Structural complexity in monodisperse systems of isotropic particles

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Multi-scale structural similarity for image quality assesment

MULTI-SCALE STRUCTURAL SIMILARITY FOR IMAGE QUALITY ASSESSMENT Zhou Wang1,Eero P.Simoncelli1and Alan C.Bovik2(Invited Paper)1Center for Neural Sci.and Courant Inst.of Math.Sci.,New York Univ.,New York,NY10003 2Dept.of Electrical and Computer Engineering,Univ.of Texas at Austin,Austin,TX78712 Email:zhouwang@,eero.simoncelli@,bovik@ABSTRACTThe structural similarity image quality paradigm is based on the assumption that the human visual system is highly adapted for extracting structural information from the scene,and therefore a measure of structural similarity can provide a good approxima-tion to perceived image quality.This paper proposes a multi-scale structural similarity method,which supplies moreﬂexibility than previous single-scale methods in incorporating the variations of viewing conditions.We develop an image synthesis method to calibrate the parameters that deﬁne the relative importance of dif-ferent scales.Experimental comparisons demonstrate the effec-tiveness of the proposed method.1.INTRODUCTIONObjective image quality assessment research aims to design qual-ity measures that can automatically predict perceived image qual-ity.These quality measures play important roles in a broad range of applications such as image acquisition,compression,commu-nication,restoration,enhancement,analysis,display,printing and watermarking.The most widely used full-reference image quality and distortion assessment algorithms are peak signal-to-noise ra-tio(PSNR)and mean squared error(MSE),which do not correlate well with perceived quality(e.g.,[1]–[6]).Traditional perceptual image quality assessment methods are based on a bottom-up approach which attempts to simulate the functionality of the relevant early human visual system(HVS) components.These methods usually involve1)a preprocessing process that may include image alignment,point-wise nonlinear transform,low-passﬁltering that simulates eye optics,and color space transformation,2)a channel decomposition process that trans-forms the image signals into different spatial frequency as well as orientation selective subbands,3)an error normalization process that weights the error signal in each subband by incorporating the variation of visual sensitivity in different subbands,and the vari-ation of visual error sensitivity caused by intra-or inter-channel neighboring transform coefﬁcients,and4)an error pooling pro-cess that combines the error signals in different subbands into a single quality/distortion value.While these bottom-up approaches can conveniently make use of many known psychophysical fea-tures of the HVS,it is important to recognize their limitations.In particular,the HVS is a complex and highly non-linear system and the complexity of natural images is also very signiﬁcant,but most models of early vision are based on linear or quasi-linear oper-ators that have been characterized using restricted and simplistic stimuli.Thus,these approaches must rely on a number of strong assumptions and generalizations[4],[5].Furthermore,as the num-ber of HVS features has increased,the resulting quality assessment systems have become too complicated to work with in real-world applications,especially for algorithm optimization purposes.Structural similarity provides an alternative and complemen-tary approach to the problem of image quality assessment[3]–[6].It is based on a top-down assumption that the HVS is highly adapted for extracting structural information from the scene,and therefore a measure of structural similarity should be a good ap-proximation of perceived image quality.It has been shown that a simple implementation of this methodology,namely the struc-tural similarity(SSIM)index[5],can outperform state-of-the-art perceptual image quality metrics.However,the SSIM index al-gorithm introduced in[5]is a single-scale approach.We consider this a drawback of the method because the right scale depends on viewing conditions(e.g.,display resolution and viewing distance). In this paper,we propose a multi-scale structural similarity method and introduce a novel image synthesis-based approach to calibrate the parameters that weight the relative importance between differ-ent scales.2.SINGLE-SCALE STRUCTURAL SIMILARITYLet x={x i|i=1,2,···,N}and y={y i|i=1,2,···,N}be two discrete non-negative signals that have been aligned with each other(e.g.,two image patches extracted from the same spatial lo-cation from two images being compared,respectively),and letµx,σ2x andσxy be the mean of x,the variance of x,and the covariance of x and y,respectively.Approximately,µx andσx can be viewed as estimates of the luminance and contrast of x,andσxy measures the the tendency of x and y to vary together,thus an indication of structural similarity.In[5],the luminance,contrast and structure comparison measures were given as follows:l(x,y)=2µxµy+C1µ2x+µ2y+C1,(1)c(x,y)=2σxσy+C2σ2x+σ2y+C2,(2)s(x,y)=σxy+C3σxσy+C3,(3) where C1,C2and C3are small constants given byC1=(K1L)2,C2=(K2L)2and C3=C2/2,(4)Fig.1.Multi-scale structural similarity measurement system.L:low-passﬁltering;2↓:downsampling by2. respectively.L is the dynamic range of the pixel values(L=255for8bits/pixel gray scale images),and K1 1and K2 1aretwo scalar constants.The general form of the Structural SIMilarity(SSIM)index between signal x and y is deﬁned as:SSIM(x,y)=[l(x,y)]α·[c(x,y)]β·[s(x,y)]γ,(5)whereα,βandγare parameters to deﬁne the relative importanceof the three components.Speciﬁcally,we setα=β=γ=1,andthe resulting SSIM index is given bySSIM(x,y)=(2µxµy+C1)(2σxy+C2)(µ2x+µ2y+C1)(σ2x+σ2y+C2),(6)which satisﬁes the following conditions:1.symmetry:SSIM(x,y)=SSIM(y,x);2.boundedness:SSIM(x,y)≤1;3.unique maximum:SSIM(x,y)=1if and only if x=y.The universal image quality index proposed in[3]corresponds to the case of C1=C2=0,therefore is a special case of(6).The drawback of such a parameter setting is that when the denominator of Eq.(6)is close to0,the resulting measurement becomes unsta-ble.This problem has been solved successfully in[5]by adding the two small constants C1and C2(calculated by setting K1=0.01 and K2=0.03,respectively,in Eq.(4)).We apply the SSIM indexing algorithm for image quality as-sessment using a sliding window approach.The window moves pixel-by-pixel across the whole image space.At each step,the SSIM index is calculated within the local window.If one of the image being compared is considered to have perfect quality,then the resulting SSIM index map can be viewed as the quality map of the other(distorted)image.Instead of using an8×8square window as in[3],a smooth windowing approach is used for local statistics to avoid“blocking artifacts”in the quality map[5].Fi-nally,a mean SSIM index of the quality map is used to evaluate the overall image quality.3.MULTI-SCALE STRUCTURAL SIMILARITY3.1.Multi-scale SSIM indexThe perceivability of image details depends the sampling density of the image signal,the distance from the image plane to the ob-server,and the perceptual capability of the observer’s visual sys-tem.In practice,the subjective evaluation of a given image varies when these factors vary.A single-scale method as described in the previous section may be appropriate only for speciﬁc settings.Multi-scale method is a convenient way to incorporate image de-tails at different resolutions.We propose a multi-scale SSIM method for image quality as-sessment whose system diagram is illustrated in Fig. 1.Taking the reference and distorted image signals as the input,the system iteratively applies a low-passﬁlter and downsamples theﬁltered image by a factor of2.We index the original image as Scale1, and the highest scale as Scale M,which is obtained after M−1 iterations.At the j-th scale,the contrast comparison(2)and the structure comparison(3)are calculated and denoted as c j(x,y) and s j(x,y),respectively.The luminance comparison(1)is com-puted only at Scale M and is denoted as l M(x,y).The overall SSIM evaluation is obtained by combining the measurement at dif-ferent scales usingSSIM(x,y)=[l M(x,y)]αM·Mj=1[c j(x,y)]βj[s j(x,y)]γj.(7)Similar to(5),the exponentsαM,βj andγj are used to ad-just the relative importance of different components.This multi-scale SSIM index deﬁnition satisﬁes the three conditions given in the last section.It also includes the single-scale method as a spe-cial case.In particular,a single-scale implementation for Scale M applies the iterativeﬁltering and downsampling procedure up to Scale M and only the exponentsαM,βM andγM are given non-zero values.To simplify parameter selection,we letαj=βj=γj forall j’s.In addition,we normalize the cross-scale settings such thatMj=1γj=1.This makes different parameter settings(including all single-scale and multi-scale settings)comparable.The remain-ing job is to determine the relative values across different scales. Conceptually,this should be related to the contrast sensitivity func-tion(CSF)of the HVS[7],which states that the human visual sen-sitivity peaks at middle frequencies(around4cycles per degree of visual angle)and decreases along both high-and low-frequency directions.However,CSF cannot be directly used to derive the parameters in our system because it is typically measured at the visibility threshold level using simpliﬁed stimuli(sinusoids),but our purpose is to compare the quality of complex structured im-ages at visible distortion levels.3.2.Cross-scale calibrationWe use an image synthesis approach to calibrate the relative impor-tance of different scales.In previous work,the idea of synthesizing images for subjective testing has been employed by the“synthesis-by-analysis”methods of assessing statistical texture models,inwhich the model is used to generate a texture with statistics match-ing an original texture,and a human subject then judges the sim-ilarity of the two textures [8]–[11].A similar approach has also been qualitatively used in demonstrating quality metrics in [5],[12],though quantitative subjective tests were not conducted.These synthesis methods provide a powerful and efﬁcient means of test-ing a model,and have the added beneﬁt that the resulting images suggest improvements that might be made to the model[11].M )distortion level (MSE)12345Fig.2.Demonstration of image synthesis approach for cross-scale calibration.Images in the same row have the same MSE.Images in the same column have distortions only in one speciﬁc scale.Each subject was asked to select a set of images (one from each scale),having equal quality.As an example,one subject chose the marked images.For a given original 8bits/pixel gray scale test image,we syn-thesize a table of distorted images (as exempliﬁed by Fig.2),where each entry in the table is an image that is associated witha speciﬁc distortion level (deﬁned by MSE)and a speciﬁc scale.Each of the distorted image is created using an iterative procedure,where the initial image is generated by randomly adding white Gaussian noise to the original image and the iterative process em-ploys a constrained gradient descent algorithm to search for the worst images in terms of SSIM measure while constraining MSE to be ﬁxed and restricting the distortions to occur only in the spec-iﬁed scale.We use 5scales and 12distortion levels (range from 23to 214)in our experiment,resulting in a total of 60images,as demonstrated in Fig.2.Although the images at each row has the same MSE with respect to the original image,their visual quality is signiﬁcantly different.Thus the distortions at different scales are of very different importance in terms of perceived image quality.We employ 10original 64×64images with different types of con-tent (human faces,natural scenes,plants,man-made objects,etc.)in our experiment to create 10sets of distorted images (a total of 600distorted images).We gathered data for 8subjects,including one of the authors.The other subjects have general knowledge of human vision but did not know the detailed purpose of the study.Each subject was shown the 10sets of test images,one set at a time.The viewing dis-tance was ﬁxed to 32pixels per degree of visual angle.The subject was asked to compare the quality of the images across scales and detect one image from each of the ﬁve scales (shown as columns in Fig.2)that the subject believes having the same quality.For example,one subject chose the images marked in Fig.2to have equal quality.The positions of the selected images in each scale were recorded and averaged over all test images and all subjects.In general,the subjects agreed with each other on each image more than they agreed with themselves across different images.These test results were normalized (sum to one)and used to calculate the exponents in Eq.(7).The resulting parameters we obtained are β1=γ1=0.0448,β2=γ2=0.2856,β3=γ3=0.3001,β4=γ4=0.2363,and α5=β5=γ5=0.1333,respectively.4.TEST RESULTSWe test a number of image quality assessment algorithms using the LIVE database (available at [13]),which includes 344JPEG and JPEG2000compressed images (typically 768×512or similar size).The bit rate ranges from 0.028to 3.150bits/pixel,which allows the test images to cover a wide quality range,from in-distinguishable from the original image to highly distorted.The mean opinion score (MOS)of each image is obtained by averag-ing 13∼25subjective scores given by a group of human observers.Eight image quality assessment models are being compared,in-cluding PSNR,the Sarnoff model (JNDmetrix 8.0[14]),single-scale SSIM index with M equals 1to 5,and the proposed multi-scale SSIM index approach.The scatter plots of MOS versus model predictions are shown in Fig.3,where each point represents one test image,with its vertical and horizontal axes representing its MOS and the given objective quality score,respectively.To provide quantitative per-formance evaluation,we use the logistic function adopted in the video quality experts group (VQEG)Phase I FR-TV test [15]to provide a non-linear mapping between the objective and subjective scores.After the non-linear mapping,the linear correlation coef-ﬁcient (CC),the mean absolute error (MAE),and the root mean squared error (RMS)between the subjective and objective scores are calculated as measures of prediction accuracy .The prediction consistency is quantiﬁed using the outlier ratio (OR),which is de-Table1.Performance comparison of image quality assessment models on LIVE JPEG/JPEG2000database[13].SS-SSIM: single-scale SSIM;MS-SSIM:multi-scale SSIM;CC:non-linear regression correlation coefﬁcient;ROCC:Spearman rank-order correlation coefﬁcient;MAE:mean absolute error;RMS:root mean squared error;OR:outlier ratio.Model CC ROCC MAE RMS OR(%)PSNR0.9050.901 6.538.4515.7Sarnoff0.9560.947 4.66 5.81 3.20 SS-SSIM(M=1)0.9490.945 4.96 6.25 6.98 SS-SSIM(M=2)0.9630.959 4.21 5.38 2.62 SS-SSIM(M=3)0.9580.956 4.53 5.67 2.91 SS-SSIM(M=4)0.9480.946 4.99 6.31 5.81 SS-SSIM(M=5)0.9380.936 5.55 6.887.85 MS-SSIM0.9690.966 3.86 4.91 1.16ﬁned as the percentage of the number of predictions outside the range of±2times of the standard deviations.Finally,the predic-tion monotonicity is measured using the Spearman rank-order cor-relation coefﬁcient(ROCC).Readers can refer to[15]for a more detailed descriptions of these measures.The evaluation results for all the models being compared are given in Table1.From both the scatter plots and the quantitative evaluation re-sults,we see that the performance of single-scale SSIM model varies with scales and the best performance is given by the case of M=2.It can also be observed that the single-scale model tends to supply higher scores with the increase of scales.This is not surprising because image coding techniques such as JPEG and JPEG2000usually compressﬁne-scale details to a much higher degree than coarse-scale structures,and thus the distorted image “looks”more similar to the original image if evaluated at larger scales.Finally,for every one of the objective evaluation criteria, multi-scale SSIM model outperforms all the other models,includ-ing the best single-scale SSIM model,suggesting a meaningful balance between scales.5.DISCUSSIONSWe propose a multi-scale structural similarity approach for image quality assessment,which provides moreﬂexibility than single-scale approach in incorporating the variations of image resolution and viewing conditions.Experiments show that with an appropri-ate parameter settings,the multi-scale method outperforms the best single-scale SSIM model as well as state-of-the-art image quality metrics.In the development of top-down image quality models(such as structural similarity based algorithms),one of the most challeng-ing problems is to calibrate the model parameters,which are rather “abstract”and cannot be directly derived from simple-stimulus subjective experiments as in the bottom-up models.In this pa-per,we used an image synthesis approach to calibrate the param-eters that deﬁne the relative importance between scales.The im-provement from single-scale to multi-scale methods observed in our tests suggests the usefulness of this novel approach.However, this approach is still rather crude.We are working on developing it into a more systematic approach that can potentially be employed in a much broader range of applications.6.REFERENCES[1] A.M.Eskicioglu and P.S.Fisher,“Image quality mea-sures and their performance,”IEEE munications, vol.43,pp.2959–2965,Dec.1995.[2]T.N.Pappas and R.J.Safranek,“Perceptual criteria for im-age quality evaluation,”in Handbook of Image and Video Proc.(A.Bovik,ed.),Academic Press,2000.[3]Z.Wang and A.C.Bovik,“A universal image quality in-dex,”IEEE Signal Processing Letters,vol.9,pp.81–84,Mar.2002.[4]Z.Wang,H.R.Sheikh,and A.C.Bovik,“Objective videoquality assessment,”in The Handbook of Video Databases: Design and Applications(B.Furht and O.Marques,eds.), pp.1041–1078,CRC Press,Sept.2003.[5]Z.Wang,A.C.Bovik,H.R.Sheikh,and E.P.Simon-celli,“Image quality assessment:From error measurement to structural similarity,”IEEE Trans.Image Processing,vol.13, Jan.2004.[6]Z.Wang,L.Lu,and A.C.Bovik,“Video quality assessmentbased on structural distortion measurement,”Signal Process-ing:Image Communication,special issue on objective video quality metrics,vol.19,Jan.2004.[7] B.A.Wandell,Foundations of Vision.Sinauer Associates,Inc.,1995.[8]O.D.Faugeras and W.K.Pratt,“Decorrelation methods oftexture feature extraction,”IEEE Pat.Anal.Mach.Intell., vol.2,no.4,pp.323–332,1980.[9] A.Gagalowicz,“A new method for textureﬁelds synthesis:Some applications to the study of human vision,”IEEE Pat.Anal.Mach.Intell.,vol.3,no.5,pp.520–533,1981. [10] D.Heeger and J.Bergen,“Pyramid-based texture analy-sis/synthesis,”in Proc.ACM SIGGRAPH,pp.229–238,As-sociation for Computing Machinery,August1995.[11]J.Portilla and E.P.Simoncelli,“A parametric texture modelbased on joint statistics of complex wavelet coefﬁcients,”Int’l J Computer Vision,vol.40,pp.49–71,Dec2000. [12]P.C.Teo and D.J.Heeger,“Perceptual image distortion,”inProc.SPIE,vol.2179,pp.127–141,1994.[13]H.R.Sheikh,Z.Wang, A. C.Bovik,and L.K.Cormack,“Image and video quality assessment re-search at LIVE,”/ research/quality/.[14]Sarnoff Corporation,“JNDmetrix Technology,”http:///products_services/video_vision/jndmetrix/.[15]VQEG,“Final report from the video quality experts groupon the validation of objective models of video quality assess-ment,”Mar.2000./.PSNRM O SSarnoffM O S(a)(b)Single−scale SSIM (M=1)M O SSingle−scale SSIM (M=2)M O S(c)(d)Single−scale SSIM (M=3)M O SSingle−scale SSIM (M=4)M O S(e)(f)Single−scale SSIM (M=5)M O SMulti−scale SSIMM O S(g)(h)Fig.3.Scatter plots of MOS versus model predictions.Each sample point represents one test image in the LIVE JPEG/JPEG2000image database [13].(a)PSNR;(b)Sarnoff model;(c)-(g)single-scale SSIM method for M =1,2,3,4and 5,respectively;(h)multi-scale SSIM method.。

