Modeling of Stationary Processes
Autodesk BIM工作流程：线性基础设施建模说明书
© 2018 Autodesk, Inc.Jens Luetzelberger Advanced BIM Workflows forLinear Infrastructure ModellingJens WachterEngineering & Consulting Implementation Consultant BIMTeam LeadBuilding Information ModelingModeling Concepts Dynamo BasicsIntroWhat is perfection?Antoine de Saint-Exupéry, french writer and aviator3 key-experiences I had to makeNo.1Data: When in doubt, leave it outSufficient data for main purposeNo.2 Minimizing staff means neglecting roles BIM isn`t just about technicNo.3 Throw away existing workModel concept Revit-Dynamo in practical useSec 1tunnel RastattSec 7Sec 8 ⏹Ca. 83 km line (Upgrade + New line) ⏹6 stations and 2 stop points ⏹61 existing buildingsExtent ⏹Existing conditions modeling⏹Comparison of alignment and routing variationsMain use casesRheintalbahn (Upgrade)Offenburg Lahr 7.37.2 7.4The railway-projectIntegration of Karlsruhe-Basel / Pilot project of the “Stufenplan ”Freiburg New lineModeling of the existing conditions •61 bridges •LOG of 1cm •Minimum 75% demolition →Level of accuracy →Time needed5 month completion→Motivation of teamMuch work, no real benefitSufficient data for main purposeSpecificationof the EIR ▪Modeling of the existing conditions ▪Modeling without planning •Lack of coordinationSufficient data for main purposeSpecification of the EIR ▪Modeling of the existing conditions ▪Irregular scan with 2cm distance •Use heliscan•Combine with stationary scan•No minimizing→No experience in offending→Overload for later models▪Modeling without planningSufficient data for main purposeSpecification of the EIR ▪Modeling of the existing conditions → LOD follows purpose▪Irregular scan with 2cm distance →set a well performing baseinstead of useless detailsGAINED EXPERIENCEGIVES SELF-CONFIDENCEFOR FUTURE CONSULTING▪Modeling without planning→ Status: Pilot projectNo. 2 Neglecting roles Unite roles means increases role conflicts Manager CoordinatorProduct Owner Scrum Master Dev.-Team Data Drop Desgin Review Incremtental Working LESSONS LEARNED ▪Who is in charge of providing a model-structure? ▪Who defines sprint-backlog? ▪Who collects user stories for the the next sprint ? ▪When is the right time to confirm the model?Unexperienced teams = Concentrated methodNo. 3 Throw away existing work Unite roles means increases role conflicts 1 2 3 Start Development Alignment choice 5 Month Existing conditions LOD 100 3 Month Version modeling LOD 2005 Month Modeling team 1 2 M. Modeling team 2 M.+▪Lack of fundamental specifications ▪Mature planning base to model it▪Well-rehearsed team modeling with interactingsoftwareFacing technical challenges Tool set for main benefitalignement Overall model visualisation and surfaceevaluation LOD 200Revit Navisworks Infraworks▪Modeling existing bridges/under-pass based onpoint cloud ▪Assembley ofcoordinationmodel▪Model LOD 100 forsupport of thealignment choice(conventional process)▪Modeling newalignment▪Clash detection▪Inspection of theexisting buildingscompared wirtpoint cloudCDE of the employer: At frist (Adv. processing)Infraworks Generating a new LODInfraWorks for trendsetting decision Requirement fulfillment1 ▪Bridges constructed by Infraworks / dimension from point cloud▪Digital terrain model included (only helicopter)▪Main buildings included▪Surface use included (e.g. protective areas)▪Alignment includedMain use:▪Mass calculation +/- 10%▪Visual clash detectionFacing technical challengesTool set for main benefit▪Modeling existingbridges/under-pass based on point cloud ▪Assembley ofcoordination model▪Modeling new alignement▪Clash detectionOverall model visualisationand surfaceevaluation LOD 200▪Inspection of theexisting buildings compared wirt point cloud▪Overall model visualisation and surfaceevaluation LOD200 CDE of the employer: At frist (Adv. processing) ProVI Dynamo Revit NavisworksInfraworks▪Conversion & editingAlignement data▪Visual script of the Revit workflow▪Modeling existing bridges/under-pass based on point cloud ▪Assembley of coordination model ▪Model LOD 100 for support of the alignement choice (conventional process)▪Output oflisted points extracted from cross sections▪Modeling new alignement ▪Clash detection ▪Inspection of the existing buildings compared wirt point cloud CDE of the employer: At frist (Adv. processing), at last BIM 360 (Adv. AD compatibility)Dynamo basics and why it is used Modeling Concepts▪What is Dynamo?▪Open-source platform▪Visual interface to construct logic routines▪Geometry creation▪Workflow automation▪Interface for multiple software tools▪Resources▪/(Download, Blog, Forum…)▪/de/(Online-Manual in different languages)Do I have to watch all the …spaghettis“?Dynamo BasicA Dynamo script can be viewed and used directly, but also via the Dynamo Player. The Dynamo Player automatically generates an input mask based on the structure of the Dynamo script.Applications / Use Cases Modeling ConceptsExcel Interoperability Modeling ConceptsOverview of the Modeling ConceptsWorkflow OverviewTask Revit Families Dynamo script Noterail track •2x rail profiles•2x sleepers•rail track solid geometry, placement of sleepers existing and new rail track modelling purposes•bedding•subgrade•placing of bedding and subgrade families along therail trackdeviations in input make it necessary to revise the script forrail tracks next to train stationsequipment •1x power pole U140 with 29x Types•cantilever•overhead lines•placement of power poles, cantilever and overheadlines according defined rulesexisting conditions modeling; new in preparation•automated creation of Revit family types by usingExcel input data (steel power pole types)-•KS-Signal•placement of existing signals•placement of new signalsexisting conditions modeling; new in preparationtrain platform(existing conditions; LOD100) •precast concrete element BSK55•foundation•placement of BSK55 & foundation•solid from boundary (DWG)existing conditions modeling LOD100; new currently not infocusnoise barrier (existing conditions) •noise protection elements•joint-forming profile family•precast concrete base element•column cap•foundation•noise barrier script 1(calculation of top of nb and Excel export)•noise barrier script 2(automated modeling based on Excel import)•noise barrier script 3(replacement of base element(s))existing conditions modeling; new in preparationdrainage culvert •round profile•rectangular profile•placement according rail track axis parameter for rotation in Revit family includedPilot project …Karlsruhe –Basel“WorkflowsThere are different workflows for existing conditions modeling and the modeling of a newly planned track section. The major difference is the structure of the input data for automated and semi-automated processes provided by the main authoring tool(s).▪Existing Conditions Modelling(e.g. overhead lines)▪track data per track and per IVL-section (1 km section of whole track)▪pylon data read from existing DWG or PDF files▪no automatic assignment to rail track available (rotation)▪no z-height, no pylon-height, no pylon type etc.▪point clouds are used to detail and verify the Revit model▪manual task▪New▪track data per track and per IVL-section existing▪spreadsheets of all relevant information for new pylons available▪automated control of parameters such as e-value and others possibleWorkflowsRail Profile▪RFA Template▪Generic model adaptive▪Structure of the RFA▪two adaptive points are placed in a side view at a distance of 1500 mm▪the rail profile is placed under one of these points▪rail profile with reduced detailing▪Idea / Concept▪to make rotation easy, you have to work with one RFAfor SO left and one for SO right (SO = top of rail)▪placement of 2-point AC takes care of the cant▪Level of automation▪100% - placement of the profiles and rail solids which are created by Dynamo ▪Dynamo script to be used in project environment of RevitSleeper▪RFA Template▪Generic model adaptive▪Structure of RFA▪two adaptive points are placed in a side view at a distance of 1500 mm ▪depending on the adaptive points, the shape of the sleeperis modeled including a parameter to control the distance to the SO▪Idea/ Concept▪placement of 2-point AC takes care of the cant▪the parameter controlling the distance from SO to sleeper ensures usability with different rail profiles (different heights)▪Level of automation▪100% - placement of the sleepers▪Dynamo script to be used in project environment of RevitRail Track EquipmentSignal existing conditions▪RFA Template▪Generic model adaptive▪Structure of RFA▪2x adaptive points ensure coordinate-correct placement and rotation to the track▪Parameters control the position of the light spot to the track axis, the extension of the mast to the ground and the length of the arm ▪Idea / Concept▪Signaling is required both in inventory and in planning,so two placement methods are needed▪the signal is placed over a coordinate list and automatically rotated to the track axis▪Level of automation▪90-100% - placement and rotation are automated, the extension to the ground is partially manual to solveJens Luetzelberger © 2018 Autodesk, Inc.Rail Track EquipmentSignal 2.0 - detailed▪RFA Template▪Generic model adaptive (1-Point)▪face-based Revit families included, carrying the shrink-wrap geometry from inventor files▪Structure of RFA▪1x adaptive point ensure coordinate-correct placement on the rail track axis; rotation provided from authoring tool (parameter driven) ▪all standardized signal types are provided by Revit families▪Idea / Concept▪the signal is placed over a coordinate list and automatically rotated to the track axis▪relevant signal types are provided by the authoring tool (ProSig)▪exported FBX files of the RFAs can be reused as InfraWorks content▪Level of automation▪100% - placement, rotation and type selection are automatedSignal Placement 2.0Highly flexible Revit-FamilySignal Family 2.0▪1-point adaptive▪offset and rotation all axis▪made out of Inventor fileswith a minimum of effortDocumentation of the WorkflowsInsight into the Documents Documentation of the WorkflowsPractical Example: Noise Barrier …Herbolzheim“Modeling of a Noise Barrier Practical ExampleModeling of a Noise Barrier - Drawings Practical ExampleModeling of a Noise Barrier - BOM Practical ExampleEmergency solution to first choice Using Infraworks1 2 3 ▪Model LOD 100 for support of the alignement choice(conventional process)▪Import the high-detailed Revit model ofthe hole alignment created by dynamo▪Attaching LOD 200-Revit models of brigdes▪Developing details for visualisation▪Export surface-model with airial image to NavisworksInefficientmodelstructureQ&AEngineering & ConsultingAutodesk and the Autodesk logo are registered trademarks or trademarks of Autodesk, Inc., and/or its subsidiaries and/or affiliates in the USA and/or other countries. All other brand names, product names, or trademarks belong to their respective holders. Autodesk reserves the right to alter product and services offerings, and specifications and pricing at any time without notice, and is not responsible for typographical or graphical errors that may appear in this document.© 2018。
工程仿真技术在石油化工油气设备应用-俞斌根
石油石化行业工程仿真研讨会————油气设备报告俞斌跟流体业务部安世亚太科技（北京）有限公司© 2010 PERA Global石化油气设备的流体仿真需求 涉及的物理问题及仿真分析模型 应用案例介绍石化油气设备的流体仿真需求 涉及的物理问题及仿真分析模型 应用案例介绍石油化工油气设备仿真需求输油/气管道泄漏着火与控制问题海洋钻井平台（钻探、抽油泵、分离、管系）原油储运（分离设备、多相混输磨损、泵……)天然气储运(分离器,过滤器、压缩机、阀门、伴热设计……)海上输油管水下的动力特征原油加热设备塔器设备火灾危险区域安全分析……石化油气设备的流体仿真需求 涉及的物理问题及仿真分析模型应用案例介绍多相流动输运多相流动磨蚀多相流动分离相变共轭传热其他–管道设备流致振动–管道泄漏、检测–应急事故模拟与风险评估拉格朗日多相流DDPM extended to the packing limitlid fparticles ¾DPM欧拉多相流¾VOF solids vof ¾Mixture ¾Eulerian 多相流•IAC 模型•欧拉粒子流•Immiscible Fluid Model （捕捉两相接触面，explicit VOF ）•Dense Discrete Phase Model （DDPM 模型）：拉格朗日与欧拉方法的组合附加模型¾人口平衡模型DEM+Fluent (BETA )颗粒运动方程¾对连续相采用欧拉方法求解其NS 方程，对离散相采用拉格朗日方法跟踪其轨迹，离散相和连续相之间可以进行质量、动量、能量交换。
¾颗粒运动方程•Fluent drag law ：9Spherical Drag Law•其他作用力：Spherical Drag Law 9Non-spherical Drag Law9Stokes-Cunningham Drag Law9High-Mach-Number Drag Law D i D M d l Th (9旋转参考系centripetal and Coriolis forces9浮力：buoyancy forces (由于与连续相之间的密度差异）9Dynamic Drag Model Theory(粒子动态的变形）9Dense gas-solid flow•CFX drag law99Virtual mass force (颗粒加速或减速引发的流体惯性阻力）9Small particles Ishii-Zuber9Schiller Naumann 9Grace Drag9Wen YU （固相）Thermophoretic Force （热泳，Small particles ）9Brownian Force （布朗运动力，亚微米粒子）9Saffman‘s Lift Force （亚微米粒子）9Gidaspow (固相)湍流耗散对颗粒运动轨迹的影响¾速度脉动影响混合效果，同样会对颗粒的运动轨迹产生影响。
数学标准布朗运动
数学标准布朗运动Brownian motion is a fundamental concept in mathematics that has wide-ranging applications in various fields. It was first observed by the botanist Robert Brown in 1827, when he noticed that pollen grains suspended in water moved randomly and unpredictably. This seemingly erratic motion of particles led to the development of the mathematical theory of Brownian motion, which describes the random movement of particles in a fluid.布朗运动是数学中的基本概念，在各个领域有着广泛的应用。
它首次被植物学家罗伯特·布朗在1827年观察到，当他注意到在水中悬浮的花粉颗粒随机且不可预测地移动。
这些颗粒表现出的看似无规律的运动导致了布朗运动的数学理论的发展，描述了液体中颗粒的随机运动。
One of the key characteristics of Brownian motion is its unpredictability. The motion of particles in Brownian motion is governed by random forces, leading to a stochastic process that is inherently uncertain. This unpredictability is a fundamental aspect of Brownian motion that has important implications in fields such as finance, physics, and biology.布朗运动的一个关键特征是其不可预测性。
Archive of Applied Mechanics manuscript No. (will be inserted by the editor) Developments i
