Methods in the LO Evolution of Nondiagonal Parton Distributions The DGLAP Case

726第二军医大学学报Acad J Sec M il M ed U niv2006Jul;27(7)专题报道ROC 曲线下面积估计的参数法与非参数法的应用研究宋花玲1,贺 佳2*,黄品贤1,李素云3(1.上海中医药大学预防教研室,上海201203;2.第二军医大学卫生勤务学系卫生统计学教研室,上海200433;3.上海中医药大学病理学教研室,上海201203)[摘要]目的:阐明ROC 曲线下面积估计的参数法和非参数法并进行比较,为其在诊断试验评价中的应用提供依据。

方法:用双正态模型的参数法和M annW itney 统计量的非参数法估计ROC 曲线下面积,并以其在肺癌诊断试验准确度评价中的应用来具体说明。

结果:非参数法估计的肺癌两个标志物Cyfr a21 1和CEA 的ROC 曲线下面积分别为0.77、0.87,参数法估计的面积分别为0.78、0.87,表明在样本量较大时参数法和非参数法估计的R OC 曲线下面积近似相等。

结论:样本量较小时可选择非参数法估计RO C 曲线下面积,样本量较大时可根据实际情况选择参数法或非参数法。

[关键词] ROC 曲线下面积;参数法;非参数法[中图分类号] R 195.1 [文献标识码] A [文章编号] 0258 879X(2006)07 0726 03Application of parametric method and non parametric method in estimation of area under ROC curveSO N G H ua ling 1,HE Jia 2*,HU AN G Pin x ian 1,LI Su y un 3(1.Department of P reventiv e M edicine,Shang hai U niv ersity of T raditional Chinese M edicine,Shanghai 201203,China; 2.Depar tment o f H ealth Statistics,F aculty of H ealth Ser vices,Second M ilitar y M edical U niv ersity ,Shang hai 200433; 3.Department o f Pat ho lo gy ,Shanghai U niversity of T raditio na l Chinese M edi cine,Shanghai 201203)[ABSTRACT] Objective:T o elucidate and co mpar e the par amet ric method and non parametr ic met ho d in estimatio n o f t he area under RO C curv e,so as to pr ovide a basis fo r their applicatio n in diag no sis assessment.Methods:T he ar eas under RO C curv es wer e estimated by parametr ic method of fitting binomial mo del and by non par ametric method of M ann W itney statist ics.T he met ho d w as employed in the diag nostic tests of lung cancer.Results:By non par ametric methods,the areas under ROC curv es of Cyfr a21 1and CEA wer e respectiv ely 0.77and 0.87in the lung cancer diag no st ic tests;by par ametric metho ds,they w ere 0.78and 0.87,respect ively.It was indicated that w hen the sample size was larg e,the v alues o f a reas under R OC Cur ves w ere similar between par ametric method and non parametr ic metho d.C onclusion:No n paramet ric method sho uld be used to evaluate the ar ea under RO C curv e if the sample size is small,and for larg e sample size,the par ametric method o r no nparametr ic met ho d should be cho sen according to the actual situation.[KEY WORDS] ar ea under RO C curv e;parametr ic metho d;no n par amet ric method[A cad J Sec M il M ed U niv,2006,27(7):726 728][作者简介] 宋花玲,讲师,硕士.*Corres ponding author.E mail:h ejia@一项新的诊断试验的诊断性能如何,它能否替代旧的诊断试验,这在很大程度上依赖于新的诊断试验的准确度大小。

Methods in Nonlinear AnalysisNonlinear analysis is a branch of mathematics that deals with the study of equations involving nonlinear functions. It plays a crucial role in various fields, including physics, engineering, economics, and biology. In this article, we will explore some of the methods used in nonlinear analysis and their applications.1. Fixed Point TheoryFixed point theory is a fundamental tool in nonlinear analysis. It provides a powerful framework for studying the existence and uniqueness of solutions to equations. The basic idea behind fixed point theory is to find a point that remains unchanged under the action of a given function.One of the most famous fixed point theorems is the Banach fixed point theorem, also known as the contraction mapping principle. It states that if a complete metric space is mapped into itself by a contraction mapping, then there exists a unique fixed point.The Banach fixed point theorem has numerous applications in various areas of mathematics and its extensions have been developed to handle more general situations. It is widely used in proving the existence of solutions to differential equations, optimization problems, and functional equations.2. Nonlinear OptimizationNonlinear optimization is concerned with finding the optimal solution to a problem involving nonlinear functions. It is a powerful tool in many practical applications, such as engineering design, portfolio optimization, and machine learning.There are various methods used in nonlinear optimization, including gradient-based meth ods, Newton’s method, and evolutionary algorithms. Gradient-based methods, such as the steepest descent and Newton’s method, use the gradient or Hessian matrix of the objective function to iteratively update the solution. Evolutionary algorithms, such as genetic algorithms and particle swarm optimization, mimic natural evolution to search for the optimal solution.Nonlinear optimization problems are often challenging due to the presence of multiple local optima. Therefore, finding the global optimum is a major concern in nonlinear optimization. Several strategies, such as initialization techniques and global optimization algorithms, have been developed to overcome this issue.3. Nonlinear Partial Differential EquationsNonlinear partial differential equations (PDEs) are mathematical models that describe various physical phenomena, such as fluid flow, heat transfer, and reaction-diffusion processes. They involve nonlinear functions of the unknown variables and their derivatives.Solving nonlinear PDEs is a challenging task due to their complexity and lack of analytical solutions. Numerical methods, such as finite difference, finite element, and spectral methods, are commonly used to approximate the solutions. These methods discretize the PDEs into a system of algebraic equations, which can be solved iteratively.Nonlinear PDEs arise in many areas of science and engineering. For example, the Navier-Stokes equations describe the motion of fluid and are essential in understanding turbulence and predicting weather patterns. The reaction-diffusion equations are used to model chemical reactions and pattern formation in biology.4. Chaos TheoryChaos theory is a branch of mathematics that studies the behavior of nonlinear dynamical systems that are highly sensitive to initial conditions. It deals with the concept of deterministic chaos, where small changes in the initial conditions can lead to drasticallydifferent outcomes.Chaotic systems exhibit complex and unpredictable behavior, even though they are governed by deterministic laws. They have applications in various fields, including physics, biology, finance, and cryptography. For example, chaotic systems are used in secure communication systems to generate random numbers.The study of chaos theory involves the analysis of nonlinear equations and the use of numerical simulations. Various techniques, such as Lyapunov exponents and bifurcation diagrams, are used to characterize the behavior of chaotic systems.ConclusionMethods in nonlinear analysis provide powerful tools for studying equations involving nonlinear functions. Fixed point theory, nonlinear optimization, nonlinear PDEs, and chaos theory are some of the key areas in this field. These methods have wide-ranging applications in science, engineering, and other disciplines. As researchers continue to advance these methods, they will contribute to our understanding of complex phenomena and help solve real-world problems.。