Expressiveness vs. complexity in nonmonotonic knowledge bases propositional case

Expressiveness plexity in nonmonotonic knowledge bases:propositional caseRiccardo RosatiAbstract.We study the trade-off between the expressive abili-ties and the complexity of reasoning in propositional nonmonotonicknowledge bases.Weﬁrst analyze,in an expressive epistemic modalframework,the most popular forms of nonmonotonic mechanismsused in knowledge representation:in particular,we prove that epis-temic queries and epistemic integrity constraints are naturally ex-pressed through the notion of negation as failure.Based on the aboveanalysis,we then characterize the complexity of reasoning with dif-ferent subsets of such nonmonotonic constructs.This characteriza-tion induces a complexity based classiﬁcation of the various formsof nonmonotonic reasoning considered:in particular,we prove thatnegation as failure is computationally harder than epistemic disjunc-tion,which apparently contradicts previous complexity results ob-tained in the logic programming setting.1MOTIV ATIONResearch in nonmonotonic logics has extensively studied the prob-lem of establishing the complexity of various forms of common-sense reasoning(see e.g.[2,7]).In particular,the complexity of rea-soning in several propositional nonmonotonic formalisms has beenestablished.The results obtained show that the problem of reason-ing in propositional nonmonotonic logics lies typically at the secondlevel of the polynomial hierarchy(e.g.in default logic,autoepistemiclogic,propositional circumscription,disjunctive logic programming,McDermott and Doyle’s modal logics,and the modal logic of onlyknowing).Hence,it is harder(unless the polynomial hierarchy col-lapses)than reasoning in standard propositional logic.However,despite the amount of results obtained,a systematicstudy of the relationship between the expressive abilities of a non-monotonic formalism and the complexity of reasoning in the pres-ence of such abilities has not been fully analyzed,which has beenpartly due to the lack of a common formal framework in which thedifferent forms of nonmonotonic reasoning could be compared andstudied.In particular,a careful analysis of the most used nonmono-tonic mechanisms for representing commonsense knowledge,withthe aim of identifying the contribution of each single feature to thecomplexity of reasoning,has not been pursued so far.Such an analy-sis would allow for precisely identifying the complexity of reasoningwith a given set of nonmonotonic abilities,thus enabling for design-ing optimized inference methods for nonmonotonic knowledge rep-resentation systems.The present work is aﬁrst step towards a principled analysis of therelationship between the expressive abilities and the complexity oflogically implies a formula in MKNF.Theorem1Let be an MKNF knowledge base,and be an epis-temic query.Then,iff the theoryis unsatisﬁable in MKNF.The most important consequence of the above theorem is that epis-temic queries are a form of nonmonotonic reasoning which can be reconducted to the notion of negation as failure(since they can be ex-pressed by modal sentences using only the operator).This allows for a better classiﬁcation of nonmonotonic abilities,since all reason-ing tasks involving epistemic queries actually correspond to using the negation as failure ability.Another consequence of the above theo-rem is that in MKNF it is possible to reduce all the main reasoning problems to satisﬁability/unsatisﬁability.Moreover,we prove that integrity constraints,under the epistemic interpretation of[9],can actually be expressed as MKNF sentences inside the knowledge base.Roughly speaking,under this interpreta-tion,an integrity constraint is not analogous to a usual piece of information in the knowledge base,which causes inconsistency of iff contradicts other information in:it is a property which must hold in in order to preserve consistency of.Theorem2Any integrity constraint in MKNF is equivalent to a sub-jective-formula.2As in the case of epistemic queries,this result shows that integrity constraints are naturally expressed through the notion of negation as failure.3COMPLEXITY RESULTSThe results above presented,together with previous studies on the expressive abilities of MKNF,allow for expressing all the best known nonmonotonic mechanisms used in knowledge representa-tion(like rules,defaults,negation as failure(NAF),epistemic dis-junction(ED),epistemic queries(EQs),integrity constraints(ICs)) inside MKNF.We are now thus able to characterize the complexity of reasoning with different subsets of such nonmonotonic features. In particular,by exploiting previous results on the computational as-pects of MKNF[10],we establish the complexity of the unsatisﬁa-bility problem for MKNF knowledge bases under various syntactic restrictions,corresponding to allowing different kinds of nonmono-tonic features inside the knowledge base.LPfacts(objective knowledge)PcoNPfacts and NAF(ICs,EQs)Prules with ED coNPWe call subjective-formula a formula such that each non-modal sub-formula from lies within the scope of an operator.A summary of the complexity results obtained is reported in the second column of the displayed table,3which must be read as fol-lows:for each row of the table,the complexity of checking unsatis-ﬁability in MKNF under the syntactic restriction reported in theﬁrst column(i.e.,considering only formulas whose structure corresponds to the nonmonotonic mechanisms reported)is complete with respect to the complexity class appearing in the second column.The new complexity results appear in the third and theﬁfth row of the table, while the others were already known.However,the classiﬁcation of epistemic queries(and integrity constraints)as a form of negation as failure allows for a new reading of the computational properties of several nonmonotonic formalisms.As an example,it is worthwhile to compare the results obtained in MKNF with the results obtained in the case of propositional logic programs,which are reported in the third column of the displayed table.It is known that logic programs (interpreted under the stable model semantics)correspond to MKNF theories of a special form,in which:(i)only atoms(or literals)are allowed within the scope of modalities;(ii)nested occurrences of the modalities are not allowed.Interestingly,under our classiﬁcation (i.e.separating standard queries from epistemic queries,which are expressed in terms of negation as failure)it turns out that reasoning in logic programs with disjunction in the head of the clauses is eas-ier than reasoning in logic programs with negation as failure in the body of the clauses.This result only apparently contradicts previous complexity analyses of logic programming[3],which establish that reasoning with positive disjunctive logic programs lies at the second level of the polynomial hierarchy,since in such analyses the contri-bution of negation as failure given by queries was not considered. REFERENCES[1] A.Bochman.Biconsequence relations for nonmonotonic rea-soning.In Proc.of KR-96,482–492,1996.[2]M.Cadoli and M.Schaerf.A survey of complexity results fornonmonotonic logics.JLP,17,127–160,1993.[3]T.Eiter and G.Gottlob.On the computational cost of disjunc-tive logic programming.AMAI,15(3,4),1995.[4]J.Y.Halpern and Y.Moses.Towards a theory of knowledge andignorance:Preliminary report.Technical Report CD-TR92/34, IBM,1985.[5]V.Lifschitz.Nonmonotonic databases and epistemic queries.In Proc.of IJCAI-91,381–386,1991.[6] F.Lin and Y.Shoham.A logic of knowledge and justiﬁed as-sumptions.AIJ,57:271–289,1992[7]W.Marek and M.Truszczy´n ski.Nonmonotonic Logics–Context-Dependent Reasoning.Springer-Verlag,1993.[8]R.C.Moore.Semantical considerations on nonmonotoniclogic.AIJ,25:75–94,1985.[9]R.Reiter.What should a database know?JLP14,1990.[10]R.Rosati.Reasoning with minimal belief and negation as fail-ure:algorithms and complexity.In AAAI-97,430–435,1997.[11]G.Schwarz and M.Truszczy´n ski.Minimal knowledge prob-lem:a new approach.AIJ,67:113–141,1994.。

化学化工专业英语

The shift of electron density in a covalent bond toward the more electronegative atom or group can be observed in several ways. For bonds to hydrogen, acidity is one criterion. If the bonding electron pair moves away from the hydrogen nucleus the proton will be more easily transfered to a base (it will be more acidic). Methane is almost non-acidic, since the C–H bond is nearly non-polar. The O–H bond of water is polar, and it is at least 25 powers of ten more acidic than methane. H–F is over 12 powers of ten more acidic than water as a consequence of the greater electronegativity difference in its atoms. Electronegativity differences may be transmitted through connecting covalent bonds by an inductive effect. This inductive transfer of polarity tapers off as the number of transmitting bonds increases, and the presence of more than one highly electronegative atom has a cumulative effect. For example, trifluoro ethanol, CF3CH2– O–H is about ten thousand times more acidic than ethanol, CH3CH2–O–H.

氧化锆制备技术的研究现状与进展

氧化锆制备技术的研究现状与进展张铭媛1, 2，康娟雪1, 2，普婧1, 2，黄秀兰1, 2，段利平1, 2，彭金辉1, 2, 3，陈菓1, 2, 3, *（1.云南省高校民族地区资源清洁转化重点实验室，云南民族大学，云南昆明650500；2.云南省跨境民族地区生物质资源清洁利用国际联合研究中心，云南民族大学，云南昆明650500；3. 非常规冶金教育部重点实验室，昆明理工大学，云南昆明650093）摘要：氧化锆被广泛用作高温、负载及侵蚀性介质条件下的抗磨损结构构件，对工业生产具有重要意义。

现今生产氧化锆的稳定化制备工艺较多，现对几种常见的制备氧化锆的生产技术进行了介绍，并分析了这些制备技术的优势，化学法制备出的氧化锆粒径分布均匀且方法简单易行。

溶胶-凝胶法生产的氧化锆粒径小、单分散性能优异。

水热法生产出的氧化锆粒径小、纯度高。

电熔法生产的氧化锆杂质含量低，致密度高且生产工艺简单。

微波热处理制备的氧化锆反应时间短、升温速率快、能耗小。

氧化锆的多种制备工艺技术使得其性能应用更加的多样化。

关键词:氧化锆；化学法；溶胶-凝胶法；水热法；电熔法；微波热处理中图分类号：TF841.4文献标识码：A 文章编号：Research status and progress of zirconia preparationtechnologyZHANG Mingyuan 1, 2, KANG Juanxue 1, 2, PU Jing 1, 2, HUANG Xiulan 1, 2,DUAN Liping 1, 2, CHEN Guo 1, 2, 3, *(1. Key Laboratory of Resource Clean Conversion in Ethnic Regions, Education Departmentof Yunnan, Yunnan Minzu University, Kunming Yunnan, 650500, China;2. Joint Research Centre for International Cross-border Ethnic Regions Biomass Clean Utilizationin Yunnan, Yunnan Minzu University, Kunming Yunnan, 650500, China; 3. Key Laboratory of Unconventional Metallurgy, Ministry of Education, Kunming University of Science and Technology,Kunming Yunnan, 650093, China)Abstract:Zirconia was widely used as an anti-wear structural element under high temperature, load and aggressive media conditions and was of great importance to industrial manufacture. The current manufacture of zirconia was more stabilization of the preparation process, several common preparation of zirconia manufacture technology were introduced and analyzed the advantages of these preparation techniques, the zirconium oxide prepared by chemical method uniform particle size distribution and the method was simple and easy to do. Sol-gel method to produce fine powder particles, monodisperse excellent stability of zirconia powder. Hydrothermal production of zirconium oxide was small particle size, high purity. Fused zirconium oxide produced by low content of impurities, high density and production process was simple. Zirconia prepared by microwave heat treatment has short reaction time, fast heating rate and low energy consumption. The various preparation technology of zirconia makes its application more diversified.Keywords:zirconia; chemical method; sol-gel method; hydrothermal method; electrofusion; microwave heat treatment1前言氧化锆（ZrO2）是一种耐高温、耐腐蚀、高硬度的一种材料。