LEOPOLD-FRANZENS UNIVERSITYChair of Engineering Mechanicso.Univ.-Prof.Dr.-Ing.habil.G.I.Schu¨e ller,Ph.D.G.I.Schueller@uibk.ac.at Technikerstrasse13,A-6020Innsbruck,Austria,EU Tel.:+435125076841Fax.:+435125072905 mechanik@uibk.ac.at,http://mechanik.uibk.ac.atIfM-Publication2-407G.I.Schu¨e ller.Developments in stochastic structural mechanics.Archive of Applied Mechanics,published online,2006.Archive of Applied Mechanics manuscript No.(will be inserted by the editor)Developments in Stochastic Structural MechanicsG.I.Schu¨e llerInstitute of Engineering Mechanics,Leopold-Franzens University,Innsbruck,Aus-tria,EUReceived:date/Revised version:dateAbstract Uncertainties are a central element in structural analysis and design.But even today they are frequently dealt with in an intuitive or qualitative way only.However,as already suggested80years ago,these uncertainties may be quantified by statistical and stochastic procedures. In this contribution it is attempted to shed light on some of the recent advances in the now establishedfield of stochastic structural mechanics and also solicit ideas on possible future developments.1IntroductionThe realistic modeling of structures and the expected loading conditions as well as the mechanisms of their possible deterioration with time are un-doubtedly one of the major goals of structural and engineering mechanics2G.I.Schu¨e ller respectively.It has been recognized that this should also include the quan-titative consideration of the statistical uncertainties of the models and the parameters involved[56].There is also a general agreement that probabilis-tic methods should be strongly rooted in the basic theories of structural en-gineering and engineering mechanics and hence represent the natural next step in the development of thesefields.It is well known that modern methods leading to a quantification of un-certainties of stochastic systems require computational procedures.The de-velopment of these procedures goes in line with the computational methods in current traditional(deterministic)analysis for the solution of problems required by the engineering practice,where certainly computational pro-cedures dominate.Hence,their further development within computational stochastic structural analysis is a most important requirement for dissemi-nation of stochastic concepts into engineering practice.Most naturally,pro-cedures to deal with stochastic systems are computationally considerably more involved than their deterministic counterparts,because the parameter set assumes a(finite or infinite)number of values in contrast to a single point in the parameter space.Hence,in order to be competitive and tractable in practical applications,the computational efficiency of procedures utilized is a crucial issue.Its significance should not be underestimated.Improvements on efficiency can be attributed to two main factors,i.e.by improved hard-ware in terms of ever faster computers and improved software,which means to improve the efficiency of computational algorithms,which also includesDevelopments in Stochastic Structural Mechanics3 utilizing parallel processing and computer farming respectively.For a con-tinuous increase of their efficiency by software developments,computational procedure of stochastic analysis should follow a similar way as it was gone in the seventieth and eighties developing the deterministic FE approach. One important aspect in this fast development was the focus on numerical methods adjusted to the strength and weakness of numerical computational algorithms.In other words,traditional ways of structural analysis devel-oped before the computer age have been dropped,redesigned and adjusted respectively to meet the new requirements posed by the computational fa-cilities.Two main streams of computational procedures in Stochastic Structural Analysis can be observed.Thefirst of this main class is the generation of sample functions by Monte Carlo simulation(MCS).These procedures might be categorized further according to their purpose:–Realizations of prescribed statistical information:samples must be com-patible with prescribed stochastic information such as spectral density, correlation,distribution,etc.,applications are:(1)Unconditional simula-tion of stochastic processes,fields and waves.(2)Conditional simulation compatible with observations and a priori statistical information.–Assessment of the stochastic response for a mathematical model with prescribed statistics(random loading/system parameters)of the param-eters,applications are:(1)Representative sample for the estimation of the overall distribution.4G.I.Schu¨e ller Indiscriminate(blind)generation of samples.Numerical integration of SDE’s.(2)Representative sample for the reliability assessment by gen-erating adverse rare events with positive probability,i.e.by:(a)variance reduction techniques controlling the realizations of RV’s,(b)controlling the evolution in time of sampling functions.The other main class provides numerical solutions to analytical proce-dures.Grouping again according to the respective purpose the following classification can be made:Numerical solutions of Kolmogorov equations(Galerkin’s method,Finite El-ement method,Path Integral method),Moment Closure Schemes,Compu-tation of the Evolution of Moments,Maximum Entropy Procedures,Asymp-totic Stability of Diffusion Processes.In the following,some of the outlined topics will be addressed stressing new developments.These topics are described within the next six subject areas,each focusing on a different issue,i.e.representation of stochastic processes andfields,structural response,stochastic FE methods and parallel processing,structural reliability and optimization,and stochastic dynamics. In this context it should be mentioned that aside from the MIT-Conference series the USNCCM,ECCM and WCCM’s do have a larger part of sessions addressing computational stochastic issues.Developments in Stochastic Structural Mechanics5 2Representation of Stochastic ProcessesMany quantities involving randomfluctuations in time and space might be adequately described by stochastic processes,fields and waves.Typical ex-amples of engineering interest are earthquake ground motion,sea waves, wind turbulence,road roughness,imperfection of shells,fluctuating prop-erties in random media,etc.For this setup,probabilistic characteristics of the process are known from various measurements and investigations in the past.In structural engineering,the available probabilistic characteristics of random quantities affecting the loading or the mechanical system can be often not utilized directly to account for the randomness of the structural response due to its complexity.For example,in the common case of strong earthquake motion,the structural response will be in general non-linear and it might be too difficult to compute the probabilistic characteristics of the response by other means than Monte Carlo simulation.For the purpose of Monte Carlo simulation sample functions of the involved stochastic pro-cess must be generated.These sample functions should represent accurately the characteristics of the underlying stochastic process orfields and might be stationary and non-stationary,homogeneous or non-homogeneous,one-dimensional or multi-dimensional,uni-variate or multi-variate,Gaussian or non-Gaussian,depending very much on the requirements of accuracy of re-alistic representation of the physical behavior and on the available statistical data.6G.I.Schu¨e ller The main requirement on the sample function is its accurate represen-tation of the available stochastic information of the process.The associ-ated mathematical model can be selected in any convenient manner as long it reproduces the required stochastic properties.Therefore,quite different representations have been developed and might be utilized for this purpose. The most common representations are e.g.:ARMA and AR models,Filtered White Noise(SDE),Shot Noise and Filtered Poisson White Noise,Covari-ance Decomposition,Karhunen-Lo`e ve and Polynomial Chaos Expansion, Spectral Representation,Wavelets Representation.Among the various methods listed above,the spectral representation methods appear to be most widely used(see e.g.[71,86]).According to this procedure,samples with specified power spectral density information are generated.For the stationary or homogeneous case the Fast Fourier Transform(FFT)techniques is utilized for a dramatic improvements of its computational efficiency(see e.g.[104,105]).Advances in thisfield provide efficient procedures for the generation of2D and3D homogeneous Gaus-sian stochasticfields using the FFT technique(see e.g.[87]).The spectral representation method generates ergodic sample functions of which each ful-fills exactly the requirements of a target power spectrum.These procedures can be extended to the non-stationary case,to the generation of stochastic waves and to incorporate non-Gaussian stochasticfields by a memoryless nonlinear transformation together with an iterative procedure to meet the target spectral density.Developments in Stochastic Structural Mechanics7 The above spectral representation procedures for an unconditional simula-tion of stochastic processes andfields can also be extended for Conditional simulations techniques for Gaussianfields(see e.g.[43,44])employing the conditional probability density method.The aim of this procedure is the generation of Gaussian random variates U n under the condition that(n−1) realizations u i of U i,i=1,2,...,(n−1)are specified and the a priori known covariances are satisfied.An alternative procedure is based on the so called Kriging method used in geostatistical application and applied also to con-ditional simulation problems in earthquake engineering(see e.g.[98]).The Kriging method has been improved significantly(see e.g.[36])that has made this method theoretically clearer and computationally more efficient.The differences and similarities of the conditional probability density methods and(modified)Kriging methods are discussed in[37]showing the equiva-lence of both procedures if the process is Gaussian with zero mean.A quite general spectral representation utilized for Gaussian random pro-cesses andfields is the Karhunen-Lo`e ve expansion of the covariance function (see e.g.[54,33]).This representation is applicable for stationary(homoge-neous)as well as for non-stationary(inhomogeneous)stochastic processes (fields).The expansion of a stochastic process(field)u(x,θ)takes the formu(x,θ)=¯u(x)+∞i=1ξ(θ) λiφi(x)(1)where the symbolθindicates the random nature of the corresponding quan-tity and where¯u(x)denotes the mean,φi(x)are the eigenfunctions andλi the eigenvalues of the covariance function.The set{ξi(θ)}forms a set of8G.I.Schu¨e ller orthogonal(uncorrelated)zero mean random variables with unit variance.The Karhunen-Lo`e ve expansion is mean square convergent irrespective of its probabilistic nature provided it possesses afinite variance.For the im-portant special case of a Gaussian process orfield the random variables{ξi(θ)}are independent standard normal random variables.In many prac-tical applications where the random quantities vary smoothly with respectto time or space,only few terms are necessary to capture the major part of the randomfluctuation of the process.Its major advantage is the reduction from a large number of correlated random variables to few most important uncorrelated ones.Hence this representation is especially suitable for band limited colored excitation and stochastic FE representation of random me-dia where random variables are usually strongly correlated.It might also be utilized to represent the correlated stochastic response of MDOF-systems by few most important variables and hence achieving a space reduction.A generalization of the above Karhunen-Lo`e ve expansion has been proposed for application where the covariance function is not known a priori(see[16, 33,32]).The stochastic process(field)u(x,θ)takes the formu(x,θ)=a0(x)Γ0+∞i1=1a i1(x)Γ1(ξi1(θ))+∞i1=1i1i2=1a i1i2(x)Γ2(ξi1(θ),ξi2(θ))+ (2)which is denoted as the Polynomial Chaos Expansion.Introducing a one-to-one mapping to a set with ordered indices{Ψi(θ)}and truncating eqn.2Developments in Stochastic Structural Mechanics9 after the p th term,the above representations reads,u(x,θ)=pj=ou j(x)Ψj(θ)(3)where the symbolΓn(ξi1,...,ξin)denotes the Polynomial Chaos of order nin the independent standard normal random variables.These polynomialsare orthogonal so that the expectation(or inner product)<ΨiΨj>=δij beingδij the Kronecker symbol.For the special case of a Gaussian random process the above representation coincides with the Karhunen-Lo`e ve expan-sion.The Polynomial Chaos expansion is adjustable in two ways:Increasingthe number of random variables{ξi}results in a refinement of the random fluctuations,while an increase of the maximum order of the polynomialcaptures non-linear(non-Gaussian)behavior of the process.However,the relation between accuracy and numerical efforts,still remains to be shown. The spectral representation by Fourier analysis is not well suited to describe local feature in the time or space domain.This disadvantage is overcome in wavelets analysis which provides an alternative of breaking a signal down into its constituent parts.For more details on this approach,it is referred to[24,60].In some cases of applications the physics or data might be inconsistent with the Gaussian distribution.For such cases,non-Gaussian models have been developed employing various concepts to meet the desired target dis-tribution as well as the target correlation structure(spectral density).Cer-tainly the most straight forward procedures is the above mentioned memo-ryless non-linear transformation of Gaussian processes utilizing the spectralrepresentation.An alternative approach utilizes linear and non-linearfil-ters to represent normal and non-Gaussian processes andfields excited by Gaussian white noise.Linearfilters excited by polynomial forms of Poisson white noise have been developed in[59]and[34].These procedures allow the evaluation of moments of arbitrary order without having to resort to closure techniques. Non-linearfilters are utilized to generate a stationary non-Gaussian stochas-tic process in agreement with a givenfirst-order probability density function and the spectral density[48,15].In the Kontorovich-Lyandres procedure as used in[48],the drift and diffusion coefficients are selected such that the solutionfits the target probability density,and the parameters in the solu-tion form are then adjusted to approximate the target spectral density.The approach by Cai and Lin[15]simplifies this procedure by matching the spec-tral density by adjusting only the drift coefficients,which is the followed by adjusting the diffusion coefficient to approximate the distribution of the pro-cess.The latter approach is especially suitable and computationally highly efficient for a long term simulation of stationary stochastic processes since the computational expense increases only linearly with the number n of dis-crete sample points while the spectral approach has a growth rate of n ln n when applying the efficient FFT technique.For generating samples of the non-linearfilter represented by a stochastic differential equations(SDE), well developed numerical procedures are available(see e.g.[47]).3Response of Stochastic SystemsThe assessment of the stochastic response is the main theme in stochastic mechanics.Contrary to the representation of of stochastic processes and fields designed tofit available statistical data and information,the output of the mathematical model is not prescribed and needs to be determined in some stochastic sense.Hence the mathematical model can not be selected freely but is specified a priori.The model involves for stochastic systems ei-ther random system parameters or/and random loading.Please note,due to space limitations,the question of model validation cannot be treated here. For the characterization of available numerical procedures some classifi-cations with regard to the structural model,loading and the description of the stochastic response is most instrumental.Concerning the structural model,a distinction between the properties,i.e.whether it is determinis-tic or stochastic,linear or non-linear,as well as the number of degrees of freedom(DOF)involved,is essential.As a criterion for the feasibility of a particular numerical procedure,the number of DOF’s of the structural system is one of the most crucial parameters.Therefore,a distinction be-tween dynamical-system-models and general FE-discretizations is suggested where dynamical systems are associated with a low state space dimension of the structural model.FE-discretization has no essential restriction re-garding its number of DOF’s.The stochastic loading can be grouped into static and dynamic loading.Stochastic dynamic loading might be charac-terized further by its distribution and correlation and its independence ordependence on the response,resulting in categorization such as Gaussian and non-Gaussian,stationary and non-stationary,white noise or colored, additive and multiplicative(parametric)excitation properties.Apart from the mathematical model,the required terms in which the stochastic re-sponse should be evaluated play an essential role ranging from assessing thefirst two moments of the response to reliability assessments and stabil-ity analysis.The large number of possibilities for evaluating the stochas-tic response as outlined above does not allow for a discussion of the en-tire subject.Therefore only some selected advances and new directions will be addressed.As already mentioned above,one could distinguish between two main categories of computational procedures treating the response of stochastic systems.Thefirst is based on Monte Carlo simulation and the second provides numerical solutions of analytical procedures for obtaining quantitative results.Regarding the numerical solutions of analytical proce-dures,a clear distinction between dynamical-system-models and FE-models should be made.Current research efforts in stochastic dynamics focus to a large extent on dynamical-system-models while there are few new numerical approaches concerning the evaluation of the stochastic dynamic response of e.g.FE-models.Numerical solutions of the Kolmogorov equations are typical examples of belonging to dynamical-system-models where available approaches are computationally feasible only for state space dimensions one to three and in exceptional cases for dimension four.Galerkin’s,Finite El-ement(FE)and Path Integral methods respectively are generally used tosolve numerically the forward(Fokker-Planck)and backward Kolmogorov equations.For example,in[8,92]the FE approach is employed for stationary and transient solutions respectively of the mentioned forward and backward equations for second order systems.First passage probabilities have been ob-tained employing a Petrov-Galerkin FE method to solve the backward and the related Pontryagin-Vitt equations.An instructive comparison between the computational efforts using Monte Carlo simulation and the FE-method is given e.g.in an earlier IASSAR report[85].The Path Integral method follows the evolution of the(transition)prob-ability function over short time intervals,exploiting the fact that short time transition probabilities for normal white noise excitations are locally Gaus-sian distributed.All existing path integration procedures utilize certain in-terpolation schemes where the probability density function(PDF)is rep-resented by values at discrete grid points.In a wider sense,cell mapping methods(see e.g.