附录I 英文翻译第一部分英文原文文章来源：书名：《自然赋予灵感的元启发示算法》第二、三章出版社：英国Luniver出版社出版日期：2008Chapter 2Genetic Algorithms2.1 IntroductionThe genetic algorithm (GA), developed by John Holland and his collaborators in the 1960s and 1970s, is a model or abstraction of biolo gical evolution based on Charles Darwin’s theory of natural selection. Holland was the first to use the crossover and recombination, mutation, and selection in the study of adaptive and artificial systems. These genetic operators form the essential part of the genetic algorithm as a problem-solving strategy. Since then, many variants of genetic algorithms have been developed and applied to a wide range of optimization problems, from graph colouring to pattern recognition, from discrete systems (such as the travelling salesman problem) to continuous systems (e.g., the efficient design of airfoil in aerospace engineering), and from financial market to multiobjective engineering optimization.There are many advantages of genetic algorithms over traditional optimization algorithms, and two most noticeable advantages are: the ability of dealing with complex problems and parallelism. Genetic algorithms can deal with various types of optimization whether the objective (fitness) functionis stationary or non-stationary (change with time), linear or nonlinear, continuous or discontinuous, or with random noise. As multiple offsprings in a population act like independent agents, the population (or any subgroup) can explore the search space in many directions simultaneously. This feature makes it ideal to parallelize the algorithms for implementation. Different parameters and even different groups of strings can be manipulated at the same time.However, genetic algorithms also have some disadvantages.The formulation of fitness function, the usage of population size, the choice of the important parameters such as the rate of mutation and crossover, and the selection criteria criterion of new population should be carefully carried out. Any inappropriate choice will make it difficult for the algorithm to converge, or it simply produces meaningless results.2.2 Genetic Algorithms2.2.1 Basic ProcedureThe essence of genetic algorithms involves the encoding of an optimization function as arrays of bits or character strings to represent the chromosomes, the manipulation operations of strings by genetic operators, and the selection according to their fitness in the aim to find a solution to the problem concerned. This is often done by the following procedure:1) encoding of the objectives or optimization functions; 2) defining a fitness function or selection criterion; 3) creating a population of individuals; 4) evolution cycle or iterations by evaluating the fitness of allthe individuals in the population,creating a new population by performing crossover, and mutation,fitness-proportionate reproduction etc, and replacing the old population and iterating again using the new population;5) decoding the results to obtain the solution to the problem. These steps can schematically be represented as the pseudo code of genetic algorithms shown in Fig. 2.1.One iteration of creating a new population is called a generation. The fixed-length character strings are used in most of genetic algorithms during each generation although there is substantial research on the variable-length strings and coding structures.The coding of the objective function is usually in the form of binary arrays or real-valued arrays in the adaptive genetic algorithms. For simplicity, we use binary strings for encoding and decoding. The genetic operators include crossover,mutation, and selection from the population.The crossover of two parent strings is the main operator with a higher probability and is carried out by swapping one segment of one chromosome with the corresponding segment on another chromosome at a random position (see Fig.2.2).The crossover carried out in this way is a single-point crossover. Crossover at multiple points is also used in many genetic algorithms to increase the efficiency of the algorithms.The mutation operation is achieved by flopping the randomly selected bits (see Fig. 2.3), and the mutation probability is usually small. The selection of anindividual in a population is carried out by the evaluation of its fitness, and it can remain in the new generation if a certain threshold of the fitness is reached or the reproduction of a population is fitness-proportionate. That is to say, the individuals with higher fitness are more likely to reproduce.2.2.2 Choice of ParametersAn important issue is the formulation or choice of an appropriate fitness function that determines the selection criterion in a particular problem. For the minimization of a function using genetic algorithms, one simple way of constructing a fitness function is to use the simplest form F = A−y with A being a large constant (though A = 0 will do) and y = f(x), thus the objective is to maximize the fitness function and subsequently minimize the objective function f(x). However, there are many different ways of defining a fitness function.For example, we can use the individual fitness assignment relative to the whole populationwhere is the phenotypic value of individual i, and N is the population size. The appropriateform of the fitness function will make sure that the solutions with higher fitness should be selected efficiently. Poor fitness function may result in incorrect or meaningless solutions.Another important issue is the choice of various parameters.The crossover probability is usually very high, typically in the range of 0.7~1.0. On the other hand, the mutation probability is usually small (usually 0.001 _ 0.05). If is too small, then the crossover occurs sparsely, which is not efficient for evolution. If the mutation probability is too high, the solutions could still ‘jump around’ even if the optimal solution is approaching.The selection criterion is also important. How to select the current population so that the best individuals with higher fitness should be preserved and passed onto the next generation. That is often carried out in association with certain elitism. The basic elitism is to select the most fit individual (in each generation) which will be carried over to the new generation without being modified by genetic operators. This ensures that the best solution is achieved more quickly.Other issues include the multiple sites for mutation and the population size. The mutation at a single site is not very efficient, mutation at multiple sites will increase the evolution efficiency. However, too many mutants will make it difficult for the system to converge or even make the system go astray to the wrong solutions. In reality, if the mutation is too high under high selection pressure, then the whole population might go extinct.In addition, the choice of the right population size is also very important. If the population size is too small, there is not enough evolution going on, and there is a risk for the whole population to go extinct. In the real world, a species with a small population, ecological theory suggests that there is a real danger of extinction for such species. Even the system