基于属性增强的神经传感融合网络的人脸识别算法论文

Attribute-Enhanced Face Recognition with Neural Tensor Fusion Networks Guosheng Hu1Yang Hua1,2Yang Yuan1Zhihong Zhang3Zheng Lu1 Sankha S.Mukherjee1Timothy M.Hospedales4Neil M.Robertson1,2Yongxin Yang5,61AnyVision2Queen’s University Belfast3Xiamen University 4The University of Edinburgh5Queen Mary University of London6Yang’s Accounting Consultancy Ltd {guosheng.hu,yang.hua,yuany,steven,rick}@,N.Robertson@ zhihong@,t.hospedales@,yongxin@yang.acAbstractDeep learning has achieved great success in face recog-nition,however deep-learned features still have limited in-variance to strong intra-personal variations such as large pose changes.It is observed that some facial attributes (e.g.eyebrow thickness,gender)are robust to such varia-tions.We present theﬁrst work to systematically explore how the fusion of face recognition features(FRF)and fa-cial attribute features(FAF)can enhance face recognition performance in various challenging scenarios.Despite the promise of FAF,weﬁnd that in practice existing fusion meth-ods fail to leverage FAF to boost face recognition perfor-mance in some challenging scenarios.Thus,we develop a powerful tensor-based framework which formulates fea-ture fusion as a tensor optimisation problem.It is non-trivial to directly optimise this tensor due to the large num-ber of parameters to optimise.To solve this problem,we establish a theoretical equivalence between low-rank ten-sor optimisation and a two-stream gated neural network. This equivalence allows tractable learning using standard neural network optimisation tools,leading to accurate and stable optimisation.Experimental results show the fused feature works better than individual features,thus proving for theﬁrst time that facial attributes aid face recognition. We achieve state-of-the-art performance on three popular databases:MultiPIE(cross pose,lighting and expression), CASIA NIR-VIS2.0(cross-modality environment)and LFW (uncontrolled environment).1.IntroductionFace recognition has advanced dramatically with the ad-vent of bigger datasets,and improved methodologies for generating features that are variant to identity but invari-ant to covariates such as pose,expression and illumination. Deep learning methodologies[41,40,42,32]have proven particularly effective recently,thanks to end-to-endrepre-Figure1:A sample attribute list is given(col.1)which per-tains to the images of the same individual at different poses (col.2).While the similarity scores for each dimension vary in the face recognition feature(FRF)set(col.3),the face at-tribute feature(FAF)set(col.4)remains very similar.The fused features(col.5)are more similar and a higher similar-ity score(0.89)is achieved.sentation learning with a discriminative face recognition ob-jective.Nevertheless,the resulting features still show im-perfect invariance to the strong intra-personal variations in real-world scenarios.We observe that facial attributes pro-vide a robust invariant cue in such challenging scenarios.For example gender and ethnicity are likely to be invariant to pose and expression,while eyebrow thickness may be invariant to lighting and resolution.Overall,face recogni-tion features(FRF)are very discriminative but less robust;while facial attribute features(FAF)are robust but less dis-criminative.Thus these two features are potentially com-plementary,if a suitable fusion method can be devised.To the best of our knowledge,we are theﬁrst to systematically explore the fusion of FAF and FRF in various face recog-nition scenarios.We empirically show that this fusion can greatly enhance face recognition performance.Though facial attributes are an important cue for face recognition,in practice,weﬁnd the existing fusion meth-ods including early(feature)or late(score)fusion cannot reliably improve the performance[34].In particular,while 1offering some robustness,FAF is generally less discrimina-tive than FRF.Existing methods cannot synergistically fuse such asymmetric features,and usually lead to worse perfor-mance than achieved by the stronger feature(FRF)only.In this work,we propose a novel tensor-based fusion frame-work that is uniquely capable of fusing the very asymmet-ric FAF and FRF.Our framework provides a more powerful and robust fusion approach than existing strategies by learn-ing from all interactions between the two feature views.To train the tensor in a tractable way given the large number of required parameters,we formulate the optimisation with an identity-supervised objective by constraining the tensor to have a low-rank form.We establish an equivalence be-tween this low-rank tensor and a two-stream gated neural network.Given this equivalence,the proposed tensor is eas-ily optimised with standard deep neural network toolboxes. Our technical contributions are:•It is theﬁrst work to systematically investigate and ver-ify that facial attributes are an important cue in various face recognition scenarios.In particular,we investi-gate face recognition with extreme pose variations,i.e.±90◦from frontal,showing that attributes are impor-tant for performance enhancement.•A rich tensor-based fusion framework is proposed.We show the low-rank Tucker-decomposition of this tensor-based fusion has an equivalent Gated Two-stream Neural Network(GTNN),allowing easy yet effective optimisation by neural network learning.In addition,we bring insights from neural networks into theﬁeld of tensor optimisation.The code is available:https:///yanghuadr/ Neural-Tensor-Fusion-Network•We achieve state-of-the-art face recognition perfor-mance using the fusion of face(newly designed‘Lean-Face’deep learning feature)and attribute-based fea-tures on three popular databases:MultiPIE(controlled environment),CASIA NIR-VIS2.0(cross-modality environment)and LFW(uncontrolled environment).2.Related WorkFace Recognition.The face representation(feature)is the most important component in contemporary face recog-nition system.There are two types:hand-crafted and deep learning features.Widely used hand-crafted face descriptors include Local Binary Pattern(LBP)[26],Gaborﬁlters[23],-pared to pixel values,these features are variant to identity and relatively invariant to intra-personal variations,and thus they achieve promising performance in controlled environ-ments.However,they perform less well on face recognition in uncontrolled environments(FRUE).There are two main routes to improve FRUE performance with hand-crafted features,one is to use very high dimensional features(dense sampling features)[5]and the other is to enhance the fea-tures with downstream metric learning.Unlike hand-crafted features where(in)variances are en-gineered,deep learning features learn the(in)variances from data.Recently,convolutional neural networks(CNNs) achieved impressive results on FRUE.DeepFace[44],a carefully designed8-layer CNN,is an early landmark method.Another well-known line of work is DeepID[41] and its variants DeepID2[40],DeepID2+[42].The DeepID family uses an ensemble of many small CNNs trained in-dependently using different facial patches to improve the performance.In addition,some CNNs originally designed for object recognition,such as VGGNet[38]and Incep-tion[43],were also used for face recognition[29,32].Most recently,a center loss[47]is introduced to learn more dis-criminative features.Facial Attribute Recognition.Facial attribute recog-nition(FAR)is also well studied.A notable early study[21] extracted carefully designed hand-crafted features includ-ing aggregations of colour spaces and image gradients,be-fore training an independent SVM to detect each attribute. As for face recognition,deep learning features now outper-form hand-crafted features for FAR.In[24],face detection and attribute recognition CNNs are carefully designed,and the output of the face detection network is fed into the at-tribute network.An alternative to purpose designing CNNs for FAR is toﬁne-tune networks intended for object recog-nition[56,57].From a representation learning perspective, the features supporting different attribute detections may be shared,leading some studies to investigate multi-task learn-ing facial attributes[55,30].Since different facial attributes have different prevalence,the multi-label/multi-task learn-ing suffers from label-imbalance,which[30]addresses us-ing a mixed objective optimization network(MOON). Face Recognition using Facial Attributes.Detected facial attributes can be applied directly to authentication. Facial attributes have been applied to enhance face veriﬁca-tion,primarily in the case of cross-modal matching,byﬁl-tering[19,54](requiring potential FRF matches to have the correct gender,for example),model switching[18],or ag-gregation with conventional features[27,17].[21]deﬁnes 65facial attributes and proposes binary attribute classiﬁers to predict their presence or absence.The vector of attribute classiﬁer scores can be used for face recognition.There has been little work on attribute-enhanced face recognition in the context of deep learning.One of the few exploits CNN-based attribute features for authentication on mobile devices [31].Local facial patches are fed into carefully designed CNNs to predict different attributes.After CNN training, SVMs are trained for attribute recognition,and the vector of SVM scores provide the new feature for face veriﬁcation.Fusion Methods.Existing fusion approaches can be classiﬁed into feature-level(early fusion)and score-level (late fusion).Score-level fusion is to fuse the similarity scores after computation based on each view either by sim-ple averaging[37]or stacking another classiﬁer[48,37]. Feature-level fusion can be achieved by either simple fea-ture aggregation or subspace learning.For aggregation ap-proaches,fusion is usually performed by simply element wise averaging or product(the dimension of features have to be the same)or concatenation[28].For subspace learn-ing approaches,the features areﬁrst concatenated,then the concatenated feature is projected to a subspace,in which the features should better complement each other.These sub-space approaches can be unsupervised or supervised.Un-supervised fusion does not use the identity(label)informa-tion to learn the subspace,such as Canonical Correlational Analysis(CCA)[35]and Bilinear Models(BLM)[45].In comparison,supervised fusion uses the identity information such as Linear Discriminant Analysis(LDA)[3]and Local-ity Preserving Projections(LPP)[9].Neural Tensor Methods.Learning tensor-based compu-tations within neural networks has been studied for full[39] and decomposed[16,52,51]tensors.However,aside from differing applications and objectives,the key difference is that we establish a novel equivalence between a rich Tucker [46]decomposed low-rank fusion tensor,and a gated two-stream neural network.This allows us achieve expressive fusion,while maintaining tractable computation and a small number of parameters;and crucially permits easy optimisa-tion of the fusion tensor through standard toolboxes. Motivation.Facial attribute features(FAF)and face recognition features(FRF)are complementary.However in practice,weﬁnd that existing fusion methods often can-not effectively combine these asymmetric features so as to improve performance.This motivates us to design a more powerful fusion method,as detailed in Section3.Based on our neural tensor fusion method,in Section5we system-atically explore the fusion of FAF and FRF in various face recognition environments,showing that FAF can greatly en-hance recognition performance.3.Fusing attribute and recognition featuresIn this section we present our strategy for fusing FAF and FRF.Our goal is to input FAF and FRF and output the fused discriminative feature.The proposed fusion method we present here performs signiﬁcantly better than the exist-ing ones introduced in Section2.In this section,we detail our tensor-based fusion strategy.3.1.ModellingSingle Feature.We start from a standard multi-class clas-siﬁcation problem setting:assume we have M instances, and for each we extract a D-dimensional feature vector(the FRF)as{x(i)}M i=1.The label space contains C unique classes(person identities),so each instance is associated with a corresponding C-dimensional one-hot encoding la-bel vector{y(i)}M i=1.Assuming a linear model W the pre-dictionˆy(i)is produced by the dot-product of input x(i)and the model W,ˆy(i)=x(i)T W.(1) Multiple Feature.Suppose that apart from the D-dimensional FRF vector,we can also obtain an instance-wise B-dimensional facial attribute feature z(i).Then the input for the i th instance is a pair:{x(i),z(i)}.A simple ap-proach is to redeﬁne x(i):=[x(i),z(i)],and directly apply Eq.(1),thus modelling weights for both FRF and FAF fea-tures.Here we propose instead a non-linear fusion method via the following formulationˆy(i)=W×1x(i)×3z(i)(2) where W is the fusion model parameters in the form of a third-order tensor of size D×C×B.Notation×is the tensor dot product(also known as tensor contraction)and the left-subscript of x and z indicates at which axis the ten-sor dot product operates.With Eq.(2),the optimisation problem is formulated as:minW1MMi=1W×1x(i)×3z(i),y(i)(3)where (·,·)is a loss function.This trains tensor W to fuse FRF and FAF features so that identity is correctly predicted.3.2.OptimisationThe proposed tensor W provides a rich fusion model. However,compared with W,W is B times larger(D×C vs D×C×B)because of the introduction of B-dimensional attribute vector.It is also almost B times larger than train-ing a matrix W on the concatenation[x(i),z(i)].It is there-fore problematic to directly optimise Eq.(3)because the large number of parameters of W makes training slow and leads to overﬁtting.To address this we propose a tensor de-composition technique and a neural network architecture to solve an equivalent optimisation problem in the following two subsections.3.2.1Tucker Decomposition for Feature FusionTo reduce the number of parameters of W,we place a struc-tural constraint on W.Motivated by the famous Tucker de-composition[46]for tensors,we assume that W is synthe-sised fromW=S×1U(D)×2U(C)×3U(B).(4) Here S is a third order tensor of size K D×K C×K B, U(D)is a matrix of size K D×D,U(C)is a matrix of sizeK C×C,and U(B)is a matrix of size K B×B.By restricting K D D,K C C,and K B B,we can effectively reduce the number of parameters from(D×C×B)to (K D×K C×K B+K D×D+K C×C+K B×B)if we learn{S,U(D),U(C),U(B)}instead of W.When W is needed for making the predictions,we can always synthesise it from those four small factors.In the context of tensor decomposition,(K D,K C,K B)is usually called the tensor’s rank,as an analogous concept to the rank of a matrix in matrix decomposition.Note that,despite of the existence of other tensor de-composition choices,Tucker decomposition offers a greater ﬂexibility in terms of modelling because we have three hyper-parameters K D,K C,K B corresponding to the axes of the tensor.In contrast,the other famous decomposition, CP[10]has one hyper-parameter K for all axes of tensor.By substituting Eq.(4)into Eq.(2),we haveˆy(i)=W×1x(i)×3z(i)=S×1U(D)×2U(C)×3U(B)×1x(i)×3z(i)(5) Through some re-arrangement,Eq.(5)can be simpliﬁed as ˆy(i)=S×1(U(D)x(i))×2U(C)×3(U(B)z(i))(6) Furthermore,we can rewrite Eq.(6)as,ˆy(i)=((U(D)x(i))⊗(U(B)z(i)))S T(2)fused featureU(C)(7)where⊗is Kronecker product.Since U(D)x(i)and U(B)B(i)result in K D and K B dimensional vectors re-spectively,(U(D)x(i))⊗(U(B)z(i))produces a K D K B vector.S(2)is the mode-2unfolding of S which is aK C×K D K B matrix,and its transpose S T(2)is a matrix ofsize K D K B×K C.The Fused Feature.From Eq.(7),the explicit fused representation of face recognition(x(i))and facial at-tribute(z(i))features can be achieved.The fused feature ((U(D)x(i))⊗(U(B)z(i)))S T(2),is a vector of the dimen-sionality K C.And matrix U(C)has the role of“clas-siﬁer”given this fused feature.Given{x(i),z(i),y(i)}, the matrices{U(D),U(B),U(C)}and tensor S are com-puted(learned)during model optimisation(training).Dur-ing testing,the predictionˆy(i)is achieved with the learned {U(D),U(B),U(C),S}and two test features{x(i),z(i)} following Eq.(7).3.2.2Gated Two-stream Neural Network(GTNN)A key advantage of reformulating Eq.(5)into Eq.(7)is that we can nowﬁnd a neural network architecture that does ex-actly the computation of Eq.(7),which would not be obvi-ous if we stopped at Eq.(5).Before presenting thisneural Figure2:Gated two-stream neural network to implement low-rank tensor-based fusion.The architecture computes Eq.(7),with the Tucker decomposition in Eq.(4).The network is identity-supervised at train time,and feature in the fusion layer used as representation for veriﬁcation. network,we need to introduce a new deterministic layer(i.e. without any learnable parameters).Kronecker Product Layer takes two arbitrary-length in-put vectors{u,v}where u=[u1,u2,···,u P]and v=[v1,v2,···,v Q],then outputs a vector of length P Q as[u1v1,u1v2,···,u1v Q,u2v1,···,u P v Q].Using the introduced Kronecker layer,Fig.2shows the neural network that computes Eq.(7).That is,the neural network that performs recognition using tensor-based fu-sion of two features(such as FAF and FRF),based on the low-rank assumption in Eq.(4).We denote this architecture as a Gated Two-stream Neural Network(GTNN),because it takes two streams of inputs,and it performs gating[36] (multiplicative)operations on them.The GTNN is trained in a supervised fashion to predict identity.In this work,we use a multitask loss:softmax loss and center loss[47]for joint training.The fused feature in the viewpoint of GTNN is the output of penultimate layer, which is of dimensionality K c.So far,the advantage of using GTNN is obvious.Direct use of Eq.(5)or Eq.(7)requires manual derivation and im-plementation of an optimiser which is non-trivial even for decomposed matrices(2d-tensors)[20].In contrast,GTNN is easily implemented with modern deep learning packages where auto-differentiation and gradient-based optimisation is handled robustly and automatically.3.3.DiscussionCompared with the fusion methods introduced in Sec-tion2,we summarise the advantages of our tensor-based fusion method as follows:Figure3:LeanFace.‘C’is a group of convolutional layers.Stage1:64@5×5(64feature maps are sliced to two groups of32ones, which are fed into maxout function.);Stage2:64@3×3,64@3×3,128@3×3,128@3×3;Stage3:196@3×3,196@3×3, 256@3×3,256@3×3,320@3×3,320@3×3;Stage4:512@3×3,512@3×3,512@3×3,512@3×3;Stage5:640@ 5×5,640@5×5.‘P’stands for2×2max pooling.The strides for the convolutional and pooling layers are1and2,respectively.‘FC’is a fully-connected layer of256D.High Order Non-Linearity.Unlike linear methods based on averaging,concatenation,linear subspace learning [8,27],or LDA[3],our fusion method is non-linear,which is more powerful to model complex problems.Further-more,comparing with otherﬁrst-order non-linear methods based on element-wise combinations only[28],our method is higher order:it accounts for all interactions between each pair of feature channels in both views.Thanks to the low-rank modelling,our method achieves such powerful non-linear fusion with few parameters and thus it is robust to overﬁtting.Scalability.Big datasets are required for state-of-the-art face representation learning.Because we establish the equivalence between tensor factorisation and gated neural network architecture,our method is scalable to big-data through efﬁcient mini-batch SGD-based learning.In con-trast,kernel-based non-linear methods,such as Kernel LDA [34]and multi-kernel SVM[17],are restricted to small data due to their O(N2)computation cost.At runtime,our method only requires a simple feed-forward pass and hence it is also favourable compared to kernel methods. Supervised method.GTNN isﬂexibly supervised by any desired neural network loss function.For example,the fusion method can be trained with losses known to be ef-fective for face representation learning:identity-supervised softmax,and centre-loss[47].Alternative methods are ei-ther unsupervised[8,27],constrained in the types of super-vision they can exploit[3,17],or only stack scores rather than improving a learned representation[48,37].There-fore,they are relatively ineffective at learning how to com-bine the two-source information in a task-speciﬁc way. Extensibility.Our GTNN naturally can be extended to deeper architectures.For example,the pre-extracted fea-tures,i.e.,x and z in Fig.2,can be replaced by two full-sized CNNs without any modiﬁcation.Therefore,poten-tially,our methods can be integrated into an end-to-end framework.4.Integration with CNNs:architectureIn this section,we introduce the CNN architectures used for face recognition(LeanFace)designed by ourselves and facial attribute recognition(AttNet)introduced by[50,30]. LeanFace.Unlike general object recognition,face recognition has to capture very subtle difference between people.Motivated by theﬁne-grain object recognition in [4],we also use a large number of convolutional layers at early stage to capture the subtle low level and mid-level in-formation.Our activation function is maxout,which shows better performance than its competitors[50].Joint supervi-sion of softmax loss and center loss[47]is used for training. The architecture is summarised in Fig.3.AttNet.To detect facial attributes,our AttNet uses the ar-chitecture of Lighten CNN[50]to represent a face.Speciﬁ-cally,AttNet consists of5conv-activation-pooling units fol-lowed by a256D fully connected layer.The number of con-volutional kernels is explained in[50].The activation func-tion is Max-Feature-Map[50]which is a variant of maxout. We use the loss function MOON[30],which is a multi-task loss for(1)attribute classiﬁcation and(2)domain adaptive data balance.In[24],an ontology of40facial attributes are deﬁned.We remove attributes which do not characterise a speciﬁc person,e.g.,‘wear glasses’and‘smiling’,leaving 17attributes in total.Once each network is trained,the features extracted from the penultimate fully-connected layers of LeanFace(256D) and AttNet(256D)are extracted as x and z,and input to GTNN for fusion and then face recognition.5.ExperimentsWeﬁrst introduce the implementation details of our GTNN method.In Section5.1,we conduct experiments on MultiPIE[7]to show that facial attributes by means of our GTNN method can play an important role on improv-Table1:Network training detailsImage size BatchsizeLR1DF2EpochTraintimeLeanFace128x1282560.0010.15491hAttNet0.050.8993h1Learning rate(LR)2Learning rate drop factor(DF).ing face recognition performance in the presence of pose, illumination and expression,respectively.Then,we com-pare our GTNN method with other fusion methods on CA-SIA NIR-VIS2.0database[22]in Section5.2and LFW database[12]in Section5.3,respectively. Implementation Details.In this study,three networks (LeanFace,AttNet and GTNN)are discussed.LeanFace and AttNet are implemented using MXNet[6]and GTNN uses TensorFlow[1].We use around6M training face thumbnails covering62K different identities to train Lean-Face,which has no overlapping with all the test databases. AttNet is trained using CelebA[24]database.The input of GTNN is two256D features from bottleneck layers(i.e., fully connected layers before prediction layers)of LeanFace and AttNet.The setting of main parameters are shown in Table1.Note that the learning rates drop when the loss stops decreasing.Speciﬁcally,the learning rates change4 and2times for LeanFace and AttNet respectively.Dur-ing test,LeanFace and AttNet take around2.9ms and3.2ms to extract feature from one input image and GTNN takes around2.1ms to fuse one pair of LeanFace and AttNet fea-ture using a GTX1080Graphics Card.5.1.Multi-PIE DatabaseMulti-PIE database[7]contains more than750,000im-ages of337people recorded in4sessions under diverse pose,illumination and expression variations.It is an ideal testbed to investigate if facial attribute features(FAF) complement face recognition features(FRF)including tra-ditional hand-crafted(LBP)and deeply learned features (LeanFace)to improve the face recognition performance–particularly across extreme pose variation.Settings.We conduct three experiments to investigate pose-,illumination-and expression-invariant face recogni-tion.Pose:Uses images across4sessions with pose vari-ations only(i.e.,neutral lighting and expression).It covers pose with yaw ranging from left90◦to right90◦.In com-parison,most of the existing works only evaluate perfor-mance on poses with yaw range(-45◦,+45◦).Illumination: Uses images with20different illumination conditions(i.e., frontal pose and neutral expression).Expression:Uses im-ages with7different expression variations(i.e.,frontal pose and neutral illumination).The training sets of all settings consist of the images from theﬁrst200subjects and the re-maining137subjects for testing.Following[59,14],in the test set,frontal images with neural illumination and expres-sion from the earliest session work as gallery,and the others are probes.Pose.Table2shows the pose-robust face recognition (PRFR)performance.Clearly,the fusion of FRF and FAF, namely GTNN(LBP,AttNet)and GTNN(LeanFace,At-tNet),works much better than using FRF only,showing the complementary power of facial features to face recognition features.Not surprisingly,the performance of both LBP and LeanFace features drop greatly under extreme poses,as pose variation is a major factor challenging face recognition performance.In contrast,with GTNN-based fusion,FAF can be used to improve both classic(LBP)and deep(Lean-Face)FRF features effectively under this circumstance,for example,LBP(1.3%)vs GTNN(LBP,AttNet)(16.3%), LeanFace(72.0%)vs GTNN(LeanFace,AttNet)(78.3%) under yaw angel−90◦.It is noteworthy that despite their highly asymmetric strength,GTNN is able to effectively fuse FAF and FRF.This is elaborately studied in more detail in Sections5.2-5.3.Compared with state-of-the-art methods[14,59,11,58, 15]in terms of(-45◦,+45◦),LeanFace achieves better per-formance due to its big training data and the strong gener-alisation capacity of deep learning.In Table2,2D meth-ods[14,59,15]trained models using the MultiPIE images, therefore,they are difﬁcult to generalise to images under poses which do not appear in MultiPIE database.3D meth-ods[11,58]highly depend on accurate2D landmarks for 3D-2D modellingﬁtting.However,it is hard to accurately detect such landmarks under larger poses,limiting the ap-plications of3D methods.Illumination and expression.Illumination-and expression-robust face recognition(IRFR and ERFR)are also challenging research topics.LBP is the most widely used handcrafted features for IRFR[2]and ERFR[33].To investigate the helpfulness of facial attributes,experiments of IRFR and ERFR are conducted using LBP and Lean-Face features.In Table3,GTNN(LBP,AttNet)signiﬁ-cantly outperforms LBP,80.3%vs57.5%(IRFR),77.5% vs71.7%(ERFR),showing the great value of combining fa-cial attributes with hand-crafted features.Attributes such as the shape of eyebrows are illumination invariant and others, e.g.,gender,are expression invariant.In contrast,LeanFace feature is already very discriminative,saturating the perfor-mance on the test set.So there is little room for fusion of AttrNet to provide beneﬁt.5.2.CASIA NIR-VIS2.0DatabaseThe CASIA NIR-VIS2.0face database[22]is the largest public face database across near-infrared(NIR)images and visible RGB(VIS)images.It is a typical cross-modality or heterogeneous face recognition problem because the gallery and probe images are from two different spectra.The。

语言学重要概念梳理（中英文对照版）

语⾔学重要概念梳理（中英⽂对照版）第⼀节语⾔的本质⼀、语⾔的普遍特征（Design Features）1.任意性 Arbitratriness：shu 和Tree都能表⽰“树”这⼀概念；同样的声⾳，各国不同的表达⽅式2.双层结构Duality：语⾔由声⾳结构和意义结构组成（the structure ofsounds and meaning）3.多产性productive: 语⾔可以理解并创造⽆限数量的新句⼦，是由双层结构造成的结果（Understand and create unlimited number withsentences）4.移位性 Displacemennt：可以表达许多不在场的东西，如过去的经历、将来可能发⽣的事情，或者表达根本不存在的东西等5.⽂化传播性 Cultural Transmission：语⾔需要后天在特定⽂化环境中掌握⼆、语⾔的功能（Functions of Language）1.传达信息功能 Informative：最主要功能The main function2.⼈际功能 Interpersonal：⼈类在社会中建⽴并维持各⾃地位的功能establish and maintain their identity3.⾏事功能 performative：现实应⽤——判刑、咒语、为船命名等Judge，naming，and curses4.表情功能 Emotive Function：表达强烈情感的语⾔，如感叹词/句exclamatory expressions5.寒暄功能 Phatic Communion：应酬话phatic language，⽐如“吃了没？”“天⼉真好啊！”等等6.元语⾔功能 Metalingual Function：⽤语⾔来谈论、改变语⾔本⾝，如book可以指现实中的书也可以⽤“book这个词来表达作为语⾔单位的“书”三、语⾔学的分⽀1. 核⼼语⾔学 Core linguistic1)语⾳学 Phonetics：关注语⾳的产⽣、传播和接受过程，着重考察⼈类语⾔中的单⾳。

