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Physical properties of the shallow sediments in late Pleistocene formations,Ursa Basin,Gulf of Mexico,and their implications for generation and preservation of shallow overpressuresN.T.T.Binh a ,*,T.Tokunaga a ,1,T.Nakamura b ,2,K.Kozumi b ,2,M.Nakajima b ,2,M.Kubota b ,2,H.Kameya c ,3,M.Taniue c ,3aDepartment of Environment Systems,School of Frontier Sciences,University of Tokyo,5-1-5Kashiwanoha,Kashiwa-shi,Chiba 277-8563,Japan bGeotechnical Department,Dia Consultants,Saitama,Japan cCore Laboratory,Oyo Corporation,Niigata,Japana r t i c l e i n f oArticle history:Received 14September 2007Received in revised form 20January 2009Accepted 23January 2009Available online 31January 2009Keywords:Basin modellingShallow overpressure Fluid ﬂowDeepwater environmenta b s t r a c tUnderstanding the evolution of abnormally high ﬂuid pressures within sedimentary formations is critical for analysing hydrogeological processes and assessing drilling risks.We have constructed a two-dimensional basin model and have performed numerical simulations to increase the understanding of the history of ﬂuid ﬂow and shallow overpressures in the Pleistocene and Holocene formations in the Ursa basin,deepwater Gulf of Mexico.We measured physical properties of sediments,such as porosity and permeability,in the laboratory and estimated in situ pore pressures from preconsolidation pressures.We obtained porosity–effective stress relationships from measurements of bulk density,grain density and preconsolidation pressures in the laboratory.Porosity–effective stress relationships were also obtained from downhole density logs and measured pore pressures.The porosity–effective stress and porosity–permeability relationships obtained were applied in two-dimensional basin simulations.Results showed that high pore pressures developed shortly after sediment deposition.Peaks in pore pressure ratios were related to high sedimentation rates of mass transport deposits and the incision of the Ursa teral ﬂows from the area where the overburden is thick towards the area where it is thin have occurred at least since 30ka.Present pore pressure and temperature distributions suggest that lateral ﬂows play a role in re-distributing heat in the basin.Ó2009Elsevier Ltd.All rights reserved.1.IntroductionUnderstanding pore pressure regimes and ﬂuid ﬂow patterns in sedimentary basins and their temporal changes is important for investigating the evolution of sedimentary basins,the stability of slopes,and related geodynamics.For example,lateral ﬂuid ﬂows in a sedimentary formation are controlled by pore pressure gradients,sedimentation rates,and permeability distribution (Bethke,1986).Flemings et al.(2005)speculated that focusing of ﬂuid ﬂows may result in slope instability on continental slopes.In 2005,the Inte-grated Ocean Drilling Program (IODP)conducted Expedition 308tostudy shallow overpressure and ﬂuid ﬂow in the Ursa region,continental slope of the Gulf of Mexico.Eight holes were drilled at three well sites U1324,U1323and U1322.The wells were logged and in situ measurements were made.Geopressured sediments from Pleistocene and Holocene formations were found in these wells (Expedition 308Scientists,2005;Myers et al.,2007).In this area,Byrd et al.(1996),Eaton (1999),Ostermeier et al.(2002),and Flemings et al.(2005)described the existence of shallow water ﬂow phenomena,and discussed the problems encountered with shallow water ﬂows.Shallow water ﬂows are associated with a variety of drilling problems and seaﬂoor damage,including uncontrolled ﬂow of sands in the annulus of the borehole being drilled,borehole washouts,craters,and cracks on the seaﬂoor (Alberty et al.,1997;Alberty,2000;Faul et al.,2000;Ostermeier et al.,2002).They are considered to be the cause of major problems for the oil and gas industry working in the Mark-Ursa region (Alberty,2000).The loss of many wells in the Ursa Development Project in the Mississippi Canyon Block 810was an extreme example of severe damage due to violent shallow water ﬂows (Furlow,1998).*Corresponding author.Department of Earth Sciences,Durham University,Durham DH13LE,United Kingdom.Tel.:þ44(0)1913343972;fax:þ44(0)1913342301.E-mail address:binh.nguyen@ (N.T.T.Binh).1Tel.:þ81471364708;fax:þ81471364709.2Tel.:þ81486543011;fax:þ81486543833.3Tel.:þ81252745656;fax:þ81252716765.Contents lists available at ScienceDirectMarine and Petroleum Geologyjournal homepage :www.else /locate /marpetgeo0264-8172/$–see front matter Ó2009Elsevier Ltd.All rights reserved.doi:10.1016/j.marpetgeo.2009.01.018Marine and Petroleum Geology 26(2009)474–486Recently,Sawyer et al.(2007a)described the lithology of the main depositional elements in the Mars-Ursa region and interpreted the geological evolution of the area for the past70ky based on high resolution3D seismic data and well log data.Long et al.(2007)and Flemings et al.(2006,2008)presented pore pressure penetrometer measurements made during IODP Expedition308and documented vertical and lateral variation in overpressure at Sites U1322and U1324.They showed that overpressures have reached60%of the hydrostatic effective stress at Site U1322and70%at Site U1324. Sawyer et al.(2007b)and Dugan et al.(2007)integrated physical properties of mass transport complexes(MTCs)and concluded that consolidation behaviour in MTCs is different from that in the sedi-ments encasing the MTCs.Furthermore,low permeability in MTCs precludes drainage of overpressure in the Ursa region.In this study,we conducted numerical modelling,supported by data from laboratory experiments using samples obtained from IODP Expedition308,to examine lateralﬂuidﬂow and pore pressure evolution in the Pleistocene and Holocene sedimentary sections of the Ursa basin.In the geotechnical laboratory,we measured physical properties of sediments such as porosity and permeability and estimated in situ pore pressures from preconsolidation pressures. Then porosity–effective stress relationships were constructed from IODP results and the estimated in situ pore pressures.The porosity–effective stress and porosity–permeability relationships obtained were applied in two-dimensional basin simulations.Two-dimensional modelling cases presented here include the effects of both the uneven sedimentation rates along the cross-section and the ancient channel activities on the hydrogeological system.The modelling results can help to improve the under-standing of the history ofﬂuidﬂow and overpressure in the study area.2.Geological settingIn this section,we summarize what is known from previous research results about the lithology and deposition of sediments in the Ursa basin.This information will be used to construct the geological model for our basin modelling study which will be dis-cussed in Section4.The Ursa Basin is located about200km southeast of New Orleans on the continental slope of the Gulf of Mexico(Fig.1).It is a salt-withdrawal mini-basin with water depth in the range800–1400m,and with sediments originating from the Mississippi River system(Expedition308Scientists,2005).This study focuses on the Late Pleistocene to Holocene sedimentary section of the Ursa Basin, i.e.,from the base of the Blue Unit to the seaﬂoor(Fig.2).According to Sawyer et al.(2007a)and Expedition308Scientists (2005),sediments deposited from the base of the Blue Unit to the seaﬂoor can be divided intoﬁve units from bottom to top:the Blue Unit,the Ursa Canyon channel-levee system,the Southwest Pass Canyon channel-levee system,mass transport deposits,and the distal deposits and hemipelagic drape(Fig.2).The Blue Unit is composed of interbedded sands and clays with a maximum two-way travel time of250–300ms(Sawyer et al.,2007a).In the study area,the Blue Unit thins towards west due to the incision by the Southwest Pass and Ursa Canyon systems(Fig.2)(Sawyer et al., 2007a).The Blue Unit was most likely deposited during the MIS Stage4eustatic sea level fall and at the time of an eastward shift in the drainage pattern of the Mississippi River(Piggott and Pulham, 1993;Winker and Booth,2000;Ruddiman,2001).The base of the Blue Unit was interpreted to have been deposited at around68ka based on biostratigraphic data from nannofossils assemblages (Winker and Booth,2000).Note that Urgeles et al.(2007)recently suggested the age of the base of the Blue Unit to be89ka,but without giving a reason.Thus,we assumed the age of deposition to be68ka. The Ursa Canyon runs from the northwest to the southeast(Sawyer et al.,2007a).The channel-levee system is composed of a channelﬁll, channel-margin slides,and levees(Sawyer et al.,2007a).The channel margin and levees consist of silty clays while the channelﬁll consists of sands and silty clays(Sawyer et al.,2007a).The Ursa Canyon incised the Blue Unit and channel-margin slides play as hydraulic barrier within the Blue Unit(Fig.2)(Sawyer et al.,2007a).Fig.1.Location of the study area and the cross-section used for two-dimensional basin modelling.Contours show water depth.The names of the blocks are also shown.After Sawyer et al.(2007b).N.T.T.Binh et al./Marine and Petroleum Geology26(2009)474–486475The Southwest Pass Canyon system is younger than,and lies to the west of the Ursa Canyon system (Fig.2).It eroded much of the western levee of the Ursa channel and completely buried its ﬁll and eastern levee.It has similar characteristics as the Ursa Canyon system but is even larger (Sawyer et al.,2007a ).The Southwest Pass Canyon also contains a belt of rotated channel-margin slides (Fig.2),which is up to 5.5km wide.The canyon ﬁll itself is approximately 1.3–1.6km wide (Sawyer et al.,2007a ).Mass transport deposits (MTDs)are situated within the mud-rich levee deposits above the Blue Unit in the studied area (Fig.2)(Sawyer et al.,2007a ).MTDs consist of silty clays (Sawyer et al.,2007b;Dugan et al.,2007).The ﬁrst mass transport deposit is located on the eastern levee of the Ursa Canyon channel-levee system,and the second mass transport deposit is located within the eastern levee of the Southwest Pass Canyon channel-levee system (Fig.2b)(Sawyer et al.,2007a ).Distal deposits and hemipelagic drape overlie the MTDs.The distal deposits are composed primarily of olive-green and reddish brown clay interbedded with black clay and are capped by hemi-pelagic clay that is rich in nannofossils and foraminifera (Expedi-tion 308Scientists,2005).3.Physical properties of the soft sedimentsSoft sediment samples obtained from IODP Expedition 308were analysed,focusing mainly on porosity–permeability relationships.Index properties of the samples used for consolidation tests are shown in Table 1.Grain densities were in the range 2760–2900kg/m 3and were found to be higher than those measured onboard the ship (2667–2780kg/m 3)(Expedition 308Scientists,2005).We fol-lowed JIS A 1202-1999(Japanese Industrial Standards Committee,1999)and JGS 0111-2000(Japanese Geotechnical Society,2000a )for the measurements of the grain densities of the samples,so the reasons for the discrepancy between the density measurements are unknown.However,the effects of the differences of the grain densities on the calculated porosities were rather small,i.e.,less than 2%,and hence were not signiﬁcant,at least for this study.3.1.Porosity–permeability relationships3.1.1.Evaluation of permeability from 1-D consolidation testsA total of fourteen samples were tested either by using an incremental loading oedometer (IL)or aconstant-rate-of-strainFig.2.(a)Seismic line AA 0and (b)interpreted cross-section.The dotted blue box shows the modelled area.Modiﬁed from Sawyer et al.(2007a)(For interpretation of the references to colour in this ﬁgure legend,the reader is referred to the web version of this article).N.T.T.Binh et al./Marine and Petroleum Geology 26(2009)474–486476oedometer (CRS).Both the IL and CRS oedometers had a loading capability up to 10MPa.Prior to testing,the samples were removed from the sealed core liner and trimmed to a height of 2.5cm and a diameter of 6.0cm.Once trimmed,the wet mass of the sample was recorded before the sample was transferred into the consolidation cell.The cells for both oedometers were made of stainless steel so that only vertical sediment deformation could occur.The sample was set into the cell and placed between two porous stones.Saturated ﬁlter papers were used to separate the sample from the porous stones and to prevent ﬁne-grained sediment particles from blocking the drainage paths through the porous stones.After the cell was assembled,it was transferred to the oedometer.Side friction was minimized using silicone grease.General sample preparation and testing procedures followed the guidelines set out by JIS A 1217-2000and JIS A 1227-2000(Japanese Industrial Standards Committee,2000b,c )and JGS 0411-2000and JGS 0412-2000(Japanese Geotechnical Society,2000c,d ).In the IL tests,the cells were submerged in saline water to keep the samples fully water-saturated.The samples were allowed to settle for 24h after the application of each incremental load.Time,loading pressure,and the change in specimen height were measured.The end of primary consolidation was evaluated using the square root of time method proposed by Taylor (1948).Then,the coefﬁcient of vertical consolidation (C v )and the coefﬁcient of volume compressibility (m v )were indirectly evaluated based on the one-dimensional Terzaghi’s consolidation theory (Terzaghi,1943;Lambe and Whitman,1979).In the CRS tests,backpressure of 200kPa was applied to the samples for 24h to ensure they were fully water-saturated before loading with a constant axial strain rate of 0.03%/min.Backpressure was kept constant at 200kPa until the end of the tests.Time,axial load,the change in specimen height,and excess pore pressure at the base of the sample were measured.C v and m v were calculated using linear ﬁnite strain theory (Smith and Wahls,1969).Hydraulic conductivity (K )was estimated from C v ,m v and density of water (r w )byK ¼C v m v r w g(1)where g is gravitational acceleration (Lambe and Whitman,1979).In the equation (1),the unit of K is m/s,C v is m 2/s,m v is 1/Pa,g is m/s 2,r w is kg/m 3.Intrinsic permeability can be obtained from hydraulic conduc-tivity through the following relationship (Hubbert,1940)K ¼k r w gm(2)where k is intrinsic permeability (m 2)and m is viscosity of pore ﬂuid (Pa s).The intrinsic permeability reported here is for seawater with density and viscosity to be 1025kg/m 3and 0.963Â10À3Pa s,respectively.3.1.2.ResultsThe permeability values obtained from fourteen consolidation tests ranged from 5Â10À17to 1Â10À19m 2.For comparison,the results are plotted in Figs.3and 4with data from a previous study in the Gulf of Mexico (Bryant et al.,1975).Bryant et al.(1975)classiﬁed their measurements based on the grain size of the samples.For grain size analysis,we used a hydrometer and fol-lowed the method JIS A 1204-2000(Japanese Industrial Standards Committee,2000a )and JGS 0131-2000(Japanese Geotechnical Society,2000b ).We also estimated shale volume of the sediments from gamma ray log data.The analysed samples from MTDs and silty clays have shale volumes in the range 59–78%(Table 1).Thus,the data obtained for MTDs and silty clays were compared with group 2of Bryant et al.(1975),which had clay content in the range 60–80%(Fig.3).Since the gamma ray data were not good enough at very shallow depths near the seaﬂoor,we did not use these datatoTable 11E-191E-181E-171E-161E-15PorosityP e r m e a b i l i t y (m 2)Silty clays MTDs Group 2 (Bryant et al., 1975)Fig. 3.Porosity and permeability obtained from IL tests and CRS tests for mass transport deposits (open triangles)and silty clays (open squares)in the Ursa Basin,and the published results in the Gulf of Mexico (black circles)(Bryant et al.,1975).The dotted line and the black line were modelled porosity–vertical permeability relation-ship used for MTDs and for silty clays,respectively.N.T.T.Binh et al./Marine and Petroleum Geology 26(2009)474–486477calculate shale volumes for hemipelagic clay.Based on visual core description and smear slide analyses,hemipelagic clay samples are mainly composed of clay (Expedition 308Scientists,2005).Therefore,we assume that the data for hemipelagic clays should be comparable with the group 1samples of Bryant et al.(1975)which had clay content higher than 80%(Fig.4).Based on these compar-isons,the results from this study were conﬁrmed to be consistent with data from the previous study.An IL test and a CRS test were conducted with specimens from the same sample U1324B-19H-CC for comparison.The intrinsic permeability values obtained from the CRS test were about 1.17–1.2times higher than those obtained from the IL test for the same porosity (Fig.5).However,the slopes of the permeability–porosity lines on the semi logarithmic plots were parallel (Fig.5),suggesting that the trend of permeability reduction by mechanical consoli-dation can be estimated from this slope.We used the equationk ¼A exp ðB f Þ(3)where A and B are lithology-dependent constants to express rela-tionships between permeability (k )and porosity (4)(Table 2).We used the porosity–permeability relationships shown in Table 2for the two-dimensional simulation described in the next section.3.2.Preconsolidation pressure measurementsPreconsolidation pressure is usually interpreted to be the maximum effective overburden stress that sediments have expe-rienced (Casagrande,1936;Lambe and Whitman,1979).Knowing the preconsolidation pressure (P c )and in situ overburden stress (S v ),we can estimate the in situ pore pressure (P p )through the following equation assuming that the sediments are normally consolidated.P p ¼S v ÀP c (4)In this study,preconsolidation pressures of the sediments were determined by using Casagrande (1936)’s method to analyse the IL and CRS test data.In situ pore pressures calculated from P c are shown in Table 3.The values obtained are consistent with in situ measurements (Flemings et al.,2008)(Fig.6)except for the point at 1566mbsl (metres below sea level)at site U1324.Also,in situ pore pressuremeasurements at 1316.5mbsl and 1366.5mbsl seemed to be lower than the expected pore pressures from preconsolidation pressures.The in situ measurements were conducted with good coupling between the measurement device and the formation at these depths (Flemings et al.,2008).These differences could be explained by the declines of pore pressures due to recent drilling activity,as suggested at the site U1322by Flemings et al.(2008)even though the precise reason is quite difﬁcult to state.Based on the data shown in Fig.6,we consider that the sections between seaﬂoor and 1127mbsl at site U1324and between seaﬂoor and 1375.5mbsl at site U1322are normally consolidated.3.3.Porosity–effective stress relationshipsPorosity–effective stress relationships were constructed for normal consolidation sections based on porosities calculated from density logs at sites U1324and U1322(Expedition 308Scientists,2005)(Fig.7).Overburden stress was estimated from density logs and water depths.In hemipelagic clay sections,porosities decrease quickly from 80%to 55%(Fig.7(a)).For mass transport deposits and silty clays,porosities can be modelled by porosity–effective stress relation-ships of the Athy (1930)type,with different parameters for the silty clays and mass transport deposits (Fig.7(b),(c)).4.Two-dimensional pore pressure and ﬂuid ﬂow modelling Basin modelling is one of the methods which can be used to predict pore pressure as well as ﬂuid ﬂow in a sedimentary basin(e.g.,Welte and Yalcin,1988;Burrus et al.,1992;Dore´et al.,1993;Hermanrud,1993;Neuzil,2003).Fully coupled and integrated basin simulators provide information on pore pressures,porosities,temperatures and ﬂuid ﬂow patterns through time.We conducted two-dimensional simulation using SIGMA-2D (Okui et al.,1996,1998).1.E-191.E-181.E-171.E-16PorosityP e r m e a b i l i t y (m 2)Fig.5.Porosity–permeability relationships obtained from the IL test (black squares)andCRS test (open squares)on the sample 1324B-19H-CC.The lines were obtained by least square ﬁts.Table 2Constants obtained for vertical permeability–porosity relationships.See text for 1.E-191.E-181.E-171.E-161.E-151.E-14PorosityP e r m e a b i l i t y (m 2)Fig.4.Porosity and permeability obtained from hemipelagic clays in the Ursa Basin using IL consolidation tests (open squares),and the published results in the Gulf of Mexico (black circles)(Bryant et al.,1975).The black line was used as the modelled porosity–vertical permeability relationship for hemipelagic clays.N.T.T.Binh et al./Marine and Petroleum Geology 26(2009)474–4864784.1.Model construction:geological model,lithology model,and physical propertiesThe cross-section was chosen to be parallel to the local slope direction in the study area (Fig.1).Hence,it is considered to contain the principal direction of ﬂuid ﬂow related to sedimentation and topography.There are three IODP Expedition 308well sites along this section,i.e.,U1324,U1323and U1322,and data from these wells can be used for calibration.A lithology model was made based on previous studies in the area (Expedition 308Scientists,2005;Sawyer et al.,2007a ).Because of the lack of detailed data,it was necessary to simplify the lithology distribution.Four types of lithology,hemipelagic clays,mass transport deposits,sands,and silty clays,were used to model the sediments in the study area (Fig.8).Since sheet sands in the Blue Unit have been considered to be the source of shallow water ﬂows in the Ursa area (Eaton,1999;Ostermeier et al.,2002;Winker and Shipp,2002),the distribution of sheet sands in the Blue Unit and the Ursa channel ﬁll sands were modelled in detail,whereas the other lithologies were rather simpliﬁed.The modelled cross-section was part of cross-section AA 0in Fig.2(b),and was divided into 28columns,26layers and one erosion event (Fig.8).Based on previous research results (Expedition 308Scientists,2005;Sawyer et al.,2007a ),the distribution of the sediments was set as follows (Table 4).From seaﬂoor to seismic horizon S10,the lithology is hemipelagic clays (layers 1and 2).From seismic horizon S10to seismic horizon S20,the lithology is silty clays and MTDs (layers 3–6).From seismic horizon S20to seismic horizon S30,the lithology is MTDs (layers 7–9).From seismic horizon S30to seismic horizon S60,the lithology is silty clays and MTDs with small amounts of sand (layers 10–13).The Ursa channel (layers 14–17)was modelled as sands and silty clays with the lithology distribu-tion being based on the seismic interpretation (Fig.2)(Sawyer et al.,2007a ).The Blue Unit includes sands and interbedded silty clays (layers 18to 24).Layers 25and 26are silty clays below the Blue Unit.The simulation was run for a total period of 85ky.Erosion by the incision of the Ursa Canyon was considered (Fig.8).Based on previous studies on sea level cycles in the Mis-sissippi river depositional system and in the Gulf of Mexico (Ruddiman,2001;LoDico et al.,2006),we considered that erosion had occurred after the deposition of the Blue Unit and before the sea level rise at 58ka.The timing of erosion was set to lie between 60ka and 58ka (Table 4).The eroded thickness was modelled to be between 50and 250m,assuming that the thickness of the Blue Unit had been constant throughout this cross-section before erosion occurred.During erosion,the palaeo-water depth in the eroded area increased by an amount equals to the eroded thickness.The relationships between porosity and vertical effective stress and between porosity and permeability for hemipelagic clays,MTDs and silty clays obtained from our experimental study (Figs.3,4and 7and Table 2)were used as input data for basin simulation.We also used published relationships between porosity and vertical effective stress and between porosity and permeability for sands in the Gulf of Mexico (Aniekwena et al.,2003)(Fig.9).Anisotropy in permeability was considered.Freeze and Cherry (1979)suggested that clays and shales show horizontal toverticalTable 3aU1324U1322Pressure (MPa)D e p t h (m b s l )Overburden stressCalculated from PcPressure (MPa)Fig.6.Pore pressures calculated from preconsolidation pressures (ﬁlled squares)(a)at site U1324and (b)at site U1322.The in situ measurements (Flemings et al.,2008)are also shown by open triangles.The units mbsl are metres below sea level.N.T.T.Binh et al./Marine and Petroleum Geology 26(2009)474–486479anisotropy ratio in the range3:1–10:1.In this study,the ratios between horizontal permeability and vertical permeability were chosen to be10:1for hemipelagic clays and mass transport deposits,and to be3:1for silty clays.For sands,since the perme-ability in the horizontal direction was found to be nearly equal to,or only slightly larger than that in the vertical direction(Hatanaka et al.,1997),the horizontal to vertical anisotropy ratio was chosen to be1:1.Other physical properties for each lithology required for the two-dimensional basin modelling include grain density,matrix thermal conductivity,and heat capacity.These data were estimated from published results(Expedition308Scientists,2005;Rossane et al.,2004;Waples and Waples,2004)(Table5).4.2.Boundary and initial conditionsGeological information can be used to select the appropriate boundary conditions.For this purpose,the distribution of faults in the layers of higher hydraulic conductivity was studied.Because the majority of sediments are clays and silty clays,and fault displacements tend to produceﬁne-grained material in general(e.g.,Ferrill and Morris,2001),all faults are assumed to act as sealing faults.Faults are located at the eastern boundary of the cross-section(Fig.2(b)).At the western boundary,the Blue Unit is completely eroded by the incision of the Southwest Pass channel (Fig.2).Thus,the body of low hydraulic conductivity in the Southwest Pass channel can be treated as a seal for westwardﬂuid Fig.8.The geological model used for the basin modelling.Dotted line shows erosion by the Ursa Canyon.50100150200250PorosityVerticaleffectivestress(kPa)100200300400500600700800PorosityVerticaleffectivestress(kPa)Porositya b cFig.7.Porosity–effective stress relationships for(a)hemipelagic clays,(b)MTDs,and(c)silty clays.Lines in theﬁgure and the equations show the modelled porosity–effective stress relationships used for two-dimensional basin modelling.N.T.T.Binh et al./Marine and Petroleum Geology26(2009)474–486480ﬂow in the Blue Unit.Therefore,no-ﬂow boundary conditions were assigned to both side boundaries.For the basal boundary,no ﬂuid ﬂow conditions were set because the base of the cross-section is composed of a mud-rich layer.The initial pore pressures at each time step were set to be the calculated pore pressures at the previous time step.For the newly added grids,hydrostatic pore pressures were applied.The initial temperatures were set to be those obtained from the previous time step for existing grids,and seawater temperature for the newly added grids.Boundary conditions for the heat ﬂow in SIGMA-2D are speciﬁed temperatures along the upper boundary,a speciﬁed heat ﬂux along the bottom,and no ﬂux along side boundaries (Okui et al.,1996,1998).At the upper surface of the model,temperatureand pressure were modelled as seaﬂoor temperature and seaﬂoor pressure.Temperatures at the seaﬂoor were set to be in the range 4.0–4.5 C depending on the water depth.Pressure at the seaﬂoor was considered to be hydrostatic.4.3.Model calibrationDuring calibration,the measured data from wells,i.e.,pore pressure,temperature,and porosity,were compared with the simulated results at the well locations to optimize the parameters used in the model.Sedimentation rates were adjusted by trial-and-error to obtain reasonable agreement between simulated and observed values.The geological model used in this study was made up of 26layers (Fig.8and Table 4).In the modelled cross-section,the ages of the top of the layers 1,2,6,9,12,13,15and 19were determined based on the estimated ages of the known key horizons (Table 4)(Expedition 308Scientists,2005;Sawyer et al.,2007a ).Because other layers were situated between horizons of known ages,the ages of these layers were adjusted to lie within the constrained ranges.Through this procedure,the appropriate sedimentation rates (Table 4)were chosen to obtain good correlations between the calculated results and the measured data.Present-day heat ﬂow at the base of the model was estimated by ﬁtting temperatures calculated from the model and in situ measurements (Expedition 308Scientists,2005).The heat ﬂow value obtained was 36.3mW/m 2.This value is quite low in comparison with heat ﬂow values seen in the continental crust,which ranges from 40to 46mW/m 2(Smith and Dees,1982).Mello and Karner (1996),Nagihara and Jones (2005)and Jones et al.(2003)suggested that the rapid deposition of thick sections of young sediments in the Mississippi fan has suppressed regional isotherms,resulting in anomalously low heat ﬂow.We assumed that the palaeo heat ﬂows were the same as present-day heatﬂows.Table 4Ages and thicknesses for the modelled cross-section AA 0.S10to S80represents keyTable 5Physical properties of four types of lithology estimated from Expedition 308050010001500200025003000350040000.30.40.50.60.70.8PorosityV e r t i c a l e f f e c t i v e s t r e s s (k P a)1.0E-151.0E-141.0E-131.0E-121.0E-111.0E-100.10.20.30.40.5PorosityP e r m e a b i l i t y (m 2)abFig.9.(a)Porosity–effective stress and (b)porosity–permeability relationships for sand.From Aniekwena et al.(2003).N.T.T.Binh et al./Marine and Petroleum Geology 26(2009)474–486481。

The Random Oracle Methodology, Revisited

1.1
The Setting
For the purpose of the following discussion, a cryptographic system consists of a set of parties, which are modeled by probabilistic polynomial time interactive Turing machines. A cryptographic application comes with a security requirement specifying the adversary’s abilities and when the latter is considered successful. The abilities of the adversary include its computational power (typically, an arbitrary polynomial-time machine) and the ways in which it can interact with the other parties. The success of the adversary is deﬁned by means of a predetermined polynomial-time predicate of the application’s global view.1 A system is considered secure if any adversary with the given abilities has only a negligible probability of success.
Keywords:

商务英语阅读(下册)(第二版) unit 10 知识产权

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Comparative calculations of alkali metal cation basicities of some Lewis bases