[38,39])can be regarded as special setups of the path integral procedure.As documented in[9],cumulant neglect closure described in section7.3 has been automated putational procedures for the automated generation and solutions of the closed set of moment equations have been developed.The method can be employed for an arbitrary number of states and closed at arbitrary levels.The approach,however,is limited by available computational resources,since the computational cost grows exponentially with respect to the number of states and the selected closurelevel.The above discussed developments of numerical procedures deal with low dimensional dynamical systems which are employed for investigating strong non-linear behavior subjected to(Gaussian)white noise excitation. Although dynamical system formulations are quite general and extendible to treat non-Gaussian and colored(filtered)excitation of larger systems,the computational expense is growing exponentially rendering most numerical approaches unfeasible for larger systems.This so called”curse of dimen-sionality”is not overcome yet and it is questionable whether it ever will be, despite the fast developing computational possibilities.For this reason,the alternative approach based on Monte Carlo simu-lation(MCS)gains importance.Several aspects favor procedures based on MCS in engineering applications:(1)Considerably smaller growth rate of the computational effort with dimensionality than analytical procedures.(2) Generally applicable,well suited for parallel processing(see section5.1)and computationally straight forward.(3)Non-linear complex behavior does not complicate the basic procedure.(4)Manageable for complex systems.Contrary to numerical solutions of analytical procedures,the employed structural model and the type of stochastic loading does for MCS not play a deceive role.For this reason,MCS procedures might be structured ac-cording to their purpose i.e.where sample functions are generated either for the estimation of the overall distribution or for generating rare adverse events for an efficient reliability assessment.In the former case,the prob-ability space is covered uniformly by an indiscriminate(blind)generationof sample functions representing the random quantities.Basically,at set of random variables will be generated by a pseudo random number generator followed by a deterministic structural analysis.Based on generated random numbers realizations of random processes,fields and waves addressed in section2,are constructed and utilized without any further modification in the following structural analysis.The situation may not be considered to be straight forward,however,in case of a discriminate MCS for the reliability estimation of structures,where rare events contributing considerably to the failure probability should be gener-ated.Since the effectiveness of direct indiscriminate MCS is not satisfactory for producing a statistically relevant number of low probability realizations in the failure domain,the generation of samples is restricted or guided in some way.The most important class are the variance reduction techniques which operate on the probability of realizations of random variables.The most widely used representative of this class in structural reliability assess-ment is Importance Sampling where a suitable sampling distribution con-trols the generation of realizations in the probability space.The challenge in Importance Sampling is the construction of a suitable sampling distribu-tion which depends in general on the specific structural system and on the failure domain(see e.g.[84]).Hence,the generation of sample functions is no longer independent from the structural system and failure criterion as for indiscriminate direct MCS.Due to these dependencies,computational procedures for an automated establishment of sampling distributions areurgently needed.Adaptive numerical strategies utilizing Importance Direc-tional sampling(e.g.[11])are steps in this direction.The effectiveness of the Importance sampling approach depends crucially on the complexity of the system response as well as an the number of random variables(see also section5.2).Static problems(linear and nonlinear)with few random vari-ables might be treated effectively by this approach.Linear systems where the randomness is represented by a large number of RVs can also be treated efficiently employingfirst order reliability methods(see e.g.[27]).This ap-proach,however,is questionable for the case of non-linear stochastic dynam-ics involving a large set of random variables,where the computational effort required for establishing a suitable sampling distribution might exceed the effort needed for indiscriminate direct MCS.Instead of controlling the realization of random variables,alternatively the evolution of the generated sampling can be controlled[68].This ap-proach is limited to stochastic processes andfields with Markovian prop-erties and utilizes an evolutionary programming technique for the genera-tion of more”important”realization in the low probability domain.This approach is especially suitable for white noise excitation and non-linear systems where Importance sampling is rather difficult to apply.Although the approach cannot deal with spectral representations of the stochastic processes,it is capable to make use of linearly and non-linearlyfiltered ex-citation.Again,this is just contrary to Importance sampling which can be applied to spectral representations but not to white noisefiltered excitation.4Stochastic Finite ElementsAs its name suggests,Stochastic Finite Elements are structural models rep-resented by Finite Elements the properties of which involve randomness.In static analysis,the stiffness matrix might be random due to unpredictable variation of some material properties,random coupling strength between structural components,uncertain boundary conditions,etc.For buckling analysis,shape imperfections of the structures have an additional impor-tant effect on the buckling load[76].Considering structural dynamics,in addition to the stiffness matrix,the damping properties and sometimes also the mass matrix might not be predictable with certainty.Discussing numerical Stochastic Finite Elements procedures,two cat-egories should be distinguished clearly.Thefirst is the representation of Stochastic Finite Elements and their global assemblage as random structural matrices.The second category addresses the evaluation of the stochastic re-sponse of the FE-model due to its randomness.Focusingfirst on the Stochastic FE representation,several representa-tions such as the midpoint method[35],the interpolation method[53],the local average method[97],as well as the Weighted-Integral-Method[94,25, 26]have been developed to describe spatial randomfluctuations within the element.As a tendency,the midpoint methods leads to an overestimation of the variance of the response,the local average method to an underestima-tion and the Weighted-Integral-Method leads to the most accurate results. Moreover,the so called mesh-size problem can be resolved utilizing thisrepresentation.After assembling all Finite Elements,the random structural stiffness matrix K,taken as representative example,assumes the form,K(α)=¯K+ni=1K Iiαi+ni=1nj=1K IIijαiαj+ (4)where¯K is the mean of the matrix,K I i and K II ij denote the determinis-ticfirst and second rate of change with respect to the zero mean random variablesαi andαj and n is the total number of random variables.For normally distributed sets of random variables{α},the correlated set can be represented advantageously by the Karhunen-Lo`e ve expansion[33]and for non-Gaussian distributed random variables by its Polynomial chaos ex-pansion[32],K(θ)=¯K+Mi=0ˆKiΨi(θ)(5)where M denotes the total number of chaos polynomials,ˆK i the associated deterministicfluctuation of the matrix andΨi(θ)a polynomial of standard normal random variablesξj(θ)whereθindicates the random nature of the associated variable.In a second step,the random response of the stochastic structural system is determined.The most widely used procedure for evaluating the stochastic response is the well established perturbation approach(see e.g.[53]).It is well adapted to the FE-formulation and capable to evaluatefirst and second moment properties of the response in an efficient manner.The approach, however,is justified only for small deviations from the center value.Since this assumption is satisfied in most practical applications,the obtainedfirst two moment properties are evaluated satisfactorily.However,the tails of the。
振动疲劳介绍
Introduction
Traditionally fatigue damage is associated with time dependent loading, in the form of local stress or strain histories. However there often are situations where these loading time signals can not easily be determined. As examples one can think of shaker tables, or signals like the wind load on a wind mill where just the length of necessary measurements enforces to use other representations of the loads. In these cases power spectral densities define the loads.
The post-processing features are especially adapted to the given applications and they are not only intended to really fast identify the critical regions but give answers to how to solve the durability problems. Due to the efficient solving and the tight integration multiple designs can be analyzed without big manual interaction to reach the design that fulfills both weight and durability requirements.
适合表面活性剂理论计算投稿的一些期刊-自己整理
1. Langmuir 影响因子4.186 (ACS)Langmuir is an interdisciplinary（多学科）journal publishing articles in the following subject categories:（1）Colloids: Surfactants and self-assembly, dispersions, emulsions, foams（2）Interfaces: Adsorption, reactions, films, forces.（3）Biological Interfaces: Bio-colloids, bio-molecular and bio-mimetic materials(生物仿生材料)（4）Materials: nano - structured and meso-structured materials（纳米和中间结构材料）, polymers, gels, liquid crystals（5）Electrochemistry: Interfacial charge transfer (界面电子转移), charge transport, electro-catalysis(电催化作用), electro-kinetic phenomena（电动力学现象）, bio-electrochemistry（生物电化学）（6）Devices and Applications: Sensors(传感器), fluidics（射流技术）, patterning （仿生）, catalysis（催化）, photonic crystals（光电子晶体）期刊地址：/journal/langd52. JPC (A)影响因子2.946(ACS)The Journal of Physical Chemistry A (Isolated Molecules, Clusters, Radicals（自由基）, and Ions; Environmental Chemistry, Geochemistry（地球化学）, and Astrochemistry（天体化学）; Theory) publishes studies on kinetics and dynamics; spectroscopy, photochemistry, and excited states（激发态）; environmental and atmospheric chemistry, aerosol processes（气溶胶过程）, geochemistry, and astrochemistry; and molecular structure, quantum chemistry, and general theory期刊地址：/journal/jpcafh3. JPC (B)影响因子3.696(ACS)The Journal of Physical Chemistry B (Biophysical Chemistry, Biomaterials, Liquids, and Soft Matter) publishes studies on biophysical chemistry and biomolecules; biomaterials, surfactants, and membranes（细胞膜）; liquids; chemical and dynamical processes in solution; glasses, colloids, polymers, and soft matter期刊地址：/journal/jpcbfk4. JPC (C)影响因子4.805(ACS)The Journal of Physical Chemistry C (Energy Conversion and Storage, Optical and Electronic Devices, Interfaces, Nanomaterials, and Hard Matter) publishes studies on energy conversion and storage; energy and charge transport; surfaces, interfaces, porous materials, and catalysis; plasmonics(等离子体), optical materials, and hard matter; physical processes in nanomaterials and nanostructures.期刊地址：/journal/jpccck5. Journal of Colloid and Interface Science 影响因子3.070(Elsevier)The Journal of Colloid and Interface Science publishes original research findings and insights regarding the fundamental principles of colloid and interface science, and conceptually novel applications of these principles in chemistry, chemical engineering, physics, applied mathematics, materials science, polymer science, electrochemistry, geology, agronomy, biology, medicine, fluid dynamics, and related fields The Journal of Colloid and Interface Science emphasizes fundamental scientific innovation within the following categories:A. Colloidal Materials and NanomaterialsB. Surfactants and Soft MatterC. Adsorption, Catalysis and ElectrochemistryD. Interfacial Processes, Capillarity（毛细管作用）and WettingE. Biomaterials and NanomedicineF. Novel Phenomena and Techniques期刊地址：/journal-of-colloid-and-interface-science/ 6. Journal of Surfactants and Detergents 影响因子1.545(Springer)Journal of Surfactants and Detergents（洗涤剂）, a journal of the American Oil Chemists Society (AOCS) publishes scientific contributions in the surfactants and detergents area. This includes the basic and applied science of petrochemical（石油化学）and oleochemical（油化学）surfactants, the development and performance of surfactants in all applications, as well as the development and manufacture of detergent ingredients（材料）and their formulation into finished products. Manuscripts involving performance, test method development, analysis, and the environmental fate of surfactants and detergent ingredients are welcome.