Monodisperse NaYF4 Yb,Er Core and Core Shell-Structured Nanocrystals

Highly Efficient Multicolor Up-Conversion Emissions and Their Mechanisms of Monodisperse NaYF4:Yb,Er Core and Core/Shell-Structured NanocrystalsHao-Xin Mai,Ya-Wen Zhang,*Ling-Dong Sun,and Chun-Hua Yan*Beijing National Laboratory for Molecular Sciences,State Key Lab of Rare Earth Materials Chemistry andApplications&PKU-HKU Joint Lab in Rare Earth Materials and Bioinorganic Chemistry,Peking Uni V ersity,Beijing100871,ChinaRecei V ed:May21,2007;In Final Form:July6,2007This paper comprehensively presents the highly efficient multicolor up-conversion(UC)emissions and relatedmechanisms of monodisperse NaYF4:Yb,Er core(R-and -NaYF4:Yb,Er)and core/shell(R-NaYF4:Yb,-Er@R-NaYF4and -NaYF4:Yb,Er@R-NaYF4)nanocrystals with controlled size(5-14nm for R-phasenanocrystals and20-300nm for -phase nanocrystals),chemical composition,and surface state.Thesenanoparticles were synthesized via a unique delayed nucleation pathway using trifluoroacetates as precursorsin hot solutions of oleic acid/oleylamine/1-octadecene.With the naked eye and natural light the intensemulticolor UC emissions(red,yellow,or green,without filters)can be observed in cyclohexane dispersionsof as-prepared nanocrystals(1wt%)excited by a980nm laser source(power density1.22W cm-2).On thebasis of compositional optimization for -NaYF4:Yb,Er nanocrystals,the intensity ratio of green to red emission(f g/r)reaches ca.30,the highest value to our knowledge.The highly efficient multicolor UC emissions werearoused from the controlled crystallite size,phase,and associated defect state.R-NaYF4:Yb,Er and large-NaYF4:Yb,Er nanocrystals displayed a normal two-photon UC process excited by a980nm NIR laser,while small -NaYF4:Yb,Er nanocrystals showed an unusual partially three-photon process.Shell formationcould remarkably decrease the surface defects and surface ligands influence and thus decrease the associatednonradiative decays.As a result,the core/shell-structured R-NaYF4:Yb,Er@R-NaYF4and -NaYF4:Yb,Er@R-NaYF4nanocrystals exhibited greatly enhanced green emission intensity,f g/r,and saturation power with respectto their counterparts.The remarkable UC properties of the as-synthesized NaYF4:Yb,Er core and core/shellnanocrystals demonstrate that they are promising UC nanophosphors of adequate theoretical and practicalinterest.IntroductionRecently,studies on up-conversion(UC)phosphors have grown rapidly owing to their wide applications in solid-state lasers,three-dimensional flat-panel displays,optical fiber-based telecommunications,low-intensity IR imaging,1,2and especially bioprobes and bioimages.3Compared with the traditionally used biological labels such as rhodamine,4a fluorescein,4b isothio-cyanates,4c cyanine dyes,4d and semiconductors,4e-h conceivable advantages of near-infrared(NIR)to visible up-conversion fluorescent bioprobes include a weak autofluorescence back-ground,thus avoiding photodegradation in biotagging applica-tions(which will simplify detection of the labeled target molecules and increase the sensitivity),the noninvasive excita-tion of980nm NIR light,the strong ability of penetration for NIR radiation(which will make in vivo imaging easy),the resistance to photobleaching,high detection limits,and low toxicity(compared with the majority of current commercialized labels such as organic dyes and quantum dots).4,5However, commercially available UC phosphors normally consist of submicrometer-or micrometer-sized grains which do not form transparent colloids and are much too large to substitute for molecular dyes in biological tagging applications.Therefore,it is a challenge to synthesize differently sized UC nanomaterials selectively from5to300nm for various use for probes,tags,and imaging technologies.6Among the UC materials,hexagonal NaYF4( -NaYF4)isthe most efficient host material for green and blue UC phosphorswhen activated by Yb3+and Er3+/Tm3+ions due to the verylow phonon energy of its lattice.1,7As NaYF4is such a superiormaterial,many works have been done to study the structureand UC mechanism of the bulk NaYF4:Yb,Er.1,7 -NaYF4hasthree cation sites.Site A at(2/3,1/3,1/2),showing occupationaldisorder involving a1:1ratio of Na+and rare-earth cations(RE3+),is coordinated by nine F-ions,forming tricappedtrigonal prisms with crystallographic C3h symmetry.Site B at(0,0,0),which is fully occupied by RE3+,shows a low C1symmetry due to the different directions of the three F1atomscapping the F2prism.The third cation site,with irregularoctahedral coordination,is half vacant and half occupied by Na+.The presence of two independent sites for both Yb3+and Er3+ions in the most efficient UC material -NaYF4:Yb,Er qua-druples the number of possible Yb3+f Er3+energy-transfer processes and thus significantly increases the probability of anextremely efficient resonant or near resonant process.7e,f Thered(4F9/2)and green(4S3/2)UC of the -NaYF4:Yb,Er are two-photon processes at room temperature due to the highly efficientYb3+2F5/2f Er3+4F11/2energy transfer dominating the whole UC process,while at low temperature(5K)the red4F9/2is a three-photon process because of the inefficient cross-relaxation*To whom correspondence should be addressed.Fax:+86-10-6275-4179.E-mail:yan@.13721J.Phys.Chem.C2007,111,13721-1372910.1021/jp073920d CCC:$37.00©2007American Chemical SocietyPublished on Web08/24/2007process increase.7b,c However,for nanosized -NaYF 4:Yb,Er,the systemic study of the UC mechanism is scare.Therefore,it remains an open topic to obtain NaYF 4:Yb,Er nanomaterials with different sizes,high UC intensity,and various high-purity colors.More recently,we obtained monodisperse R -NaYF 4:Yb,Er and -NaYF 4:Yb,Er nanocrystals with controlled size,chemical composition,and surface state using trifluoroacetates as precur-sors in hot surfactant solutions via a unique delayed nucleation pathway.6j In this article,we report the UC properties and mechanisms of these differently sized monodisperse NaYF 4:Yb,Er core and core/shell-structured nanocrystals.Interestingly,the nanocrystals show intense light emissions in various colors without using any filters (Figures 1a -c,e).On the basis of the uncovered UC mechanism,diverse NaYF 4:Yb,Er nanostructures with higher UC intensity and intensity ratio of green to red (f g/r )can be synthesized designedly.Experimental SectionSynthesis was carried out using standard oxygen-free pro-cedures and commercially available reagents.Rare-earth oxides (RE )Y,Yb,Er,and Tm),oleic acid (OA;90%,Alpha),oleylamine (OM;>80%,Acros),1-octadecene (ODE;>90%,Acros),trifluoroacetic acid (99%,Acros),CF 3COONa (>97%,Acros),absolute ethanol,and cyclohexane were used as received.RE(CF 3COO)3were prepared by the literature method.8,6c1.Synthesis of r -NaYF 4:20%Yb,2%Er Core and -NaYF 4:20%Yb,2%Er Nanocrystals.Differently sized R -and -NaYF 4:20%Yb,2%Er core nanocrystals were obtained by following the synthetic procedure reported elsewhere (Table 1).6j2.Synthesis of NaYF 4:20%Yb,2%Er@r -NaYF 4(Core/Shell)Nanocrystals.A 1.3mmol amount of NaYF 4:20%Yb,2%Er core (R or ),0.5mmol of Y(CF 3COO)3,and 0.5mmol of CF 3COONa were added to a mixture of OA (20mmol)and ODE (20mmol)in a three-necked flask at room temperature.Then the slurry was heated to 100°C to remove water and oxygen with vigorous magnetic stirring under vacuum for 30min in a temperature-controlled electromantle,thus forming an optically transparent solution.The solution was then heated to 250°C at a heating rate of 20°C min -1and maintained at this temperature for 0.5h under an Ar atmosphere.When the reaction was completed,an excess amount of ethanol was poured into the solution at room temperature.The resultant mixture was centrifugally separated,and the products were collected.The as-precipitated nanocrystals without any size selection were washed several times with ethanol and then dried in air at 70°C overnight.The afforded nanocrystals could be easily redispersed in various nonpolar organic solvents (e.g.,cyclohexane).3.Synthesis of -NaYF 4:30%Yb,0.5%Tm Nanocrystals. -NaYF 4:30%Yb,0.5%Tm nanocrystals were obtained by fol-lowing the synthetic procedure of -NaYF 4:20%Yb,2%Er nanocrystals.6j4.Instrumentation.Inductively coupled plasma (ICP-AES)(Plasma-Spec,Leeman Labs.Inc.)was used to determine Y 3+,Yb 3+,and Er 3+contents in NaYF 4:Yb,Er nanocrystals.Powder X-ray diffraction (XRD)patterns of the dried powders were recorded on a Rigaku D/MAX-2000diffractometer (Japan)with a slit of 1/2°at a scanning rate of 2°min -1using Cu K R radiation (λ)1.5418Å).The lattice parameters were calculated with the least-squares method.Samples for transmission electron microscopy (TEM)analysis were prepared by drying a drop of nanocrystal dispersion in cyclohexane on amorphous carbon-coated copper grids.Particle sizes and shapes were examined by a TEM (200CX,JEOL,Japan)operated at 160kV.High-resolution TEM (HRTEM)was performed on a Philips Tecnai F30FEG-TEM (USA)operated at 300kV.The UC emission spectra of NaYF 4:Yb,Er/Tm nanocrystals redispersed in cyclo-hexane (1wt %)were measured on a modified Hitachi F-4500spectrophotometer with an external tunable 2W 980nm laser diode (50mW for NaYF 4:Yb,Er nanocrystals and 380mW as the high pumping power for NaYF 4:Yb,Er nanocrystals (Figure S4))as the excitation source in place of the xenon lamp in the spectrometer.Results and Discussion1.Characteristics of NaYF 4:Yb,Er Core and Core/Shell-Structured Nanocrystals.Via a unique delayed nucleation pathway,differently sized monodisperse R -NaYF 4:Yb,Er (5-14nm)and -NaYF 4:Yb,Er (20-300nm)core nanocrystals were synthesized from trifluoroacetate precursors at temperatures ranging from 250to 330°C in OA/OM/ODE (Table 1).6j The nanocrystals are single crystallites in the shape of a polyhedron and truncated cube for the R -phase and spheres,hexagonal prism,and hexagonal plate for the -phase.6jIn the presence of CF 3COONa and Y(CF 3COO)3precursors at 250°C for 30min in OA/ODE,R -NaYF 4:Yb,Er@R -NaYF 4nanocrystals were synthesized using 8.0nm R -NaYF 4:Yb,Er nanopolyhedra as the core nanocrystals,while -NaYF 4:Yb,-Er@R -NaYF 4nanocrystals were obtained using 27.7nm -NaYF 4:Yb,Er as the core nanocrystals (Figures 2and S1).The size of monodisperse R -NaYF 4:Yb,Er@R -NaYF 4nanoc-rystals is 13.6(0.7nm,5nm larger than that of the R -NaYF 4:Yb,Er core (Figure 2a).In addition,the energy-dispersive X-ray analysis spectra of the core and core/shell nanoparticles (which is detected in three different areas of the copper grid)imply the increased tendency of the atomic ratio of Y:Yb of an individual nanoparticle after the core/shell treatment(FigureFigure 1.Fluorescence photographs of (a)185nm -NaY 0.78Yb 0.2-Er 0.02F 4hexagonal nanoplates,(b) 5.1nm R -NaY 0.78Yb 0.2Er 0.02F 4nanopolyhedra,(c)8.0nm R -NaY 0.78Yb 0.2Er 0.02F 4nanopolyhedra,(d)R -NaY 0.78Yb 0.2Er 0.02F 4@R -NaYF 4nanopolyhedra,(e)20.2nm -NaY 0.78-Yb 0.2Er 0.02F 4nanospheres,(f) -NaY 0.78Yb 0.2Er 0.02F 4@R -NaYF 4nano-spheres,and (g)100nm -NaY 0.695Yb 0.3Tm 0.005F 4nanoplates (λex )980nm;pumping power,50mW).Figure 2.TEM and HRTEM (inset)images of the R -NaY 0.78Yb 0.2-Er 0.02F 4@R -NaYF 4nanopolyhedra (a)and -NaY 0.78Yb 0.2Er 0.02F 4@R -NaYF 4nanospheres (b).13722J.Phys.Chem.C,Vol.111,No.37,2007Mai et al.S1c,d).As a result,we conclude that the core/shell structure of R -NaYF 4:Yb,Er@R -NaYF 4is formed.Figure 2b shows that the size of monodisperse -NaYF 4:Yb,Er@R -NaYF 4nanospheres is 32.6(1.1nm,around 5nm larger than that of the -NaYF 4:Yb,Er core.The XRD pattern shows the reflections of hexagonal NaYF 4along with a little cubic phase (Figure S1b).As the core nanocrystals are of pure hexagonal phase,the size of the particles increases uniformly,and no tiny particles are found with TEM after adding CF 3COONa and Y(CF 3COO)3precursors,we suggest that the appreciable amount of cubic phase must originate from the shells and R -NaYF 4should be grown on the surface of the -NaYF 4:Yb,Er cores.6h The HRTEM image inset in Figure 2b indicates an interplanar spacing of 0.52nm corresponding to the (10-10)crystal plane for the -NaYF 4:Yb,Er core and 0.30nm corresponding to the (111)crystal plane for the R -NaYF 4shell,again confirming formation of a -NaY 0.78Yb 0.2Er 0.02F 4@R -NaYF 4core/shell structure. 2.UC Properties of Differently Sized r -NaYF 4:Yb,Er, -NaYF 4:Yb,Er Nanocrystals and Their Core/Shell Struc-tures.2.1.UC Emission of Differently Sized R -NaYF 4:Yb,Er and -NaYF 4:Yb,Er Nanocrystals.All the NaYF 4:Yb,Er nanoc-rystal dispersions (1wt %)in cyclohexane show intensive emission under 980nm laser diode excitation into the 2F 7/2f 2F 5/2absorption of the Yb3+ion with a power density of 1.22W cm -2(Figure 1a -e),which can be observed by the naked eye even under lamplight.Figure 3a shows the UC spectra of the differently sized R -NaYF 4:Yb,Er nanopolyhedra dispersions (1wt %)in cyclohexane.Three typical emission peaks at 525,541,and 655nm are observed attributable to the energy transfer from (4H 11/2,4S 3/2)to 4I 15/2(green,weak)and from 4F 9/2to 4I 15/2(red,intensive)of Er 3+,respectively (Figure 3a).The intensity ratio of green to red emission (f g/r )1,2,6of the R -NaYF 4:Yb,Er nanopolyhedra is dependent upon the size of the nanocrystals.As the size of R -NaYF 4:Yb,Er nanopolyhedra decreases fromTABLE 1:Crystal Structure,Space Group,Size,Morphology,and Calculated Lattice Constants of the As-Obtained NaYF 4:Yb,Er Core and Core/Shell-Structured Nanocrystalsstructurespace group size/nm morphology lattice constants R -NaYF 4:Yb,Er cubic F m-3m 13.7(0.8a polyhedron a )5.546(8)Å8.0(1.0polyhedron a )5.485(6)Å5.1(0.6truncated cube a )5.480(3)Å -NaYF 4:Yb,ErhexagonalP -620.2(0.9spherea )5.99(2)Åc )3.54(2)Å47.1(2.0hexagonal prism a )5.97(1)Åc )3.51(1)Å185(9hexagonal plate a )5.991(5)Åc )3.524(4)ÅR -NaYF 4:Yb,Er@R -NaYF 4cubic F m-3m13.6(0.7polyhedron a )5.490(3)Å-NaYF 4:Yb,Er@R -NaYF 432.6(1.1sphereaThe standard deviation statistic from at least 100particles.Figure 3.UC spectra of differently sized (a)R -NaY 0.78Yb 0.2Er 0.02F 4and (b) -NaY 0.78Yb 0.2Er 0.02F 4nanocrystal dispersions (1wt %)in cyclohexane.(c)Diagram of f r/g versus the size of -NaY 0.78Yb 0.2Er 0.02F 4nanocrystals.Highly Efficient Multicolor Up-Conversion Emissions J.Phys.Chem.C,Vol.111,No.37,20071372313.7to8.0nm,f g/r decreases from0.63to0.43.As thenanopolyhedra shrink to 5.1nm in size,f g/r significantlydiminishes to0.13.An intense red emission is observablewith the naked eye(Figure1b)from their dispersion incyclohexane(1wt%)under980nm excitation,implying thatthese nanocrystals are good candidates for red emission UCmaterials.Under980nm NIR excitation intense green light is observedwith the naked eye for differently sized -NaYF4:Yb,Er nano-crystal dispersions(1wt%)in cyclohexane(Figure1a and e).As seen from the UC spectra shown in Figure3b,two intensivegreen emission bands in the range of510-530nm and530-560nm are ascribed to2H11/2f4I15/2and4S3/2f4I15/2 transitions,respectively,while the weak red emission bandbetween640and670nm is assigned to a4F9/2f4I15/2 transition.3-5,7,8Similar to the case of the R-NaYF4:Yb,Er nanocrystals,f g/r increases with the size of -NaYF4:Yb,Er nanocrystals(Figure3b)below70nm.Figure3c is the diagram of f g/r(calculated from Figure3b)versus the size of -NaY0.78-Yb0.2Er0.02F4nanocrystals.It can be seen that f g/r is7.3, 8.3,11,12,and17for20.2,27.7,38.1,47.1,and72.1nm -NaYF4:Yb,Er nanocrystals,respectively.When the size of the -NaYF4:Yb,Er nanocrystals exceeds70nm,f g/r is unaltered as f g/r)17(Figure3b,c).This result indicates that the -NaYF4: Yb,Er nanocrystals show similar UC properties to its bulk counterpart and f g/r is size independent for nanocrystals over 70nm in size.positional Optimization of Differently Sized -NaYF4:Yb,Er Nanocrystals.It is well known that the I UC and f g/r canbe tuned by changing the proportion of Yb and Er concentration.Recently,it was reported that for bulk UC materials the bestgreen UC emissions are obtained for Er3+ions with several Yb3+neighbors rather than Er3+for excitation energy transfer in thepresence of enough optically inactive Y3+ions for restrainingthe energy migration from the active ions to killer traps.1,7,2cFor bulk hexagonal NaYF4:Yb,Er,Kramer et al.obtained f g/rvalues between1.923and5.162c and Huang et al.reported aconsiderably high f g/r value of approximately12.2e In the presentwork,as discussed above,when the particle size of the -NaYF4:Yb,Er exceeds70nm,the UC properties of -NaYF4:Yb,Ershow faint size dependence and approach to the bulk materials.Therefore,we choose20.2nm -NaYF4:Yb,Er nanospheres and185nm -NaYF4:Yb,Er nanoplates as typical examples of smalland large nanocrystals to optimize the composition,respectively.For the185nm -NaYF4:Yb,Er nanoplates,I UC and f g/r decreasemonotonically with the increase of the Yb3+concentration from10to30mol%(Figure4a,b)with the concentration of Er3+fixed at2mol%.It seems that the presence of more Yb3+ionsin the lattice makes the distance between Yb3+and Er3+ionsshorter,resulting in energy back transfer from Er3+to Yb3+ions.Also,the cross-relaxation between Er3+and Yb3+will beincreased,causing the population to diminish in(2H11/2or4S3/2)levels(Er3+)and enhancement of the population in the4F9/2level(Er3+),leading to the decrease of I UC and f g/r,respectively.Similarly,I UC and f g/r also decrease monotonically with theincrease of the Er3+concentration from0.5to3mol%(Figure4c,d)with the concentration of Yb3+fixed at20mol%,whichis due to the increasing interactions between neighboring Er3+ions.This is in good agreement with that reported previouslyto obtain the highest output of the green luminescence for itspractical application.2a,7a,d Therefore,the optimized componentof the large-sized -NaYF4:Yb,Er is10%Yb and0.5%Er doped,the f g/r of which is as high as30(Figure S2),which is the highestf g/r value reported to our knowledge.For the20.2nm -NaYF4:Yb,Er nanospheres,when the concentration of Er3+is fixed at2mol%,I UC increases with Yb3+concentration from10to20mol%and decreases with further increasing the Yb3+concentration from20to30mol% (Figure4e),while f g/r increases slightly when the Yb3+ concentration increases from10to15mol%and decreases abruptly with further increasing the Yb3+concentration from 15to30mol%(Figure4f).I UC increases obviously when the Er3+concentration increases from0.5to2mol%with the fixed concentration of Yb3+at20mol%and then decreases with further increasing the concentration of Er3+ions(Figure4g), while f g/r decreases monotonically with increasing the Er3+ concentration from0.5to5mol%(Figure4h).On the basis of these results,it can be confirmed that higher Yb3+and Er3+ concentrations will cause the concentration quenching of the -NaYF4:Yb,Er nanocrystals,and the quenching concentration for the small -NaYF4:Yb,Er nanocrystals is higher than that for the large ones.2.3.UC Properties of R-NaYF4:Yb,Er and -NaYF4:Yb,Er Core/Shell-Structured Nanocrystals.Generally,the emission efficiency of doped nanoparticles is usually lower than that of the corresponding bulk material as a result of energy-transfer processes to the surface through adjacent dopant ions or because the luminescence of surface dopant ions is quenched.As we known,coating a shell made up of a material through which energy cannot be transferred around the doped nanoparticles can suppress these quenched processes and enhance the emission of the phosphors greatly.6h,9In the present work,panels a and b of Figure5depict the comparison of the UC spectra of the core/shell-structured nanocrystals with their core counterparts.Formation of core/shell-structured R-NaYF4: Yb,Er@R-NaYF4enhances both the intensity of green emission by200%and f g/r from0.4to2.0.The intensity of red emission decreases markedly to only0.55times as high as that ofR-NaYF4:Yb,Er nanocrystals(Figure5a).Therefore,8.0nmR-NaYF4:Yb,Er nanopolyhedra exhibit an intense yellow emis-sion(Figure1c),while the emission of the R-NaYF4:Yb,Er@R-NaYF4nanopolyhedra is yellowish-green(Figure1d).As discussed above,we know that an increase in particle size and a lower concentration of Er3+and Yb3+dopants will also lead to increased I UC and f g/r for NaYF4:Yb,Er.As a result,13.6nmR-NaY0.88Yb0.1Er0.02F4nanopolyhedra were synthesized,and the corresponding UC spectrum is shown in Figure S3.It can be seen that the UC spectra of13.6nm R-NaY0.88Yb0.1Er0.02F4 nanopolyhedra and10.8nm R-NaY0.78Yb0.2Er0.02F4core are similar,but both are quite different from that of the core/shell structures.The lower concentration of Er3+and Yb3+dopants of R-NaYF4led to the increase in the green to red ratio,but the variations of the I UC and f g/r are slight.Therefore,the great enhancement of I UC and f g/r for R-NaY0.78Yb0.2Er0.02F4@R-NaYF4compared with the R-NaY0.78Yb0.2Er0.02F4core shown in Figure5a is indeed caused by the core/shell structures. For the core/shell-structured -NaYF4:Yb,Er@R-NaYF4the value of f g/r is17(Figure5b),significantly greater than f g/r of 8for27.7nm -NaYF4:Yb,Er nanospheres but closer to that of the large-sized -NaYF4:Yb,Er nanoplates(Figure3c).The green emission of -NaYF4:Yb,Er@R-NaYF4nanospheres is more intense by50%compared with that of -NaYF4:Yb,Er nanocrystals,while the intensity of the red emission is decreased to only0.71times as high as that of -NaYF4:Yb,Er nanocrystals (Figures1e,f and5b).It can be seen that the core/shell structure is both effective for enhancing the UC emission of R-NaYF4: Yb,Er and -NaYF4:Yb,Er.13724J.Phys.Chem.C,Vol.111,No.37,2007Mai et al.Figure 4.Diagrams of I UC and f g/r versus (a,b)doping content of Yb 3+ions at a fixed Er 3+content of 2mol %and (c,d)doping content of Er 3+ions at a fixed Yb 3+content of 20mol %for 185nm -NaYF 4:Yb,Er nanoplates.Diagrams of I UC and f g/r versus (e,f)doping content of Yb 3+ions at a fixed Er 3+content of 2mol %and (g,h)doping content of Er 3+ions at a fixed Yb 3+content of 20mol %for 20.2nm -NaYF 4:Yb,Er nanospheres.The relative metal contents of the NaYF 4:Yb,Er nanocrystals were determined by ICP method.Highly Efficient Multicolor Up-Conversion Emissions J.Phys.Chem.C,Vol.111,No.37,2007137253.UC Mechanisms of NaYF 4:Yb,Er Nanocrystals.3.1.R -NaYF 4:Yb,Er and R -NaYF 4:Yb,Er@R -NaYF 4Nanocrystals.The UC spectra of 13.6,8.0,and 5.1nm R -NaYF 4:Yb,Er nanopolyhedra redispersed in cyclohexane (1wt %)under 980nm excitation (50mW)are shown in Figure 3a.f g/r decreases with the size of the nanocrystals.To determine the number of photons involved in the UC process of these nanocrystals,the intensities of the UC emissions are recorded as a function of the 980nm excitation intensity density in log -log plots.1,6a,d,e,7,10Both the slopes for the red and green curves are about 2at relatively low excitation densities (Figure 6a -c),indicating a two-photon emission process.At high excitation density the slope of the curve is reduced due to saturation of the UC process.As suggested by Suyver et al.,7g this saturation will prove that the present up-conversion process is a sensitized up-conversion process involving Yb 3+and Er 3+rather than a purely ESA-type process involving only Er 3+.The saturation power of 5.1nm R -NaYF 4:Yb,Er nanopolyhedra is 428mW,significantly higher than that (242mW)of 13.6and 8.0nm R -NaYF 4:Yb,Er nanopolyhedra.The two-photon UC mechanism is described in Figure 7a.An Yb 3+ion in ground-state 2F 7/2absorbs a photon and transits to excited-state 2F 5/2,and then drops back to the ground state while transferring the energy to an adjacent Er 3+ion,which populates the 4I 11/2level from the ground-state 4I 15/2(energy transfer,ET).Then a second 980nm photon,or energy transfer from an Yb 3+ion,can populate the 4F 7/2level of the Er 3+ion.The Er 3+ion can then relax nonradiatively to the 2H 11/2and 4S 3/2levels,and the green 2H 11/2f 4I 15/2and 4S 3/2f 4I 15/2emissions occur.Alternatively,the Er 3+ion can further relax and populate the 4F 9/2level,leading to the red 4F 9/2f 4I 15/2emission.Also,the Er 3+ion in the 4I 11/2state maynonradiativelyFigure 5.UC spectra of the (a)R -NaY 0.78Yb 0.2Er 0.02F 4@R -NaYF 4nanopolyhedra and (b) -NaY 0.78Yb 0.2Er 0.02F 4@R -NaYF 4nanospheres.Figure 6.log -log diagrams of green and red luminescence intensities for 13.6(a),8.0(b),and 5.1nm (c)R -NaY 0.78Yb 0.2Er 0.02F 4nanopolyhedra and (d)R -NaYF 4:Yb,Er@R -NaYF 4nanocrystals redispersed in cyclohexane (1wt %).13726J.Phys.Chem.C,Vol.111,No.37,2007Mai et al.decay to the 4I 13/2state and then populate the 4F 9/2level via energy transfer from the excited Yb 3+ions,leading to the red 4F 9/2f 4I 15/2emission.These ET processes are shown in the Figure 7a.As the slopes of 2H 11/2f 4I 15/2and 4F 9/2f 4I 15/2of the differently sized R -NaY 0.78Yb 0.2Er 0.02F 4nanopolyhedra are close to 2,we suggest that the nonradiative decay is the main process to quench the green emission of the R -NaYF 4:Yb,Er nanocrystals.From the mechanism discussed above,the ratio of nonradia-tive decay increases when the size of R -NaYF 4:Yb,Er nanopo-lyhedra decreases from 8.0to 5.1nm,leading to the decrease of f g/r .It is well known that f g/r is influenced by several factors,such as doping levels,excitation density,preparation temper-ature,oxygen impurities,crystallinity,surface ligands,and defects.1,2,6,7,11For the differently sized R -NaYF 4:Yb,Er nan-opolyhedra (which were prepared by extending the reaction time),the excitation power and preparation temperature are fixed and the doping levels are nearly unaltered after 1h reaction (Table S1).Moreover,as f g/r of the products decreases by prolonging the reaction time from 1to 5h and increases from 5to 10h,the crystallinity is excluded as the most important factor,considering its influence on f g/r should be monotonic with the reaction time and all of the nanocrystals in the present work are highly crystallized.Therefore,the variation of f g/r is correlated with the oxygen impurities,surface ligands,and surface defect of the R -NaYF 4:Yb,Er nanocrystals.We consider that the smaller the nanocrystals,the smaller the f g/r since a large number of Er 3+ions should be located close to the surface and hence in proximity to defects,surface states,ligands,and impurities and should therefore be able to quench the excited Er 3+states.Considering the growth of the whole process of the rare-earth fluoride nanocrystals,the initial stage is the replacement of the RE -O bond with a RE -F bond,9c resulting in oxygen impurities in the NaYF 4:Yb,Er nanocrystals decreas-ing the size growth (by monomer supply)stage due to the finished fluorination.In the size shrinkage (by dissolution)stage,the large particles are redissolved by OA and transformed into small truncated nanocubes.As revealed in the IR spectra of our previous article,the smaller the particle size,the stronger the bonding between OA molecules and the surface atoms of the nanocrystals.It is rational that the surfaces of the 5.1nm R -NaYF 4:Yb,Er truncated cube are deeply etched by the strongly coordinative OA ligands.In addition,the influence of the surface increases with the shrinkage in particle size.As a result,for the smaller particles,the high vibrational energies of the strong binding organic ligands and the increased surface defect will have a greater combined influence on the Er 3+ions.11As mentioned above,we suggested that the nonradiative decay of the NaYF 4:Yb,Er nanocrystals might be partially correlated with the influence between the surface defect,ligands,and surface Er 3+ions.As the ratio of the surface defects increases with decreasing size of the nanocrystals,the nonra-diative decay is enhanced.Also,as the surfaces of small truncated cubes are presumably deeply etched by the strongly coordinative OA ligands,the proximity of these high vibrational quanta will cause the red up-conversion pathway to be favored,especially due to nonradiative relaxation of the 4I 11/2to 4I 13/2level in the Er 3+ion.Therefore,the red 4F 9/2f 4I 15/2emission coming from the two-photon process is favored and f g/r decreases.Moreover,this suggestion is supported by the UC mechanism results of the core/shell nanostructures.As it is well known,the nonradiative centers existing on the surface of the nanocrystals will be eliminated mostly by the shielding of the shell,resulting in the nonradiative decay on the nanocrystals surfaces,obviously decreasing in the R -NaYF 4:Yb,Er@R -NaYF 4core/shell nanostructure,which enhances its f g/r and greatly enhances the whole I UC (Figures 5a,6d,and S3).Its saturation power is 659mW,much higher than those of R -NaYF 4:Yb,Er nanocrystals.3.2. -NaYF 4:Yb,Er and -NaYF 4:Yb,Er@R -NaYF 4Nanoc-rystals.The UC spectra of 20.2nm -NaYF 4:Yb,Er nanospheres,47.1nm ×45.8nm nanoprisms,and 185nm ×75nm -NaYF 4:Yb,Er nanoplates redispersed in cyclohexane (1wt %)under 980nm NIR excitation (50mW),shown in Figure 3b,reveal that the f g/r increases with the size of the nanocrystals (Figure 3c).Interestingly,the slopes for the green and red emission curves of the 20.2and 47.1nm -NaYF 4:Yb,Er nanocrystals are adjacent 2.0at low pumping power density (lower than 1.93W cm -2),displaying a simple two-photon mechanism,while becoming larger than 2.5at relatively high pumping power density (from 1.93to 12.2W cm -2),displaying a partially three-photon mechanism (Figure 8a,b),which are quite different from the related reports.For 185nm -NaYF 4:Yb,Er nanoplates,however,the slopes for green and red emission curves arenearlyFigure 7.Schematic illustration of the (a)two-photon and (b)three-photon mechanism UC process of Er 3+with Yb 3+as the promoter for NaYF 4:Yb,Er nanocrystals (left,red emission at 655nm;right,green emissions at 525and 543nm,and violet emission at 415nm).Highly Efficient Multicolor Up-Conversion Emissions J.Phys.Chem.C,Vol.111,No.37,200713727。

