A simple and efficient method for solvent
今日事今日毕的英语作文
In the hustle and bustle of modern life,the adage finish todays tasks today holds significant importance.This principle,when applied to our daily routines,can lead to increased productivity,reduced stress,and a greater sense of accomplishment.Here is an essay that delves into the concept and its practical implications.Title:The Importance of Finish Todays Tasks TodayIntroductionIn an era where time is a precious commodity,the saying finish todays tasks today serves as a guiding light for those seeking to maximize their efficiency and effectiveness.This essay will explore the reasons behind this approach,its benefits,and strategies for implementation.The ConceptThe phrase finish todays tasks today is a call to action,urging individuals to complete their daily responsibilities without procrastination.It emphasizes the importance of immediate action and discourages the habit of postponing tasks to a later date.Benefits of the Principle1.Increased Productivity:By focusing on completing tasks as they arise,individuals can avoid the accumulation of work,which can lead to overwhelming workloads and decreased productivity.2.Reduced Stress:Procrastination often leads to stress as deadlines approach.Tackling tasks promptly can alleviate this pressure and create a more relaxed work environment.3.Enhanced Time Management:Adhering to this principle helps individuals develop better time management skills,ensuring that time is used wisely and not wasted.4.Sense of Achievement:Completing tasks as they come provides a sense of accomplishment and satisfaction,which can boost morale and motivation. Strategies for Implementation1.Prioritization:Identify the most critical tasks and tackle them first.This ensures that the most important work is completed on time.2.Time Management Tools:Utilize calendars,todo lists,and apps to organize and schedule tasks effectively.3.Setting Realistic Goals:Break down larger tasks into smaller,manageable goals to make them less daunting and more achievable.4.Avoiding Distractions:Minimize interruptions and distractions to maintain focus and complete tasks efficiently.5.Regular Reviews:Regularly review progress and adjust plans as necessary to stay on track with daily tasks.ConclusionThe practice of finishing todays tasks today is not just a time management technique it is a lifestyle choice that can lead to a more organized,stressfree,and productive life.By adopting this approach,individuals can enhance their professional and personal lives, achieving more in less time and with greater satisfaction.ReflectionReflecting on the days work at its end,one can take pride in knowing that all tasks have been addressed,leaving the mind at ease and ready to tackle new challenges the following day.This practice fosters a sense of control over ones life and work, contributing to overall wellbeing and success.In conclusion,embracing the philosophy of finish todays tasks today is a proactive step towards mastering the art of time management and personal productivity.It is a principle that,when consistently applied,can transform ones approach to life,leading to a more fulfilling and efficient existence.。
单片微型计算机原理与接口技术_高锋版_课后答案全集
单片微型计算机原理与接口技术高锋版课后答案第一章略第二章【单片机的基本结构】思考与练习题解析【2-1】8()C51单片机在片内集成了哪些主要逻辑功能部件?各个逻辑部件的最主要功能是什么?【答】80C51单片机在片内主要包含中央处理器CPU(算术逻辑单元ALU及控制器等)、只读存储器ROM、读/写存储器RAM、定时器/计数器、并行I/O口Po~P3、串行口、中断系统以及定时控制逻辑电路等,各部分通过内部总线相连。
1.中央处理器(CPU)单片机中的中央处理器和通用微处理器基本相同,是单片机的最核心部分,主要完成运算和控制功能,又增设了“面向控制”的处理功能,增强了实时性。
80C51的CPU是一个字长为8位的中央处理单元。
2.内部程序存储器根据内部是否带有程序存储器而形成三种型号:内部没有程序存储器的称为80C31;内部带ROM的称为80c51,80c51共有4 KB掩膜ROM;内部以EPROM代替RoM的称为87C51。
程序存储器用于存放程序和表格、原始数据等。
3.内部数据存储器(RAM)在单片机中,用读/写存储器(RAM)来存储程序在运行期间的工作变量和数据。
80C51中共有256个RAM单元。
4.I/O口单片机提供了功能强、使用灵活的I/O引脚,用于检测与控制。
有些I/O引脚还具有多种功能,比如可以作为数据总线的数据线、地址总线的地址线或控制总线的控制线等。
有的单片机I/0引脚的驱动能力增大。
5.串行I/O口目前高档8位单片机均设置了全双工串行I/0口,用以实现与某些终端设备进行串行通信,或与一些特殊功能的器件相连的能力,甚至用多个单片机相连构成多机系统。
有些型号的单片机内部还包含两个串行I/O口。
6·定时器/计数器80c51单片机内部共有两个16位定时器/计数器,80C52则有3个16位定时器/计数器。
定时器/计数器可以编程实现定时和计数功能。
7.中断系统80C51单片机的中断功能较强,具有内、外共5 个中断源,具有两个中断优先级。
写作业换个优雅的说法英语
Composing academic assignments with a touch of sophistication in English can be referred to in various ways,depending on the context and the level of formality desired. Here are some elegant alternatives to doing homework:1.Crafting Scholarly Compositions This term implies a thoughtful and deliberate approach to writing assignments,suggesting a focus on quality and depth.2.Engaging in Intellectual Endeavors This phrase elevates the act of homework to a pursuit of knowledge and understanding,emphasizing the intellectual effort involved.3.Pursuing Academic Excellence This suggests a commitment to high standards and a dedication to performing at ones best in academic tasks.4.Participating in the Pursuit of Knowledge This phrase frames homework as part of a broader journey of learning and discovery.5.Executing Academic Tasks with Grace This option combines the idea of performing homework with an elegant and refined approach.6.Authoring Academic Works This term positions the student as an author,suggesting a creative and original contribution to their field of study.7.Conducting Scholarly Research This phrase emphasizes the investigative and analytical aspects of homework,particularly when it involves gathering and synthesizing information.8.Embarking on an Academic Journey This metaphorical expression paints homework as part of a larger educational adventure.9.Fostering Intellectual Growth through Assignments This option highlights the developmental aspect of homework,suggesting that it contributes to the students overall intellectual progress.10.Refining Cognitive Skills through Scholarly Activities This term underscores the idea that homework is not just about completing tasks but also about honing critical thinking and other cognitive abilities.11.Elaborating on Theoretical Concepts This phrase is particularly apt for assignments that require a deep understanding and application of theoretical frameworks.12.Illustrating Understanding through Written Expression This option highlights the communicative aspect of homework,suggesting that it serves as a medium for demonstrating comprehension.13.Delving into the Realm of Academic Inquiry This phrase casts homework as an exploration of academic subjects,indicating a deep dive into specific areas of interest. 14.Synthesizing Information into Coherent Arguments This term is suitable for assignments that require the integration of various ideas into a wellstructured argument.15.Evolving as a Scholar through Written Reflection This phrase positions homework asa tool for personal and academic development,with an emphasis on reflection and growth.Each of these alternatives can be used to describe the process of doing homework in a way that is not only more elegant but also reflects the intellectual and creative effort involved in academic work.。
高中牛津英语模块三unit Project课件
Enjoy some old pictures
Enjoy some cave paintings in Dunhuang China:
Ancient City of Rome:
Alexander the Great 亚历山大大帝
公元前356年7月20日 -前323年6月10日
6. What happened to his kingdom after he died? His generals divided his kingdom among themselves.
• What do you think of Alexander the Great? • Do you think he is great because he occupied more land than anyone before? • Do you know any other famous person who played key roles in history?
The father of Western philosophy
• Do you interest in philosophy? • What do you know about philosophy? • Who is the father of Western philosophy? • What do you know about the father of
Distant Greece. 2. When did Alexander become king?
At the age of twenty after his father died. 3. What was his ambition?
LucasKanadeMeetsHornSchunckCombiningLocaland GlobalOptic
International Journal of Computer Vision61(3),211–231,2005c 2005Springer Science+Business Media,Inc.Manufactured in The Netherlands. Lucas/Kanade Meets Horn/Schunck:Combining Local and Global OpticFlow MethodsANDR´ES BRUHN AND JOACHIM WEICKERTMathematical Image Analysis Group,Faculty of Mathematics and Computer Science,Saarland University,Building27,66041Saarbr¨u cken,Germanybruhn@mia.uni-saarland.deweickert@mia.uni-saarland.deCHRISTOPH SCHN¨ORRComputer Vision,Graphics and Pattern Recognition Group,Faculty of Mathematics and Computer Science,University of Mannheim,68131Mannheim,Germanyschnoerr@uni-mannheim.deReceived August5,2003;Revised April22,2004;Accepted April22,2004First online version published in October,2004Abstract.Differential methods belong to the most widely used techniques for opticflow computation in image sequences.They can be classified into local methods such as the Lucas–Kanade technique or Big¨u n’s structure tensor method,and into global methods such as the Horn/Schunck approach and its extensions.Often local methods are more robust under noise,while global techniques yield denseflowfields.The goal of this paper is to contribute to a better understanding and the design of novel differential methods in four ways:(i)We juxtapose the role of smoothing/regularisation processes that are required in local and global differential methods for opticflow computation.(ii)This discussion motivates us to describe and evaluate a novel method that combines important advantages of local and global approaches:It yields denseflowfields that are robust against noise.(iii)Spatiotemporal and nonlinear extensions as well as multiresolution frameworks are presented for this hybrid method.(iv)We propose a simple confidence measure for opticflow methods that minimise energy functionals.It allows to sparsify a dense flowfield gradually,depending on the reliability required for the resultingflparisons with experiments from the literature demonstrate the favourable performance of the proposed methods and the confidence measure. Keywords:opticflow,differential techniques,variational methods,structure tensor,partial differential equations, confidence measures,performance evaluation1.IntroductionIll-posedness is a problem that is present in many im-age processing and computer vision techniques:Edgedetection,for example,requires the computation of im-age derivatives.This problem is ill-posed in the senseof Hadamard,1as small perturbations in the signalmay create largefluctuations in its derivatives(Yuilleand Poggio,1986).Another example consists of opticflow computation,where the ill-posedness manifestsitself in the nonuniqueness due to the aperture prob-lem(Bertero et al.,1988):The data allow to computeonly the opticflow component normal to image edges.Both types of ill-posedness problems appear jointlyin so-called differential methods for opticflow recov-ery,where opticflow estimation is based on computing212Bruhn,Weickert and Schn¨o rrspatial and temporal image derivatives.These tech-niques can be classified into local methods that may optimise some local energy-like expression,and global strategies which attempt to minimise a global en-ergy functional.Examples of thefirst category include the Lucas–Kanade method(Lucas and Kanade,1981; Lucas,1984)and the structure tensor approach of Big¨u n and Granlund(1988)and Big¨u n et al.(1991), while the second category is represented by the clas-sic method of Horn and Schunck(Horn and Schunck, 1981)and its numerous discontinuity-preserving vari-ants(Alvarez et al.,1999;Aubert et al.,1999;Black and Anandan,1991;Cohen,1993;Heitz and Bouthemy, 1993;Kumar et al.,1996;Nagel,1983;Nesi,1993; Proesmans et al.,1994;Schn¨o rr,1994;Shulman and Herv´e,1989;Weickert and Schn¨o rr,2001).Differential methods are rather popular:Together with phase-based methods such as(Fleet and Jepson,1990)they belong to the techniques with the best performance(Barron et al.,1994;Galvin et al.,1998).Local methods may offer relatively high robustness under noise,but do not give denseflowfields.Global methods,on the other hand, yieldflowfields with100%density,but are experimen-tally known to be more sensitive to noise(Barron et al., 1994;Galvin et al.,1998).A typical way to overcome the ill-posedness prob-lems of differential opticflow methods consists of the use of smoothing techniques and smoothness as-sumptions:It is common to smooth the image se-quence prior to differentiation in order to remove noise and to stabilise the differentiation process.Lo-cal techniques use spatial constancy assumptions on the opticflowfield in the case of the Lucas–Kanade method,and spatiotemporal constancy for the Big¨u n method.Global approaches,on the other hand,sup-plement the opticflow constraint with a regularising smoothness term.Surprisingly,the actual role and the difference between these smoothing strategies,how-ever,has hardly been addressed in the literature so far. In afirst step of this paper we juxtapose the role of the different smoothing steps of these methods.We shall see that each smoothing process offers certain advantages that cannot be found in other cases.Conse-quently,it would be desirable to combine the different smoothing effects of local and global methods in or-der to design novel approaches that combine the high robustness of local methods with the full density of global techniques.One of the goals of the present pa-per is to propose and analyse such an embedding of local methods into global approaches.This results in a technique that is robust under noise and givesflow fields with100%density.Hence,there is no need for a postprocessing step where sparse data have to be interpolated.On the other hand,it has sometimes been criticised that there is no reliable confidence measure that al-lows to sparsify the result of a denseflowfield such that the remainingflow is more reliable(Barron et al., 1994).In this way it would be possible to compare the real quality of dense methods with the character-istics of local,nondense approaches.In our paper we shall present such a measure.It is simple and applica-ble to the entire class of energy minimising global op-ticflow techniques.Our experimental evaluation will show that this confidence measure can give excellent results.Our paper is organised as follows.In Section2 we discuss the role of the different smoothing pro-cesses that are involved in local and global opticflow approaches.Based on these results we propose two combined local-global(CLG)methods in Section3, one with spatial,the other one with spatiotemporal smoothing.In Section4nonlinear variants of the CLG method are presented,while a suitable multiresolu-tion framework is discussed in Section5.Our nu-merical algorithm is described in Section6.In Sec-tion7,we introduce a novel confidence measure for all global opticflow methods that use energy func-tionals.Section8is devoted to performance evalua-tions of the CLG methods and the confidence mea-sure.A summary and an outlook to future work is given in Section9.In the Appendix,we show how the CLG principle has to be modified if one wants to replace the Lucas–Kanade method by the struc-ture tensor method of Big¨u n and Granlund(1988)and Big¨u n et al.(1991).1.1.Related WorkIn spite of the fact that there exists a very large number of publications on motion analysis(see e.g.(Mitiche and Bouthemy,1996;Stiller and Konrad,1999)for reviews),there has been remarkably little work de-voted to the integration of local and global opticflow methods.Schn¨o rr(Schn¨o rr,1993)sketched a frame-work for supplementing global energy functionals with multiple equations that provide local data constraints. He suggested to use the output of Gaussianfilters shifted in frequency space(Fleet and Jepson,1990)orLucas/Kanade Meets Horn/Schunck213local methods incorporating second-order derivatives (Tretiak and Pastor,1984;Uras et al.,1988),but did not consider methods of Lucas–Kanade or Big¨u n type. Our proposed technique differs from the majority of global regularisation methods by the fact that we also use spatiotemporal regularisers instead of spa-tial ones.Other work with spatiotemporal regularisers includes publications by Murray and Buxton(1987), Nagel(1990),Black and Anandan(1991),Elad and Feuer(1998),and Weickert and Schn¨o rr(2001). While the noise sensitivity of local differential methods has been studied intensively in recent years (Bainbridge-Smith and Lane,1997;Ferm¨u ller et al., 2001;J¨a hne,2001;Kearney et al.,1987;Ohta,1996; Simoncelli et al.,1991),the noise sensitivity of global differential methods has been analysed to a signifi-cantly smaller extent.In this context,Galvin et al. (1998)have compared a number of classical methods where small amounts of Gaussian noise had been added.Their conclusion was similar to thefindings of Barron et al.(1994):the global approach of Horn and Schunck is more sensitive to noise than the local Lucas–Kanade method.A preliminary shorter version of the present paper has been presented at a conference(Bruhn et al.,2002). Additional work in the current paper includes(i)the use of nonquadratic penalising functions,(ii)the ap-plication of a suitable multiresolution strategy,(iii)the proposal of a confidence measure for the entire class of global variational methods,(iv)the integration of the structure tensor approach of Big¨u n and Granlund (1988)and Big¨u n et al.(1991)and(v)a more extensive experimental evaluation.2.Role of the Smoothing ProcessesIn this section we discuss the role of smoothing tech-niques in differential opticflow methods.For simplicity we focus on spatial smoothing.All spatial smoothing strategies can easily be extended into the temporal domain.This will usually lead to improved results (Weickert and Schn¨o rr,2001).Let us consider some image sequence g(x,y,t), where(x,y)denotes the location within a rectangular image domain ,and t∈[0,T]denotes time.It is com-mon to smooth the image sequence prior to differentia-tion(Barron et al.,1994;Kearney et al.,1987),e.g.by convolving each frame with some Gaussian Kσ(x,y) of standard deviationσ:f(x,y,t):=(Kσ∗g)(x,y,t),(1)The low-pass effect of Gaussian convolution removes noise and other destabilising high frequencies.In a sub-sequent opticflow method,we may thus callσthe noise scale.Many differential methods for opticflow are based on the assumption that the grey values of image objects in subsequent frames do not change over time:f(x+u,y+v,t+1)=f(x,y,t),(2) where the displacementfield(u,v) (x,y,t)is called opticflow.For small displacements,we may perform afirst order Taylor expansion yielding the opticflow constraintf x u+f y v+f t=0,(3) where subscripts denote partial derivatives.Evidently, this single equation is not sufficient to uniquely com-pute the two unknowns u and v(aperture problem): For nonvanishing image gradients,it is only possible to determine theflow component parallel to∇f:= (f x,f y) ,i.e.normal to image edges.This so-called normalflow is given byw n=−f t∇f.(4)Figure1(a)depicts one frame from the famous Hamburg taxi sequence.2We have added Gaussian noise,and in Fig.1(b)–(d)we illustrate the impact of presmoothing the image data on the normalflow. While some moderate presmoothing improves the re-sults,great care should be taken not to apply too much presmoothing,since this would severely destroy im-portant image structure.In order to cope with the aperture problem,Lucas and Kanade(1981)and Lucas(1984)proposed to assume that the unknown opticflow vector is constant within some neighbourhood of sizeρ.In this case it is possible to determine the two constants u and v at some location (x,y,t)from a weighted least squarefit by minimising the functionE L K(u,v):=Kρ∗(f x u+f y v+f t)2.(5) Here the standard deviationρof the Gaussian serves as an integration scale over which the main contribution of the least squarefit is computed.A minimum(u,v)of E L K satisfies∂u E L K=0and ∂v E L K=0.This gives the linear systemKρ∗f2xKρ∗(f x f y)Kρ∗(f x f y)Kρ∗f2yuv=−Kρ∗(f x f t)−Kρ∗(f y f t)(6)214Bruhn,Weickert and Schn¨orrFigure 1.From left to right,and from top to bottom :(a)Frame 10of the Hamburg taxi sequence,where Gaussian noise with standard deviationσn =10has been added.The white taxi turns around the corner,the left car drives to the right,and the right van moves to the left.(b)Normal flow magnitude without presmoothing.(c)Normal flow magnitude,presmoothing with σ=1.(d)Ditto,presmoothing with σ=5.(e)Lucas-Kanade method with σ=0,ρ=7.5.(f)Ditto,σ=0,ρ=15.(g)Optic flow magnitude with the Horn-Schunck approach,σ=0,α=105.(h)Ditto,σ=0,α=106.which can be solved provided that its system matrix is invertible.This is not the case in flat regions where the image gradient vanishes.In some other regions,the smaller eigenvalue of the system matrix may be close to 0,such that the aperture problem remains present and the data do not allow a reliable determination of the full optic flow.All this results in nondense flow fields.They constitute the most severe drawback of local gradient methods:Since many computer vision applications require dense flow estimates,subsequent interpolation steps are needed.On the other hand,one may use the smaller eigenvalue of the system matrix as a confidence measure that characterises the reliability of the estimate.Experiments by Barron et al.(1994)indicated that this performs better than the trace-based confidence measure in Simoncelli et al.(1991).Figure 1(e)and (f)show the influence of the integra-tion scale ρon the final result.In these images we haveLucas/Kanade Meets Horn/Schunck215 displayed the entireflowfield regardless of its localreliability.We can see that in each case,theflowfieldhas typical structures of orderρ.In particular,a suffi-ciently large value forρis very successful in renderingthe Lucas–Kanade method robust under noise.In order to end up with denseflow estimates one mayembed the opticflow constraint into a regularisationframework.Horn and Schunck(Horn and Schunck,1981)have pioneered this class of global differen-tial methods.They determine the unknown functionsu(x,y,t)and v(x,y,t)as the minimisers of the globalenergy functionalE HS(u,v)=((f x u+f y v+f t)2+α(|∇u|2+|∇v|2))dx dy(7)where the smoothness weightα>0serves as regu-larisation parameter:Larger values forαresult in astronger penalisation of largeflow gradients and leadto smootherflowfields.Minimising this convex functional comes down tosolving its corresponding Euler–Lagrange equations(Courant and Hilbert,1953;Elsgolc,1961).They aregiven by0= u−1αf2x u+f x f y v+f x f t,(8)0= v−1f x f y u+f2y v+f y f t.