Proc. Estonian Acad. Sci. Chem., 2007, 56, 3, 107–121Comparative calculations of alkali metal cation basicities of some Lewis basesPeeter Burk*, Mari-Liis Sults, and Jaana Tammiku-TaulInstitute of Chemical Physics, University of Tartu, Jakobi 2, 51014 Tartu, EstoniaReceived 16 May 2007Abstract. Lithium, sodium, and potassium cation basicities were calculated for some Lewis bases using the DFT B3LYP/6-311+G**, G2, G2(MP2), G3, and CBS-QB3 methods and compared with corresponding experimental values. The best results for lithium cation basicities (LCB) were obtained with the G2, G2(MP2), and CBS-QB3 methods. So, the G2(MP2) method seems to be the best compromise between speed and accuracy for the calculations of LCBs. Also the quicker DFT B3LYP/6-311+G** level of theory can be used for quantitative prediction of LCBs, if the systematic error is taken into account. For sodium cation basicities (SCB) both G2 and G2(MP2) methods gave excellent correlation and mean absolute deviation (0.6–0.7 kcal/mol), thus G2(MP2) can be suggested for calculation of SCB. The less expensive DFT B3LYP/6-311+G** method gave also good correlation with the experiment. Potassium cation affinities can be calculated with equal accuracy using three methods: G2, G2(MP2), and B3LYP/6-311+G**.Key words: lithium cation basicity, sodium cation basicity, potassium ion basicity.INTRODUCTIONAlkali metal ions were the first metal cations studied in the gas phase for their Lewis acid properties. This was due to their relatively easy production under vacuum. In contrast with transition metal ions, alkali metal cations reactivity towards ligands is simple: they form adducts, or clusters, which can be considered as ions ‘solvated’ by one or several ligands. Moreover, the possibility of measuring accurate alkali metal cation affinities with high accuracy by means of different experimental techniques (equilibrium constant determination by high pressure mass spectrometry (HPMS) [1–4] or ion cyclotron resonance (ICR) [5–9], unimolecular dissociation – Cook’s kinetic method [4, 10, 11], energy resolved collision-induced dissociation (CID) [12–14], photodissociation and radiative association kinetics [15, 16]), has stimulated a growing interest in the * Corresponding author, peeter.burk@ut.ee107108study of these interactions [17–19]. Such measurements generate data that help understand the fundamental interactions important in analytical mass spectro-metry, organic synthesis, catalysis, lithium battery electrochemistry [20], and cation transport in living systems ion channels [21].The gas-phase lithium, sodium, and potassium cation basicities (LCB, SCB, and PCB, respectively) are defined as the Gibbs free energies associated with the thermodynamic equilibria:1[],K B M B M +++− (1)where e.g. 1Li ln G RT K +∆=− and Li LCB .G +=−∆ In the similar manner, the gas-phase lithium, sodium, and potassium cation affinities (LCA, SCA, and PCA, respectively) are defined as the negative values of the enthalpy changes of reaction (1), e.g. Li LCA .H +=−∆Numerous computational studies [22–43] have been carried out to study the structure and energetics of interactions between alkali metal cations and different Lewis bases. Such studies have usually been limited to a small number of similar bases, and limited relationships have been established between experimental and theoretically calculated cation basicity (or affinity) values.Burk et al. calculated lithium cation basicities for 37 compounds at G2 and G2(MP2) levels of theory and for 63 compounds at DFT B3LYP/6-311+G** level of theory [44]. It was concluded that G2 and G2(MP2) methods estimate the LCB values with equal accuracy so that there is no need to use a computationally more demanding G2 method for predicting LCBs. A similar conclusion was reached for sodium cation basicities by Remko & Šarišsky [30]. The DFT method had a somewhat larger average error, but was also found to give adequate LCB values. It was noted that in the LCB region above 36 kcal/mol the discrepancies between calculated and experimental values were considerably greater.Good agreement between experimental (HPMS) and computational (MP2/6-31+G*) SCBs was found by Hoyau et al. for 40 bases [45]. Similar results were obtained by Rodgers & Armentrout, who used MP2(full)/6-311G** level of calculation [46]. In 2000 McMahon & Ohanessian studied sodium binding to 50 molecules and put together an extensive affinity scale [47]. Experiments were done using FT-ICR and calculations using the MP2(full)/6-31G* method. A SCA scale from 6 to 45 kcal/mol was put together. A good agreement was found between the experimental and theoretical values. In 2001 Petrie studied the SCA of 38 molecules using the CPd-G2thaw and c-SLW3 methods [48]. Calculated values were systematically ca 0.7 kcal/mol higher than the experimental values. At the same time these values are 0.5 to 0.7 kcal/mol lower than obtained in previous works. Bloomfield et al. used the high-level ab initio CP-dG2thaw method to recalculate the sodium affinity scale for 22 bases in 2006 [49]. The results were in good agreement with the experiment (±0.3 kcal/mol) and ensured the addition of 97 new ligands to the scale.Siu et al. studied the BSSE corrections for the G2 and G3 methods in 2001 [50]. A detailed analysis is given on Li +, Na +, and K + complexes with Lewisbases (small chain alcohols and amines). It was found that applying the full BSSE often gives misleading results. This can be corrected by taking into account the geometry effect. For potassium complexes the geometry effect is negligible but for lithium and sodium ion complexes the value is large and of similar magnitude but opposite sign to the core size effect.In 2004 Tsang et al. looked into the theoretical potassium and sodium affinities of amides in order to validate these values experimentally using the kinetic method [51]. The theoretical calculations were done at the G2(MP2,SVP) theory. The results for relative and absolute affinities were in good agreement with the experimental values. Several earlier inconsistencies in sodium and potassium affinity scales were resolved.Lau et al. made the most thorough calculations of the potassium cation basicity scale in 2003 [52]. The calculations carried out using the DFT B3LYP/ 6-311+G(3df,2p) method and 136 ligands were considered. Experimental PCA values for 70 bases were used for comparison. The experimental and theoretical values were in good agreement with the mean average error of 1.1 kcal/mol.In 2000 Ma et al. suggested the use of a smaller core size for potassium ion when calculating its complexes [53]. Short chain alcohols were used for a test and differences between Li+, Na+, and K+ were noted. Calculations were made using the G2(MP2,SVP) and G3 methods. The nature of interactions between cations and bases was also discussed.There are a large number of publications about sodium and potassium cation binding to the amino acids and peptides. In 2003 Kish et al. studied SCAs of the amino acids experimentally by collision activated dissociation and converted their results to the ladder of sodium affinities via Cooks’s kinetic method [54]. The SCA scale was verified with calculations at MP2(full)/6-31G*. A similar experimental method was used by Gapeev & Dunbar, who also obtained a satisfactory affinity scale for amino acids [55]. However, a large uncertainty remains about the anchoring of the scale to the SCA value of glycine. Potassium bindings to dipeptides and corresponding structures were determined theoretically by Wong et al. in 2002 using the DFT method B3LYP/6-31G* [56]. In 2007 Wang et al. studied experimentally and theoretically (MP2(full)/6-311+G**) the sodium ion affinities of simple di-, tri-, and tetrapeptides [57]. The experimental sodium affinities agreed excellently with the theoretical predictions.In the current paper we study the ability of DFT B3LYP/6-311+G** and high level G2, G2(MP2), G3, and CBS-B3 methods to predict the affinities of different bases towards alkali metal (lithium, sodium, and potassium) cations.METHODSAll calculations were carried out with the Gaussian 03 program package [58]. The DFT B3LYP functional [59–62] with 6-311+G** basis, G2 [63], G2(MP2) [64], G3 [65], and CBS-QB3 [66, 67] methods were used. Geometries were optimized and the frequencies calculated at respective levels. All stationary points109110were found to be true minima (number of imaginary frequencies, NImag = 0). The calculated (unscaled) frequencies were also used for the calculations of the enthalpies and free energies using standard procedures [68]. No corrections for the basis set superposition error were made.RESULTS AND DISCUSSIONLithium cation basicitiesIn the current work we calculated LCB values using the high-level G2, G3, and CBS-QB3 methods. The calculated LCB values are reported in Table 1 alongTable 1. Experimental and calculated at different levels lithium cation basicities (LCB) and differences between the experimental [44] and calculated LCBs (∆). MAD is the mean absolute difference between the experimental and calculated LCBs at the given level of theory. All values are in kcal/molB3LYP G2 G2(MP2) G3 CBS-QB3Base Exp. LCB∆ LCB ∆ LCB ∆ LCB ∆ LCB ∆CH 3SH 20.3 22.5 2.2 20.8 0.520.8 0.521.3 1.0 19.6 0.7 CH 3CH2SH 21.4 24.8 3.4 22.4 1.022.4 1.0 22.9 1.5 21.1 –0.3 i-CH 3CH 2CH 2SH 22.4 25.8 3.4 23.5 1.123.5 1.1 24.4 2.0 22.5 0.1 (CH 3)2S 23.4 26.5 3.1 24.6 1.224.6 1.2 25.2 1.8 23.2 –0.2 H 2O 24.7 29.7 5.0 26.4 1.726.1 1.4 27.4 2.7 26.3 1.6 H 2CO 25.4 30.6 5.2 28.0 2.628.0 2.6 28.8 3.4 27.8 2.4 HCN 25.9 27.7 1.8 25.4 –0.525.5 –0.4 26.7 0.8 25.6 –0.3 CH 3OH 28.5 32.4 3.9 29.1 0.629.0 0.5 30.4 1.9 29.0 0.5 NH 3 30.2 34.2 4.0 30.2 0.029.9 –0.3 31.4 1.2 30.3 0.1 CH 3CH 2OH 30.4 35.0 4.6 30.9 0.530.8 0.4 32.2 1.8 31.0 0.6 CH 3NH 2 31.3 34.8 3.5 32.0 0.731.8 0.5 33.1 1.8 31.9 0.6 CH 3CHO 31.8 36.5 4.7 33.0 1.233.0 1.2 35.1 3.3 32.8 1.0 (CH 3)3N 32.0 34.1 2.1 32.4 0.432.2 0.2 33.5 1.5 32.0 0.0 (CH 3)2NH 32.1 34.8 2.7 32.5 0.432.3 0.2 33.7 1.6 32.3 0.2 i-CH 3CH 2CH 2OH 32.3 36.8 4.5 33.5 1.233.4 1.1 34.8 2.5 33.3 1.0 HCO 2CH 3 32.4 36.3 3.9 33.2 0.833.2 0.8 34.2 1.8 32.8 0.4 CH 3CH 2CHO 32.8 37.6 4.8 34.1 1.334.1 1.3 35.1 2.3 33.9 1.1 Pyrazol 33.6 38.5 4.9 35.3 1.735.2 1.6 36.7 3.1 35.0 1.4 CH 3CN 34.0 39.2 5.2 35.5 1.535.5 1.5 36.6 2.6 35.4 1.4 (CH 3)2CO 35.3 40.9 5.6 36.7 1.436.7 1.4 38.0 2.7 36.7 1.4 CH 3CH 2CH 2CN 35.7 41.0 5.3 37.7 2.037.7 2.0 39.7 4.0 38.6 2.9 4-Methylpyrazole 35.7 41.0 5.3 37.5 1.837.4 1.7 38.9 3.2 38.2 2.5 HCONH 2 36.4 43.7 7.3 40.0 3.639.9 3.5 41.3 4.9 40.5 4.1 CH 3CO 2CH 2CH 3 36.0 42.1 6.1 37.6 1.637.6 1.6 39.7 3.7 37.5 1.5 Imidazole 38.1 45.0 6.9 42.0 3.941.9 3.8 43.4 5.3 41.9 3.8 CH 3CONH 2 39.3 47.0 7.7 43.5 4.243.5 4.2 45.0 5.7 43.0 3.7 HCONCH 3 39.6 47.4 7.8 44.0 4.444.0 4.4 45.3 5.7 44.2 4.6 (CH 3)2SO 41.8 53.6 11.8 47.0 5.247.1 5.3 48.0 6.2 46.9 5.1 HCON(CH 3)2 41.5 49.2 7.7 46.0 4.546.0 4.5 47.5 6.0 46.2 4.7 MAD 4.6 1.51.42.7 1.6with our earlier B3LYP/6-311+G** and G2(MP2) values. The results of correla-tion analysis (squares of correlation coefficients, slopes, and intercepts of correla-tion lines) are given in Table 2 and the correlations are graphically represented in Fig. 1.Comparison of the experimental and calculated LCBs indicates that practically in all cases the calculated LCBs are greater than the experimental ones. This trend is most pronounced in the case of LCBs above 36 kcal/mol as noted earlier [44]. Comparison of the mean absolute deviations (MAD) of the calculated LCBs from experimental ones for the high level G2, G3, and CBS-QB3 methods used indicates that the G2(MP2), G2, and CBS-QB3 methods are practically of the same quality, while the performance of the G3 method is somewhat worse. So, there is no need to calculate LCBs at the more expensive G2 or CBS-B3 levels as the improvement of the results compared to G2(MP2) is negligible. The MAD of B3LYP/ 6-311+G** calculations are worse than of the other methods used.The correlations between experimental and calculated LCBs are also of high quality: R2 values are 0.97 or higher, but the slopes of correlation lines are all greater than one and intercepts are not zeroes (slope one and zero intercept correspond to ideal correlation, free of systematic errors). However, note that in Table 2. Results of the regression analysis of the correlation between the calculated and experi-mental alkali metal cation basicities: equations of the correlation lines and squares of correlation coefficients (R2)Method Equation of the correlation line R2Lithium cation basicitiesB3LYP LCB calc = (1.27±0.04) · LCB exp – 3.61±1.42 0.969B3LYP* LCB calc = (1.12±0.05) · LCB exp – 0.37±1.35 0.968G2 LCB calc = (1.16±0.03) · LCB exp – 3.60±1.11 0.977G2* LCB calc = (1.028±0.03) · LCB exp – 0.22±0.97 0.981G2(MP2) LCB calc = (1.17±0.04) · LCB exp – 3.72±1.15 0.975G2(MP2)* LCB calc = (1.027±0.03) · LCB exp – 0.25±1.00 0.979G3 LCB calc = (1.21±0.03) · LCB exp – 3.66±1.02 0.982G3* LCB calc = (1.09±0.03) · LCB exp – 0.33±1.03 0.980CBS-QB3 LCB calc = (1.22±0.03) · LCB exp – 5.53±1.07 0.981CBS-QB3* LCB calc = (1.11±0.04) · LCB exp – 2.59±1.11 0.978Sodium cation basicitiesB3LYP SCB calc = (1.03±0.05) · SCB exp + 2.39±1.02 0.942G2 SCB calc = (0.99±0.04) · SCB exp + 0.91±0.78 0.963G2(MP2) SCB calc = (0.99±0.04) · SCB exp + 0.66±0.77 0.964G3 SCB calc = (1.02±0.05) · SCB exp + 3.23±1.10 0.936Potassium cation basicitiesB3LYP PCB calc = (1.11±0.05) · PCB exp – 1.57±0.87 0.958G2 PCB calc = (0.96±0.05) · PCB exp + 1.04±0.74 0.959G2(MP2) PCB calc = (0.97±0.05) · PCB exp + 0.86±0.76 0.959 ————————* Correlation analysis results with bases that have LCBs below 36 kcal/mol.111112(a)192429343944ExperimentalG 2(b)192429343944ExperimentalG 2(M P 2)(c)192429343944ExperimentalG 3(d)192429343944ExperimentalC B S -Q B 3(e)192429343944ExperimentalB 3L Y PFig. 1. Correlations between experimental and calculated lithium cation basicities calculated at the G2 (a), G2(MP2) (b), G3 (c), CBS-B3 (d), and DFT B3LYP/6-311+G** (e) levels. All LCB values are in kcal/mol.the region of bases with LCB exp < 36 kcal/mol the statistical characteristics of the fit are significantly better: the slopes and intercepts are now appreciably closer to their ideal values (1.0 and 0.0) and in some cases (G2 and G2(MP2)) statistically indistinguishable from perfect values. We note, however, that the standard deviation of the points from the correlation line (calculated vs. experimental LCBs) of the fastest method used – B3LYP/6-311+G** – was close to that ofother methods, and the correlation coefficient was only slightly lower than in the other cases. So, the DFT B3LYP/6-311+G** level of theory can be used for quantitative prediction of LCBs, if the systematic error is taken into account.In an earlier study we observed that in the LCB region above 36 kcal/mol the discrepancies between calculated and experimental values are considerably larger than in a region of lower basicities [15]. Possible explanations of this observation include insufficient accuracy of the computational methods used, experimental errors due to unsystematic build-up of the absolute LCB scale, and some possible incorrect equilibrium measurements.We tried to check for the insufficient accuracy of computational methods used by applying a recent W1 methodology [69, 70], which is claimed to yield energies within 0.3 kcal/mol accuracy [69, 70]. As the W1 methodology is computationally quite demanding we used it only for the smallest base in the high LCB region – dimethylsulphoxide. The calculated LCB was 48.3 kcal/mol, by 8.1 kcal/mol higher than the experimental value (40.2 kcal/mol). This indicates that the discrepancy between experimental and computational LCBs is not due to insufficient accuracy of the computational methods used. New experimental measurements in the high LCB (>36 kcal/mol) region are needed to clarify the origin of the discrepancy between the experiment and calculations.Sodium cation basicitiesIn the current work we calculated SCB values using the high-level G2, G2(MP2), and G3 methods. The DFT B3LYP/6-311+G** method was also tested. The calculated SCB values are reported in Table 3, the results of correlation analysis (squares of correlation coefficients, slopes, and intercepts of correlation lines) are given in Table 2 and the correlations are graphically represented in Fig. 2.Again, the experimental SCB values are consistently smaller than the G3 or DFT calculated ones. However, at variance with LCBs, the G2 and G2(MP2) methods provide excellent correlations (slopes are practically 1 and intercepts 0) between calculated and experimental SCBs, with small random deviations. Comparison of the MAD of the calculated SCBs with experimental ones for the high level G2(MP2), G2, and G3 methods used shows that the G2(MP2) and G2 methods are practically of equal excellent quality, while the performance of the G3 method is somewhat worse. So, again there is no need to calculate SCBs at the more expensive G2 level as the improvement of the results compared to G2(MP2) is negligible. The MAD of B3LYP/6-311+G** calculations are worse than of the other methods used, but the correlation coefficient is high, so that this method can provide SCB values fast and accurately, if empirical corrections are taken into account.113Table 3. Experimental and calculated at different levels sodium cation basicities (SCB) and thedifferences between the experimental [45, 47] and calculated SCBs (∆). MAD is the mean absolutedifference between the experimental and calculated SCBs at the given level of theory. All valuesare in kcal/molB3LYP G2 G2(MP2) G3 Base Exp.SCB ∆SCB ∆SCB ∆SCB ∆1.4 11.8 1.3 13.9 3.411.9CH3SH 10.52.813.312.2–0.0–0.012.214.32.1n-C4H9Br 12.20.2 14.4 0.2 17.0 2.814.416.0(CH3)2S 14.21.818.2 3.8 18.0 3.6 21.7 7.36.7Pyrrole 14.421.1H2O 15.715.6–0.1 15.5 –0.2 18.3 2.62.718.41.3 16.7 1.0 19.5 3.817.01.717.4C6H6 15.7C6H5OH 16.70.2 16.8 0.1 20.4 3.716.918.41.75.8 18.7 1.9 18.5 1.7 23.66.822.63-Methylpyrrole 16.8CH3OH 17.30.2 17.4 0.1 20.0 2.717.52.720.0–0.3 17.2 –0.2 19.4 2.01.717.1H2CO 17.419.10.5 18.1 0.5 20.4 2.818.14.2(CH3)2O 17.621.8–0.1 18.3 –0.3 20.8 2.218.52.8NH3 18.621.40.3 19.3 0.3 21.5 2.519.32.8CH3CH2OH 19.021.819.10.1 18.9 –0.1 22.1 3.120.3(CH3)3N 19.01.3CH3NH2 19.50.0 19.4 –0.1 22.0 2.519.52.321.8–0.1 19.4 –0.2 22.2 2.619.521.31.7(CH3)2NH 19.62.720.4 0.0 20.4 0.0 23.6 3.223.1i-CH3(CH2)2OH 20.423.4 3.0 23.4 3.0 26.1 5.75.1Pyridine 20.425.522.2 1.2 22.1 1.1 25.0 4.03.524.5Pyrazole 21.0(CH3CH2)2O 21.3–0.3 21.0 –0.3 24.7 3.421.02.123.44-Methylpyrazole 21.5 26.3 4.8 23.9 2.4 23.9 2.4 26.9 5.40.0 23.4 0.1 26.0 2.723.32.225.5CH3CO2CH3 23.3CH3CN 23.60.5 24.1 0.5 26.4 2.824.126.12.523.8–0.3 23.8 –0.3 26.4 2.32.726.8(CH3)2CO 24.127.9 0.6 27.8 0.5 30.7 3.42.9Imidazole 27.330.2CH3CONH2 27.428.71.3 28.8 1.4 31.7 4.34.131.530.31.1 30.3 1.1 33.4 4.25.034.2CH3CONHCH3 29.20.5 30.7 0.6 35.1 5.030.62.9HCON(CH3)2 30.133.03.332.1 0.8 32.2 0.9 35.5 4.234.6CH3CON(CH3)2 31.30.63.60.73.1MAD114115(a)10.015.020.025.030.035.010.015.020.025.030.035.0ExperimentalG 2(b)10.015.020.025.030.035.0ExperimentalG 2(M P 2)(c)10.015.020.025.030.035.0ExperimentalG 3(d)10.015.020.025.030.035.040.05.010.015.020.025.030.035.0ExperimentalD F TFig. 2. Correlations between the experimental and calculated sodium cation basicities calculated atthe G2 (a), G2(MP2) (b), G3 (c), and DFT B3LYP/6-311+G** (d) levels. All SCB values are in kcal/mol.Potassium cation basicitiesIn the current work we calculated PCB values using the high-level G2, G2(MP2), and G3 methods. The DFT B3LYP/6-311+G** method was also tested. The calculated PCB values are reported in Table 4, the results of correlation analysis (squares of correlation coefficients, slopes, and intercepts of correlation lines) are given in Table 2 and the correlations are graphically represented in Fig. 3.The calculated PCB values are in an excellent correlation with the experimental ones – for all methods used the MAD is 0.7–0.8 kcal/mol, R 2 are 0.96, and the correlation lines have slopes and intercepts close to ideal, in the case of G2(MP2) even equal with those within statistical error. So, the DFT B3LYP/6-311+G** method seems to be the best compromise between speed and accuracy for the calculation of PCBs.Experimental Experimental Experimental Experimental116Table 4. Experimental and calculated at different levels potassium cation basicities (PCB) and the differences between the experimental [52] and calculated PCBs (∆). MAD is the mean absolute difference between the experimental and calculated PCBs at the given level of theory. All values are in kcal/molB3LYP G2 G2(MP2) BaseExp. PCBPCB∆ PCB ∆ PCB ∆C 6H 5OH 11.0 10.9 –0.1 12.9 1.9 12.9 1.9H 2O 11.5 12.2 0.7 10.8 –0.7 10.8 –0.7 NH 3 11.8 12.9 1.1 12.1 0.3 12.0 0.2 C 6H 6 11.9 9.2 – 2.7 13.8 1.9 13.9 2.0 CH 3NH 2 12.7 13.0 0.3 12.9 0.2 12.9 0.2 (CH 3)3N 13.0 11.6 – 1.4 12.9 –0.1 12.9 –0.1 Pyrrole 13.0 12.9 – 0.1 13.9 0.9 13.9 0.9 (CH 3)2NH 13.1 12.4 – 0.7 13.1 0.0 13.1 0.0 (CH 3)2O 13.4 13.6 0.2 12.9 –0.5 12.9 –0.5 Pyrazole 13.9 15.3 1.4 15.5 1.6 15.6 1.7 n-CH 3(CH 2)2NH 2 14.2 14.1 – 0.1 14.4 0.2 14.4 0.2 (CH 3CH 2)2O 14.9 14.6 – 0.3 14.8 –0.1 14.9 0.0 Pyridine 15.2 16.4 1.2 16.6 1.4 16.6 1.4 4-Methylpyrazole 16.2 17.3 1.1 17.0 0.8 17.1 0.9 2-Methylpyridine 16.7 16.0 – 0.7 16.5 –0.2 16.6 –0.1 CH 3CN 18.0 17.8 – 0.2 17.8 –0.2 17.9 –0.1 (CH 3)2CO 19.0 18.5 –0.5 18.0 –1.0 18.1 –0.9 Imidazole 20.0 20.2 0.2 20.4 0.4 20.4 0.4 HCON(CH 3)2 23.0 23.6 0.6 23.6 0.6 23.8 0.8 CH 3CON(CH 3)2 24.0 25.4 1.4 25.1 1.1 25.3 1.3 (CH 3)2SO 25.0 27.1 2.1 24.3 –0.7 24.5 –0.5 MAD 0.8 0.4 0.4(a)(b)10.012.014.016.018.020.022.024.026.0ExperimentalG 210.015.020.025.030.0ExperimentalG 2(M P 2)(c)10.012.014.016.018.020.022.024.026.0ExperimentalD F TFig. 3. Correlations between the experimentaland calculated potassium cation basicities calculated at the G2 (a), G2(MP2) (b), and DFT B3LYP/6-311+G** (c) levels. All PCB values are in kcal/mol.Experimental Experimental ExperimentalCONCLUSIONSWe compared the ability of the B3LYP/6-311+G**, G2(MP2), G2, G3, and CBS-QB3 methods to predict the gas-phase complexation free energies of lithium, sodium, and potassium cations with Lewis bases.The best results for lithium cation basicities were obtained with the G2, G2(MP2), and CBS-QB3 methods. So, the G2(MP2) method seems to be the best compromise between speed and accuracy for the calculations of LCBs. Also the quicker DFT B3LYP/6-311+G** level of theory can be used for quantitative prediction of LCBs, if the systematic error is taken into account. Calculations of dimethylsulphoxides LCB at the W1 level suggest that the discrepancy between experimental and calculated values in the high LCB region might originate from the accumulation of experimental errors and new experimental measurements in the high LCB (>36 kcal/mol) region are needed to clarify the origin of the discrepancy [44] between the experiment and calculations.SCB and PCB values seem to be much easier to predict. For SCB both G2 and G2(MP2) methods give excellent correlation and small MAD (0.6–0.7 kcal/mol) so that G2(MP2) can be suggested for the calculation of SCB. The less expensive DFT B3LYP/6-311+G** method gave also good correlation with the experiment. Potassium cation affinities can be calculated with equal accuracy using the G2, G2(MP2), and B3LYP/6-311+G** methods.ACKNOWLEDGEMENTThis research was partly funded by Estonian Science Foundation grant No. 6695.REFERENCES1. Dzidic, I. & Kebarle, P. Hydration of the alkali ions in the gas phase. 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Experimental and theoretical study oflithium(1+) affinities of methyldiazoles. J. Phys. Chem., 1990, 94, 4796–4804.118。

Correlated neuronal activity and the flow of neural information SalinasSejnowski(2001)

REVIEWSa 50S p i k e s s –1b c500 ms–500 50 03–5050 03 –500 5003Time shift (ms)d e f250 ms–100100 01–100100 01 –1001001Time shift (ms)Time shift (ms)Time shift (ms)Time shift (ms)Time shift (ms)50S p i k e s s –10Figure 1 |Synthetic computer-generated spike trains with various correlation patterns.Each panel includes a raster plotR E V I E W S83.Fellous, J.-M. & Sejnowski, T. J. Cholinergic induction ofspontaneous oscillations in the hippocampal slice in theslow (0.5–2 Hz), theta (5–12 Hz) and gamma (35–70 Hz)bands. Hippocampus10, 187–197 (2000).Three kinds of rhythmic activity are observed ina hippocampal slice preparation, and a singleneuromodulator can shift the dynamics from onemode to another. A model for this concentration-dependent switching is developed in the referencebelow.84.Tiesinga, P. H. E., Fellous, J.-M., José, J. V. & Sejnowski, T. J.Computational model of carbachol-induced delta, theta and gamma oscillations in the hippocampus. 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Whenattention shifts to the recorded neuron’s receptivefield, the unit and its neighbours become moresynchronized with respect to rapid (50-Hz)fluctuations, but less so with respect to slow (10-Hz)fluctuations. Attention seems to cause a complex yetstereotyped change in the dynamics of the localcircuit of visual neurons.114.Frost, J. D. Jr An averaging technique for detection ofEEG–intracellular potential relationships. Electroencephalogr.Clin. Neurophysiol. 23, 179–181 (1967).115.Goto, Y. & O’Donnell, P. Network synchrony in the nucleusaccumbens in vivo. J. Neurosci. 21, 4498–4504 (2001).116.Fries, P., Neuenschwander, S., Engel, A. K., Goebel, R. &Singer, W. Rapid feature selective neuronal synchronizationthrough correlated latency shifting. 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USA98, 1282–1287(2001).A model for speech recognition in which a set ofsensory units responds, a downstream populationbecomes activated and synchronized, and a thirdpopulation further downstream responds selectivelyto the evoked synchrony patterns. The model showshow oscillations generated centrally could confera functional advantage to a neural circuit.124.Tuckwell, H. C. Introduction to Theoretical NeurobiologyVols 1 & 2 (Cambridge Univ. Press, New York, 1988).125.Koch, C. Biophysics of Computation(Oxford Univ. Press,New York, 1999).AcknowledgementsResearch was supported by the Howard Hughes Medical Institute.We thank P. Steinmetz for providing us with Figure 3, and P. Friesfor providing us with Figure 4. We also thank J. Reynolds andP. Tiesinga for helpful comments.550| |。

New variants of the Bell-Kochen-Specker theorem

a rXiv:q uant-ph/9761v13J u n1997New variants of the Bell-Kochen-Specker theorem ∗Ad´a n Cabello †Jos´e M.Estebaranz Guillermo Garc´ıa-Alcaine Departamento de F´ısica Te´o rica,Universidad Complutense,28040Madrid,Spain.May 20,1996Abstract We discuss two new demonstrations of the Bell-Kochen-Specker theorem:a state-independent proof using 14propositions in R 4,based on a suggestion made by Clifton,and a state-speciﬁc proof involving 5propositions on the singlet state of two spin-1∗Phys.Lett.A 218,115(1996).†Electronic address:ﬁte1z1@sis.ucm.esWe recently found[1]a proof of the Bell-Kochen-Specker(BKS in the following)theorem[2,3]using18vectors in R4.While the paper was in press,Clifton[4]suggested to us a modiﬁcation allowing a further reduction of this number.In theﬁrst part of this paper we will discuss Clifton’s idea. In the second part we will take advantage of the properties of the singlet state of two spin-1subspaces complementary to these common vectors,we reduce the system to only5equations with14vectors,for instance,v(0,0,1,0)+v(1,1,0,0)+v(1,−1,0,0)=(1)v(0,1,0,0)+v(1,0,1,0)+v(1,0,−1,0),v(1,−1,−1,1)+v(1,1,0,0)+v(0,0,1,1)=(2)v(1,1,1,1)+v(1,0,−1,0)+v(0,1,0,−1),v(0,0,1,0)+v(0,1,0,0)+v(1,0,0,1)=(3)v(1,−1,−1,1)+v(1,1,1,1)+v(0,1,−1,0),v(1,1,−1,1)+v(1,0,1,0)+v(0,1,0,−1)=(4)v(1,1,1,−1)+v(1,0,0,1)+v(0,1,−1,0),v(1,1,−1,1)+v(1,1,1,−1)+v(1,−1,0,0)+v(0,0,1,1)=1.(5) We can now formulate the following version of the BKS theorem: There is no set of values v(u i)verifying eqs.(1–5).The proof involves a parity argument:if we add theseﬁve equations,each value v(u i)appears either twice on the same side of the resulting equation, with an even contribution(0or2),or once on each side,with a cancella-tion of both contributions;the extra term1on the right-hand side makes it impossible to satisfy the equality.This14-vector set(or any of the many others that we can obtain similarly) leads to a proof of the BKS theorem based on the explicit use of(c),according to Clifton’s suggestion.Condition(c)is a direct consequence of(a)and(b), and does not impose any new requirement on hidden variables.Its use is not an artiﬁce to leave out some propositions when counting the number of them involved in the proof,because no concrete value for the propositions eliminated is assumed;the proof stands,whatever the values of the omitted propositions.Let us now consider a system of two spin-1Replacing these values into eqs.(7,8)of ref.[1](or into eqs.(5)and(4)in this paper,respectively,but the intermediate step to obtain(4)from(8,9) in[1]is actually unnecessary)we obtainv(1,1,−1,1)+v(1,−1,0,0)+v(0,0,1,1)=1,(6)v(1,1,−1,1)+v(1,0,1,0)+v(0,1,0,−1)=1.(7) The hidden variables values for the four non-repeated propositions in(6,7) satisfy the following relation in the singlet state:v(1,−1,0,0)+v(0,0,1,1)+v(1,0,1,0)+v(0,1,0,−1)=1.(8)The proof of(8)is straightforward:ﬁrst,the value of a factorizable propo-sition(like the ones appearing in(8))is the product of the values of its factors,v (a,b)(1)⊗(c,d)(2) =v (a,b)(1) v (c,d)(2) 1;secondly,if in the singlet state we measure the spin component of each particle along thesame(arbitrary)direction,the results are perfectly correlated(always op-posite),and the same relations must exist between the values of proposi-tions in any deterministic hidden variables theory,v (1,0)(1) =v (0,1)(2) , v (1,1)(1) =v (1,−1)(2) ,etc...Therefore the left-hand side of(8)can be written as follows:v (1,0)(1) v (1,−1)(2) +v (0,1)(1) v (1,1)(2)+v (1,1)(1) v (1,0)(2) +v (1,−1)(1) v (0,1)(2)(9) = v (1,0)(1) +v (0,1)(1) × v (1,1)(1) +v (1,−1)(1) =1.The last equality in(9)is a consequence of condition(b)in the2-dimensional space of the spin states of theﬁrst particle,q.e.d.Theﬁrst two vectors in eq.(8)are not orthogonal to the last two,and the corresponding projectors do not commute;moreover,there is no common eigenstate to the four projectors.Then,we can neither prepare a system in a state free of dispersion for the four propositions,nor check experimentally the relation by a simultaneous measurement of them;in this sense this relation is diﬀerent from those that implement condition(b)(4 i=1|u i u i|=1⇒4 i=1v(u i)=1),for which there are states simultaneously free of dispersion for each tetrad of compatible projectors,and which can(in principle)be experimentally veriﬁed in any state.But in deterministic hidden variables theories non-compatible observables can have well deﬁnite values in the same individual system,and therefore eq.(8),although not consequence of a resolution of the identity,is a legitimate relation between hidden variables values,based on the properties of the singlet state.We now state our second no-go theorem:There is no set of values v(u i)verifying eqs.(6–8).The proof rests once more on a parity argument:each proposition appears twice in(6–8),but the sum of the right-hand sides is3.We willﬁnish with a reﬂexion on the number of propositions involved in the proof;we have counted them explicitly using another consequence of requisites(a)and(b):(d)Given an individual system prepared in a state w and a set of vectors{u i}spanning a subspace U that contains w,then i v(u i)=1.A reason for this was given in[6]:any vector v in the subspace complemen-tary to U is orthogonal to w;v(w)=1and(b)imply v(v)=0;therefore the sum of values of any complete set of compatible propositions on the subspace U must be1.This can be justiﬁed too,without explicitly quoting the values v(v)=0,if we keep in mind that the probability ofﬁnding the system in a state in the subspace U is1.Condition(d)is a consequence of(a)and(b), but in contradistinction to what happened in(c),now the propositions omit-ted have deﬁnite zero values,and therefore it could be argued that using(d) is essentially a way to count the number of vectors appearing in state-speciﬁc4proofs[6],leaving out the vectors orthogonal to the initial state2.In our example,both triads of vectors on the left-hand sides of(6,7)span subspaces that contain the singlet(0,1,−1,0),and therefore both equations are a direct application of rule(d).If we use this condition,we only need to count the5vectors explicitly appearing in(6,7);a count of the number of propositions with deﬁnite values involved in the theorem,based strictly on (a)and(b),should also include the omitted vectors(1,1,1,−1),(−1,1,1,1) (and perhaps the initial state too).In the second part of this paper we have proved the impossibility of as-signing non-contextual values to a set of propositions in the singlet state. The contradiction arises in only3equations involving5propositions in R4: in terms of number of propositions,this state-speciﬁc BKS proof is the most economic no-go theorem that we know of.We would like to thank Rob Clifton for the private communication that got this paper started,the anonymous referee for his comments on the second part,and Asher Peres and Gabriel´Alvarez for their patience and advice.References[1]A.Cabello,J.M.Estebaranz and G.Garc´ıa-Alcaine,Phys.Lett.A212(1996)183.[2]J.S.Bell,Rev.Mod.Phys.38(1966)447.[3]S.Kochen and E.P.Specker,J.Math.Mech.17(1967)59.[4]R.K.Clifton(private communication).[5]A.Einstein,B.Podolsky and N.Rosen,Phys.Rev.47(1935)777.[6]M.Kernaghan and A.Peres,Phys.Lett.A198(1995)1.[7]J.S.Bell,Phys.1(1964)195.[8]N.D.Mermin,Phys.Rev.Lett.65(1990)3373;Rev.Mod.Phys.65(1993)803.[9]L.Hardy,Phys.Rev.Lett.68(1992)2981;71(1993)1665;R.K.Cliftonand P.Niemann,Phys.Lett.A166(1992)177;S.Goldstein,Phys.Rev.Lett.72(1994)1951.[10]A.Peres,Phys.Lett.A151(1990)107.6。