carries on, there is still a danger of premature convergence. In a small population, if a significantly more fit individual appears too early, it may reproduces enough offsprings so that they overwhelm the whole (small) population. This will eventually drive the system to a local optimum (not the global optimum). On the other hand, if the population is too large, more evaluations of the objectivefunction are needed, which will require extensive computing time.Furthermore, more complex and adaptive genetic algorithms are under active research and the literature is vast about these topics.2.3 ImplementationUsing the basic procedure described in the above section, we can implement the genetic algorithms in any programming language. For simplicity of demonstrating how it works, we have implemented a function optimization using a simple GA in both Matlab and Octave.For the generalized De Jong’s test function where is a positive integer andr > 0 is the half length of the domain. This function has a minimum of at . For the values of , r = 100 and n = 5 as well as a population size of 40 16-bit strings, the variations of the objective function during a typical run are shown in Fig. 2.4. Any two runs will give slightly different results dueto the stochastic nature of genetic algorithms, but better estimates are obtained as the number of generations increases.For the well-known Easom functionit has a global maximum at (see Fig. 2.5). Now we can use the following Matlab/Octave to find its global maximum. In our implementation, we have used fixedlength 16-bit strings. The probabilities of crossover and mutation are respectivelyAs it is a maximization problem, we can use the simplest fitness function F = f(x).The outputs from a typical run are shown in Fig. 2.6 where the top figure shows the variations of the best estimates as they approach while the lower figure shows the variations of the fitness function.% Genetic Algorithm (Simple Demo) Matlab/Octave Program% Written by X S Yang (Cambridge University)% Usage: gasimple or gasimple(‘x*exp(-x)’);function [bestsol, bestfun,count]=gasimple(funstr)global solnew sol pop popnew fitness fitold f range;if nargin<1,% Easom Function with fmax=1 at x=pifunstr=‘-cos(x)*exp(-(x-3.1415926)^2)’;endrange=[-10 10]; % Range/Domain% Converting to an inline functionf=vectorize(inline(funstr));% Generating the initil populationrand(‘state’,0’); % Reset the random generatorpopsize=20; % Population sizeMaxGen=100; % Max number of generationscount=0; % counternsite=2; % number of mutation sitespc=0.95; % Crossover probabilitypm=0.05; % Mutation probabilitynsbit=16; % String length (bits)% Generating initial populationpopnew=init_gen(popsize,nsbit);fitness=zeros(1,popsize); % fitness array% Display the shape of the functionx=range(1):0.1:range(2); plot(x,f(x));% Initialize solution <- initial populationfor i=1:popsize,solnew(i)=bintodec(popnew(i,:));end% Start the evolution loopfor i=1:MaxGen,% Record as the historyfitold=fitness; pop=popnew; sol=solnew;for j=1:popsize,% Crossover pairii=floor(popsize*rand)+1; jj=floor(popsize*rand)+1;% Cross overif pc>rand,[popnew(ii,:),popnew(jj,:)]=...crossover(pop(ii,:),pop(jj,:));% Evaluate the new pairscount=count+2;evolve(ii); evolve(jj);end% Mutation at n sitesif pm>rand,kk=floor(popsize*rand)+1; count=count+1;popnew(kk,:)=mutate(pop(kk,:),nsite);evolve(kk);endend % end for j% Record the current bestbestfun(i)=max(fitness);bestsol(i)=mean(sol(bestfun(i)==fitness));end% Display resultssubplot(2,1,1); plot(bestsol); title(‘Best estimates’); subplot(2,1,2); plot(bestfun); title(‘Fitness’);% ------------- All sub functions ----------% generation of initial populationfunction pop=init_gen(np,nsbit)% String length=nsbit+1 with pop(:,1) for the Signpop=rand(np,nsbit+1)>0.5;% Evolving the new generationfunction evolve(j)global solnew popnew fitness fitold pop sol f;solnew(j)=bintodec(popnew(j,:));fitness(j)=f(solnew(j));if fitness(j)>fitold(j),pop(j,:)=popnew(j,:);sol(j)=solnew(j);end% Convert a binary string into a decimal numberfunction [dec]=bintodec(bin)global range;% Length of the string without signnn=length(bin)-1;num=bin(2:end); % get the binary% Sign=+1 if bin(1)=0; Sign=-1 if bin(1)=1.Sign=1-2*bin(1);dec=0;% floating point.decimal place in the binarydp=floor(log2(max(abs(range))));for i=1:nn,dec=dec+num(i)*2^(dp-i);enddec=dec*Sign;% Crossover operatorfunction [c,d]=crossover(a,b)nn=length(a)-1;% generating random crossover pointcpoint=floor(nn*rand)+1;c=[a(1:cpoint) b(cpoint+1:end)];d=[b(1:cpoint) a(cpoint+1:end)];% Mutatation operatorfunction anew=mutate(a,nsite)nn=length(a); anew=a;for i=1:nsite,j=floor(rand*nn)+1;anew(j)=mod(a(j)+1,2);endThe above Matlab program can easily be extended to higher dimensions. In fact, there is no need to do any programming (if you prefer) because there are many software packages (either freeware or commercial) about genetic algorithms. For example, Matlab itself has an extra optimization toolbox.Biology-inspired algorithms have many advantages over traditional optimization methods such as the steepest descent and hill-climbing and calculus-based techniques due to the parallelism and the ability of locating the very good approximate solutions in extremely very large search spaces.Furthermore, more powerful new generation algorithms can be formulated by combiningexisting and new evolutionary algorithms with classical optimization methods.Chapter 3Ant AlgorithmsFrom the discussion of genetic algorithms, we know that we can improve the search efficiency by using randomness which will also increase the diversity of the solutions so as to avoid being trapped in local optima. The selection of the best individuals is also equivalent to use memory. In fact, there are other forms of selection such as using chemical messenger (pheromone) which is commonly used by ants, honey bees, and many other insects. In this chapter, we will discuss the nature-inspired ant colony optimization (ACO), which is a metaheuristic method.3.1 Behaviour of AntsAnts are social insects in habit and they live together in organized colonies whose population size can range from about 2 to 25 millions. When foraging, a swarm of ants or mobile agents interact or communicate in their local environment. Each ant can lay scent chemicals or pheromone so as to communicate with others, and each ant is also able to follow the route marked with pheromone laid by other ants. When ants find a food source, they will mark it with pheromone and also mark the trails to and from it. From the initial random foraging route, the pheromone concentration varies and the ants follow the route with higher pheromone concentration, and the pheromone is enhanced by the increasing number of ants. As more and more ants follow the same route, it becomes the favoured path. Thus, some favourite routes (often the shortest or more efficient) emerge. This is actually a positive feedback mechanism.Emerging behaviour exists in an ant colony and such emergence arises from simple interactions among individual ants. Individual ants act according to simple and local