(9)with reflecting boundary conditions. denotes the spa-tial Laplace operator::=∂xx+∂yy.(10) The solution of these diffusion–reaction equations is not only unique(Schn¨o rr,1991),it also benefits from thefilling-in effect:At locations with|∇f|≈0,no reliable localflow estimate is possible,but the reg-ulariser|∇u|2+|∇v|2fills in information from theneighbourhood.This results in denseflowfields and makes subsequent interpolation steps obsolete.This is a clear advantage over local methods.It has,however,been criticised that for such global differential methods,no good confidence measures are available that would help to determine locations where the computations are more reliable than elsewhere (Barron et al.,1994).It has also been observed that they may be more sensitive to noise than local differential methods(Barron et al.,1994;Galvin et al.,1998).An explanation for this behaviour can be given as follows.Noise results in high image gradients.They serve as weights in the data term of the regularisation functional(7).Since the smoothness term has a con-stant weightα,smoothness is relatively less important at locations with high image gradients than elsewhere. As a consequence,flowfields are less regularised at noisy image structures.This sensitivity under noise is therefore nothing else but a side-effect of the desired filling-in effect.Figure1(g)and(h)illustrate this be-haviour.Figure1(g)shows that theflowfield does not reveal a uniform scale:It lives on afine scale at high gra-dient image structures,and the scale may become very large when the image gradient tends to zero.Increasing the regularisation parameterαwillfinally also smooth theflowfield at noisy structures,but at this stage,it might already be too blurred inflatter image regions (Fig.1(h)).3.A Combined Local–Global MethodWe have seen that both local and global differential methods have complementary advantages and short-comings.Hence it would be interesting to construct a hybrid technique that constitutes the best of two worlds:It should combine the robustness of local methods with the density of global approaches.This shall be done next.We start with spatial formulations before we extend the approach to the spatiotemporal domain.3.1.Spatial ApproachIn order to design a combined local–global(CLG) method,let usfirst reformulate the previous ing the notationsw:=(u,v,1) ,(11)|∇w|2:=|∇u|2+|∇v|2,(12)∇3f:=(f x,f y,f t) ,(13) Jρ(∇3f):=Kρ∗(∇3f∇3f )(14)it becomes evident that the Lucas–Kanade method min-imises the quadratic formE L K(w)=w Jρ(∇3f)w,(15)216Bruhn,Weickert and Schn¨o rrwhile the Horn–Schunck technique minimises the functionalE HS(w)=(w J0(∇3f)w+α|∇w|2)dx dy.(16)This terminology suggests a natural way to extend the Horn–Schunck functional to the desired CLG func-tional.We simply replace the matrix J0(∇3f)by the structure tensor Jρ(∇3f)with some integration scale ρ>0.Thus,we propose to minimise the functionalE CLG(w)=w Jρ(∇3f)w+α|∇w|2dx dy.(17)Its minimisingflowfield(u,v)satisfies the Euler–Lagrange equations0= u−1αKρ∗f2xu+Kρ∗(f x f y)v+Kρ∗(f x f t),(18)0= v−1Kρ∗(f x f y)u+Kρ∗f2yv+Kρ∗(f y f t).(19)It should be noted that these equations are hardly more complicated than the original Horn–Schunck Eqs.(8) and(9).All one has to do is to evaluate the terms con-taining image data at a nonvanishing integration scale. The basic structure with respect to the unknown func-tions u(x,y,t)and v(x,y,t)is identical.It is there-fore not surprising that the well-posedness proof for the Horn–Schunck method that was presented in(Schn¨o rr, 1991)can also be extended to this case.3.2.Spatiotemporal ApproachThe previous approaches used only spatial smooth-ness operators.Rapid advances in computer technol-ogy,however,makes it now possible to consider also spatiotemporal smoothness operators.Formal exten-sions in this direction are straightforward.In general, one may expect that spatiotemporal formulations give better results than spatial ones because of the additional denoising properties along the temporal direction.In the presence of temporalflow discontinuities smooth-ing along the time axis should only be used moderately. However,even in this case one can observe the benefi-cial effect of temporal information.A spatiotemporal variant of the Lucas–Kanade ap-proach simply replaces convolution with2-D Gaus-sians by spatiotemporal convolution with3-D Gaus-sians.This still leads to a2×2linear system of equa-tions for the two unknowns u and v. Spatiotemporal versions of the Horn-Schunck method have been considered by Elad and Feuer (1998),while discontinuity preserving global methods with spatiotemporal regularisers have been proposed in different formulations in Black and Anandan(1991), Murray and Buxton(1987),Nagel(1990),Weickert and Schn¨o rr(2001).Combining the temporal extended variant of both the Lucas–Kanade and the Horn–Schunck method we obtain a spatiotemporal version of our CLG functional given byE C LG3(w)=×[0,T](w Jρ(∇3f)w+α|∇3w|2)dx dy dt(20) where convolutions with Gaussians are now to be un-derstood in a spatiotemporal way and|∇3w|2:=|∇3u|2+|∇3v|2.(21)Due to the different role of space and time the spa-tiotemporal Gaussians may have different standard de-viations in both directions.Let us denote by J nm the component(n,m)of the structure tensor Jρ(∇3f). Then the Euler–Lagrange equations for(20)are given by3u−1α(J11u+J12v+J13)=0,(22) 3v−1(J12u+J22v+J23)=0.(23)One should note that they have the same structure as(18)–(19),apart from the fact that spatiotempo-ral Gaussian convolution is used,and that the spa-tial Laplacean is replaced by the spatiotemporal Laplacean3:=∂xx+∂yy+∂tt.(24)The spatiotemporal Lucas–Kanade method is similar to the approach of Big¨u n and Granlund(1988)and Big¨u n et al.(1991).In the Appendix we show how the latter method can be embedded in a global energy functional.Lucas/Kanade Meets Horn/Schunck217 4.Nonquadratic ApproachSo far the underlying Lucas–Kanade and Horn–Schunck approaches are linear methods that are basedon quadratic optimisation.It is possible to replacethem by nonquadratic optimisation problems that leadto nonlinear methods.From a statistical viewpointthis can be regarded as applying methods from ro-bust statistics where outliers are penalised less severelythan in quadratic approaches(Hampel et al.,1986;Huber,1981).In general,nonlinear methods give bet-ter results at locations withflow discontinuities.Ro-bust variants of the Lucas–Kanade method have beeninvestigated by Black and Anandan(1996)and byYacoob and Davis(1999),respectively,while a surveyof the numerous convex discontinuity-preserving reg-ularisers for global opticflow methods is presented inWeickert and Schn¨o rr(2001).In order to render our approach more robust againstoutliers in both the data and the smoothness term wepropose the minimisation of the following functional:E CLG3−N(w)=×[0,T](ψ1(w Jρ(∇3f)w)+αψ2(|∇3w|2))dx dy dt(25)whereψ1(s2)andψ2(s2)are nonquadratic penalisers.Encouraging experiments with related continuous en-ergy functionals have been performed by Hinterbergeret al.(2002).Suitable nonquadratic penalisers can bederived from nonlinear diffusionfilter design,wherepreservation or enhancement of discontinuities is alsodesired(Weickert,1998).In order to guarantee well–posedness for the remaining problem,we focus onlyon penalisers that are convex in s.In particular,we usea function that has been proposed by Charbonnier et al.(1994):ψi(s2)=2β2i1+s2i,i∈1,2(26)whereβ1andβ2are scaling parameters.Under some technical requirements,the choice of convex penalis-ers ensures a unique solution of the minimisation prob-lem and allows to construct simple globally convergent algorithms.The Euler–Lagrange equations of the energy func-tional(25)are given by0=div(ψ 2(|∇3w|2)∇3u)−1ψ 1(w Jρ(∇3f)w)(J11u+J12v+J13),(27) 0=div(ψ 2(|∇3w|2)∇3v)−1ψ 1(w Jρ(∇3f)w)(J21v+J22u+J23).(28) withψ i(s2)=11+s2β2i,i∈1,2(29)One should note that for large values ofβi the nonlinearcase comes down to the linear one sinceψi(s2)≈ 1.5.Multiresolution ApproachAll variants of the CLG method considered so far are based on a linearisation of the grey value con-stancy assumption.As a consequence,u and v are re-quired to be relatively small so that the linearisation holds.Obviously,this cannot be guaranteed for arbi-trary sequences.However,there are strategies that al-low to overcome this limitation.These so called multi-scale focusing or multiresolution techniques(Ananden, 1989;Black and Anandan,1996;M´e min and P´e rez, 1998;M´e min and P´e rez,2002)incrementally compute the opticflowfield based on a sophisticated coarse-to-fine strategy:Starting from a coarse scale the resolution is refined step by step.However,the estimatedflowfield at a coarser level is not used as initalisation at the nextfiner scale.In particular for energy functionals with a global minimum,such a proceeding would only lead to an ac-celeration of the convergence,since the result would not change.Instead,the coarse scale motion is used to warp(correct)the original sequence before going to the nextfiner level.This compensation for the already computed motion results in a hierarchy of modified problems that only require to compute small displace-mentfields,the so called motion increments.Thus it is not surprising that thefinal displacementfield obtained by a summation of all motion increments is much more accurate regarding the linearisation of the grey value constancy assumption.218Bruhn,Weickert and Schn¨o rrLet δw m denote the motion increment at resolutionlevel m ,where m =0is the coarsest level with ini-talisation w 0=(0,0,0) .Then δw m is obtained by optimisation of the following spatiotemporal energy functional:E m CLG3−N (δw m )= ×[0,T ](ψ1(δw m J ρ(∇3f (x +w m ))δw m )+αψ2(|∇3(w m +δw m )|2))dxwhere w m +1=w m +δw m and x =(x ,y ,t ).One shouldnote that warping the original sequence does only af-fect the data term.Since the smoothness assumption applies to the complete flow field,w m +δw m is used as argument of the penaliser.If we denote the structure tensor of the corrected se-quence by J mρ=J ρ(∇3f (x +w m )),the corresponding Euler–Lagrange equations are given by0=div (ψ2(|∇3(w m +δw m )|2)∇3δu m )−1αψ 1 δw m J m ρδw J m 11δu +J m 12δv +J m13,(30)0=div (ψ2(|∇3(w m +δw m )|2)∇3δv m )−1αψ 1 δw m J m ρδw J m 21δv +J m 22δu +J m23.(31)6.Algorithmic Realisation 6.1.Spatial and Spatiotemporal ApproachLet us now discuss a suitable algorithm for the CLG method (18)and (19)and its spatiotemporal variant.To this end we consider the unknown functions u (x ,y ,t )and v (x ,y ,t )on a rectangular pixel grid of size h ,and we denote by u i the approximation to u at some pixel i with i =1,...,N .Gaussian convolution is realised in the spatial/spatiotemporal domain by discrete con-volution with a truncated and renormalised Gaussian,where the truncation took place at 3times the stan-dard deviation.Symmetry and separability has been exploited in order to speed up these discrete convolu-tions.Spatial derivatives of the image data have been approximated using a sixth-order approximation with the stencil (−1,9,−45,0,45,−9,1)/(60h ).Tempo-ral derivatives are either approximated with a sim-ple two-point stencil or the fifth-order approximation(−9,125,−2250,2250,−125,9)/(1920h ).Let us denote by J nmi the component (n ,m )of the structure tensor J ρ(∇f )in some pixel i .Furthermore,let N (i )denote the set of (4in 2-D,6in 3-D)neigh-bours of pixel i .Then a finite difference approxima-tion to the Euler–Lagrange equations (18)–(19)is given by0= j ∈N (i )u j −u i h 2−1(J 11i u i +J 12i v i +J 13i ),(32)0= j ∈N (i )v j −v i h 2−1α(J 21i u i +J 22i v i +J 23i )(33)for i =1,...,N .This sparse linear system of equationsmay be solved iteratively.The successive overrelax-ation (SOR)method (Young,1971)is a good compro-mise between simplicity and efficiency.If the upper index denotes the iteration step,the SOR method can be written asu k +1i =(1−ω)u k i +ωj ∈N −(i )u k +1j + j ∈N +(i )u k j −h 2αJ 12i v k i +J 13i|N (i )|+h 2αJ 11i,(34)v k +1i =(1−ω)v k i +ωj ∈N −(i )v k +1j + j ∈N +(i )v k j −h 2αJ 21i u k +1i+J 23i|N (i )|+h αJ 22i(35)whereN −(i ):={j ∈N (i )|j <i },(36)N +(i ):={j ∈N (i )|j >i }(37)and |N (i )|denotes the number of neighbours of pixel i that belong to the image domain.The relaxation parameter ω∈(0,2)has a strong influence on the convergence speed.For ω=1one obtains the well-known Gauß–Seidel method .We usu-ally use values for ωbetween 1.9and 1.99.This nu-merically inexpensive overrelaxation step results in a speed-up by one order of magnitude compared with the Gauß–Seidel approach.We initialised the flow compo-nents for the first iteration by 0.The specific choice。
学术英语提纲格式范文
学术英语提纲格式范文Alright, here's a sample outline of an academic English essay that follows the given requirements:Paragraph 1: Introduction to the Topic.You know, I've been thinking a lot about the impact of technology on our daily lives. It's just amazing howquickly things have changed over the years. Smartphones, social media, AI these are just a few examples of how technology has revolutionized our world.Paragraph 2: Discussing the Positives.On the bright side, technology has made our lives easier. We can access information anytime, anywhere. Distance is no longer a barrier with video calls andinstant messaging. Plus, technology has opened up new opportunities for education and employment.Paragraph 3: Exploring the Negatives.But there's a flipside too. Too much screen time can be harmful to our health. Social media can lead to comparison culture and anxiety. And sometimes, we get so caught up in technology that we forget to appreciate the simple pleasures of life.Paragraph 4: Balancing Technology with Real Life.I think the key is to find a balance. Technology is a tool, not a master. We should use it wisely and in moderation. There's so much more to life than scrolling through social media or being glued to our phones. Let's not forget to appreciate.。
默写英语作文科学方法
默写英语作文科学方法The Scientific Method of Writing an English EssayThe process of writing an effective English essay follows a systematic approach similar to the scientific method. Just as scientists employ a structured methodology to investigate the natural world, writers can adopt a comparable framework to produce well-organized and persuasive academic compositions. By applying the principles of the scientific method, students can enhance their critical thinking skills, develop coherent arguments, and craft essays that engage and enlighten their readers.The first step in the scientific method is to identify a research question or problem that warrants investigation. Likewise, the initial stage of the essay-writing process involves selecting a topic that is sufficiently focused and thought-provoking. A good essay topic should be narrow enough to allow for in-depth exploration, yet broad enough to accommodate a comprehensive analysis. It should also be a subject that the writer finds genuinely interesting and is motivated to explore further.Once the topic has been chosen, the next phase entails conducting thorough research to gather relevant information and evidence. This may involve consulting scholarly sources such as academic journals, books, and databases, as well as considering diverse perspectives and counterarguments. Just as scientists must meticulously collect data to support their hypotheses, essayists must amass a wealth of credible information to substantiate their claims.After the research phase, the writer must organize and synthesize the collected material in a logical and coherent manner. This is akin to the "hypothesis" stage of the scientific method, where researchers formulate a tentative explanation for the phenomenon under study. In the context of essay writing, this involves developing a clear thesis statement that outlines the essay's central argument or claim.With the thesis statement in place, the writer can then proceed to construct the body of the essay, much like scientists design and execute experiments to test their hypotheses. Each body paragraph should introduce a distinct piece of evidence or supporting point that directly relates to and reinforces the overall thesis. Just as scientific experiments must be carefully designed and executed, each body paragraph should flow seamlessly from the previous one, creating a cohesive and well-structured argument.Throughout the writing process, the essayist must continuously evaluate and refine their work, much like scientists analyze their experimental data and adjust their hypotheses accordingly. This involves carefully reviewing the essay for logical coherence, grammatical accuracy, and the strength of the supporting evidence. The writer may also solicit feedback from peers or instructors, akin to the peer review process in scientific research, to identify areas for improvement and ensure the essay meets the desired standards of academic rigor.Finally, the writer must effectively communicate their findings and conclusions to the reader, just as scientists present their research findings through scholarly publications and conference presentations. The essay's conclusion should succinctly restate the thesis, summarize the key points, and leave the reader with a clear understanding of the essay's significance and implications.By adopting the principles of the scientific method, writers can approach the essay-writing process with a greater sense of structure, organization, and intellectual rigor. This systematic approach not only helps to ensure the production of well-crafted and persuasive essays but also fosters the development of critical thinking skills that are invaluable in academic and professional settings.In the same way that scientists must constantly refine theirhypotheses and experimental designs, essayists must be willing to engage in an iterative process of writing, revising, and refining their work. This iterative approach allows writers to identify and address weaknesses in their arguments, incorporate new evidence and perspectives, and ultimately produce essays that are both intellectually compelling and rhetorically effective.Moreover, the application of the scientific method to essay writing encourages writers to approach their topics with an open and inquiring mindset. Just as scientists must be willing to challenge their own assumptions and consider alternative explanations, essayists must be prepared to engage with diverse viewpoints and consider counterarguments to their own positions. This openness to new ideas and perspectives not only strengthens the essay's overall argument but also enhances the writer's critical thinking skills and intellectual growth.In conclusion, the scientific method provides a valuable framework for the essay-writing process, allowing writers to approach their work with a systematic and rigorous approach. By adopting the principles of hypothesis formulation, evidence gathering, logical reasoning, and iterative refinement, essayists can craft compositions that are not only well-structured and persuasive but also intellectually engaging and academically sound. Through the application of the scientific method, the art of essay writing can be transformed into a moresystematic and analytical pursuit, ultimately leading to the production of essays that are both intellectually compelling and rhetorically effective.。
学问靠钻研的英语作文
Learning is a lifelong process that requires continuous effort and dedication.In the pursuit of knowledge,it is essential to delve deeply into various subjects to truly understand and master them.Here are some key points to consider when discussing the importance of thorough study:1.Foundation Building:A solid foundation in any subject is crucial.It provides a strong base for further learning and helps in building a comprehensive understanding of the subject matter.2.Critical Thinking:Delving deeply into a subject encourages critical thinking.It allows learners to question,analyze,and evaluate information,which is vital for intellectual growth.3.Innovation and Creativity:When one studies a subject in depth,they are more likely to come up with innovative ideas and creative solutions to problems.This is because a deep understanding of a field often reveals new possibilities and connections.4.Specialization:In todays world,where there is a vast amount of information available, specialization is key.By focusing on a particular area and studying it thoroughly,one can become an expert and contribute significantly to that field.5.ProblemSolving Skills:Thorough study helps in developing problemsolving skills.It equips individuals with the ability to approach complex issues methodically and find effective solutions.6.Adaptability:A deep understanding of a subject allows for better adaptability in a rapidly changing world.It enables individuals to keep up with new developments and apply their knowledge to new situations.7.Lifelong Learning:The habit of thorough study fosters a love for lifelong learning.It ensures that individuals remain curious and engaged with the world around them, continually seeking to expand their knowledge.8.Career Advancement:In professional settings,having a deep understanding of ones field can lead to career advancement.Employers value individuals who are knowledgeable and can think critically.9.Cultural Appreciation:When studying subjects like history,literature,or languages,a deep dive allows for a richer appreciation of different cultures and perspectives.10.Personal Satisfaction:Finally,there is a personal satisfaction that comes from mastering a subject.It boosts selfesteem and provides a sense of accomplishment.In conclusion,thorough study is not just about acquiring knowledge its about enhancing ones ability to think,adapt,and contribute meaningfully to society.It is a journey that requires patience,curiosity,and perseverance,but the rewards are well worth the effort.。
专业英语翻译
2. Of more resent introduction is mushroom cultivation which probably dates back many hundreds of years for Japanese shii-ta-ke cultivation and about 300 years for the Agaricus mushroom now widely cultivated throughout the temperate world.