21世纪大学英语读写教程(第四册)修订版 TEXTA B课文翻译

unit1TEXTA谁是伟大的？迈克尔?赖恩阿尔伯特?爱因斯坦小时候在学校里的成绩很糟糕，老师们都认为他迟钝。

拿破仑?波拿巴年轻时只是法国陆军中几百名炮兵中尉中的一几乎没有受过正规教育的乔治?华盛顿，十几岁时不是受训当兵而是受训做土地测量员。

尽管他们的起步平淡无奇，但是每个人后来都为自己在历史上赢得了一席之地。

是什么使得他们变得伟大呢？是他们生来就具备一些特殊的东西？还是他们的伟大与时机掌握、献身精神和也许是一种坚定的个性更为有关？几十年来，科学家们一直在问这样的问题。

在过去几年里，他们已经发现了证据，这些证据有助于解释为什么有些人出类拔萃，而另外的人——也许同样很有才华——却被甩在了后面。

他们的发现可能对我们所有的人都有启示。

谁是伟大的？伟人的定义取决于如何衡量成功。

但标准还是有一些的。

“对人类文明作出永久性贡献的人是伟大的，”基思?西蒙顿院长说。

他是加州大学戴维斯分校的一名心理学教授，1994年出版的《伟大：谁创造历史，以及为什么》一书的作者。

但他又提醒说：“有时侯伟人并没有被载入史册。

许多女性取得了巨大成就，或者颇具影响力，但却没有得到承认。

”在这本书的写作中，西蒙顿把有关伟大人物的历史知识和遗传学、精神病学及社会科学领域的最新发现融合在了一起。

他所聚焦的伟人包括获得过诺贝尔奖、领导过伟大的国家或赢得过战争、谱写过流芳百世的交响乐或在科学、哲学、政治学或艺术上引起过革命性巨变的男性和女性。

虽然他没有一个公式来解释某些人怎样或为什么出类拔萃（其中涉及的因素太多了），但他却提出了一些共同的特点。

一种“永不屈服”的态度。

西蒙顿说，如果事业上取得巨大成就者具有什么共性的话，那就是一种持续不断地追求成功的动力。

“人们往往认为他们天生具有一些超常非凡的东西，”他解释道。

“但研究结果表明，有的伟人并没有惊人的智力。

有的只是程度上的差异而已。

伟大是建立在大量的学习、实践和献身精神的基础之上的。

”他举出二战时期的英国首相温斯顿?丘吉尔作为一个永不放弃的冒险者的典范。

Conformal Field Theory Approach to the Kondo Effect

a r X i v :c o n d -m a t /9512099v 1 12 D e c 1995Conformal Field Theory Approach to the Kondo Eﬀect ∗Ian AﬄeckCanadian Institute for Advanced Research and Physics Department,University of British Columbia,Vancouver,BC,V6T 1Z1,CanadaRecently,a new approach,based on boundary conformal ﬁeld theory,has been applied to a variety of quantum impurity problems in condensed matter and particle physics.A particularly enlightening example is the multi-channel Kondo problem.In this review some earlier approaches to the Kondo problem are discussed,the needed material on boundary conformal ﬁeld theory is developed and then this new method is applied to the multi-channel Kondo problem.OUTLINEI.Renormalization Group and Fermi Liquid Approaches to the Kondo Eﬀect A)Introduction to The Kondo Eﬀect B)Renormalization Group Approach C)Mapping to a One Dimensional Model D)Fermi Liquid Approach at Low TII.Conformal Field Theory (“Luttinger Liquid”)Techniques:Separation of Charge and Spin De-grees of Freedom,Current Algebra,“Gluing Conditions”,Finite-Size SpectrumIII.Conformal Field Theory Approach to the Kondo Eﬀect:“Completing the Square”A)Leading Irrelevant Operator,Speciﬁc Heat,Susceptibility,Wilson Ratio,Resistivity at T >0IV.Introduction to the Multi-Channel Kondo Eﬀect:Underscreening and Overscreening A)Large-k LimitB)Current Algebra Approach V.Boundary Conformal Field TheoryVI.Boundary Conformal Field Theory Results on the Multi-Channel Kondo Eﬀect:A)Fusion and the Finite-Size Spectrum B)Impurity EntropyC)Boundary Green’s Functions:Two-Point Functions,T=0Resistivity D)Four-Point Boundary Green’s Functions,Spin-Density Green’s Function E)Boundary Operator Content and Leading Irrelevant Operator:Speciﬁc Heat,Susceptibility,Wilson Ratio,Resistivity at T >0I.RENORMALIZATION GROUP AND FERMI LIQUID APPROACHES TO THE KONDO EFFECTA.Introduction to the Kondo EﬀectMost mechanisms contributing to the resistivity of metals,ρ(T),give eitherρ(T)decreasing to0, as T→0(phonons or electron-electron interactions),orρ(T)→constant,as T→0(non-magnetricimpurities).However,metals containing magnetic impurities show aρ(T)which increases as T→0.This was explained by Kondo1in1964using a simple Hamiltonian:H= kαψ†α kψ kαǫ(k)+λ S· k k′ψ† k σ+...]2(1.2)THere D is the band-width,νthe density of states.This result stimulated an enormous amount oftheoretical work.As Nozi`e res put it,“Theorists‘diverged’on their own,leaving the experimentrealities way behind”.2What happens at low T,i.e.T∼T K=De−1ψ( 0,t) ,2where theﬁelds are in the interaction picture.EFk F2D’Ek2DFIG.1.Reduction of the cut-oﬀfrom D to D ′.As S(t )is independent of t ,we simply multiply powers of S using [S a ,S b ]=iǫabc S c ,S2=s (s +1).We must time-order S ’s which don’t commute.The ﬁrst few diagrams are shown in Figure (2).In 2nd order in λ,we have:−λ22ψ(t )ψ†(t ′)σb2λ2dt dt ′ψ†σa2ψT ψ(t )ψ†(t ′) (θ(t −t ′)S a S b +θ(t ′−t )S b S a )=λ22ψ· Ssn(t −t ′) ψ(t )ψ†(t ′) ,(1.3)where sn (t −t ′)is the sign-function which arises from T -ordering spins.FIG.2.Feynman diagrams contributing to renormalization of the Kondo coupling constant to third order.We see that the integraldtǫ(t )G (t )=−idt(2π)3dωiω+δ+1ω−ǫk +iδsn(ǫk )(1.5)=d 3 k|ǫk |≈2νDD ′dǫD ′.(1.6)Thusδλ=νλ2lnDd ln D=−νλ2.(1.8)We see that lowering the band cut-oﬀincreases λor,deﬁning a length-dependent cut-oﬀ,l ∼v F /D ,dλ1−νλ0lnD 0νλ0,If λ0<0(ferromag-netic),λeﬀ(D )→0.See Figure(3).effλFIG.3.RG ﬂow of the Kondo coupling.The behaviour at temperature T is determined by λeﬀ(T ):ρ(T )→0as T →0for the ferromag-netic case.What happens for the antiferromagnetic case?C.Mapping to a One-Dimensional ModelThe above discussion can be simpliﬁed if we map the model into a one dimensional one.Weassume a spherically symmetricǫ( k),ǫ(k)=k2√2ψ0,k′· S,(1.12)whereν=k2F/2π2v F is the density of states per spin.This can also be written in terms of radialco-ordinate.We eliminate all modes except for a band width2D:|k−k F|<D.Deﬁning left andright movers(incoming and outgoing waves),ΨL,R(r)≡ ∧−∧dke±ikrψ0(k+k F),⇒ψL(0)=ψR(0),(1.13) we haveH0=v FdrψL−ψ†R i d2ψL(0)· S.(1.14)Here we have redeﬁned a dimensionless Kondo coupling,λ→λν.Using the notationψL=ψL(x,τ)=ψL(z=τ+ix),ψR(x,τ)=ψR(z∗=τ−ix),(1.15) whereτis imaginary time and x=r,(and we set v F=1)we haveψL(z)ψ+L(0) =1z∗.(1.16) Alternatively,sinceψL(0,τ)=ψR(0,τ)ψL=ψL(z),ψR=ψR(z∗),(1.17) we may considerψR to be the continuation ofψL to the negative r-axis:ψR(x,τ)≡ψL(−x,τ).(1.18) Now we obtain a relativistic(1+1)dimensionalﬁeld theory(a“chiral”one,containing left-moversonly)interacting with the impurity at x=0withH0=v FdxψL(1.19)and H INT as in Eq.(1.14).See Figure(4).LLL RFIG.4.Reﬂecting the left-movers to the negative axis.D.Fermi Liquid Approach at Low TWhat is the T →0behavior of the antiferromagetic Kondo model?The simplest assumption is λeﬀ→∞.But what does that really mean?Consider the strong coupling limit of a lattice model,2for convenience,in spatial dimension D =1.(D doesn’t really matter since we can always reduce the model to D =1.)H =ti(ψ†i ψi +1+ψ†i +1ψi )+λ S ·ψ†0σl(n +1/2)λ=∞:k =πnNear the Fermi surface the energies are linearly spaced.Assuming particle-hole symmetry,the Fermi energy lies midway between levels or on a level.[See Figures(5)and(6).]The two situations switch with the phase shift.Wilson’s numerical RG scheme3involves calculating the low-lying spectrum numerically and looking for this shift.This indicates thatλrenormalizes to∞even if it is initially small.However,now we expect the screening to take place over a longer length scaleξ∼v FDe1/νλ.(1.24)In other words,the wave function of the screening electron has this scale.We get low energy Bloch states of free electrons only for|k−k F|<<1/ξ(so we must take l>>ξ).[See Figure(7).]The free electron theory with a phase shift corresponds to a universal stable low energyﬁxed point for the Kondo problem.This observation determines the T=0resistivity for an array of Kondo impurities at random locations of low density n i.It is the same as for non-magnetic s-wave scatterers with a π/2phase shift at the Fermi energy.δ=π/2gives the so-called unitary limit resistivity:ρu=3n ik-kFconditions.FIG.6.Free fermion energy levels with periodic boundary1k-kFFIG.7.Non-interacting Bloch states with a vanishing boundary condition occur for|k−k F|<<v F/T K.The low-T behaviour,so far,seems trivial.Much of the interesting behaviour comes from the leading irrelevant operator.The impurity spin has disappeared(screened)from the description ofthe low-T physics.However certain interactions between electrons are generated(at the impuritysite only)in the process of eliminating the impurity spin.We can determine these by simply writingthe lowest dimension operators allowed by symmetry.It is simplest to work in the1D formulation,with left-movers only.We write the interaction in terms ofψL,obeying the new boundary condition(but notψL+....(1.26)dxThe length and time dimensions are equivalent(we convert with v F),[H]=E⇒[ψ]=E1The interactions are localδH= iλi O i(x=0),[λi]+[O i]=1.Soλi has negative energy dimension if[O i]>1,implying that it is irrelevant.In RG theory one usually deﬁnes a dimensionless coupling constant by multiplying powers of the cut-oﬀD,if[λi]=E−a,˜λi≡λi D a,˜λidecreases as we lower D:d˜λidx ψα(0)−id3v FlT+aT3v FT+ln2.(1.30)At low T,the impurity entropy decreases to0:S(T)=πlT K.(1.31)In general we may write:S(T)−πl2πv F +b2πv F +12πv F +1ln(T/T K)+... .(1.35)In general,we may write:χ−lTf(T/T K),(1.36)where f(T/T K)is another universal scaling function.See Figure(9).T KKTKln (T/T )4T+...Tχ 1 - 1imp1bFIG.9.Qualitative behaviour of the impurity susceptibility.The temperature dependent part of the low T resistivity for the dilute random array is 2nd orderin perturbation theory,ρ=ρu [1−dTWe start by considering a left-moving spinless fermion ﬁeld with Hamiltonian density:H =1dxψL .(2.1)Deﬁne the current (=density)operator,J L (x −t )=:ψ+L ψL :(x,t )=lim ǫ→0[ψL (x )ψL (x +ǫ)− 0|ψL (x )ψL (x +ǫ)|0 ](2.2)(Henceforth we generally drop the subscripts “L”.)We will reformulate the theory in terms ofcurrents (key to bosonization).Consider:J (x )J (x +ǫ)asǫ→0=:ψ†(x )ψ(x )ψ†(x +ǫ)ψ(x +ǫ):+[:ψ†(x )ψ(x +ǫ):+:ψ(x )ψ†(x +ǫ):]G (ǫ)+G (ǫ)2G (ǫ)= 0|ψ(x )ψ†(x +ǫ)|0 =1ǫ2]=lim ǫ→01dxψ:H =1(x −y −iδ)2+1dx1x −y +iδ=2πid2∂φ2∂φ∂tφ(y )]=iδ(x −y )(2.7)We can again decompose it into the left and right-moving parts,(∂t 2−∂x 2)φ=(∂t +∂x )(∂t −∂x )φφ(x,t )=φL (x +t )+φR (x −t )(∂t −∂x )φL ≡∂−φL =0,∂+φR =0H =14(∂+φ)2=14(∂+φL )2(2.8)Consider the Hamiltonian density for a left-moving boson ﬁeld:H =1dxδ(x −y )(2.9)Comparing to the Fermionic case,we see that:J L =√π∂+φ,(2.10)12since the commutation relations and Hamiltonian are the same.That means the operators are the same with appropriate boundary conditions.Let’s compare the spectra.For the Fermionic case,choose boundary condition:ψ(l)=−ψ(−l)(i.e.ψL(l)+ψR(l)=0),k=π2),n=0,±1,±2...(2.11)[See Figure(5).Note that we have shifted k by k F.]Consider the minimum energy state of chargeQ(relative to the ground state).See Figure(11).We have the single Fermion energy:E=v F k,(2.12) so:E(Q)=v F π2)=v Fπl ( 1k-kFFIG.12.A particle-hole excitation in which three electrons are raised four levels and then one electron is raised three levels.Now consider the bosonic spectrum.What are the boundary conditions?Try the periodic one,φ(l)=φ(−l)⇒k=πml (∞ 1n m·m),n m=occupation number:0,1,2,...(2.16)Where does the Q2term in Eq.(2.14)come from?We need more general boundary condition on the bosonﬁeld.Letφbe an angular variable:φL(−l)=φL(l)+√πl·(x+t)+∞m=114πm(e−iπm2 ∂φ2 ∂φl[14πφL,(2.19) which gives the correct Green’s function and implies the same angular deﬁnition ofφL.For the Kondo eﬀect we are also interested in the phase-shifted boundary condition:[See Figure(6).]ψL(l)=+ψL(−l),k=πl Q(Q−1)We have the degenerate ground state,Q=0or1,which correspond to an anti-periodic boundary condition onφ,φ(l)=φ(−l)+√2)E=π2(Q−1l(1dxψα,(α=1,2,summed).(2.22)Now we have charge and spin currents(or densities).We can write H in a manifestly SU(2)invariant way,quadratic in charge and spin currents:J=:ψα†ψα:, J=ψ†ασβα4:ψ†αψαψ†βψβ:+3idxψα+c-number,J2=:ψ†αψαψ†βψβ:+2iψα+d8πJ2+12[J↑+J↓,J↑−J↓]=0.(2.26) From[J, J]=0,we see that H is sum of commuting charge and spin parts.[J a(x),J b(y)]=2πψ†[σ2b]ψ·δ(x−y)+tr[σ2b]2πiddxδ(x−y).(2.27)We obtain the Kac-Moody algebra of central charge k=1.More generally the coeﬃcient of the second term is multiplied by an integer k.Fourier transforming,Jn≡1lx J(x),[J an,J b m]=iǫabc J c n+m+1l18πJ2+14π(J2↑+J2↓)=14[(∂+(φ↑+φ↓2))2+(∂+(φ↑−φ↓2))2] =1Now we have introduced two commuting charge and spin free massless bosons.SU(2)symmetry is now concealed but boundary condition on φs must respect it.Consider the spectrum of fermion theory with boundary condition:ψ(l )=−ψ(−l ),E =πV22+Q ↓2(Q ↑−Q ↓)E =πv F4Q 2+(S z )2+∞ 1mn c m+∞1mn s m ](2.32)=E c +E sφc =√2√l (x +t )+...φs=π2S zl[1l[12),(±1,0).(2.36)Now Q =2S z +1(mod 2);i.e.we “glue”together charge and spin excitations in two diﬀerent ways,either(even,integer)⊕(odd,half-integer)or (even,half-integer)⊕(odd,integer),(2.37)depending on the boundary conditions.Theπl·(integer).Likewise for all half-integer spin states,(s z )2=1dxψLα+λψ†αLσβα8πJ 2+1The Kondo interaction involves spinﬁelds only,not chargeﬁelds:H=H s+H c.Henceforth we only consider the spin part.In Fourier transformed form,H s=π3∞ n=−∞ J−n· J n+λ∞ n=−∞ J n· S)[J a n,J b m]=iǫabc J c n+m+ndlnD =−λ2+···.That is a smallλ>0grows.What is the infrared stableﬁxed point?Considerλ=23l∞ n=−∞[( J−n+ S)·( J n+ S)−32δabδn,−m.(3.4)H is quadratic in the new currents, J n≡ J n+ S,which obey the same Kac-Moody algebra!Whatis the spectrum of H(λ=21−32-Integer.See Figure(13).This is equivalent to aπ2-integer)(even,16πJ(x)2+λ1 J(0)2δ(x).(3.9)This is the only dimension-2rotationally invariant operator in the spin sector.We have succeededin reducing two dimension-2operators to one.The other one is the charge-operatorλ2J(0)2δ(x),λ2=0because there is no interaction in the charge sector(with other regularization we expectλ1∼1D<<λ1).171/2 integer-s tower towerinteger-s FIG.13.At λ=2/3the 1/2-integer-spin conformal tower is mapped into the integer-spin conformal tower.Now we calculate the speciﬁc heat and susceptibility to 1st order in λ1.Susceptibility of left-moving free fermions:0-th orderM =12)−n (ǫ−h2π(forT <<D )1st order χ=13T 2[dx J(x )]2 J (0)2 +...(3.10)A simplifying trick is to replace:δH =λ1 J2(0)δ(x )−→λ16π+λ1l)H.(3.13)Equivalently in a thermal average,T →Tl≡T (λ1)(3.14)χ(λ1,T )=11+3πλ1/l χ(0,T (λ1))≈[1−3πλ12π−3λ1where in the last equality theﬁrst term represents the bulk part and the second one,of order∼13.(3.16) Each free left-moving boson makes an identical contribution.1st order inλ1C s(λ1,T)=∂3T3−π2λ1T(3.17)δC sl =2δC sδC/C =2=C8π2r1r2[e−ik F(r1+r2)(G LR(r1,r2)−G LR,0(r1,r2))+h.c.]=G03(r1)ΣG03(r2).(3.20) The self-energyΣdepends only on the frequency.It gets multiplied by the impurity concentationfor aﬁnite density(in the dilute limit).We must calculate the1D Green’s function G LR(r1,r2,ω) perturbatively inλO(λ01):G LR(r1,r2)=−G0LL(r1,−r2)=−G0LL(r1+r2)=−G0LR(r1,r2),(3.21) where the(−)sign comes from the change in boundary conditions,G LR−G0LR=−2G0LR+O(λ1)(3.22) To calculate to higher orders it is convenient to write the interaction as:J2=−34(ψ†αddxψα)(3.23)To second order inλ1,we have the Feynman diagrams shown in Figure(14),giving:ΣR(ω)=−in2(3πλ1)2ω2−12πν[1−e2iδ(ω)]+ΣR inel(ω)δ=π2+...1τ(ω)=n i2(3πλ1)2ω2−1The leadingλ1dependence is O(λ21)in this case.The O(λ1)term inΣR is real.We calculate the conductivity from the Kubo formula.(There is no contribution from the scattering vertex for pure s-wave scattering.)σ(T)=2e2(2π)3 −∂n2n i[1+18(3πλ1)2(ǫ2k+(π2T2)]ρ(T)=1π(ev Fν)2[1−9T K.Numerical or Bethe ansatz methods are needed toﬁnd the precise value ofλ1(D,λ)∝1d lnD =−νλ2+k2ψ0,(4.3)forλ>0(antiferromagnetic case)the minimum energy state has maximum spin for electrons at0i.e.spin=k/2.Coupling this spin-k/2to a spin-s,we don’t get a singlet if s=k/2,but ratheran eﬀective spin of size|s−k/2|.[See Figure(15).]The impurity is underscreened(k/2<s)or overscreened(k/2>s).20FIG.15.Formation of an eﬀective spin at strong Kondo coupling.k=3,s=1and s eﬀ=1/2. Now let tλ2<<1See Figure(16).What is the sign ofλeﬀ?The coupling of the electron spins is antiferromagnetic:λeﬀ S e1,0· S e1,1,withλeﬀ>0(as in the Hubbard model).But we must combine spinsSeﬀ= S+ Sel,0.(4.4)For k2>s, S ef f||+ S el,0.So,ultimately,λeﬀ<0in the underscreenedcase andλeﬀ>0in the overscreened case.In theﬁrst(underscreened)case,the assumptionλ→∞was consistent since a ferromagneticλeﬀ→0under renormalizaton and this impliesλ→∞,since λeﬀ∼−tFIG.18.The overscreened case with s=1/2,k=2.rge-k LimitTheβ-function is:β=λ2−kdk λc=2λc−3k.(4.7) This implies that the leading irrelevant coupling constant at the non-trivial(infrared)ﬁxed pointhas dimension2/k at large k,so that(λ−λc)scales asΛ2/k.Thus the leading irrelevant operatorhas dimension(1+2/k).This is not an integer!This implies that this critical point is not a Fermiliquid.B.Current Algebra ApproachWe can gain some insight into the nature of the non-trivial critical point using the current algebraapproach discussed in the previous section for the k=1case.It is now convenient to use a formof bosonization which separates spin,charge andﬂavour(i.e.channel)degrees of freedom.This representation is known as a conformal embedding.We introduce charge(J),spin( J)andﬂavour(J A)currents.A runs over the k2−1generators of SU(k).The corresponding elements of thealgebra are written T A.These are traceless Hermitean matrices normalized so that:tr T A T B=12 δb cδd a−12ψiβJ A≡ψ†iα(T A)j iψjα.(4.11) (All repeated indices are summed.)It can be seen using Eq.(4.9)that the free fermion Hamiltoniancan be written in terms of these currents as:H=12π(k+2) J2+1C V(G)+k,(4.14) where Dim(G)is the dimension of the group and C V(G)is the quadratic Casimir in the fundamental representation.For SU(k)this has the value:C V(SU(k))=k.(4.15) Thus the total value of the central charge,c,is:c TOT=1+3·kk+2=2k,(4.16)the correct value for2k species of free plicated“gluing conditions”must be imposed tocorrectly reproduce the free fermion spectra,with various boundary conditions.These were workedout in general by Altshuler,Bauer and Itzykson.27The SU(2)k sector consists of k+1conformaltowers,labelled by the spin of the lowest energy(“highest weight”)state:s=0,1/2,1,...k/2.32,33 We may now treat the Kondo interaction much as in the single channel case.It only involves thespin sector which now becomes:H s=12+k,(4.18) where the Hamiltonian reduces to its free form after a shift of the current operators by S whichpreserves the KM algebra.We note that at large k this special value ofλreduces to the one corresponding to the critical point:λc→2/k.While this observation is tantalizing,it leaves many open questions.We might expect that some rearranging of the(k+1)SU(2)k conformal towers takes place at the critical point but preciselywhat is it?Does it correspond to some sort of boundary condition?If so what?How can wecalculate thermodynamic quantities and Green’s functions?To answer these questions we need to understand some more technical aspects of CFT in the presence of boundaries.V.BOUNDARY CONFORMAL FIELD THEORY We will assume that the critical point corresponds to a conformally invariant boundary conditionon the free ing the general theory of conformally invariant boundary conditions developed by Cardy28we can completely solve for the critical properties of the model.Why assume that the critical point corresponds to such a boundary condition?It is convenient to work in the space-(imaginary)time picture.The impurity then sits at the boundary,r=0of the half-plane r>0 on which the Kondo eﬀect is deﬁned.If we consider calculating a two-point Green’s function when both points are taken very far from the boundary(with their separation heldﬁxed)then we expectto obtain bulk behaviour,unaﬀected by the boundary.[See Figure(19).]This,at long distances and times is the conformally invariant behaviour of the free fermion system.Very close to the boundary,we certainly do not expect the behaviour to be scale invariant(let alone conformallyinvariant)because various microscopic scales become important.The longest of these scales is presumably the Kondo scale,ξK≈v F/T L≈ae1/νλ.Beyond this distance,it is reasonable to expect scale-invariant behaviour.However,if the two points are far from each other compared to theirdistance from the boundary[Figure(20)]then the behaviour is still inﬂuenced by the boundary even when both points are far from it.We have a sort of boundary-dependent termination of the bulk conformally invariant behaviour.The dependence on the details of the boundary(such as the value ofξK)drops out.We may think of various types of boundaries as falling into universality classes,each corresponding to a type of conformally invariant behaviour.Rather remarkably,the above statements hold true whether we are dealing with a2-dimensional classical statistical system with some boundary condition imposed,or dealing with a(1+1)-dimensional quantum system with some dynamical degrees of freedom living on the boundary.In fact,we already saw an example of this in the single-channel Kondo problem.The dynamical impurity drops out of the description of the low-energy physics and is replaced by a simple,scale-invariant boundary condition,ψL=−ψR.FIG.19.The bulk limit.ξFIG.20.The boundary limit.Precisely what is meant by a conformally invariant boundary condition?Without boundaries,conformal transformations are analytic mappings of the complex plane:z ≡τ+ix,(5.1)into itself:z →w (z ).(5.2)(Henceforth,we set the Fermi velocity,v F =1.)We may Taylor expand an arbitrary conformaltransformation around the origin:w (z )=∞ 0a n z n ,(5.3)where the a n ’s are arbitrary complex coeﬃcients.They label the various generators of the conformalgroup.It is the fact that there is an inﬁnite number of generators (i.e.coeﬃcients)which makesconformal invariance so powerful in (1+1)dimensions.Now suppose that we have a boundary atx =0,the real axis.At best,we might hope to have invariance under all transformations whichleave the boundary ﬁxed.This implies the condition:w (τ)∗=w (τ).(5.4)We see that there is still an inﬁnite number of generators,corresponding to the a n ’s of Eq.(5.3)except that now we must impose the conditions:a ∗n =a n .(5.5)We have reduced the (still ∞)number of generators by a factor of 1/2.The fact that there is still an∞number of generators,even in the presence of a boundary,means that this boundary conformalsymmetry remains extremely powerful.To exploit this symmetry,following Cardy,it is very convenient to consider a conformally invariantsystem deﬁned on a cylinder of circumference βin the τ-direction and length l in the x direction,with conformally invariant boundary conditions A and B at the two ends.[See Figure (21).]Fromthe quantum mechanical point of view,this corresponds to a ﬁnite temperature,T =1/β.Thepartition function for this system is:Z AB =tr e −βH lAB ,(5.6)where we are careful to label the Hamiltonian by the boundary conditions as well as the length ofthe spatial interval,both of which help to determine the spectrum.Alternatively,we may make amodular transformation,τ↔x .Now the spatial interval,of length,β,is periodic.We write thecorresponding Hamiltonian as H βP .The system propagates for a time interval l between initial andﬁnal states A and B .Thus we may equally well write:Z AB =<A |e −lH βP |B >.(5.7)Equating these two expressions,Eq.(5.6)and (5.7)gives powerful constraints which allow us todetermine the conformally invariant boundary conditions.βBlA FIG.21.Cylinder of length l ,circumference βwith boundary conditions A andB at the two ends.To proceed,we make a further weak assumption about the boundary conditions of interest.We assume that the momentum density operator,T−¯T vanishes at the boundary.This amounts to a type of unitarity condition.In the free fermion theory this becomes:ψ†αi L ψLαi(t,0)−ψ†αiRψRαi(t,0)=0.(5.8)Note that this is consistent with both boundary conditions that occured in the one-channel Kondoproblem:ψL=±ψR.Since T(t,x)=T(t+x)and¯T(t,x)=¯T(t−x),it follows that¯T(t,x)=T(t,−x).(5.9) i.e.we may regard¯T as the analytic continuation of T to the negative axis.Thus,as in ourprevious discussion,instead of working with left and right movers on the half-line we may work withleft-movers only on the entire line.Basically,the energy momentum density,T is unaware of theboundary condition.Hence,in calculating the spectrum of the system with boundary conditions Aand B introduced above,we may regard the system as being deﬁned periodically on a torus of length2l with left-movers only.The conformal towers of T are unaﬀected by the boundary conditions,A,B.However,which conformal towers occur does depend on these boundary conditions.We introducethe characters of the Virasoro algebra,for the various conformal towers:χa(e−πβ/l)≡ i e−βE a i(2l),(5.10) where E a i(2l)are the energies in the a th conformal tower for length2l.i.e.:E a i(2l)=π24l,(5.11)where the x a i’s correspond to the(left)scaling dimensions of the operators in the theory and c is theconformal anomaly.The spectrum of H l AB can only consist of some combination of these conformaltowers.i.e.:Z AB= a n a ABχa(e−πβ/l),(5.12)where the n a AB are some non-negative integers giving the multiplicity with which the various con-formal towers occur.Importantly,only these multiplicities depend on the boundary conditions,notthe characters,which are a property of the bulk left-moving system.Thus,a speciﬁcation of allpossible multiplicities,n a AB amounts to a speciﬁcation of all possible boundary conditions A.Theproblem of specifying conformally invariant boundary conditions has been reduced to determiningsets of integers,n a AB.For rational conformalﬁeld theories,where the number of conformal towersisﬁnite,only aﬁnite number of integers needs to be speciﬁed.Now let us focus on the boundary states,|A>.These must obey the operator condition:[T(x)−¯T(x)]|A>=0(∀x).(5.13) Fourier transforming with respect to x,this becomes:[L n−¯L n]|A>=0.(5.14) This implies that all boundary states,|A>must be linear combinations of the“Ishibashi states”:29|a>≡ m|a;m>⊗|a;0>.(5.17)(Note that while the states,|a;m>⊗S a0n a AB= b N a bc n b AA.(5.26)Here0labels the conformal tower of the identity operator.Importantly,the new boundary stateand multiplicities so obtained,obey Cardy’s equation.The right-hand side of Eq.(5.23)becomes:S a c<A|a0><a0|B>=<A|a0><a0|A>.(5.29)S a0This gives:b S a b n b AB=S ac S a0<A|a0><a0|A>=<A|a0><a0|B>,(5.30)proving that fusion does indeed give a new solution of Cardy’s equations.The multiplicities,n a BBare given by double fusion:n a BB= b,d N a bc N b dc n d AA.(5.31)[Recall that|B>is obtained from|A>by fusion with the primary operator c.]It can be checkedthat the Cardy equation with A=B is then obeyed.It is expected that,in general,we can generatea complete set of boundary states from an appropriate reference state by fusion with all possibleconformal towers.VI.BOUNDARY CONFORMAL FIELD THEORY RESULTS ON THE MULTI-CHANNEL KONDOEFFECTA.Fusion and the Finite-Size SpectrumWe are now in a position to bring to bear the full power of boundary conformalﬁeld theory on the Kondo problem.By the arguments at the beginning of Sec.V,we expect that the infraredﬁxed points describing the low-T properties of the Kondo Hamiltonian correspond to conformallyinvariant boundary conditions on free fermions.We might also expect that we could determinethese boundary conditions and corresponding boundary states by fusion with appropriate operatorsbeginning from some convenient,trivial,reference state.We actually already saw a simple example of this in Sec.III in the single channel,s=1/2,Kondo problem.There we observed that the free fermion spectrum,with convenient boundary conditionscould be written:(0,even)⊕(1/2,odd).(6.1) Here0and1/2label the SU(2)1KM conformal towers in the spin sector,while“even”and“odd”label the conformal towers in the charge sector.We argued that,after screening of the impurityspin,the infraredﬁxed point was described by free fermions with aπ/2phase shift,correspondingto a spectrum:(1/2,even)⊕(0,odd).(6.2) The change in the spectrum corresponds to the interchange of SU(2)1conformal towers:0↔1/2.(6.3) This indeed corresponds to fusion,with the spin-1/2primaryﬁeld of the WZW model.To see thisnote that the fusion rules for SU(2)1are simply[from Eq.(5.25)]:。