期刊地址：/chemistry/journal/117437. Journal of Dispersion Science and Technology 影响因子0.628(Taylor & Francis Group content )Journal of Dispersion Science and Technology is an international journal covering fundamental and applied aspects of dispersions, emulsions, vesicles（囊泡）, microemulsions, liquid crystals, particle suspensions（悬浮液）and sol-gel processes. Fundamental areas that are covered include new surfactants, polymers and indigenous stabilizers; surfactant and polymer association as well as phase equilibria （相平衡）in systems water and oil; surfactant and polymer films, monolayers and interfacial films; adsorption and desorption onto solid surfaces; stability and destabilization of dispersions, emulsions and particle suspensions; collodal templates and sol-gel processing. Industrial applications cover chemicals (surfactants, polymers, stabilizers, inhibitors), crude oils, food, pharmaceuticals, agriculture, nanotechnology, and soft condensed materials.期刊地址：/action/aboutThisJournal?show=aimsScope&journalCode =ldis208. Journal of Molecular Modeling （J MOL MODEL，JMM）影响因子1.797The Journal of Molecular Modeling was founded in 1995 as the first purely electronic journal in chemistry with the aim of publishing original articles on all aspects of molecular modeling. One reason for the electronic format was the ability to publish in full color at no extra cost and to be able to provide multimedia features or supplemental material electronically. From January 1st 2003 the Journal of Molecular Modeling is also published six times per year as a classical, but still full color, print journal. The electronic publication in advance of the printed issues continues as for the purely electronic journal. Electronic supplementary material will also be available from Springer's internet service as before. To our knowledge, the Journal of Molecular Modeling is the first scientific journal to make the move from purely electronic (with subsequent publication of the Molecular Modeling Annuals) to a more classical print format. We have decided to use the opportunity of the birth of the print edition of theJournal of Molecular Modeling to redefine the aims and scope of the journal to fit the fast-changing field of molecular modeling.The Journal of Molecular Modeling publishes all quality science that passes the critical review of expert reviewers and falls within the scope of the journal coverage, including:Life Science Modeling· Computer-aided molecular design· Rational drug design, de novo ligand design, receptor modeling and docking· Cheminformatics（化学信息学）, data analysis, visualization and mining（采矿）· Computational medicinal chemistry· Homology modeling（同源建模）· Simulation of peptides, DNA and other biopolymers· Quantitative structure-activity relationships (QSAR)· Quantitative structure-property relationships (QSAR) and ADME-modeling· Modeling of biological reaction mechanisms·Combined experimental/computational studies in which calculations play a major roleMaterials Modeling· Classical or quantum mechanical modeling of materials· Modeling mechanical and physical properties· Computer-based structure determination of materials· Catalysis-modeling· Modeling zeolites（沸石）, layered minerals（矿物）etc.· Modeling catalytic reaction mechanisms and computational catalysis optimization · Polymer modeling· Nanomaterials, fullerenes（富勒烯）and nanotubes· Modeling stationary phases in separation scienceNew Methods· New classical modeling techniques and parameter sets·New quantum mechanical techniques, including ab inito DFT and semiempiricalMO-methods, basis sets etc.· New hybrid QM/MM techniques· New computer-based methods for interpreting experimental data· New visualization techniques· New statistical methods for treating biopolymers· New software and new versions of existing software· New techniques for simulating environments or solventComputational Chemistry· Classical and quantum mechanical modeling of chemical structures and reactions · Molecular recognition· Modeling sensors· New desktop modeling software and techniques· Theories of chemical structure and reactions· Neural nets and genetic algorithms in chemistry期刊地址：/chemistry/journal/894。
台式电脑和笔记本电脑的区别英语作文
台式电脑与笔记本电脑的区别In today's digital era, computers have become an integral part of our lives, enabling us to work, learn, and entertain ourselves efficiently. Among the various types of computers available, desktop computers and laptops stand out as two popular choices. While both serve similar functions, they differ significantly in terms of design, performance, portability, and usage scenarios. This essay aims to explore the key differences between desktop computers and laptops, highlighting their unique characteristics and applications.Firstly, the most apparent difference lies in their physical design and form factor. Desktop computers are typically larger, with separate monitors, keyboards, and towers containing the processing unit, graphics card, and storage devices. This design offers ample space for high-performance components and cooling systems, resulting in superior processing power and graphics capabilities. On the other hand, laptops are designed for portability, with all the necessary components integrated into a single, compact unit. While this makes them convenient to carry around, italso limits their internal space and cooling capabilities, potentially affecting performance.In terms of performance, desktop computers often excel due to their ability to accommodate more powerful hardware components. They can be equipped with high-end processors, dedicated graphics cards, and large amounts of RAM, making them ideal for tasks that require intense computational power or graphics rendering, such as gaming, video editing, and 3D modeling. Laptops, while capable of handling many common tasks, are typically limited by their size and power constraints, resulting in lower overall performance compared to desktops.Portability is another significant difference between the two. Laptops are designed to be lightweight and easy to carry, making them perfect for students, business professionals, and anyone who needs to work or access information on the go. They can be easily packed into a bag and taken anywhere, allowing users to stay connected and productive regardless of their location. Desktop computers, on the other hand, are typically stationary and require a dedicated workspace. While they offer a more stable andergonomic working environment, they lack the flexibility and convenience of laptops.Moreover, the usage scenarios for desktop computers and laptops often differ. Desktop computers are often preferred for home or office use, where a permanent setup is desired. They can be customized with multiple monitors, keyboards, and other peripherals to enhance productivity and comfort. Laptops, on the other hand, are ideal for mobile workers, students, and travelers who need to access information or work remotely. They are also commonly used for personal entertainment, such as streaming movies or playing games. In conclusion, desktop computers and laptops each have their unique strengths and applications. Desktop computers excel in terms of performance and customization, making them suitable for tasks that require high computational power or a permanent setup. Laptops, on the other hand, offer portability and convenience, allowing users to stay connected and productive wherever they go. When choosing between the two, it is essential to consider your specific needs and usage scenarios to make the most informed decision.**台式电脑与笔记本电脑的区别**在当今数字化时代，电脑已成为我们生活中不可或缺的一部分，使我们能够高效地进行工作、学习和娱乐。
基于ADAMS的并联机器人运动学和动力学仿真
第22卷 第8期计 算 机 仿 真2005年8月 文章编号:1006-9348(2005)08-0181-05基于ADA M S的并联机器人运动学和动力学仿真游世明,陈思忠,梁贺明(北京理工大学机械与车辆工程学院,北京100081)摘要:应用机械系统动力学仿真分析软件ADAM S,建立了Stewart型并联机构的虚拟样机模型,包括对并联机器人各部件的简化方法、在ADAM S中的模型描述及仿真过程控制,并利用该虚拟样机模型对并联机器人进行了运动学和动力学分析。
为并联机器人系统的设计、制造和模拟运动作业提供了理论依据和主要参数。
实现了在计算机上通过使用CAE仿真软件来对并联机器人的运动和动力性能进行分析,为并联机器人的设计提供了一套有效的分析方法。
关键词:并联机器人;运动学;动力学;虚拟样机中图分类号:TP391.9 文献标识码:AK i nema tics and D ynam ics S i m ula tion of P M T Ba sed O n ADAM SY OU Shi-m ing,CHEN Si-zhong,L I A N G He-m ing(School ofM echanical and Vehicle Engineering,Beijing Institute of Technology,Beijing100081,China)ABSTRACT:This paper uses mechanic dynam ic analysis soft ware ADAM S to build a virtual p rototype of theStewart Parallel KinematicsM achine Tool,gives the detail of si mp lified method of model,ADAM S descrip tion ofmodel,control of si m ulating p rocess.The virtual p rototyp ing model of the P M T p rovides the theoretic foundationand main parameters for the system design,p roduction and app lication in experi m ent.It show s the si mulation forthe kinematics and dynam ics of P M T,realizes an effective method for the engineering design w ith the CAEsoft ware on computer.KEYWO RD S:Parallel kinematics machine tool;Kinematics;Dynam ics;V irtual p rototype1 引言1965年,德国学者Stewart提出了一种新型的、6自由度并联机器人平台机构,称为Stewart平台。
科技英语部分课后练习答案
III: Text Organization
Part I (1-3):
A multibillion-dollar craft called the Crew Exploration Vehicle (CEV).
Part II (4-7): David Gump and Gary Hudson;
.
4
Page 52:
III: Translation
11.每个化学元素在周期表中都有一定的原子数和 位置，可以据此来推测其特性：如何同别的元素 相互作用，能形成什么样的化合物，以及它的物 理属性。
12.固体加热到足够温度时，它所含的电子就会有 一部分离开固体表面而飞到周围的空间中去；这 种现象称为热电子放射；通常，电子管就利用这 种现象产生自由电子。
.
5
Page 74-75:
II: Abstract Correction
The current calibration methods of the projectilevelocity measurement system are introduced, and the problem and the unreasonableness of these methods are analyzed. Based on the principle of the mathematical statistics, the calibration method is investigated that measures the projectile-velocity at the same time by the multi-group zone-block device is unbiased, uniform and efficient and uses the average of the measured value as the true value of the projectile-velocity at the point.
英语翻译
第一课设计实践可能是一个最令人兴奋的活动和履行工程师可以承担。
有一种强烈的满足感和自豪感在看到自己的创造努力结果出现在实际产品和过程中受益的人。
做设计也需要大量的特点。
the design engineer should not only have adequate technical training, but must be a person of sound judgment and wide experience, qualities which are usually acquired only after considerable time has been spent in actual professional work设计工程师不仅要有足够的技术训练，但必须是一个健全的判断力和广泛的经验，这些特质通常只能在相当长的时间后已被用在专业实际工作a start in this direction can be made with a good teacher while the student is still at the university当学生仍在大学时，一个方向的开始可以由一个好的老师做出。
however, the beginning designer must expect to get a substantial portion of this training after leaving school through further reading and study, and especially by being associated with other competent engineers.然而，开始设计师必须指望得到相当一部分这种培训学校毕业后通过深入的阅读和研究，尤其是与其他主管工程师。
the more any one engineer knows about all phrases of design, the better.任何一个工程师更了解所有词组的设计，更好的。
空间统计学
Statistics for spatial data; Noel A.C. Cressie, Wiley& Sons,1991空间统计学 0 引言 0.1定义空间统计学由于许多学科的需求发展迅速。
空间统计学涉及的领域：生物学、空间经济学、遥感科学、图像处理、环境与地球科学( 大地测量、地球物理、空间物理、大气科学等等)、生态学、地理学、流行病学、农业经济学、林学及其它学科空间过程或随机场定义：{}(),Z s s =∈Z S （1） 式中S 是空间位置s 的集合，可以是预先确定的，也可以随机的，2dd ⊆=S R 是二维欧氏空间；()Z s 取值于状态空E 。
空时过程：如考虑时间，则{}(,),,(,)d Z s t s s t +=∈∈⨯Z S R R式中S 是空间位置s 的集合，可以是预先确定的，也可以随机的；t +∈R ；()Z s 取值于状态空E 。
注意：上述为标量值过程，但也可扩展为向量过程。
0.2 空间数据类型0.2.1 连续型地学统计数据（Geostatistical data ） 此时， 2dd ⊆=S R是连续欧氏子空间，即连续点的集合，随机场{}(),Z s s ∈S 在实值空间E 上的n 个固定位置n s s s ,,,21 取值。
如图为连续型空间数据（a ）降雨量分布图；(b) 土壤孔穴分布图。
（符号大小正比于属性变量值）Geostatistical (spatial) data is usually processed by the geostatistical method that has been set out in considerable detail since Krige published his important paper. In summary, this method consists of an exploratory spatial data analysis, positing a model of (non-stationary) mean plus ( intrinsically stationary) error, non-parametrically estimating variogram or covariogram, fitting a valid model to the estimate, and kriging ( predicting )unobserved parts from the available data. This last step yields not only a predictor, but a mean squared prediction error.0.2.2 离散型格网数据（Lattice data ）此时， 2dd ⊆=S R是固定的离散空间点，非随机点集合，随机场{}(),Z s s ∈S 在2d d ⊆=S R 的空间点采样。
Furthermore
where G(z) = G(z1 ; z2) is some su ciently smooth, scalar valued function, u0 denotes the initial value of the ow, denotes the control and l is a positive weight. Applying a Lagrangian approach to problem (P) leads to an optimality system involving the NSE, a backwards in time parabolic linear system for the adjoint variables and the optimality condition for (see 1]). To solve (1) numerically for realistic ows necessitates a computing power that is beyond the presently available resources. Instead of looking for an optimal control for problem (P) and thus for an optimal cost reduction, we apply a time integration scheme to the NSE and solve at each time step an optimization problem with an instantaneous cost function related to the one of problem (1). As we shall demonstrate this allows a signi cant reduction of the costs with readily available computing power. In order to explain the approach consider the rst order semi implicit time integration scheme
供应链下的多级存货管理【外文翻译】
本科毕业论文（设计）外文翻译原文：Multi-echelon inventory management in supply chains Historically, the echelons of the supply chain, warehouse, distributors, retailers, etc., have been managed independently, buffered by large inventories. Increasing competitive pressures and market globalization are forcing firms to develop supply chains that can quickly respond to customer needs. To remain competitive and decrease inventory, these firms must use multi-echelon inventory management interactively, while reducing operating costs and improving customer service.Supply chain management (SCM) is an integrative approach for planning and control of materials and information flows with suppliers and customers, as well as between different functions within a company. This area has drawn considerable attention in recent years and is seen as a tool that provides competitive power .SCM is a set of approaches to integrate suppliers, manufacturers, warehouses, and stores efficiently, so that merchandise is produced and distributed at right quantities, to the right locations and at the right time, in order to minimize system-wide costs while satisfying service-level requirements .So the supply chain consists of various members or stages. A supply chain is a dynamic, stochastic, and complex system that might involve hundreds of participants.Inventory usually represents from 20 to 60 per cent of the total assets of manufacturing firms. Therefore, inventory management policies prove critical in determining the profit of such firms. Inventory management is, to a greater extent, relevant when a whole supply chain (SC), namely a network of procurement, transformation, and delivering firms, is considered. Inventory management is indeed a major issue in SCM, i.e. an approach that addresses SC issues under an integrated perspective.Inventories exist throughout the SC in various forms for various reasons. Thelack of a coordinated inventory management throughout the SC often causes the bullwhip effect, namely an amplification of demand variability moving towards the upstream stages. This causes excessive inventory investments, lost revenues, misguided capacity plans, ineffective transportation, missed production schedules,and poor customer service.Many scholars have studied these problems, as well as emphasized the need of integration among SC stages, to make the chain effectively and efficiently satisfy customer requests (e.g. reference). Beside the integration issue, uncertainty has to be dealt with in order to define an effective SC inventory policy. In addition to the uncertainty on supply (e.g. lead times) and demand, information delays associated with the manufacturing and distribution processes characterize SCs.Inventory management in multi-echelon SCs is an important issue, because thereare many elements that have to coordinate with each other. They must also arrangetheir inventories to coordinate. There are many factors that complicate successful inventory management, e.g. uncertain