译文:生物工程反应可以是分解代谢反应或合成代谢反应(也叫生物合成反应)。分解代谢反应中复杂的化合物被分解为简单物质(葡萄糖生成乙醇),而合成代谢反应或生物合成反应,是简单的分子合成为更复杂的物质(抗生素的合成)。
3.biotechnology includes fermentation process(ranging from beers and wines to bread, cheese, antibiotics and vaccines), water and waste treatment, parts of food technology, and an increasing range of novel applications(ranging from biomedical to metal recovery from low grade ores
译文: 生物工程是属于应用生物科学和技术的一个领域,它包含生物或其亚细胞组分在制造业、服务业和环境管理等方面的应用。
2.The reactions of biotechnological processes can be catabolic, in which complex compounds are broken down to simpler ones (glucose to ethanol ), or anabolic or biosynthetic, whereby simple molecules are built up into more complex ones(antibiotic synthesis).
高二下学期英语阶段性阅读理解魔法带练(08)
【passage 15】①While often seen as a negative(消极的)emotion,anger can also be a powerful motivator (促进因素)for people to achieve challenging goals in their lives,according to research published by the American Psychological Association.②“People often believe that a state of happiness is perfect, " said lead author Heather Lench, PhD, a professor in the department of psychological and brain sciences at Texas A & M University, “but previous research suggests that a mix of emotions, including negative emo tions like anger, results in good outcomes."③The functionalist theory of emotion suggests that all emotions, good or bad, are reactions to events within a person's environment and help that person to make proper actions, according to Lench. For example, sadness may suggest that a person needs to seek help or emotional support,while anger may indicate a person needs to take action to overcome an obstacle(障碍).④To better understand the role of anger in achieving goals, researchers conducted a series of experiments involving more than 1,000 participants and analyzed survey data from more than 1,400 respondents. In each experiment, participants either had an emotional response (such as anger, amusement,desire or sadness)or a neutral(中性的)emotional state,and then were presented with a challenging goal. Across all the experiments, anger improved people's ability to reach their goals compared with a neutral condition in a variety of challenging situations.⑤“Our research adds to the growing evidence that a mix of positive and negative emotions promotes well-being, and that using negative emotions as tools can be particularly effective in some situations,” Lench said.(素材来源:云南省大理州2023-2024学年上学期教学质量监测高二英语试题)53. What is commonly believed concerning people's emotions?A. It is believed that a state of joy is great.B. A feeling of sadness leads to poor effect.C. Anger is actually a positive emotion.D. Pride acts as an obstacle to success.54. Why did researchers do a series of experiments?A. They hoped to overturn the previous findings.B.They hoped to prove that a state of happiness is ideal.C.They hoped to find the relationship between positive and negative emotions.D.They hoped to have a better understanding of the role of anger in attaining goals.55. What's Paragraph 4 mainly about?A.The problem of the research.B.The background of the research.C. The process of the research.D. The significance of the research.56. What's Lench's attitude to their research?A. Skeptical.B. Favorable.C. Uncaring.D. Critical.【魔法带练】串联题干53.What is commonly believed concerning people's emotions?54.Why did researchers do a series of experiments?55.What's Paragraph 4 mainly about?56.What's Lench's attitude to their research?得出主题词:experiments→research→people’s emotions这个实验结果饱受争议引起公众的担忧或者这个实验专门针对人类的情感?53. What is commonly believed concerning people's emotions?关于人的情绪,人们通常认为什么A.It is believed that a state of joy is great.(同义替换:great=perfect)人们相信快乐的状态是完美的。
NGUYEN DUNG DUC
Studies on Improving the Efficiency of Support Vector MachinesbyNGUYEN DUNG DUCsubmitted toJapan Advanced Institute of Science and Technology in partial fulfillment of the requirementsfor the degree ofDoctor of PhilosophySupervisor:Professor Ho Bao TuSchool of Knowledge ScienceJapan Advanced Institute of Science and TechnologyMarch,2006AbstractMotivation and Objective:In recent years support vector machine(SVM)has emerged as a powerful learning approach and successfully be applied in a wide variety of applications.The high generalization ability of SVMs is guaranteed by special properties of the optimal hyperplane and the use of kernel.However,SVM is considered slower than other learning approaches in both testing and training phases.In testing phase SVMs have to compare the test pattern with every support vectors included in their solutions.When the number of support vectors increases,the speed of testing phase decreases proportionally.To reduce this computational expense,reduced set methods try to replace original SVM solution by a simplified one which consists of much fewer number of vectors,called reduced vectors.However,the main drawback of former reduced set methods lies in the construction of each new reduced vector:it is required to minimize a multivariate function with local minima.Thus,in order to achieve a good simplified solution the construction must be repeated many times with different initial values.Our first objective was aiming at building a better reduced set method which overcomes the mentioned local minima problem.The second objective was tofind a simple and effective way to reduce the training time in a model selection process.This objective was motivated by the fact that the selection of a good SVM for a specific application is a very time consuming task.It generally demands a series of SVM training with different parameter settings;and each SVM training solves a very expensive optimization problem.Methodology:Starting from a mechanical point of view,we proposed to simplify support vector solutions by iteratively replacing two support vectors with a newly created vector;or to substitute two member forces in an equilibrium system by an equivalent force.This approach also faces the difficulties caused by the so called pre-image problem of kernel-based methods where generally there is no exact substitution of two support vectors in a kernel-induced feature space by image of a vector in input space.However this bottom-up approach possess a big advantage that the computation of the new vector involves only two support vectors being replaced,not to involve all vectors as in the former top-down approach.The extra task of the bottom-up method is tofind a heuristic to select a good pair of support vectors to substitute in each iteration.This heuristic aims at minimizing the difference between the original solution and the simplified one.Also,it is necessary to design a stopping condition to terminate the simplification process before it makes the simplified solution too different from the original one,thus the possible loss in generalization performance can get out of control.For the second problem,our intensive investigation reconfirmed that different SVMs trained by different parameter settings share a big portion of common support vectors.This observation suggests a simple technique to use the results of previously trained SVMs to initialize the search in training a new machine.In a general decomposition framework for SVM training, this initialization makes the initial local optimized solution closer to the global optimized solution;hence the optimization process for SVM training converges more quickly.Finding and Conclusion:The bottom-up approach leads to a conceptually simpler and computationally less expensive method for simplifying SVM solutions.We found that it is reasonable to select a close support vector pair to replace with a newly constructed vector,and this construction only requiresfinding the unique maximum point of a uni-variate function.The uniqueness of solution does not only make the algorithm run faster, but it also makes the reduce set method easier to use in ers do not have to run many trials and wonder about different results returned in different runs.Experimental results on real life datasets shown that our proposed method can reduce a large number of support vectors and keeps generalization performance paring with for-mer methods,the proposed one produced slightly better results,and more importantly it is much more efficient.For the second problem,experiments on various real life datasets showed that by initializing thefirst working set using the result of trained SVMs,the training time for each subsequent SVM can be reduced by22.8-85.5%.This reduction is significant in speeding up the whole model selection process.AcknowledgmentsThis work was carried out at Knowledge Creating Methodology Lab,School of Knowl-edge Science,Japan Advanced Institute of Science and Technology.I wish to express my gratitude to the many people who have supported me during my work.I am most grateful to my supervisor,Prof.Ho Tu Bao,for providing me with his help, supervision and motivation throughout the course of this work.His insight and breadth of knowledge have been invaluable to me.Without his care,supervision and friendship I would not be able to complete this work.I want to thank Prof.Kenji Satou,who has kindly accepted me to do a minor theme research under his supervision.I wish to express my gratefulness to the official referees of the dissertation,Prof.Kenji Satou,Prof.Yoshiteru Nakamori,Prof.Tsutomu Fujinami,and Prof.Hiroshi Motoda, for their valuable comments and suggestions on this dissertation.I would like to express my appreciation to the Ministry of Education,Culture,Sports, Science,and Technology of Japan,and the International Information Science Foundation for providing me the scholarship and thefinancial support for attending international conferences.My special thank goes to the members of the Knowledge Creating Laboratory,and the many friends of mine in JAIST for providing their helps,a friendly and enjoyable environment.Finally,I am indebted to my parents for their forever affection,patience,and constant encouragement,to my wife for sharing me difficulties and happiness.To my son,the greatest source of inspiration.ContentsAbstract iAcknowledgments iii1Introduction11.1Efforts in Improving the Efficiency of Support Vector Learning (1)1.2Problem and Contribution (4)1.3Thesis Outline (6)2Preliminaries on Support Vector Machines72.1Introduction (7)2.2Linear Support Vector Classification (7)2.2.1The Maximal Margin Hyperplane (7)2.2.2Finding the Maximal Margin Classifier (12)2.2.3Soft Margin Classifiers (12)2.2.4Optimization (13)2.3Nonlinear Support Vector Classification (17)2.3.1Learning in Feature Space (17)2.3.2Kernels (19)2.3.3VC Dimension and Generalization Ability of Support Vector Machine212.4Support Vector Regression (23)2.5Implementation Techniques (26)2.6Summary (30)3Simplifying Support Vector Solutions313.1Introduction (31)3.2Simplifying Support Vector Machines (32)3.2.1Reducing Complexity of SVMs in Testing Phase (32)3.2.2Reduced Set Construction (33)3.2.3Reduced Set Selection (35)3.3A Bottom-up Method for Simplifying Support Vector Solutions (36)3.3.1Simplification of Two Support Vectors (37)3.3.2Simplification of Support Vector Solution (43)3.3.3Pursuing a Better Approximation (46)3.4Experiment (46)3.5Discussion (50)4Speeding-up Support Vector Training in Model Selection544.1Introduction (54)4.2Model Selection for Support Vector Machine (55)4.2.1What is Model Selection (55)4.2.2Model Selection for Support Vector Machines (57)4.3Speeding-up Model Selection SVM (60)4.3.1Speeding-up by Improving Search Strategy (60)4.3.2Speeding-up by Improving Model Evaluation (61)4.4Speeding-up SVM Training in Model Selection (61)4.4.1The General Decomposition Algorithm for SVM Training (61)4.4.2Initializing Working Set (63)4.5Experiments (66)4.6Discussion (68)5Conclusions and Future Work69 References72 Publications80List of Figures2.1Margin of a set of examples with respect to a hyperplane.The origin has−bperpendicular Euclidian distance to the hyperplane (8)w2.2Among liner machines,the maximal margin classifier is intuitively preferable.102.3Two-dimensional example of a classification problem:separate’o’from’+’using a straight line.Suppose that we add bounded noise to each pattern.If the optimal margin hyperplane has marginρ,and the noise is boundedby r<ρ,then the line will correctly separates even the noisy patterns.[53]102.4Noisy pattern will be treated softly by permitting constraint violation(e.g.having functional marginξ<1),but the objective function will be penalizea cost C(1−ξ),whereξis functional margin (13)2.5An illustration of kernel-based algorithms.By mapping the original inputspace to other high dimensional feature space,the linearly inseparable datamay become linearly separable in the feature space (18)2.6Three points in R2shattered by oriented lines (21)2.7Gaussian RBF SVMs of sufficiently small width can classify an arbitrarylarge number of training points correctly,and thus have infinite VC dimen-sion[50] (23)2.8In -SV regression,training examples inside the tube of radius are notconsidered as mistakes.The trade-offbetween model complexity(or theflatness of the hyperplane)and points lying outside the tube is controlledby weighted -insensitive losses (24)+(1−m)C k2ij with m=0.4,C ij=0.7 (39)3.1f(k)=mC(1−k)2ij3.2Projection of vector z on the plane(x i,x j)in the input space (40)3.3Illustration of the marginal difference of a(original)support vector x withrespect to the original and simplified solutions (44)3.4Illustration of simplified support vector solution using proposed method.The decision boundaries constructed by the simplified machines with4SVs(right-top)and20SVs(right-bottom)are almost identical with thoseconstructed by the original machines with61SVs(left-top)and75SVs(left-bottom).The cracked lines represent vectors with approximately1marginal distance to the optimal hyperplane (46)3.5Thefirst100digits in the USPS dataset (47)3.6Performance comparison between the former top-down the the proposedbottom-up approach on the USPS dataset.With the same reduction ratethe bottom-up preserved better predictive accuracy,while computationalefficiency is guaranteed by theoretical result.Note:Top-down:the resultoffix-point iteration method in[37](Phase I);bottom-up:the result of pro-posed method(Phase I);Phase II:the result of proposed method runningwith both two phases optimization (52)3.7Display of all vectors in simplified solutions.The original10classifierstrained with polynomial kernel of degree three and the cost C=10consistof4538SVs and produce88errors(on2007testing data).The simplified10classifiers consist of270vectors and produce95errors.The numberbelow each image indicates the new weight of a reduced vector (53)4.1Relations among model complexity(horizontal axis),empirical risk(thedotted line),and expected risk(the solid line).The dash-dotted line is theupper-bound on the complexity term(confidence).[73] (56)4.2Different kernels produce different type of discriminant function (58)4.3Trade offbetween model complexity and empirical risk (59)4.4Common support vectors in two different machines learned from threedatasets sat-image,letter recognition,and shuttle:(a)linear machineslearned with different error penalties C=1and C=2,(b)polynomialmachines of degree two and three learned with the same C=1,(c)RBFmachines learned with different error penalties C=1and C=2 (64)4.5Illustration of initializing working set using result of previously trainedSVM.The optimized solution for machine(γ=10,C=10)(d)can bereached normally from an random initial solution(a),or more efficientlyfrom solution of a trained machine(γ=5,C=10)or(γ=10,C=1) (65)4.6Reduction in number of required optimization loops and training time onthree datasets sat-image(a-d-g),letter recognition(b-e-h),and shuttle(c-f-i),and in different situations:the same linear kernel with different cost(a-b-c),polynomial kernels of different degree with the same cost,and differentRBF kernels with different costs.”org.”denotes the original method withrandomly working set selection;”WS”denotes the proposed method.Allmeasures(average number of loops and training time)are normalized in to[0,1] (67)List of Tables2.1Decomposition algorithm for SVM training (28)3.1The simplification algorithm (45)3.2Reduction in number of support vectors and the corresponding loss in gen-eralization performance with different values of MMD.Original machines(the3rd and14th lines)were trained on the USPS training data usingGaussian and polynomial kernels.Errors were evaluated on the testing data.483.3Experimental results on45binary classifiers learned from the USPS datasetusing thefirst phase of the proposed method.Left-bottom:number of sup-port vectors in original classifiers/number of vectors in simplified classifiers.Right-top:number of errors on the test data of original classifiers-simpli-fied classifiers (49)3.4Experimental results on various applications (50)4.1Datasets used in experiments (66)Chapter1IntroductionIn this chapter wefirstly review the many efforts currently being made to improve the efficiency of the support vector learning approach.After that we mention some limitations of the previous methods and briefly introduce our solutions in simplifying support vector solutions and in speeding-up support vector training in a model selection process.Outline of this thesis will be given in the last section of this chapter.1.1Efforts in Improving the Efficiency of SupportVector LearningThe support vector learning[1,2,3,4]implements the following idea:itfinds an optimal hyperplane in feature space according to some optimization criterion,e.g.it is the optimal hyperplane that maximizes the distance to training examples in a two-class classification task,or maximizes theflatness of a function in regression.Thus,training a support vector machine(SVM)is equivalent to solving an optimization problem in which the number of variables to be optimized is l,and the number of parameter is l2,where l is the size of training data.This is apparently an expensive task in both memory requirement and computational power.Moreover,the optimal hyperplane lies in feature space which is constructed based on the choice of kernel.Selecting a suitable kernel for a specific application is still an open problem and SVM users have to do intensive trials of training and testing with different types of kernel and different values of parameters.Also,since the feature space does not exist explicitly the hyperplane,e.g.a classifier or a regressor,is characterized by a set of training examples called support vectors.To test a new pattern SVMs have to compare it with all these support vectors and this becomes a very time consuming work when the number of support vectors is large.In short,support vector is a rather computationally demanding learning approach,and in return,it can producehigh generalization ability machines in many practical applications.There have been different directions to deal with the high resource-demanding prop-erty of support vector training.The algorithmic approach tries tofind intelligent solutions for a quick convergence to the optimal solution with a limited memory available.From the observation that the SVM solutions are sparse,or many of training examples do not play any role in the forming of SVM solutions,chunking and decomposition methods[1,5] decompose the original quadratic programming(QP)problem into a series of smaller QP problems.These method has been shown to be able to handle problems with size exceeding the capacity of the computer,e.g.RAM memory.The sequential minimal optimization (SMO)[6]can be viewed as the extreme case of decomposition methods.In each iteration SMO solves a QP problem of size two using an analytical solution,thus no optimizer is required.The remaining problem of SMO is to choose a good pair of variable to optimize. The original heuristics presented in[6]are based on the level of violating the optimal con-dition.There have been several works,e.g.[7,8],trying to improve these heuristics.The general decomposition framework and some other implementation techniques like shrink-ing,kernel caching have been implemented in most currently available SVM softwares, e.g.SVM light[9],LIBSVM[10],SVMTorch[11],HeroSvm[12].The main obstacle for this approach is still the huge memory required for storing kernel elements when the number of training example exceeds a few hundreds of thousands.The second approach to solving large scale SVM is to parallelize the optimization.The main idea is to split training data into subsets and perform optimization on these subsets separately.The partial results are then combined andfiltered again into a”cascade”of SVMs[13,14],or a mixture of SVMs[15].However,the price we must pay is the possibility of losing predictive accu-racy because the combination of partial SVMs does not guarantee an optimal hyperplane, thus we might get a machine with lower performance than those trained by other learn-ing approaches[16].The third approach is to properly remove”unnecessary”examples from training data,thus simultaneously reducing the memory requirement as well as the training time.The reduced support vector machines method[17,18]reduce the size of the original kernel matrix from l×l to l×m,where m is the size of a randomly selected subset of training data considered as candidates of support vectors.The smaller matrix of size l×m(with m is much smaller than l)can be stored in memory,so optimization algorithms such as Newton method can be applied.Instead of random sampling,different techniques have been used to intelligently sample a small number of training examples from training data,ing cross-training[19],boosting[19],clustering[20,21],active learning[22,23,24],on-line and active learning[22].Another way to reduce the size of the optimization problem is applying different techniques to obtain low-rank approxima-tions on the kernel matrix using Nystr¨o m method[25],greedy approximation[26],matrix sampling[26]or matrix decomposition[27].The drawback of this approach still is that the resulted machines can only achieve a”similar”or a comparable performance with the machines trained on the original training data.There have been also many other efficient implementation techniques to achieve approximate support vector solutions with a low cost.The core support machines in[28]reformulates the optimization in SVM training as a minimum enclosing ball(MEB)problem in computational geometry,and then adopt an efficient approximate MEB algorithm to obtain approximately optimal solution.In [29]the authors consider the application of quantum computing to solve the problem of effective SVM training.Though training SVMs is computationally very expensive,SVM users have to spend most time for choosing a suitable kernel and appropriate parameter setting for their applications,or to deal with the model selection problem.In order to achieve a good machine,model selection has to solve two main tasks:to conduct a search in model space (a space of all available SVMs),and to evaluate the goodness of a model.Different search strategies have been proposed to improve the search,including grid search with different grid size[30],pattern search[31],and all common search strategies when applicable like gradient descent[32,32,33],genetic algorithms[34].The difficulty in conducting the search in model space is that there have been no theories to suggest this type of kernel will work better than the other on a given domain,or to determine the region of parameter values where we canfind the best one.Another way to speed-up model selection process is to efficiently evaluate each model in our consideration.In[35],the author proposed ξα-estimator specially designed for support vector machines.Theξα-estimator is based on the general leave-one-out method,but it is much more efficient because it does not require to perform re-sampling and retraining.The open question for model evaluation is that there is no dominated method in estimating the goodness of a model.In practice, SVM users estimate error rate of a machine mainly based on cross validation techniques like k-fold cross validation,which is very time consuming.One common property between support vector learning and instance-based learning is that they have to compare all instances included in their solution with the new pattern in testing phase(these instances are support vectors in SVMs and all training examples in nearest neighbor machines).Except for linear SVMs where the norm vector of the optimal hyperplane can be represented by a vector in input space,the solution of a nonlinear SVM is characterized by a linear combination of support vectors in feature space.Thus to classify a new pattern,SVMs have to compare it with every support vectors via kernel calculations.This computation becomes very expensive when the number of supportvector is large.The reduced set methods,e.g.