The Properties of Photonics

The Properties of PhotonicsPhotonics refers to the study and application of photons, or particles of light, in modern technology. This field has grown rapidly in recent years, both in terms of theoretical understanding and practical applications. In this article, we will explore some of the properties of photonics and examine how they are utilized in various fields.One property of photons is their wavelength, which determines the color of light. Visible light, the range that can be perceived by the human eye, ranges from about 400 to 700 nanometers. Longer wavelengths correspond to colors such as red and orange, while shorter wavelengths correspond to blue and purple. Light with wavelengths shorter than visible light are called ultraviolet, X-rays, or gamma rays, while those with longer wavelengths are called infrared, microwave, or radio waves.Another important property of photons is their polarization. Polarization refers to the orientation of the electric field of the photon, which affects how it interacts with other materials. Polarizers, which selectively transmit or block certain polarizations of light, are commonly used in optics and electronics.A third property of photons is their coherence, or how well their waves align with each other. Coherent light waves can interfere constructively or destructively, leading to phenomena such as diffraction and interference patterns. Laser light, which is highly coherent, is used in a wide range of applications, from cutting and welding materials to reading data on compact discs.Photonics is also important in communication technologies. Fiber-optic cables, which use light to transmit data over long distances, are widely used in telecommunications. The speed and bandwidth of fiber-optic communication make it critical for modern communication networks.Medical applications of photonics have also seen notable advancements, such as optical imaging techniques that can be used to diagnose disease. For instance, optical coherence tomography (OCT) uses low-power light to visualize tissue structures in theeye, allowing detection of early signs of eye diseases. Similarly, fluorescence imaging uses specific molecules that emit light when excited by photons to locate cancer cells or study biological processes.In conclusion, photonics is a fascinating field with a wide range of both fundamental and practical properties. The combination of its various properties and applications has made photonics an indispensable tool in various fields such as telecommunications, medical imaging, and material processing. As research and development continue, photonics will undoubtedly play an increasingly important role in shaping the technologies of the future.。

高光谱光谱解混

Spectral Unmixing
José M. Bioucas Dias Instituto Superior Técnico Instituto de Telecomunicações Lisbon, Portugal
First European School on Hyperspectral Imaging – Caceres, October 2007
4
Remote Sensing: Basics
E(λ) L(λ)
ρ(λ)
Surface
E ρ L λ
– Irradiance (W/m2) – Reflectance – Radiance (W/Sr/m2) – Wavelength (µm)
5
Remote Sensing: Atmosphere
3. Inversion
For each pixel, identify the vector of proportions
19
Dimensionality Reduction
Dim(A) = [L × p]
L
p
Problem: Identify the subspace generated by the columns of
Coefficients a and b depend on
• • • • • • • Terrain topography Reflectance of the surroudings Sun position (local time) Water-vapor Aerosols Gases Clouds
m1 m2
∆x
m3 m4 m6
m5 m7
• Spectral Domain {m1, m2, L, mp} linearly independent

馒头品质的研究外文

Characteristics of wheat dough and Chinese steamed bread added with sodium alginates or konjac glucomannanS.Y.Sim,A.A.Noor Aziah,L.H.Cheng *Food Technology Division,School of Industrial Technology,Universiti Sains Malaysia,11800Minden,Penang,Malaysiaa r t i c l e i n f oArticle history:Received 27January 2010Accepted 17September 2010Keywords:DoughChinese steamed bread Sodium alginates Konjac glucomannana b s t r a c tIn this study,wheat ﬂour was dry-blended with sodium alginates (ALG)and konjac glucomannan (KGM)at 0.2%and 0.8%w/w ﬂour as-is moisture basis.Dough mixing and stretching properties were assessed by farinograph and extensograph,respectively.Chinese steamed bread (CSB)samples prepared were compared in terms of spread ratio,speci ﬁc volume and staling behaviour.In general,ALG and KGM addition was found to produce dough with rigid and weak network,respectively.Chinese steamed bread with ALG (0.2%)or KGM (0.8%)addition was relatively low in spread ratio and speci ﬁc volume,but softer and more resistant to staling on storage as compared to the control sample.Ó2010Elsevier Ltd.All rights reserved.1.IntroductionChinese steamed bread (CSB),a kind of wheat-based traditional fermented Chinese food has been consumed for almost two millennia in China (Su,Ding,Li,Su,&Zheng,2005).It is gaining popularity and widely consumed by people reside in the Southeast Asia region.The basic ingredients for making CSB are wheat ﬂour,water,yeast and salt;sugar and shortening are optional (Pomeranz,Huang,&Rubenthaler,1991).There are three major types of steamed bread made in China.The Northern-style steamed bread has a very cohesive,elastic and dense texture and it is usually prepared from strong gluten ﬂour.Whereas,the Southern-style steamed bread,is commonly known for a more open crumb structure,softer texture and a white surface,and it is usually prepared from weak gluten ﬂour.In the very southern part of China,Cantonese-style steamed bread or bun is popular.This type of steamed bread is very unique whereby the crumb is extremely white in colour,very soft but not cohesive in texture and tastes very sweet (Crosbie,Huang,&Barclay,1998;Jiang,Hao,&Tian,2008).Consumers prefer steamed bread which has a smooth surface,a soft,moist,and uniform white crumb with higher speci ﬁc volume (Rubenthaler,Huang,&Pomeranz,1990).The processing method of CSB is different from that of bread in which the CSB is made by cooking the fermented dough through steaming whereas bread is produced by baking in an oven.This steaming method produces product with a soft,moist,and uniform crumb texture,and a thin,smooth,white skin rather than the brown crust of traditional bread (Rubenthaler et al.,1990;Su et al.,2005).Qin,Cheng,and Ma (2007)reported that the shelf life of CSB was 1e 3days only when being stored at room temperature and the shelf life becomes shorter at higher storage temperature or reduced storage relative humidity.Most of the time bread quality loss is not due to microorganism or endogenous enzyme deteriorative activity but staling (Bárcenas &Rosell,2005).Gums consist of a number of water-soluble polysaccharides come with different chemical structures and provide diverse functional properties such as gelling,thickening,stabilising,foaming,emulsi-fying,as well as inhibiting syneresis during a freeze e thaw cycle,water-retention and textural enhancing properties,by controlling the water molecules mobility (Rosell,Collar,&Haros,2007).They have been used to retard the baked goods from staling and improve the quality of the fresh produce (Bárcenas &Rosell,2005),and to enhance frozen dough shelf life (Asghar,Anjum,Butt,Tariq,&Hussain,2007).Apart from these,guar and xanthan gums have been reported when used in bread at 7%and 2%addition levels,respectively,are able to impart therapeutic effects (Kohajdová,Karovi c ová,&Schmidt,2009).In this study,sodium alginates (ALG)and konjac glucomannan (KGM)were added to CSB.Alginates are extracted from marine brown algae of the genera Ascophyllum ,Alaria ,Cystoseira ,Ecklonia ,Eisenia ,Fucus ,Laminaria ,Macrocystis ,Nereocystis ,and Sargassum (Khotimchenko,Kovalev,Savchenko,&Ziganshina,2001).Alginate and its salts consisted of the radicals of b -D-mannuronic and a -L-guluronic acids linked with (1/4)-bonds in an unbranched chain (Brownlee et al.,2005;Khotimchenko et al.,2001).Alginate and its salts have wide applications due to their thickening,emulsifying,gelling and sta-bilising behaviours besides their capability to retain water (Draget,2000;Khotimchenko et al.,2001).The usage levels for alginates are cost-driven and ranged between 0.5and 1.5%in food application*Corresponding author.Tel.:þ6046535209;fax:þ6046573678.E-mail address:lhcheng@usm.my (L.H.Cheng).Contents lists available at ScienceDirectFood Hydrocolloidsjournal homepa ge:/locate/foodhyd0268-005X/$e see front matter Ó2010Elsevier Ltd.All rights reserved.doi:10.1016/j.foodhyd.2010.09.009Food Hydrocolloids 25(2011)951e 957(Brownlee et al.,2005).Guarda,Rosell,Benedito de Barber,and Galotto(2004)and Rosell,Rojas,and Benedito de Barber(2001a) reported that alginates showed an anti-staling effect.The ability of alginates to decrease staling rate of bread samples was attributed to inhibiting interactions between gluten and starch(Davidou,Le Meste,Debever,&Bekaert,1996).Konjac glucomannan(KGM),a high molecular weight and water-soluble non-ionic polysaccharide is extracted from the root tuber of Amorphophallus konjac C.Koch(Davé&McCarthy,1997;Nishinari& Zhang,2004).KGM is a type of neutral heteropolysaccharide consists of b-1,4-linked D-mannose and D-glucose in the ratio of 1.6:1with a low degree of acetyl groups at the C-6position(Kato& Matsuda,1969).KGM is one of the most viscous dietaryﬁbres known because of its effective water-absorbing ability(Chua,Baldwin, Hocking,&Chan,2010).Konjacﬂour has wide usage in food appli-cation as it served as an agent for thickening,texturing,gelling and water binding(Takigami,2000).It is a key ingredient to make konnyaku gels used in Japanese traditional dishes and it is also used as a gelling agent in dessert jellies(Nishinari&Zhang,2004).In making of low-fat and fat-free meat products,it may be used to offer fat replacement properties(Takigami,2000).From the literature,the inﬂuences of gums on dough functional performance and bread quality are depending upon the nature, origin,particle size,molecular structure and ionic charges of the gums,and also on the dosages of gums added to the dough formulations(Collar,Andreu,Martínez,&Armero,1999).According to Collar et al.(1999),gums added at less than1%(w/w,ﬂour basis), are expected to provide higher water holding capacity and loaf volume,as well as decrease crumbﬁrmness by delaying starch retrogradation.Although the macroscopic effect of gums on wheat dough has been ascribed to structural changes induced by inter-actions between gum molecules and main components present in the wheatﬂour,however there is no general consensus about the mechanism of action of the gums(Rosell et al.,2007).There is scarce information on the quality of CSB added with gums of various kinds.Hence,this study was conducted in order to deter-mine the effects of sodium alginates and konjac glucomannan on wheat dough rheological properties and also study the possibility of retarding staling and enhancing shelf life of CSB.2.Materials and methods2.1.MaterialsWheatﬂour with10%protein(14%moisture basis),0.47%ash and 13.3%moisture(dry basis)was supplied by United Malayan Flour Mill(Butterworth,Malaysia).Konjac glucomannan(KGM)was obtained from Hung Thong Food Technology Sdn.Bhd.(Penang, Malaysia).Sodium alginates(ALG)from brown algae(Fluka brand, product of United Kingdom)was procured from Sigma-Aldrich Sdn. Bhd(Selangor,Malaysia).Sucrose and sodium chloride(SYSTERMÒbrand)were purchased from Merck Sdn.Bhd.(Selangor,Malaysia) and Classic Chemicals Sdn.Bhd.(Selangor,Malaysia),respectively. Calcium propionate was bought from Sim Company Sdn.Bhd. (Penang,Malaysia).Crisco shortening was manufactured by J.M. Smucker Company(Orrville,OH,U.S.A.)while fresh yeast was obtained from AB Mauri Malaysia Sdn.Bhd.(Selangor,Malaysia).2.2.Farinograph testA constantﬂour weight method was performed on Brabender FarinographÒ-E(Brabender OHG,Duisburg,Germany)according to AACC method54-21(AACC,2000).Approximately300g of wheat ﬂour(corrected to14%moisture basis)with or without addition of ALG or KGM was mixed in a300-g mixing bowl for50min.Parameters such as water absorption,dough development time, dough stability and mixing tolerance index(MTI)were recorded. Average of triplicate measurements was reported.2.3.Extensograph testDough was prepared in a Brabender FarinographÒ-E with formulation as aforementioned with an addition of6g of sodium chloride dissolved in part of the water.The dough wasﬁrst mixed for1min,rested for5min and mixing continues until a500FU consistency was reached.Water addition wasﬁxed by subtracting 2%of the water absorption determined by farinograph to counter-balance for salt addition effect.The dough was stretchedusingFig.1.Farinograph parameters of wheatﬂour with or without addition of food gums. The error bar representsÆstandard deviation(n¼3).Bars followed by the same letter and with the same capital letter are not signiﬁcantly different at5%probability level.(a)Water absorption corrected to500FU,(b)dough development time,(c)dough stability,(d)mixing tolerance index(MTI).S.Y.Sim et al./Food Hydrocolloids25(2011)951e957 952Brabender ExtensographÒ(Brabender OHG,Duisburg,Germany) until rupture after45min,90min,and135min of resting time ina humidiﬁed chamber(>90%relative humidity)conditioned at30 C.This test was done following AACC method54-10(AACC, 2000).Dough maximum resistance,extensibility,and work applied to stretch the dough(area under the curve)were measured from extensogram obtained with the aid of a planimeter.Average of triplicate measurements was reported.2.4.Preparation of Chinese steamed breadRecipe formulation consisted of wheatﬂour with or without addition of ALG or KGM at0.2%and0.8%,80%water based on far-inograph water absorption,8%sucrose,3%fresh yeast,1%sodium chloride,2%Crisco shortening,and0.2%w/w(based on as-is moisture basis of wheatﬂour)calcium propionate.The levels of ALG and KGM addition were arbitrarilyﬁxed.No-time fermentation was used in this sample preparation.Before mixing the dough, fresh yeast was dissolved in warm water(35e40 C)containing5g sucrose and left for10min.Sodium chloride,calcium propionate, and the remaining sucrose were dissolved in warm water.To begin dough mixing,sucrose solution was poured slowly into wheatﬂour in a mixing bowl of KitchenAidÒMixer(Model:5KSM150PS, KitchenAid,USA)with a dough hook.Mixing was conducted at speed2of the mixer.Fresh yeast solution was added and mixed before sodium chloride solution,calcium propionate solution,and the remaining cold water were added while mixing.After being mixed for2min,shortening was added and mixing was continued for another8min until the dough was not sticky to the bowl.The dough was then divided into pieces of100-g and rounded with the balling unit of extensograph.This was followed by prooﬁng in a proofer conditioned at30 C and85%relative humidity for30min.Finally,the dough was steamed in a steamer for15min and tested after cooling for1h.2.5.Storage study of Chinese steamed bread(CSB)The samples of Chinese steamed bread(CSB)prepared were kept in air-tight containers until analysis at27 C.The properties studied are spread ratio,speciﬁc volume,andﬁrmness.Average of triplicate measurements was reported for each storage period and preparation of CSB was repeated twice.2.6.Spread ratio of CSBThe heights and bottom widths of CSB were measured at three different locations with a ruler and the average was recorded. Spread ratio(width/height)was then calculated.This was modiﬁed from Lijuan,Guiying,Guoan,and Zaigui(2007).2.7.Speciﬁc volume of CSBThe volume of CSB was determined by using rapeseed displacement method.Weight of CSB was measured using a top-pan balance and measured to the nearest of0.01g.Speciﬁc volume (ml gÀ1)is the ratio of volume to weight of CSB.2.8.Textural properties of CSBFresh and storage samples were subjected to a penetration test using a TA-XT Plus Texture Analyzer(Stable Micro Systems,Surrey, United Kingdom)equipped with a30-kg load cell and a1-inch diameter Delrin ball probe.Measurements were conducted with a pre-test speed of1.0mm sÀ1,a test speed of1.7mm sÀ1,a post-test speed of10.0mm sÀ1,and5.0g trigger force.The deformation level was75%of the sample height and the samples were pene-trated once.The maximum force orﬁrmness of samples was determined.Change inﬁrmness during storage was used as a parameter in the evaluation of CSB staling.Staling index or increase ofﬁrmness is calculated as follows:2.9.Statistical analysisA complete randomize design was adopted for all analyses con-ducted.For farinograph and extensograph tests on dough samples, triplicate measurements were performed.As for CSB characteriza-tion on storage,three sub-samples from each duplicate preparation were measured.Where necessary,means were compared using Duncan test at95%signiﬁcance level by SPSS software for Windows Release15.0(SPSS Inc.,Chicago,Illinois,USA).3.Results and discussion3.1.Dough mixing propertiesFig.1shows the dough mixing properties of wheatﬂour with and without addition of sodium alginates(ALG)or konjac gluco-mannan(KGM)at0.2and0.8%addition level.At0.2%addition level,water absorption of sample added with ALG or KGM was not signiﬁcantly different from the control sample, but at0.8%addition level water absorption of sample increased in the order of control<KGM<ALG.It is clearly evident that water absorption of sample added with0.8%ALG or KGM are signiﬁcantly higher than those added at0.2%(Fig.1a).There means to say,to reach500FU,relatively higher amount of water is required in the presence of excessive gums.This suggests that gums molecules compete with gluten molecules for water due to the fact that gums showing higher water binding capacity than gluten(Ghodke Shalini&Laxmi,2007;Guarda et al.,2004).This observation was in line with the work of Rao,Indrani,and Shurpalekar(1985), Rosell,Rojas,and Benedito de Barber(2001b),Guarda et al. (2004),Asghar et al.(2007)and Ghodke Shalini and Laxmi(2007).Dough development time is deﬁned as the time between the point ofﬁrst addition of water and the point at whichﬁrst indi-cation of weakening of dough is detected.On the other hand, stability is deﬁned as the difference in time between the point at which the top of the curveﬁrst intercepts the500FU line and the point at which the top of the curve leaves the500FU line.Both parameters give an indication of dough strength and tolerance towards mixing,respectively.In general,ALG was found to be more effective in increasing dough development time and dough stability than KGM.However, when compared to the control sample,ALG at0.2%and0.8% addition levels increased dough development time;dough stabilityS.Y.Sim et al./Food Hydrocolloids25(2011)951e957953was increased and decreased at 0.2and 0.8%,respectively (Fig.1b and c).Meanwhile,samples added with KGM demonstrated lower dough development time and dough stability values than the control sample at both levels of addition.Mixing tolerance index (MTI)shows a reverse trend with dough stability.It is de ﬁned as the difference in Farinograph unit between the top of the curve at the peak and the position of the curve measured 5min after the peak is reached.Henceforth,dough with low MTI value is commonly known to possess good tolerance towards mixing.In other words,the weaker the dough,the higher the MTI value.From the result,it is found that inclusion of KGM at both addition levels showed the highest MTI value (Fig.1d).Therefore,KGM is anticipated to be weaker relatively.This ﬁnding is consistent with the ﬁndings of dough development time and dough stability.3.2.Dough extensibilitySingh and MacRitchie (2001)have applied the knowledge from polymer science studies to explain the properties of gluten.They attributed the dough extensibility to the extension of the large glutenin molecules in a dough system.It is reported that entan-glement coupling between glutenin molecules is responsible to maintain the elasticity of the dough.According to the theory of Termonia and Smith (1987),the extensional properties of polymers are governed by two main kinetic processes,namely the breaking of secondary valence bonds and slippage of entangled chains.The relative rates of the two kinetic processes then determine the extensional behaviour of a polymer.For dough,a biopolymer system,when the rate of chain slippage is much greater than the rate of elongation of the chain,resistance towards stretching is low.As a result,the tensile strength and the elongation will both be low.However,if the rate of chain slippage is relatively low,meaning chains slippage occur insuf ﬁciently rapidly in response to the applied stress,the chains will break and resulting very short distance of elongation.When the rate of slippage is optimum in the sense that the chains will slip free suf ﬁciently rapidly to avoid breakage of covalent bonds,the entanglement points will contribute to resistance.In this case,both the tensile strength and elongation will be maximised.Henceforth,any factors that modify the degree of chain slippage and elongation will directly in ﬂuence the dough stretching properties (Singh &MacRitchie,2001).Figs.2e 4show the extensograph properties (R max ,maximum resistance;A,work applied;R max /E,ratio of maximum resistance to extensibility)of dough prepared in this study as a function of resting time.Results showed that R max ,A and R max /E increased with progressive increase in resting time from 45to 90min,and a plateau or a slight drop was evident between 90and 135min.This indicates that 90min resting time is required for an optimum gluten network formation.This result trends might throw some light on what we might expect when dough is over proofed.WhenFig.2.Maximum resistance of dough with or without food gums added as a function of rest time.(a)0.2%addition level,(b)0.8%addition level.Typical coef ﬁcient of variation for triplicate measurements did not exceed10%.Fig.3.Work applied to stretch the dough with or without food gums added as a function of rest time.(a)0.2%addition level,(b)0.8%addition level.Typical coef ﬁcient of variation for triplicate measurements did not exceed 10%.S.Y.Sim et al./Food Hydrocolloids 25(2011)951e 957954rest time was prolonged,structural relaxation of the work-hard-ened dough could account for the drop in R max ,A and R max /E values (Hlynka,1955).With fewer secondary valence bonds and entan-glement couplings,chain slippage is facilitated when stress is applied,thus low resistance to extension and work done result (Singh &MacRitchie,2001).However,this phenomenon is delayed with higher level of gums addition,which could be explained by an enhanced chain e chain interaction between gums molecules and gluten molecules.Therefore,it is desirable,if not necessary to obtain gluten network formation through proper dough prepara-tion and formulation because the dough may not rise optimally during baking or steaming due to either rupture of a weaker gluten network,rigid or “heavy ”dough weight upon gas bubbles expan-sion (Campbell,2003;Hru s ková, Svec,&Jirsa,2006;Larroque,Gianibelli,&MacRitchie,1999).This will confer an impact on loaf volume and crumb texture (Goesaert et al.,2005).When compared to the control sample,extensograph properties of both samples added with ALG and KGM were found to be addition level and rest time dependent.In general,at both addition levels,R max (Fig.2)and R max /E (Fig.4)of the samples decreased in the order of ALG >Control >KGM,except for R max and R max /E of sample added with 0.2%KGM were higher than those of the control sample at 45min resting time.The energy required to stretch the dough was reduced when gums was added to wheat ﬂour.This is clearly shown in Fig.3where A value of samples added with ALG or KGM was found to be lower than the control sample.However,the trend was different at different level of addition,i.e.Control >ALG >KGM at 0.2%level and Control >KGM >ALG at 0.8%level.In general,ALG at 0.2%addition level was found to produce dough with high R max ,R max /E,and A values.These are character-istics of a rigid dough that when being stretched,the chains could not slip suf ﬁciently rapidly in response to the applied stress and break apart easily,resulting in high resistance to extension but short elongation at break (Singh &MacRitchie,2001).On the other hand,KGM at 0.2%addition level has resulted in weaker dough with relatively lower R max ,R max /E and A values.At a molecular level,it could be envisaged that when KGM added dough is stretched,chain slippage is greater than the rate of chain elongation,as if spacing between entanglement knots are far enough to render the chain strands ﬂexible (Singh &MacRitchie,2001).3.3.Characteristics of Chinese steamed bread (CSB)Spread ratio and speci ﬁc volume of CSB :Spread ratio and speci ﬁc volume of CSB prepared are tabulated in Table 1.Overall,spreadratio and speci ﬁc volume of CSB was decreased upon addition of gums at both 0.2and 0.8%levels.This result trend is in accordance with result presented earlier on extensograph and substantiate the theory that a strong gluten network is crucial in giving strength to the gas cells in the dough to expand during initial stages of baking or steaming (Bell,1990;Dziezak,1991;Haque,Richardson,Morris,Gidley,&Caswell,1993;Sarkar &Walker,1995).This observation suggests that an appropriate balance between dough resistance and extensibility is important in governing the dough properties.No doubt,a high dough strength can provoke an increase in loaf volume but dough rise can be hindered when dough is too strong (Rosell et al.,2001b;Goesaert et al.,2005).On the other hand,with too much of elasticity may bring about dif ﬁculty to CSB processing and causing shrinkage of ﬁnished product,whereas too high extensibility may result in ﬂat-shaped product (Hou &Popper,2007).Firmness of CSB upon storage at 27 C :Bread staling is a complicated phenomenon and it is attributable to many factors.Nevertheless,retrogradation of starch molecules remains as the main factor of bread staling (Gray &BeMiller,2003).Upon storage,the increase of ﬁrmness is usually serves as an index of bread staling (Hareland &Puhr,1998).As seen in Fig.5,at 0.2%addition level,ALG gave a softening effect to CSB and ﬁrming was delayed on storage,whilst CSB added with KGM became ﬁrmer when compared with the control counterpart.The initial staling rate as denoted by the initial slope value was found to decrease upon gums addition.However,at 0.8%addition level,control sample showed the highest ﬁrmness increment value throughout the storage and it is interesting to ﬁnd out that upon addition of ALG and KGM at 0.8%level,a drastic drop in staling rate was evident.ThisleadsFig.4.Ratio of maximum resistance to extensibility of dough with or without food gums added as a function of rest time.(a)0.2%addition level,(b)0.8%addition level.Typical coef ﬁcient of variation for triplicate measurements did not exceed 10%.Table 1Spread ratio and speci ﬁc volume of Chinese steam bread prepared with and without gums ’addition.SampleSpread ratio Speci ﬁc volume (ml g À1)0.2%Addition level Control 1.69Æ0.09a 4.14Æ0.20a ALG 1.61Æ0.14b 3.76Æ0.32b KGM1.64Æ0.09ab 4.16Æ0.13a 0.8%Addition level Control 1.69Æ0.09A 4.14Æ0.20A ALG 1.64Æ0.06B 3.80Æ0.10B KGM1.73Æ0.10A3.55Æ0.14CALG,sodium alginates;KGM,konjac glucomannan.Means Æstandard deviation (n ¼6).Values followed by the same letter and with the same capital letter in the same column are not signi ﬁcantly different at the 5%probability level.S.Y.Sim et al./Food Hydrocolloids 25(2011)951e 957955support to the view that gums present in dough could hinder the development of macromolecular entanglements and retard starch recrystallization (Collar et al.,1999;Davidou et al.,1996;Gujral,Haros,&Rosell,2004).4.ConclusionsSodium alginates (ALG)and konjac glucomannan (KGM)show different effects on wheat dough and Chinese steamed bread (CSB)properties due probably to their distinctly different molecular structures and functionalities.The results from this study suggest that at 0.2%addition level ALG is better than KGM in delaying staling of CSB though slight reduction in spread ratio and speci ﬁc volume were evident.However,at 0.8%level,KGM seems to be better than ALG in enhancing CSB properties.AcknowledgementsThis work was ﬁnancially supported by Kuok Foundation and Malayan Sugar Manufacturing Co.Bhd through a research grant (304/PTEKIND/650441/K132).S.Y.Sim wishes to thank Institute of Post-graduate Studies,Universiti Sains Malaysia for fellowship support.NomenclaturesA:work applied to stretch dough ALG:sodium alginates BU:Brabender unit CSB:Chinese steamed bread FU:farinograph unitKGM:konjac glucomannanR max /E:ratio of maximum resistance to extensibility R max :maximum resistanceReferencesAACC.(2000).Approved methods of the American association of Cereal Chemists 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education and meaning-making outcomes in Japan and the United States