information (such as pheromone concentration) to carry out their activities. Although there is no master ant overseeing the entire colony and broadcasting instructions to the individual ants, organized behaviour still emerges automatically. Therefore, such emergent behaviour is similar to other self-organized phenomena which occur in many processes in nature such as the pattern formation in animal skins (tiger and zebra skins).The foraging pattern of some ant species (such as the army ants) can show extraordinary regularity. Army ants search for food along some regular routes with an angle of about apart. We do not know how they manage to follow such regularity, but studies show that they could move in an area and build a bivouac and start foraging. On the first day, they forage in a random direction, say, the north and travel a few hundred meters, then branch to cover a large area. The next day, they will choose a different direction, which is about from the direction on the previous day and cover a large area. On the following day, they again choose a different direction about from the second day’s direction. In this way, they cover the whole area over about 2 weeks and they move out to a different location to build a bivouac and forage again.The interesting thing is that they do not use the angle of (this would mean that on the fourth day, they will search on the empty area already foraged on the first day). The beauty of this angle is that it leaves an angle of about from the direction on the first day. This means they cover the whole circle in 14 days without repeating (or covering a previously-foraged area). This is an amazing phenomenon.3.2 Ant Colony OptimizationBased on these characteristics of ant behaviour, scientists have developed a number ofpowerful ant colony algorithms with important progress made in recent years. Marco Dorigo pioneered the research in this area in 1992. In fact, we only use some of the nature or the behaviour of ants and add some new characteristics, we can devise a class of new algorithms.The basic steps of the ant colony optimization (ACO) can be summarized as the pseudo code shown in Fig. 3.1.Two important issues here are: the probability of choosing a route, and the evaporation rate of pheromone. There are a few ways of solving these problems although it is still an area of active research. Here we introduce the current best method. For a network routing problem, the probability of ants at a particular node to choose the route from node to node is given bywhere and are the influence parameters, and their typical values are .is the pheromone concentration on the route between and , and the desirability ofthe same route. Some knowledge about the route such as the distance is often used so that ,which implies that shorter routes will be selected due to their shorter travelling time, and thus the pheromone concentrations on these routes are higher.This probability formula reflects the fact that ants would normally follow the paths with higher pheromone concentrations. In the simpler case when , the probability of choosing a path by ants is proportional to the pheromone concentration on the path. The denominator normalizes the probability so that it is in the range between 0 and 1.The pheromone concentration can change with time due to the evaporation of pheromone. Furthermore, the advantage of pheromone evaporation is that the system could avoid being trapped in local optima. If there is no evaporation, then the path randomly chosen by the first ants will become the preferred path as the attraction of other ants by their pheromone. For a constant rate of pheromone decay or evaporation, the pheromone concentration usually varies with time exponentiallywhere is the initial concentration of pheromone and t is time. If , then we have . For the unitary time increment , the evaporation can beapproximated by . Therefore, we have the simplified pheromone update formula:where is the rate of pheromone evaporation. The increment is the amount of pheromone deposited at time t along route to when an ant travels a distance . Usually . If there are no ants on a route, then the pheromone deposit is zero.There are other variations to these basic procedures. A possible acceleration scheme is to use some bounds of the pheromone concentration and only the ants with the current global best solution(s) are allowed to deposit pheromone. In addition, certain ranking of solution fitness can also be used. These are hot topics of current research.3.3 Double Bridge ProblemA standard test problem for ant colony optimization is the simplest double bridge problem with two branches (see Fig. 3.2) where route (2) is shorter than route (1). The angles of these two routes are equal at both point A and pointB so that the ants have equal chance (or 50-50 probability) of choosing each route randomly at the initial stage at point A.Initially, fifty percent of the ants would go along the longer route (1) and the pheromone evaporates at a constant rate, but the pheromone concentration will become smaller as route (1) is longer and thus takes more time to travel through. Conversely, the pheromone concentration on the shorter route will increase steadily. After some iterations, almost all the ants will move along the shorter route. Figure 3.3 shows the initial snapshot of 10 ants (5 on each route initially) and the snapshot after 5 iterations (or equivalent to 50 ants have moved along this section). Well, there are 11 ants, and one has not decided which route to follow as it just comes near to the entrance.Almost all the ants (well, about 90% in this case) move along the shorter route.Here we only use two routes at the node, it is straightforward to extend it to the multiple routes at a node. It is expected that only the shortest route will be chosen ultimately. As any complex network system is always made of individual nodes, this algorithms can be extended to solve complex routing problems reasonably efficiently. In fact, the ant colony algorithms have been successfully applied to the Internet routing problem, the travelling salesman problem, combinatorial optimization problems, and other NP-hard problems.3.4 Virtual Ant AlgorithmAs we know that ant colony optimization has successfully solved NP-hard problems such asthe travelling salesman problem, it can also be extended to solve the standard optimization problems of multimodal functions. The only problem now is to figure out how the ants will move on an n-dimensional hyper-surface. For simplicity, we will discuss the 2-D case which can easily be extended to higher dimensions. On a 2D landscape, ants can move in any direction or , but this will cause some problems. How to update the pheromone at a particular point as there are infinite number of points. One solution is to track the history of each ant moves and record the locations consecutively, and the other approach is to use a moving neighbourhood or window. The ants ‘smell’ the pheromone concentration of their neighbourhood at any particular location.In addition, we can limit