Astructurefordeoxyribosenucleicacid

The characteristics of DNA include its double stranded helix structure, semi conservative replication, and the ability to store and transmit genetic information
Complexity: The sequence of bases on one strand of DNA determines the sequence on the potential strand through the principle of completeness This means that the order of bases on one strand is complete to the order on the other strand, with A pairing with T and G pairing with C
Structural constraint elements
The structural constraint elements of DNA include the nucleotides, which are made up of a phase group, a deoxyribose sugar, and a nitrogen base (A, T, G, or C)
In addition to the nucleotides, DNA also contains epigenetic marks such as methylation and acetylation, which can affect gene expression without altering the DNA sequence itself

structural dependency 语言学名词解释

structural dependency 语言学名词解释Structural dependency is a linguistic term that refers to the syntactic relationship between words in a sentence. It involves analyzing the dependency or hierarchical structure of a sentence by identifying heads (governing words) and dependents (words that are governed). The concept is commonly used in dependency grammar, a framework for syntactic analysis.1. In the sentence "The cat is sleeping on the mat," the word "cat" is the head and "the" is its dependent.在句子"The cat is sleeping on the mat"中，单词"cat"是主要词（head），而"the"是它的从属词（dependent）。

2. The structural dependency between a verb and itsdirect object is crucial in understanding the sentence's meaning.动词和其直接宾语之间的结构依存关系对于理解句子的意思至关重要。

3. The structural dependency analysis revealed that the modifier "very" in the sentence added emphasis to the adjective.结构依存分析揭示了这个句子中的修饰语"very"增强了形容词的强调程度。

complexity,accuracy and fluency in sla

Complexity, Accuracy and Fluency in Second Language Acquisition1ALEX HOUSEN AND 2FOLKERT KUIKEN1Vrije Universiteit Brussel, 2Universiteit van AmsterdamINTRODUCTIONThis special issue addresses a general question that is at the heart of much research in applied linguistics and second language acquisition (SLA): What makes a second or foreign language (L2) user, or a native speaker for that matter, a more or less proficient language user?Many researchers and language practicioners believe that the constructs of L2 performance and L2 proficiency are multi-componential in nature , and that their principal dimensions can be adequately, and comprehensively, captured by the notions of complexity, accuracy and fluency (e.g. Skehan 1998; Ellis 2003, 2008; Ellis and Barkhuizen 2005). As such, complexity, accuracy and fluency (henceforth CAF) have figured as major research variables in applied linguistic research. CAF have been used both as performance descriptors for the oral and written assessment of language learners as well as indicators of learners’ proficiency underlying their performance; they have also been used for measuring progress in language learning.A review of the literature suggests that the origins of this triad lie in research on L2 pedagogy where in the 1980s a distinction was made between fluent versus accurate L2 usage to investigate the development of oral L2 proficiency in classroom contexts. One of the first to use this dichotomy was Brumfit (1984), who distinguished between fluency-oriented activities, which foster spontaneous oral L2 production, and accuracy-oriented activities, which focus on linguistic form and on the controlled production of grammatically correct linguistic structures in the L2 (cf. also Hammerly 1991).The third component of the triad, complexity, was added in the 1990s, following Skehan(1989) who proposed an L2 model which for the first time included CAF as the three principal proficiency dimensions. In the 1990s the three dimensions were also given their traditional working definitions, which are still used today. Complexity has thus been commonly characterized as ‘[t]he extent to which the language produced in performing a task is elaborate and varied’ (Ellis 2003: 340), accuracy as the ability to produce error-free speech, and fluency as the ability to process the L2 with ‘native-like rapidity’ (Lennon 1990: 390) or ‘the extent to which the language produced in performing a task manifests pausing, hesitation, or reformulation’ (Ellis 2003: 342).CAF in SLA researchSince the 1990s these three concepts have appeared predominantly, and prominently, as dependent variables in SLA research. Examples include studies of the effects on L2 acquisition of age, instruction, individuality features, task type, as well as studies on the effects of learning context (e.g. Bygate 1999; Collentine 2004; Derwing and Rossiter 2003; Skehan and Foster 1999; Freed 1995; Freed, Segalowitz and Dewey 2004; Kuiken and Vedder 2007; Muñoz 2006; Spada and Tomita 2007; Yuan and Ellis 2003). From this diverse body of research, CAF emerge as distinct components of L2 performance and L2 proficiency which can be separately measured and which may be variably manifested under varying conditions of L2 use, and which may be differentially developed by different types of learners under different learning conditions.From the mid-1990s onwards, inspired by advances in cognitive psychology and psycholinguistics (cf. Anderson 1993; Levelt 1989), CAF have also increasingly figured as the primary foci or even as the independent variables of investigation in SLA (e.g. Guillot 1999; Hilton, 2008; Housen, Pierrard and Van Daele 2005; Larsen-Freeman 2006; Lennon 2000; Riggenbach 2000; Robinson 2001; Segalowitz 2007; Skehan 1998; Skehan and Foster 2007; Tonkyn 2007; Towell 2007; Towell and Dewaele 2005; Tavakoli and Skehan 2005; Van Daele,Housen and Pierrard 2007). Here CAF emerge as principal epiphenomena of the psycholinguistic mechanisms and processes underlying the acquisition, representation and processing of L2 knowledge. There is some evidence to suggest that complexity and accuracy are primarily linked to the current state of the learner’s (partly declarative, explicit and partly procedural, implicit) interlanguage knowledge (L2 rules and lexico-formulaic knowledge) whereby complexity is viewed as ‘the scope of expanding or restructured second language knowledge’ and accuracy as ‘the conformity of second language knowledge to target language norms’ (Wolfe-Quintero et al. 1998: 4). Thus, complexity and accuracy are seen as relating primarily to L2 knowledge representation and to the level of analysis of internalized linguistic information. In contrast, fluency is primarily related to learners’ control over their linguistic L2 knowledge, as reflected in the speed and ease with which they access relevant L2 information to communicate meanings in real time, with ‘control improv[ing] as the learner automatizes the process of gaining access’ (Wolfe-Quintero et al. 1998: 4).Defining CAFIn spite of the long research interest in CAF, none of these three constructs is uncontroversial and many questions remain, including such fundamental questions as how complexity, accuracy and fluency should be defined as constructs. Despite the belief that we share a common definition of CAF as researchers and language teachers, there is evidence that agreement cannot be taken for granted and that various definitions and interpretations coexist. Accuracy (or correctness) is probably the oldest, most transparent and most consistent construct of the triad, referring to the degree of deviancy from a particular norm (Hammerly 1991; Wolfe-Quintero et al. 1998). Deviations from the norm are usually characterized as errors. Straightforward though this characterization may seem, it raises the thorny issue of criteria for evaluating accuracy and identifying errors, including whether these criteria should be tuned to prescriptive standard norms(as embodied by an ideal native speaker of the target language) or to non-standard and even non-native usages acceptable in some social contexts or in some communities (Ellis 2008; James 1998; Polio 1997).There is not the same amount of (relative) denotative congruence in the applied linguistics community with regard to fluency and complexity as there is with regard to accuracy. Historically, and in lay usage, fluency typically refers to a person's general language proficiency, particularly as characterized by perceptions of ease, eloquence and ‘smoothness’ of speech or writing (Chambers 1997; Freed 2000; Guillot 1999; Hilton 2008; Lennon 1990; Koponen and Riggenbach 2000). Language researchers for their part have mainly analyzed oral production data to determine exactly which quantifiable linguistic phenomena contribute to fluency in L2 speech (e.g. Lennon 1990; Kormos and Dénes 2004; Cucchiarini, Strik and Boves 2002; Towell, Hawkins and Bazergui 1996). This research suggests that speech fluency is a multi-componential construct in which different sub-dimensions can be distinguished, such as speed fluency (rate and density of delivery), breakdown fluency (number, length and distribution of pauses in speech) and repair fluency (number of false starts and repetitions) (Tavakoli and Skehan 2005).As befits the term, complexity is the most complex, ambiguous and least understood dimension of the CAF triad. For a start, the term is used in the SLA literature to refer both to properties of language task (task complexity) and to properties of L2 performance and proficiency (L2 complexity) (e.g., Robinson 2001; Skehan 2001). L2 complexity in turn has been interpreted in at least two different ways: as cognitive complexity and as linguistic complexity (DeKeyser 2008; Housen, Pierrard and Van Daele 2005; Williams and Evans 1998). Both types of complexity in essence refer to properties of language features (items, patterns, structures, rules) or (sub)systems (phonological, morphological, syntactic, lexical) thereof. However, whereas cognitive complexity is defined from the perspective of the L2 learner-user, linguistic complexity is defined from the perspective of the L2 system or the L2 features. Cognitive complexity (ordifficulty) refers to the relative difficulty with which language features are processed in L2 performance and acquisition. The cognitive complexity of an L2 feature is a variable property which is determined both by subjective, learner-dependent factors (e.g. aptitude, memory span, motivation, L1 background) as well as by more objective factors, such as its input saliency or its inherent linguistic complexity. Thus, cognitive complexity is a broader notion than linguistic complexity, which is one of the (many) factors that may (but need not) contribute to learning or processing difficulty.Linguistic complexity, in turn, has been thought of in at least two different ways: as a dynamic property of the learner’s interlanguage system at large and as a more stable property of the individual linguistic elements that make up the interlanguage system. Accordingly, when considered at the level of the learner’s interlanguage system, linguistic complexity has been commonly interpreted as the size, elaborateness, richness and diversity of the learner’s linguistic L2 system. When considered at the level of the individual features themselves, one could speak of structural complexity, which itself can be further broken down into the formal and the functional complexity of an L2 feature (DeKeyser 1998; Williams and Evans 1988; Housen, Pierrard and Van Daele 2005).Operationalizing and measuring CAFClearly, then, accuracy and particularly fluency and complexity are multifaceted and multidimensional concepts. Related to the problems of constructed validity discussed above (i.e. the fact that CAF lack appropriate definitions supported by theories of linguistics and language learning), there are also problems concerning their operationalization, that is, how CAF can be validly, reliably and efficiently measured. CAF have been evaluated across various language domains by means of a wide variety of tools, ranging from holistic and subjective ratings by lay or expert judges, to quantifiable measures (frequencies, ratios, formulas) of general or specificlinguistic properties of L2 production so as to obtain more precise and objective accounts of an L2 learner’s level within each (sub-)dimension of proficiency (e.g. range of word types and proportion of subordinate clauses for lexical and syntactic complexity, number and type of errors for accuracy, number of syllables and pauses for fluency; for inventories of CAF measures, see Ellis and Barkhuizen 2005; Iwashita, Brown, McNamara and O'Hagan 2008; Polio 2001; Wolfe-Quintero et al. 1998). However, critical surveys of the available tools and metrics for gauging CAF have revealed various problems, both in terms of the analytic challenges which they present and in terms of their reliability, validity and sensitivity (Norris and Ortega 2003; Ortega 2003; Polio 1997, 2001; Wolfe-Quintero et al. 1998). Also the (cor)relation between holistic and objective measures of CAF, and between general and more specific, developmentally-motivated measures, does not appear to be straightforward (e.g. Halleck 1995; Skehan 2003; Robinson and N. Ellis 2008).Interaction of CAF componentsAnother point of discussion concerns the question to what extent these three dimensions arein(ter)dependent in L2 performance and L2 development (Ellis 1994, 2008; Skehan 1998; Robinson 2001; Towell 2007). For instance, according to Ellis, increase in fluency in L2 acquisition may occur at the expense of development of accuracy and complexity due to the differential development of knowledge analysis and knowledge automatization in L2 acquisition and the ways in which different forms of implicit and explicit knowledge influence the acquisition process. The differential evolution of fluency, accuracy and complexity would furthermore be caused by the fact that ‘the psycholinguistic processes involved in using L2 knowledge are distinct from acquiring new knowledge. To acquire the learner must attend consciously to the input and, perhaps also, make efforts to monitor output, but doing so may interfere with fluent reception and production’ (Ellis 1994: 107). Researchers who subscribe tothe view that the human attention mechanism and processing capacity are limited (e.g. Bygate 1999; Skehan 1998; Skehan and Foster 1999) also see fluency as an aspect of L2 production which competes for attentional resources with accuracy, while accuracy in turn competes with complexity. Learners may focus (consciously or subconsciously) on one of the three dimensions to the detriment of the other two. A different view is proposed by Robinson (2001, 2003) who claims that learners can simultaneously access multiple and non-competitional attentional pools; as a result manipulating task complexity by increasing the cognitive demands of a task can lead to simultaneous improvement of complexity and accuracy.OVERVIEW OF THE VOLUMEAs the above discussion demonstrates, many challenges remain in attempting to understand the nature and role of CAF in L2 use, L2 acquisition and in L2 research. But despite these challenges, complexity, accuracy and fluency are concepts that are still widely used to evaluate L2 learners, both in SLA research as in L2 education contexts. We therefore thought it timely to take stock of what L2 research on CAF has brought us so far and in which directions future research could or should develop. With this broad goal in mind, four central articles were invited (by Rod Ellis; Peter Skehan; John Norris and Lourdes Ortega; Peter Robinson, Teresa Cadierno and Yasuhiro Shirai), and two commentary articles were commissioned (by Diane Larsen-Freeman and Gabriele Pallotti).Controversial issuesThe following issues were offered to the contributors as guidelines for reflection and discussion: 1.The constructs of CAF: definition, theoretical base and scopeExactly what is meant by complexity, accuracy and fluency, i.e. how can they be defined as constructs? To what extent do CAF adequately and exhaustively capture all relevant aspects anddimensions of L2 performance and L2 proficiency? To what extent are the three constructs themselves multi-componential? How do they manifest themselves in the various domains of language (e.g. phonology and prosody, lexis, morphology, syntax)? How do they relate to theoretical models of L2 competence, L2 proficiency and L2 processing? And how do CAF relate to L2 development (i.e. are CAF valid indicators of language development)?2.Operationalization and measurement of CAFHow can the three constructs best be operationalized as components of L2 performance and L2 proficiency in a straightforward, objective and non-intuitive way in empirical research designs? How can they be most adequately (i.e. validly, reliably and practically) measured?3.Interdependency of the CAF component sTo what extent are the three CAF components independent of one another in either L2 performance, L2 proficiency and L2 development? To what extent can they be measured separately?4.Underlying correlates of CAFWhat are the underlying linguistic, cognitive and psycholinguistic correlates of CAF? How do the three constructs relate to a learner’s knowledge bases (e.g. implicit-explicit, declarative-procedural), memory stores (working, short-term or long-term), and processing mechanisms and learning processes (e.g. attention, automatization, proceduralization)?5.External factors that influence CAFWhich external factors can influence the manifestation and development of CAF in L2 learning and use, such as, for example characteristics of language tasks (e.g. type and amount of planning), personality and socio-psychological features of the L2 learner (e.g. degree of extraversion, language anxiety, motivation, language aptitude), and features of pedagogic intervention (e.g. what types of instruction are effective for developing each of these dimensions within a classroom context?)The contributions to this special issue all explicitly focus on either one, two or all three of the CAF constructs in relation to one or several of the five issues listed above, which in some cases are illustrated with new empirical research. We will now present a short overview of the topics and questions that are raised by the authors in the four central articles and in the two commentaries.EllisThe first article by Rod Ellis addresses the role and effects of one type of external factor, planning, on CAF in L2 performance and L2 acquisition. Ellis first presents a state-of-art/comprehensive survey of the research on planning. Three types of planning seem to be relevant with respect to CAF: rehearsal, strategic planning and within-task planning. Ellis concludes that all three types of planning have a beneficial effect on fluency, but the results for complexity and accuracy are more mixed, reflecting both the type of planning and also the mediating role of various other external factors, including task design, implementation variables and individual difference factors.Ellis then provides a theoretical account for the role of planning in L2 performance in terms of Levelt’s (1989) model of speech production and the distinction between implicit and explicit L2 knowledge. Rehearsal provides an opportunity for learners to attend to all three components in Levelt’s model – conceptualization, formulation and articulation – and thus benefits all three dimensions of L2 production. According to the author, strategic planning assists conceptualization in particular and thus contributes to greater message complexity and also to enhanced fluency. Unpressured within-task planning eases formulation and also affords time for monitoring, that is, for using explicit L2 knowledge; in this way accuracy increases.SkehanThe second article, by Peter Shehan, addresses the issue of operationalization and measurement of CAF. Skehan claims that fluency needs to be rethought if it is to be measured effectively. In addition he argues that CAF measures need to be supplemented by measures of lexical use. Not only because empirical evidence suggests that the latter is a separate aspect of overall performance, but also because lexical access and retrieval figure prominently in all models of speech production. Skehan also points to the lack of native speaker data in CAF research. Such data are of crucial importance, as they constitute a baseline along which L2 learners can be compared. Skehan presents a number of empirical studies in which, for identical tasks and similar task conditions, both native and non-native participants are involved, and for which measures of complexity, accuracy (for non-native speakers only), fluency, and lexis were obtained. Results suggest that the difference between native and non-native performance on tasks is related more to aspects of fluency and lexis than to the grammatical complexity of the language produced. Regarding fluency, the major difference between the two groups is the pattern of pause locations, in that native speakers use end-of-clause points for more effective, listener-friendly pausing, pausing there slightly more often albeit for shorter periods, while non-natives pause more mid-clause. Lexical performance is noticeably different between the two groups, both in terms of lexical density and of lexical variety (i.e. the use of less frequent words). Especially interesting is the difference in disruptiveness for fluency of the use of less frequent words, as non-natives are derailed in speech planning when they are pushed to use such words more because of task demands.Skehan also considers the issue of interdependency between CAF measures; in particular between accuracy and complexity, since positive correlations between these two aspects have been less common in the literature. In order to account for these correlations. Skehan explores rival claims from his own Trade-off Hypothesis and Robinson’s Cognition Hypothesis. Skehan argues that such joint raised performance in accuracy and complexity is not a function of taskdifficulty (as Robinson’s Cognition Hypothesis would predict) but, rather, that it reflects the joint operation of separate task and task condition factors. Like Ellis, Skehan tries to link the research findings to Levelt’s (1989) model of speaking.Robinson, Cadierno and ShiraiThe article by Peter Robinson, Teresa Cadierno and Yasuhiro Shirai exemplifies a particularly prolific strand of empirical research on CAF, namely research on the impact of task properties on learners’ L2 performance. The authors present results of two studies that measure the effects of increasing the complexity of task demands in two conceptual domains (time and motion) using specific rather than general measures of the accuracy and complexity of L2 speech production. The studies are carried out within the theoretical framework of Robinson’s Cognition Hypothesis. This hypothesis claims that pedagogic tasks should be sequenced for learners in an order of increasing cognitive complexity, and that along resource-directing dimensions of task demands increasing effort at conceptualization promotes more complex and more grammaticized L2 speech production.The specific measures used are motivated by research into the development of tense-aspect morphology for reference time, and by typological, cross-linguistic research into the use of lexicalization patterns for reference to motion. Results show that there is more complex, developmentally advanced use of tense-aspect morphology on conceptually demanding tasks compared to less demanding tasks, and a trend to more accurate, target-like use of lexicalization patterns for referring to motion on complex tasks. By using specific measures of complexity and accuracy (alongside general measures), these authors address the issue of measurement of CAF in their contribution. They contrast the effectiveness of these conceptually specific metrics with the general metrics for assessing task-based language production used in previous studies, and argue for the use of both. In addition, Robinson, Cadierno and Shirai also argue for a higher sensitivityof the specific measures which are used in order to gauge cognitive processing effects on L2 speech production along selected dimensions of task complexity.Norris and OrtegaThe article by John Norris and Lourdes Ortega addresses the crucial issue of the operationalization and measurement of CAF. They critically examine current practices in the measurement of complexity, accuracy, and fluency in L2 production to illustrate the need for what they call more organic and sustainable measurement practices. Building from the case of syntactic complexity, they point to impoverished operationalizations of multi-dimensional CAF constructs and the lack of attention to CAF as a dynamic and inter-related set of constantly changing sub-systems. They observe a disjuncture among the theoretical claims researchers make, the definition of the constructs that they attempt to measure, and the grain size and focus of the operationalizations via which measurement happens. Furthermore they question current reasoning, under which a linear or co-linear trajectory of greater accuracy, fluency, and complexity is expected. Instead they want to consider measurement demands that stem from a dynamic, variable, and non-linear view of L2 development. They therefore call for a closer relation between theory and measurement and argue for a more central role for multi-dimensionality, dynamicity, variability, and non-linearity in future CAF research.This overview of the four central articles in this volume shows that the authors approach CAF from various perspectives, focus on different issues and investigate distinct research topics. What they share is their desire to build further on the results to date. This is where the commentaries by Diane Larsen-Freeman and Gabriele Pallotti come in.Larsen-FreemanLarsen-Freeman starts by reminding us of the fact that, historically, CAF research has come out of the search for an L2 developmental index. The big challenge has always been how to operationalize CAF. According to Larsen-Freeman the measures we have been using to date may be too blunt and not suitable because we may not have been looking at the right things in the right places. She therefore seconds Robinson, Cadierno and Shirai’s suggestion not to stick to general measures, but to use more specific measures and to look at more detailed aspects of performance. She further points out that the operationalization and measurement issue is complicated by the interdependency of the CAF components. As mentioned by some of the authors in this volume, there is an increasing amount of evidence, that complexity, accuracy and fluency do not operate in complete independence form each other, and that findings obtained by CAF measures depend on the participants involved and on the context in which the data have been collected. For those reasons Larsen-Freeman does not expect much from studying the CAF components one by one to see what effect they have on learner performance in a linear causal way. In her view such a reductionist approach does little to advance our understanding, as we risk ignoring their mutual interaction. Instead, we should try to capture the development of multiple sub-systems over time, and in relation to each other. With reference to Wolfe-Quintero et al. (1998) who have demonstrated that many, if not all, aspects of language development are non-linear, Larsen-Freeman calls for a broader conceptual framework and for more longitudinal and non-linear research, in which difference and variation occupy a central role. She considers a dynamic or complex systems theory, in which more socially-oriented measures of development are employed as the best candidates for such a framework.PallottiPallotti starts by signaling some definitional and operationalizational problems of CAF constructs. As an example of an unresolved question in this area he opposes Skehan – whodoubts whether lexical and syntactic complexity are ‘different aspects of the same performance area’ or two separate areas – to Norris and Ortega, who consider syntactic complexity to be a multi-dimensional construct with several sub-constructs. Pallotti considers CAF to be a good starting point for describing linguistic performance, but they do not constitute a theory or a research program in themselves. He emphasizes that a clear distinction should be made between on the one hand CAF, referring to the properties of language performance as a product, and linguistic development on the other, referring to a process, with its sub-dimensions such as route and rate.In line with Larsen-Freeman, and with specific reference to the contributions by Norris and Ortega and Robinson et al., Pallotti welcomes the use of specific measures in addition to the more general ones, as one cannot expect that ‘all sorts of task complexification lead to higher complexity of any linguistic feature.’ He questions, however, what the use of specific measures may contribute to theorizing about CAF. Although by using specific measures the relationship between task difficulty and linguistic complexity may become more reliable, ‘discovering such relationships looks more like validating the tasks as elicitation procedures for specific linguistic features than like confirmations of general theories about speech production.’Pallotti agrees with Larsen-Freeman’s call for a more central role of non-linearity in L2 acquisition. He illustrates this by referring to Norris and Ortega’s example that syntactic complexity as measured by means of a subordination ratio may not always increase linearly, but that syntactic complexity may grow in other ways, for example by phrasal and clausal complexification. And also for accuracy it is not always the case that ‘more is better’. He does not, however, embrace Larsen-Freeman’s idea that variation should move to the front of CAF research. This is what he calls ‘the necessary variation fallacy’: research should not only be concerned with variations and differences, but also with constants and similarities. Instead he argues that adequacy be included as a separate dimension of L2 production and proficiency,。