International Journal of Intercultural Relations24(2000)741–761Training in culture:the case of aikido education and meaning-making outcomes in Japanand the United StatesC.Jeﬀrey Dykhuizen*Lakeland College JapanAbstractThis study investigated whether a relationship existed between instructional style and points of emphasis in the training context of the martial art aikido and the perceptions which practitioners of aikido generated for aikido-related concepts.Theﬁndings were gathered within and compared across aikido training settings in two cultures}Japan and the United States.Analysis of the quantitative and qualitative data gathered for this investigation revealed several potent diﬀerences between the manner in which Japanese and American aikido practitioners represented their understandings of aikido-related concepts.Diﬀerences in the manner in which aikido practitioners in Japan and the United States represented their understandings of aikido reﬂected the teaching emphasis observed in the respective cultures.It was concluded that aikido instructors represented the values of their own culture in the context of aikido training,and thus served as important mediating forces inﬂuencing the meaning which practitioners generated for aikido.An additionalﬁnding revealed that in neither culture were participants able to accurately represent how practitioners in the‘‘other’’culture structured their understandings of aikido.It was reasoned that both cultural groups generated faulty perceptions of how the‘‘other’’group understood aikido because they utilized a similar pattern of projection,using their own meanings of aikido to represent the understandings of practitioners in the‘‘other’’cultural group.#2000Elsevier Science Ltd. All rights reserved.Keywords:Aikido;Cultural diﬀusion;Education;Cross-cultural studies;Psychology;Semiotics*Correspondence address.9320Ravine Ridge,Caledonia,MI,49316,USA.E-mail address:cjdyk@(C.J.Dykhuizen).0147-1767/00/$-see front matter#2000Elsevier Science Ltd.All rights reserved.PII:S0147-1767(00)00029-81.IntroductionWhen persons from diﬀerent cultures come into contact,there is an inevitable exchange of cultural elements.Generally,the meaning and function of a cultural artifact or practice is altered as it is transferred from one culture to another (Hunter &Whitten,1976).Developments in transportation and communications technology in the contemporary world have resulted in information being shared between cultures at ever-increasing rates.It is therefore becoming increasingly important to have a clear understanding of the process of information transfer across cultures.This study investigated the process by which an artifact from one culture was received into another.In the past several decades the Asian martial arts have become quite popular and extensively practiced in the United States (Trulson,1986).Several authors (Min,1979;Back &Kim,1984)have suggested that there are diﬀerences in martial arts instruction in American and Japanese dojo s (training halls).It has been argued that the process of recontextualizing the martial arts into the culture of the United States has resulted in new understandings of the martial arts (Columbus &Rice,1991;Trulson,1986;Deshimaru in Wertz,1984).The majority of the research which has generated these ﬁndings,however,has involved hard,linear,combat-oriented martial arts.Aikido,which was used as an example in this study,is a relatively new,soft,spiritually based martial art.1.1.The nature of aikidoAikido is a soft,circular Japanese martial way which is commonly translated into English as ‘‘the way of harmony’’.In aikido,the goal of training is to generate a balance of body,mind,and spirit (Ueshiba,1984).This is accomplished by training to centralize and extend ‘‘ki ’’or vital energy,and to coordinate it harmoniously with the surrounding circumstances (Ratti &Westerbrook,1973,p.359).Aikido’s founder,Morihei Ueshiba,believed that violence and aggression could be guided,led or turned aside by the harmonious coordination of spirit.The manifestations of this principle can be observed in watching an aikido practitioner whirl and spin,leading the aggressor’s force to a harmonious,non-violent outcome.From its inception,aikido has emphasized a spiritual component (Ueshiba,1984;Saotome,1993),and this emphasis has diﬀerentiated aikido from other,more combative martial arts.Aikido was founded by Morihei Ueshiba in Japan in 1942(Crawford,1992;Ueshiba,1984)and it is practiced widely in Japan by persons of both genders and various ages.Aikido was ﬁrst introduced in the United States in 1953,and it is currently estimated that there are approximately 1000aikido dojo s in the continental United States (Pranin,1991).Aikido has recently received attention due to the success of Steven Segal’s movies.1.2.Research questionsThis study sought to clarify whether,and if so how the meaning of aikido was altered in its diﬀusion to the United States.Although the investigation was broadlyC.J.Dykhuizen /International Journal of Intercultural Relations 24(2000)741–761742C.J.Dykhuizen/International Journal of Intercultural Relations24(2000)741–761743contextualized within theﬁeld of cultural diﬀusion,it speciﬁcally explored the relationship between the presentation and instruction of aikido,and students’understandings of it.Three interrelated research questions guided the inquiry.(1)How is the instruction and practice of aikido in the United States diﬀerent thanthe instruction and practice of aikido in Japan?(2)What diﬀerences,if any,exist between what aikido means to practitioners in theUnited States and Japan?(3)In what manner are diﬀerences in instruction and practice related to diﬀerencesin the meaning which aikido has to practitioners in diﬀerent cultures?1.3.Culture as a research variableIn this research project,as in many cross-cultural studies,culture‘‘entails some sort of‘treatment’or‘condition’’’(Berry,Poortinga,Segall&Dasen,1992,p.220). When culture serves as an antecedent or independent variable,individuals’beliefs and behaviors are dependent variables(Berry,1980).By comparing the manner in which aikido practitioners in Japan and the United States structured their understandings of aikido-related concepts,this project compared the dependent variables of diﬀerent‘‘treatment’’groups.The terms America(employed to parsimoniously refer to the United States)and Japan were used in this project to refer to speciﬁc research settings,and not to entire nation-states.As pointed out by Berry et al.(1992),the contrast between large cultural populations‘‘is rarely of more psychological interest than between the people of two small groups within the two areas’’(p.228).This investigation took place within the socially constructed world within which speciﬁc groups of aikido practitioners trained.2.Methodology2.1.Multiple case study design and mixed-methods methodologyA multiple-case study comparative research design using mixed methods was used to conduct this investigation.The multiple-case study design accommodated an essential feature of this study}across case analysis.Yin(1984)stated that in the multiple case study design,the use of multiple sources of data aids in the generation of‘‘more convincing and accurate’’ﬁndings.The comparative nature of the investigation’s research design was facilitated by being structured within a format of constant–comparative analysis(Glasser&Strauss,1967).Berry and his colleagues(1992)have stated that for cross-cultural comparative studies,‘‘an important strategy is to use more than one method of measurement’’(p.223).Both qualitative and quantitative data gathering and analysis strategies were used in this comparative investigation.Data were gathered using in-depthinterviews with participants (LeCompte &Preissle,1993;Glesne &Peshkin,1992;Yin,1984),participant–observation (LeCompte &Preissle,1993;Glesne &Peshkin,1992;Yin,1984),direct,structured observation (Yin,1984;LeCompte &Preissle,1993)semantic diﬀerentials (Osgood,Suci &Tannenbaum,1957),and a demographic questionnaire.Berry and his colleagues (1992)have asserted that by using more than one method of measurement,‘‘one can have more conﬁdence in a ﬁnding’’(p.223).2.2.Semantic diﬀerentialsThe semantic diﬀerential (Osgood et al.,1957)is a quantitative tool which measures the meaning of concepts.The semantic diﬀerential is a ﬁtting tool to use in gathering data cross-culturally,particularly between populations speaking diﬀerent languages.One reason for this is that the semantic diﬀerential utilizes the principles of synesthesia.Synesthesia is the act of using descriptors from one sensory mode to describe a sensation experienced through another sense.For example,when someone says,‘‘That’s a hot suit.’’when referring to the color and style of someone’s outﬁt,they are using the principles of synesthesia.Instruction in aikido uses a variety of sensory modes to convey teachings }visual,tactile,aural,kinesthetic,etc.Because practitioners acquire information about aikido from a variety of sensory modes,the semantic diﬀerential was chosen as a tool to gather data on this topic.The concepts measured in this study were deliberately selected for purposes of comparison }to compare the manner in which two diﬀerent cultural groups structure the meaning of a shared activity,aikido.Concepts were also selected to evaluate participants’perceptions of how members of the ‘‘other’’group perceives aikido.The concepts whose meaning was measured were:(a)‘‘‘Ki ’is’’;(b)‘‘‘Aikido’is’’;(c)‘‘Aikido practitioners in the United States think aikido is’’;and (d)‘‘Aikido practitioners in Japan think aikido is’’.Twelve descriptive scales were used to generate a semantic diﬀerential for the four concepts.The descriptive scales,comprised of pairs of polar adjectives,were selected from scales published in Osgood et al.(1957).Scales were selected for use in this study based upon several criteria,the primary criteria being that the scales were relevant to the martial arts-related concepts under investigation.Additionally,scales were given selection priority if they had also been shown to account for a large amount of variance in previous studies presented in Osgood et al.(1957).2.3.Back-translationIn cross-cultural comparative studies translation is a factor which must be addressed.This research project used the back-translation technique described by Brislin (1980).In this study,back-translation consisted of a bilingual person translating the tool from one language (English)to another (Japanese),after which another bilingual person independently translated the tool back to the original language.The ﬁrst and third versions of the documents were then compared for consistency.This technique has the advantage of ‘‘decentering’’the material awayC.J.Dykhuizen /International Journal of Intercultural Relations 24(2000)741–761744C.J.Dykhuizen/International Journal of Intercultural Relations24(2000)741–761745 from the semantic bias of the original language.The result of the process of decentering‘‘means that the research project is not centered around any one culture or language’’(Brislin,1980,p.433).The questions used to guide interviews,as well as the semantic diﬀerentials used in this study were back-translated.2.4.ParticipantsAll participants in the study were adult practitioners of aikido in dojo s in Japan and the United States.Several of the practitioners in each culture who participated in this study were instructors.In each culture,data was gathered only from participants who were‘native’to that culture;for example,in Japan data was gathered only among Japanese aikido practitioners,and in the United States only from American practitioners.Data was not collected among non-Japanese aikido practitioners training in Japan,nor among non-Americans training in dojo s in the United States.2.5.Quantitative populationThe quantitative population in this study consisted of aikido practitioners who trained at dojo s in the research settings,and who volunteered to participate inﬁlling out a semantic diﬀerential packet.One hundred twenty aikido practitioners training at12dojo s in the Japanese research setting completed the quantitative measures.In the research area in the United states,128aikido practitioners training at nine dojo s participated in completing the forms.A minimum of120participants training in each culture was required to maintain the statistical integrity of the semantic diﬀerential.2.6.Qualitative interview participantsIn-depth,structured interviews with aikido practitioners were conducted in Japan and the United States.The majority of interview participants were drawn from among practitioners training in aikido dojo s which served as the participant observation sites in the study.After a minimum of two months of training with participants,individual practitioners were approached if their beliefs,attitudes and training practices were judged to be representative of other individuals at the same level of expertise training in dojo s in that research setting.In this sense, interview participants were selected purposively.In several cases,instructors teaching at sites in the research area where systematic observation was conducted were also approached for interviews.All practitioners who were asked volunteered to participate in the interviewing process.Interviews with Japanese participants were conducted in Japanese.Although the primary researcher is functionallyﬂuent in spoken Japanese,interviews in Japan preceded with a native Japanese speaker present to ensure that theﬂow and focus of the inquiry was maintained.The interviews were audio-taped,translated in the case of the Japanese interviews,and transcribed to text for analysis.Participants possessed a variety of demographic characteristics and experiences.Ten Japanese interview participants,eight males and two females,participated in interviews between June and August,1995.All were actively engaged in aikido training at aikido dojo s at the time the interviews were conducted.Their ages ranged from 20to 55years.One participant had been training for just over one year,while two others had been training for over 40years.Participants had aikido rankings ranging from fourth kyu (pre-black belt ranking)to eighth dan (black belt ranking).There were three instructors,all of whom were male,among Japanese interview participants.Seven participants,one of whom was female,were interviewed in the research area in the United States.They ranged in age from 20to 50.Their aikido experience ranged from slightly less than one year to more than 25,and they had ranks ranging from ﬁfth kyu to fourth dan .Three of the participants in the United States were male instructors.One had seven,one ﬁve,and the other three years teaching experience.3.FindingsThis section presents the ﬁndings of the study.The ﬁndings concerning the instructional emphasis observed and experienced while training in aikido dojo s is presented ﬁrst.This is followed by a section describing practitioners’perceptions of aikido-related concepts,and a comparison of these perceptions across cultures.The implications of the ﬁndings are addressed in the discussion section.3.1.Instructional style and content emphasis observed at the participantobservation sitesParticipant observation was conducted on two sites simultaneously for a three month period in the Japanese research area.Participant observation occurred at one site in the United States for a total period of six months.Dojos were selected for participant observation if they (a)provided training in Aikikai-aﬃliated aikido,(b)granted access,(c)were accessible to the researcher in terms of commuting time,and (d)were representative of aikido dojos in the research areas.For this investigation,participant observation included involvement in all aspects of training,maintenance,and after training activities both inside and outside the dojo .Field notes were taken to record observations,experiences,and conversations with informants which occurred before,during,and after training sessions.The following section provides a brief description of the teaching emphasis observed and experienced at each of the participant observation sites.Pseudonyms are used to refer to all persons and places.3.2.Participant observation sites in Japan3.2.1.Akiyama dojoAkiyama dojo was a small dojo ,both in size of training area and number of students.Typically,8–10students trained during each of the three-night a weekC.J.Dykhuizen /International Journal of Intercultural Relations 24(2000)741–761746C.J.Dykhuizen/International Journal of Intercultural Relations24(2000)741–761747 classes.Classes were very structured,beginning with a bow to shomen(the front of the dojo),continuing through a short meditation session,group warm-ups,and speciﬁc instruction in and practice of aikido techniques.All activities at Akiyama dojo,including partner selection and technique training,were scripted,even ritualized,and Akiyama sensei(teacher;instructor)insured that the behavioral patterns were strictly adhered to.Akiyama sensei placed emphasis on kokyu ho(breathing method)during training.A seated,meditative version of the exercise was performed at the beginning and end of each training session.During kokyu ho,Akiyama sensei instructed students to keep their posture straight,their chins pulled in,and their breath slow:‘‘When inhaling,concentrate on the incoming breath,bringing ki energy into your tanden (center point),then hold it there.When exhaling,force ki out through every part of your body;do not try to keep ki in your body’’.We were instructed to literally ‘‘watch’’our breathing(Fieldnotes,June,1995).Akiyama sensei also provided speciﬁc instruction on the‘‘proper’’positioning of the hands,feet,and hips when performing aikido movements.For example,we were told to hold the bokken(a wooden training sword)with our hands on the top of the hilt,gripping only with the two smallestﬁngers.‘‘Hold the bokken straight in front of you,the butt twoﬁsts from your hara(belly)’’.He stated that the cut had to originate in the hara,and that ki should‘‘ﬂow through the bokken’’.He insisted that the only way to get a smooth,fast,yet powerful cut‘‘was to concentrate ki through the tip of the ken(colloquial for‘‘bokken’’).The hips are also important,because speed and power come from the hara’’(Fieldnotes,June3,1995).These comments exemplify Akiyama sensei’s consistent emphasis on ki control and extension as it related to coordinating breathing and bodily movement,as well as the precise manner in which he gave speciﬁc instruction.3.2.2.Sakamoto dojoSakamoto sensei,the instructor at the second participant observation site in Japan,also placed emphasis upon aspects of spirit and energy in aikido,although such imagery was employed in a context of educating toward practical applica-tion of aikido principles and techniques to martial situations.Approximately 25–30practitioners participated in each of the twice-a-week training sessions.I observed that the aikido practiced at Sakamoto dojo was sharp,clean,and in many ways‘‘harder’’than the aikido observed at the majority of dojo s visited in the Japanese research setting.Although demonstrations of and instruction in the performance of speciﬁc aikido techniques was provided by Sakamoto sensei,the practice sessions were less formally structured than at Akiyama dojo,and students freely chose partners with whom they trained at mutually agreed-upon levels of rigor.Occasionally,individuals would receive personal instruction from Sakamoto sensei during a training session.For instance,once while instructing me to work on body movements,he told me to‘‘Move with your whole body,not just your feet. Don’t show your opponent where you are moving,don’t show him your heart.Feel from hara’’(Fieldnotes,June10,1995).He continued giving individualizedinstruction during pair-work,telling me to ‘‘Look at the eyes (of your partner),only at the eyes.Don’t look at the weapon or your hands.Eyes.Capture the spirit of your opponent’’(Fieldnotes,June 10,1995).As his comment illustrates,although Sakamoto sensei taught within a context which emphasized martial engagement,he did occasionally refer to spirit to illustrate his instructional points.3.3.The American participant observation site:White Hall dojoAt the American participant observation site,labeled White Hall dojo because there was more than one regular instructor,there was an easily discernable ‘‘script’’of dojo procedures for activities (such as opening and closing class procedures,partner selection,and technique demonstration).Unlike instruction in the Japanese settings,however,there was no recognizable systematic approach to teaching speciﬁc techniques or movements.This coincides with the two American instructors’descriptions of their teaching styles as ‘‘idea-driven’’.Both Frank and William,the two primary instructors at the dojo ,taught from a non-scripted,thematic framework which emphasized the basic principles of aikido.As William stated during an interview:‘‘Usually for teaching,I just come in with an idea.And we explore the idea,and we try to make as many connections as possible with the various techniques based on an idea’’(Interview,November 8,1995,p.9).Among the ‘‘ideas’’around which training sessions frequently revolved at White Hall dojo were:being centered,extension,establishing and maintaining a connection with the training partner,and circularity.Additionally,both instructors placed emphasis upon the application of aikido principles for martial eﬀectiveness.For example,while giving an explanation to the class,Frank stated that,‘‘Aikido must be able to work against anybody from any martial art.Aikido is a martial art.Otherwise you’re just dancing around and feeling good’’(Fieldnotes,January 6,1996).This statement reﬂects an instructional style aimed at generating an understanding of aikido as a martial activity,and not primarily as a practice designed for psychological or spiritual development.White Hall dojo instructors rarely spoke of energy when providing explanations,and they were never heard to mention ki during a training session.Instead,their explanations generally focused on the principles upon which aikido’s dynamic movements were founded,as exempliﬁed in their utilization of terms such as ‘‘centering’’,and ‘‘connection’’.The instructors taught martial practicality in a manner which did not deny,but certainly did not give primacy to,psychological or spiritual considerations.3.4.Practitioners ’perceptions of aikido-related concepts:integrated ﬁndings from the semantic diﬀerential and interview dataThis project utilized the constant comparative method (Glasser &Strauss,1967);analysis of interview and participant observation data were on-going throughout the study.The ﬁndings generated from the analysis of these data were integrated with the empirical ﬁndings which emerged from the analysis of the semantic diﬀerentialC.J.Dykhuizen /International Journal of Intercultural Relations 24(2000)741–761748C.J.Dykhuizen/International Journal of Intercultural Relations24(2000)741–761749 data.Theﬁndings generated from the analysis of the semantic diﬀerential data provide a structured representation of participants’understandings of aikido-related concepts,while the qualitativeﬁndings generate a fuller,more detailed description of participants’dynamic and personal understandings of aikido.A principle component analysis(factor analysis)with verimax rotation was applied to analyze the semantic diﬀerential data.The data collected from aikido practitioners in Japan and the United States were treated separately,and items loading at0.60or higher were retained to represent the factors extracted from the analysis.Theﬁndings are presented below.3.5.Concept1:Ki is‘‘Ki’’is typically translated into English as‘‘spirit’’,‘‘mind’’,‘‘will’’,and‘‘intrinsic or inner energy’’(O’Neill,1973;Ratti&Westerbrook,1973).Three distinct factors for‘‘ki is’’were extracted from the Japanese semantic diﬀerential data,while only two factors were extracted from the American data.This indicates that Japanese practitioners structured their understanding of ki in a more complex manner than American practitioners.An examination of the items comprising the factors for each group reveals further diﬀerences in how members of the two cultural groups constructed meaning for the same concept.For clarity,the items comprising a factor are not presented here as polar adjective pairs,but as single adjectives,in accordance with the positive or negative sign extracted during analysis.It should be also noted that theﬁrst factor extracted during analysis typically serves as the‘‘central’’factor around which the meaning for a particular concept is structured.Theﬁrst factor extracted from the Japanese data consisted of theﬁve items‘‘kind’’,‘‘graceful’’,‘‘peaceful’’,‘‘soft’’,and ‘‘rounded’’.These items connoted a sense of ethicalﬂuidness,characterizing harmony.The second factor consisted of the items‘‘strong’’,‘‘deep’’and‘‘active’’, and the third factor of the items‘‘heavy’’and‘‘tenacious’’(Table1).Theﬁrst factor extracted from data gathered among participants in the United States consisted of the items‘‘cruel’’,‘‘ferocious’’,‘‘hard’’and‘‘tenacious’’.The connotative quality of this factor was intrusive,even aggressive.No factor extracted from the Japanese data carried a similar quality of meaning.The second factor contained the items‘‘beautiful’’,‘‘graceful’’,‘‘strong’’,‘‘deep’’,‘‘tenacious’’,and ‘‘active’’.The two factors extracted from the American data seemed to exist on a semantic continuum;a continuum which ranged from‘aggression’,to a sense of aesthetic movement.The meaning of ki was less diﬀerentiated by the American aikido practitioners than by their Japanese counterparts,indicating that American practitioners had a less complex understanding of the concept.This is not surprising,as the kanji (Chinese character)‘‘ki’’is found in words and phrases used everyday in Japanese society;for example,the word for weather,‘‘tenki’’,contains the kanji for‘‘heaven’’and‘‘ki’’.The analysis of the Japanese interview data also revealed that ki plays an important role in Japanese participants’conceptions of aikido.For example,a female aikidoist stated,‘‘If you don’t have harmonious ki,you can’t do aikido’’(Participant interview,June 29,1995,p.3).Not only does her statement illuminate the central importance of the practice and philosophy of harmony to aikido,but it contextualizes this idea in the concept of ki .For English speakers,however,‘‘ki ’’is a foreign concept.Although Americans training in aikido have more opportunities to reﬁne their understanding of ‘‘ki ’’than do non-aikido practicing Americans,the lesser diﬀerentiation of the concept among American participants may simply be due to their relative unfamiliarity with the concept.It was also found that in the educational settings American instructors referred to ki less frequently during training sessions than did instructors teaching in the Japanese settings.Table 1Results of the principle component analysis of the semantic diﬀerential data by culture (Concept:ki is)Factors123LoadingsJapanBeautiful/ugly0.430.59À0.35Kind/cruel0.740.20À0.18Graceful/awkward0.810.25À0.20Peaceful/ferocious0.800.14À0.01Hard/soft À0.85À0.23À0.01Heavy/light0.080.050.78Strong/weak 0.400.630.15Deep/shallow 0.160.85À0.06Tenacious/yielding À0.12À0.090.69Active/passive 0.220.740.14Complex/simpleÀ0.140.390.50Angular/rounded À0.81À0.13À0.04Factors12LoadingsUSABeautiful/ugly À0.350.72Kind/cruelÀ0.760.37Graceful/awkward À0.260.72Peaceful/ferociousÀ0.720.26Hard/soft0.72À0.20Heavy/light0.53À0.05Strong/weak À0.250.64Deep/shallow À0.150.73Tenacious/yielding0.690.03Active/passive0.190.67Complex/simple0.56À0.07Angular/rounded 0.55À0.36C.J.Dykhuizen /International Journal of Intercultural Relations 24(2000)741–761750。

新世纪大学英语第四册第二版听力原文

U1 听力原文： The Importance of Protecting Sea Resources During the 19th century, people in Europe and Americaclimed 声称 that marine resources were unlimited. For example, a noted biologist a t the time commented that none of theworld ’s great sea fisheries wereever going to be exhausted疲劳 .Today though, there is evidence showing thatmarine 海洋resources are as seriously endangered as those of the land and the air. I n fact, in some ways the threats to fish are more alarming than the thre ats to animals and birds. This is because fish is a much needed food res ource, as people throughout the world depend on fish as an important part of their diet. It is reported that to satisfy food demands, 20 billionpounds of fish are harvested 收获every year in the North Atlantic alone. Sea resources are alsorapidly declining in many other parts of the world.Scientists now believe that food supplies from the seawon’t last forever. They warn that excessive fishing will destroy fish reserves within the next few years. They also warn that the decline in fish supply will cause starvationin some parts of the world. What Has Technology Brought Us?U2Technology plays a vital role in our society. It makes our life more comfortable and convenient. Without it, wecouldn ’t evolve or cope up with theever changing world we live in.Firstly, technology shortens the distance between people and makes co mmunication much easier. Today, the Internet is widely used not only f or the collection of information but also for correspondence.Secondly, modern means of transportation, such as airplanes and high-speed trains make our journey smoother and faster. With the help of m odern transportation, we can go almost anywhere we want to. To journ ey into outer space and other planets is not a dream any more. Rockets and space shuttles have made the dream come true.Thirdly, modern medicineprolongsour life and relieves patients from p ain. Some deadly diseases, such as cancer and AIDS can be treated now , and we can live longer and better.Last but not least, technology expands our vision of the world. It provid es us with larger possibilities by giving us ideas that never occurred to u s in the past.It is hard to imagine what the world would be like without technology.U3▇Script: Four Steps to Successful Goal-settingSuccessful people always have clear goals. Great musicians, athlegreates, successful salespeople and inspiring leaders know what they want i n life, and they go after it. No one becomes successful by accident! And yet, a lot of young people that I know just live their lives with no g oals at all, or with onlyvaguedreams, hopes and wishes. No wonder th ey have achieved so much less than they could!For those who have not yet experienced the joy of setting and achievin g magnificent goals, here is a powerful set of principles that have work ed for thousands of myclients. They will work for you, too. I call them“Four Steps to Successful GoalSetting- ”:1.Decide what you want. Choose the life you prefer! Youcan?thave everything in life. Butyou can have anything you choose if you will focus, pay the price, and p ursue it with all your heart.2.Make clear your values. Too often, people choose goals that are inco nsistent with theirpriorities and daily behavior. Do you value health, or comfort? Is financi al independence a priority, or merely a wish? Make sure that your goals are consistent with your most important values.3.Write them down. Have the courage to put your intentions on paper and in your ownwords. Bespecificand describe your goals in detail. When will you achi eve them? What will success look like? Write down the details and readyour goals every day.4.Take action. To run a marathon, you must jog every day. A loving mar riage or happykids require your time, your attention and your love, every day. Your da ily actions need not be profound extraordinary, but they must be co nsistent and persistent.Success does not“ justhappen”Just. as an artist will make preliminary s ketches and work out the details in his mind, so your success requires written goals, careful choices, clear commitments and daily persistence. You can do this. Make something great of your life! U4Attitude Makes a DifferenceAttitudes affect the way people get along at home, at school, and at work. Your attitude will influence your feelings of job satisfaction and your career success. Attitude is the way you think about things and act towa rd others.In fact, many employers believe that the most important factor in job s uccessis a positive attitude. They know that employeean’s work perfor mance is closelyrelated to his or her attitude. Employees with a positiv e attitude enjoy better business performance.If you view a new job as an opportunity, a chance to learn new things, and act with interest and enthusiasm, you are expressing a positive attitude. You also demonstrate a positive attitude when you are polite,coo perative and considerate with your co-workers and superiors. People w ith a positive attitude view the world as a friendly place. They take resp onsibility for their decisions and have the ability to control their feeling s. People with a positive attitude are easy to get along with. Theyare ho nest in expressing their thoughts and feelings. And they are open to su ggestions andconstructivecriticism.As you begin your new job, guard against a negative attitude. People wi th a negative attitude frequently complain and have careless work habi ts. They always blame others for their own problems. Besides, they are critical and indifferent to the needs of others.U5Living a Frugal LifePeople who live a frugal lifestyle often live with less stress. This is bec ause they know how to take control of their money and, therefore, the y have more control over their lives in general. And if you have more co ntrol of your life, you are likely to have more peace of mind. With that peace of mind comes what may be called "frugal freedom", namely, fre edom from debt, freedom from envy, freedom from shame, freedom fr om worry, and freedom from loss of one's identity. Living a frugal life d oes not mean having nothing or living poor and cheap. Neither does itmean denying oneself theluxuriesof life. A frugal lifestyle simply mean s thatyou have theintelligenceto live a happy and fulfilling life without possessing a lot. Much of the transition from being extravagant to bei ng frugalis within the mind. For example, before you leave your room, remember to turn off the light; keep all windows and outside doors clo sed when the airconditioningis on; turn off thetap water immediately after use; when you go shopping, use your brain to save money on cert ain things for something else you need or want. In short, living a frugal lifestyle means that you don't have to "keep up with the Joneses" and t hat you do not have to follow trends and fads.We should always remember that it is easier to spend less than it is to make more. It is easier to be frugal than to free oneself fromfinancial s tress. So, letus be frugal and live within ourmeans. It is a great way of l ife.U6Always Be PositiveBeing positive is a discipline. There are so many things we cannot contr ol, forces we are powerless to change because so much of life is unpred ictable. We cannot control diseases. We cannot control injuries. We can not control the weather and so many other things that are a part of our lives.But we can control our moods. A mood is simply a reflection of our atti tude and we certainly can change our attitude.For example, when one of your co-workers asks you howyou’ redoingwith your work, your answer may be“I feel great ”But. do you really fe el great? Probably not.If your answer were negative, you would make your co-workers feel bad and uncomfortable. In that case,you’ vebegun a whole cycle of negat ivity. Again,it ’s anattitude. A good attitude and a bad attitude are reall y just two different ways of looking at the same situation.Here ’s another example. You have a big work project due on theboss’s desk tomorrow morning. You are up against a deadline. And you are ha lf-done. Now there are two ways to look at the project. You can worry a bout all the work still left for you to do. Or you can tell yourself that hal f of it is already done, and you are certainly better off than when you first started the projectThat. ’s your choice: is the glass half-empty or hal f-full? That ’s the choice we have to make every morning when we get o ut of bed. Looking honestly at the reality of the situation and seeing the positive s ide of it may indeed increase the quality of our life. Self-motivated peo ple look at each day as a new opportunity. They love what they do. The y cannot wait to get to work in the morning.U7Reading EfficientlyYou know you have to read“ betweenthe lines to” get the most out of anything. I want to persuade you to do somethingequally 一样important in the course of your reading, that is:“writebetween thelines ”Unless. you do, you are not likely to do the most efficient kind of reading. Icontendthat marking up a book is an act of love.There are two ways in which one can own a book.The first is the property right youestablishby paying for it, just as you payfor clothes and furniture. But this act of purchase is onlypreludthee 序幕to possession. Full ownership comes only when you have made it a par t of yourself, and the best way to make yourself a part of it is by writing in it.Why is marking up a bookindispensableto reading? First, it keeps you awake. And Idon’t mean merelyconscious; I mean wide awake. In the second place, reading, if it is active, is thinking, and thinking tends to e xpress itself in words, spoken or written. The marked book is usually th e thought-through book. Finally, writing helps you remember the thoug hts you had, or the thoughts the authorexpressed.If reading is toaccomplishanything more than passing time, it must beactive. Youcan’t let your eyes glide across the lines of a book and come up with an understandingof what you have read. The books you read for pleasure can be read in a state relaxationof and nothing is lost. But a great book, rich in ideas and beauty, a book thraisestand tries to answer fundamental questions,demandsthe most active reading. When you’ vefinished reading a book, and the pages are filled with your notes , you know that you readactively.。