[36,37,38],try to replace the original SVM by a simplified SVM which consists of fewer number of support vectors,called reduced SVs.The support vectors in the simplified solution can be newly created,or selected from the set of original support vectors.The limitation of this approach lies in the construction/selection of reduced SVs that faces local minimum problem.Another approach to speed-up SVMs is to approximate the comparison in the testing phase.In [39,40],the authors proposed to treat kernel machines as a special form of k-nearest neighbor machines.The result of testing phase is based on comparisons with nearest support vectors,where these SVs are determined in a pre-query analysis.These methods have been shown to produce very promising speed-up rate,but they require an extensive pre-query analysis and depend much on very sensitive parameters,thus cause practical difficulties for real life applications.In summary,support vector learning is a resource demanding learning approach.There have been a huge number of works trying to make support vector machines run faster in all training,model selection,and in testing phases.Our effort described in this dissertation is two folds:making SVMs run faster in testing phase and speeding-up the support vector training in a model selection process.1.2Problem and ContributionIn comparing with making support vector training and model selection run faster,speeding-up SVMs in testing phase is practically important,especially for real-time or on-line appli-cations like detection of objects in streaming video or in image[41,42,14,43,44,45,46], abnormal events detection[46,47],real-time business intelligence systems[20].In these applications,it is possible to train the machines in hours,or days,but the respond time must be limited in a restrictive period.The reduced set methods briefly introduced above have been successfully used for reducing the complexity of SVMs in many applications like handwritten character recognition[48,49],face detection in a large collection of im-ages[14].However,the main difficulty still lies in the fact that it is impossible to exactly replace a complicated linear combination of many vectors in feature space by a simple one,except for linear SVMs.For linear SVMs we can represent the optimal hyperplane by only two parameters:the norm vector which is also a vector in the input space,and the bias.For nonlinear SVMs,because the feature space is constructed implicitly then the normal vector must be represented by a linear combination of images of input support vectors.The reduced set approach has no way but approximates the original combination by a fewer number of SVs,called the reduced SVs.In previous methods,constructingeach new support vector requires to minimize a multivariate function with local minima. Because we cannot know the global minimum has been reached or not,the construction has to repeat the search many times with different initial guesses.This repetition must be applied for every reduced SV in order to arrive at thefinal reduced solution,and there is also no way but to determine the goodness of the reduced solution experimentally. Our attempt in this research direction is to propose a conceptually simpler and compu-tationally less expensive method to simplify support vector solutions.Starting from a mechanical point of view in which if each SV exerts a force on the optimal hyperplane then support vector solutions satisfy the conditions of mechanical equilibrium[50],and in an equilibrium system if we replace two member forces by an equivalent one,the stable state will not change.Thus,instead of constructing reduced vectors set incrementally like in the previous reduced set methods,two nearest SVs will be iteratively considered and replaced by a newly constructed vector.This approach leads to the construction of each new vector only requiring tofind the unique maximum point of a one-variable function on(0,1),and thefinal reduced set is unique for each running time.Experimental results showed that this method is effective in reducing the number of support vectors and preserving generalization performance.To control the possible lost in generalization performance,we propose a quantity called maximal marginal difference to estimate the difference between the original SVM solution and the simplified one.The simplification process will stop before it makes the estimated difference exceed a given threshold.Our second contribution is devoted for speeding-up the support vector training in a model selection process.By conducting intensive experiments we reconfirm that two different machines trained by two different parameter settings,or even two different choices of kernel,share a big number of support vectors.This observation suggests an inheritance mechanism in which training a new SVM in a model selection process can benefit from the results of previously trained machines.In the general decomposition framework,we propose to initialize each new working set by a set of all SVs found in previously trained machines.Moreover,if two machines use the same kernel function then one’s solution can be adjusted and used as the initial point in searching for the the other’s solution. This initialization makes thefirst local solution closer to the global solution,and the decomposition algorithm converges more quickly.Experimental results indicated that we can reduce22.8-85.5%the training time without any impact on the result of model selection.1.3Thesis OutlineChapter2introduces basic concepts in support vector learning.Especially it em-phasizes critical properties of the optimal hyperplane and the use of kernel in classical classification and regression tasks.We intended to discuss in more detail the two most commonly used kernels:Gaussian RBF and polynomial,and the decomposition algorithm for SVM training.These fundamentals will be used in other chapters.Chapter3describes attempts in making SVMs run faster in testing phase.Firstly it reviews existing methods for reducing the complexity of SVMs by reducing the number of necessary SVs included in SVM solutions.It then describes our proposed bottom-up method for replacing two SVs by a new one and the whole iterative simplification process, including a selection heuristic and a stopping condition.Experiments will be reported next,and this chapter ends with conclusions.Chapter4introduces the model selection problem for support vector machines and the many efforts in making this process more efficient.It then describes a technique to speed-up SVM training in a model selection process by inheriting the result among different SVMs under consideration.Experiments on various benchmark datasets are described next for illustrating the effectiveness of the proposed method.Chapter5concludes this dissertation with summarization of methodology,contribu-tion,as well as limitation of the proposed methods.It alsofigures out open problems for a further research in future.。
黎曼的就职资格论文(几何基础假设英文版)
On the Hypotheses which lie at the Bases ofGeometry.Bernhard RiemannTranslated by William Kingdon Clifford [Nature,Vol.VIII.Nos.183,184,pp.14–17,36,37.]Transcribed by D.R.WilkinsPreliminary Version:December1998On the Hypotheses which lie at the Bases ofGeometry.Bernhard RiemannTranslated by William Kingdon Clifford[Nature,Vol.VIII.Nos.183,184,pp.14–17,36,37.]Plan of the Investigation.It is known that geometry assumes,as things given,both the notion of space and thefirst principles of constructions in space.She gives definitions of them which are merely nominal,while the true determinations appear in the form of axioms.The relation of these assumptions remains consequently in darkness;we neither perceive whether and how far their connection is necessary,nor a priori,whether it is possible.From Euclid to Legendre(to name the most famous of modern reform-ing geometers)this darkness was cleared up neither by mathematicians nor by such philosophers as concerned themselves with it.The reason of this is doubtless that the general notion of multiply extended magnitudes(in which space-magnitudes are included)remained entirely unworked.I have in thefirst place,therefore,set myself the task of constructing the notion of a multiply extended magnitude out of general notions of magnitude.It will follow from this that a multiply extended magnitude is capable of different measure-relations,and consequently that space is only a particular case of a triply extended magnitude.But henceflows as a necessary consequence that the propositions of geometry cannot be derived from general notions of magnitude,but that the properties which distinguish space from other con-ceivable triply extended magnitudes are only to be deduced from experience. Thus arises the problem,to discover the simplest matters of fact from which the measure-relations of space may be determined;a problem which from the nature of the case is not completely determinate,since there may be several systems of matters of fact which suffice to determine the measure-relations of space—the most important system for our present purpose being that which Euclid has laid down as a foundation.These matters of fact are—like all1matters of fact—not necessary,but only of empirical certainty;they are hy-potheses.We may therefore investigate their probability,which within the limits of observation is of course very great,and inquire about the justice of their extension beyond the limits of observation,on the side both of the infinitely great and of the infinitely small.I.Notion of an n-ply extended magnitude.In proceeding to attempt the solution of thefirst of these problems,the development of the notion of a multiply extended magnitude,I think I may the more claim indulgent criticism in that I am not practised in such under-takings of a philosophical nature where the difficulty lies more in the notions themselves than in the construction;and that besides some very short hints on the matter given by Privy Councillor Gauss in his second memoir on Biquadratic Residues,in the G¨o ttingen Gelehrte Anzeige,and in his Jubilee-book,and some philosophical researches of Herbart,I could make use of no previous labours.§1.Magnitude-notions are only possible where there is an antecedent general notion which admits of different specialisations.According as there exists among these specialisations a continuous path from one to another or not,they form a continuous or discrete manifoldness;the individual special-isations are called in thefirst case points,in the second case elements,of the manifoldness.Notions whose specialisations form a discrete manifoldness are so common that at least in the cultivated languages any things being given it is always possible tofind a notion in which they are included.(Hence mathematicians might unhesitatingly found the theory of discrete magni-tudes upon the postulate that certain given things are to be regarded as equivalent.)On the other hand,so few and far between are the occasions for forming notions whose specialisations make up a continuous manifoldness, that the only simple notions whose specialisations form a multiply extended manifoldness are the positions of perceived objects and colours.More fre-quent occasions for the creation and development of these notions occurfirst in the higher mathematic.Definite portions of a manifoldness,distinguished by a mark or by a boundary,are called Quanta.Their comparison with regard to quantity is accomplished in the case of discrete magnitudes by counting,in the case of continuous magnitudes by measuring.Measure consists in the superposition of the magnitudes to be compared;it therefore requires a means of using one magnitude as the standard for another.In the absence of this,two magnitudes can only be compared when one is a part of the other;in which2case also we can only determine the more or less and not the how much.The researches which can in this case be instituted about them form a general division of the science of magnitude in which magnitudes are regarded not as existing independently of position and not as expressible in terms of a unit, but as regions in a manifoldness.Such researches have become a necessity for many parts of mathematics,e.g.,for the treatment of many-valued analytical functions;and the want of them is no doubt a chief cause why the celebrated theorem of Abel and the achievements of Lagrange,Pfaff,Jacobi for the general theory of differential equations,have so long remained unfruitful.Out of this general part of the science of extended magnitude in which nothing is assumed but what is contained in the notion of it,it will suffice for the present purpose to bring into prominence two points;thefirst of which relates to the construction of the notion of a multiply extended manifoldness,the second relates to the reduction of determinations of place in a given manifoldness to determinations of quantity,and will make clear the true character of an n-fold extent.§2.If in the case of a notion whose specialisations form a continuous manifoldness,one passes from a certain specialisation in a definite way to another,the specialisations passed over form a simply extended manifold-ness,whose true character is that in it a continuous progress from a point is possible only on two sides,forwards or backwards.If one now supposes that this manifoldness in its turn passes over into another entirely different,and again in a definite way,namely so that each point passes over into a definite point of the other,then all the specialisations so obtained form a doubly extended manifoldness.In a similar manner one obtains a triply extended manifoldness,if one imagines a doubly extended one passing over in a definite way to another entirely different;and it is easy to see how this construction may be continued.If one regards the variable object instead of the deter-minable notion of it,this construction may be described as a composition of a variability of n+1dimensions out of a variability of n dimensions and a variability of one dimension.§3.I shall show how conversely one may resolve a variability whose region is given into a variability of one dimension and a variability of fewer dimen-sions.To this end let us suppose a variable piece of a manifoldness of one dimension—reckoned from afixed origin,that the values of it may be compa-rable with one another—which has for every point of the given manifoldness a definite value,varying continuously with the point;or,in other words, let us take a continuous function of position within the given manifoldness, which,moreover,is not constant throughout any part of that manifoldness.3Every system of points where the function has a constant value,forms then a continuous manifoldness of fewer dimensions than the given one.These man-ifoldnesses pass over continuously into one another as the function changes; we may therefore assume that out of one of them the others proceed,and speaking generally this may occur in such a way that each point passes over into a definite point of the other;the cases of exception(the study of which is important)may here be left unconsidered.Hereby the determination of position in the given manifoldness is reduced to a determination of quantity and to a determination of position in a manifoldness of less dimensions.It is now easy to show that this manifoldness has n−1dimensions when the given manifold is n-ply extended.By repeating then this operation n times, the determination of position in an n-ply extended manifoldness is reduced to n determinations of quantity,and therefore the determination of position in a given manifoldness is reduced to afinite number of determinations of quantity when this is possible.There are manifoldnesses in which the deter-mination of position requires not afinite number,but either an endless series or a continuous manifoldness of determinations of quantity.Such manifold-nesses are,for example,the possible determinations of a function for a given region,the possible shapes of a solidfigure,&c.II.Measure-relations of which a manifoldness of n dimensions is capable on the assumption that lines have a length independent of position,and consequently that every line may be measured by every other.Having constructed the notion of a manifoldness of n dimensions,and found that its true character consists in the property that the determina-tion of position in it may be reduced to n determinations of magnitude,we come to the second of the problems proposed above,viz.the study of the measure-relations of which such a manifoldness is capable,and of the condi-tions which suffice to determine them.These measure-relations can only be studied in abstract notions of quantity,and their dependence on one another can only be represented by formulæ.On certain assumptions,however,they are decomposable into relations which,taken separately,are capable of geo-metric representation;and thus it becomes possible to express geometrically the calculated results.In this way,to come to solid ground,we cannot,it is true,avoid abstract considerations in our formulæ,but at least the results of calculation may subsequently be presented in a geometric form.The foun-dations of these two parts of the question are established in the celebrated memoir of Gauss,Disqusitiones generales circa superficies curvas.§1.Measure-determinations require that quantity should be independent of position,which may happen in various ways.The hypothesis whichfirst4presents itself,and which I shall here develop,is that according to which the length of lines is independent of their position,and consequently every line is measurable by means of every other.Position-fixing being reduced to quantity-fixings,and the position of a point in the n -dimensioned manifold-ness being consequently expressed by means of n variables x 1,x 2,x 3,...,x n ,the determination of a line comes to the giving of these quantities as functions of one variable.The problem consists then in establishing a mathematical expression for the length of a line,and to this end we must consider the quan-tities x as expressible in terms of certain units.I shall treat this problem only under certain restrictions,and I shall confine myself in the first place to lines in which the ratios of the increments dx of the respective variables vary continuously.We may then conceive these lines broken up into elements,within which the ratios of the quantities dx may be regarded as constant;and the problem is then reduced to establishing for each point a general expression for the linear element ds starting from that point,an expression which will thus contain the quantities x and the quantities dx .I shall sup-pose,secondly,that the length of the linear element,to the first order,is unaltered when all the points of this element undergo the same infinitesimal displacement,which implies at the same time that if all the quantities dx are increased in the same ratio,the linear element will vary also in the same ratio.On these suppositions,the linear element may be any homogeneous function of the first degree of the quantities dx ,which is unchanged when we change the signs of all the dx ,and in which the arbitrary constants are continuous functions of the quantities x .To find the simplest cases,I shall seek first an expression for manifoldnesses of n −1dimensions which are everywhere equidistant from the origin of the linear element;that is,I shall seek a continuous function of position whose values distinguish them from one another.In going outwards from the origin,this must either increase in all directions or decrease in all directions;I assume that it increases in all directions,and therefore has a minimum at that point.If,then,the first and second differential coefficients of this function are finite,its first differential must vanish,and the second differential cannot become negative;I assume that it is always positive.This differential expression,of the second order remains constant when ds remains constant,and increases in the duplicate ratio when the dx ,and therefore also ds ,increase in the same ratio;it must therefore be ds 2multiplied by a constant,and consequently ds is the square root of an always positive integral homogeneous function of the second order of the quantities dx ,in which the coefficients are continuous functions of the quantities x .For Space,when the position of points is expressed by rectilin-ear co-ordinates,ds = (dx )2;Space is therefore included in this simplest5case.The next case in simplicity includes those manifoldnesses in which the line-element may be expressed as the fourth root of a quartic differential ex-pression.The investigation of this more general kind would require no really different principles,but would take considerable time and throw little new light on the theory of space,especially as the results cannot be geometrically expressed;I restrict myself,therefore,to those manifoldnesses in which the line element is expressed as the square root of a quadric differential expres-sion.Such an expression we can transform into another similar one if we substitute for the n independent variables functions of n new independent variables.In this way,however,we cannot transform any expression into any other;since the expression contains 12n (n +1)coefficients which are arbitrary functions of the independent variables;now by the introduction of new vari-ables we can only satisfy n conditions,and therefore make no more than n of the coefficients equal to given quantities.The remaining 1n (n −1)are then entirely determined by the nature of the continuum to be represented,and consequently 12n (n −1)functions of positions are required for the determina-tion of its measure-relations.Manifoldnesses in which,as in the Plane and in Space,the line-element may be reduced to the form √ dx 2,are therefore only a particular case of the manifoldnesses to be here investigated;they re-quire a special name,and therefore these manifoldnesses in which the square of the line-element may be expressed as the sum of the squares of complete differentials I will call flat .In order now to review the true varieties of all the continua which may be represented in the assumed form,it is necessary to get rid of difficulties arising from the mode of representation,which is ac-complished by choosing the variables in accordance with a certain principle.