《程序设计实践》生词表--云大软件学院大二上专英单词

《程序设计实践》生词表--云大软件学院大二上专英单词1.1 Namesformatted 格式化commented 注释prescription 规定consistency 一致性convention 协议arbitrary 武断的，专治的illustrate 举例说明multiple 多样的brevity 短暂crucial 重要的evolve 发展margin 空白处suffice 足够memorable 显著的concise 简洁的clarity 清晰brevity 简洁adherence 依附context 上下文boolean 布尔数学体系unambiguous 清楚的accurate 精确的octal 八进制implementation 安装启用contradiction 矛盾，否认1.2 Expression and Statements increment 增加negation 否认，拒绝ambiguity 不明确parentheses 圆括号omit 省略precedence 优先pernicious 恶性的yield 出产equivalent 等价的version 版本mysterious 难解的explicit 清楚的underlying 优先的1.3Consistency and Idioms brace 大括号layout 布局exemplify 例示initialize 初始化index 索引detract 转移sprawl 蔓延terminate 结束alternative 供选择的correspond 符合sequence 序列duplicate 复制1.4 Function Macros subtle 精细的textual 本文的expand 扩张syntax 语法1.5 Magic Numbers literal 逐字的histogram 柱状图opaque 不透明的alphabet 字母表principal 首要的coordinate 坐标simplification 简单化idiomatic 惯用的feasible 可行的demystify 阐明blunt 钝的lexical 词汇的integer 整数contiguous 临近的documentation 文件材料potential 可能的1.6 Commentsplainly 朴素的elaborate 详尽的briefly 短暂的increment 增加summary 扼要的tendency 倾向decoder 译码器muddle 混合的negation 否定session 会议routine 程序1.7 Why Bother straightforward 简单的arbitrary 任意的toss 投掷automatic 自动的subconscious 下意识的2.1Searching sophisticated 复杂的parsiny 剖析lest 以免intricate 详尽的in a nutshell 概括的说verbatim 逐字的proportional 成比例的excerpt 摘录textnal 全文eliminate 消除sequential 连续的linear 线性的2.2 SortingBubble 气泡Myriad 无数的Pivot 中心点Random 任意的Interchange 互换Sophisticated 精致的，复杂的2.3 LibrariesRoutines 日常生活Robust 强健的Temporary 暂时的Vice 代替Versa 反的Explicit 清楚的2.4A Java Quicksort Mechanism 原理Components 部件，成分Significant 有意义的Penalty 处罚Incurred 遭受2.5Q-NotationPrecise 精确的Essentially 本质上Approximately 大约Allocation 分配tricky 棘手的swap交换overlap 重叠provoke 激怒terminate 终止complexity 复杂2.6 Growing Arrays Flabby 软弱的Chunk 大块Allocation 分配Subscript 脚注Tricky 机警的Squeeze 挤unbeatable 最佳选择matrix multiplication 矩阵乘法set partitioning 集合划分2.7 ListsBroadly 宽广的Item 条款Rearrange 重新排列Invalidate 使无效Fundamental 基本上Dynamically 不断变化的Flexible 灵活的Awkward 不合适的Clumsy 笨拙的Fluctuations 变动Shrink 收缩2.8 TreesHierarchical 分层的Demonstrate 证明Essential 基本的Descend 下降Duplicate 复制品Terminate 终止Random 随机的Intrinsic 本质的2.9 Hash TableDynamic 动态的Illustrate 图解Empirically 以经验为主的Modulo 以…为模板Symbol 符号Allocate 分配Simulation 模拟Concurrent 并发的3.1 The Markov Chain Algorithm Elegant 优雅的Prefix 前缀Suffix 后缀Overlap 重叠Paraphrase 释义Coherent 连贯的Implement 实现Alphabetic 字母的Restriction 约束Punctuation 标点Sneak 暗中进行Permit 许可3.2 Data Structure Alternatives Alternative 交替的Simplistic 过分简单化Associat 联盟Correspond 符合Terminology 术语Dynamic 动态的Negligible 可忽略的3.3 Building the Data Structure in C Implementation 安装启用Critical 临界的Pictorial 形象的Irritation 刺激Peculiar 独特的Arbitrary 任意的,武断的Previous 以前的3.4 Generating Ouoput Terminate 终止Encountered 遭遇Insufficient 不足的Fabricate 装配Sentinel 哨兵，防备Sketch 略图Reclaim 再利用Razor—sharp 锋利的刀3.5 Java Encapsulated 压缩的Associate 聚合的Terminology 术语Instance 实例Vector 矢量Current 通用的Explicit 清楚的Significant 意义深远的Compact 紧密的3.6C++Version 版本Mechanism 原理Deque 双队列Arbitrary 任意的Subscript 脚注Measurement 测量3.7 Awk and Per Specialize 详细说明Correspond 符合Brace 大括号Scalar 标量Anonymous 无名的Phase 阶段3.8 Performance Dominate 控制Snapshot 急照Approximate 近似于Immature 不成熟的Voluminous 大量的4.1-4.2csv 逗号分隔的值allocation 分配reclamation 回收constructor 构造函数destructor 析构函数Mechanism 机制Deallocate 释放；解除分配reentrant 可重入的initialization 初始化Prototype 原型4.3-4.4Robust 强壮的，健全的Sequence 先后次序Adjacent 临近的Precautions 预防措施Allocate 分配Implementation 执行Corresponding 相当的，对应的Boundary 分界线Parallel 类似的，相对应的NailingVisible 明显的，可察觉的Diagram 图表accumulate 堆积，积累multiple 多重的，倍数4..5-4.6encapsulation 包装，封装abstraction 提取enforce 实施，强迫violate 违反，违背static 静态的perennial 经常出现的，长久的terminate 结束，终结garbage collection 碎片帐集4.7-4.8Abort 使失败Terminate 结束，终止Alternative 两者选一Graceful 得体的Mechanism 体制，结构方式，方法Mathematical 数学上的Lumping 很多否认，重大的Alternate 轮流Convoluted 冗长复杂的Component 组成部分Straightforward (人或行为) 坦诚的；坦率的；率直的简单的；易懂的；不复杂的Diagnositc 诊断的Syntax句法;句法规则Graphical图解的Clarity清楚, 明晰5..1-5.2Abstraction 提取Modeling 建模，造型Verification 证明，核实，查对Eliminate 消除，排除Tendency 倾向，趋势Evidence 证词，证据，迹象Erroneous 错误的，不正确的Signature 签名，名字Novice 新手Astronomy 天文学Verify 证实，核实Garbage 垃圾，废物Optional 可选择的，非强制性的Alphabet 字母表5.3-5.4Reproducible 可重现的Crucial 关键的Hypothesis 假设Numerology 计数Interval 间隔abnormally 反常的tactic 战术flush 刷新buffer 缓冲区analogous 相似的revert 恢复lurk 潜伏5.5-5.6Voluminous 大的，容量大的Anomalies 异常现象Segment 部分，片段Duplicate 完全一样的Portability 可携带，轻便5.7-5.8inevitably 必然地；不可避免地metaphor 比喻说法；暗喻，隐喻clue 线索；（故事等的）情节Track down 追查，查找sufficiently 充分地；足够地credibility 可信性；确实性propagate 传播；繁殖；传送novice 新手，初学者legitimately 正当地；合理地demonstrate 证明；论证elaborate 精心制作的；煞费苦心的provoke 煽动；驱使strategy 战略，策略conquer 战胜，征服；攻克leak 漏洞；泄漏violation 违背；违反；妨碍6.1-6.2Systematic 有系统的，有规则的Demonstrate 说明，演示，论证，证明Simulate 假装，模仿，模拟Idiom 习语，成语，风格，特色Trivial 琐碎的，没有价值的，没有意义的Abort 终止Assertion 有力的声言或陈词; 坚持强硬陈词; 断言Violate 违反，违背Fragile 易碎的Validate 证实Permutation 序列，排列Encryption 加密术,密码术Decryption解密;译码,密电码回译Ambiguous 引起歧义的，含糊不清的Profiler 靠模工具机;仿形铣床;靠模工,仿形工,模制者6.3-6.4Automation 自动化，自动操作Regression 衰退Infinite 无限的，无穷的，无边无际的Miscellaneous不同种类的,多种多样的;混杂的Tricky 复杂的，棘手的Leverage 杠杆作用，力量，影响。

结构照明显微术原理

结构照明显微术原理Structural illumination is a technique that involves using light to enhance the visibility of small structures and features in a sample. By manipulating the angle and intensity of the light source, researchers can highlight specific details that may otherwise be difficult to see. This can be especially useful in fields such as biology and materials science, where the study of intricate structures is essential for furthering our understanding of the natural world.结构照明是一种技术，涉及使用光来增强样本中小结构和特征的可见性。

通过调节光源的角度和强度，研究人员可以突出特定细节，否则这些细节可能很难看到。

这在生物学和材料科学等领域尤其有用，这些领域中，研究复杂结构对于推动我们对自然界的理解至关重要。

One of the key principles behind structural illumination microscopy is the use of oblique illumination. This technique involves shining light at an angle onto the sample, which creates shadows that help to accentuate surface features. By carefully adjusting the angle of the light source, researchers can control the contrast and resolution ofthe image, allowing them to visualize tiny structures with greater clarity and detail.结构照明显微镜背后的一个关键原则是倾斜照明的使用。

石墨烯研究进展

石墨烯复合材料的研究进展石墨烯以其优异的性能和独特的二维结构成为材料领域研究热点。

本文综述了石墨烯的制备方法并简单介绍了石墨烯的力学、光学、电学及热学性能,并对石墨烯的复合材料应用做了展望。

1制备方法熔融共混法:将原始石墨氧化,经过剥离并还原制成石墨烯,与聚合物在熔融状态下共混制得复合材料。

原位聚合法:将石墨烯与聚合物单体混合,加入引发剂引发反应,最后制得复合材料。

溶液混合法:在溶液共混法中,常常先制备氧化石墨烯,对其进行改性得到在有机溶剂中能够分散的分散液,通过还原得到石墨烯,然后与聚合物进行溶液共混制备石墨烯/ 聚合物复合材料。

乳液混合法:利用氧化石墨烯在水中具有良好的分散性,可将氧化石墨烯的水性分散液与聚合物胶乳进行混合,通过还原制备石墨烯/ 聚合物复合材料。

2性能特点导电性:石墨烯结构非常稳定,迄今为止,研究者仍未发现石墨烯中有碳原子缺失的情况。

石墨烯中各碳原子之间的连接非常柔韧,当施加外部机械力时,碳原子面就弯曲变形,从而使碳原子不必重新排列来适应外力,也就保持了结构稳定导热性能:石墨烯优异的热传输性能可应用于微型电子设备的热管理如导热膏热驱动、形状记忆聚合物等。