demands, lead times, production times, product prices, costs, etc., especially the uncertainty in demand and lead times where the inventory cannot be managed between echelons optimally.Most manufacturing enterprises are organized into networks of manufacturingand distribution sites that procure raw material, process them into finished goods, and distribute the finish goods to customers. The terms ‘multi-echelon’ or ‘multilevel‘production/distribution networks are also synonymous with such networks(or SC), when an item moves through more than one step before reaching the final customer. Inventories exist throughout the SC in various forms for various reasons. Atany manufacturing point, they may exist as raw materials, work in progress, or finished goods. They exist at the distribution warehouses, and they exist in-transit, or‘in the pipeline’, on each path linking these facilities.Manufacturers procure raw material from suppliers and process them into finished goods, sell the finished goods to distributors, and then to retail and/or customers. When an item moves through more than one stage before reaching thefinal customer, it forms a ‘multi-echelon’ inventory system. The echelon stock of a stock point equals all stock at this stock point, plus in-transit to or on-hand at any of its downstream stock points, minus the backorders at its downstream stock points.The analysis of multi-echelon inventory systems that pervades the business world has a long history. Multi-echelon inventory systems are widely employed to distribute products to customers over extensive geographical areas. Given the importance of these systems, many researchers have studied their operating characteristics under a variety of conditions and assumptions. Since the development of the economic order quantity (EOQ) formula by Harris (1913), researchers and practitioners have been actively concerned with the analysis and modeling of inventory systems under different operating parameters and modeling assumptions .Research on multi-echelon inventory models has gained importance over the last decade mainly because integrated control of SCs consisting of several processing and distribution stages has become feasible through modern information technology. Clark and Scarf were the first to study the two-echelon inventory model. They proved the optimality of a base-stock policy for the pure-serial inventory system and developed an efficient decomposing method to compute the optimal base-stock ordering policy. Bessler and Veinott extended the Clark and Scarf model to include general arbores cent structures. The depot-warehouse problem described above was addressed by Eppen and Schrage who analyzed a model with a stockless central depot. They derived a closed-form expression for the order-up-to-level under the equal fractile allocation assumption. Several authors have also considered this problem in various forms. Owing to the complexity and intractability of the multi-echelon problem Hadley and Whitin recommend the adoption of single-location, single-echelon models for the inventory systems.Sherbrooke considered an ordering policy of a two-echelon model for warehouse and retailer. It is assumed that stock outs at the retailers are completely backlogged. Also, Sherbrooke constructed the METRIC (multi-echelon technique for coverable item control) model, which identifies the stock levels that minimize the expected number of backorders at the lower-echelon subject to a bud get constraint. This modelis the first multi-echelon inventory model for managing the inventory of service parts. Thereafter, a large set of models which generally seek to identify optimal lot sizes and safety stocks in a multi-echelon framework, were produced by many researchers. In addition to analytical models, simulation models have also been developed to capture the complex interaction of the multi-echelon inventory problems.So far literature has devoted major attention to the forecasting of lumpy demand, and to the development of stock policies for multi-echelon SCs Inventory control policy for multi-echelon system with stochastic demand has been a widely researched area. More recent papers have been covered by Silver and Pyke. The advantage of centralized planning, available in periodic review policies, can be obtained in continuous review policies, by defining the reorder levels of different stages, in terms of echelon stock rather than installation stock.Rau et al. , Diks and de Kok , Dong and Lee ,Mitra and Chatterjee , Hariga , Chen ,Axsater and Zhang , Nozick and Turnquist ,and So and Zheng use a mathematic modeling technique in their studies to manage multi-echelon inventory in SCs. Diks and de Kok’s study considers a divergent multi-echelon inventory system, such as a distribution system or a production system, and assumes that the order arrives after a fixed lead time. Hariga, presents a stochastic model for a single-period production system composed of several assembly/processing and storage facilities in series. Chen, Axsater and Zhang, and Nozick and Turnquist consider a two-stage inventory system in their papers. Axsater and Zhang and Nozickand Turnquist assume that the retailers face stationary and independent Poisson demand. Mitra and Chatterjee examine De Bodt and Graves’ model (1985), which they developed in their paper’ Continuous-review policies for a multi-echelon inventory problem with stochastic demand’, for fast-moving items from the implementation point of view. The proposed modification of the model can be extended to multi-stage serial and two -echelon assembly systems. In Rau et al.’s model, shortage is not allowed, lead time is assumed to be negligible, and demand rate and production rate is deterministic and constant. So and Zheng used an analytical model to analyze two important factors that can contribute to the high degree of order-quantity variability experienced bysemiconductor manufacturers: supplier’s lead time and forecast demand updating. They assume that the external demands faced by there tailor are correlated between two successive time periods and that the retailer uses the latest demand information to update its future demand forecasts. Furthermore, they assume that the supplier’s delivery lead times are variable and are affected by the retailer’s order quantities. Dong and Lee’s paper revisits the serial multi-echelon inventory system of Clark and Scarf and develops three key results. First, they provide a simple lower-bound approximation to the optimal echelon inventory levels and an upper bound to the total system cost for the basic model of Clark and Scarf. Second, they show that the structure of the optimal stocking policy of Clark and Scarf holds under time-correlated demand processing using a Martingale model of forecast evolution. Third, they extend the approximation to the time-correlated demand process and study, in particular for an autoregressive demand model, the impact of lead times, and autocorrelation on the performance of the serial inventory system.After reviewing the literature about multi-echelon inventory management in SCs using mathematic modeling technique, it can be said that, in summary, these papers consider two, three, or N-echelon systems with stochastic or deterministic demand. They assume lead times to be fixed, zero, constant, deterministic, or negligible. They gain exact or approximate solutions.Dekker et al. analyses the effect of the break-quantity rule on the inventory costs. The break-quantity rule is to deliver large orders from the warehouse, and small orders from the nearest retailer, where a so-called break quantity determines whether an order is small or large. In most l-warehouse–N-retailers distribution systems, it is assumed that all customer demand takes place at the retailers. However, it was shown by Dekker et al. that delivering large orders from the warehouse can lead to a considerable reduction in the retailer’s inventory costs. In Dekker et al. the results of Dekker et al. were extended by also including the inventory costs at the warehouse. The study by Mohebbi and Posner’s contains a cost analysis in the context of a continuous-review inventory system with replenishment orders and lost sales. The policy considered in the paper by V ander Heijden et al. is an echelon stock, periodicreview, order-up-to policy, under both stochastic demand and lead times.The main purpose of Iida’s paper is to show that near-myopic policies are acceptable for a multi-echelon inventory problem. It is assumed that lead times at each echelon are constant. Chen and Song’s objective is to minimize the long-run average costs in the system. In the system by Chen et al., each location employs a periodic-review, or lot-size reorder point inventory policy. They show that each location’s inventory positions are stationary and the stationary distribution is uniform and independent of any other. In the study by Minner et al., the impact of manufacturing flexibility on inventory investments in a distribution network consisting of a central depot and a number of local stock points is investigated. Chiang and Monahan present a two-echelon dual-channel inventory model in which stocks are kept in both a manufacturer warehouse (upper echelon) and a retail store (lower echelon), and the product is available in two supply channels: a traditional retail store and an internet-enabled direct channel. Johansen’s system is assumed to be controlled by a base-stock policy. The independent and stochastically dependent lead times are compared.To sum up, these papers consider two- or N-echelon inventory systems, with generally stochastic demand, except for one study that considers Markov-modulated demand. They generally assume constant lead time, but two of them accept it to be stochastic. They gain exact or approximate solutions.In multi-echelon inventory management there are some other research techniques used in literature, such as heuristics, vary-METRIC method, fuzzy sets, model predictive control, scenario analysis, statistical analysis, and GAs. These methods are used rarely and only by a few authors.A multi-product, multi-stage, and multi-period scheduling model is proposed by Chen and Lee to deal with multiple incommensurable goals for a multi-echelon SC network with uncertain market demands and product prices. The uncertain market demands are modeled as a number of discrete scenarios with known probabilities, and the fuzzy sets are used for describing the sellers’ and buyers’ incompatible preference on product prices.In the current paper, a detailed literature review, conducted from an operational research point of view, is presented, addressing multi-echelon inventory management in supply chains from 1996 to 2005.Here, the behavior of the papers, against demand and lead time uncertainty, is emphasized.The summary of literature review is given as: the most used research technique is simulation. Also, analytic, mathematic, and stochastic modeling techniques are commonly used in literature. Recently, heuristics as fuzzy logic and GAs have gradually started to be used.Source: A Taskin Gu¨mu¨s* and A Fuat Gu¨neri Turkey, 2007. “Multi-echelon inventory management in supply chains with uncertain demand and lead times: literature review from an operational research perspective”. IMechE V ol. 221 Part B: J. Engineering Manufacture. June, pp.1553-1570.译文：供应链下的多级存货管理从历史上看，多级供应链、仓库、分销商、零售商等，已经通过大量的库存缓冲被独立管理。
趋势平稳过程的均值和方差
趋势平稳过程的均值和方差## Definitions of Stationary Processes: ##。
Mean of a Stationary Process: The mean of a stationary process is a constant. This means that the expected value of the process does not change over time.Variance of a Stationary Process: The variance of a stationary process is also a constant. This means that the spread of the process around its mean does not change over time.## Covariance of a Stationary Process: ##。
Covariance of a Stationary Process: The covariance of a stationary process is a function of the time difference between two observations.It measures the degree to which two observations are related. The covariance of a stationary process is constantfor all time differences.## Examples of Stationary Processes: ##。
1. White noise: White noise is a stationary process with a constant mean of 0 and a constant variance. The covariance of white noise is 0 for all time differences.2. Random walk: A random walk is a stationary process with a constant mean of 0 and a variance that is proportional to time. The covariance of a random walk is proportional to the time difference between two observations.3. AR(1) process: An AR(1) process is a stationary process with a constant mean and a variance that is proportional to the squared autocorrelation coefficient. The covariance of an AR(1) process is proportional to the autocorrelation coefficient raised to the power of the time difference between two observations.## Applications of Stationary Processes: ##。
adaptive filter
Introduction to Adaptive Filters1. Basic ConceptsIn the last decades, the field of digital signal processing, and particularly adaptive signal processing, has developed enormously due to the increasingly availability of technology for the implementation of the emerging algorithms. These algorithms have been applied to an extensive number of problems including noise and echo canceling, channel equalization, signal prediction, adaptive arrays as well as many others.An adaptive filter may be understood as a self-modifying digital filter that adjusts its coefficients in order to minimize an error function. This error function, also referred to as the cost function, is a distance measurement between the reference or desired signal and the output of the adaptive filter.2. Basic configuration of an adaptive filterThe basic configuration of an adaptive filter, operating in thediscrete-time domain k, is illustrated in Figure 2.1. In such a scheme, the input signal is denoted by x(k), the reference signal d(k) represents the desired output signal (that usually includes some noise component), y(k) is the output of the adaptive filter, and the error signal is defined as e(k) = d(k).y(k).Fig. 2.1 Basic block diagram of an adaptive filter.The error signal is used by the adaptation algorithm to update the adaptive filter coefficient vector w(k) according to some performance criterion. In general, the whole adaptation process aims at minimizing some metric of the error signal, forcing the adaptive filter output signal to approximate the reference signal in a statistical sense.Fig. 2.2Channel equalization configuration of an adaptive filter: The output signal y(k) estimates the transmitted signal s(k).Fig. 2.3 Predictor configuration of an adaptive filter: The output signaly(k) estimates the presentinput sample s(k) based on past values of this same signal. Therefore, when the adaptive filter output y(k) approximates the reference, the adaptive filter operates as a predictor system.3. Adaptation algorithmSeveral optimization procedures can be employed to adjust the filter coefficients, including, for instance, the least mean-square (LMS) and its normalized version, the data-reusing (DR) including the affine projection (AP), and the recursive least-squares (RLS) algorithms. All these schemes are discussed in Section 2.3, emphasizing their main convergence and implementation characteristics. The remaining of the book focuses on the RLS algorithms, particularly, those employing QR decomposition, which achieve excellent overall