§2.For this purpose let us imagine that from any given point the system of shortest limes going out from it is constructed;the position of an arbitrary point may then be determined by the initial direction of the geodesic in which it lies,and by its distance measured along that line from the origin.It can therefore be expressed in terms of the ratios dx 0of the quantities dx in this geodesic,and of the length s of this line.Let us introduce now instead of the dx 0linear functions dx of them,such that the initial value of the square of the line-element shall equal the sum of the squares of these expressions,so that the independent varaibles are now the length s and the ratios of the quantities dx .Lastly,take instead of the dx quantities x 1,x 2,x 3,...,x n proportional to them,but such that the sum of their squares =s 2.When we introduce these quantities,the square of the line-element is dx 2for infinitesimal values of the x ,but the term of next order in it is equal to a homogeneous function of the second order of the 1n (n −1)quantities (x 1dx 2−x 2dx 1),(x 1dx 3−x 3dx 1)...an infinitesimal,therefore,of the fourth order;so that6we obtain a finite quantity on dividing this by the square of the infinitesimal triangle,whose vertices are (0,0,0,...),(x 1,x 2,x 3,...),(dx 1,dx 2,dx 3,...).This quantity retains the same value so long as the x and the dx are included in the same binary linear form,or so long as the two geodesics from 0to x and from 0to dx remain in the same surface-element;it depends therefore only on place and direction.It is obviously zero when the manifold represented is flat,i.e.,when the squared line-element is reducible to dx 2,and may therefore be regarded as the measure of the deviation of the manifoldness from flatness at the given point in the given surface-direction.Multiplied by −34it becomes equal to the quantity which Privy Councillor Gauss has called the total curvature of a surface.For the determination of the measure-relations of a manifoldness capable of representation in the assumed form we found that 12n (n −1)place-functions were necessary;if,therefore,the curvature at each point in 1n (n −1)surface-directions is given,the measure-relations of the continuum may be determined from them—provided there be no identical relations among these values,which in fact,to speak generally,is not the case.In this way the measure-relations of a manifoldness in which the line-element is the square root of a quadric differential may be expressed in a manner wholly independent of the choice of independent variables.A method entirely similar may for this purpose be applied also to the manifoldness in which the line-element has a less simple expression,e.g.,the fourth root of a quartic differential.In this case the line-element,generally speaking,is no longer reducible to the form of the square root of a sum of squares,and therefore the deviation from flatness in the squared line-element is an infinitesimal of the second order,while in those manifoldnesses it was of the fourth order.This property of the last-named continua may thus be called flatness of the smallest parts.The most important property of these continua for our present purpose,for whose sake alone they are here investigated,is that the relations of the twofold ones may be geometrically represented by surfaces,and of the morefold ones may be reduced to those of the surfaces included in them;which now requires a short further discussion.§3.In the idea of surfaces,together with the intrinsic measure-relations in which only the length of lines on the surfaces is considered,there is al-ways mixed up the position of points lying out of the surface.We may,however,abstract from external relations if we consider such deformations as leave unaltered the length of lines—i.e.,if we regard the surface as bent in any way without stretching,and treat all surfaces so related to each other as equivalent.Thus,for example,any cylindrical or conical surface counts as equivalent to a plane,since it may be made out of one by mere bend-ing,in which the intrinsic measure-relations remain,and all theorems about7a plane—therefore the whole of planimetry—retain their validity.On the other hand they count as essentially different from the sphere,which cannot be changed into a plane without stretching.According to our previous in-vestigation the intrinsic measure-relations of a twofold extent in which the line-element may be expressed as the square root of a quadric differential, which is the case with surfaces,are characterised by the total curvature.Now this quantity in the case of surfaces is capable of a visible interpretation,viz., it is the product of the two curvatures of the surface,or multiplied by the area of a small geodesic triangle,it is equal to the spherical excess of the same.Thefirst definition assumes the proposition that the product of the two radii of curvature is unaltered by mere bending;the second,that in the same place the area of a small triangle is proportional to its spherical excess. To give an intelligible meaning to the curvature of an n-fold extent at a given point and in a given surface-direction through it,we must start from the fact that a geodesic proceeding from a point is entirely determined when its initial direction is given.According to this we obtain a determinate surface if we prolong all the geodesics proceeding from the given point and lying initially in the given surface-direction;this surface has at the given point a definite curvature,which is also the curvature of the n-fold continuum at the given point in the given surface-direction.§4.Before we make the application to space,some considerations about flat manifoldness in general are necessary;i.e.,about those in which the square of the line-element is expressible as a sum of squares of complete differentials.In aflat n-fold extent the total curvature is zero at all points in every direction;it is sufficient,however(according to the preceding investigation), for the determination of measure-relations,to know that at each point thecurvature is zero in12n(n−1)independent surface directions.Manifoldnesseswhose curvature is constantly zero may be treated as a special case of those whose curvature is constant.The common character of those continua whose curvature is constant may be also expressed thus,thatfigures may be viewed in them without stretching.For clearlyfigures could not be arbitrarily shifted and turned round in them if the curvature at each point were not the same in all directions.On the other hand,however,the measure-relations of the man-ifoldness are entirely determined by the curvature;they are therefore exactly the same in all directions at one point as at another,and consequently the same constructions can be made from it:whence it follows that in aggregates with constant curvaturefigures may have any arbitrary position given them. The measure-relations of these manifoldnesses depend only on the value of the curvature,and in relation to the analytic expression it may be remarked8that if this value is denoted byα,the expression for the line-element may be written1 1+1αx2dx2.§5.The theory of surfaces of constant curvature will serve for a geometric illustration.It is easy to see that surface whose curvature is positive may always be rolled on a sphere whose radius is unity divided by the square root of the curvature;but to review the entire manifoldness of these surfaces,let one of them have the form of a sphere and the rest the form of surfaces of revolution touching it at the equator.The surfaces with greater curvature than this sphere will then touch the sphere internally,and take a form like the outer portion(from the axis)of the surface of a ring;they may be rolled upon zones of spheres having new radii,but will go round more than once. The surfaces with less positive curvature are obtained from spheres of larger radii,by cutting out the lune bounded by two great half-circles and bringing the section-lines together.The surface with curvature zero will be a cylinder standing on the equator;the surfaces with negative curvature will touch the cylinder externally and be formed like the inner portion(towards the axis)of the surface of a ring.If we regard these surfaces as locus in quo for surface-regions moving in them,as Space is locus in quo for bodies,the surface-regions can be moved in all these surfaces without stretching.The surfaces with positive curvature can always be so formed that surface-regions may also be moved arbitrarily about upon them without bending,namely(they may be formed)into sphere-surfaces;but not those with negative-curvature. Besides this independence of surface-regions from position there is in surfaces of zero curvature also an independence of direction from position,which in the former surfaces does not exist.III.Application to Space.§1.By means of these inquiries into the determination of the measure-relations of an n-fold extent the conditions may be declared which are neces-sary and sufficient to determine the metric properties of space,if we assume the independence of line-length from position and expressibility of the line-element as the square root of a quadric differential,that is to say,flatness in the smallest parts.First,they may be expressed thus:that the curvature at each point is zero in three surface-directions;and thence the metric properties of space are determined if the sum of the angles of a triangle is always equal to two right angles.9Secondly,if we assume with Euclid not merely an existence of lines in-dependent of position,but of bodies also,it follows that the curvature is everywhere constant;and then the sum of the angles is determined in all triangles when it is known in one.Thirdly,one might,instead of taking the length of lines to be independent of position and direction,assume also an independence of their length and direction from position.According to this conception changes or differences of position are complex magnitudes expressible in three independent units.§2.In the course of our previous inquiries,wefirst distinguished between the relations of extension or partition and the relations of measure,and found that with the same extensive properties,different measure-relations were conceivable;we then investigated the system of simple size-fixings by which the measure-relations of space are completely determined,and of which all propositions about them are a necessary consequence;it remains to discuss the question how,in what degree,and to what extent these assumptions are borne out by experience.In this respect there is a real distinction between mere extensive relations,and measure-relations;in so far as in the former, where the possible cases form a discrete manifoldness,the declarations of experience are indeed not quite certain,but still not inaccurate;while in the latter,where the possible cases form a continuous manifoldness,every deter-mination from experience remains always inaccurate:be the probability ever so great that it is nearly exact.This consideration becomes important in the extensions of these empirical determinations beyond the limits of observation to the infinitely great and infinitely small;since the latter may clearly become more inaccurate beyond the limits of observation,but not the former.In the extension of space-construction to the infinitely great,we must distinguish between unboundedness and infinite extent,the former belongs to the extent relations,the latter to the measure-relations.That space is an unbounded three-fold manifoldness,is an assumption which is developed by every conception of the outer world;according to which every instant the region of real perception is completed and the possible positions of a sought object are constructed,and which by these applications is for ever confirming itself.The unboundedness of space possesses in this way a greater empirical certainty than any external experience.But its infinite extent by no means follows from this;on the other hand if we assume independence of bodies from position,and therefore ascribe to space constant curvature,it must necessarily befinite provided this curvature has ever so small a positive value.If we prolong all the geodesics starting in a given surface-element, we should obtain an unbounded surface of constant curvature,i.e.,a surface which in aflat manifoldness of three dimensions would take the form of a10。
科技英语中常用写作方法
Clear and accurate communication of scientific ideas enables researchers to share knowledge, collaboration, and build on each other's work
Improves understanding and learning
Effective scientific writing helps readers to understand complex concepts, methods, and results, then enhancing their learning experience
The Characteristics of Scientific English Writing
Objectivity
Scientific writing aims to present facts and evidence objectively, without bias or personal opinion
Use third person point of view
02
Writing in the third person helps to maintain objectivity
and credibility
Reply on evidence
03
Support claims and arguments with evidence from
Interrogtive presence
Asking questions
一种新科学方法英文
一种新科学方法英文*Abstract:*The scientific method has been the backbone of experimental research for centuries. However, in recent years, there has been a growing need for a new scientific method that can keep up with the rapid advancements in technology and expanding knowledge. This article proposes a new approach to scientific inquiry that takes into account the complexities of the modern world.IntroductionSince its inception, the scientific method has provided a systematic way to acquire knowledge, test hypotheses, and make accurate predictions. However, with the advent of new technologies and the increasing complexity of scientific questions, the traditional scientific method may no longer be sufficient. This article aims to introduce a new scientific method that can better cater to the demands of the modern scientific community.The Limitations of the Traditional Scientific MethodThe traditional scientific method, often referred to as the "hypothesis-driven" approach, is a linear process that involves making observations, formulating a hypothesis, conducting experiments, analyzing data, and drawing conclusions. While this method has been immensely successful in advancing scientific knowledge, it has itslimitations.One significant limitation is the exclusion of complex real-world systems. Traditional experiments are often conducted in highly controlled environments, which fail to capture the complex interactions and interdependencies that exist in nature. Additionally, the traditional scientific method tends to favor reductionism, dissecting complex problems into simpler components, and focusing only on one variable at a time. This reductionist approach may work in some cases, but it fails to address the holistic nature of interconnected systems found in nature. Introducing the Holistic Scientific MethodThe proposed holistic scientific method recognizes the limitations of the traditional approach and aims to bridge the gap between reductionism and the complexity of real-world systems. This method combines elements from other scientific approaches, such as systems thinking, network analysis, and computational modeling, to provide a more comprehensive understanding of complex systems.The holistic scientific method employs a multidisciplinary approach, integrating knowledge from various fields. Instead of starting with a single hypothesis, this method begins with a "conceptual framework" that represents the system under investigation. The conceptual framework takes into account the interdependencies, feedback loops, and emergent properties of the system. This framework is then used toguide the collection of data, design experiments, and analyze results.A key aspect of this method is the use of computational modeling and simulation techniques. These tools allow researchers to simulate the behavior of complex systems under different conditions, making it possible to explore scenarios that would be challenging or unethical to study in real life. The holistic scientific method also emphasizes the importance of collecting large datasets and utilizing advanced data analysis techniques, such as machine learning, to extract meaningful patterns and insights.Advantages and Potential ApplicationsThe holistic scientific method offers several advantages over the traditional approach. By considering the complexity and interconnectedness of natural systems, this method allows researchers to study phenomena that were previously difficult to tackle. It provides a more realistic representation of the real world, enabling the development of more accurate models and predictions.Moreover, the holistic scientific method can be applied to a wide range of disciplines, such as biology, ecology, economics, and social sciences. It can help understand complex biological systems, analyze social networks, predict economic fluctuations, and optimize resource allocation.ConclusionIn conclusion, the holistic scientific method aims to address the limitations of the traditional scientific method by incorporating interdisciplinary knowledge, computational modeling, and large datasets. This method provides a more comprehensive understanding of complex systems and enables researchers to tackle the challenges of the modern scientific landscape. By embracing the holistic scientific method, scientists can advance our knowledge and find solutions to the intricate problems we face in the 21st century.*Keywords: scientific method, holistic approach, complex systems, computational modeling, data analysis.*。
Graphene1
cost mass production of graphene with high quality.
Surface modification : deal with chemical modification、 doping、surface functional groups, produce grapene and related materials.
Advantages: nice and simple Disadbantages: Low yield, messy, not scalable.
2 The Solution-Phase Method
Reduction of single layer graphene oxide.
Oxidation of graphite Delamination of graphitic oxide Reduction of gration : Many properties of graphene are not fully
understood which have attracted much attention, expediting the application of graphene.
Graphene
石墨烯
Content
Introduction Preparation of Graphene Application of Graphene Conclusion
Introduction
Graphene is a rigid planar(平面的) nanostructure made of a single layer of carbon atoms arranged in an hexagonal crystal lattice(六方晶型) Graphene can be also considered as a sort of two-dimensional macromolecule, where benzene is the repeating structural unit.
Instructional_design
Instructional designFrom Wikipedia, the free encyclopediaInstructional Design(also called Instructional Systems Design (ISD)) is the practice of maximizing the effectiveness, efficiency and appeal of instruction and other learning experiences. The process consists broadly of determining the current state and needs of the learner, defining the end goal of instruction, and creating some "intervention" to assist in the transition. Ideally the process is informed by pedagogically(process of teaching) and andragogically(adult learning) tested theories of learning and may take place in student-only, teacher-led or community-based settings. The outcome of this instruction may be directly observable and scientifically measured or completely hidden and assumed. There are many instructional design models but many are based on the ADDIE model with the five phases: 1) analysis, 2) design, 3) development, 4) implementation, and 5) evaluation. As a field, instructional design is historically and traditionally rooted in cognitive and behavioral psychology.HistoryMuch of the foundations of the field of instructional design was laid in World War II, when the U.S. military faced the need to rapidly train large numbers of people to perform complex technical tasks, fromfield-stripping a carbine to navigating across the ocean to building a bomber—see "Training Within Industry(TWI)". Drawing on the research and theories of B.F. Skinner on operant conditioning, training programs focused on observable behaviors. Tasks were broken down into subtasks, and each subtask treated as a separate learning goal. Training was designed to reward correct performance and remediate incorrect performance. Mastery was assumed to be possible for every learner, given enough repetition and feedback. After the war, the success of the wartime training model was replicated in business and industrial training, and to a lesser extent in the primary and secondary classroom. The approach is still common in the U.S. military.[1]In 1956, a committee led by Benjamin Bloom published an influential taxonomy of what he termed the three domains of learning: Cognitive(what one knows or thinks), Psychomotor (what one does, physically) and Affective (what one feels, or what attitudes one has). These taxonomies still influence the design of instruction.[2]During the latter half of the 20th century, learning theories began to be influenced by the growth of digital computers.In the 1970s, many instructional design theorists began to adopt an information-processing-based approach to the design of instruction. David Merrill for instance developed Component Display Theory (CDT), which concentrates on the means of presenting instructional materials (presentation techniques).[3]Later in the 1980s and throughout the 1990s cognitive load theory began to find empirical support for a variety of presentation techniques.[4]Cognitive load theory and the design of instructionCognitive load theory developed out of several empirical studies of learners, as they interacted with instructional materials.[5]Sweller and his associates began to measure the effects of working memory load, and found that the format of instructional materials has a direct effect on the performance of the learners using those materials.[6][7][8]While the media debates of the 1990s focused on the influences of media on learning, cognitive load effects were being documented in several journals. Rather than attempting to substantiate the use of media, these cognitive load learning effects provided an empirical basis for the use of instructional strategies. Mayer asked the instructional design community to reassess the media debate, to refocus their attention on what was most important: learning.[9]By the mid- to late-1990s, Sweller and his associates had discovered several learning effects related to cognitive load and the design of instruction (e.g. the split attention effect, redundancy effect, and the worked-example effect). Later, other researchers like Richard Mayer began to attribute learning effects to cognitive load.[9] Mayer and his associates soon developed a Cognitive Theory of MultimediaLearning.[10][11][12]In the past decade, cognitive load theory has begun to be internationally accepted[13]and begun to revolutionize how practitioners of instructional design view instruction. Recently, human performance experts have even taken notice of cognitive load theory, and have begun to promote this theory base as the science of instruction, with instructional designers as the practitioners of this field.