the number of directions the ants can move by quantizing the directions. For example, ants are only allowed to move left and right, and up and down (only 4 directions). We will use this quantized approach here, which will make the implementation much simpler. Furthermore, the objective function or landscape can be encoded into virtual food so that ants will move to the best locations where the best food sources are. This will make the search process even more simpler. This simplified algorithm is called Virtual Ant Algorithm (VAA) developed by Xin-She Yang and his colleagues in 2006, which has been successfully applied to topological optimization problems in engineering.The following Keane function with multiple peaks is a standard test functionThis function without any constraint is symmetric and has two highest peaks at (0, 1.39325) and (1.39325, 0). To make the problem harder, it is usually optimized under two constraints:This makes the optimization difficult because it is now nearly symmetric about x = y and the peaks occur in pairs where one is higher than the other. In addition, the true maximum is, which is defined by a constraint boundary.Figure 3.4 shows the surface variations of the multi-peaked function. If we use 50 roaming ants and let them move around for 25 iterations, then the pheromone concentrations (also equivalent to the paths of ants) are displayed in Fig. 3.4. We can see that the highest pheromoneconcentration within the constraint boundary corresponds to the optimal solution.It is worth pointing out that ant colony algorithms are the right tool for combinatorial and discrete optimization. They have the advantages over other stochastic algorithms such as genetic algorithms and simulated annealing in dealing with dynamical network routing problems.For continuous decision variables, its performance is still under active research. For the present example, it took about 1500 evaluations of the objective function so as to find the global optima. This is not as efficient as other metaheuristic methods, especially comparing with particle swarm optimization. This is partly because the handling of the pheromone takes time. Is it possible to eliminate the pheromone and just use the roaming ants? The answer is yes. Particle swarm optimization is just the right kind of algorithm for such further modifications which will be discussed later in detail.第二部分中文翻译第二章遗传算法2.1 引言遗传算法是由John Holland和他的同事于二十世纪六七十年代提出的基于查尔斯·达尔文的自然选择学说而发展的一种生物进化的抽象模型。

Package‘r2glmm’October14,2022Type PackageTitle Computes R Squared for Mixed(Multilevel)ModelsDate2017-08-04Version0.1.2Description The model R squared and semi-partial R squared for the linear and generalized linear mixed model(LMM and GLMM)are computed with confidence limits.The R squared measure from Edwards et.al(2008)<DOI:10.1002/sim.3429> is extended to the GLMM using penalized quasi-likelihood(PQL)estimation(see Jaeger et al.2016<DOI:10.1080/02664763.2016.1193725>).Three methods of computation are provided and described as follows.First,TheKenward-Roger approach.Due to some inconsistency between the'pbkrtest'package and the'glmmPQL'function,the Kenward-Roger approach in the'r2glmm'package is limited to the LMM.Second,The method introducedby Nakagawa and Schielzeth(2013)<DOI:10.1111/j.2041-210x.2012.00261.x>and later extended by Johnson(2014)<DOI:10.1111/2041-210X.12225>.The'r2glmm'package only computes marginal R squared for the LMM and doesnot generalize the statistic to the GLMM;however,confidence limits andsemi-partial R squared forfixed effects are useful stly,anapproach using standardized generalized variance(SGV)can be used forcovariance model selection.Package installation instructions can be foundin the readmefile.Imports mgcv,lmerTest,Matrix,pbkrtest,ggplot2,afex,stats,MASS,gridExtra,grid,data.table,dplyrSuggests lme4,nlme,testthatLicense GPL-2LazyData TRUERoxygenNote6.0.1URL https:///bcjaeger/r2glmmBugReports https:///bcjaeger/r2glmm/issuesNeedsCompilation noAuthor Byron Jaeger[aut,cre]12calc_sgvMaintainer Byron Jaeger<**********************>Repository CRANDate/Publication2017-08-0510:26:17UTCR topics documented:calc_sgv (2)cmp_R2 (3)glmPQL (4)pSym (5)make.partial.C (5)plot.R2 (6)pqlmer (7)print.R2 (8)r2beta (8)r2dt (10)Index12 calc_sgv Compute the standardized generalized variance(SGV)of a blockeddiagonal matrix.DescriptionCompute the standardized generalized variance(SGV)of a blocked diagonal matrix.Usagecalc_sgv(nblocks=NULL,blksizes=NULL,vmat)Argumentsnblocks Number of blocks in the matrix.blksizes vector of block sizesvmat The blocked covariance matrixValueThe SGV of the covariance matrix vmat.Exampleslibrary(Matrix)v1=matrix(c(1,0.5,0.5,1),nrow=2)v2=matrix(c(1,0.2,0.1,0.2,1,0.3,0.1,0.3,1),nrow=3)v3=matrix(c(1,0.1,0.1,0.1,1,0.2,0.1,0.2,1),nrow=3)calc_sgv(nblocks=3,blksizes=c(2,3,3),vmat=Matrix::bdiag(v1,v2,v3))cmp_R23 cmp_R2Compute R2with a specified C matrixDescriptionCompute R2with a specified C matrixUsagecmp_R2(c,x,SigHat,beta,method,obsperclust=NULL,nclusts=NULL)Argumentsc Contrast matrix forfixed effectsx Fixed effects design matrixSigHat estimated model covariance(matrix or scalar)betafixed effects estimatesmethod the method for computing r2betaobsperclust number of observations per cluster(i.e.subject)nclusts number of clusters(i.e.subjects)ValueA vector with the Wald statistic(ncp),approximate Wald F statistic(F),numerator degrees of free-dom(v1),denominator degrees of freedom(v2),and the specified r squared value(Rsq)Exampleslibrary(nlme)library(lme4)library(mgcv)lmemod=lme(distance~age*Sex,random=~1|Subject,data=Orthodont)X=model.matrix(lmemod,data=Orthodont)SigHat=extract.lme.cov(lmemod,data=Orthodont)beta=fixef(lmemod)p=length(beta)obsperclust=as.numeric(table(lmemod$data[, Subject ]))nclusts=length(obsperclust)C=cbind(rep(0,p-1),diag(p-1))partial.c=make.partial.C(p-1,p,2)cmp_R2(c=C,x=X,SigHat=SigHat,beta=beta,obsperclust=obsperclust,nclusts=nclusts,method= sgv )cmp_R2(c=partial.c,x=X,SigHat=SigHat,beta=beta,obsperclust=obsperclust,nclusts=nclusts,method= sgv )4glmPQLglmPQL Compute PQL estimates forfixed effects from a generalized linearmodel.DescriptionCompute PQL estimates forfixed effects from a generalized linear model.UsageglmPQL(glm.mod,niter=20,data=NULL)Argumentsglm.mod a generalized linear modelfitted with the glm function.niter maximum number of iterations allowed in the PQL algorithm.data The data used by thefitted model.This argument is required for models with special expressions in their formula,such as offset,log,cbind(sucesses,trials),etc.ValueA glmPQL object(i.e.a linear model using pseudo outcomes).Examples#Load the datasets package for example codelibrary(datasets)library(dplyr)#We ll model the number of world changing discoveries per year for the#last100years as a poisson outcome.First,we set up the datadat=data.frame(discoveries)%>%mutate(year=1:length(discoveries))#Fit the GLM with a poisson link functionmod<-glm(discoveries~year+I(year^2),family= poisson ,data=dat)#Find PQL estimates using the original GLMmod.pql=glmPQL(mod)#Note that the PQL model yields a higher R Squared