convergence performance.3.1 Error MeasurementsAdaptation of the filter coefficients follows a minimization procedure of aparticular objective or cost function. This function is commonly defined as a norm of the error signal e(k). The three most commonly employed norms are the mean-square error (MSE), the instantaneous square error (ISE), and the weighted least-squares (WLS), which are introduced below.3.2 The mean-square errorThe MSE is defined as(k) = E[e2(k)] = E[|d(k)−y(k)|2].Where R and p are the input-signal correlation matrix and thecross-correlation vector between the reference signal and the input signal, respectively, and are defined asR = E[x(k)x T(k)],p = E[d(k)x T(k)].Note, from the above equations, that R and p are not represented as a function of the iteration k or not time-varying, due to the assumed stationarity of the input and reference signals.From Equation (2.5), the gradient vector of the MSE function with respect to the adaptive filter coefficient vector is given byThe so-called Wiener solution w o, that minimizes the MSE cost function, is obtained by equating the gradient vector in Equation (2.8) to zero. Assuming that R is non-singular, one gets that3.3 The instantaneous square errorThe MSE is a cost function that requires knowledge of the error function e(k) at all time k. For that purpose, the MSE cannot be determined precisely in practice and is commonly approximated by other cost functions. The simpler form to estimate the MSE function is to work with the ISE given byIn this case, the associated gradient vector with respect to the coefficient vector is determined asThis vector can be seen as a noisy estimate of the MSE gradient vector defined in Equation (2.8) or as a precise gradient of the ISE function,which, in its own turn, is a noisy estimate of the MSE cost function seen in Section 2.2.1.3.4 The weighted least-squaresAnother objective function is the WLS function given bywhere 0_⎣ < 1 is the so-called forgetting factor. The parameter ⎣ k−i emphasizes the most recent error samples (where i ≈k) in the composition of the deterministic cost function ⎩D(k), giving to this function the ability of modeling non-stationary processes. In addition, since the WLS function is based on several error samples, its stochastic nature reduces in time, being significantly smaller than the noisy ISE nature as k increases.2.3 Adaptation AlgorithmsIn this section, a number of schemes are presented to find the optimal filter solution for the error functions seen in Section 2.2. Each scheme constitutes an adaptation algorithm that adjusts the adaptive filter coefficients in order to minimize the associated error norm.The algorithms seen here can be grouped into three families, namely the LMS, the DR, and the RLS classes of algorithms. Each group presents particular characteristics of computational complexity and speed ofconvergence, which tend to determine the best possible solution to an application at hand.2.3.1 LMS and normalized-LMS algorithmsDetermining the Wiener solution for the MSE problem requires inversion of matrix R, which makes Equation (2.9) hard to implement in real time. One can then estimate the Wiener solution, in a computationally efficient manner, iteratively adjusting the coefficient vector w at each time instant k, in such a manner that the resulting sequence w(k) converges to the desired w o solution, possibly in a sufficiently small number of iterations. The LMS algorithm is summarized in Table 2.1, where the superscripts . and H denote the complex-conjugate and the Hermitian operations, respectively.The LMS algorithm is very popular and has been widely used due to its extreme simplicity. Its convergence speed, however, is highly dependent on the condition number p of the input-signal autocorrelation matrix[1–3],defined as the ratio between the maximum and minimum Eigen values of this matrix.In the NLMS algorithm, when = 0, one has w(k) = w(k−1) and theupdating halts. When υ= 1, the fastest convergence is attained at the price of a higher misadjustment then the one obtained for 0 <υ< 1.2.3.2 Data-reusing LMS algorithmsAs remarked before, the LMS algorithm estimates the MSE function with the current ISE value, yielding a noisy adaptation process. In this algorithm, information from each time sample k is disregarded in future coefficient updates. DR algorithms [9–11] employ present and past samples of the reference and input signals to improve convergence characteristics of the overall adaptation process.As a generalization of the previous idea, the AP algorithm [13–15] is among the prominent adaptation algorithms that allow trade-off between fast convergence and low computational complexity. By adjusting the number of projections, or alternatively, the number of data reuses, one obtains adaptation processes ranging from that of the NLMS algorithm to that of the sliding-window RLS algorithm [16, 17].2.3.3 RLS-type algorithmsThis subsection presents the basic versions of the RLS family of adaptive algorithms. Importance of the expressions presented here cannot be overstated for they allow an easy and smooth reading of the forthcoming chapters.The RLS-type algorithms have a high convergence speed which is independent of the Eigen value spread of the input correlation matrix. These algorithms are also very useful in applications where the environment is slowly varying.The price of all these benefits is a considerable increase in the computational complexity of the algorithms belonging to the RLS family.The main advantages associated to the QR-decomposition RLS(QRD-RLS) algorithms, as opposed to their conventional RLS counterpart, are the possibility of implementation in systolic arrays and the improved numerical behavior in limited precision environment.2.5 ConclusionIt was verified how adaptive algorithms are employed to adjust the coefficients of a digital filter to achieve a desired time-varying performance in several practical situations. Emphasis was given on the description of several adaptation algorithms. In particular, the LMS and the NLMS algorithms were seen as iterative schemes for optimizing the ISE, an instantaneous approximation of the MSE objective function. Data-reuse algorithms introduced the concept of utilizing data from past time samples, resulting in a faster convergence of the adaptive process. Finally, the RLS family of algorithms, based on the WLS function, was seen as the epitome of fast adaptation algorithms, which use all available signal samples to perform the adaptation process. In general, RLSalgorithms are used whenever fast convergence is necessary, for input signals with a high Eigen value spread, and when the increase in the computational load is tolerable. A detailed discussion on the RLS family of algorithms based on the QR decomposition, which also guarantees good numerical properties in finite-precision implementations, constitutes the main goals of this book. Practical examples of adaptive system identification and channel equalization were presented, allowing one to visualize convergence properties, such as misadjustment, speed, and stability, of several distinct algorithms discussed previously.。
计量经济学中英文词汇对照
Controlled experiments Conventional depth Convolution Corrected factor Corrected mean Correction coefficient Correctness Correlation coefficient Correlation index Correspondence Counting Counts Covaห้องสมุดไป่ตู้iance Covariant Cox Regression Criteria for fitting Criteria of least squares Critical ratio Critical region Critical value
Asymmetric distribution Asymptotic bias Asymptotic efficiency Asymptotic variance Attributable risk Attribute data Attribution Autocorrelation Autocorrelation of residuals Average Average confidence interval length Average growth rate BBB Bar chart Bar graph Base period Bayes' theorem Bell-shaped curve Bernoulli distribution Best-trim estimator Bias Binary logistic regression Binomial distribution Bisquare Bivariate Correlate Bivariate normal distribution Bivariate normal population Biweight interval Biweight M-estimator Block BMDP(Biomedical computer programs) Boxplots Breakdown bound CCC Canonical correlation Caption Case-control study Categorical variable Catenary Cauchy distribution Cause-and-effect relationship Cell Censoring
肠道有益大肠杆菌在营养诱导的细菌生长之后产生蛋白质激活宿主饱感信号通路
Article Gut Commensal E.coli Proteins Activate Host Satiety Pathways following Nutrient-Induced Bacterial GrowthGraphical AbstractHighlightsd Regular nutrition stabilizes the E.coli exponential(Exp)growth for20mind E.coli proteome changes in the stationary(Stat)phased Exp and Stat E.coli proteins intra-GI tract stimulate GLP-1and PYY,respectivelyd Stat E.coli proteins i.p.activate anorexigenic neurons in thebrain AuthorsJonathan Breton,Naouel Tennoune, Nicolas Lucas,...,Tomas Ho¨kfelt,Pierre De´chelotte,Sergueı¨O.FetissovCorrespondenceserguei.fetissov@univ-rouen.frIn BriefBreton et al.show that nutrient availability stabilizes the exponential growth ofE.coli within20min with accompanying proteome changes,such as the a-MSH mimetic bacterial protein ClpB,which induces satiety in the host.In vivo administration of E.coli proteins affected rodent food intake,depending on their growthphases. Breton et al.,2016,Cell Metabolism23,1–11February9,2016ª2016Elsevier Inc./10.1016/j.cmet.2015.10.017Gut Commensal E.coli Proteins ActivateHost Satiety Pathways followingNutrient-Induced Bacterial GrowthJonathan Breton,1,5Naouel Tennoune,1,5Nicolas Lucas,1,5Marie Francois,1,5Romain Legrand,1,5Justine Jacquemot,1,5 Alexis Goichon,1,5Charle`ne Gue´rin,1,5Johann Peltier,2,5Martine Pestel-Caron,2,5,6Philippe Chan,3,5David Vaudry,3,5 Jean-Claude do Rego,4,5Fabienne Lie´nard,7Luc Pe´nicaud,7Xavier Fioramonti,7Ivor S.Ebenezer,8Tomas Ho¨kfelt,9 Pierre De´chelotte,1,5,6and Sergueı¨O.Fetissov1,5,*1Inserm UMR1073,Nutrition,Gut and Brain Laboratory,Rouen76183,France2Microbiology Laboratory GRAM,EA2656,Rouen76183,France3PISSARO Proteomic Platform,Mont-Saint-Aignan76821,France4Animal Behavior Platform(SCAC),Rouen76183,France5Institute for Research and Innovation in Biomedicine(IRIB),Rouen University,Normandy University,Rouen76000,France6Rouen University Hospital,CHU Charles Nicolle,Rouen76183,France7Centre for Taste and Feeding Behaviour,UMR6265-CNRS,1324-INRA,Bourgogne Franche Comte´University,Dijon F21000,France8Neuropharmacology Research Group,School of Pharmacy and Biomedical Sciences University of Portsmouth,Portsmouth PO12DT,UK 9Department of Neuroscience,Karolinska Institutet,Stockholm17176,Sweden*Correspondence:serguei.fetissov@univ-rouen.fr/10.1016/j.cmet.2015.10.017SUMMARYThe composition of gut microbiota has been associ-ated with host metabolic phenotypes,but it is not known if gut bacteria may influence host appetite. Here we show that regular nutrient provision stabi-lizes exponential growth of E.coli,with the stationary phase occurring20min after nutrient supply accom-panied by bacterial proteome changes,suggesting involvement of bacterial proteins in host satiety. Indeed,intestinal infusions of E.coli stationary phase proteins increased plasma PYY and their intraperito-neal injections suppressed acutely food intake and activated c-Fos in hypothalamic POMC neurons, while their repeated administrations reduced meal size.ClpB,a bacterial protein mimetic of a-MSH, was upregulated in the E.coli stationary phase, was detected in plasma proportional to ClpB DNA in feces,and stimulatedfiring rate of hypothalamic POMC neurons.Thus,these data show that bacte-rial proteins produced after nutrient-induced E.coli growth may signal meal termination.Furthermore, continuous exposure to E.coli proteins may influ-ence long-term meal pattern.INTRODUCTIONThe composition of gut microbiota has been associated with host metabolic phenotypes(Ley et al.,2006),and transfer of ‘‘obese’’microbiota can induce adiposity(Turnbaugh et al., 2006)and hyperphagia(Vijay-Kumar et al.,2010),suggesting that gut microbiota may influence host feeding behavior. Although the mechanisms underlying effects of gut bacteria on host appetite are unknown,it is likely that they may use the host molecular pathways.The current model of appetite control involves gut-derived hunger and satiety hormones signaling to brain circuitries regu-lating homeostatic and hedonic aspects of feeding(Berthoud, 2011;Murphy and Bloom,2006).Prominent among these are the anorexigenic and orexigenic pathways originating in the hy-pothalamic arcuate nucleus(ARC)such as the proopiomelano-cortin(POMC)and neuropeptide Y(NPY)/agouti-related protein (AgRP)neurons,which are relayed to the paraventricular nucleus (PVN)(Cowley et al.,1999;Garfield et al.,2015;Shi et al.,2013). The ARC and PVN pathways also converge in the lateral parabra-chial nucleus,which sends anorexigenic calcitonin gene-related peptide(CGRP)projections to the central amygdala(CeA) (Carter et al.,2013).The CeA,among other forebrain areas, was shown to integrate homeostatic and motivational aspects of feeding and also receives a sensory input from the brainstem (Areias and Prada,2015;Becskei et al.,2007;Morris and Dolan, 2001).Putative mechanisms linking gut microbiota with the host con-trol of food intake may involve energy harvesting activities of gut bacteria(Turnbaugh et al.,2006)or their production of neuroac-tive transmitters and metabolites(Dinan et al.,2015;Forsythe and Kunze,2013).Another possibility,explored in this study,is that bacterial proteins may act directly on appetite-controlling pathways locally in the gut or via the circulation.In fact,several bacterial proteins have been shown to display sequence homol-ogy with peptide hormones(Fetissov et al.,2008),and we have recently identified that the caseinolytic protease(Clp)B,pro-duced by Escherichia coli(E.coli),is an antigen-mimetic of a-melanocyte-stimulating hormone(a-MSH)(Tennoune et al., 2014).a-MSH is a POMC-derived neuropeptide playing a key role in signaling satiation by activation of the melanocortin recep-tors4(MC4R)(Cone,2005).Although MC4R-mediated a-MSH anorexigenic effects have been mainly ascribed to its central sites of actions(Mul et al.,2013),a recent study shows that Cell Metabolism23,1–11,February9,2016ª2016Elsevier Inc.1activation of the MC4R in the gut enteroendocrine cells stimu-lates release of the satietogenic hormones glucagon-like pep-tide-1(GLP-1)and peptide YY (PYY)(Panaro et al.,2014).Hence,local gut signaling by microbiota-derived a -MSH-like molecules to the enteroendocrine cells is possible (Manning and Batter-ham,2014).Most studies linking nutrition and microbiota have so far focused on the bacterial biodiversity (Parks et al.,2013),but little is known about how nutrient-induced bacterial growth may affect host metabolism.In fact,the dynamics of bacterial growth depend on nutrient supply,implying that regular daily meals should trigger the growth of gut bacteria.After a single provision of nutrients to cultured bacteria long-lasting exponential (Exp)and stationary (Stat)growth phases are observed which differ in protein expressions (Wick et al.,2001).It is,hence,possible that during the different growth phases induced by regular nutrient supply,gut bacteria will synthetize proteins that may differentially influence host appetite acting via intestinal and/or systemic routes.To test this hypothesis,we studied growth dynamics of E.coli K12,a model organism of commensal strains of gut E.coli bacteria,exposed to regular nutrient supply,modeling two daily ing proteomic approaches we compared proteins extracted from E.coli during