[14]Finally Clark, Nguyen and Sweller[15]published a textbook describing how Instructional Designers can promote efficient learning using evidence-based guidelines of cognitive load theory.Instructional Designers use various instructional strategies to reduce cognitive load. For example, they think that the onscreen text should not be more than 150 words or the text should be presented in small meaningful chunks.[citation needed] The designers also use auditory and visual methods to communicate information to the learner.Learning designThe concept of learning design arrived in the literature of technology for education in the late nineties and early 2000s [16] with the idea that "designers and instructors need to choose for themselves the best mixture of behaviourist and constructivist learning experiences for their online courses" [17]. But the concept of learning design is probably as old as the concept of teaching. Learning design might be defined as "the description of the teaching-learning process that takes place in a unit of learning (eg, a course, a lesson or any other designed learning event)" [18].As summarized by Britain[19], learning design may be associated with:∙The concept of learning design∙The implementation of the concept made by learning design specifications like PALO, IMS Learning Design[20], LDL, SLD 2.0, etc... ∙The technical realisations around the implementation of the concept like TELOS, RELOAD LD-Author, etc...Instructional design modelsADDIE processPerhaps the most common model used for creating instructional materials is the ADDIE Process. This acronym stands for the 5 phases contained in the model:∙Analyze– analyze learner characteristics, task to be learned, etc.Identify Instructional Goals, Conduct Instructional Analysis, Analyze Learners and Contexts∙Design– develop learning objectives, choose an instructional approachWrite Performance Objectives, Develop Assessment Instruments, Develop Instructional Strategy∙Develop– create instructional or training materialsDesign and selection of materials appropriate for learning activity, Design and Conduct Formative Evaluation∙Implement– deliver or distribute the instructional materials ∙Evaluate– make sure the materials achieved the desired goals Design and Conduct Summative EvaluationMost of the current instructional design models are variations of the ADDIE process.[21] Dick,W.O,.Carey, L.,&Carey, J.O.(2004)Systematic Design of Instruction. Boston,MA:Allyn&Bacon.Rapid prototypingA sometimes utilized adaptation to the ADDIE model is in a practice known as rapid prototyping.Proponents suggest that through an iterative process the verification of the design documents saves time and money by catching problems while they are still easy to fix. This approach is not novel to the design of instruction, but appears in many design-related domains including software design, architecture, transportation planning, product development, message design, user experience design, etc.[21][22][23]In fact, some proponents of design prototyping assert that a sophisticated understanding of a problem is incomplete without creating and evaluating some type of prototype, regardless of the analysis rigor that may have been applied up front.[24] In other words, up-front analysis is rarely sufficient to allow one to confidently select an instructional model. For this reason many traditional methods of instructional design are beginning to be seen as incomplete, naive, and even counter-productive.[25]However, some consider rapid prototyping to be a somewhat simplistic type of model. As this argument goes, at the heart of Instructional Design is the analysis phase. After you thoroughly conduct the analysis—you can then choose a model based on your findings. That is the area where mostpeople get snagged—they simply do not do a thorough-enough analysis. (Part of Article By Chris Bressi on LinkedIn)Dick and CareyAnother well-known instructional design model is The Dick and Carey Systems Approach Model.[26] The model was originally published in 1978 by Walter Dick and Lou Carey in their book entitled The Systematic Design of Instruction.Dick and Carey made a significant contribution to the instructional design field by championing a systems view of instruction as opposed to viewing instruction as a sum of isolated parts. The model addresses instruction as an entire system, focusing on the interrelationship between context, content, learning and instruction. According to Dick and Carey, "Components such as the instructor, learners, materials, instructional activities, delivery system, and learning and performance environments interact with each other and work together to bring about the desired student learning outcomes".[26] The components of the Systems Approach Model, also known as the Dick and Carey Model, are as follows:∙Identify Instructional Goal(s): goal statement describes a skill, knowledge or attitude(SKA) that a learner will be expected to acquire ∙Conduct Instructional Analysis: Identify what a learner must recall and identify what learner must be able to do to perform particular task ∙Analyze Learners and Contexts: General characteristic of the target audience, Characteristic directly related to the skill to be taught, Analysis of Performance Setting, Analysis of Learning Setting∙Write Performance Objectives: Objectives consists of a description of the behavior, the condition and criteria. The component of anobjective that describes the criteria that will be used to judge the learner's performance.∙Develop Assessment Instruments: Purpose of entry behavior testing, purpose of pretesting, purpose of posttesting, purpose of practive items/practive problems∙Develop Instructional Strategy: Pre-instructional activities, content presentation, Learner participation, assessment∙Develop and Select Instructional Materials∙Design and Conduct Formative Evaluation of Instruction: Designer try to identify areas of the instructional materials that are in need to improvement.∙Revise Instruction: To identify poor test items and to identify poor instruction∙Design and Conduct Summative EvaluationWith this model, components are executed iteratively and in parallel rather than linearly.[26]/akteacher/dick-cary-instructional-design-mo delInstructional Development Learning System (IDLS)Another instructional design model is the Instructional Development Learning System (IDLS).[27] The model was originally published in 1970 by Peter J. Esseff, PhD and Mary Sullivan Esseff, PhD in their book entitled IDLS—Pro Trainer 1: How to Design, Develop, and Validate Instructional Materials.[28]Peter (1968) & Mary (1972) Esseff both received their doctorates in Educational Technology from the Catholic University of America under the mentorship of Dr. Gabriel Ofiesh, a Founding Father of the Military Model mentioned above. Esseff and Esseff contributed synthesized existing theories to develop their approach to systematic design, "Instructional Development Learning System" (IDLS).The components of the IDLS Model are:∙Design a Task Analysis∙Develop Criterion Tests and Performance Measures∙Develop Interactive Instructional Materials∙Validate the Interactive Instructional MaterialsOther modelsSome other useful models of instructional design include: the Smith/Ragan Model, the Morrison/Ross/Kemp Model and the OAR model , as well as, Wiggins theory of backward design .Learning theories also play an important role in the design ofinstructional materials. Theories such as behaviorism , constructivism , social learning and cognitivism help shape and define the outcome of instructional materials.Influential researchers and theoristsThe lists in this article may contain items that are not notable , not encyclopedic , or not helpful . Please help out by removing such elements and incorporating appropriate items into the main body of the article. (December 2010)Alphabetic by last name∙ Bloom, Benjamin – Taxonomies of the cognitive, affective, and psychomotor domains – 1955 ∙Bonk, Curtis – Blended learning – 2000s ∙ Bransford, John D. – How People Learn: Bridging Research and Practice – 1999 ∙ Bruner, Jerome – Constructivism ∙Carr-Chellman, Alison – Instructional Design for Teachers ID4T -2010 ∙Carey, L. – "The Systematic Design of Instruction" ∙Clark, Richard – Clark-Kosma "Media vs Methods debate", "Guidance" debate . ∙Clark, Ruth – Efficiency in Learning: Evidence-Based Guidelines to Manage Cognitive Load / Guided Instruction / Cognitive Load Theory ∙Dick, W. – "The Systematic Design of Instruction" ∙ Gagné, Robert M. – Nine Events of Instruction (Gagné and Merrill Video Seminar) ∙Heinich, Robert – Instructional Media and the new technologies of instruction 3rd ed. – Educational Technology – 1989 ∙Jonassen, David – problem-solving strategies – 1990s ∙Langdon, Danny G - The Instructional Designs Library: 40 Instructional Designs, Educational Tech. Publications ∙Mager, Robert F. – ABCD model for instructional objectives – 1962 ∙Merrill, M. David - Component Display Theory / Knowledge Objects ∙ Papert, Seymour – Constructionism, LOGO – 1970s ∙ Piaget, Jean – Cognitive development – 1960s∙Piskurich, George – Rapid Instructional Design – 2006∙Simonson, Michael –Instructional Systems and Design via Distance Education – 1980s∙Schank, Roger– Constructivist simulations – 1990s∙Sweller, John - Cognitive load, Worked-example effect, Split-attention effect∙Roberts, Clifton Lee - From Analysis to Design, Practical Applications of ADDIE within the Enterprise - 2011∙Reigeluth, Charles –Elaboration Theory, "Green Books" I, II, and III - 1999-2010∙Skinner, B.F.– Radical Behaviorism, Programed Instruction∙Vygotsky, Lev– Learning as a social activity – 1930s∙Wiley, David– Learning Objects, Open Learning – 2000sSee alsoSince instructional design deals with creating useful instruction and instructional materials, there are many other areas that are related to the field of instructional design.∙educational assessment∙confidence-based learning∙educational animation∙educational psychology∙educational technology∙e-learning∙electronic portfolio∙evaluation∙human–computer interaction∙instructional design context∙instructional technology∙instructional theory∙interaction design∙learning object∙learning science∙m-learning∙multimedia learning∙online education∙instructional design coordinator∙storyboarding∙training∙interdisciplinary teaching∙rapid prototyping∙lesson study∙Understanding by DesignReferences1.^MIL-HDBK-29612/2A Instructional Systems Development/SystemsApproach to Training and Education2.^Bloom's Taxonomy3.^TIP: Theories4.^Lawrence Erlbaum Associates, Inc. - Educational Psychologist -38(1):1 - Citation5.^ Sweller, J. (1988). "Cognitive load during problem solving:Effects on learning". Cognitive Science12 (1): 257–285.doi:10.1016/0364-0213(88)90023-7.6.^ Chandler, P. & Sweller, J. (1991). "Cognitive Load Theory andthe Format of Instruction". Cognition and Instruction8 (4): 293–332.doi:10.1207/s1532690xci0804_2.7.^ Sweller, J., & Cooper, G.A. (1985). "The use of worked examplesas a substitute for problem solving in learning algebra". Cognition and Instruction2 (1): 59–89. doi:10.1207/s1532690xci0201_3.8.^Cooper, G., & Sweller, J. (1987). "Effects of schema acquisitionand rule automation on mathematical problem-solving transfer". Journal of Educational Psychology79 (4): 347–362.doi:10.1037/0022-0663.79.4.347.9.^ a b Mayer, R.E. (1997). "Multimedia Learning: Are We Asking theRight Questions?". Educational Psychologist32 (41): 1–19.doi:10.1207/s1*******ep3201_1.10.^ Mayer, R.E. (2001). Multimedia Learning. Cambridge: CambridgeUniversity Press. ISBN0-521-78239-2.11.^Mayer, R.E., Bove, W. Bryman, A. Mars, R. & Tapangco, L. (1996)."When Less Is More: Meaningful Learning From Visual and Verbal Summaries of Science Textbook Lessons". Journal of Educational Psychology88 (1): 64–73. doi:10.1037/0022-0663.88.1.64.12.^ Mayer, R.E., Steinhoff, K., Bower, G. and Mars, R. (1995). "Agenerative theory of textbook design: Using annotated illustrations to foster meaningful learning of science text". Educational TechnologyResearch and Development43 (1): 31–41. doi:10.1007/BF02300480.13.^Paas, F., Renkl, A. & Sweller, J. (2004). "Cognitive Load Theory:Instructional Implications of the Interaction between InformationStructures and Cognitive Architecture". Instructional Science32: 1–8.doi:10.1023/B:TRUC.0000021806.17516.d0.14.^ Clark, R.C., Mayer, R.E. (2002). e-Learning and the Science ofInstruction: Proven Guidelines for Consumers and Designers of Multimedia Learning. San Francisco: Pfeiffer. ISBN0-7879-6051-9.15.^ Clark, R.C., Nguyen, F., and Sweller, J. (2006). Efficiency inLearning: Evidence-Based Guidelines to Manage Cognitive Load. SanFrancisco: Pfeiffer. ISBN0-7879-7728-4.16.^Conole G., and Fill K., “A learning design toolkit to createpedagogically effective learning activities”. Journal of Interactive Media in Education, 2005 (08).17.^Carr-Chellman A. and Duchastel P., “The ideal online course,”British Journal of Educational Technology, 31(3), 229-241, July 2000.18.^Koper R., “Current Research in Learning Design,” EducationalTechnology & Society, 9 (1), 13-22, 2006.19.^Britain S., “A Review of Learning Design: Concept,Specifications and Tools” A report for the JISC E-learning Pedagogy Programme, May 2004.20.^IMS Learning Design webpage21.^ a b Piskurich, G.M. (2006). Rapid Instructional Design: LearningID fast and right.22.^ Saettler, P. (1990). The evolution of American educationaltechnology.23.^ Stolovitch, H.D., & Keeps, E. (1999). Handbook of humanperformance technology.24.^ Kelley, T., & Littman, J. (2005). The ten faces of innovation:IDEO's strategies for beating the devil's advocate & driving creativity throughout your organization. New York: Doubleday.25.^ Hokanson, B., & Miller, C. (2009). Role-based design: Acontemporary framework for innovation and creativity in instructional design. Educational Technology, 49(2), 21–28.26.^ a b c Dick, Walter, Lou Carey, and James O. Carey (2005) [1978].The Systematic Design of Instruction(6th ed.). Allyn & Bacon. pp. 1–12.ISBN020*******./?id=sYQCAAAACAAJ&dq=the+systematic+design+of+instruction.27.^ Esseff, Peter J. and Esseff, Mary Sullivan (1998) [1970].Instructional Development Learning System (IDLS) (8th ed.). ESF Press.pp. 1–12. ISBN1582830371. /Materials.html.28.^/Materials.htmlExternal links∙Instructional Design - An overview of Instructional Design∙ISD Handbook∙Edutech wiki: Instructional design model [1]∙Debby Kalk, Real World Instructional Design InterviewRetrieved from "/wiki/Instructional_design" Categories: Educational technology | Educational psychology | Learning | Pedagogy | Communication design | Curricula。
Verifying the Franz-Wiedemann Law in the Undergrad
Journal of Physical Science and Application 3 (5) (2013) 328-331Verifying the Franz-Wiedemann Law in the Undergraduate LaboratoryAndrea Clark, Logan Jacobson and Patrick PolleyDepartment of Physics and Astronomy, Beloit College, Beloit WI 53511Received: May 05, 2013 / Accepted: May 10, 2013 / Published: May 15, 2013.Abstract: The Franz-Wiedemann law is similar to Ohm’s law in that it applies to an important but narrow set of materials.In 1853 R. Franz and G. Wiedemann observed that in metals the ratio of the thermal conductivity to the electrical conductivity is a constant.This observation suggests that whatever mechanism or particle is involved in the transmission of electrical current through a metal may also be responsible for the transmission of heat.In this paper we present an inexpensive and quick experiment through which this law may be verified for copper, aluminum and zinc.Key words: Thermal conductivity, thermal diffusivity, electrical conductivity, undergraduate laboratory.1. IntroductionThe motivation for developing this experiment wasto provide an inexpensive method for demonstrating the Franz-Wiedemann law [1]. We denote the thermal conductivity of metal by K and the electrical conductivity by σ, and following Franz and Wiedemann we can write:K/σ= constant (1) The electrical resistivity ρel is the reciprocal of the electrical conductivity so we chose to write Eq. (1) as:Kρel= constant (2) The electrical properties of metals are easy to measure. Determining the electrical resistivity of a metal in the form of a wire for constant currents requires a straightforward application of Ohm’s law. The thermal conductivity is more difficult. If we use an analogy to electrical current to describe the heat current in a metal, voltage and electrical current are easy to measure, and the heat equivalent to voltage, temperature, is easy to measure, but the amount of heat flowing through a wire or slab is more difficultCorresponding author: Patrick Polley, professor, research fields: optics and solid-state physics. E-mail: ******************.to measure. Charge and current are confined to the wire, but heat can radiate through the sides as well as along the wire. We get around this by using a transient method to determine the rate of temperature change in an insulated metal rod when the temperature of one end of the rod is suddenly changed.2. TheoryThe first theoretical explanation of the Franz-Wiedemann law in terms of the electron was given by Drude [2]. In Drude’s model the electrons are treated as a classical gas. The thermal conductivity K is given by [3]:K =Cvl/3 (3) In Eq. (3), C is the specific heat per unit volume, v is the velocity of the electrons, l is the mean free path between collisions. The electrical conductivity σ is given by [3]:σ = (ne2l)/(mv) (4) In Eq. (4), n is the number of conduction electrons per unit volume, e is the electron charge, and m is the mass of the electrons. Taking the ratio K/σ we obtain:K/σ = (3ne2)/(Cmv2) (5)Treating the electrons as a classical gas with aAll Rights Reserved.classical spe for mv 2 in t K /σ that is a paper he ma [4] that led agreement electronic sp 3k B /2, wher kinetic ener electrons n conduction. Fermi-Dirac agreement w by [5]:In Eq. (6)The deter experimenta heat flow conditions, i temperature investigation rod. In one takes the for In Eq. (7)distance x an The thermal In Eq. (8)C is the ma given positio by:In Eq. (9)our experim observing th for different certain value product (αt)the same di relative valu Ver ecific heat and erms of a kin a factor of at ade a compen to unexpect with the pecific heat in re k B is Bo rgy term is near the Fe The free-el c statistics fo with the expe K /σ =, T is the tem rmination of al challenges through a in our experim of one en n and monito e dimension rm [6]:∂T (x ,), T (x , t ) is the nd time t , an l diffusivity is ) ρm is the ma aterial’s spec on d along th T (d ,t ) = X ), X (d ) is a f ment we obta he temperatur t metals. Wh e, we can use ) is the same stance from ues for K for t rifying the Fr d any sort of netic energy least 2 too s nsating error o tedly and un experimenta n metals is m oltzmann’s c much larger ermi energy lectron mod for the electr erimental valu = (πk B )2T /(3e mperature of th the value of . Rather tha metal und ment we sud nd of the m or the tempe the heat di t )/∂t= α ∂2T (e temperature nd α is the the s defined by:α = K/(ρm C) ass density of cific heat per he rod the tem (d )[1 −exp(-function of th ain relative v re at a fixed hen the temp e Eq. (9) and for those di the end of th the metals us ranz-Wiedema f reasonable v yields a valu small. In Dru of a factor of ndeservedly g al values. much smaller onstant, but r since only y participate el that emp ron yields g ues, and is g 2)he metal. K presents s n measuring der steady-s denly change metal rod un erature along ffusion equa x ,t )/∂x 2e as a functio ermal diffusivf the material r unit mass.A mperature isg αt)] he distance d values for K point on the peratures reac d the fact that ifferent meta he rod to ext ing Eq. (8).ann Law in th valueue of ude’s f two good The than the the e in ploys good given (6) ome g the state e the nder g the ation (7) on of vity. (8) l and At a given (9) d. In K by e rod ch a t the ls at tract 3. E In atta clam Wir wra and allig inte late note resi Ohm U area grap whe cros slop M invo alon The elec con resi rod min T amo samFig.app he Undergrad Experimen n order to m ached two ver mps to the en re samples o apped around d connected gator clips. V ervals, marke er. The curren ed at each istance of eac m’s Law. Using Eq. (1a of each s phing:ere, ρel is ss-sectional a pe of the grap Measuring th olved proces ng metal sam e sensors are ctrical tape t ntacting the istance of 0.0setup with nimize heat lo Though we ount of heat mples it is im . 1 Simplifiedaratus.duate Laborat ntsmeasure the el rtical steel ro nds of a table of zinc, copp d these meter to the circui Voltage read ed by tape f nt from the v measuremen ch wire samp 10) and the sample, the R = (ρthe electrica area and L is ph yields the r he thermal ss. Five LM mple rods app e held in pla to prevent th rod. Insula 0196 W/m is w h the wires oss through th have taken t lost throug mportant to ad circuit diag tory lectrical cond ods clad in PV about 2 mete per, and alum sticks in a zig it shown in dings were ta for precise m ariable powe nt. From th le can be calc measured cr resistivity i el /A )L al resistivity the length of resistivity ρel conductivity M335 sensors proximately 6ace with hea he sensors’ ation tape w wrapped arou gathered at he sides of th measures to gh the sides account for tgram of electri 329ductivity, we VC pipe with ers in length.minum were g-zag pattern Fig. 1 with aken at 4 m measurement er supply was his data, the culated using ross-sectional is found by (10)y, A is the f sample. The l . is a more s are spaced 6’’ in length.at grease and prongs from with a heat und the entire the top to e sample. o reduce the s of the rod the heat leaksical resistivity 9eh . e n h m t s e g l y )e e e d . d m t e o e d sy All Rights Reserved.330through the relies on the where, ΔQ i is the temp barrier, A is thickness of cylindrical g In Eq.(12the insulatio the literatur Eq. (11), we rods for respectively thermal con manufacture the heat loss These sen Boiling wat directly ben rod is subme Voltage rea intervals for will determi the calculati the sensors l produce the 1-D approx far away fro too slowly used to find K Al and K Cu in terms o resistivities agreement w 4. ResultsFor the el 16.8n Ω·m, n Ω·m. The l Al, 28.2 n Ω·Ver insulation tap e steady-state ΔQ /Δt is the heat lo perature diffe s the cross s f the barrier geometry lead ΔQ /Δt = ), r o and r i ar on, and L is th e values for e calculated th aluminum, , 4.7 W, 2.3nductivity of er and Eq. (1s through the nsors connect ter stored in eath the rod erged approx adings of all r 7 min. Grap ine which se ion of therm located neare desired data imation inva om the boilin and did not α, the therma u . Then these of K Cu . Usin and Eq. (2with the Franz lectrical resis ρ(Cu) is 28.literature valu · m for Cu, a rifying the Fr pe. Our worst version of Eq = K·A (Δss, Δt is the t ference acros sectional area [7]. Adaptin ds to:2π·K·L·(ΔT)re the inner a he length of th thermal con he rate of hea zinc, and 3 W, and 7.f the tape p 12) we calcu tape to be 0.0t to the circui n Styrofoam sample so th imately 1 cen l sensors are phs of each ensor should mal resistivity est to the boil since their lo alid. The sen ng water reac yield useful al diffusivity can be solved ng the calc ), we observ z-Wiedemann stivity we fou .4 n Ω·m, an ues[8] of ρel a and 59.0 n ranz-Wiedema t-case calcula q.(7).T /Δx ) time interval ss the insula a, and Δx is ng Eq. (11) f /ln(r o /r i )and outer rad he sample. U nductivity [8]at flows along copper to .9 W. Using published by ulated the rat 05 W t shown in Fi cups is pla hat the end of ntimeter in w e taken at 1sample’s sen be observed y. We found ing water did ocation made nsors located ched equilibr data. Eq. (8, in terms of d for K Zn and culated electr ve the expe n Law.und that ρ(A nd ρ(Zn) is are 16.8 n Ω·m Ω·m for Zn. Th ann Law in th ation(11), ΔT ating s the for a (12)dii of Using and g the be, g the the te of ig. 2. aced f the ater. 15 s nsors d for that d not e the too rium 8) is K Zn , d K Al rical ected Al) is62.1 m for hereFig.appis le to l equ whe resi area of F com wou esca whi the valu Fig dist for to th W alum K Zn term is 0weFig.he Undergrad . 2 Simplified aratus.ess than a thr literature valu uation:ere, ρel is istance, L is t a of the wire R versus L , as For the therm mplicated. Fi uld be lost s aped heat in ich contribute thermal con ues we plotte . 4. From h tance for two the temperatu he thermal di We find tha minum, 2.90 n for zinc. W ms of K Cu and 0.3689 K Cu .want to show . 3Electrical duate Laborat d circuit diagr ree percent er ues. We fou ρel = (the electrica the length, an e. We found R s seen in Fig. mal conductiv irst we calc so we would the system. W es an error of nductivity. T ed the voltage ere we used o rods the rat ure to reach a iffusivity α of t 2/t 1 =at αvalues a × 10-4 K Cu fo We can expre d found that To verify th w that:resistivity resu toryram of therma rror in our da und these val (R /L )A al resistivity nd A is the cr /L by plotti 3. vity our resu culated how d know the We found thi f less than fiv To find our e versus time d the fact tha tio of the tim a certain valu f each rod by = α1/α2 are 4.13 × or copper and essall the α v K Al is 0.664 he Franz-Wieults.al conductivity ata compared lues with the (13)y, R is the ross sectional ing the graph ults are more much heat error due to s to be 4.5 J,ve percent to experimental e as shown in at at a fixed me t required ue T is related y:(14)10-4K Al for d 3.63 × 10-4values in the K Cu and K Znedemann law yd e )e l h e t o , o l n d d d )r4e n wAll Rights Reserved.Fig. 4 ThermAt some experimenta Ω·mK Cu , Cu This shows t 5. Conclus We haveVer mal conductivi ρel · specific te al values, we u is 18.7 Ω·m the approxim sionse developed rifying the Fr ity results. K = constantemperature T find that ρel m K Cu , and Zn mate agreemenan inexpens ranz-Wiedema T.By using K for Al is n is 21.8 Ω·m nt we expectesive method ann Law in thour17.8 K Cu . ed.for veri und the con Re [1][2][3][4][5][6][7][8]he Undergrad ifying the dergraduate la student the nductivity and ferencesR. Franz, G.conductivity 497-531. (in P. Drude, On der Physik 30C. Kittel, In Wiley, New J N.W. Ashcro New Jersey, N.W. Ashcro New Jersey, J. Fourier, Th French)H.C. Ohania Scientists, 3rd http://www.e duate Laborat Franz-Wied aboratory. Th intimate con d electrical co Wiedemann, O of metals, Ann German) n the theory of 06 (1900) 566-6ntroduction to Jersey 1996, p. oft, N.D. Mermi 1976, p. 23. oft, N.D. Mermi 1976, p. 255. he Analytic The n, J.T. Marker d ed.,2006, p. 6ngineeringtoolb tory demann La his method br nnection betw onductivity.On the thermal nalen der Physik f electrons in m 613. Solid State Ph 132. in, Solid State P in, Solid State P eory of Heat, 18rt, Physics for 650. .331aw in the rings home to ween thermal l and electrical k 165 (8) (1853metals, Annalen hysics, 7th ed.,Physics, Wiley,Physics, Wiley,822, p. 135. (in Engineers and eo l l3) n , , , n d All Rights Reserved.。