Procedings of the IASTED International Conference APPLIED SIMULATION AND MODELLING

Procedings of the IASTED International ConferenceAPPLIED SIMULATION AND MODELLINGSeptember3-5,2003,Marbella,SpainA time-frequency approach to blind separation of under-determinedmixture of sourcesA.MANSOURLab.E I,ENSIETA,29806Brest cedex09,(FRANCE).mansour@M.KAW AMOTODept.of Electronic andControl Systems Eng.,Shimane University,Shimane690-8504,(JAPAN)kawa@ecs.shimane-u.ac.jpC.PuntonetDepartamento de Arquitectura yTecnologia de computadores,Universidad de Granada,18071Granada,(SPAIN).carlos@atc.ugr.esABSTRACTThis paper deals with the problem of blind separation of under-determined or over-complete mixtures(i.e.more sources than sensors).Atﬁrst a global scheme to sepa-rate under-determined mixtures is presented.Then a new approach based on time-frequency representations(TFR) is discussed.Finally,some experiments are conducted and some experimental results are given.KEY WORDSICA,BSS,Time-Frequency domain,over-complete or under-determined mixtures1.IntroductionBlind separation of sources problem is a recent and an im-portant signal processing problem.This problem involves recovering unknown sources by only observing some mixed signals of them[1].Generally,researchers assume that the sources are statistically independent from each other and at most one of them can be a Gaussian signal[2]. Other assumptions can be also founded in the literature concerning the nature of the transmission channel(i.e.an instantaneous or a memoryless channel,a convolutive or a memory channel,and a non-linear channel).In addition,a widely used assumption considers that the number of sen-sors should be equal or greater(for subspace approaches) than the number of sources.These assumptions are fairly satisﬁed in many divers applications such as robotics, telecommunication,biomedical engineering,radars,etc., see[3].In recent applications linked to special scenarios in telecommunication(as satellite communication in double-talk mode),robotics(for exemple,robots which imitate human behavior)or radar(in ELectronic INTelli-gence”ELINT”applications),the assumption about the number of sensors can not be satisﬁed.In fact,in the latter applications the number of sensors is less than the number of sources and often we should deal with a mono-sensor system with two or more sources.Recently,few authors have considered the under-determined mixtures.Thus by using overcomplete repre-sentations,Lewicki and Sejnowski in[4]present an algo-rithms to learn overcomplete basis.Their algorithm uses a Gaussian approximation of probability density function (PDF)to maximize the probability of the data given the model.Their approach can be considered as a generaliza-tion of the Independent Component Analysis(ICA)[2]in the case of instantaneous mixtures.However,in this ap-proach,the sources should be sparse enough to get good ex-perimental results,otherwise the sources are being mapped down to a smaller subspace and there is necessary a loss of ing the previous approach,Lee et al.[5] separate successfully three speech signals using two micro-phones.On the other hand,When the sources are sparsely distributed,at any time t,at most one of sources could be signiﬁcantly different from zero.In this case,estimating the mixing matrix[6,7,8]consists ofﬁnding the direc-tions of maximum data density by simple clustering ing Reimannian metrics and Lie group structures on the manifolds of over-complet mixture matrices,Zhang et al.[9]present a theoretical approach and develop an al-gorithm which can be considered as a generalization of the one presented in[10].The algorithm of Zhang et al.up-date the weight matrix by minimizing a kullback-Leibler divergence by using natural learning algorithm[11].In the general case,one can consider that separation of over-complete mixtures still a real challenge for the sci-entiﬁc community.However,some algorithms have been proposed to deal with particular applications.Thus for bi-nary signals used in digital communication,Diamantaras and Chassioti[12,13]propose an algorithm based on the PDF of the observed mixed signals.The pdf of the ob-servation signals have been modeled by Gaussian pdf and estimated from the histogram of the observed -ing differential correlation function,Deville and Savoldelli [14]propose an algorithm to separate two sources from noisy convolutive mixtures.The proposed approach re-quires the sources to be long-term non-stationary signals and the noise should be long-terme stationary ones.The previous statement means that the sources(resp.noise) should have different(resp.identically)second order statis-tics at different instances separated by a long period.2.Channel ModelHereinafter,we consider that the sources are non Gaus-sian signals and statistically independent from each other.In addition,we assume that the noise is an additive whiteGaussian noise (AWGN).Letdenote the source vector at any time t,is mixing vector and is a AWGN vector.The channel is represented by a full rank real and constant matrix ().H ( )Channel+B(t)S(t)Figure 1.General structure.The separation is considered achieved when the sources are estimated up to a scale factor and a permuta-tion.That means the global matrix can be written as:here,is a weight matrix,is a permutation matrix and is a non-zero diagonal matrix.For a sake of simplic-ity and without loss of generality,we will consider in the following that:Where is an invertible matrix and is a full rankrectangular matrix.3.A Separation SchemeIn the case of over-complete mixtures (),the invert-ibility of the mixing matrix becomes an ill-conditioned problem.That means the Independent Component Analy-sis (ICA)will be reduced to extract independent signals which are not necessarily the origine sources,i.e.the sep-aration can not give a unique solution.Therefore,further assumptions should be considered and in consequence suit-able algorithms could be developed.Thus,two strategies can be considered:At ﬁrst one can identify the mixing matrix then us-ing this estimated matrix along with important infor-mation about the nature or the distributions of the sources,we should retrieve the original sources.In many applications (such as speech signals,telecom-munications,etc ),one can assume the sources havespecial features (constant modulus,frequency prop-erties,etc ).Using sources’speciﬁcs,the separation becomes possible in the classic manner,i.e.up to per-mutation and a scale factor.Beside the algorithms cited and discussed in the intro-duction of our manuscript,few more algorithms can be founded in the literature.The latter publications are dis-cussed in this section.3.1Identiﬁcation &SeparationOne of the ﬁrst publications on the identiﬁcation of under-determined mixtures was proposed by Cardoso [15].In his manuscript,Cardoso proposed an algorithm based only on fourth-order cumulant.In fact,using the symmetries of quadricovariance tensor,an identiﬁcation method based on the decomposition of the quadricovariance was proposed.Recently,Comon [16]proved using an algebraic approach,that the identiﬁcation of static MIMO (Multiple Inputs Multiple Outputs)with fewer outputs than inputs is possible.In other words,he proved that the CANonical Decomposition (CAND)of a fourth-order cross-cumulant tensor can be considered to achieve the identiﬁcation.In addition,he proved that ICA is a symmetric version of ing a Sylveter’s theorem in multilinear algebra and the fourth order cross cumulant tensor,he proposed an algorithm to identify the mixing matrix in the general case.To recover d-psk sources,comon proposes alsoa non-linear inversion ofby adding some non-linear equations and using the fact that the d-psk signals satisfyspecial polynomial properties (i.e.).Later on,Comon and Grellier [17]proposed an extension of the previous algorithm to deal with different communication signals (MSK,QPSK and QAM4).Similar approach was also proposed by De Lathauwer et al.,see [18].Finally,Taleb [19]proposes a blind identiﬁcation al-gorithm of M-inputs and 2-outputs channel.He proved thatthe coefﬁcients of the mixing matrixare the roots of a polynomial equations based on the derivative of the sec-ond characteristic function of the observed signals.The uniqueness of the solution is proved using Darmois’Theo-rem [20].3.2Direct SeparationHere,we discuss methods to separate special signals.As it is mentioned in the previous subsection that Comon et al.[16,17]proposed an algorithm to separate communication signals.Nakadai et al.[21,22]addressed the problem of a blind separation of three mixed speech signals with the help of two microphones by integrating auditory and vi-sual processing in real world robot audition systems.Theirapproach is based on direction pass-ﬁlters which are imple-mented using the interaural phase difference and the inter-aural intensity difference in each sub-band -ing Dempster-Shafer theory,they determine the direction of each sub-band frequency.Finally,the waveform of one sound can be obtained by a simple inverse FFT applied to the addition of the sub-band frequencies issued by the spe-ciﬁc direction of that speaker.Their global system can per-form sound source localization,separation and recognition by using audio-visual integration with active movements.4.Time-Frequency ApproachThe algorithm proposed in this section is based on time-frequency distributions of the observed signals.To our knowledge,few time-frequency methods have been devoted to the blind separation of MIMO channel.In fact,for MIMO channel with more sensors than sources, Belouchrani and Moeness[23]proposed a time-frequency separation method exploiting the difference in the time-frequency signatures of the sources which are assumed to be nonstationary multi-variate process.Their idea consists on achieving a joint diagonalization of a combined set of spatial time-frequency distributions which have been deﬁned in their paper.It is clear from the discussion of the previous sections that the identiﬁcation of MIMO channel is possible.How-ever,the separation is not evident in the general case.The few published algorithms for the under-determined matter are very linked to signal features of theirs applications.In our applications,an instantaneous static under-determined mixture of speech signals is considered.This problem can be divided into two steps:Atﬁrst an identiﬁcation algorithm should be applied.For the moment,we didn’t develop a speciﬁc identi-ﬁcation algorithm.Therefore,any identiﬁcation algo-rithms previously mentioned can be used.Let us assume that the coefﬁcient of the mixing matrixhave been estimated.The question becomes How can we recover the sources from fewer sensors?To answer this question,we consider in this section the separation of a few speech signals(for the instance, we are considering just two or at most three sources) using the output of a single microphone(i.e.Multiple Inputs Single output,MISO channel).Recently,time-frequency representations(TFR) have been developed by many researchers[24]and they are considered as very powerful signal processing tools.In the literature,many different TFR have been developed as Wigner-Ville,Pseudo-Wigner-Ville,Smooth Pseudo-Wigner-Ville,Cho-Willims,Born-Jordan,etc.In a previous study[25],we found that for simplicity and performance reasons,the Pseudo-Wigner-Ville can be considered as a good TFR candidate.Here we present a new algorithm based on time-frequency representations of the observed signals(TFR)to separate a MISO channel with speech sources.It is known that speech signals are non-stationary signals.However within phonemes(about80ms of duration)the statistics of the signal are relatively constant[26].On the other hand,It is well known that voiced speech are quasi-periodic signals and the non-voiced signals can be considered as white ﬁltered noise[27].Within a small window corresponding to51ms,the pitch can be slightly change.Therefore,one can use this property to pick up the frequency segments of a speaker.The pitch can be estimated using divers techniques[28].Using the previous facts and Pseudo-Wigner-Ville representations,one can separate up to three speech signals from one observed mixed signal of them.To achieve that goal,we assume that the time-frequency signatures of the sources are disjoints.Atﬁrst,one should calculate the TFR of the observed signal.Then,in the time-frequency space, we plot a regular grilled.The dimensions of the a small cell of the grilled are evaluated based on the properties of the speech signals and the sampling frequency.Therefore, these dimensions can be considered as10to20ms in length (i.e.time axis)and5to10%of the sampling frequency value in the vertical axis.Once we plot the grilled,we estimate the energy average in each cell and a threshold is applied to distinguish noisy cells from other.Then the cell with the maximum energy is considered as a potential pitch of one speaker and it is pointed out.After that,we merge in a set of cells,all cells with high level of energy in the neighborhood of the previous cell.At least one har-monic of the pitch should be also selected.The previous steps should repeated as necessary.Finally,the obtained map can be considered as a bi-dimensional time-frequency ﬁlters which should be applied on the mixed -ing a simple correlation maximization algorithm,one can ﬁnd the different pieces corresponding to the speech of one speaker.5.Experimental ResultsTo demonstrate the validity of the proposed algorithm men-tioned in section4,many computer simulations were con-ducted.Some results are shown in this section.We consid-ered the following two-input and one-output system.(1)The sources were male and female voices which were recorded by8[KHz]sampling fre-quency.The TFR was calculated by using128data of the observed signal.Figure2shows the results obtained by applying the proposed algorithm(last paragraph in section4)to the ob-served signal.From thisﬁgure,one might think that the estimated signals are different from the original signals. However,if one hear the estimated signals,one can see that the two original sources and are separated from the observed signal by the proposed algorithm.6.ConclusionThis paper deals with the problem of blind separation of under-determined(or over-complete)mixtures(i.e.more sources than sensors).Atﬁrst,a survey on blind separation algorithms for under-determined mixtures is given.A sep-aration scheme based on identiﬁcation or direct separation is discussed.A new time-frequency algorithm to separate speech signals has been proposed.Finally,some experi-ments have been conducted and the some experimental re-sults are given.Actually,we are working on a project con-cern the separation of under-determined mixtures.Further results will be the subject of future communications.References[1]A.Mansour, A.Kardec Barros,and N.Ohnishi,“Blind separation of sources:Methods,assumptions and applications.,”IEICE Transactions on Funda-mentals of Electronics,Communications and Com-puter Sciences,vol.E83-A,no.8,pp.1498–1512, August2000.[2]on,“Independent component analysis,a newconcept?,”Signal Processing,vol.36,no.3,pp.287–314,April1994.[3]A.Mansour and M.Kawamoto,“Ica papers classi-ﬁed according to their applications&performances.,”IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences,vol.E86-A,no.3,pp.620–633,March2003.[4]M.Lewicki and T.J.Sejnowski,“Learning non-linear overcomplete representations for efﬁcient cod-ing,”Advances in neural Information Processing Sys-tems,vol.10,pp.815–821,1998.[5]T.W.Lee,M.S.Lewicki,M.Girolami,and T.J.Se-jnowski,“Blind source separation of more sources than mixtures using overcomplete representations,”IEEE Signal Processing Letters,vol.6,no.4,pp.87–90,April1999.[6]P.Boﬁll and M.Zibulevsky,“Blind separation ofmore sources than mixtures using sparsity of their short-time fourier transform,”in International Work-shop on Independent Component Analysis and blind Signal Separation,Helsinki,Finland,19-22June 2000,pp.87–92.[7]P.Boﬁll and M.Zibulevsky,“Underdetermined blindsource separation using sparse representations,”Sig-nal Processing,vol.81,pp.2353–2363,2001. [8]P.Boﬁll,“Undetermined blind separation of delayedsound sources in the frequency domain,”NeuroCom-puting,p.To appear,2002.[9]L.Q.Zhang,S.I.Amari,and A.Cichocki,“Nat-ural gradient approach to blind separation of over-and under-complete mixtures,”in First International Workshop on Independent Component Analysis and signal Separation(ICA99),J.F.Cardoso,Ch.Jutten, and Ph.loubaton,Eds.,Aussois,France,11-15Jan-uary1999,pp.455–460.[10]M.Lewicki and T.J.Sejnowski,“Learning overcom-plete representations,”Neural Computation,vol.12, no.2,pp.337–365,2000.[11]S.I.Amari,A.Cichocki,and H.H.Yang,“A newlearning algorithm for blind signal separation,”in Neural Information Processing System8,Eds.D.S.Toureyzky et.al.,1995,pp.757–763.[12]K.Diamantaras and E.Chassioti,“Blind separationof n binary sources from one observation:A deter-ministic approach,”in International Workshop on In-dependent Component Analysis and blind Signal Sep-aration,Helsinki,Finland,19-22June2000,pp.93–98.[13]K.Diamantaras,“Blind separation of multiple binarysources using a single linear mixture,”in Proceed-ings of International Conference on Acoustics Speech and Signal Processing2001,ICASSP2000,Istanbul, Turkey,Jun2000,pp.2889–2892.[14]Y.Deville and S.Savoldelli,“A second order dif-ferential approach for underdetermined convolutive source separation,”in Proceedings of International Conference on Acoustics Speech and Signal Process-ing2001,ICASSP2001,Salt Lake City,Utah,USA, May7-112001.[15]J.F.Cardoso,“Super-symetric decomposition of thefourth-order cumulant tensor.blind identiﬁcation of more sources than sensors.,”in Proceedings of Inter-national Conference on Speech and Signal Process-ing1991,ICASSP’91,Toronto-Canada,May1991, pp.3109–3112.[16]on,“Blind channel identiﬁcation and extrac-tion of more sources than sensors,”in In SPIE Confer-ence on Advanced Algorithms and Architectures for Signal Processing,San Diego(CA),USA,July19-24 1998,pp.2–13,Keynote address.[17]on and O.Grellier,“Non-linear inversion ofunderdetermined mixtures,”in First International Workshop on Independent Component Analysis andFigure2.Simulations Results:(a)Source signal(b)Source signal(c)Observed signal(d)Estimated signal of(e)Estimated signal ofsignal Separation(ICA99),J.F.Cardoso,Ch.Jut-ten,and Ph.loubaton,Eds.,Aussois,FRANCE,11-15 January1999,pp.461–465.[18]L.De Lathauwer,on,B.De Moor,and J.Van-dewalle,“ICA algorithms for3sources and2sen-sors,”in IEEE SP Int Workshop on High Order Statis-tics,HOS99,Caeserea,Israel,12-14June1999,pp.116–120.[19]A.Taleb,“An algorithm for the blind identiﬁcation ofn independent signals with2sensors,”in Sixth Inter-national Symposium on Signal Processing and its Ap-plications(ISSPA2001),M.Deriche,Boashash,and W.W.Boles,Eds.,Kuala-Lampur,Malaysia,August 13-162001.[20]G.Darmois,“Analyse g´e n´e rale des liaisons stochas-tiques,”Rev.Inst.Intern.Stat.,vol.21,pp.2–8,1953.[21]K.I.Nakadai,K.Hidai,H.G.Okuno,and H.ki-tano,“Real-time speaker localization and speech sep-aration by audio-visual integration,”in17th inter-national Joint Conference on Artiﬁcial Intelligence (IJCAI-01),Seatle,USA,August2001,pp.1425–1432.[22]H.G.Okuno,K.Nakadai,T.Lourens,and H.kitano,“Separating three simultaneous speeches with two microphones by integrating auditory and visual pro-cessing,”in European Conference on Speech Process-ing,Aalborg,Denmark,September2001,pp.2643–2646.[23]A.Belouchrani and M.G.Amin,“Blind source sep-aration based on time-frequency signal representa-tions,”IEEE Trans.on Signal Processing,vol.46, no.11,pp.2888–2897,1998.[24]P.Flandrin,Time-Frequency/Time-Scale analysis,Academic Press,Paris,1999.[25]D.Le Guen and A.Mansour,“Automatic recogni-tion algorithm for digitally modulated signals,”in6th Baiona workshop on signal processing in communi-cations,Baiona,Spain,25-28June2003,p.To ap-pear.[26]J.Thiemann,Acoustic noise suppression for speechsignals using auditory masking effects,Ph.D.thesis, Department of Electrical&Computer Engineering, McGill University,Canada,July2001.[27]R.Le Bouquin,Traitemnet pour la reduction du bruitsur la parole application aux communications radio-mobiles.,Ph.D.thesis,L’universit´e de Rennes I,July 1991.[28]A.Jefremov and B.Kleijn,“Sline-based continuous-time pitch estimation,”in Proceedings of Interna-tional Conference on Acoustics Speech and Signal Processing2002,ICASSP2002,Orlando,Florida, U.S.A,13-17May2002.。