the established pattern of alternations of the Exp and Stat growth phases and analyzed the identified proteins for their relevance to energy metabolism.ClpB,a bacterial protein mimetic of a -MSH,was used as a marker of E.coli proteins which can be involved in signaling satiety.We have developed a ClpB immunoassay,in order to determine if ClpB production and plasma concentrations may depend on the bacterial growth and intestinal delivery of nutri-ents.Pursuing the possibility that gut bacterial proteins may directly influence peripheral and central pathways involved in appetite control,we examined in mice and rats the effects of E.coli proteins from the Exp or Stat growth phases on food intake and meal pattern,plasma GLP-1and PYY and activa-tions of some key appetite-regulating neurons in the brain such as in the ARC and CeA.RESULTSE.coli Growth Dynamics In Vitro after Regular Nutrient Provision and Proteomic AnalysisA regular,each 12hr provision of Mu¨eller-Hinton (MH)nutritional medium to cultured E.coli increased bacterial biomass and shortened the Exp growth phase (Figures 1A and 1B).As such,after the fifth MH medium supply,the Exp growth phase lasted for 20min,and it did not further change after the following nutrient provisions with the same (D 0.3)relative increase in OD,reflecting an identical bacterial growth (Figures 2A–2C).According to the McFarland standards,an increase of 0.3OD corresponds to an increment of 108–109of bacteria.Because the growth dynamics of regularly fed bacteria can be associated with the host prandial and postprandial phases,we have compared the proteomes of E.coli extracted in the middle of the Exp phase,i.e.,10min after nutrient provision,and in the Stat phase 2hr later,a time normally characterized by a feeling of satiety in the host (Figure 1C).Two-dimensional polyacrylamide gel electrophoresis was performed separately on membrane andcytoplasmic fractions (Figures 1D and 1E).The total number of detected protein spots was 2,895(1,367membrane and 1,528cytoplasmic).Comparative analysis revealed 20differentially ex-pressed (by at least 1.5-fold)membrane proteins (see Figures S1A and S1B).Among them,17proteins showed increased expression in the Exp phase and of these,15were identified by mass spectrometry (Figure S1C).Contrary to the membrane proteins,19from 20differentially expressed cytoplasmic pro-teins showed increased levels during the Stat phase (Fig-ure S2C).Only flagellin had higher expression in the Exp phase.The majority of identified proteins were implicated in either anabolic or catabolic processes (Tables S1and S2),showing an overall mixed metabolic profile in both growth phases,as summarized in Figure 1F.Thus,these data show that the pro-teomes of regularly fed E.coli are qualitatively different between growth phases,although their metabolic profiles are not clearly distinguishable.After the ninth nutrient provision,total bacterial proteins were extracted in the Exp and Stat phases displaying concentrations of 0.088mg/ml and 0.15mg/ml,respectively,and were used in the ATP production assay,tested for ClpB levels and used for in-tracolonic infusions and intraperitoneal (i.p.)injections.ATP Production Capacity by E.coli Proteins In Vitro Is Similar for Both Growth PhasesTo verify if proteome changes between growth phases may influ-ence bacterial energy extraction capacities,the adenosine-50-triphosphate (ATP)production by E.coli K12proteins from the Exp and Stat phases was tested in vitro.We found that proteins from both growth phases were able to increase ATP production from different energy sources (Figure 1G).The ATP concentrations were higher,when a protein-containing mixed energy source,such as the MH medium was used as compared to a sucrose solution.The ATP production increased dose-dependently with concentrations of bacterial proteins.However,no significant differences were found between ATP-producing effects of proteins from the Exp or Stat phases (Figure 1G).These results confirm that bacterial proteins may continue to catalyze ATP production after bacterial lysis suggesting that nutrient-induced bacterial growth may contribute to increased ATP production in the gut.Changes in whole-body ATP content can be relevant to appetite control via regulation of the activity of adenosine-50-monophosphate-activated protein kinase,result-ing in increased food intake,when ATP levels are low and vice versa (Dzamko and Steinberg,2009).Furthermore,increased in-traluminal ATP production may contribute to the digestive pro-cess via gut relaxation (Glasgow et al.,1998).Increased E.coli ClpB Production in the Stat Growth PhaseE.coli ClpB is a conformational protein mimetic of a -MSH,and may be potentially involved in E.coli effects on host feeding.We developed and validated an enzyme-linked immunosorbent assay (ELISA)for detection of E.coli ClpB (for details,see Exper-imental Procedures and Figure S3).To investigate whether ClpB production is different between two growth phases,we used western blot and ELISA.ClpB-corresponding,96KDa bands was detected in all proteins preparations,with an increased mean level during the Stat phase (Figures 1H and 1I).These2Cell Metabolism 23,1–11,February 9,2016ª2016ElsevierInc.changes were further confirmed using the ClpB ELISA,showing almost doubling of ClpB concentrations in the Stat phase (Figure 1J).In Vivo Nutrient-Induced Bacterial Growth &Effects of E.coli Proteins in the Gut to Stimulate GLP-1and PYY Plasma ReleaseTo verify if our in vitro model of regular nutrient-induced E.coli growth is relevant to gut bacterial growth dynamics in vivo,MH medium or water were infused into the colon of anaesthetized rats.We found that instillations of the MH medium,but not water,induced bacterial proliferation in the gut with the Exp growth phase lasting for 20min (Figure 2A),consistent with our in vitro data.To see if the bacterial growth in the gut may be ac-companied by increased plasma ClpB,its concentrations were measured in the portal vein before and after colonic infusions.ClpB was readably detected in rat plasma but no significant differences were found 30or 60min after MH infusion (Figure 2B).Nevertheless,plasma ClpB concentration correlated posi-tively with ClpB DNA content in rat feces (Figure 2C);presence of such correlations was confirmed by an independent study in rats (data not shown).Furthermore,plasma ClpB levels were increased in mice after 3weeks of gavage with wild-type but not with ClpB mutant E.coli (Figure S3F).These data indicate that plasmatic ClpB depends on number ofClpB-expressingFigure 1.Effects of Repeated Nutrient Supply on E.coli Growth In Vitro and Proteomic Analysis(A and B)Dynamics of E.coli K12bacterial growth during nine regular provisions of MH medium.(C–E)Proteins were extracted during the last growth cycle as indicated by arrows during the Exp phase (Exp,a)and the Stat phase (Stat,b).Representative images of Coomassie brilliant blue-stained 2D gels of cytoplasmic proteins extracted from E.coli K12in Exp (D)and in Stat (E)phases;MW,molecular weight.(F)Number of E.coli proteins increased in each growth phase in relation to their catabolic or anabolic properties.(G)Effects of bacterial proteins on ATP production in vitro from different food substrates.(H–J)ClpB protein levels ±SEM in two growth phases was analyzed by western blot (H and I)and ELISA (J).OD,optical density.Student’s t test,*p <0.05.Cell Metabolism 23,1–11,February 9,2016ª2016Elsevier Inc.3bacteria in the gut,but it cannot be a short-term signal of satia-tion to the brain.Next,we determined if growth-dependent changes of E.coli proteomes may influence host appetite control locally in the gut via release of satiety hormones.In a separate experiment,anaesthetized rats received 20min colonic infusions of E.coli proteins from the Exp or Stat phases,both at 0.1mg/kg.The concentrations of ClpB in the colonic mucosa measured 20min after the infusion were higher in rats receiving the Stat phase proteins (Figure 2D)but it was not affected in plasma (Figure 2E).The colonic infusions of E.coli proteins from the Exp,but not the Stat phase,stimulated plasma levels of GLP-1(Figure 2F)and,in contrast,increased plasma levels of PYY were detected after infusion of proteins from the Stat,but not the Exp,phase (Figure 2G).Thus,E.coli proteins produced dur-ing nutrient-induced bacterial growth in the gut may influence short-term appetite control via stimulation of gut satiety hormone release.Food Intake and Hypothalamic and Amygdala c-Fos Activation After Acute E.coli Protein Administration in RatsBecause E.coli -derived ClpB was present in plasma of rats and mice,it is possible that plasmatic E.coli -derived proteins might influence long-term appetite control via a systemic route.Testing this possibility in overnight fasted rats,we found that a single i.p.administration (0.1mg/kg)of the Stat phase membrane fraction of E.coli proteins,decreased 1and 2hr food intake during re-feeding as compared with the control group (Figure 3A).In contrast,administration of the cytoplasmic fraction of the Exp phase E.coli proteins increased 4hr food intake (Figure 3B).Because both cell protein fractions are simultaneously present and in order to see if E.coli proteome may influence spontaneous food intake,free feeding rats were injected before the onset of the dark phase with total bacterial proteins (0.1mg/kg,i.p.).Food intake was measured for 2hr after injec-tions,and was followed by the immunohistochemical analysis of c-Fos expression in brain.We found that rats injected with bacterial proteins from the Stat phase ate less than controls,while food intake was not significantly affected by injections of bacterial proteins from the Exp phase (Figure 3C).An increased number of c-Fos-positive cells was found in the ARC of rats receiving Stat phase proteins (Figure 4D).The majority of c-Fos expressing cells in the ARC contained b -endorphin (controls,71.31%±12.81%,E.coli exp.phase,73.56%±10.45%, E.coli stat.phase,80.50%±9.68%,ANOVA,p =0.36),i.e.,were identified as anorexigenic POMC neurons (Figures 4A–4C).Although the total numbers of b -endor-phin-positive cells were not significantly different among the groups (controls,54.82±10.67cells,E.coli exp.phase,66.03±11.43cells,E.coli stat.phase,66.03±5.06cells,ANOVA,p =0.09),the relative number of c-Fos activated b -endor-phin neurons was increased in rats receiving Stat phase proteins (Figure 4E).Furthermore,the number of c-Fos activated b -endor-phin neurons correlated inversely with food intake (Figure 4F).C-Fos expressing cells were also analyzed in the ARC neigh-boring ventromedial nucleus (VMN,Figures 4G–4I)showing their increased number in rats receiving bacterial proteins from the Stat phase (Figure 4J).Similarly,in the CeA,the number of c-Fos-positive neurons was increased in rats injected with the Stat phase proteins (Figure 4O)and correlated inversely with food intake (Figure 4P).We used dou-ble-immunostaining to see if c-Fos activated CeA neurons were located in the terminal field of CGRP-positive fibers (Figures 4K–4M).A confocal microscopy revealed that c-Fos expressing neu-rons in the CeA were often surrounded by CGRP fibers (Figure 4N),confirming that they were located in the terminal field of anorexi-genic projections from the parabrachial nucleus.Thus,acute systemic increase in E.coli proteins from the Stat phase was asso-ciated with decreased food intake and activation of anorexigenic neurons including both accessible to circulating factors such as in the ARC and in the brain-downstream nuclei such as CeA.Feeding Pattern and Hypothalamic Neuropeptides After Chronic E.coli Protein Injections in MiceTo determine the potential long-term effects of systemic pres-ence of E.coli proteins on body weight,food intake and feeding pattern,two daily injections of E.coli total proteins (0.1mg/kg,i.p.)were administered for one week to free feeding mice.The first day after injections was characterized by significantly lower body weight and food intake in mice receiving bacterial proteins from the Stat but not the Exp phase (Figures 5A and5B).Figure 2.Effects of Intestinal Infusions of Nutrients and E.coli Proteins in Rats(A and B)Bacterial growth dynamics after colonic instillations of MH medium or water,and (B)plasma ClpB before and during instillations.(C)Detection of ClpB DNA in rat feces and its correlation with plasma ClpB levels before in-stillations.(D–G)Effects of colonic infusion of E.coli proteins from Exp and Stat growth phases on concentrations of ClpB in colonic mucosa (D)and plasma (E),and plasma concentrations of GLP-1(F)and PYY (G),before (T 0)and 20min after infusions (T 20).(D)Student’s t test,*p <0.05;(F and G),ANOVA,p =0.02and p =0.0004,respectively,Tukey’s post-test *p <0.05and ***p <0.001.All data are shown as mean ±SEM.4Cell Metabolism 23,1–11,February 9,2016ª2016ElsevierInc.Although daily meal size was not significantly different among the groups,its decrease relative to the day before injection was observed after one week in mice receiving Stat phase pro-teins as compared to controls (Figure 5C).In contrast,meal fre-quency in these mice showed an increasing trend (Figure 5D).Whereas total food intake among the three groups was not significantly different during the study period,mice injected with the Exp phase proteins had increased food intake during the light (inactive)period (Figure 5E),and decreased during the dark (active)period (Figure 5F).In contrast,mice receiving the Stat phase proteins displayed lower food intake than controls in the dark period without any effect in the light period (Figures 5E and 5F).While during the first day after injections mice receiving proteins from the Stat phase displayed increased satiety ratio (Figure 5G),reflecting an increased duration of post-meal intervals relative to the amount of food eaten during pre-ceding meals,the same group showed a tendency toward a decrease at the end of the study (Figure 5H).To get an insight into the molecular changes underlying altered feeding pattern observed after 6days of bacterial protein injec-tions,we analyzed hypothalamic mRNA expression levels of several neuropeptides involved in appetite control.We found that mice receiving the Stat phase proteins showed elevated precursor mRNA levels of anorexigenic brain-derived neurotro-phic factor (BDNF)and of orexin as compared to controls,and of anorexigenic corticotropin-releasing hormone (CRH)as compared to mice injected with proteins from the Exp phase.The latter also showed elevated mRNA levels of BDNF but decreased mRNA levels of the precursor of orexigenic pyrogluta-mylated RFamide peptide (QRFP)(Table S3).These data suggest that long-term systemic effects of E.coli proteins in mice may in-fluence their meal pattern without affecting total energy balance via modulation of hypothalamic neuropeptide expression.Electrophysiological Activation of Hypothalamic POMC Neurons by E.coli -Derived ClpBTo confirm that bacterial proteins present in the circulation may directly activate feeding-related brain circuitry,we used an elec-trophysiological approach.We looked if ClpB,an a -MSH mimetic protein and a marker of E.coli proteins upregulated in the Stat phase,may directly activate ARC POMC neurons.Brain slices from POMC-eGFP mice were examined using cell-attached patch-clamp electrophysiology (Figures 6A and 6B).We found that,bath addition of ClpB (1nM)increased action potential frequency of 50%of the tested ARC POMC neurons (n =7/13)by 229%±109%(basal:2.02±0.78Hz versus ClpB:3.82±1.36Hz;Figures 6C and 6D).In general,POMC neurons did not fully return to their basal firing rate until at least 10min af-ter ClpB application (Figures 6C and6D).Figure 4.Effects of E.coli Proteins on c-Fos Expression in the BrainImmunohistochemical detection of c-Fos (green)in the ARC (A–C),VMN (G–I),and CeA (K–N)2hr after i.p.injections of Exp.and Stat.E.coli proteins in rats.Double staining with b -endorphin (b -end,red)in the ARC (A–C)and with CGRP in the CeA (red,K–N)including a confocal image (N).c-Fos-positive cell number in the ARC (D),VMN (J),and CeA (O).Percentage of b -end activated cells in the ARC (E)and their correlation with food intake (F).Correlation between number of c-Fos cells in the CeA and food intake (P).