机械特性:石墨烯是人类已知强度最高的物质,比钻石还坚硬,强度比世界上最好的钢铁还要高上100倍。

相互作用:石墨烯中电子间以及电子与蜂窝状栅格间均存在着强烈的相互作用。

化学性质:类似石墨表面,石墨烯可以吸附和脱附各种原子和分子。

从表面化学的角度来看,石墨烯的性质类似于石墨,可利用石墨来推测石墨烯的性质。

3结论与展望目前,无论是在理论还是实验研究方面,石墨烯均已展示出重大的科学意义和应用价值,且已在生物、电极材料、传感器等方面展现出独特的应用优势。

随着对石墨烯研究的不断深入,其内在的一些特殊性能如荧光性能、模板性能等也相继被发现。

由于石墨烯具有较大的比表面积、径厚比、热导率和电导率,与传统填料相比,石墨烯增强的复合材料具有更加优异的物理性能。

219316071_常温酸奶发酵剂产胞外多糖对DSS诱导肠炎的改善作用

户行宇，姚梦柯，孙婷，等. 常温酸奶发酵剂产胞外多糖对DSS 诱导肠炎的改善作用[J]. 食品工业科技，2023，44（12）：378−387.doi: 10.13386/j.issn1002-0306.2022080243HU Hangyu, YAO Mengke, SUN Ting, et al. Improvement Effect of the EPS Produced by Long Shelf-life Yogurt Culture on DSS-Induced Enteritis[J]. Science and Technology of Food Industry, 2023, 44(12): 378−387. (in Chinese with English abstract). doi:10.13386/j.issn1002-0306.2022080243· 营养与保健 ·常温酸奶发酵剂产胞外多糖对DSS 诱导肠炎的改善作用户行宇1，姚梦柯1，孙　婷2，马　霞2，孙　倩2，洪维鍊2, *，杨贞耐1,*（1.北京工商大学食品与健康学院，北京 100048；2.内蒙古伊利实业集团股份有限公司，内蒙古呼和浩特 010080）摘　要：为探究常温酸奶发酵剂发酵过程中分泌的胞外多糖（EPS ）对小鼠结肠炎的缓解作用，本研究在常温酸奶发酵剂A 和发酵剂B 分别发酵后的常温酸奶中提取纯化胞外多糖，测定其分子特性、结构形貌和单糖组成，同时通过葡聚糖硫酸钠（DSS ）诱导小鼠结肠炎，分析了两种胞外多糖对小鼠体重、结肠组织病理学、小鼠炎症因子、髓过氧化物酶（MPO ）和紧密连接蛋白（ZO-1、Occludin ）表达量的影响。

结果表明，EPS-A 和EPS-B 在分子形貌和单糖组成上均有明显差异。

EPS-A 结构略疏松，由盐酸氨基半乳糖、半乳糖和葡萄糖3种单糖构成，其摩尔比为0.345:0.21:0.435；EPS-B 结构致密，由盐酸氨基半乳糖、盐酸氨基葡萄糖、半乳糖和葡萄糖4种单糖构成，其摩尔比为0.421:0.05:0.207:0.322。

monocistronic and polycistronic名词解释

monocistronic and polycistronic名词解释Monocistronic and Polycistronic: An ExplanationMonocistronic and polycistronic are two terms used to describe the structure and organization of genetic material in living organisms, particularly in relation to messenger RNA (mRNA) molecules.Monocistronic refers to a genetic arrangement where an mRNA molecule carries the genetic code for a single protein. In simple terms, it means that one mRNA molecule encodes only one protein. This organization is commonly found in eukaryotes, such as animals and plants. Monocistronic mRNA is transcribed from a single gene and typically contains a single open reading frame (ORF), which is the sequence that is translated into a protein.On the other hand, polycistronic refers to a genetic arrangement where a single mRNA molecule carries the genetic code for multiple proteins. In this case, the mRNA contains multiple ORFs, each encoding a distinct protein. Polycistronic organization is commonly found in prokaryotes, such as bacteria. The polycistronic mRNA is transcribed from a gene cluster that contains several genes coding for related or functionally associated proteins.The key difference between monocistronic and polycistronic structures lies in the number of proteins encoded by a single mRNA molecule. Monocistronic mRNA produces one protein, while polycistronic mRNA produces multiple proteins. This variation in genetic organization has significant implications for gene regulation, expression, and overall cellular functionality.Monocistronic and polycistronic structures are differentially regulated in cells. Monocistronic mRNA expression is often tightly controlled, allowing for precise and specific regulation of protein production. In contrast, polycistronic mRNA expression canbe regulated in a coordinated manner, whereby all the genes within the cluster are transcribed and translated together. This coordination allows for the production of multiple proteins required for a particular biological process.Understanding the distinctions between monocistronic and polycistronic structures is essential in the fields of genetics and molecular biology. It aids in comprehending the complexity and diversity of gene regulation mechanisms across different organisms. The structural organization of genetic material plays a fundamental role in determining the functionality and diversity of living systems, and studying these concepts helps shed light on various biological processes.。

对照品结构解析的英语

对照品结构解析的英语Structural Analysis of Reference StandardsStructural analysis of reference standards, often referred to as "control samples" or "reference compounds," plays a pivotal role in the fields of pharmacology, toxicology, and chemistry. These compounds are utilized as benchmarks to ensure the accuracy and reliability of analytical methods, such as chromatography, spectroscopy, and mass spectrometry.The process of structural analysis begins with the isolation and purification of the reference standard. This involves the use of techniques like liquid chromatography or preparative thin-layer chromatography to separate the compound from its impurities. Once purified, the compound undergoes a range of spectroscopic and spectrometric techniques to determine its chemical structure.Techniques such as nuclear magnetic resonance (NMR) spectroscopy and infrared (IR) spectroscopy provide detailed information about the bonds and functional groups within the compound. NMR spectroscopy, in particular, is a powerful tool that can reveal the presence of specific atoms and their connectivity within the molecule. IR spectroscopy, on the other hand, can identify the presence of certain functional groups, such as hydroxyl, carbonyl, or amine groups.In addition to spectroscopy, mass spectrometry (MS) is another crucial technique in structural analysis. MS allows researchers to determine the molecular weight of the compound, which provides valuable insights into its structure. By combining the results of these spectroscopic and spectrometric techniques, researchers can piece together the chemical structure of the reference standard.The importance of structural analysis of reference standards cannot be overstated. Accurate structural information is crucial for understanding the properties and behavior of compounds, which is essential for drug development, toxicology studies, and other applications. By ensuring the reliability of analytical methods, structural analysis of reference standards helps to safeguardthe integrity of scientific research and ensures that accurate and reliable data is used to make informed decisions.。

“复杂性”研究的若干哲学问题

“复杂性”研究的若干哲学问题【内容提要】本文讨论了复杂、复杂性等概念和问题，研究了复杂性的本体论、认识论和方法论的各种意义；讨论了复杂性与非线性、混沌、分形、涨落和突变，以及与简单性、随机性、确定性等关系问题。

给出了复杂性的一种本体论和认识论定义．英文摘要】in this treatise, concepts and problems of the plex and plexity are discussed, meanins of plexity on ontoloy,epistemoloy and methodoloy are too researched. connections between plexity and nonlinear, chaos, fractal, fluctuations , mutations, randomicity, determinism, too.a definition of plexity on ontoloy and on epistemoloy is present.关键词】复杂性/简单性/本体复杂性/认识复杂性/随机性plexity/simplicity/plexity on ontoloy/plexityon epistemoloy/ randomicity正文】复杂性是什么？20世纪70年代以前，它是“无法认识”和“难以处理”的代名词。

简单性被认为是世界自身的基本属性，复杂性从没有被认为是世界的属性，至多被认为是简单性复合产物，是现象。

复杂性甚至被认为是认识主体运用简单性原则处理问题能力不足所致的结果。

因此，无论在认识论或本体论上，“简单性”与“复杂性”的地位都是不对称的。

近年来，人们仍争论“复杂性”是不是世界的属性，表明“复杂性”本体论地位仍未得到认同。

而“复杂性”认识论意义更未澄清。

本文提出复杂性有关的问题和观点，就教于同行，以期引起更深入的研究。

The Structural Complexity Column by

EATCS Bulletin Feb. 95 (no. 55) pp. 136-138The Structural Complexity ColumnbyJuris HARTMANISCornell University, Department of Computer ScienceIthaca, NY 14853, U.S.A.jh@On the Weight of ComputationsOne of the Grand Challenges to computer science is to understand what is and is not feasibly computable. Recursive function theory clarified what is and is not effectively computable and in the process extended our understanding of Goedel incompleteness results about the limits of the power of formal mathematical methods. Since computing is universal and encompasses the power of mathematics, the understanding of the limits of the feasibly computable could give a deeper understanding of the limits of rational intellectual processes and insights into the power and limits of scientific theories.The search for what is and is not feasibly computable has two distinct aspects. The first is to determine (estimate) how much and what kind of computing power will be available in the foreseeable future. The other problem is to determine what kind of problems can be solved with these available computing resources. The first problem is a technological assessment of the existing and potential computing technologies to estimate what kind and how much computing work our machines will be able to render. The other problem leads us to the central questions of complexity theory: what is the intrinsic complexity of important classes of problems we wish to solve. Clearly, the P=NP=PSPACE? problems are among the best known in this area.In all these considerations, the exponential function seems to give a crude upper limit for the feasibly computable. May it be time, memory or weight requirements, if they grow exponentially, then the computations are not feasible. We do not know what computations require exponential amounts of resources, nor do all instances of problems in exponential complexity classes require exponential resources. Even if the exact solutions require exponential resources, good approximations to the solution may not. But if indeed the problem requires exponential resources, then it is clearly not feasibly solvable already for moderate size instances of the problem.New Computing TechnologiesRecently there have been some very interesting results about new modes of -computing with hints that for some computations there may be an escape from the exponential curse. The two most interesting technologies are quantum computing and molecular (DNA) computing.We will leave quantum computing for a later time and reflect on Adleman's exciting paper, "Molecular Computation of Solutions to Combinatorial Problems" [1]. Adleman's paper appeared on November 11, 1994, and has received a lot of national publicity since then, including a New York Times article on December 13, 1995, about Adleman's scientific career by Gina Kolata.The paper describes how the Hamiltonian path problem for a seven node graph was encoded in DNA sequences and the Hamiltonian path was extracted as a single DNA string using standard lab techniques after seven days of lab work. This is indeed a very impressive achievement andmay stimulate through exploration of the potential of molecular computing. As pointed out in this article, the numbers of "operations" performed in biological computing can be very high and the energy requirements are surprisingly small. At the same time, as we will observe later, even these computations can not escape the exponential curse; if the computations are indeed exponential, then their weight is prohibitive.The following nondeterministic algorithm was used to solve the directed Hamiltonian path problem:Step 1: Generate random paths through the graph.Step 2: Keep only paths that begin with in-node and end with out-node.Step 3: If the graph has n vertices, then keep only those paths that enter exactly nvertices.Step 4: Keep only those paths that enter all of the vertices of the graph at least once.Step 5: If any paths remain, say "Yes'; otherwise, say "No".If this algorithm is used then in step 1 one has to expect to generate almost all possible paths through the graph to find a path satisfying the remaining conditions. Though the paths are encoded molecularly (with DNA strands) they have weight and the question arises: How heavy will these computations get? In short, we have to consider a new computational complexity measure: the weight of the computation to assess its feasibility.Consider a graph with 200 nodes and assume that to extract a Hamiltonian path we need to generate an exponential number of paths of length n. A lower bound for the encoding of the edges and the paths in the graph in DNA sequences is log4200 bases per edge and a low estimate of the weight per base is 10-25kg. Thus the biologically encoded set of paths will weigh more than2200 • log4200 • 10-25 kg ≥ (24)50 • 3 • 10-25 kg ≥ 3 • 1025 kgwhich is more than the weight of the Earth.Adleman's molecular solution of the Hamiltonian path problem is indeed a magnificent achievement and may initiate a more intensive exploration of molecular computing and computing in biological systems. At the same time, the exponential function grows too fast and the atoms are a bit too heavy to hope that molecular computing can break the exponential barrier, this time the weight barrier.This leaves us with the difficult task of understanding what computations can be performed below the exponential computational resource bounds imposed by nature.ReferencesAdleman, Leonard M, "Molecular Computation of Solutions of Combinatorial Problems." Science, Vol. 266, 11 November, 1994, pages 1021-1024.* Supported in part by National Science Foundation Grant CCR-91-23730.。