New Developments in Luminescence for Solar Energy Utilization
See discussions, stats, and author profiles for this publication at: https:///publication/222741013 New Developments in Luminescence for Solar Energy UtilizationArticle in Optical Materials · July 2010DOI: 10.1016/j.optmat.2010.04.034CITATIONS 73READS 2141 author:Renata ReisfeldHebrew University of Jerusalem454 PUBLICATIONS 11,346 CITATIONSSEE PROFILEAll content following this page was uploaded by Renata Reisfeld on 05 December 2016.The user has requested enhancement of the downloaded file. All in-text references underlined in blue are added to the original document and are linked to publications on ResearchGate, letting you access and read them immediately.Invited PaperNew developments in luminescence for solar energy utilizationRenata Reisfeld *,1Chemistry Institute,Hebrew University of Jerusalem,Givat-Ram,Jerusalem 91904,Israela r t i c l e i n f o Article history:Received 8April 2010Accepted 19April 2010Available online 21May 2010Keywords:Luminescent dyes SilverPhotonic crystals Glass platesOrgano-inorganic matrices Non-self-absorbing systemsa b s t r a c tAs our fossil sources of energy diminish constantly search for alternative energy solutions becomes vital.The interest in exploiting solar energy for photovoltaic electricity has grown exponentially in recent dec-ade,however,its high cost is still a limiting factor for massive uses.Static luminescent concentrator could provide a partial solution if properly designed.The paper summarizes the requirements for efficient and photostable luminescent concentrators,provides the latest results and ideas and shows how they can be materialized.It is demonstrated how the plate efficiency can be improved by applying a thin film with optical contact to transparent plate,silver plasmons that increase the transition probability of the colo-rants,photonic systems preventing the escape of the luminescence from the plate when traveling to the cell,creating fluorescence in the UV and visible part of the spectrum,using materials in which the absorp-tion and emission from different electronic levels prevent self-absorption.Ó2010Elsevier B.V.All rights reserved.1.IntroductionSince the shortage of fossil fuel is treating our future it became obvious that a search of alternative energy sources is of crucial importance.Photovoltaic electricity is one of the most promising solutions,however,it is still too expensive for massive uses.Lumi-nescent solar concentrators are one of possible solutions for chip-per photovoltaic electricity.Luminescent solar concentrator (LSC)has been already proposed several decades ago [1–6],after which the research stopped because of the availability of cheap fossil en-ergy.In recent years because of the rapid increase of the cost of fuel there is a renewed great interest in solar energy and specifically in LSC [7–15].Limitations to produce effective stable LSC still exist and these have to be improved.The theory of LSC,which is based on internal reflection of fluo-rescent light which is subsequently concentrated at the edges,has been discussed in detail for inorganic materials [3,4]and organic dyes incorporated in bulk polymers [5–15].A transparent plate doped by fluorescent species should absorb the major part of solar spectrum.The resulting high yield luminescence is emitted at the longer wavelength part of the spectrum.Repeated reflections of the fluorescent light in a transparent matrix should carry the radi-ation to the edges of the plate where the light will emerge in a con-centrated form.The concentration factor is the ratio of the plate surface to the plate edge.Theoretically about 75–80%of the lumi-nescence would be trapped by total internal reflection in the plate having a refractive index of about 1.51.Photovoltaic cells can be coupled to the edges and receive the concentrated light.Such an arrangement should substantially decrease the amount of photo-voltaic cells needed to produce a given amount of electricity and thus reduce the cost of the system of photovoltaic electricity.If all these ideal conditions could be realized our calculations show that the collecting efficiency,which is the amount of energy reach-ing the photovoltaic cell divided by the energy falling on the plate should be about 20%[5].This result is obtained by taking into ac-count the overall absorption of the colorants in the plate,their fluorescent efficiency,the trapping efficiency (depending of the refractive index of the plate medium),and the Stock efficiency (which is the ratio of the average energy emitted to the average en-ergy absorbed).So far the efficiency of LSC was never above 7%,one of the main reasons for the relatively low efficiency is the self-absorption of the luminescent dyes as a result of overlapping of the absorption and luminescence of the dyes.Another difficulty is the escaping of the luminescence emitted beyond the critical an-gle.In what follows I shall shortly describe the advantages and dis-advantages of the different colorants:organic dyes vs.rare earth elements and other inorganic 2species,trapping media glass or polymer,balks vs.thin films deposited on transparent plates,use of photonic systems as a band gaps preventing the escape of the trapped radiation,using materials in which the absorption of light and the emission arise from different electronic levels preventing self-absorption,and finally utilizing solar cells with their sensitivity0925-3467/$-see front matter Ó2010Elsevier B.V.All rights reserved.doi:10.1016/j.optmat.2010.04.034*Tel.:+97226585323.E-mail address:Renata@vms.huji.ac.il 1Enrique Berman Professor of Solar Energy.2The inorganic species will be described in the next paper.Optical Materials 32(2010)850–856Contents lists available at ScienceDirectOptical Materialsj o u r n a l ho m e p a g e :w w w.e l s e v i e r.c o m /l o c a t e /o p t m atmatching maximum emission of the colorants.A possible solution to the problems is a luminescent material in which the excitation and emission arise from different electronic levels.It will also be shown that increase of transitional probabilities of the colorants from the ground to the excited state may be achieved as a result of interaction of the electronic state of the dye with silver plasmons.In what follows I will summarize the principle of LSC perfor-mance,the spectroscopic properties of organic colorants which ab-sorb in major part of solar spectrum and emit light close to the maximum sensitivity of different solar cells,the optimum hosts for incorporation of the dyes,an example will be given how silver plasmons can increase the intensity of the dyes luminescence and how an overlap between absorption andfluorescence of the colo-rants responsible for lowering the plate efficiencies can be circum-vented by new type of compounds in which the absorption and luminescence originate from different electronic states.It will be shown how photonic band gaps prevent escaping of thefluores-cence traveling to the solar cell.An example will be also give of a possibility to incorporate efficiently emitting quantum dots in the LSC system.2.The state of art of luminescent solar concentratorsLuminescent solar concentrators(LSCs)are based on the en-trance of solar radiation into a homogeneous medium collector containing afluorescent species in which the emission bands have little or no overlap with the absorption bands.This emission is trapped by total internal reflection and concen-trated at the edge of the collector by the geometrical shape which is usually a thin plate.Thus the concentration of light trapped in the plate is proportional to the ratio of the surface area to the edges.The advantages of LSC over conventional solar concentrators are the following:(a)High collection efficiency of solar light direct and diffuse.(b)Good heat dissipation from the large area of the collectorplate in contact with air,so that essentially‘‘cold light’’is used for converter devices such as silicon cells,whose effi-ciency is reduced by high temperatures.(c)Tracking of the sun unnecessary;and(d)Choice of the luminescent species allows optimal spectralmatching of the concentrated light to the maximum sensi-tivity of the photovoltaic(PV)process,minimizing undesir-able side reactions in the PV cells.The performance of LSC is given by the effective concentration ratio which is the product of the geometrical factor and the optical conversion efficiency of the collector.The geometrical factor is the ratio of the surface Area As to the area of the plate edges A l.The optical conversion efficiency g opt of the collector plate may be de-fined as the ratio of light delivered from the total edges to the light incident on the collector plategopt¼P out=P inwhere P in is the total solar power in watts incident on the collector and P out is the power delivered from the total edges.The optical efficiency of the plate,which is the energy coming out of the edges of the plate divided by the energy falling on the plate,including reflection is given by,gopt¼ð1ÀRÞg abs g f g s g t g tr g selfð1Þwhere R is reflectance.The parameters determining the optical plate efficiency depend on the following factors:(a)The fraction g abs which is the ratio of photons absorbed bythe plate to the number of photons falling on the plate. (b)The quantum efficiency offluorescence g f which is the ratioof the number of photons emitted to the number of photons absorbed.(c)The Stokes efficiency g S which is the ratio of the averageenergy of emitted photons to the average energy of the absorbed photons and is given bygS¼m em=m absð2Þ(d)The trapping efficiency g t of the light trapped in the collec-tor given bygt¼ð1À1=n2Þ1=2ð3Þwhere n is the refractive index of the light-emitting medium.If the surrounding medium does not have a refractive index n0very close to1(such as air),the quantity(1/n)is everywhere replaced by the ratio(n0/n).Numerical examples of the trapping efficiency g trap are shown below:n=1.414 1.556 1.743 2.000g trap=0.70710.76600.81910.8660In a recent paper it has been shown[11]how the trapping efficiency can be increased by applying photonic crystals.A photonic structure which serves as a band-stop reflectionfilter for the light-emitted from the dye,increases the trapping efficiency of a collector pre-venting the escape from the system(as shown below).(e)The transport efficiency g tr which takes into account thetransport losses due.To matrix absorption and scattering.(f)The efficiency g self due to losses arising from self-absorp-tion of the colorants.The overall power efficiency obtained from afluorescent plate connected to photovoltaic cells is the product of plate efficiency to that of the solar cells.Such efficiencies was recently elaborated by Rau et al.[8]who uses in his computation the thermodynamic limits of photovoltaic solar energy conversion applying Shockley–Queisser calculations.Slooff et al.[12]have recently reported power conversion efficiency of5.2%of5Â5Â0.5cm3LSC con-nected to silicon cell and7.1%of the similar plate connected to GaAs cell.The concept of photonic crystals introduced recently [11]by Goldschmidt et al.allows to increase the plate collector efficiency.It has been shown that one-dimensional photonic struc-ture increases the efficiency of5Â10Â0.5cm3of afluorescent concentrator by12%by increasing the trapping efficiency.As a re-sult of recent work it is now evident that additional factors such as inefficient luminescent trapping and overlap offluorescence with the absorption spectra are still limiting factors to the calculated theoretical efficiency of24%[8]of photovoltaic conversion effi-ciency of LSC.anic dyesMajor part of solar spectrum can be covered by organic colo-rants suitable for LSC.The requirements of the dyes(colorants) are similar to laser dyes which absorb in UV,visible and IR part of the spectra[16,17].Thus their combination can cover most of the solar spectrum and thefluorescence can be match to the excit-ing solar cells.Organic laser dyes generally contain extended conjugated p bonds which determine the resonant optical absorption bands of the dye molecule.To afirst approximation,the conjugated p-elec-trons can be analyzed as a quantum mechanical particle in a poten-tial well[16,17].However,the spectra can be significantly modified by adding functional groups with various electronegativ-R.Reisfeld/Optical Materials32(2010)850–856851ities (e.g.electron withdrawing groups such as ketones)to the ba-sic conjugated anic dyes usually have very broad spec-tra,which are affected by solvent polarity.Dye molecules tend not to be water soluble unless charged or highly polar side groups are added.In addition,their large size (as compared to ions)lends it-self to various photochemical processes that result in photobleach-ing.They can also form dimers or aggregates,often resulting in large changes in their spectral anic dyes come in families,which are characterized by a basic structure to which var-ious substituent groups are added.A recent review of the organic laser dyes can be found in Ref.[17].And Fig.3.Emission spectra of the dyes presented in Fig.2and quantum dots with solar cells.For practical uses the dyes have to be desolved in an appropri-ate matrix in a monomolecular form to prevent fluorescent quenching due to dimers and higher aggregates.For this reson spe-cial photostable matrices have to be designed.In addition as will be shown father the matrices have to be optically attached to trans-parent glass or polymer plates.The optimum solution is a glass plate which covered by an or-ganic–inorganic (ormocer)thin film incorporated by fluorescent dyes as shown in Fig.1.Such matrices are prepared by combination of organic and inorganic parts (ormocers).In what follows we present examples of ormocer matrices which can be used for luminescent solar concentrators.4.Examples of ormocer matricesAs seen above,an important goal in designing LSC is to find proper hosts in which the organic colorants dissolve well in mono-molecular form preventing aggregation.To achieve this goal we have developed several ormocer matrices prepared by the sol–gel method.The sol–gel process allows to design inorganic or org-ano-inorganic (ormocer)balks or thin films which can be incorpo-rated by variety of inorganic,organic units,quantum dots and metallic nanoparticles.The process utilizes metalo-organic precur-sors,organic solvents,low processing temperatures,processing versatility of the colloidal state,etc.and allow the introduction of ‘‘fragile’’organic molecules inside an inorganic network.Inorganic and organic components can be mixed at the nanometer scale,in virtually any ration,leading to the formation of the so-called hy-brids organic–inorganic nanocomposites.These hybrids are extre-mely versatile in their composition,processing and optical and mechanical properties.The properties of the hybrid materials do not depend only of interface between both phases,which can be used to tune many properties.The general tendency is to increase interfacial interactions by creating intimate mixing and/or inter-penetration at the nanometer scale between both components.The nature of the interface between the organic and inorganic components has used to classify these hybrid nanocomposites into two classes.Class I corresponds to all those systems where organic and inorganic components only exchange weak interactions such as van der Waals,hydrogen bonds or electrostatic forces.On the contrary in class II materials,at least parts of the organic and inor-ganic components are linked by strong chemical bonds (covalent or iono-covalent).Among hybrid compounds,siloxane-based materials present several advantages for the design of materials for photonics,as many precursors are commercially available.In our case,the me-tal–organic compounds are silicon alkoxides such as tetraalkoxysi-lane,of the general formula Si(OR)4(where R is an alkyl group)and organofunctional-trialkoxysilane,which has the general formula G–Si–(OR)3(G is an organic group).If G is a simple non-hydroliz-able group bonded to silicon through a Si–C bond,it will have a network modifying effect (e.g.Si–CH 3).On the other hand,if G can react with itself (G contains a metacrylate group for example)or additional components,it will behave as a network -work modifiers and network former functionalities can also be used to target many other specific properties (e.g.photochemical,electrochemical and optical).Therefore numerous siloxane-based hybrid organic–inorganic materials have been developed in the past few years.This develop-ment yields many interesting new materials,with improved mechanical properties tunable between those of glasses andthoseFig.1.Scheme of LSC.Profile of transparent glass plate covered by an ormocer matrix incorporated by a number ofcolorants.Fig.2.Absorption spectra of several organic dyes and one quantum dot overlapping the solar spectrum which can be used in solarconcentrators.Fig.3.Emission spectra of a number of dyes and sensitivity curves of several solar cells,as specified in the figure.852R.Reisfeld /Optical Materials 32(2010)850–856of polymers.Sol–gel processed hybrid materials can be used as, optical devices,new sensors and biosensors.The mechanical and optical properties of glasses prepared by the sol–gel are being improved constantly by modifying the sol process and using a variety of organofunctional silicon alkoxides. Modified alkoxide precursors,RSi(OEt)3or RSi(OMet)3,,such as methyltriethoxysilane(MTEOS),vinyltriethoxysilane(VTEOS), amyltriethoxysilane(ATEOS),3-(trimethoxysilyl)propylmethacry-late(TMSPMA),3-glycidoxypropyltrimethoxysilane(GLYMO)or methyltrimethoxysilane(MTMOS)lead to organic–inorganic hy-brid matrices.The permanent organic group decreases the mechanical ten-sions during the drying process.Functionalized alkoxides F–G–Si(OEt)3,where F is a chemical function such as an amino or isocy-anate group and G is an alkyl spacer,allow to covalently graft onto the xerogel matrix to avoid phase separation and consequently to increase the concentration of the guest molecules.After drying, optically clear and dense inorganic–organic hybrid xerogels (30mm diameter and15mm thick)where obtained as shown in Ref.