statistic#than the fit of a strictly linear model.This is attributed#to correctly modelling the distribution of outcomes and then#linearizing the model to measure goodness of fit,rather than#simply fitting a linear modelsummary(mod.pql)pSym5summary(linfit<-lm(discoveries~year+I(year^2),data=dat))r2beta(mod.pql)r2beta(linfit)pSym Checks if a matrix is Compound Symmetric.DescriptionChecks if a matrix is Compound Symmetric.UsagepSym(mat,tol=1e-05)Argumentsmat The matrix to be tested.tol a number indicating the smallest acceptable difference between off diagonal val-ues.ValueTrue if the matrix is compound symmetric.Examplesgcmat<-matrix(c(1,0.2,0.1,0.2,1,0.3,0.1,0.3,1),nrow=3)csmat<-matrix(c(1,0.2,0.2,0.2,1,0.2,0.2,0.2,1),nrow=3)pSym(csmat)make.partial.C Generate partial contrast matricesDescriptionGenerate partial contrast matricesUsagemake.partial.C(rows,cols,index)6plot.R2Argumentsrows Number of rows in the contrast matrixcols Number of columns in the contrast matrixindex A number corresponding to the position of thefixed effect in the vector offixed effect parameter estimates.ValueA contrast matrix designed to test thefixed effect corresponding to index in the vector offixedeffects.Examplesmake.partial.C(4,5,2)make.partial.C(4,5,3)make.partial.C(4,5,2:4)plot.R2Visualize standardized effect sizes and model R squaredDescriptionVisualize standardized effect sizes and model R squaredUsage##S3method for class R2plot(x,y=NULL,txtsize=10,maxcov=3,r2labs=NULL,r2mthd="sgv",cor=TRUE,...)Argumentsx An R2object from the r2beta function.y An R2object from the r2beta function.txtsize The text size of the axis labels.maxcov Maximum number of covariates to include in the semi-partial plots.r2labs a character vector containing labels for the models.The labels are printed as subscripts on a covariance model matrix.r2mthd The method used to compute R2cor An argument to be passed to the r2dt function.Only relevant if comparing two R2objects....Arguments to be passed to plotpqlmer7ValueA visual representation of the model and semi-partial R squared from the r2object provided.Exampleslibrary(nlme)library(r2glmm)data(Orthodont)#Linear mixed modellmemod=lme(distance~age*Sex,random=~1|Subject,data=Orthodont)r2=r2beta(model=lmemod,partial=TRUE,method= sgv )plot(x=r2)pqlmer pqlmerDescriptionFit a GLMM model with multivariate normal random effects using Penalized Quasi-Likelihood for mermod objects.Usagepqlmer(formula,family,data,niter=40,verbose=T)Argumentsformula The lme4model formula.family a family function of the error distribution and link function to be used in the model.data the dataframe containing the variables in the model.niter Maximum number of iterations to perform.verbose if TRUE,iterations are printed to console.ValueA pseudo linear mixed model of class"lme".See AlsoglmmPQLExamples#Compare lmer PQL with lme PQLlibrary(MASS)lmePQL=glmmPQL(y~trt+week+I(week>2),random=~1|ID,family=binomial,data=bacteria,verbose=FALSE)merPQL=pqlmer(y~trt+week+I(week>2)+(1|ID),family=binomial,data=bacteria,verbose=FALSE)summary(lmePQL)summary(merPQL)print.R2Print the contents of an R2objectDescriptionPrint the contents of an R2objectUsage##S3method for class R2print(x,...)Argumentsx an object of class R2...other arguments passed to the print function.r2beta r2beta Compute R Squared for Mixed ModelsDescriptionComputes coefficient of determination(R squared)from edwards et al.,2008and the generalized R squared from Jaeger et al.,2016.Currently implemented for linear mixed models with lmer and lme objects.For generalized linear mixed models,only glmmPQL are supported.Usager2beta(model,partial=TRUE,method="sgv",data=NULL)Argumentsmodel afitted mermod,lme,or glmmPQL model.partial if TRUE,semi-partial R squared are calculated for eachfixed effect in the mixed model.method Specifies the method of computation for R squared beta:if method=’sgv’then the standardized generalized variance approach is applied.This method is rec-ommended for covariance model selection.if method=’kr’,then the KenwardRoger approach is applied.This option is only available for lme models.ifmethod=’nsj’,then the Nakagawa and Schielzeth approach is applied.This op-tion is available for lmer and lme objects.if method=’lm’,the classical Rsquared from the linear model is computed.This method should only be usedon glm and lm object.data The data used by thefitted model.This argument is required for models with special expressions in their formula,such as offset,log,cbind(sucesses,trials),etc.ValueA dataframe containing the model F statistic,numerator and denominator degrees of freedom,non-centrality parameter,and R squared statistic with95If partial=TRUE,then the dataframe also contains partial R squared statistics for allfixed effects in the model.ReferencesEdwards,Lloyd J.,et al."An R2statistic forfixed effects in the linear mixed model."Statistics in medicine27.29(2008):6137-6157.Nakagawa,Shinichi,and Holger Schielzeth."A general and simple method for obtaining R2from generalized linear mixed effects models."Methods in Ecology and Evolution4.2(2013):133-142.Jaeger,Byron C.,et al.,"An R Squared Statistic for Fixed Effects in the Generalized Linear Mixed Model."Journal of Applied Statistics(2016).Exampleslibrary(nlme)library(lme4)data(Orthodont)#Linear mixed modelsmermod=lmer(distance~age*Sex+(1|Subject),data=Orthodont)lmemod=lme(distance~age*Sex,random=~1|Subject,data=Orthodont)#The Kenward-Roger approachr2beta(mermod,method= kr )#Standardized Generalized Variancer2beta(mermod,method= sgv )r2beta(lmemod,method= sgv )10r2dt#The marginal R squared by Nakagawa and Schielzeth(extended by Johnson)r2beta(mermod,method= nsj )#linear and generalized linear modelslibrary(datasets)dis=data.frame(discoveries)dis$year=1:nrow(dis)lmod=lm(discoveries~year+I(year^2),data=dis)glmod=glm(discoveries~year+I(year^2),family= poisson ,data=dis)#Using an inappropriate link function(normal)leads to#a poor fit relative to the poisson link function.r2beta(lmod)r2beta(glmod)#PQL models#Currently only SGV method is supportedlibrary(MASS)PQL_bac=glmmPQL(y~trt+I(week>2),random=~1|ID,family=binomial,data=bacteria,verbose=FALSE)r2beta(PQL_bac,method= sgv )r2dt R Squared Difference Test(R2DT).Test for a statistically significantdifference in generalized explained variance between two candidatemodels.DescriptionR Squared Difference Test(R2DT).Test for a statistically significant difference in generalized ex-plained variance between two candidate models.Usager2dt(x,y=NULL,cor=TRUE,fancy=FALSE,onesided=TRUE,clim=95,nsims=2000,mu=NULL)Argumentsx An R2object from the r2beta function.y An R2object from the r2beta function.If y is not specified,Ho:E[x]=mu is tested(mu is specified by the user).r2dt11 cor if TRUE,the R squared statistics are assumed to be positively correlated anda simulation based approach is used.If FALSE,the R squared are assumedindependent and the difference of independent beta distributions is used.Thisonly needs to be specified when two R squared measures are being considered.fancy if TRUE,the output values are rounded and changed to characters.onesided if TRUE,the