Dendrimer维基百科

DendrimerFrom Wikipedia, the free encyclopediaJump to: ,Figure 1: Dendrimer and dendronDendrimers are repetitively . The name comes from the word "δένδρον" (pronounced dendron), which translates to "tree". Synonymous terms for dendrimer include arborols and cascade molecules. However, dendrimer is currently the internationally accepted term. A dendrimer is typically symmetric around the core, and often adopts a spherical three-dimensional morphology. The word dendron is also encountered frequently. A dendron usually contains a single chemically addressable group called the focal point. The difference between dendrons and dendrimers is illustrated in figure one, but the terms are typically encountered interchangeably.Figure 2: Crystal structure of a first-generation polyphenylene dendrimer reported by Müllen et al.The first dendrimers were made by divergent synthesis approaches by in 1978, at in 1981, at in 1983 and in 1985, and by in 1985. In 1990 a convergent synthetic approach was introduced by . Dendrimer popularity then greatly increased, resulting in more than 5,000 scientific papers and patents by the year 2005.Contentsoooooo[] PropertiesDendritic molecules are characterized by structural perfection. Dendrimers and dendrons are and usually highly , spherical compounds. The field of dendritic molecules can be roughly divided into low- andhigh-molecular weight species. The first category includes dendrimers and dendrons, and the latter includes , hyperbranched polymers, and the .The properties of dendrimers are dominated by the on the , however, there are examples of dendrimers with internal functionality. Dendritic of functional molecules allows for the isolation of the active site, a structure that mimics that of active sites in biomaterials. Also, it is possible to make dendrimers water soluble, unlike most , by functionalizing their outer shell with charged species or other groups. Other controllable properties of dendrimers include , , tecto-dendrimer formation, and .Dendrimers are also classified by generation, which refers to the number of repeated branching cycles that are performed during its synthesis. For example if a dendrimer is made by convergent synthesis (see below), and the branching reactions are performed onto the core molecule three times, the resulting dendrimer is considered a third generation dendrimer. Each successive generation results in a dendrimer roughly twice the molecular weight of the previous generation. Higher generation dendrimers also have more exposed functional groups on the surface, which can later be used to customize the dendrimer for a given application.[] SynthesisFigure 3: Synthesis to second generation arborolOne of the very first dendrimers, the Newkome dendrimer, was synthesized in 1985. This is also commonly known by the name arborol. Figure 3 outlines the mechanism of the first two generations of aborol through adivergent route (discussed below). The synthesis is started by of1-bromopentane by triethyl sodiomethanetricarboxylate in and . The groups were then by to a in a step. Activation of the chain ends was achieved by converting the alcohol groups to groups with and . The tosyl group then served as in another reaction with the tricarboxylate, forming generation two. Further repetition of the two steps leads to higher generations of arborol.Poly(amidoamine), or PAMAM, is perhaps the most well known dendrimer. The core of PAMAM is a diamine (commonly ), which is reacted with , and then another ethylenediamine to make the generation-0 (G-0) PAMAM. Successive reactions create higher generations, which tend to have different properties. Lower generations can be thought of as flexible molecules with no appreciable inner regions, while medium sized (G-3 or G-4) do have internal space that is essentially separated from the outer shell of the dendrimer. Very large (G-7 and greater) dendrimers can be thought of more like solid particles with very dense surfaces due to the structure of their outer shell. The functional group on the surface of PAMAM dendrimers is ideal for , which gives rise to many potential applications.Dendrimers can be considered to have three major portions: a core, an inner shell, and an outer shell. Ideally, a dendrimer can be synthesized to have different functionality in each of these portions to control properties such as solubility, thermal stability, and attachment of compounds for particular applications. Synthetic processes can also precisely control the size and number of branches on the dendrimer. There are two defined methods of dendrimer synthesis, and . However, because the actual reactions consist of many steps needed to protect the , it is difficult to synthesize dendrimers using either method. This makes dendrimers hardto make and very expensive to purchase. At this time, there are only a few companies that sell dendrimers; commercializes biocompatiblebis-MPA dendrimers and Dendritech is the only kilogram-scale producers of PAMAM dendrimers. Dendritic Nanotechnologies Inc., from Mount Pleasant, Michigan, USA produces PAMAM dendrimers and other proprietary dendrimers.[] Divergent MethodsFigure 4: Schematic of divergent synthesis of dendrimersThe dendrimer is assembled from a multifunctional core, which is extended outward by a series of reactions, commonly a . Each step of the reaction must be driven to full completion to prevent mistakes in the dendrimer, which can cause trailing generations (some branches are shorter than the others). Such impurities can impact the functionality and symmetry of the dendrimer, but are extremely difficult to purify out because the relative size difference between perfect and imperfect dendrimers is very small.[] Convergent MethodsFigure 5: Schematic of convergent synthesis of dendrimersDendrimers are built from small molecules that end up at the surface of the sphere, and reactions proceed inward building inward and are eventually attached to a core. This method makes it much easier to remove impurities and shorter branches along the way, so that the final dendrimer is more monodisperse. However dendrimers made this way are not as largeas those made by divergent methods because crowding due to along the core is limiting.[] Click chemistryFigure 6: Dendrimer DA reaction Mullen 1996Dendrimers have been prepared via , employing , thiol-ene reactions and azide-alkyne reactions. An example is the synthesis of certain polyphenylene dendrimers can be seen in figure 6..There are ample avenues that can be opened by exploring this chemistry in dendrimer synthesis.[] ApplicationsApplications of dendrimers typically involve conjugating other chemical species to the dendrimer surface that can function as detecting agents (such as a molecule), affinity , targeting components, , , or . Dendrimers have very strong potential for these applications because their structure can lead to systems. In other words, one dendrimer molecule has hundreds of possible sites to couple to an active species. Researchers aimed to utilize the hydrophobic environments of the dendritic media to conduct photochemical reactions that generate the products that are synthetically challenged. Carboxylic acid and phenol terminated water soluble dendrimers were synthesized to establish their utility in drug delivery as well as conducting chemical reactions in their interiors. This might allow researchers to attach both targeting molecules and drug molecules to the same dendrimer, which could reduce negative side effects of medications on healthy cells.Dendrimers can also be used as a solubilizing agent. Since their introduction in the mid-1980s, this novel class of dendrimer architecture has been a prime candidate for hosts guest chemistry. Dendrimers with hydrophobic core and hydrophilic periphery have shown to exhibitmicelle-like behavior and have container properties in solution. The use of dendrimers as unimolecular micelles was proposed by Newkome in 1985. This analogy highlighted the utility of dendrimers as solubilizing agents. The majority of drugs available in pharmaceutical industry are hydrophobic in nature and this property in particular creates major formulation problems. This drawback of drugs can be ameliorated by dendrimeric scaffolding, which can be used to encapsulate as well as to solubilize the drugs because of the capability of such scaffolds to participate in extensive hydrogen bonding with water. Dendrimer labs throughout the planet are persistently trying to manipulate dendrimer’s solubilizing trait, in their way to explore dendrimer as drug delivery and target specific carrier.[] Drug DeliveryFigure 7: Schematic of a G-5 PAMAM dendrimer conjugated to both a dye molecule and a strand of DNA.Approaches for delivering unaltered natural products using polymeric carriers is of widespread interest, dendrimers have been explored for the encapsulation of compounds and for the delivery of anticancer drugs. The physical characteristics of dendrimers, including their monodispersity, water solubility, encapsulation ability, and large number of functionalizable peripheral groups, make these appropriate candidatesfor evaluation as drug delivery vehicles. There are three methods for using dendrimers in drug delivery: first, the drug is covalently attached to the periphery of the dendrimer to form dendrimer prodrugs, second the drug is coordinated to the outer functional groups via ionic interactions, or third the dendrimer acts as a unimolecular by encapsulating a pharmaceutical through the formation of a dendrimer-drug supramolecular assembly. The use of dendrimers as drug carriers by encapsulating hydrophobic drugs is a potential method for delivering highly active pharmaceutical compounds that may not be in clinical use due to their limited water solubility and resulting suboptimal . Dendrimers have been widely explored for controlled delivery of antiretroviral bioactives The inherent antiretroviral activity of dendrimers enhances their efficacy as carriers for antiretroviral drugs The dendrimer enhances both the uptake and retention of compounds within cancer cells, a finding that was not anticipated at the onset of studies. The encapsulation increases with dendrimer generation and this method may be useful to entrap drugs with a relatively high therapeutic dose. Studies based on this dendritic polymer also open up new avenues of research into the further development of drug-dendrimer complexes specific for a cancer and/or targeted organ system. These encouraging results provide further impetus to design, synthesize, and evaluate dendritic polymers for use in basic drug delivery studies and eventually in the clinic.[] Gene DeliveryThe ability to deliver pieces of to the required parts of a cell includes many challenges. Current research is being performed to find ways to use dendrimers to traffic genes into cells without damaging or deactivating the DNA. To maintain the activity of DNA during dehydration, the dendrimer/DNA complexes were encapsulated in a water soluble polymer, andthen deposited on or sandwiched in functional polymer films with a fast degradation rate to mediate gene . Based on this method, PAMAM dendrimer/DNA complexes were used to encapsulate functional biodegradable polymer films for substratemediated gene delivery. Research has shown that the fast degrading functional polymer has great potential for localized transfection.[] SensorsScientists have also studied dendrimers for use in technologies. Studied systems include or sensors using poly(propylene imine),cadmium-sulfide/polypropylenimine tetrahexacontaamine dendrimer composites to detect signal , and poly(propylenamine) first and second generation dendrimers for metal amongst others. Research in this field is vast and ongoing due to the potential for multiple detection and binding sites in dendritic structures.[] See also•••[] References1. D. Astruc, E. Boisselier, C. Ornelas (2020). "Dendrimers Designedfor Functions: From Physical, Photophysical, and SupramolecularProperties to Applications in Sensing, Catalysis, Molecular Electronics, and Nanomedicine". 110 (4): 1857–1959. :.2.Vögtle, Fritz / Richardt, Gabriele / Werner, Nicole DendrimerChemistry Concepts, Syntheses, Properties, Applications 2020 ISBN-10: 3-527-32066-03.^ Nanjwade, Basavaraj K.; Hiren M. Bechraa, Ganesh K. Derkara, .Manvia, Veerendra K. Nanjwade (2020). "Dendrimers: Emerging polymers for drug-delivery systems". European Journal of Pharmaceutical Sciences (Elsevier) 38 (3): 185–196. : .4. Roland E. Bauer, Volker Enkelmann, Uwe M. Wiesler, Alexander J.Berresheim, Klaus Müllen (2002). "Single-Crystal Structures ofPolyphenylene Dendrimers". 8 (17): 3858. :5.Egon Buhleier, Winfried Wehner, Fritz Vögtle (1978). ""Cascade"-and "Nonskid-Chain-like" Syntheses of Molecular Cavity Topologies".1978 (2): 155–158. :.6. Denkewalter, Robert G., Kolc, Jaroslav, Lukasavage, William J.7.8.9. D. A. Tomalia, H. Baker, J. Dewald, M. Hall, G. Kallos, S. Martin,J. Roeck, J. Ryder and P. Smith (1985). "A New Class of Polymers: Starburst-Dendritic Macromolecules". Polymer Journal17: 117. :10. . Science News. 1996. .11.^ George R. Newkome, Zhongqi Yao, Gregory R. Baker, Vinod K.Gupta (1985). "Micelles. Part 1. Cascade molecules: a new approach to micelles. A [27]-arborol". 50 (11): 2003. :.12.Hawker, C. J.; Fréchet, J. M. J. (1990). "Preparation of polymerswith controlled molecular architecture. A new convergent approach to dendritic macromolecules". 112 (21): 7638. :.13.Bifunctional Dendrimers: From Robust Synthesis and AcceleratedOne-Pot Postfunctionalization Strategy to Potential Applications P.Antoni, Y. Hed, A. Nordberg, D. Nyström, H. von Holst, A. Hult and M.Malkoch Angew. Int. Ed., 2020, 48 (12), pp 2126-2130 :14. J. R. McElhanon and D. V. McGrath , 2000, 65 (11), pp 3525-3529 :15. C. O. Liang and J. M. J. Fréchet , 2005, 38 (15), pp 6276-6284 :16.S. Hecht, J. M. J. Fréchet (2001). "Dendritic Encapsulation ofFunction: Applying Nature's Site Isolation Principle from Biomimetics to Materials Science". 40 (1): 74–91. : .17. Frechet, Jean M.; Donald A. Tomalia (March 2002). Dendrimers andOther Dendritic Polymers. New York, NY: John Wiley & Sons. .18. M. Fischer, F. Vogtle (1999). "Dendrimers: From Design toApplication—A Progress Report". 38 (7): 884. :19.^ Holister, Paul; Christina Roman Vas, Tim Harper (October2003). . Cientifica. Retrieved 17 March 2020.20.^ Hermanson, Greg T. (2020). "7". Bioconjugate Techniques (2nded.). London: Academic Press of Elsevier. .21. Polymer Factory AB, Stockholm, Sweden.22. Dendritech Inc., from Midland, Michigan, USA..23.24.Diels–Alder “Click” Chemistry in Designing DendriticMacromolecules, Gregory Franc and Ashok K. Kakkar 2020 :25.Robust, Efficient, and Orthogonal Synthesis of Dendrimers viaThiol-ene “Click” Chemistry Kato L. Killops, Luis M. Campos and Craig J. Hawker , 2020, 130 (15), pp 5062–5064 :26. A chemoselective approach for the accelerated synthesis ofwell-defined dendritic architectures P. Antoni, D. Nyström, C. J. Hawker,A. Hult and M. Malkoch , 2007, 22, pp 2249-2251 :27.New methodologies in the construction of dendritic materials A.Carlmark, C. J. Hawker, A. Hult and M. Malkoch , 2020, 38, pp 352 - 362 : 28.Dendrimer Design Using Cu(I)-Catalyzed Alkyne-Azide ClickChemistry, G. Franc, A. Kakkar , 2020, pp 5267 - 5276 :29.Polyphenylene Dendrimers: From Three-Dimensional toTwo-Dimensional Structures Volume 36, Issue 6, Date: April 4, 1997, Pages: 631-634 Frank Morgenroth, Erik Reuther, Klaus Müllen :30.Dendrimers as Photochemical Reaction Media. PhotochemicalBehavior of Unimolecular and Bimolecular Reactions in Water-Soluble Dendrimers" Lakshmi S. Kaanumalle,† R. Ramesh,‡ Murthy V. S. N.Maddipatla,† Jayaraj Nithyanandhan,‡ Narayanaswamy Jayaraman,*‡ and V.Ramamurthy. , 2005, (70), 5062 - 506931. . Tomalia, . Naylor, . Goddard III (1990). "Starburst Dendrimers:Molecular-Level Control of Size, Shape, Surface Chemistry, Topology, and Flexibility from Atoms to Macroscopic Matter". 29 (2): 138–175. :.32. J. M. J. Frechet (1994). "Functional Polymers and Dendrimers:Reactivity, Molecular Architecture, and Interfacial Energy". 263(5154): 1710–1715. :.33. M. Liu, K Kono, J. Frechet (2000). "Water-soluble unimolecularmicelles: their potential as drug delivery agents". 65: 121–131. 34. . Newkome, . Yao, . Baker, . Gupta (1985). "Micelles Part 1.Cascade molecules: a new approach to micelles, A-arborol". 50:155–158.35. Stevelmens, S., Hest, J. C. M., Jansen, J. F. G. A., Boxtel, D.A. F. J., de Bravander-van den,B., Miejer, E. W. (1996). "Synthesis,characterisation and guest-host properties of inverted unimolecular micelles". 118 (31): 7398–7399. :.36. Gupta, U; Agashe, .; Asthana, A.; Jain, . (2006). "Dendrimers:Novel Polymeric Nanoarchitectures for Solubility EnhancementBiomacromolecules". 7: 155–158.37. . Thomas, . Majoros, A. Kotlyar, . Kukowska-Latallo, A. Bielinska,A. Myc, . Baker Jr. (2005). "Targeting and Inhibition of Cell Growth byan Engineered Dendritic Nanodevice". 48 (11): 3729–3735. :.38. D. Bhadra, S. Bhadra, P. Jain, (2002). "Pegnology: a review ofPEG-ylated systems". 57: 5–29.39. Asthana, A.; Chauhan, A. S.; Diwan, P. V.; Jain, N. K. (2005)."Poly (amidoamine) (PAMAM) dendritic nanostructures for controlled site-specific delivery of anti-inflammatory active ingredient". 6(3): E536–E542. :.40. D. Bhadra, S. Bhadra, S. Jain, . Jain (2003). "A PEGylated,dendritic nanoparticulate carrier of fluorouracil". 257: 111–124.41. . Khopade, F. Caruso, P. Tripathi, S. Nagaich, . Jain (2002).""Cascade"- and " Effect of dendrimer on entrapment and release of bioactive from liposomes". 232: 157–162. :.42. Prajapati RN, Tekade RK, Gupta U, Gajbhiye V, Jain NK (2020)."Dendimer-Mediated Solubilization, Formulation Development and in Vitro-in Vivo Assessment of Piroxicam". 6: 940–950.43. . Chauhan, S. Sridevi, . Chalasani, . Jain, . Jain, . Jain, . Diwan(2003). "Dendrimer-mediated transdermal delivery: enhancedbioavailability of indomethacin". 90: 335–343.44. . Kukowska-Latallo, . Candido, Z. Cao, . Nigavekar, . Majoros, .Thomas, . Balogh, . Khan, . Baker Jr. (2005). "Nanoparticle Targeting of Anticancer Drug Improves Therapeutic Response in Animal Model of Human Epithelial". 65: 5317–5324.45. A. Quintana, E. Raczka, L. Piehler, I. Lee, A. Myc, I. Majoros, .Patri, T. Thomas, J. Mule, . Baker Jr. (2002). "Design and Function ofa Dendrimer-Based Therapeutic nanodevice targeted to tumor cells throughthe folate receptor". 19: 1310–1316.46.^ Morgan, Meredith T.; Yuka Nakanishi, David J. Kroll, Aaron P.Griset, Michael A. Carnahan, Michel Wathier, Nicholas H. Oberlies, Govindarajan Manikumar, Mansukh C. Wani and Mark W. Grinstaff(2006Meredith T. Morgan1). "Dendrimer-Encapsulated Camptothecins".American Association for Cancer Research(1 Department of Chemistry, DukeUniversity, 2 Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina; 3 Natural Products Laboratory, Research Triangle Institute, Research Triangle Park, North Carolina; and 4 Departments of Biomedical Engineering and Chemistry, Metcalf Center for Science and Engineering, Boston University, Boston, Massachusetts) 66(24): 11913–21. :. .47. Tekade, Rakesh Kumar; Tathagata Dutta, Virendra Gajbhiye andNarendra Kumar Jain (2020). "Exploring dendrimer towards dual drug delivery". (Pharmaceutics Research Laboratory, Department ofPharmaceutical Sciences, Dr Hari Singh Gour University,) 26 (4):287–296. :. .48. Tathagata Dutta and Targeting Potential and Anti HIV activityof mannosylated fifth generation poly (propyleneimine) Dendrimers.Biochimica et Biophysica Acta. 2007; 1770: 681-686.49. Tathagata Dutta, Minakshi Garg, Narendra K. Jain. 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超顺磁氧化铁纳米粒子特性研究

超顺磁氧化铁纳米粒子特性研究赵戎蓉;鲁芳【期刊名称】《中国医药生物技术》【年(卷),期】2012(007)003【总页数】3页(P221-223)【作者】赵戎蓉;鲁芳【作者单位】400038,重庆,第三军医大学护理学院;400038,重庆,第三军医大学护理学院【正文语种】中文基于特殊的物理和化学性质，纳米材料目前已成为表面工程学研究的理想材料。

纳米粒子作为肿瘤治疗中的靶向给药载体可以提高药物的靶向作用。

因此，对纳米粒子的修饰，将促进其在生物医学中的应用。

以超顺磁性粒子（superparamagnetic iron oxide nanoparticles，SPION）为载体的微粒靶向递送系统已成为比较热门的研究方向[1]。

SPION 由于其磁性核心的特性，可以通过外部磁铁有针对性地到达特定位置。

20 世纪 70 年代末期，外部磁场控制磁性载体的观点被认同，随后以各种磁性纳米和磁性微粒子作为载体，用于药物在体内的靶向投递的研究相继见诸报道[2]。

SPION 由两部分组成：磁性核心（通常是 Fe3O4、Fe2O3 和γ-Fe2O3）和具有生物相容性的聚合物或多孔生物相容聚合物涂层。

该涂层可以被功能化羧基、生物素、抗生物素蛋白、碳二亚胺和其他分子修饰[3]，从而起到抗原-抗体偶联以及靶向作用。

此外，聚合物涂层还可以通过共价结合，吸附或包埋药物实现药物的投递[4]。

到目前为止，载体优化的目标是：①减少药物的细胞毒性，从而降低药物副作用；②减少用量，达到靶向给药目的。

1 SPION 的稳定性及表面修饰由于诊断和治疗的需要，SPION 已在生物学和医学领域被广泛应用。

SPION 具有胶体特性，稳定性取决于粒子的大小及粒子与界面的空间位阻和库仑排斥作用。

SPION颗粒足够小能够抵消引力造成的沉降，在中性 pH 环境和生理盐水中也很稳定。

另一方面，在制备和合成后期进行生物相容性聚合物涂层，既能避免生物降解，也起到防止粒子聚集，提高其稳定性的目的。

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a rX iv:087.875v1[c ond-ma t.m trl-s ci]5J u l28Structural complexity in monodisperse systems of isotropic particles Michael Engel ∗and Hans-Rainer Trebin Institut f¨u r Theoretische und Angewandte Physik,Universit¨a t Stuttgart,Pfaﬀenwaldring 57,D-70550Stuttgart,Germany July 5,2008Abstract It has recently been shown that identical,isotropic particles can form complex crystals and quasicrystals.In order to understand the relation between the particle interaction and the structure,which it stabilizes,the phase behavior of a class of two-scale potentials is studied.In two dimensions,the phase diagram features many phases previously observed in experiment and simulation.The three-dimensional system includes the sigma phase with 30particles per unit cell,not grown in sim-ulations before,and an amorphous state,which we found impossible to crystallize in molecular dynamics.We suggest that the appearance of structural complexity in monodisperse systems is related to competing nearest neighbor distances and discuss implications of our result for the self-assembly of macromolecules.1Introduction When kept long enough at low temperatures,most systems develop long-range order.The easiest crystallization is expected for monodisperse systems,because only topological,butno additional chemical ordering is necessary.If the particles are isotropic,then a ﬁrst reasoning suggests a preference to form simple,close-packed crystals,since this allows all of them to have the same,high ﬁrst coordination numbers.A look at the periodic table reveals that indeed the ground states of most metals are bcc,fcc,or hcp [1].Similar simple crystals are found in mesoscopic or macroscopic systems like for example globular proteins[2],monodispersed colloids [3],and bubble rafts on liquid surfaces [4].In some of the systems above,quantum mechanics does not play an important role.Hence,it should in principle be possible to understand crystallization by using classicalpair interactions.For monodisperse systems there is only one type of interaction given by the shape of the potential function.Many common potentials are smooth and have a single minimum only.Simulations with these potentials usually lead to simple crystals. An example is the Lennard-Jones(LJ)potential.Nevertheless,the situation is not always that easy.Over the last years,more complex structures have been observed in experiments and simulations.A large part of work in this direction started after the discovery of quasicrystals in metallic alloys and was carried out with the aim to understand their formation.Another type of complex order are periodic crystals with large unit cells,so-called complex crystals.Whereas the lattice constants of a simple crystal are comparable to the range of the particle interactions,the unit cell of a complex crystal is stabilized indirectly,e.g.by geometric constraints.On the theoretical side,the obvious procedure to promote structural complexity is to use potentials with a more complicated radial dependence.It has been shown with double-minima potentials[5],oscillating potentials[6],and a repulsive barrier[7]that the energy of an icosahedral phase can be lower than the energy of a class of trial structures including close-packed phases.Surprisingly,even in the LJ system,a quasicrystal is unstable only by a small energy diﬀerence[8].These early works relied on general arguments and did not try to observe real crystals in simulations.Dzugutov was theﬁrst to actually grow a stable one-component dodecagonal quasicrystal from the melt[9],although it was later found to be only metastable[10].He used a LJ potential with an additional bump to disfavor the formation of simple crystals. Although no further work on other three-dimensional systems has been reported,there are many investigations focusing on two-dimensional systems,where computation and vi-sualization is easier.Theﬁrst such study applied a variation of the Dzugutov potential and found a planar dodecagonal quasicrystal[11].Furthermore,it was pointed out that a decagonal quasicrystal appears with a square-well[12]and a ramp potential[13].Experiments show that nature is quite ingenious in her way to assemble identical par-ticles.First of all,a few complex ground states of elemental metals are known to exist.A notable example isα-manganese,which has cubic symmetry with58atoms per unit cell.Thermodynamically stable high-temperature phases areβ-boron with105atoms and β-uranium with30atoms per unit cell[1].The latter is isostructural toσ-CrFe and known as the sigma phase.Furthermore,commensurately modulated phases are common at high pressures[14].Recently,diﬀerent kinds of macromolecules have been observed to self-assemble into complex phases:(i)Under appropriate experimental conditions,tree-like molecules(den-drons)forming spherical micelles arrange to a dodecagonal quasicrystal[15].(ii)T-shaped molecules can be designed in such a way that they organize into liquid crystalline honey-combs[16].(iii)ABC-star polymers form cylindrical columns according to square-triangle quasicrystals,a two-dimensional version of the sigma phase[17],and other Archimedean tilings.How can structural complexity be understood from bottom-up?It is instructive to study the ground state for small portions of the system–more or less spherical clusters–as a function of the particle number N.The structure of small clusters(N<200in threedimensions,N<30in two dimensions)is often diﬀerent from the bulk crystal[18].The reason is the competition between local lowest energy conﬁgurations and the necessity of continuation in space.For example in a monodisperse LJ system,icosahedral coordination occurs in small clusters[19],although the hcp phase is the lowest energy bulk crystal[20]. On the other hand,if small clusters already have simple structure,then the bulk phase will also be simple.A local order,which is incompatible with periodicity,is a necessary condition for structural complexity in the bulk.2Phase diagram for the two-dimensional system There are two mechanisms to introduce structural complexity:either by destabilizing simple phases or by stabilizing a complex phase.An example for the destabilization mech-anism is the Dzugutov potential.We adopt the stabilization mechanism,because it has the advantage that the choice of the target structure can be controlled more directly.The particles are assumed to interact with an isotropic two-scale potential.A simple ansatz is the Lennard-Jones-Gauss(LJG)potentialV(r)=1r6−ǫexp −(r−r0)2−4.5−4−3.5−3−2.5−2with a sigma crystal.Quickly,the whole system transformed into a single fcc phase.Hence, the sigma phase is also not stabilized entropically.The reason for its appearance in our simulations is the huge nucleation radius of fcc.The local order of sig is much closer to the melt.Further details will be reported elsewhere.In the case r0=1.4,all attempts to crystallize the system failed.One reason is the comparably low melting temperature:the ground state fcc melts around T=0.9.In various MD runs over several106steps in the range0.5≤T≤0.9no nucleation was observed.We conclude that the choice r0=1.4is interesting for studying a monatomic glass.Our results indicate that this LJG glass is more resistant against crystallization than the Dzugutov glass[27],which forms the dodecagonal quasicrystal rather quickly [9,10,28].4DiscussionHow does the LJG system compare to experiments?In metals,multi-body terms can only be neglected in aﬁrst approximation.Eﬀective potentials often have Friedel oscillations, which are mimicked by double-wells.It should be kept in mind thatﬁxed pair interactions between all atoms might be not applicable in complex phases,since the atoms are found in diﬀerent local environments.Furthermore,the interaction is expected to change during crystallization.Isotropic pair potentials are more applicable to macromolecular self-assembly,because the molecules as a whole interact almost classically.It has been suggested[29]that their complex arrangements originates from the competition of two length scales,which appear due to soft repulsion and strong interpenetration.The micelles forming the dodecagonal quasicrystal in[15]have two natural length scales:the inner one corresponds to the back-bone of the dendrons and the outer one to the end of the tethered chains.Our simulations suggest that the ideal ratio of the scales is close to2:3.The cylindrical phases(T-shaped molecules and ABC-star polymers)can be understood as two-dimensional tilings with an eﬀective interaction within the plane.Although not isotropic anymore,the particles still have two length scales.The tilings observed so far [30,31]consist of hexagons,squares,and triangles only.If it will be possible to stabilize pentagons or decagons,then the phases Pen/Pen2,Xi/Xi2might appear.Weﬁnish with a challenge:Can monodisperse icosahedral quasicrystals be grown in simulation or experiment?As of today,none have been found.5AcknowledgmentThis work was funded by the Deutsche Forschungsgemeinschaft(Tr154/24-1)and the Japan Society for the Promotion of Sciences.References[1]J.Donohue,The structure of the elements(Krieger pany,1982).[2]F.Rosenberger,P.G.Vekilov,M.Muschol,and B.R.Thomas,Nucleation and crys-tallization of globular proteins–what we know and what is missing,J.Cryst.Growth 168,1(1996).[3]Y.Xia,B.Gates,Y.Yin,and Y.Lu,Monodispersed colloidal spheres:Old materialswith new applications,Adv.Materials12,693(2000).[4]L.Bragg and J.F.Nye,A dynamical model of a crystal structure,Proc.R.Soc.London,Ser.A190,474(1947).[5]Z.Olami,Stable Dense Icosahedral Quasicrystals,Phys.Rev.Lett.65,2559(1990).[6]A.P.Smith,Stable one-component quasicrystals,Phys.Rev.B43,11635(1991).[7]A.R.Denton and 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