(D and J)ANOVA p <0.05,Tukey’s post-test,*p <0.05.(E)ANOVA,p =0.006,Tukey’s post-tests *p <0.05and **p <0.01.(O)ANOVA,p =0.0003,Tukey’s post-tests,**p <0.01and ***p <0.001.All data are shown as mean ±SEM.Figure 3.Food Intake after Acute Injections of E.coli Proteins in Rats(A and B)Food intake after i.p.injections of membrane (A)and cytoplasmic (B)fractions of E.coli proteins from Exp and Stat growth phases or PBS as a control (Contr)in rats during refeeding after overnight food restriction.(C)Two hour food intake in free-feeding rats injected with total E.coli proteins before the onset of the dark phase.(A)ANOVA p =0.04,Tukey’s post-test Contr.versus Stat.,*p <0.05,Student’s t test Contr.versus Stat.$p =0.01.(B)Student’s t tests,Exp.versus Contr.and versus Stat.$p <0.05.(C)ANOVA p =0.05,Tukey’s post-test Contr.versus Stat.,*p <0.05and Student’s t test Exp.versus Stat.$p =0.04.All data are shown as mean ±SEM.Cell Metabolism 23,1–11,February 9,2016ª2016Elsevier Inc.5DISCUSSIONOur study reveals that bacterial proteins may physiologically link gut E.coli to host control of appetite involving both short-term ef-fects on satiation,associated with nutrient-induced bacterial growth,acting locally in the gut,as well as long-term regulation of feeding pattern associated with plasmatic changes of bacte-rial proteins that may activate central anorexigenic circuitries.The following main results support this conclusion:(1)regular provision of nutrients stabilizes the Exp growth of E.coli lasting for 20min both in vitro and in vivo;(2)E.coli Stat growth phase was characterized by increased total bacterial protein content and a different proteome profile,including increased expression of ClpB,a bacterial protein mimetic of a -MSH;(3)E.coli proteins dose-dependently stimulated in vitro ATP production;(4)plasma levels of ClpB did not change after nutrient-induced bacterial growth in the gut,but correlated with ClpB DNA in gut microbiota and was increased after chronic E.coli supplementation;(5)in-testinal infusion of E.coli proteins from the Exp or Stat growth phases increased plasma GLP-1or PYY levels,respectively;(6)acute i.p.administrations of E.coli proteins from the Stat phase decreased food intake and led to c-Fos activation in anorexigenic ARC and CeA neurons while their chronic i.p.injec-tions reduced meal size without affecting total food intake and body weight;and finally (7)ClpB stimulated firing rate of ARC POMC neurons ex vivo.Regular Provision of Nutrients and Bacterial GrowthAmong the wide variety of bacteria in the gastrointestinal tract,E.coli is the most abundant facultative anaerobe,justifying it as a model organism for commensal gut bacteria.Here,we show that during regular nutrient supply to cultured E.coli the growth dynamics of a rich bacterial population ischaracterizedFigure 5.Effects of Chronic Injections of E.coli Proteins in Mice(A and B)Effects of bidiurnal injections during 6days,of E.coli proteins from Exp and Stat growth phases or PBS as a control (Contr)in mice on body weight (A),food intake (B),meal size (C),and meal number (D).Total mean food intake was also analyzed separately in light (E)and dark (F)periods.The satiety ratio,expressed as postmeal interval (s)31,000versus food (g)consumed in the preceding meal,at day 1(G)and day 6(H).(A and B)Two-way RM ANOVA,Bonferroni post-tests Contr.versus Stat.,***p <0.001and **p <0.01.(C)Student’s t test Contr.versus Stat.#p <0.05.(E and F)K-W test p <0.002,Dunn’s post-tests,***p <0.001and M-W test #p <0.05.(G)ANOVA p =0.005,Tukey’s post-test,**p <0.01.(H)Student’s t test,$p <0.05.All data are shown as mean ±SEM.by an immediate Exp growth entering the Stat phase 20min after nutrient sup-ply.The growth cycle,which is apparently limited to only one division of bacteria,is then identically reproduced after the next provision of nutrients,suggesting that itcan play a role of a pacemaker.A similar dynamics of bacterial growth in response to nutrient infusion was seen in the rat colon,supporting that our in vitro data can be relevant to in vivo situa-tion,e.g.,in humans taking regular meals,and that it is not limited to E.coli .The 108–109increase of bacterial number remained stable after each new provision,suggesting that the correspond-ing stable production of the bacterial biomass in the gut,including proteins,may play a role in regulation of host feeding.Given that the average prandial phase in humans is similar to the duration of the Exp growth of regularly-fed bacteria,it is tempting to speculate that host satiety may be triggered by gut bacteria reaching the Stat phase 20min after a contact with ingested nu-trients.However,bacterial content in the gastrointestinal tract ranges from 103in the stomach to 1012in the colon.Moreover,about 2hr is necessary for the advancement of ingested nutri-ents through the stomach and small intestine,and the transit through the large intestine requires about 10hr.Because of such a delay of nutrient delivery to most gut bacteria,it is likely that beside the direct contact with the nutrient bolus,bacterial growth during the prandial phase might also be initiated by nutri-ents released into the gut lumen by the Pavlovian cephalic reflex to ingestion (Power and Schulkin,2008).Bacterial Protein Expression during Growth Phases and Intestinal SensingUsing ClpB as a marker of E.coli proteins helped us to determine the putative action sites of bacterial proteins on host appetite pathways.ClpB is a chaperone protein present in both cytosolic and membrane E.coli compartments (Winkler et al.,2010);its increased expression may save bacteria from elevated protein aggregations in the Stat phase (Kwiatkowska et al.,2008).From the other hand,as an a -MSH mimetic,increased ClpB pro-duction may contribute to the activation of anorexigenic6Cell Metabolism 23,1–11,February 9,2016ª2016ElsevierInc.。
journal of chromatography a介绍
journal of chromatography a介绍Journal of Chromatography AJournal of Chromatography A is an international journal publishing original research in the areas of chromatography and related separation techniques. The scientific scope of the journal encompasses the entire field of chromatography, covering all kinds of chromatography with classical and new stationary phases, mass spectrometry, hyphenated techniques, electrochromatography, affinity chromatography, electrophoretic methods, microfluidics, nanoscale separations, capillary electrophoresis, sensors and all related techniques.The journal promotes the development of analytical and preparative chromatographic applications and applicationsin chemometrics and related fields. All aspects of instrumentation, developments in stationary phases and theoretical modeling of separations are welcome. Chromatography of all kinds of materials, be it small molecules, proteins, polysaccharides, lipids or complex mixtures, is of relevance. Authors from all over the world represent a broad range of scientific disciplines,including analytical biochemistry, clinical andpharmaceutical analysis, biotechnology, food chemistry, environmental and forensic analysis.The spectrum of topics covered in the journal includes, but is not limited to, the following:Chromatographic fundamentals and theoryChromatographic instrumentation and engineeringDevelopment of new phases and their applicationsHyphenated techniquesSensors and microfluidic applicationsCapillary electrophoresisSurface chemistry and monolithic columnsAnalysis of compounds in complex systemsDrug metabolism and pharmacokinetic studiesMetabonomics and proteomicsChemical engineering and process analysisFood analysis and environmental analysisApplication oriented papers。
Probability and Stochastic Processes
Probability and Stochastic ProcessesProbability and stochastic processes are essential concepts in the field of mathematics and statistics. They play a crucial role in modeling and analyzing random phenomena, making predictions, and understanding uncertainty. Probability theory deals with the study of random events and their likelihood of occurrence, while stochastic processes involve the study of random variables evolving over time.One of the fundamental principles of probability theory is the concept of probability space, which consists of a sample space, event space, and probability measure. The sample space represents all possible outcomes of an experiment, while the event space consists of subsets of the sample space representing different events. The probability measure assigns a probability to each event, representing the likelihood of that event occurring.In real-world applications, probability theory is used to make predictions and decisions in situations involving uncertainty. For example, in finance,probability theory is used to model stock prices and predict future returns. In healthcare, probability theory is used to assess the effectiveness of medical treatments and predict patient outcomes. In engineering, probability theory isused to analyze the reliability of systems and predict failure rates.Stochastic processes are used to model random phenomena that evolve over time. Examples of stochastic processes include random walks, Markov chains, and Brownian motion. These processes are used in a wide range of applications, including finance, telecommunications, and biology. For example, in finance, stochastic processes are used to model stock prices and interest rates. In telecommunications, stochastic processes are used to model network traffic and optimize resource allocation.One of the key concepts in stochastic processes is the concept of stationarity.A stochastic process is said to be stationary if its statistical properties do not change over time. Stationary processes are easier to analyze and model, making them useful in a wide range of applications. Non-stationary processes, on theother hand, exhibit changing statistical properties over time, making them more challenging to analyze.Overall, probability and stochastic processes are powerful tools for analyzing and modeling random phenomena. They play a crucial role in a wide range of applications, from finance to healthcare to engineering. By understanding these concepts, we can make better predictions, decisions, and assessments in situations involving uncertainty.。
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
The service times in seconds are.
105.84 28.92 98.64 55.56 128.04 45.60 67.80 105.12 48.48 51.84 173.40 51.96 54.12 68.64 93.12 68.88 84.12 68.64 41.52 127.92 42.12 17.88 33.00. The first step is to determine if the od identically distributed. The data must be given in the order collected for independence to be assessed. What if: 1.A new teller has been hired at a bank and the 23 service times represent a task that has a steep learning curve. The expected service time is likely to decrease as the new teller learns how to perform the task more efficiently. 2.The service times represent 23 times to completion of a physically demanding task during an 8-hour shift. If fatigue is a significant factor, the expected time to complete the task is likely to increase with time.
Modeling of Stationary Processes - Chapter 9
4
DATA COLLECTION
•Vending machine sales can be used to show the difficulties: 1.Wrong amount of aggregation. We want to model daily sales, but have only monthly sales. 2.Wrong distribution in time. We have sales for this month and want to model next month's sales. 3. Wrong distribution in space. We want to model sales at a vending machine in location A, but only have sales figures on a vending machine at location B. 4.Insufficient distribution resolution. We want the distribution of the number of soda cans sold at a particular vending machine, but our data is given in cases, effectively rounding the data up to the next multiple of 24, the number of cans in a case. 5.Censored data. We want to model demand, but we only have sales data. If the vending machine ever sells out, this constitutes a right-censored observation. All of these issues show up especially in existing data, and you may never know it.
Collecting data on the appropriate elements of the system of interest is one of the initial and pivotal steps in successful input modeling. • You could be or measuring incorrectly. • There are several things that can be ”wrong" with a data set.
Modeling of Stationary Processes - Chapter 9
3
DATA COLLECTION
Two approaches arise for the collection of data: 1.A designed experiment is conducted to collect the data. 2.Questions are addressed by means of existing data that the modeler had no hand in collecting. The first approach is better in terms of control and the second approach is generally better in terms of cost.
Modeling of Stationary Processes
Modeling of Stationary Processes - Chapter 9
1
Modeling of Stationary Processes
The question considered here is how to model an element (e.g., arrival process, service times) in a discrete-event simulation model given a data set collected on the element of interest. We assume that there is an existing system from which data can be drawn.. There are five basic questions to be answered in the following order: 1.Have the data been sampled in an appropriate fashion? 2.Should a trace-driven model or parametric probability model be selected as an input model? If the latter is chosen, the following three questions arise. 3.What type of distribution seems to fit" the sample? Equivalently, what type of random variate generator seems to have produced the data? Does the Exponential(m), Gamma(a,b), or Lognormal(a, b), for example, most adequately describe the data? 4.What are the value(s) of the parameter(s) that characterize the distribution? If the distribution is Exponential(m), for example, then what is the value of m? 5.How much confidence do we have in our answers to the two previous questions?
Modeling of Stationary Processes - Chapter 9 6
PRELIMINARY DATA ANALYSIS
If a simple linear regression of the service times versus the observation numbers shows a significant nonzero slope, then the identically distributed assumption is probably not right, and a non-stationary model is appropriate. Assume that there is a suspicion that a learning curve is present, which makes us suspect that the service times are decreasing. The scatter plot and least-squares regression line shown in Figure 9.1.1 indicate a slight downward trend in the service times. But is this negative slope significantly different from zero? A hypothesis test shows that the negative slope is more likely due to sampling variability, rather than a systematic decrease in service times over the 23 values collected. We assume a stationary model is appropriate, and the observations can be treated as 23 identically distributed observed service times.