alternative hypothesis is that one model explains a larger propor-tion of generalized variance.If false,the alternative is that the amount of gener-alized variance explained by the two candidate models is not equal.clim Desired confidence level for interval estimates regarding the difference in gen-eralized explained variance.nsims number of samples to draw when simulating correlated non-central beta random variables.This parameter is only relevant if cor=TRUE.mu Used to test Ho:E[x]=mu.ValueA confidence interval for the difference in R Squared statistics and a p-value corresponding to thenull hypothesis of no difference.Exampleslibrary(nlme)library(lme4)library(r2glmm)data(Orthodont)#Comparing two linear mixed modelsm1=lmer(distance~age*Sex+(1|Subject),Orthodont)m2=lmer(distance~age*Sex+(1+age|Subject),Orthodont)m1r2=r2beta(model=m1,partial=FALSE)m2r2=r2beta(model=m2,partial=FALSE)#Accounting for correlation can make a substantial difference.r2dt(x=m1r2,y=m2r2,cor=TRUE)r2dt(x=m1r2,y=m2r2,cor=FALSE)Indexcalc_sgv,2cmp_R2,3glmmPQL,7,8glmPQL,4pSym,5lme,8,9lmer,8,9make.partial.C,5plot.R2,6pqlmer,7print.R2,8r2beta,8r2dt,1012。

方法论英语作文模板Title: The Essence of Methodology in Academic Research.In the realm of academic inquiry, methodology holds a pivotal position, guiding researchers towards reliable and valid conclusions. It is the systematic approach that underpins the entire research process, ensuring that objectives are met, hypotheses are tested, and data are analyzed with precision and rigor.Methodology is not merely a set of techniques or tools; it is a philosophy that dictates how one approaches a problem, selects appropriate methods, and interprets findings. It is the backbone of any research study, informing every step from conceptualization to dissemination of results.The foundation of any methodology is the theoretical framework. This framework provides a lens through which the researcher views the world and interprets data. It is thetheoretical backdrop that guides the selection of methods, the interpretation of findings, and the ultimateconclusions drawn.In selecting a methodology, researchers must consider the nature of their research question. Is it descriptive, explanatory, or exploratory? The answer to this questionwill determine the appropriate research design, sample size, data collection methods, and analytical techniques. For example, a descriptive study might employ quantitative methods such as surveys or experiments to gather data,while an exploratory study might rely on qualitative methods like interviews or case studies.The sampling technique is also crucial. Whether the researcher opts for a probabilistic or non-probabilistic sample, the chosen method must be representative of the population being studied. Otherwise, the findings may notbe generalizable or reliable.Data collection is another vital aspect of methodology. Researchers must determine the best method for gatheringdata, whether it be through primary or secondary sources. Primary data collection methods include surveys, interviews, observations, and experiments, while secondary data can be obtained from existing databases, published studies, or government reports. The choice of data collection method should be based on the research objectives and the availability of resources.Data analysis is where the real magic happens. It is here where raw data are transformed into meaningful information that answers the research question. The analytical techniques employed should be appropriate forthe type of data collected and the research objectives. For example, quantitative data might be analyzed using descriptive statistics, inferential statistics, or regression analysis, while qualitative data might be analyzed through content analysis, thematic analysis, or grounded theory.Finally, the dissemination of results is an integralpart of the research process. Researchers must communicate their findings in a clear and concise manner, ensuring thattheir work is accessible to other scholars and practitioners. Publications in peer-reviewed journals, presentations at conferences, and the sharing of data and materials are all important avenues for disseminating research findings.In conclusion, methodology is the lifeblood of academic research. It is the compass that guides researchers through the maze of inquiry, ensuring that they stay on course and arrive at reliable and valid conclusions. By paying careful attention to the theoretical framework, research design, sampling techniques, data collection and analysis, and dissemination of results, researchers can ensure that their work makes a meaningful contribution to the field.。

本科毕业设计(论文)题目名称：离散型牛顿法在解非线性方程中的应用学院：数学学院专业年级：信息与计算科学2009级学生姓名：班级学号：200911020110指导教师：姜晓威二O一三年四月十七日摘要牛顿型方法是解非线性方程组的一类重要方法,在非线性方程组迭代解法的理论研究中占有十分重要的地位,牛顿型方法是逐步线性化方法的典型代表,牛顿法的收敛性理论及其研究方法,特别是K ahtopobnu的著名论文,对迭代的研究产生了深远的影响.在通常情况下，非线性算子方程的解不能精确解出，而是用数值方法求其近似解.牛顿法是一种普遍适用的迭代法.它的计算格式简洁，程序简单，而且收敛速度快，适用范围广.多年来，众多学者对经典牛顿法提出多种改进方案，如：萨马斯基提出的修正牛顿法，阻尼牛顿法，拟牛顿法等各种变形.经典牛顿法尽管具有很多优点,但在处理某些不可微问题或导数难计算问题时会遇到一些困难,而离散型牛顿法可以在一定程度上弥补这方面的不足.本文讨论了牛顿法及离散型牛顿法的半局部收敛性及大范围收敛性，并给出数值算例对此两种方法的执行情况.关键词：非线性方程；牛顿法；离散型牛顿法；收敛性AbstractThe Newton method is an important method for the solution of nonlinear equations，Occupies a very important position in the theory group iterative method for solving nonlinear equations.The Newton method is a typical representative of successive l inearization method, Newton method, convergence theory and research method, especially the famous paper Kahtopobnu, exerted a profound influence on the study of iteration.Generally speaking, we can not solve the nonlinear equations exactly. We always Give the approximate solution by using the numerical methods for nonlinear equations. Newton’s method is one of the most powerful and well-known iterative methods known to converge operator equation. In recent decades, scholars obtained many progresses of the classic Newton ’s method for solving nonlinear equations, Frozen- Newton method given by Samaski, damped Newton method, Quasi- Newton method and other forms.In this paper, we will give the convergence and convergence rate of the modified discrete Newton’s method, again. And numerical examples are given to verify the validity of the method. Moreover, using the modified discrete Newton’s method, we propose the modified continuous Newton’s method. We prove that it is convergence.Keywords: nonlinear equations; Newton’s method;Discret e Newton’s method; convergence目录中文摘要 (Ⅰ)英文摘要 (Ⅱ)目录 (Ⅲ)1.引言 (1)2.主要内容 (1)2.1牛顿法和牛顿型方法介绍 (1)2.2牛顿方法的收敛性 (3)2.2.1牛顿法的半局部收敛性 (3)2.2.2大范围收敛问题 (4)2.3.离散型牛顿法 (6)2.3.1半局部收敛性 (6)2.3.2大范围收敛性 (7)2.4数值算例 (8)3.总结 (10)致谢 (11)参考文献 (12)1.引 言牛顿法是牛顿在17世纪提出的一种在实数域或复数域上近似求解方程的方法.多数方程不存在求根公式,因此求精确根非常困难,甚至不可能,从而寻找方程的近似根就显得特别重要.牛顿法是求方程的重要方法之一,其最大优点是在方程0)(=x f 的单根附近具有平方收敛速度,而且该方法还可以用来求方程的重根,复根,此时线性收敛,但是可通过一些手段变成超线性收敛.该方法已广泛用于计算机编程中. 本课题主要来源于非线性算子方程数值解法.随着非线性科学的飞速发展，许多科研工作者逐渐的对非线性问题的求解产生了浓厚的兴趣.线性系统的解很容易由计算机求出，但是对于非线性问题，无论从理论上还是从计算方法上都比解线性问题要复杂的多.一般情况下，非线性问题是很难求出解析解(或精确解)，往往只能求出数值解(或近似解).经典牛顿法尽管具有很多优点,但在处理某些不可微问题或导数难计算问题时会遇到一些困难,而离散型牛顿法可以在一定程度上弥补这方面的不足.2.