Acinetobacter venetianus 7

ORIGINAL PAPERCharacterization of novel diesel-degrading strains Acinetobacter haemolyticus MJ01and Acinetobacter johnsonii MJ4isolated from oil-contaminated soilMyungjin Lee •Sung-Geun Woo •Leonid N.TenReceived:11November 2011/Accepted:24January 2012/Published online:7February 2012ÓSpringer Science+Business Media B.V.2012Abstract The diesel-degrading strains,designated as MJ01and MJ4,were isolated from oil-contaminated soil in Daejeon (South Korea)and were taxonomically charac-terized using a polyphasic approach and their diesel oil degradation abilities were analyzed.The isolates MJ01and MJ4were identiﬁed as Acinetobacter haemolyticus and Acinetobacter johnsonii ,respectively,based on their 16S rDNA gene sequences,DNA–DNA relatedness,fatty acid proﬁles and various physiological characteristics.Strains MJ01and MJ4were able to use diesel oil as the sole carbon and energy source.Both strains could degrade over 90%of diesel oil with an initial concentration of 20,000mg/l after incubation for 7days,the most signiﬁcant degradation occurred during the ﬁrst 3days.To our knowledge,this is the ﬁrst report on diesel oil-degrading microorganisms among bacterial strains belonging to A.haemolyticus and A.johnsonii .Keywords Acinetobacter haemolyticus MJ01ÁAcinetobacter johnsonii MJ4ÁBiodegradation ÁDiesel oilIntroductionSoil and ground water contamination due to petroleum-derived products,in particular diesel oil,is an important environmental problem.Importantly,diesel oil is classiﬁed as hazardous waste (Bartha and Bossert 1984)and hydro-carbon-degrading microorganisms can potentially play a central role in addressing this problem.Thus,biodegrada-tion of hydrocarbons by microorganisms represents one of the primary mechanisms by which those pollutants could be eliminated from the environment (Leahy and Colwell 1990;Van Hamme et al.2003;Gouda et al.2008;Cerqueira et al.2011).The application of hydrocarbon-degrading bacteria in oil-contaminated sites does not guarantee that all oil components will be completely metabolized because some components,such as alkanes of shorter and longer chains (\C 10and C 20–C 40),are not as readily biodegradable as are alkanes of intermediate lengths (Atlas and Cerniglia 1995;Yuste et al.2000).It is therefore important to ﬁnd a new bacterial strain that can metabolize a broad range of oil hydrocarbons,especially the highly persistent components.Many microorganisms have been reported to degrade fuel and diesel oils (Atlas and Cerniglia 1995;Hong et al.2005;Cerqueira et al.2011).Among them bacterial strains belonging to the genus Acinetobacter are known for their high ability to degrade a broad variety of hydrocarbons,including the n -alkanes (Espeche et al.1994;Marin et al.1996;Di Cello et al.1997;Akinde and Obire 2008),aromatic compounds (Adebusoye et al.2007;Fischer et al.2008)and diesel oil (Su et al.2008;Kang et al.2011).At the time of writing,the genus Acinetobacter comprises 25species with validlypublished names (Euze´by 2011),but the description of a twenty sixteenth member of the genus,Acinetobacter rudis ,is available ahead of print (Vaz-Moreira et al.2011).M.Lee (&)ÁS.-G.Woo ÁL.N.TenResearch and Development Division,H-Plus Eco Ltd.,BVC 301,KRIBB,Eoeun-dong,Yuseong-gu,Daejeon 305-333,Republic of Koreae-mail:mgeneli@S.-G.WooSchool of Civil and Environmental Engineering,Yonsei University,Seoul 120-749,Republic of KoreaL.N.Ten (&)Department of Biology and Medicinal Science,Pai Chai University,14Yeon-Ja 1Gil,Seo-Gu,Daejeon 302-735,Republic of Koreae-mail:l_ten@World J Microbiol Biotechnol (2012)28:2057–2067DOI 10.1007/s11274-012-1008-3Acinetobacter species are distributed widely throughout many environments,including soil(Prathibha and Sumathi 2008),seawater(Di Cello et al.1997),wastewater(Pei et al.2009;Vaz-Moreira et al.2011),sewage(Lee and Lee 2010),oil(Marin et al.1996)and human clinical speci-mens(Bouvet and Grimont1986),suggesting the profound adaptability of the genus to various environments and its ubiquity and metabolic versatility.Recently,Acinetobacter strain MJ01was isolated and used in mixture with two other bacteria for biodegradation of diesel,but it has not yet been characterized in detail (Lee et al.2010).At the same time,we isolated other diesel oil-degrading strain,designed as MJ4.The aim of the present study was taxonomic characterization of strains MJ01and MJ4,that capable to use diesel oil as a sole carbon and energy source,and evaluation their diesel oil degradation potentials.Materials and methodsChemicalsDiesel oil was a gift from LG-Caltex Corporation(Daej-eon,South Korea).All bacterial media components and organic solvents were at least analytical grade.Isolation of diesel oil-degrading bacteriaSoil samples were collected from a diesel oil contaminated site in Daejeon,South Korea.The samples(2g)were incubated in50ml minimal salts(MS)solution containing the following in g/l of distilled water;K2HPO4,0.9; KH2PO4,0.54;MgSO4Á7H2O,0.25;KCl,0.25;CaCl2Á2H2O,0.01;plus trace amounts of micronutrients(Widdel and Bak1992)supplemented with0.5%(v/v)diesel oil. Theﬁnal pH of the medium was7.0.The medium was shaken at150rev/min at30°C for7days,5ml of the suspension transferred to45ml of fresh medium and incubated for a further7days at30°C.The resultant suspension(1ml)was plated on solid media(1.5%w/v agar)supplemented with0.5%(v/v)diesel oil,to obtain pure cultures.Fifty of single colonies were tested for their ability to degrade diesel oil in MS solution.Two strains, designed as MJ01and MJ4,showed the highest degrada-tion activity and were selected for further study. Characterization of strains MJ01and MJ4Strains MJ01and MJ4were grown on trypticase soy agar medium(TSA;Difco)at30°C for3days in order to determine their morphological and physiological charac-teristics.The Gram reaction was determined by using a Gram-stain kit(Difco)according to the manufacturer’s instructions.Cell morphology and motility were observed under a Nikon light microscope at91000magniﬁcation using cells exponentially and stationary growing cultures. Assimilation of single carbon sources,enzyme activities and other physiological characteristics were determined with the API ID32GN,API ZYM and API20NE galleries according to the manufacturer’s instructions(bioMe´rieux). The ability of the strains to grow at different temperatures (15,25,30,37and41°C)was determined on TSA agar. Oxidase activity was tested using Bactident-Oxidase test strips(Merck)and catalase activity with3%hydrogen peroxide.Cellular fatty acids were analyzed in organisms grown on TSA for48h at28°C.The cellular fatty acids were saponiﬁed,methylated and extracted according to the protocol of the Sherlock Microbial Identiﬁcation System (MIDI).The fatty acid methyl esters were then analyzed by gas chromatography(model6890;Hewlett Packard)using the Microbial Identiﬁcation software package(Sasser 1990).Determination of DNA G?C content and DNA–DNA hybridizationFor the measurement of chromosomal DNA G?C content, the genomic DNA of the strains were extracted and puriﬁed as described by Moore and Dowhan(1995)and degraded enzymically into nucleosides;the DNA G?C contents were determined as described by Mesbah et al.(1989)by using reverse-phase HPLC.DNA–DNA hybridization to determine genomic relatedness was performedﬂuoromet-rically according to the method of Ezaki et al.(1989),by using photobiotin-labelled DNA probes(Sigma)and mic-rodilution wells(Greiner),withﬁve replications for each sample.The highest and lowest values obtained for each sample were excluded and the means of the remaining three values are quoted as the DNA–DNA hybridization values.Analysis of16S rRNA gene sequence and phylogenetic analysisGenomic DNA was extracted by using a commercial genomic DNA extraction kit(Solgent)and PCR-mediated ampliﬁcation of the16S rRNA gene and sequencing of the puriﬁed PCR product were carried out according to Kim et al.(2005).Full sequences of the16S rRNA gene were compiled by using SeqMan software(DNASTAR).The total16S rRNA gene sequence of the test strains was edited using the BioEdit program(Hall1999)and aligned using CLUSTAL_X software(Thompson et al.1997).Related sequences were obtained from the GenBank database by using the BLAST search program.The distance matrix wascalculated by using the BioEdit program and the phylo-genetic tree was constructed by using the neighbor-joining algorithm(Saitou and Nei1987)and the MEGA4program (Tamura et al.2007).The stability of relationships was assessed by a bootstrap analysis of1,000trials. Nucleotide sequence accession numbersThe16S rRNA gene sequences of strains MJ01and MJ4 determined in this study has been deposited in the Gen-Bank database under the accession numbers GU991530 and HQ650820,respectively.Other accession numbers for reference16S rRNA gene sequences used in the phyloge-netic analysis are shown in positional analysis of diesel oilDiesel oils supplied from LG-Caltex Corporation(Daejeon, South Korea)were used as target compounds for the deg-radation experiments.The diesel oil consisted of alkanes (42.7%),cycloalkanes(33.4%),and aromatics(23.9%)as described in the technical data sheets provided by LG-Caltex Corporation.The composition of the diesel oil was analyzed using a gas chromatograph(GC-17A;Shimadzu, Kyoto)equipped with a mass detector(GCMS-QP5050A; Shimadzu)and HP-1column(30m90.32mm91l m ﬁlm thickness;J&W Scientiﬁc,Folsom,CA,USA).Sam-ple volumes of1l l were injected into the column.The temperatures in the injector and detector were250°C and 300°C,respectively.The column temperature was kept at70°C for 2min,increased to 300°C at a ramp rate of 10°C/min and held at 300°C for 15min.Only n -alkanes and a few branched hydrocarbons can be identiﬁed as separate compounds out of the 2,000to 4,000hydrocarbons that diesel oil contains.However,it is possible to quantify the main structural classes,namely n -alkanes,isoalkanes,cyclo-alkanes and aromatics which comprise diesel oil (Olson et al.1999).A chromatogram proﬁle of the diesel oil used in the study is shown in Fig.2;it can be seen that all of the n -alkanes were clearly identiﬁed.Branched alkanes such as 2,6,10,14-tetramethylpentadecane (pristane)and 2,6,10,14-tetramethyl-hexadecane (phytane)were also detected.However,the major fractions of diesel oil were not identiﬁed because of the ana-lytical complexity related to the large number of components.The same batch of diesel oil was used throughout the study.Degradation of diesel oilThe bacteria were grown in triplicate in 50ml MS solution (pH 7.0)with diesel oil as the sole carbon and energy source.Initial diesel concentrations were 1,000,5,000,10,000and 20,000mg/l.The resultant preparations were inoculated with strains MJ01or MJ4to give 69106c.f.u./ml while uninoculated control ﬂasks were prepared to detect any decrease in diesel oil concentration due to factors other than microbial utilization.All ﬂasks were closed with cotton-wool plugs that allow the passage of oxygen into the ﬂasks.The cultures were incubated on a rotary shaker (300rev/min)at 30°C for 7days.Diesel oils are not homogeneously distributed in shake ﬂasks which make representative sam-pling of broths virtually impossible;hence,sacriﬁcial sam-pling of complete ﬂask contents was carried out at each day.All samples were set up in triplicate.Thus the 50ml samples were extracted in an equivalent volume of n -hexane.Selected samples were analyzed by using a gas chromato-graph ﬁtted with an FID detector (HP 5890series II;Hewlett-Packard,Palo Alto,CA,USA)and an HP-1column (30m 90.32mm 91l m;J&W Scientiﬁc)with helium as the carrier gas.The temperatures in the injector and detector were 280and 300°C,respectively.The column temperature was kept at 40°C for 2min,shifted to 300°C at 10°C/min and then held at 300°C for 15min.Two samples were injected into the GC with the total petroleum hydrocarbon (TPH)measured as the sum of all of the peak areas on the chro-matogram.The degree of degradation was calculated based on the following equation:degradation (%)=100[(A -B)/A)],where A is the area of TPH from the control experiment without inoculation,and B is the area of TPH from the inoculated culture.The rate of diesel oil degradation was calculated using calibration curves with the internal standard (97%dotriacontane,Aldrich),and the TPH measurement and the FID response factors were equal for all compounds.Oxygen uptake and growth of strain MJ01and MJ4on diesel oilThe measurements of oxygen uptake and viable cell con-centrations were parallelly performed in separate vessels.Bacterial cell concentrations were determined by using the agar plate count method on TSA medium.Inoculated Petri dishes were incubated at 30°C for 48h before cell counting.Growth of strains MJ01and MJ4was recorded as c.f.u./ml for 12days.The oxygen uptake of strains MJ01and MJ4was measured by a respirometer (Challenge AER-200,Fayette-ville,AR,USA).10,000mg/l diesel oil was added to a test respirometer vessel with 500ml MS solution (pH 7.0).The resultant preparations were inoculated with strains MJ01or MJ4to give 69106c.f.u./ml while uninoculated control vessels were prepared to detect oxygen consumption due to factors other than microbial utilization.The preparation vessels were linked to the syringe of respirometer.The cultures were incubated on 30°C water bath with rotation using magnetic stir bar (300rev/min)for 12days.The oxygen consumption of strains MJ01or MJ4were measured while the diesel oil as the sole carbon and energy source was metabolized.All experiments were performed in triplicates.Results and discussionMorphological and biochemical characteristics of strains MJ01and MJ4Several bacterial strains that used diesel oil as sole carbon source were isolated using MS agar from diesel oil con-taminated soil.Among the isolated single colonies,strains MJ01and MJ4were found to have the highest levelofFig.2Gas chromatogram of diesel oil.C numbers indicate n -alkanes;Pr,pristane (2,6,10,14-tetramethylpentadecane);Ph,phy-tane (2,6,10,14-tetramethylhexadecane)diesel oil-degrading activity and were selected for further study.The strains MJ01and MJ4were Gram-negative, non-motile cocci and their colonies were circular,convex, smooth,slightly opaque with entire margins and 1.1–1.9mm in diameter after24h of growth.Optimal growth conditions of strains MJ01and MJ4were at30°C and pH7.0.No growth occurred at41°C and under anaerobic conditions.Both strains,as well as their phylo-genetically closest relatives of the genus Acinetobacter (Table1)were positive for catalase,esterase lipase(C8),leucine arylamidase,and utilization of acetate,but negative for nitrate reduction,oxidase,N-acetyl-b-glucosaminidase, arginine dihydrolase,a-chymotrypsin,a-fucosidase,a-galac-tosidase,b-galactosidase,b-glucoronidase,a-glucosidase, a-mannosidase,trypsin,urease,and assimilation of N-acetyl-glucosamine,L-arabinose,L-fucose,gluconate,D-glucose, glycogen,inositol,itaconate,2-ketogluconate,5-ketogluconate, malonate,D-maltose,D-mannitol,D-mannose,D-melibiose, L-rhamnose,D-ribose,salicin,D-sorbitol,sucrose,and suberate. Other characteristics that support the afﬁliation of strain MJ01Table1Comparison of phenotypic characteristics of strains MJ01and MJ4with phylogenetically closely related type strains in the genus AcinetobacterCharacteristic1234567Growth at37°C--????? Growth at41°C------? Production of indole------? Gelatin hydrolysis????---Production of acid from glucose?-???--Enzyme activities(API ZYM and API20E)Acid phosphatase??----? Alkaline phosphatase??-----Cysteine arylamidase????---Esterase(C4)???-?-? b-Glucosidase??-----Lipase(C14)?-?-??-Naphthol-AS-BI-phosphohydrolase????--? Valine arylamidase----?--Assimilation of(API ID32GN and API20NE)Adipate-------L-Alanine????--? Caprate?????--Citrate--????? L-Histidine--????-3-Hydroxybenzoate------? 4-Hydroxybenzoate--??-?? 3-Hydroxybutyrate??--?--Lactate??---?? D-Malate--????? Phenylacetate-----?-Propionate???---? L-Proline?-???? L-Serine--??---Valerate???--?? DNA G?C content(mol%)44.344–45a41.740–43a42b NA NATaxa:1,strain MJ4;2,A.johnsonii KCTC12405T;3,strain MJ01;4,A.haemolyticus KCTC12404T;5,A.beijerinckii CCUG51249T;6,A. gyllenbergii DSM22705T;7,A.schindleri LMG19576T?,Positive reaction;-,negative reaction;NA,data are not availablea Data are taken from Bouvet and Grimont(1986)b Data are taken from Lee and Lee(2010)to Acinetobacter haemolyticus and strain MJ4to Acinetobacter johnsonii and differentiate them from other closely related members of the genus Acinetobacter are shown in Table1.Chemotaxonomic characteristics and DNA base compositionThe almost full-length16S rRNA gene sequences of strains MJ01(1430bp)and MJ4(1404)were obtained.In the neighbor-joining phylogenetic tree(Fig.1),based on16S rRNA gene sequence comparisons,both strains appeared within the genus Acinetobacter and MJ01joined A.hae-molyticus while MJ4joined A.johnsonii.Pairwise comparisons of the16S rRNA gene sequences via the EzTaxon program(Chun et al.2007)indicated that the closest relatives of strain MJ01were A.haemolyticus DSM 6962T(99.4%),Acinetobacter beijerinckii LUH4759T (98.7%),A.johnsonii ATCC17909T(98.4%)and Acineto-bacter gyllenbergii RUH422T(97.7%).Strain MJ4showed the highest16S rRNA gene sequence similarity to the type strains of A.johnsonii(100%),A.haemolyticus(98.9%), A.beijerinckii(98.8%),A.gyllenbergii(98.6%)and Acine-tobacter schindleri(97.8%).The generally accepted criteria for delineating bacterial species state that strains showing 16S rRNA gene sequence dissimilarity above3%or showing a level of DNA–DNA relatedness below70%(as measured by hybridization)are considered as belonging to separate species(Wayne et al.1987;Stackebrandt and Goebel1994). The recent recommendation proposed an increase from97to 98.7%in the16S rRNA gene sequence similarity threshold used to determine the uniqueness of a new strain(Stacke-brandt and Ebers2006).In view of this deﬁnition,the above-mentioned data indicate that strains MJ01and MJ4can be clearly separated from other recognized members of the genus Acinetobacter with the exception of the strains given above.DNA–DNA hybridizations were performed to clarify the taxonomic position of isolated strains.The cellular fatty acid proﬁles of strains MJ01,MJ4and their phylogenetically closest members of the genus Acinetobacter are shown in Table2.All strains contained C18:1x9c,summed feature4(C16:1x7c and/or iso-C15:0 2-OH)and C16:0as the common major fatty acids. Furthermore,the cellular fatty acid composition of strain MJ01was very close to that of A.haemolyticus KCTC 12404T but both microorganisms differed by the presence of C10:0,C17:0and C17:1x8c from strain MJ4,A.johnsonii KCTC12405T and A.schindleri LMG19576T,and by the presence of C14:0and summed feature3(iso-C16:1I and/or C14:03-OH)from A.beijerinckii CCUG51249T and A.gyllenbergii DSM22705T.Strain MJ4and the type strain,A.johnsonii KCTC12405T,had very similar fatty acid compositions that differed them from other bacteria shown in Table2.The genomic DNA G?C content of strains MJ01and MJ4were41.7and44.3mol%,respectively,which lies within the range observed for recognized Acinetobacter species(40.0–46.0mol%)(Bouvet and Grimont1986;Lee and Lee2010).As shown in Table3,strain MJ01exhibited a high level of DNA–DNA relatedness with respect to A.haemolyticus KCTC12404T(89.7%)while strain MJ4Table2Fatty acid compositions of strains MJ01and MJ4and their phylogenetically closest relatives of the genus AcinetobacterFatty acid1234567C10:0ND ND 2.2 1.4 1.3 1.8ND C12:07.58.38.97.1 6.9 4.19.1 C14:0ND ND 1.0 1.0ND ND 1.2 C16:019.318.117.515.113.417.918.9 C17:0ND ND 1.0 1.2 2.9 2.1ND C18:0 1.9 1.3 1.1 1.2 1.2 1.0 1.2 C12:02-OH 1.2 1.7 2.8 3.3 2.5 4.7ND C12:03-OH 5.8 6.212.07.37.810.6 6.1 C17:1x8c ND ND 1.1 1.0 3.0 4.1ND C18:1x9c21.723.421.029.740.835.321.9 Summed feature3a ND ND 3.9 5.3ND ND 1.7 Summed feature4a38.736.123.924.519.116.137.8 Summed feature7a 3.9 4.9 3.6 1.9 1.1 1.1 2.1Taxa:1,strain MJ4;2,A.johnsonii KCTC12405T;3,strain MJ01;4,A.haemolyticus KCTC12404T;5,A.beijerinckii CCUG51249T;6, A.gyllenbergii DSM22705T;7,A.schindleri LMG19576TValues are percentages of total fatty acids.ND,not detecteda Summed features represent groups of two or three fatty acids that could not be separated by GLC with the MIDI system.Summed feature3 contained iso-C16:1I and/or C14:03-OH.Summed feature4contained C16:1x7c and/or iso-C15:02-OH.Summed feature7contained C18:1 x7c and/or C18:1x9t and/or C18:1x12tshowed a high level of DNA–DNA relatedness of92.8% with A.johnsonii KCTC12405T.The DNA–DNA hybrid-ization levels were determined to be more than70%which is the threshold that has been suggested as delineating bacterial species(Wayne et al.1987;Stackebrandt and Goebel1994).Our results therefore support the afﬁliation of strain MJ01to A.haemolyticus and strain MJ4to A.johnsonii.Diesel oil degradation by strains MJ01and MJ4The extent and rate of diesel oil degradation by strains MJ01and MJ4gave an indication of their intrinsic deg-radation capacity.The ability of strains MJ01and MJ4to degrade diesel oil at the added concentrations of1,000, 5,000,10,000and20,000mg/l is shown in Figs.3and4. The degradation of diesel oil over time in batch cultures was monitored,and the spontaneous decrease in total petroleum hydrocarbons(TPH)in uninoculatedﬂasks was also measured.In our previous studies(Lee et al.2005)it has been shown that the decrease of diesel concentration in uninoculated cultures was mainly caused by volatilization of low molecular weight components and this effect was taken into account in the calculation of diesel degradation. The strain MJ4degraded93.3%of1,000mg/l diesel oil after incubation for3days.The degrees of degradation of diesel oil at initial concentration of5,000,10,000and 20,000mg/l were94.6,94.5and93.6%degraded,respec-tively,after incubation for7days(Fig.3).The strain MJ01 also degraded92.9%of1,000g/l diesel oil after incubation for3days.The degrees of degradation of diesel oil at initial concentration of5,000,10,000and20,000mg/l were97.7,91.8and91.4%degraded,respectively,after incubation for7days(Fig.4).In general,the most signiﬁcant degradation occurred during theﬁrst3days and degradation reached a plateau between days4and7.Many other microorganisms have been tested for biodegradation using diesel concentrations ranging from500to20,000 ppm(Hong et al.2005;Kebria et al.2009;Lee et al.2006; Mohanty and Mukherji2008;Ueno et al.2007;Wongsa et al.2004).It has been found that degradation is generally unfavorable at concentrations higher than10,000orTable3Levels of DNA–DNA relatedness(%)between strains MJ01 and MJ4and the type strains of their phylogenetically closest neighbors in the genus AcinetobacterStrain MJ01MJ4MJ01100a19.2 MJ426.4100 Acinetobacter haemolyticus KCTC12404T89.718.3 Acinetobacter johnsonii KCTC12405T27.192.8 Acinetobacter beijerinckii CCUG51249T20.317.6 Acinetobacter gyllenbergii DSM22705T18.516.7 Acinetobacter schindleri LMG19576T15.213.5 a The standard deviation for levels of reassociation was B6%15,000ppm(Bicca et al.1999;Espeche et al.1994;Lee et al.2006).Degradation at a more higher concentration ([30,000ppm of diesel)has been reported for Pseudo-monas sp.strain DRYJ3(Shukor et al.2009)and Gordonia alkanivorans S7(Kwapisz et al.2008)but,in last case, degradation requires glucose(0.2%w/v)and yeast extract (0.1%w/v).From this point of view,strains MJ01and MJ4 demonstrated a comparative advantage in their ability to tolerate relatively high diesel concentrations.Biodegrada-tion efﬁciency of various individual strains vary widely among different species of bacteria.Pseudomonas aeru-ginosa strain WatG was able to degrade diesel oil about 90%at concentration of10,000mg/l within2weeks (Wongsa et al.2004).About90%of10,000ppm diesel oil were removed by Rhodococcus erythropolis strain NTU-1 in6days of batch incubation but only32–33%diesel removal was achieved by biodegradation(Liu and Liu 2010).Kwapisz et al.(2008)reported48-60%degradation of diesel oil at concentration60ml/l by G.alkanivorans S7 for7days.The highest hydrocarbon consumption(81%) by this strain was detected in culture medium with nitrate after14days of incubation.Relatively high degradation efﬁciency were reported for other Gordonia species, Gordonia nitida strain LE31(Lee et al.2005)and G.alkanivorans strain CC-JG39(Young et al.2005).At high diesel concentrations([15,000mg/l)strains MJ01 and MJ4degraded hydrocarbons more efﬁciently than many other diesel-degrading microorganisms and their degrading ability is comparable with that of most active bacterial diesel degraders.Gas chromatograms of diesel oil degradation(10,000mg/ l)by strain MJ01at the same time points are shown in Fig.5. At the end of the incubation period,the n-alkanes were totally degraded by the inoculated microorganism,regard-less of their chain lengths.Only small amounts of methylated alkanes,such as pristane and phytane,and some unidentiﬁed compounds remained and were not completely metabolized by strain MJ01(Fig.5).The same ability to degrade diesel oil was also observed for strain MJ4.Although previous reports mentioned that Acinetobacter species can use a large variety of carbon sources(Di Cello et al.1997;Fischer et al. 2008;Su et al.2008;Kang et al.2011),not all oil-degrading strains of the genus Acinetobacter can grow on such a broad range of hydrocarbons.For example,Acinetobacter strains isolated by Kubota et al.(2008)degraded n-hexadecane (C16)and n-eicosane(C20)but did not degrade other n-alkanes or cyclic alkanes.Thus,the ability to use a broad range of diesel oil hydrocarbons would differentiate strains MJ01and MJ4as promising microorganisms within the genus Acinetobacter for bioremediation of diesel oil-con-taminated sites.In most cases described,aerobic bacterial degradation of n-alkanes starts by the oxidation of a terminal methyl group to generate a primary alcohol,which is further oxidized to the corresponding aldehyde,andﬁnally con-verted into a fatty acid.The corresponding carboxylic acidis incorporated into b -oxidation cycle via acyl-CoA for-mation (Van Hamme et al.2003;Rojo 2009).On the other hand,the Finnerty pathway,where a dioxygenase converts alkanes to aldehydes through n -alkyl hydroperoxides without an alcohol intermediate,has been described for some Acinetobacter spp.(Finnerty 1988;Sakai et al.1996).Metabolism in Acinetobacter spp.seemed to be compli-cated due to the diversity of enzymes involved in n-alkane oxidation,further research is necessary to determine the metabolic pathways involved in aerobic diesel degradation in A .haemolyticus MJ01and A .johnsonii MJ4.Oxygen uptake and growth of strain MJ01and MJ4on diesel oilThe major degradation pathways for petroleum hydrocar-bons involve oxygenases and molecular oxygen,indicating the importance of oxygen for oil degrading microorganisms (Leahy and Colwell 1990).The growth and the oxygen uptake pattern of the isolates on 10,000mg/l diesel oil were shown in Fig.6.Strains MJ01and MJ4showed over 90%degradation of diesel oil and reached a population size of about 9.19108and 7.89108c.f.u./ml at third day,respectively,while using 10,000mg/l diesel oil as substrate.This showed that diesel oil was used as the sole carbon and energy source and that the oxygen uptake also correspond-ingly increased by the strains MJ01and MJ4.Cell concen-trations of both strains slowly decrease between the fourth and the seventh days although oxygen consumption was continued.It is to be noted that in this phase the cells had to degrade more and more recalcitrant hydrocarbons contained in diesel oil.After 7days of incubation,diesel oil was almost completely degraded and this depletion of carbon source resulted in cell death due to unfavorable growth conditions.Metabolites of alkanes,such as alkanoates (Watkinson and Morgan 1990),could have accumulatedinFig.5Chromatograms of diesel oil degradation by Acinetobacter haemolyticus strain MJ01(a 0day,b second day,c fourth day and d seventh day).The added diesel oil concentration was 10,000mg/l.Two of the residual peaks at the ﬁnal day (d)were identiﬁed as the recalcitrant hydrocarbons pristane (Pr)and phytane (Ph)。