[17].Recently a new matrix of zirconia-silica-polyurethane(ZSUR) was reported by us.This matrix allows incorporation of a wide range of photonic molecules and possesses the high mechanical and thermal stability and high refractive index[17].The matrix of polyethylene urethane silica possesses the high mechanical and thermal stability and high refractive index of zir-conium oxide.By combining the strength and hardness of sol–gel matrices with the processability and ductility of polymers,novel transparent hybrid material can be obtained.Diurethane siloxane (DURS)was synthesized from3-isocyanatopropyltriethoxysilane (ICTEOS)and polyethylene glycol(PEG),chloro benzene was used as a solvent.The epoxy-silica-ormosil(ESOR)precursor was ob-tained from tetramethoxysilane(TMOS)ans3-glycid oxypropyl tri-methoxysilane(GLYMO).These two types of ormosils were combined with zirconium oxide matrix which was used as an inor-ganic hetero network and as an efficient catalyst for the epoxy-polymerization.ZrO2is the best promoter for epoxy-polymeriza-tion.However,reaction at room temperature is limited(about 27%unreacted epoxy groups)as well as at high temperature 70°C/8h(about24%),but the part of unreacted epoxy groups is still lowers that in cases of SiO2/TiO2(70–57%)or in case of SiO2 (100%).In the case of ZrO2unreacted epoxy groups(at least24%) can be reacted with secondary amino groups in urethane linkage of DURS.Due to strong chemical bonding between organic and inorganic parts,the hybrid materials offer can be easily modified or synthesized;secondly,the control of precursor reactivity can be achieved by using acid,basic or nucleophilic catalysts;thirdly, transparentfilms or monoliths having a good mechanical integrity can be easily processed;and fourthly,the resulting materials are generally not toxic.The precursors of siloxane-based hybrids are organo-substituted silicic acid esters of general formulaR0 n SiðORÞ4Àn0where R0can be any organofunctional group and R isan alkyl group.If R0is a simple non-hydrolizable group superior mechanical properties(elasticity,flexibility)and are suitable for incorporation of organic dyes.The hybrid materials are obtained by using three precursor com-posites:(a)poly(ethylene)glycol chain covalently linked by ure-thane bridges with triethoxysilane groups synthesized separately, (b)epoxy-silica ormosil precursor and(c)a zirconium oxide precur-sor.The solubility of most laser dyes is limited in pure hydrophobic or hydrophilic matrices,which can cause migration and aggrega-tion of the dyes,resulting in decreased efficiency when used in the LSC.The novel organic–inorganic ormocer matrix can efficiently entrapped a large number of colorants suitable in LSC.It was shown that the combination of two types of clusters creates two subphases with different degrees hydrophilicity/hydrophobicity,and results in an active interface.The above matrices suitable for formation of thinfilms which can be incorporated by appropriate colorants have been prepared in our laboratory based on the following mate-rials.Polymethylmethacrilate(PMMA),polyethylene glycol dimethacrilate(PEGDM),polyurethane(PU)and various epoxides have been also used as polymer hosts for solid state dye lasers[17].Some examples of common precursors for preparation of ormo-cer matrices are given below.mon precursor list for preparation of ormosils matricesSilica precursor list:Tetraalkoxysilanes(general formula:Si(OR)4):TEOS–tetraethoxysilane,Si(OC2H5)4;TMOS–tetramethoxysilane,Si(OCH3)4.Organoalkoxysilanes(general formula:G–Si–(OR)3):TMSPMA–3-(trimethoxysilyl)propylmethacrylate,C7H8O2–Si–(OCH3)3;GLYMO–3-glycidoxypropyltrimethoxysilan,C6H11O2–Si–(OCH3)3;MTMOS–methyltrimethoxysilane,CH3–Si–(OCH3)3.Zirconia precursor:Zirconium-n-propoxide,Zr(OC3H7)4.Organic monomers for polymerization:MMA–methyl methacrylate,CH2C(CH3)COOCH3;EGDM–ethylene glycol dimethacrilate;CH2C(CH3)COO–CH2–CH2-OOC(CH3)CH2.The matrices involved:silica-polyurethane(SiPU),glycidoxypro-pyltrimethoxysilane(glymo)-phenyl-silica-polyurethane(GPSPU) and zirconia-glymo(ZrGl)deposited as thinfilms on glass substrate.The process of their formation is now patent pending.5.Silver nanoparticlesFor LSC it is important to have colorants with high transition probabilities of light absorption and emission.The plate efficiency can be increased by interaction of a model luminescent dye with silver plasmons.The idea is based on the fact that the scattering light from absorbing metal nanoparticles(plasmons)can interact with a dye molecules embedded in dielectric medium and increase its radiative transition probabilities.It is well known that metal nanoparticles such as silver nano-particles can result in strong scattering of incident light and greatly enhance the localfields,the effect long time known from enhanced Raman scattering.Surface plasmons are collective oscillations of the electrons of conductors,and have attracted intense interest re-cently due to their wide range of potential applications such as nanoscale electronics,biological and chemical materials sciences [18–23].The collective oscillation of the electrons leads to a reso-nant interaction between incident light and the conductor.A similar effect was shown recently by interaction of silver plas-mons with solar cells[24].The increase of emission of luminescent dyes in presence of silver NPs in sol–gelfilms has been reported re-cently[25–31].Our preliminary experiments have demonstrated the influence of silver NPs on collection efficiency of luminescent plate which has increased by12%when compared with identical plate without silver nanoparticles.An example of sol–gel matrix in which silver nanoparticles can be formed together with dye molecules is presented below.(a)Silica-polyurethane(SiPU).(b)Glymo-phenyl-silica-polyurethane(GPSPU),(c)Zirconia-glymo(ZrGl).SiPU sol–gel matrix was prepared as follows:The corre-sponding amounts of TEOS,DURS,water and nitric acidR.Reisfeld/Optical Materials32(2010)850–856853was dissolved in ethanol with molar ratio of 1/0.5/5.5/0.02/20.The sol was allowed to hydrolyze under stirring for 24h at room temperature to obtain SiPU solution. GPSPU matrix solution was prepared using the following molar ratio of precursors:glymo/phenyl-TMOS/TEOS/DURS =1.0/1.0/3.0/0.45/which were mixed in a propa-nol/phenoxy-ethanol solution.The solution was hydro-lyzed using water and acetic acid solution,stirred at room T =22°C for 24h.The ZrGl matrix solution was prepared using the proce-dure described in Ref.[27]:Zirconium n -propoxide,gly-mo,propanol,glacial acetic acid and water were used for ZrGl matrix solution preparation.So far the efficiency of LSC reported never exceeded 7%.One of the main reasons for the relatively low efficiency is the self-absorp-tion of the luminescent dyes as a result of overlapping their absorption and luminescence spectra and loss above the critical cone of the fluorescence traveling in the plate.There are two ways to avoid these complications.There are three ways.One is by entrapping the resulting fluo-rescence in a transparent matrix the advantage of doped thin films having optical contact with the transparent plate is that the lumi-nescence emitted from the thin film is trapped in the plate while parasitic losses due to self-absorption and scattering from impuri-ties can be greatly reduced as compared to a bulk plate.This can be achieved by incorporating the fluorescent species in a monomolec-ulare form in a thin ormocer film which is in an optical contact with the transparent collector plate (see Fig.1).The trapping of the fluorescence in the transparent plate which can be guided fully to the edge will be assisted by photonic structure as will be ex-plained below.The other possible way is to use fluorescent species in which the absorption and fluorescence originate from different electronic lev-els.There is a large group of systems particularly suitable to avoid this complication.These are molecules undergoing very fast reac-tion on the level of excited state.In many cases the reaction is so fast in comparison to the primary luminescence emission that the latter is not observed.In consequence,the only fluorescence observed is the band emitted by the excited product of such reac-tion.This emission is energetically largely separated from the absorption,sometimes by 104cm À1.This phenomenon is called the anomalous Stokes shift.The best known group of such lumino-phors is represented by the systems undergoing the Excited State Intramolecular Proton Transfer (ESIPT)reactions.In majority of these systems the reaction is curing by the intramolecular hydro-gen bond [21,32–36].As an example can serve 6,60-diheptyl-bipyr-idyl-diol (DH-BP(OH)2),the structure of each shown Fig.4.This compound is proposed as a model luminescent molecule exhibiting the above described property of separation from absorp-tion spectrum.Fig.5presents an example of the absorption and emissions spectra and in addition the intensity of fluorescence in-crease as result of silver nanoparticles co-existing with the color-ant by 34%.Such phenomenon of increase of fluorescence in presents of sil-ver plasmons have been reported above,however here in addition the separation of absorption from emission prevents self-absorp-tion when the fluorescent light travels along way.It should be sta-ted that the lifetimes of the fluorescent molecules in presence of silver nanoparticles [25–31]decreased as a result of the increase of transition probabilities.In the case of DH-BP(OH)2in polyvi-nyl-butyral (PVB)[21]lifetime of the DH-BP(OH)2fluorescence in the PVB films appeared nearly the same in absence and in presence of silver nanoparticles:s f =3.55±0.02ns and s f =3.75±0.01ns,respectively,nearly constant.This important observation indicates that the increased fluorescence efficiency does not result from increasing emission rate,k f ,but rather from increasing efficiency of excitation in the neighborhood of the Ag NPs.An interaction of the fluorophore molecule,with the scattered light from silver sur-face plasmons in the neighborhood of the Ag NPs enhances the excitation of the fluorophore.This can be understood since absorp-tion and emission originate from different electronic levels.It should bee noted that DH-BP(OH)2is a new compound belonging to the family of molecules widely studied and described in the lit-erature as strongly fluorescent systems with large Stokes shift.The syntheses of similar compounds are published [32–36].In the inert solvent (hexane),at room temperature,the fluores-cence quantum yield of DH-BP(OH)2(as measured with quinine sulphate as the standard),is g f =27±2%;the fluorescence lifetime s f =3.29±0.01ns.6.Photonic structures for increased efficienciesAs mentioned before the escape cone of internal total reflection is the major loss mechanism of fluorescent concentrators .The light that is emitted by the dyes and impinges on the internal surface with an angle greater than the critical angle a c is totally internally reflected,with sin(a c )=1/n and n the refractive index of the matrix material.The light that impinges at smaller angles leaves the col-lector and is therefore lost.Thus the fluorescence traveling in the plate to the edge of the collector with the solar cell attached is lost by great amount with increasing plate dimensions a possible rem-edy to this loss are photonic structures.Photonic crystals are arti-ficial materials with a spatially periodically varied refractive index [37].The period lengths of the variation are in the range of thehalfFig.4.Structural formula of the compound 6,60-diheptyl (2,20-bipyridyl)-3,30-diol.Fig.5.Increase of the fluorescence of the compound 6,60-diheptyl (2,20-bipyridyl)-3,30-diol in presence of silver nanoparticles:1-excitation and emission spectra of the compound without silver nanoparticles;2-excitation and emission spectra of the compound with silver nanoparticles;3-extinction spectrum of the silver nanoparticle in film of polyvinyl-butyral.854R.Reisfeld /Optical Materials 32(2010)850–856。
The Art of Effective Problem-Solving
The Art of Effective Problem-SolvingProblem-solving is an essential skill that is required in all aspects of life. Whether it's in the workplace, at home, or in our personal relationships, being able to effectively solve problems is crucial for success and happiness. The artof effective problem-solving involves a combination of critical thinking, creativity, and emotional intelligence. In this response, we will explore the various perspectives on problem-solving and discuss the strategies and techniques that can be used to improve this skill. From a psychological perspective,problem-solving is a complex cognitive process that involves identifying, analyzing, and solving problems. It requires the ability to think critically, evaluate different options, and make decisions based on available information.This process can be influenced by various factors such as personality traits, past experiences, and emotional state. For example, individuals who are more open-minded and flexible in their thinking are often better at finding creativesolutions to problems, while those who are more rigid and closed-minded may struggle to see alternative perspectives. In the workplace, effective problem-solving is crucial for productivity and success. When issues arise, whether it's a technical problem, a conflict between team members, or a strategic decision that needs to be made, the ability to effectively solve these problems can make the difference between success and failure. Employers value employees who are able to think critically, remain calm under pressure, and come up with innovativesolutions to complex problems. This not only improves the overall performance of the organization but also creates a positive work environment where employees feel empowered to contribute their ideas and take ownership of their work. On a personal level, the art of effective problem-solving can greatly impact our mental well-being and relationships. When faced with challenges or conflicts, individuals who are able to approach the situation with a calm and rational mindset are more likely to find solutions that are beneficial for all parties involved. This requires a high level of emotional intelligence, the ability to empathize with others, and the willingness to communicate openly and honestly. By developingthese skills, individuals can build stronger and more fulfilling relationshipswith others, as well as improve their own sense of self-confidence and resilience.There are various strategies and techniques that can be used to improve the art of effective problem-solving. One of the most important aspects is developing strong critical thinking skills. This involves the ability to analyze information, evaluate different options, and make informed decisions. Critical thinking can be improved through practice and by seeking out new experiences that challenge our existing beliefs and perspectives. Additionally, learning to manage and regulate our emotions is crucial for effective problem-solving. When we are able to remain calm and focused, even in the face of adversity, we are better able to thinkclearly and come up with creative solutions. Another important aspect ofeffective problem-solving is the ability to collaborate and seek input from others. Oftentimes, problems are complex and multifaceted, and it can be beneficial to gather different perspectives and expertise to come up with the best possible solution. This requires strong communication skills, the ability to listen actively, and the willingness to consider alternative viewpoints. By working together with others, we can leverage the strengths and knowledge of the group to come up with more innovative and effective solutions. In conclusion, the art of effective problem-solving is a crucial skill that is required in all aspects of life. Whether it's in the workplace, at home, or in our personal relationships, being able to think critically, remain calm under pressure, and collaborate with others is essential for success and happiness. By developing strong critical thinking skills, emotional intelligence, and the ability to collaborate with others, individuals can improve their problem-solving abilities and create more fulfilling and successful lives.。
辩论的技巧
辩论的技巧英文回答:Mastering the Art of Persuasive Argumentation。
Effective argumentation is a crucial skill in various aspects of life, from academic debates to professional negotiations. To master this art, consider the following techniques:1. Define Your Terms:Before engaging in any argument, it's essential to establish a clear understanding of the terms being used. Define key concepts to avoid misunderstandings and ensure that both parties are on the same page. For example, in a debate about climate change, defining "climate" and "global warming" would be a crucial step.2. Gather Evidence and Build a Logical Case:Support your arguments with evidence from credible sources, such as scientific studies, statistical data, or expert testimony. Organize your evidence logically, present it in a clear and concise manner, and provide solid reasoning to connect your evidence to your claims. For instance, if you're arguing for stricter gun control laws, you could cite statistics showing a correlation between gun ownership and violent crime rates.3. Anticipate and Address Counterarguments:Consider the potential counterarguments that your opponent might raise and prepare your responses in advance. Acknowledge and address their points fairly, even if you disagree with them. By doing so, you'll demonstrate your willingness to engage in a thoughtful and respectful exchange of ideas. For example, if someone objects to your argument for gun control by claiming that it infringes on Second Amendment rights, you could respond by outlining reasonable regulations that balance public safety with individual liberties.4. Use Persuasive Language and Rhetorical Devices:Choose your words carefully to evoke emotions and connect with your audience. Employ rhetorical devices such as metaphors, similes, and anecdotes to make your arguments more vivid and memorable. For instance, instead of simply stating that climate change is a serious threat, you could say, "Climate change is a ticking time bomb that threatens to explode and devastate our planet."5. Practice and Refine Your Arguments:The best way to improve your argumentation skills is through practice. Engage in debates, participate in discussions, and seek feedback from others. This will help you identify areas for improvement and refine your techniques.中文回答:论辩技巧。
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A simple and efficient method for solvent-free iodinationof hydroxylated aromatic aldehydes and ketones usingiodine and iodic acid by grinding methodArchana Vibhute, Shyam Mokle, Khushal Karamunge, Vasant Gurav, Yeshwant Vibhute* Laboratory of Organic Synthesis, P.G. Department of Chemistry, Yeshwant Mahavidyalaya, Nanded 431602 (M.S.), IndiaReceived 9 October 2009AbstractGreen, mild and efficient iodination of hydroxylated aromatic aldehydes and ketones using iodine and iodic acid in the solid-state by grinding under solvent-free conditions at room temperature. This method provides several advantages such as environmentally friendly, short reaction times, high yields, non-hazardous and simple work-up procedure.⑥2010 Yeshwant Vibhute. Published by Elsevier B.V. on behalf of Chinese Chemical Society. All rights reserved. Keywords: Iodination; Aromatic aldehydes; Aromatic ketones; GrindingThe grinding method has increasingly been used in organic synthesis. Recently ball-milling grinding technique has been widely used in synthetic organic chemistry [1]. Compared with traditional methods, many organic reactions occur more efficiently in the solid-state than in solution and in many cases even more selectively, because molecules in the crystals are arranged tightly and regularly [2]. Furthermore, the solid-state reaction has many advantages: little pollution, low cost, and simplicity in progress and handling. A large number of organic reactions can be carried out simply and in high yield under mild conditions by this method [3]. Therefore, we focus on developing the novel procedure for iodination of hydroxylated aromatic aldehydes and ketones involving a solid-state reaction performed by grinding using iodine and iodic acid.The different iodoaromatic compounds have been the subject of numerous studies due to the potential of the subject toact as bacterial and fungicidal agents [4]. Aromatic iodo compounds can be easily functionalized through metal- catalyzed cross-coupling reactions [5] in the synthesis of many interesting natural products [6] and bioactive material [7].Iodoaromatic compounds are used in medicine as drug or diagnostic aids, contractors [8] and radioactively labeled markers [4]. They also have important in medicinal and pharmaceutical research [9]. The chemistry dealing with selective introduction of an iodine atom into organic molecules thus attracted broad interest in the wider specific community.Recently direct iodination methods have been intensively developed using iodonium donating system, such as iodine-nitrogen dioxide [10], iodine F-TEDA-[1-chloromethyl-4-fluoro-1, 4-diazoniabicyclo [2,2,2] octane-bis-(tetrafluor-oborate)] [11], bis N-iodosuccinimide [12], trichloroisocyanuric acid/Iz/Wet Si02 [13], mercury(In-oxide-iodine [14],iodine-monochloride [ 15], bis(pyridine)iodonium(I), tetrafiuoroborate CF3S03H [ 16], NIS-CF3S03H [ 17], iodine silver sulfate [18], iodine-mercury salts [19], NaOCI-NaI [20], iodine/Na2S20g [21] and iodine-(NH4)ZS20s-CuClz Ag2S04[22]. However most of these methods involve long reaction times, toxic reagents and organic solvents.In present work grindstone technique was used for the iodination of hydroxylated aromatic aldehydes and ketones. This method is superior since it is environmentally friendly, high yielding, shorter reaction times, no organic solvent required (except for product recrytallisation), simple and convenient.1. ExperimentalMelting points were determined in an open capillary tube and are uncorrected. IR spectra were recorded in KBr on a PerkinElmer spectrometer. 1H NMR spectra were recorded on a Gemini 300-MHz instrument in CDC13 as solvent and TMS as an internal standard. Elemental analysis was carried out on a Carlo Erba 1108 analyzer. All the products were identified by comparison of their spectral and physical data with those of the known sample. The purity of products was checked by thin-layer chromatography (TLC) on silica-gel.Hydroxylated aromatic aldehydes or ketones (10 mmol), iodine crystal (4 mmol) and iodic acid (2 mmol) and 2} drops of water were mixed in a morter. The reaction mixture was ground together in a mortar using pestle to generate violet coloured tacky solid within 20-30 min. The reaction proceeds exothermically (indicated by rise in temperature of 5-10 0C). After the reaction (TLC), saturated solution of sodium thiosulfate (5 mL) was added to remove um-eacted iodine, solid separates out. The separated solid was filtered, washed with cold water and crystallized from ethyl alcohol. By using two equivalents of iodine and iodic acid and one equivalent of substrate, diiodinated products were obtained.2. Result and山scussionIn continuation of our earlier research works on iodination of some aromatics [23-29], herein, first time we would like to report a simple, efficient and solvent-free iodination of hydroxylated aromatic aldehydes and ketones using iodine and iodic acid by grinding method (Scheme 1).A combination of iodine and iodic acid has been found to be an excellent reagent for the efficient iodination of hydroxylated aromatic aldehydes and ketones. These reactions are carried out by grinding method using mortar and pestle within shorter reaction times (20-30 min) with excellent yields (82-94%, Table 1). Few drops of water are required for easy grinding as well as iodic acid react properly in the presence of water with hydroiodic acid. A variety of ortholpara hydroxy substituted aromatic aldehydes and ketones were selected for the iodination reaction using iodine and iodic acid. The iodination occurs regioselectively and the C-iodination took place at ortho or/and para positions. When the o-position was blocked with a substituent, then iodination took place at p-position and vice versa. The diiodination occurs if ortho and para positions are unsubstituted. Iodination does not occur in the side chain, d.e. - COCHz-R or -CH3; only nuclear iodination takes place.3. ConclusionIn conclusion, we have reported a simple and efficient method for solvent-free iodination of hydroxylated aromatic aldehydes and ketones using iodine and iodic acid by grinding method. The notable merits of the present method are shorter reaction times (20-30 min), simple work-up prodoes not use any auxiliary or organic solvent. To the cedure; high yield (82-94%), environmentally friendly as it best of our knowledge this is first repot on iodination of hydroxylated aromatic aldehydes and ketones using iodine and iodic acid by grinding method.AcknowledgmentsAuthors are also grateful to UGC New Delhi for sanctioning Major Research Grant and the Director, IICT, Hyderabad for providing spectral analysis. The authors are thankful to Principal, Yeshwant Mahavi勿alaya, Nanded for providing laboratory facilities.。