【2008】Bed-Load Sediment Transport on Large Slopes

Bed-Load Sediment Transport on Large Slopes:Model Formulation and Implementation within a RANS SolverDavid D.Apsley 1and Peter K.Stansby 2Abstract:Standard bed-load sediment-transport formulas are extended using basic mechanical principles to include gravitational inﬂu-ence on large slopes of arbitrary orientation.The resulting sediment ﬂuxes are then incorporated into a morphodynamics model in a general-purpose,three-dimensional,ﬁnite-volume,Reynolds-averaged Navier–Stokes ͑RANS ͒code.Major features are:͑1͒the down-slope component of weight is combined with the ﬂuid stress to form an effective bed stress ͑similar to the work of Wu in 2004͒;͑2͒the critical effective stress is reduced in proportion to the component of gravity normal to the slope;͑3͒a simple ﬂux-based model for avalanching is implemented as a numerical means of preventing the local slope from exceeding the angle of repose;͑4͒an entirely vectorial formulation of bed-load transport is developed to account for arbitrary surface orientation;and ͑5͒methods for reducing numerical instability in the morphodynamics equation are described.Sample computations are shown for scour and accretion in a channel bend and for the movement of sand mounds on erodible and nonerodible bases.DOI:10.1061/͑ASCE ͒0733-9429͑2008͒134:10͑1440͒CE Database subject headings:Sediment transport;Slopes;Bed loads;Morphology;Channel bends;Computational ﬂuid dynamics .IntroductionThe movement of sediment by ﬂuids is an important natural pro-cess.Siltation reduces the storage capacity of reservoirs and ob-structs the free passage of water in irrigation or drainage channels.Scour may undermine the foundations of hydraulic structures,coastal defences,and the piers or abutments of bridges.The landscape has been shaped by erosion processes in air and water,whereas dunes and sandbanks provide a degree of natural protection at coastlines.The advent of powerful computers has led to the extensive use of sediment-transport formulas in numerical models—initially to establish the sediment-carrying capacity of predeﬁned waterways under steady ﬂow,and then to predict evolving surface morphol-ogy ͑Wu et al.2000;Wu 2004;Liang et al.2005͒.Sediment in motion may be divided into two components:that which rolls or slides along the bed ͑bed load ͒and that which is carried along in the moving ﬂuid ͑suspended load ͒.This paper focuses on the former.A number of predictive formulas for sedi-ment ﬂux are in widespread use.Some of these are listed in Table 1and are used in this paper.Most bed-load sediment-transport formulas are derived and calibrated for rivers of small slope.͑Where slopes do appear theyare part of the normal-ﬂow relation used to predict the bed-shear stress.͒The intention of this work is to extend these formulas using basic mechanical principles to include gravitational inﬂu-ence on large slopes and to implement them in a three-dimensional,ﬁnite-volume Reynolds-averaged Navier–Stokes ͑RANS ͒ﬂow solver coupled with a surface-movement algorithm.Other authors have extended bed-load sediment-transport for-mulas to include gravitational inﬂuence on slopes.Sekine and Parker ͑1992͒employed a stochastic model of saltating grains to deduce a formula for the ratio of cross stream to streamwise bed-load transport on a lateral slope.Kovacs and Parker ͑1994͒pro-posed a vectorial ͑and hence arbitrary slope ͒formulation for bed-load sediment transport.Assuming equilibrium between ﬂuid drag,slope-tangential component of weight,and coulombic fric-tion,they derived an expression for the ratio of critical stress on arbitrary slopes to that on a ﬂat bed in a similar manner ͑although in very different notation ͒to our own.By contrast with our model,however,they adopted a very speciﬁc,Bagnold-type hy-pothesis for the bed load.With this they were able to demonstrate,numerically,the evolution of a straight channel with trapezoidal cross section to one with a fully developed equilibrium shape.An empirical adjustment of the Meyer-Peter and Müller ͑1948͒bed-load transport model for longitudinal slope was proposed by Damgaard et al.͑1997͒.There is a standard adjustment to the critical stress for incipient motion and an additional enhancement factor for downslope ﬂow.Note that this model is still couched in terms of the ﬂuid stress ␶alone.By contrast,Wu ͑2004͒com-bined ﬂuid drag and the downslope component of weight into an effective bed stress ␶eff ,although—at least as presented in that paper ͑his equations 31–33͒—not for an arbitrary vectorial align-ment of these forces.The model was incorporated in a depth-averaged hydrodynamic solver,with a nonequilibrium approach for total ͑suspended+bed-load ͒sediment transport.Ortiz and Smolarkiewicz ͑2006͒simulated the evolution of sand dunes in severe winds.Arbitrary slopes were incorporated in a modiﬁed critical stress,but not in the subsequent direction of transport,which was always assumed aligned with the local ﬂow.1Lecturer,School of Mechanical,Aerospace and Civil Engineering,Univ.of Manchester,PO Box 88,Manchester M601QD,U.K.E-mail:d.apsley@ 2Professor of Hydrodynamics,School of Mechanical,Aerospace and Civil Engineering and Tyndall Centre for Climate-Change Research,Univ.of Manchester,PO Box 88,Manchester M601QD,U.K.E-mail:peter.k.stansby@Note.Discussion open until March 1,2009.Separate discussions must be submitted for individual papers.The manuscript for this paper was submitted for review and possible publication on January 19,2007;ap-proved on January 30,2008.This paper is part of the Journal of Hydrau-lic Engineering ,V ol.134,No.10,October 1,2008.©ASCE,ISSN 0733-9429/2008/10-1440–1451/$25.00.D o w n l o a d e d f r o m a s c e l i b r a r y .o r g b y X I 'A N J I A O T O N G U N I VE R S I T Y o n 09/28/13. C o p y r i g h t A S C E .F o r p e r s o n a l u s e o n l y ; a l l r i g h t s r e s e r v e d .They noted that sand avalanches acted as a natural slope limiterand simulated ͑the slope-limiting effect of ͒an avalanche ﬂux by a slope-dependent diffusivity.As in Wu ͑2004͒we have chosen to absorb the additional downslope gravitational force into an effective bed stress,allow-ing the user to continue using standard bed-load transport models.The handling of critical conditions for incipient motion is,how-ever,different to that of Wu.Our formulation is given in a vec-torial form,independent of any underlying coordinate system and slope alignment.The present paper also deals with the integration of sediment transport and morphology into a general-purpose three-dimensional ͑3D ͒ﬂow model.This solves the full RANS equations rather than the depth-averaged ﬂow equations;conse-quently the bed stress can be determined from the near-bed ﬂow properties,rather than from a depth/slope/roughness parameter-ization such as Chézy or Manning’s formula.The structure of the rest of the paper is as follows.The next section summarizes existing bed-load transport formulas suitable for the computational model,whereas the subsequent section de-scribes their extension for large slopes of arbitrary orientation.These extensions are “natural”in the sense that they require no additional empirical input ͑other than the angle of repose ͒.This is followed by details of how the surface-height equation is coupled with a general-purpose 3D ﬂow solver.The application of this to the generation of the equilibrium bed proﬁle in a channel bend and to the time-dependent motion of isolated sand mounds is then shown.Finally,the paper concludes with a summary of the main features of the computational model and suggests directions for future work.Existing Formulas for Bed-Load TransportBed-load transport of sediment is quantiﬁed by the bed-load ﬂux q b —the volume of nonsuspended sediment crossing unit length of surface per unit time.On horizontal beds the tendency of bed particles to move is determined by the drag imposed by the ﬂuid ﬂow and countered by the particle weight ͑reduced by buoyancy ͒,so that q b may be taken as a function of the bed-shear stress ␶b ,reduced gravity ͑s −1͒g ͑where s =␳s /␳͒,particle diameter d ,and the ﬂuid density ␳,and kinematic viscosity ␯.A formal dimen-sional analysis ͑six variables,three independent dimensions ͒dic-tates that there is a relationship between three nondimensional groups,conveniently taken asq *=q bͱ͑s −1͒gd 3͑dimensionless bed-load flux ͒͑1a ͒␶*=␶b␳͑s −1͒gd͑dimensionless shear stress or Shields stress ͒͑1b ͒d *=dͫ͑s −1͒g␯2ͬ1/3͑dimensionless particle diameter ͒͑1c ͒We have tried to maintain a consistent and meaningful nota-tion in this paper.Note,however,that elsewhere in the literature q *is often denoted by ⌽and ␶*by ␪or 1/⌿.Note also that variables such as bed-load ﬂux and bed-shear stress are vector quantities;when their direction is important they will be written in bold type.In practice,sediment moves only after a certain critical shear stress ␶crit is exceeded.Various analytic approximations to Shields’original data have been proposed.Expressed in dimen-sionless terms,two convenient formulas ͑which differ only at very small values of d *͒are those of Van Rijn ͑1984͒and Soulsby ͑1997͒.The latter is␶crit*=0.301+1.2d *+0.055͓1−exp ͑−0.020d *͔͒͑2͒and is used in all calculations presented here.Extensive lists of sediment-transport formulas may be found in,e.g.,the textbooks of Soulsby ͑1997͒and Raudkivi ͑1998͒.Table 1lists some of those more commonly used in conjunction with computational ﬂuid dynamics ͑CFD ͒.Most of the models have parameter restrictions,for which one must refer to the origi-nal papers.Table mon Bed-Load Transport Formulas ReferenceFormulaCommentsMeyer-Peter and Müller ͑1948͒q *=8͑␶*−␶crit*͒3/2The original paper assumed ␶crit*=0.047Einstein-Brown ͑Brown 1950͒q *=͕K exp ͑−0.391/␶*͒0.465,␶*Ͻ0.18240K ␶*3,␶*ജ0.182K =ͱ23+36d*3−ͱ36d *3Yalin ͑1963͒q *=0.635r ͱ␶*͓1−1␴rln ͑1+␴r ͔͒r =␶*␶crit*−1,␴=2.45ͱ␶crit*s 0.4Van Rijn ͑1984͒q *=0.053d *0.3͑␶*␶crit*−1͒2.1Nielsen ͑1992͒q *=12͑␶*−␶crit*͒ͱ␶*D o w n l o a d e d f r o m a s c e l i b r a r y .o r g b y X I 'A N J I A O T O N G U N I V E R S I T Y o n 09/28/13. C o p y r i g h t A S C E . F o r p e r s o n a l u s e o n l y ; a l l r i g h t s r e s e r v e d .Proposed Modiﬁcations for Slopes Main FeaturesThe main modiﬁcations to sediment-transport formulas for large slopes are:1.Existing bed-load models are retained,but with bed stress ␶replaced by the effective stress ␶eff formed by the vector ad-dition of ␶and the downslope component of particle weight;2.The critical effective stress is reduced in proportion to thecomponent of gravity normal to the slope;i.e.,as the cosine of the slope angle;3.An “avalanche”ﬂux is added when the slope angle exceedsthe angle of repose;and4.the ﬂux divergence from any vertical column depends onslope orientation and is computed as a line integral of the outward bed-load ﬂux around cell perimeters;the computa-tional implementation,together with a numerical smoothing technique,is described after the sediment-transport models.Although calculations in this paper were performed using the Meyer-Peter–Müller and Van Rijn models,the modiﬁcations de-scribed may be applied to any model in Table 1and,in general,to any bed-load transport models that can be written in the dimen-sionless formq b*=f ͑␶*,d *͒͑3͒An additional beneﬁt of the modiﬁcations is that they introduce anatural diffusion-like term into the morphodynamics equation which aids numerical stability.Effective StressThe orientation of the surface is completely determined by thelocal unit normal vector nˆ͑Fig.1͒.In particular,the angle ␤made by the slope with the horizontal is the same as the angle made by nˆwith the vertical.If a Cartesian coordinate system is adopted,with z vertical and z b ͑x ,y ͒the height of the bed,thennˆ=1ͱ1+͉١z b ͉2ͩ−ץz b ץx ,−ץz bץy ,1ͪ͑4͒cos ␤=n z ,sin ␤=ͱn x2+n y2=ͱ1−n z2͑5͒On sloping beds,particle motion is inﬂuenced by the compo-nent of weight down the slope.A unit vector bˆdown the line of maximum slope may be found by normalizing ͑eˆz ϫn ˆ͒ϫn ˆ,where eˆz =unit vector in the vertical direction and a vector cross product is used to generate a vector perpendicular to two given vectors.Thensin ␤b ˆ=−n z 2١z b −͑1−n z 2͒e ˆz =͑n x n z ,n y n z ,n z 2−1͒͑6͒The in-slope forces affecting the motion of a particle along thebed are ͑see Fig.2͒:•Fluid force ␶A s in the direction of ﬂow,where A s ϭrepresentative area of a sediment particle;•Component of ͑buoyancy-reduced ͒weight down the slope,W Јsin ␤bˆ;and •Resistance force with maximum magnitude ␮ϫnormal reaction =␮W Јcos ␤.Here,␮may ͑somewhat loosely ͒be termed a coefﬁcient of fric-tion,and is given by␮=tan ␾͑7͒where ␾=angle of repose—the angle at which particles begin to avalanche in the absence of ﬂow.The ﬁrst two forces may be combined to give a single effective stress ␶eff ,such that␶eff A s =␶A s +W Јsin ␤bˆ͑8͒The “friction”force always acts in the diametrically opposed di-rection to this combination.The nondimensional effective stressis found by dividing by ͑s −1͒␳gdA s ,noting that the reduced weight W Јis ͑s −1͒␳Vg ,with V the particle volume␶eff ͑s −1͒␳gd =␶͑s −1͒␳gd +V A s dsin ␤b ˆor␶eff *=␶*+D 0sin ␤b ˆ͑9͒where D 0=particle shape parameter ͑V /A s d ͒whose value is inde-pendent of slope and ﬂow .On slopes,the component of particle weight normal to the bed ͑which is what determines the normal reaction from the surface ͒is reduced by a factor cos ␤,and hence it is logical that the critical effective stress needed to initiate particle movement should be reduced by the same factor;i.e.␶eff,crit *=␶crit,0*cos ␤͑10͒where ␶crit,0*=dimensionless critical stress on horizontal beds.This provides a means of deducing D 0,as,in the absence of ﬂow,incipient motion under gravity alone occurs when ␤equals the angle of repose ␾,whence Eqs.͑9͒and ͑10͒giveFig.1.Local unit vectors onthe slopeFig.2.Forces on a particle in a plane locally tangential to the surfaceD o w n l o a d e d f r o m a s c e l i b r a r y .o r g b y X I 'A N J I A O T O N G U N I VE R S I T Y o n 09/28/13. C o p y r i g h t A S C E .F o r p e r s o n a l u s e o n l y ; a l l r i g h t s r e s e r v e d .D 0sin ␾=␶crit,0*cos ␾HenceD 0=␶crit,0*tan ␾͑11͒For typical values ␶crit,0*=0.055and ␾=32°,this would give D 0=0.09.Finally,we substitute Eq.͑6͒for the downslope vector into Eq.͑9͒to give␶eff *=␶*−D 0n z 2١z b −D 0͑1−n z 2͒e ˆz͑12͒As the formulas ͑Table 1͒for bed-load ﬂux vector q b typicallycontain a factor ␶eff −␶eff,crit ,the underlined term in Eq.͑12͒pro-vides a gradient-diffusion-like term in the morphodynamics equa-tion;this aids numerical stability,especially if the morphodynamics equation is solved implicitly.The last term in Eq.͑12͒simply ensures that the effective stress is parallel to the slope.Avalanche ModelThe avalanche model makes no pretension to modeling the inter-mittent ͑and somewhat probabilistic ͒slippages on real slopes.Here,it is simply a numerical artiﬁce to prevent the slope angle ␤signiﬁcantly exceeding the angle of repose ␾.It does so in a conservative fashion ͑i.e.,by adjusting the ﬂux ͒;speciﬁcally,it isimplemented by the addition of a further contribution q aval bˆto the bed-load ﬂux vector.Referring to Fig.3,if tan ␤exceeds tan ␾then a volume 1/2L ϫL ͑tan ␤−tan ␾͒per unit width is transferred from one side of the midpoint to the other in the course of a time step ⌬t ,corresponding to an additional ͑in-surface,rather than horizontal ͒ﬂux per unit width ofq aval =͑1−p ͒12L 2͑tan␤−tan ␾͒cos ␤⌬tif tan ␤Ͼtan ␾͑13͓͒where p =porosity—see Eq.͑20͒later ͔directed along the line ofmaximum slope bˆ.In practice,the multiplier ͑i.e.,the deﬁnition of length L ͒need not be too precise,the sole purpose of the additional ﬂux being to redistribute sediment rapidly if the slope exceeds the angle of repose.Calculations in this paper employed as L the maximum horizontal dimension ͑a diagonal ͒of the adjacent surface cell faces.Summary of Bed-Load Flux ModiﬁcationsIn summary,the modiﬁcations necessary to account for signiﬁcant slopes are:•Replacement of dimensionless bed stress by an effective stress—Eqs.͑9͒or ͑12͒;•Reduction of the critical effective stress—Eq.͑10͒;•The value of D 0—Eq.͑11͒;and•At slopes larger than the angle of repose,an additional ava-lanche ﬂux—Eq.͑13͒.Coordinate-dependent quantities ͑if required ͒are given in Eqs.͑4͒–͑6͒.Model in ContextFirst,we note that there is an important distinction between thecritical effective stress and the critical ﬂuid stress for incipient particle motion.The ﬁrst is obtained simply by multiplying the zero-slope value by the cosine of the slope ͓Eq.͑10͔͒.The latter is more complicated and may be found by combining Eqs.͑9͒and ͑10͒such that͉␶crit *+D 0sin ␤b ˆ͉=␶crit,0*cos ␤͑14͒Resolving the ﬂuid stress into components along and perpendicu-lar to the line of maximum slope ͑see Fig.2for the deﬁnition of angle ␺͒,substituting for D 0,squaring Eq.͑14͒and solving aquadratic equation for ␶crit*gives ␶crit*=sin ␤cos ␺+ͱcos 2␤tan 2␾−sin 2␤sin 2␺tan ␾␶crit,0*͑15͒This is a formula given by,e.g.,Soulsby ͑1997͒and Ortiz and Smolarkiewicz ͑2006͒,but its origin is considerably older.Comparison with other models may be affected by considering the special cases of longitudinal and lateral slopes.For longitudi-nal slopes ﬂuid and gravitational forces on sediment particles are aligned and our model gives,for the excess effective stress ͑␶eff ͒in terms of the excess ﬂuid stress ͑␶͒Upslope flow ͑␺=0°͒:␶eff*−␶eff,crit*=␶*−sin ͑␾+␤͒sin ␾␶crit,0*͑16a ͒Downslope flow ͑␺=180°͒:␶eff*−␶eff,crit*=␶*−sin ͑␾−␤͒sin ␾␶crit,0*͑16b ͒The critical ﬂuid stress is modiﬁed in exactly the same way as in Damgaard et al.͑1997͒.If the underlying bed-load model is as-sumed to be that of Meyer-Peter and Müller then the two models are exactly the same for upslope ﬂow,but our model does not contain the empirical enhancing slope factor f slope for downslope ﬂow that is suggested in Damgaard et al.͑1997͒.For lateral slopes ͑␺=90°͒,the critical ﬂuid stress is,accord-ing to Eq.͑15͒␶crit*=␶crit,0*cos ␤ͱ1−tan 2␤tan 2␾͑17͒which is consistent with other authors.The assumption in our model that the bed-load ﬂux vector remains in the direction of the effective stress after the onset of motion leads to a ratio of cross-stream ͑n ͒to streamwise ͑s ͒components given,up to the angle of repose,byFig.3.Deﬁning the avalanche ﬂuxD o w n l o a d e d f r o m a s c e l i b r a r y .o r g b y X I 'A N J I A O T O N G U N I VE R S I T Y o n 09/28/13. C o p y r i g h t A S C E .F o r p e r s o n a l u s e o n l y ; a l l r i g h t s r e s e r v e d .q nq s =1ͱtan 2␾−tan 2␤␶crit *␶*tan ␤͑18͒compared with,say,Sekine and Parker ͑1992͒q nq s =0.75ͩ␶crit *␶*ͪ1/4tan ␤͑19͒Numerical ImplementationFlow SolverFlow calculations were performed with an in-house researchcode,STREAM:a general-purpose,two-or three-dimensional,ﬁnite-volume,RANS solver.The integral equations of ﬂuid mo-tion are solved on multiblock,boundary-conforming,curvilinear meshes.The code permits the use of surface-conforming moving meshes.The code features an extensive repertoire of turbulence models ͑Leschziner and Apsley 2001͒,but the standard k –␧model ͑Launder and Spalding 1974͒is used in all calculations here.The bed-shear stress ␶b is provided by the ﬂow solver,using wall functions for arbitrarily rough surfaces ͑Apsley 2007͒.The Ni-kuradse roughness height k s is required;for sand roughness in controlled laboratory experiments,this is taken ask s =d 50where d 50=median grain size.For natural bodies of water k s can be increased to account for bed forms and surface irregularities contributing to a more generalized unresolvable roughness—a common value is k s =2.5d 50͑Raudkivi 1998͒.Variable storage is cell-centered and collocated and the code uses the standard SIMPLE pressure-correction algorithm,with Rhie-Chow interpolation for advective velocities on cell faces to eliminate “odd-even”pressure decoupling on collocated meshes.Bed MorphodynamicsThe ﬂow calculation is coupled to an equation for the moving bed.“Conservation of sand”leads to an equation for the time variation of the bed height z b ͑x ,y ͒͑1−p ͒ץz bץt=−١h ·q h ͑20͒where p =porosity ͑here taken as 0.4͒and ٌh denotes the horizon-tal divergence operator.q h =rate of horizontal transport of sand per unit horizontal length of bed and is different to the bed-load ﬂux vector q b when there are slopes because:͑1͒the actual length of the bed exceeds the projected horizontal length when there are lateral slopes and ͑2͒the horizontal projection of the bed-ﬂux vector is less than its magnitude if there are streamwise slopes.These orientation features are often ignored in depth-averaged solvers,leading to errors in the net ﬂux for large slopes.In our code the bed height equation is coded in an integral form as͑1−p ͒A h⌬z b⌬t=−ͶץAq b ·d s ϫnˆ͑21͒where the line integral is taken around the boundary of a cell face on the bed ͑Fig.4͒and gives the net outward volume ﬂux ofsediment across the cell face edges irrespective of surface orien-tation.A h =horizontal projected area of a cell face;z b =height of a cell-face-center control point with ⌬z b =height swept out over a time step.Note thatA h ⌬z b =A ⌬n͑22͒where A =sloping area of bed for this cell face and ⌬n =distance moved normal to this slope.Both sides of Eq.͑22͒represent,to leading order,the volume swept out by a cell face in one time step.Control volumes in the ﬂow are deﬁned by their vertices,but the topography of both bed and free surface is deﬁned by control points ͓Fig.5͑a ͔͒whose horizontal positions coincide with cell-face centers.The vertices on the bed and free surface are deter-mined by interpolation from adjacent control points.The mesh moves—albeit slowly—in response to the time evolution of the bed and/or free surface,with cells between bed and free surface being stretched to maintain the same constant fractions of local water depth.Finite-volume moving-mesh and free-surface tech-niques have been described by Apsley and Hu ͑2003͒.The surface orientation is everywhere determined by the unitnormal vector nˆ.At cell-face centers this is determined by nor-malizing the vector area of that cell face ͑which is routinely com-puted from its vertices ͒.At cell edges nˆedge is found by normalizing ⌬x ϫ⌬s ,where ⌬x=vector joining control points ei-ther side of the edge ⌬s .Fig.4.Geometry of a cell face on the bedFig.5.Control points and cell vertices:͑a ͒arrangement;͑b ͒constantcurvature ͑in two dimensions ͒;and ͑c ͒“sawtooth”patternD o w n l o a d e d f r o m a s c e l i b r a r y .o r g b y X I 'A N J I A O T O N G U N I VE R S I T Y o n 09/28/13. C o p y r i g h t A S C E .F o r p e r s o n a l u s e o n l y ; a l l r i g h t s r e s e r v e d .The procedure for moving cell-face-center control points ver-tically is as follows.Note that,in a conservative formulation,ﬂux vectors are required on cell edges .In STREAM this is accom-plished by ﬁrst interpolating the ﬂuid stress to the cell edges and then forming the bed-load ﬂux vector.Interpolation of the stress vector to cell edges requires care because the surface is curved:simple interpolation of Cartesian components would underesti-mate the magnitude and produce a stress vector that was not tangential to the surface.1.The ﬂuid stress is determined ͑from a wall function ͒at cell-face centers on the bed.This is then split into two in-surface components in the x –z and y –z planes;͑note:not Cartesian components ͒.2.Linear interpolation of the in-surface components is used toreconstruct the ﬂuid stress vector using the in-surface unit vectors on a cell edge.This is then combined vectorially with the downslope component of weight ͓Eq.͑9͔͒to form thedimensionless effective stress ␶eff*on each cell edge.3.The magnitude of the bed-load ﬂux vector q b is computed oncell edges by using one of the models in Table 1,but with ␶*and ␶crit *replaced by ␶eff *and ␶eff,crit*,respectively.The direc-tion of the bed-load ﬂux vector is that of ␶eff*.For slopes exceeding the angle of repose,the bed-load ﬂux vector is supplemented by an avalanche component directly down the slope.4.The net ﬂux of sediment out of each surface cell is computed by integrating the ﬂux across cell edges ⌬sDIV ͑q b ͒ϵͶq ·d s ϫnˆ→͚edgesq b ,edge ·⌬s ϫn ˆedge ͑23͒͑Upper-case DIV signiﬁes the integral divergence from thecell rather than the vector differential operator.͒5.Eq.͑21͒is stepped forward in time using a fully implicit ͑ﬁrst-order,backward-differencing ͒scheme.The height of the control point z b on each surface cell face evolvesasFig.6.Channel bend:surface morphology and near-surface ﬂowD o w n l o a d e d f r o m a s c e l i b r a r y .o r g b y X I 'A N J I A O T O N G U N I VE R S I T Y o n 09/28/13. C o p y r i g h t A S C E .F o r p e r s o n a l u s e o n l y ; a l l r i g h t s r e s e r v e d .z b n +1=z b n −⌬t͑1−p ͒A hDIV ͑q b ͒n +1͑24͒This equation is solved iteratively at each time step in con-junction with the ﬂow equations—typically,a number of it-erations of the ﬂow solver followed by one or more iterations of the bed control points ͑and consequent change in the com-putational mesh ͒.Iteration stops only when both mesh and ﬂow equations have converged.If necessary,the bed move-ment can be under-relaxed at each iteration.As q b contains a diffusive component proportional to −ٌz b we have found that numerical stability can be enhanced if Eq.͑24͒is solved implicitly for simultaneous incremental corrections ͕␦z b ͖to all control points,rather than as a predictor–corrector-type formula for individual control points.Bed SmoothingTo evaluate the integrated ﬂuxes in Eq.͑23͒requires q b at cell-face edges .How the ﬂux vector is evaluated there depends on the speciﬁc node/vertex/cell geometry of individual computer codes.In STREAM the ﬂuid stress is ﬁrst interpolated to cell edges and then the bed-load ﬂux vector is constructed on those edges.For coarse grids no additional bed smoothing is required.However,our experience is that on ﬁner grids some numerical smoothing is often necessary.To ensure that this is still conservative—i.e.,does not change the overall sediment budget–this must be accomplished by changes to the ﬂux vector.In STREAM,the surface is deﬁned by intermediate control points ͓Fig.5͑a ͔͒,with bed vertices obtained from them by inter-polation.A “sawtooth”pattern of control points can be identiﬁed by examining changes to the difference z b −z ¯b ,where z b =height of a control point and z ¯b =average height of adjacent vertices at the same horizontal location.This difference is inversely propor-tional to the radius of curvature.On ﬂat or uniformly-sloping beds it would be zero.On beds with constant curvature it would be a constant ͓Fig.5͑b ͔͒.However,if the control points were to adopt a sawtooth pattern this would alternate in sign ͓Fig.5͑c ͔͒.To prevent the growth of oscillatory wiggles,but to have no effect on beds with constant curvature,a diffusion-like term proportional to the change in z b −z ¯b can be added to the bed-load ﬂux vector.A suitable modiﬁcation was found to beq b →q b +10͉q b ͉⌬lϫ͓͑z b −z ¯b ͒L −͑z b −z ¯b ͒R ͔͑25͒where q b =component of the bed-load ﬂux vector from left ͑L ͒to right ͑R ͒nodes on either side of a cell edge and ⌬l is the distance between these nodes.The correction vanishes when there is no sediment transport ͑q b =0͒,or on surfaces of constant slope or constant curvature;in general,it is proportional to the derivative of the radius of curvature ͑in this coordinate direction ͒.Calcula-tions for the channel bend ͑see later ͒with successive values of 2,5,and 10for the numerical factor in Eq.͑25͒gave no discernible difference in ﬁnal surface proﬁles;however,the calculation was more difﬁcult to initiate with the smaller numbers.Nonerodible BasesAlthough the majority of natural applications are for deep beds of sediment,comparison with laboratory experiments sometimes in-volves cases where sediment is swept away to expose a nonerod-ible base.In these circumstances a crude but effective ﬂux limiter was employed whereby the integrated ﬂux through one cell edge was limited by that required to deplete the upstream column by half its sediment content in one time step.Once again we note that overall sediment conservation requires that any adjustment for nonerodible bases must be made by modifying the ﬂux,not by limiting bed heights directly.ApplicationsSediment Transport in a 90°Channel BendKawai and Julien ͑1996͒reported experimental measurements of bed scour in a 90°channel bend.͓Numerical simulations of this case have also been performed by Rüther and Olsen ͑1995͒.͔The channel width was 0.2m and the centerline radius of the bend was 0.6m.Two grades of bed material were used,but only the coarser type ͑0.6mm sand ͒is considered here.The initial longi-tudinal slope was 1/300.A steady ﬂow of water ͑Q =4L s −1͒and sediment discharge ͑Q s =1.44cm 3s −1͒were maintained for 200min,after which the channel bed was observed to have reached a steady state.The initial water depth ͑0.041m ͒was close to the normal depth for this channel.During the experiment secondary currents in the bend ͑outward ﬂow at the free surface,inward ﬂow at the bed ͒led to substantial scour on the outside of the bend and deposition on the inside.Our time-dependent nu-merical simulations suggest that the inner-bank structure forms ﬁrst.Numerical simulations were conducted on coarse,intermediate and ﬁne meshes of 90ϫ20ϫ12,135ϫ30ϫ18,and 185ϫ45ϫ18cells,respectively;only minor differences in bed proﬁle were found on the intermediate and ﬁne meshes.V olume ﬂuxes of water and sediment were ﬁxed at inlet and the water level was ﬁxed at outlet.The inﬂow velocity proﬁle was taken from an initial fully developed-ﬂow calculation and was scaled during the full simulation to maintain a constant volume ﬂux as the free surface and bed evolved.As our code actually solves for piezo-metric pressure,p *=p +␳g ͑z −z ref ͒,the downstream height con-dition was implemented by setting z ref to the downstream water level and p *=0on the exit boundary.The motion of both the free surface and the mobile bed was followed,with the mesh evolving to conform to them.As a ﬁnal steady state was anticipated,a larger time step was used fortheFig.7.Channel bend:bed-height changes at banksD o w n l o a d e d f r o m a s c e l i b r a r y .o r g b y X I 'A N J I A O T O N G U N I VE R S I T Y o n 09/28/13. C o p y r i g h t A S C E .F o r p e r s o n a l u s e o n l y ; a l l r i g h t s r e s e r v e d .。

TPO-44 阅读文本和对应题目文本第3篇

3. SeagrassesMany areas of the shallow sea bottom are covered with a lush growth of aquatic flowering plants adapted to live submerged in seawater. These plants are collectively called seagrasses. Seagrass beds are strongly influenced by several physical factors. The most significant is water motion: currents and waves. Since seagrass systems exist in both sheltered and relatively open areas, they are subject to differing amounts of water motion. For any given seagrass system, however, the water motion is relatively constant. Seagrass meadows in relatively turbulent waters tend to form a mosaic of individual mounds, whereas meadows in relatively calm waters tend to form flat, extensive carpets. The seagrass beds, in turn, dampen wave action, particularly if the blades reach the water surface. This damping effect can be significant to the point where just one meter into a seagrass bed the wave motion can be reduced to zero. Currents are also slowed as they move into the bed.The slowing of wave action and currents means that seagrass beds tend to accumulate sediment. However, this is not universal and depends on the currents under which the bed exists. Seagrass beds under the influence of strong currents tend to have many of the lighter particles, including seagrass debris, moved out, whereas beds in weak current areas accumulate lighter detrital material. It is interesting that temperate seagrass beds accumulate sediments from sources outside the beds, whereas tropical seagrass beds derive most of their sediments from within.Since most seagrass systems are depositional environments, they eventually accumulate organic material that leads to the creation of fine-grained sediments with a much higher organic content than that of the surrounding unvegetated areas. This accumulation, in turn, reduces the water movement and the oxygen supply. The high rate of metabolism (the processing of energy for survival) of the microorganisms in the sediments causes sediments to be anaerobic (without oxygen) below the first few millimeters. According to ecologist J. W. Kenworthy, anaerobic processes of the microorganisms in the sediment are an important mechanism for regenerating and recycling nutrients and carbon, ensuring the high rates of productivity—that is, the amount of organic material produced-that are measured in those beds. In contrast to other productivity in the ocean, which is confined to various species of algae and bacteria dependent on nutrient concentrations in the water column, seagrasses are rooted plants that absorb nutrients from the sediment or substrate. They are, therefore, capable of recycling nutrients into the ecosystem thatwould otherwise be trapped in the bottom and rendered unavailable.Other physical factors that have an effect on seagrass beds include light, temperature, and desiccation (drying out). For example, water depth and turbidity (density of particles in the water) together or separately control the amount of light available to the plants and the depth to which the seagrasses may extend. Although marine botanist W. A. Setchell suggested early on that temperature was critical to the growth and reproduction of eelgrass, it has since been shown that this particularly widespread seagrass grows and reproduces at temperatures between 2 and 4 degrees Celsius in the Arctic and at temperatures up to 28 degrees Celsius on the northeastern coast of the United States. Still, extreme temperatures, in combination with other factors, may have dramatic detrimental effects. For example, in areas of the cold North Atlantic, ice may form in winter. Researchers Robertson and Mann note that when the ice begins to break up, the wind and tides may move the ice around, scouring the bottom and uprooting the eelgrass. In contrast, at the southern end of the eelgrass range, on the southeastern coast of the United States, temperatures over 30 degrees Celsius in summer cause excessive mortality. Seagrass beds also decline if they are subjected to too much exposure to the air. The effect of desiccation is often difficult to separate from the effect of temperature. Most seagrass beds seem tolerant of considerable changes in salinity (salt levels) and can be found in brackish (somewhat salty) waters as well as in full- strength seawater.1. According to paragraph 1, which of the following is true about seagrasses in calm ocean waters?A. They will not survive for very long without the nutrients brought In by fast-moving waters.B. They tend to form beds covering large areas along the ocean floor.C. They usually are arranged in separate mounds.D. They grow more slowly than do seagrasses in fast-moving waters.2. According to paragraph 1, which of the following is MOST likely to describe a bed in which seagrasses reach the surface of the water?A. The water is almost completely still.B. The bed often has major damage from strong waves or currents.C. The bed is generally no more than one square meter in size.D. Grasses form a mosaic of individual mounds.3. Which of the sentences below best expresses the essential information in the highlighted sentence in the passage? Incorrect choices change the meaning in important ways or leave out essential information.A. Light particles and debris collect in some seagrass beds, but are washed out of those affected by strong currents.B. Seagrass beds under the influence of strong currents tend to accumulate many of the lighter particles from other bedsC. The strength of the currents determines how quickly accumulated seagrass debris is moved out of the beds.D. Seagrass debris and other light particles are often moved from areas of strong currents to areas of weak currents.4. The word "derive" in the passage is closest in meaning toA. maintainB. expelC. obtainD. enrich5. According to paragraph 3, which of the following does NOT accurately describe the sediments that collect in seagrass beds?A. Fine-grainedB. Only a few millimeters deepC. Low in oxygenD. Rich in organic matter6. The word "confined" in the passage is closest in meaning toA. relatedB. limitedC. relevantD. helpful7. According to paragraph 3，how do seagrasses affect the nutrient supply in the ecosystem?A. Because of their high rate of metabolism, they consume a large percentage of the available nutrients.B. They attract various species of algae and bacteria that produce high nutrient concentrations in the water column.C. They take up carbon and other nutrients trapped on the sea bottom and bring them back into use.D. Through anaerobic processes at their roots, they produce a very nutrient-rich sediment.8. It can be inferred from paragraph 4 that the reason seagrasses do not grow in very deep water is thatA. they cannot handle intense water pressureB. deep water is too coldC. they would not get enough lightD. deep water is too salty9. The w ord “detrimental'’ in the passage is closest in meaning toA. harmfulB. significantC. unexpectedD. distinct10. T he word “detrimental'’ in the passage is closest in meaning toA. harmfulB. significantC. unexpectedD. distinct11. Paragraph 4 suggests that which of the following would be the LEAST likely to cause major damage to eelgrass and other common seagrasses?A. Factors related to extreme temperaturesB. Exposure to airC. Major changes in salinityD. The movement of ice on the seafloor12. The phrase “tolerant of’ in the passage is closest in meaning toA. unused toB. strongly affected byC. protected fromD. able to withstand13. Look at the four squares [■] that indicate where the following sentence could be added to the passage.Seagrasses grow together in dense patches, or beds, with as many as 4,000 blades per square meter.Where would the sentence best fit? Click on a square [■] to add the sentence to the passage14. Look at the four squares [■] that indicate where the follo wing sentence could be added to the passage.Seagrasses grow together in dense patches, or beds, with as many as 4,000 blades per square meter.Where would the sentence best fit? Click on a square [■] to add the sentence to the passageDrag your answer choices to the spaces where they belong. To remove an answer choice, click on it.To review the passage, click VIEW TEXTSeagrasses are aquatic flowering plants that grow in either sheltered or open areas of the seaA. Seagrass beds are influenced by several physical factors, the most significant being the stability of the sea bottom, which must anchor them against the currents.B. Because they slow currents and waves, seagrass beds collect deposits of rich organic sediments, which are home to many anaerobic microorganisms.C. Unlike sea organisms that depend on the water column for their productivity, seagrasses ensure high rates of productivity by taking nutrients from ocean floor sediment.D. Sediments in seagrass beds vary by region, with temperate beds accumulating sediments from within, and tropical beds collecting sediments from without.E. Seagrasses under weak currents tend to have higher rates of metabolism than those under strong currents, perhaps because of differences in oxygen levels.F. Although seagrasses survive in temperatures ranging from 2 to 28 degrees Celsius, more extreme temperatures can damage them, as can desiccation and lack of light.。

21世纪大学英语读写教程第二册A B课文翻译及课后翻译题

第一单元Text A、Winston Churchill—His Other LifeMy father, Winston Churchill, began his love affair with painting in his 40s, amid disastrous circumstances. As First Lord of the Admiralty in 1915, he had been deeply involved in a campaign in the Dardanelles that could have shortened the course of a bloody world war. But when the mission failed, with great loss of life, Churchill paid the price, both publicly and privately: He was removed from the Admiralty and lost his position of political influence.我的父亲温斯顿?丘吉尔是在 40 几岁开始迷恋上绘画的，当时他正身处逆境。

1915 年，作为海军大臣，他深深地卷入了达达尼尔海峡的一场战役。

原本那次战役是能够缩短一场血腥的世界大战的，但它却失败了，人员伤亡惨重，为此丘吉尔作为公务员和个人都付出了代价：他被免去了海军部的职务，失去了显赫的政治地位。

Overwhelmed by the disaster — "I thought he would die of grief," said his wife, Clementine — he retired with his family to Hoe Farm, a country retreat in Surrey. There, as Churchill later recalled, "The muse of painting came to my rescue!"“我本以为他会因忧伤而死的。

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SOME PROPERTIES OF BELL–SEJNOWSKI PDF-MA TCHING NEURONSimone FioriDIE–UNIPG,University of Perugia,ItalyE-MAIL:SFR@UNIPG.ITABSTRACTThe aim of the present paper is to investigate the behavior of a single-input single-unit system,learning through the maximum-entropy principle,in order to understand some formal property of Bell-Sejnowski’s PDF-matching neuron. The general learning equations are presented and two case-study are discussed with details.1.INTRODUCTIONThe analysis of the behavior of adaptive activation function non-linear neurons is a challenging researchﬁeld in the neu-ral network theory,which may require analyzing non-linear differential equations of neuron’s parameters.Especially in signal processing applications,the external excitations are not deterministic but stochastic,and the aim is toﬁnd a sta-tistical description of the neural system’s response and of system features.The formal techniques known in the scien-tiﬁc literature for studying such systems beneﬁt from cross-fertilization among artiﬁcial neural networks,information theory and signal processing and neurobiology.Recently,several researchers have focused their atten-tion on this class of stochastic learning theories,with ap-plications to blind separation of sources by the independent component analysis[1,2,3,4,5,6,16,20],probability den-sity estimation[1,18,7],self-organizing classiﬁcation[19], and blind system deconvolution[2,8,9].Also,some studies on neurobiological mechanisms have suggested interesting non-linear models and information-theoretic based learning theories[10,11,13,14,15].Following the pioneering work of Linsker,Plumbley, Bell and Sejnowski[2,12,17],in recent papers,we pre-sented some results related to the use ofﬂexible non-linear units,termed FANs,trained in an stochastic way by means of an entropy-based criterion:In[7]we proposed some gen-eral structures and adapting frameworks for FAN non-linear unit,while papers[4,5,6]have been devoted to the applica-tion of these neurons to blind signal processing tasks,such as blind source separation by the independent component analysis and blind signalﬂattening;in these works we also compared the proposed structures to otherﬂexible topolo-gies known in the scientiﬁc literature,as e.g.the mixture-of-kernel,showing that the new approach may exhibit bet-ter estimation/approximation ability at a lower complexity burden.The aim of our preceding work was to introduce the new adaptive-activation-function structures and adapting theo-ries and to assess their features through numerical experi-ments on real-world data;however,due to the strong non-linearity of the involved equations we did not present any theoretical considerations about the mathematical structure and properties of the adapting equations.In the present pa-per we recall the basic adapting formulas and present the closed-form expressions of them for some special cases;our main goal is to discuss their features in an analytical way,in order to gain a deeper insight into the behavior of the non-linear differential equations governing information-theoretic FAN non-linear unit adapting,and to better explain the previous numerical results.In particular,the aim is to discuss some properties of Bell-Sejnowski probability den-sity function matching neuron.2.NEURON MODEL AND PDF-MA TCHINGLEARNING EQUATIONSIn the present paper we consider the simple neuron model depicted in the Figure1,which may be formally described by the input-output equation:(1)where and denote the neuron’s in-put stimulus and the neuron’s response signal,respectively, denotes the neuron’s connection strength and stands for the bias;the non-linear function representsa bounded(saturating)squashing activation function,whichmeets the monotonicity condition.Both the input and output signals are treated as station-ary stochastic signals,described by the probability density functions(pdfs)and.We do not make any particular hypothesis about the stimulus’statistical distribu-tion,but for requiring a sufﬁcient regularity,namelyshould be a smooth function endowed with sufﬁciently-high-order moments.194(4)(5) Note that the entropy of the stimulus does not depend on neuron parameters’values,thus its derivatives are not re-quired.An interesting observation about maximum-entropy neu-ron learning is that the neuron tries to align its transfer func-tion to the stimulus’pdf[2].This may be proven formally in the following way.We postulate that theﬁrst-order deriva-tive of neuron’s transfer function,namely in(2)tends to match the stimulus pdf:To show this it is neces-sary to deﬁne a mismatch measure and to show that it gets minimized.A pseudo-distance among pdfs is the Kullback-Leibler informational divergence,which in this case writes:Ultimately,neuron’s learning equations read:(7)−2.5−2−1.5−1−0.500.51 1.52 2.50.20.40.60.811.21.41.61.82us (u )Fig.2.Sigmoidal function for three different valuesof the integer :Dotted line:;Solid line:;Dot-dashed line:.stimulus moments:Note that,by the hypotheses made on the stimulus,the mo-ments exist at least for some value of ;it is important toremark that and,in particular,and ,always.By replacing the term in the integrals with the equivalent term ,and by making use of the binomial expansion formula,we have:and:wheredenotes the binomial coefﬁcient(10)It is also interesting to write explicitly the neuron’s entropy gap,the learning criterion deﬁned as,that reads:(11)The presence of the term(12)In this case we have,forodd,and.For ,the sigmoidal function of the neuron co-incides to the lifted error-function (’erf’),which has been investigated in a theoretical way in [7]and whose powerful in blind source separation by the independent component analysis has been numerically proven in the papers [5,6].The case has not been considered before and is interesting to study.By particularizing the learning equa-tions (9)+(10),we obtain the differential system governing the neuron’s learning phase:(13)0500100015002000250030000.10.20.30.40.50.60.70.80.9Input samplesw ,bFig.3.Case :Learning curves for the parameters (dashed-line)and (solid-line).the above learning equations to zero.In this way we obtain:(15)(16)where it is understood that and that .We have been able to identify two special cases when the above equilibrium equations may be exactly solved.3.1.CaseWhen the mean value of the input Gaussian excitation iszero,the equilibrium system noticeably simpliﬁes into:Clearly the second equation has as only feasible so-lution,because the sub-equation would lead to complex-valued solutions;by vanishing in the ﬁrst equation we also ﬁnd .As a numerical example,let us consider the case that.Also,Figure 4shows the true cdfof the input signal and the non-linear transference function of the neuron:They look nearly superimposed and,mainly,the neuron’s activation is just aligned to the true cdf.It would also be interesting to investigate the shape of the entropy-gap as a function of the learnable parameters.The entropy-gap surface and contour plot forthe present case are depicted in the Figures 5.The symmetry of the gap about the line ,as well as the fact thatFig.4.Case :Alignment of the input stimulus cdf (dashed-line)and the neuron’s activation function (solid-line).for the case .19750010001500200025003000−0.4−0.200.20.40.60.8Input 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