主要内容2.1 牛顿法和牛顿型方法介绍考虑解方程式 ()0F x =，(2.1)其中映像F :nnR RΩ⊂→ 于凸区域Ω中二次G-可微,且()F x ''于Ω连续.设*x ∈Ω为方程组(2,1)的解.选定*x 的初始近似值(0)x ∈Ω,利用Taylor 级数,我们有1(0)(0)(0)(0)(0)(0)2()()()()(())()(1)F x F xF xx xF xt x xx xt d t'''=+-++---⎰.由于(0)x 充分接近*x ,因此我们可以用线性方程组(0)(0)(0)()()()0F xF xx x'+-= (2.2)近似的代替方程组(2.1).设(2.2)的解为(1)x :(1)(0)(0)1[()]()xxFx F x-'=-. 一般说来,(1)x 应较(0)x 更近似于*x ,因而,类似的可以再(1)x 近旁用线性方程组(1)(1)(1)()()()F xFx x x'+-= 近似代替(2.1),其解(2)x 为*x 的新的近似值: (2)(1)(1)1[()]()xxFx F x-'=-. 一般地.我们有...2,1,0),()]([)(1)()()1(='-=-+k xF xF xxk k k k (2.3)这就是解方程组的牛顿程序.从上述讨论看出,解方程组的牛顿法,无论其形式或者构造方法均与方程式情形相同.对于方程组,同样可以构造简化牛顿程序,其迭代公式为...2,1,0),()]([)(1)()()1(='-=-+k xF xF xxk k k k (2.4)显然,对于方程组,这种简单化更有意义,因为他每一步减少了2n 个微商值的计算. 迭代公式(2.3)及(2.4)一般说来只是一种形式记法,因为,在空间维数n 很大,求微商的逆是困难的.实际计算时,它们分别采用下述形式:(1)()()'()()()()()0,0,1,2,k k k k k k xx x F x x F x k +⎧=+∆⎪⎨∆+==⎪⎩ , (2.5)(1)()()'(0)()()()()0,0,1,2,k k k k kxx x F x x F xk +⎧=+∆⎪⎨∆+==⎪⎩ . (2.6)即利用牛顿法或简化牛顿法计算时,每步需要解一个n 阶线性方程组,其中简化牛顿程序每步所解得方程组具有同一系数矩阵.按上述构造牛顿法的方法,我们实际上是用形如()()()()()()0k k k A xx xF x-+= (2.7)的线性方程组近似代替方程组(2.1),其解(1)()()1()[()](),0,1,2,...k k k kxxA xF xk +-=-= (2.8)即作为(2.1)的解的近似值,为保证()k x 近似于*x ,应要求A(x)近似于*()F x '.基于不 同的考虑,适当选取)(x A ,即得到牛顿法的各种变体.这类方法统称为牛顿型方法.考虑到很多问题(例如,由微积分方程离散化导出的方程组)()F x ''的计算较复杂,因此常常将()F x '的元素用相应的差商代替,即)(x A 去乘下列矩阵:(,)J x h 211111121111(()()),...,(()()).................................................................................11(()()),...,(()())nn nn n n n n n n f x h e f x f x h e f x h h f x h e f x f x h e f x h h ⎛⎫+-+- ⎪ ⎪= ⎪⎪ ⎪+-+- ⎪⎝⎭ (2.9)1(,...,),Tin h h h e=为第i 个单位向量,此时相应的迭代程序(2.8)称为离散型牛顿程序,其计算公式为(1)()()(k )()()()(,)()0,0,1,2...k k k k k k xx x J x h xF x k +⎧=+∆⎪⎨∆+==⎪⎩ (2.10) 其中()k h 为事先选定的向量序列.容易看出,为实现(2.10)每步需计算1+n 个函数向量(即(1)n n +个函数值),并且解一个n 阶线性方程组,每步计算量与牛顿法相同,但无需计算()F x '.2.2牛顿方法的收敛性2.2.1牛顿法的半局部收敛性局部收敛性定理都是在假定原方程的解*x 存在,并且初值0x 必须在真解的某个邻域中得到的.但是一般情况下，我们不知道方程是否有解,自然地，希望能从迭代过程的收敛性去确定方程解的存在性.并且对选取的初值0x ，给出保证迭代收敛性的条件，进一步还希望估计出*xx k-的误差.这样不事先假定解存在的收敛性叫做半局部收敛性.讨论牛顿法的半局部收敛性,最著名的定理是康托洛维奇定理. 定理2.1 设,X Y 均为实Banach 空间,算子0:(,)F B x X Yγ⊂→是F-可微分的,且满足: (i)0[()]F x ' 是Y到X 的有界性算子100||[()]()||F x F x α-'≤10||[()]||F x β-'≤,(ii)0||[()()]||||||,,(,)F x F y L x y x y B x γ''-≤-∈,(iii) 21L αβ<, 2αγ<.则牛顿迭代程序: 11[()](),0,1,...n n n nx x Fx F x n -+'=-= 收敛于方程()F x θ=的唯一解*0(,2),x B x α∈且有估计式:*2111||||2nn n x x q---≤,其中2qL αβ=.2.2.2大范围收敛问题Mbicobcknx 曾经指出,在Cauchy 型条件下,即使对单调函数,牛顿法也仅有局部收敛性质,并且举出方程式情形4ρ>不收敛的例子,然而,选择初始近似(0)x ,使之满足牛顿法的收敛条件是很困难的,因此,改造牛顿型方法,使之具有大范围收敛性,无论在理论上或是实际应用上都有意义的.大范围收敛的牛顿程序是按下降思想导出的,现以方程式为例介绍牛顿下降法的构造思想.考虑解方程式()0F x =(2.11)的牛顿程序(1)()()1(()()k k k kxxFx F s +-''=-,利用Taylor 公式有(1)(1)()()(1)|()||()()()()|k k k kk kF xF xF xFx x x +++'=---(k )()1()21|(x)||()()|2k k F F x F x-'''=,式中()()(1)()(),01k k k k x xx xθθ+=+-<<. 由此看出,若()()12()1|()||()||()|12k kk F xF x F x -'''<, (2.12)则有(1)()|()||()|k k F x F x +<.条件(2.2)实际上是Mbicobcknx 定理2.1中的条件2ρ<.换言之,当()F x 满足Cauchy 型条件时, 2ρ<保证了()|()|k F x 随着k 的增大而减少.上述事实启发我们适当改造牛顿程序,以减弱2ρ<的限制并保持()|()|k F x 关于k 下降的性质.基于这种考虑,构造程序(1)()()1(()()k k k kk xxFx F x ω+-'=- , (2.13)仍利用Taylor 公式导出(1)2()()12()()1|()|(|()||()||()|1)|()|2k k k kkk k F xF x F x F x F x ωω+-'''≤-+, 01k ω<≤,当()()12()1|()||()||()|12kk k k f xf xf x ω-'''<(2.14) 时,将有(1)|()||()|k kf x f x +<.只要取k ω充分小,尽管(2.2)不成立,仍可使(2.4)成立.这就解除了2ρ<的限制.鉴于上述讨论,在方程组情形考虑下述牛顿下降程序(1)()()1(()()k k k kk xxFx F x ω+-'=- 0,1,...,01k k ω=<≤ (2.15)我们有 定理2.2 设:nnFR RΩ⊂→满足下列条件:(1)(0)||()||;F x η< (2)于区域(0){||||}()x x xF x γβη'Ω=-≤⊂Ω有逆存在,且1||()||,F x x β-''≤∀∈Ω, (2.16)||()()||||||,,F x F y x y x y ρ''-≤-∀∈Ω (2.17) 则当22/1γρα≥>时,方程组()F x =于0Ω有解*x 存在,且对任何01k ω<≤,210,02,k a x αωρβη<≤≤-=.由(2.5)定义之()k x 收敛于*x ,且至少是线性收敛的.注1 当满足定理2.1的条件时,有0k 使02||()||2kx F x βα≤-,此时视0k x 为该定理中(0)x ,仍满足定理条件,因而可取1k ω≡,即此时牛顿法收敛,因而得到二阶收敛性.注2 注意到二次三项式21()12ϕωωρω=+- 在12ρ>时由1ωρ=处取最小值:11()12ρϕρρ=-,因而,为使(2.5)具有较快的敛速,可取2(k)1/||()||kx F xωβ=,换言之,程序(2.5)可以取成下列形式(1)()()1(2()()(),m i n {1,1/||()},0,1,2,... .k k k k k k k x x F x F x xF x k ωωβ+-'⎧=-⎪=⎨⎪=⎩(2.18) 对由(2.11)定义的()k x ,估计式(3.8)变成为()()22(1)2()2()212||()||,||()||;2||()||12||()||,||()||.2k k k k k F x F x X X F x X F x F x X ββββ+⎧-≥⎪⎪≤⎨⎪≤⎪⎩当当 (2.19) 注3 如果集合{||()||()||}Dx F x F x =<为有限区域,且于D 满足(2.6)(2.7)则对任何(0)(0),||()||||()||x D F x F x ∈<,故定理(2.1)的结论成立.事实上,此时由(2.5)定义的()k x 均落于D 中,否则,若有某m,使()m x D∈,而(1)m x D+∈,则有()()1()()()m m m m xx Fx F x θω-'=- ,01θ<<,使||()||||()||F xF x = ,对此x 仍有222()()1||()||(||()||1)||()||2m mm m F xX F x F x θωβθω<-+(0)||()||F x < , 这就导致了矛盾.前述定理已给出了牛顿法的大范围收敛条件.程序(2.5)早就有人研究过.2.3离散型牛顿法2.3.1半局部收敛性对于非线性算子方程 ()F x θ=其中,:FX Y→,这里Y X ,都是Banach 空间,则有以下定理:定理2.3设,X Y 均为实际Banach 空间,算子0:(,)F B x r X Y⊂→是F-可微分的,且满足:(i) 10[()]F x -'是Y 到X 的有界线性算子,100||[()]()||F x F x a-'≤ ,10||[()]||F x β-'< (ii)||()()||||||,F x F y L x y ''-≤- 0,(,)x y B x r ∈(iii)41,2,lim 0n n L a r αβτ→∞<<=则离散型牛顿法11[()]()(),0,1,...n n n n n n x x F x F x x x n τ-+'=---=收敛于式(3.1)的唯一解*0(,)x B x r ∈,且有估计式:*02||||2() (,)32n n r x x a x B x -≤∈.即对牛顿法进行简单修正后得到的离散型牛顿法 11[()]()(),0,1...n n n n n n x x F x F x x x n τ-+'=---=只要对n τ附加条件:lim 0nn τ→∞=,再加上牛顿法收敛的条件,就能收敛到非线性算子方程()F x =的解*x ,且收敛率为*2||||2()3nn x x a -≤ .2.3.2大范围收敛性由于离散牛顿法在实际应用中的重要性以及它对初始近似的苛刻限制(定理3.2及3.3),研究离散牛顿程序的大范围收敛条件更有实际意义. 相应于离散牛顿程序烤炉大范围收敛程序(1)()1()()()()11(,)(),||||||()||.k k kk kk k k k xx J xhF xhF x ωω+-⎧=-⎪⎨≤⎪⎩我们有定理2.4 设:nnFR RΩ⊂→ 满足下列条件:(1)0||()||F x η≤,(2)于(0){||||}x x xγβηΩ=-≤⊂Ω内()F x '有逆存在,且满足(2.6)及(2.7),则当35/γρα≥时,方程组()F x =于0Ω有解*x 存在,且对任何401,07k k ωαωρ<≤<≤≤,由(2.13)定义的序列(){}k x 收敛于*x ,并且至少是线性收敛的,其中,max(,1)X ρββηββ==.对于定理2.2也可以列出与定理2.1类似的注记.例如,特别可以取0Ω为集合11{||()||||()||}D x F x F x =≤,而(0)x 满足条件(0)11||()||||()||F x F x ≤其次,为提高收敛速度可以将(2.13)取成下列形式(1)()1()()()()1()()11(,)(),m in{1,25/84||()||},||||||()||.k k k k k k k k k k k x x J x h F x X F x h F x ωωββω+-⎧=-⎪=⎨⎪≤⎩此时由(2.13)定义的()k x 有下述估计式:()()11(1)1()2()11254||()||,||()||;1687||()||424||()||,||()||257.k k k k k F x X F x X F x X F x X F x ββββββββ+⎧-≤⎪⎪≤⎨⎪>⎪⎩当当最后,容易想到,仿定理2.2可以建立离散型牛顿程序的大范围收敛性定理.2.4数值算例本小节对二个经典的非线性方程组分别运用牛顿法和离散型牛顿法求解,并将所求结果进行对比,直观的说明修正的离散型牛顿法的有效性.1. 求方程组212121()0c os(/2)x x F x x x π⎧⎫-+==⎨⎬-⎩⎭的真解为*(0,1)x =,其中迭代停止准则为101||||10n n x x -+-≤.若取初值0(1,0)x =,用牛顿法收敛到(1,2)-,不收敛到所需要的真解*x .若使用离散型牛顿法来求解非线性方程组,取(0.5,0.5)x=-,则有小面的计算结果:表2.1取(0.5,0.5)x=-时的修正的离散型牛顿法迭代计算结果2. 方程组121212211(sin())22()01(1)()24x x x x x f x ex e e ex πππ⎧⎫--⎪⎪⎪⎪==⎨⎬⎪⎪--+-⎪⎪⎩⎭的真解为*(0.3,2.8)x =,其中迭代停止准则为101||||10n n x x -+-≤.若取初值0(0.1,0.1)x =,使用牛顿法进行迭代,求解结果收敛到点(-0.26059929002248,0.62253089661391), 也就是不收敛到所需要的真解*x . 而用离散型牛顿法求解,若取(0.5,3.3)x=,则有以下计算结果:表2.2取(0.5,3.3)x=时离散型牛顿法迭代计算结果对于以上两个非线性方程组,当初值取在真解附近时,使用牛顿法求解,所得结果都不收敛到真解,而使用离散型牛顿法求解,所得结果都收敛到真解,收敛准则都确定为101||||10n n x x -+-≤.数值算例的结果说明了离散型牛顿法是可行有效的.3.总 结牛顿型方法是解非线性方程组的一类重要方法,在非线性方程组迭代解法的理论研究中占有十分重要的地位,牛顿程序的构造方法是逐步线性化方法的典型代表,牛顿法的收敛性理论及其研究方法,特别是Kahtopobnu[1984,1957]的著名论文,对迭代的研究产生了深远的影响. 因此, 寻找快速可行的迭代的方法具有重要意义本文针对这种离散型牛顿法，列出完善的收敛性证明以及收敛速率，并应用数值算例验证算法的可行性.致谢本文的研究和撰写工作都是在导师姜晓威老师的悉心指导下完成的. 从论文的选题、开题、撰写直至最后的答辩, 都得到了姜老师的关心、大力帮助和耐心指导. 姜老师在学术上敏锐的洞察力、开阔活跃的学术思维、不懈进取的精神、严谨的治学风范、崇高的敬业精神、渊博的学识给我留下深刻的印象, 将使我终身受益. 最令我感动的是姜老师在我撰写论文期间给予了孜孜不倦的指导, 他严谨的科研作风给我留下了深刻的教诲和影响. 谨此之际, 向关心和培养我的导师姜晓威老师表示衷心的感谢和诚挚的敬意!同时, 感谢论文的各位评审专家能在百忙之中抽出时间对我的学士论文进行评审, 并提出宝贵的建议, 在此表示衷心的感谢! 最后, 向所有曾经关心和帮助过我的老师、同学、朋友表示诚挚的感谢!参考文献[1]关治, 陆金甫.数值分析基础（第二版）[M], 高等教育出版社,1998.52-67[2]谢如彪, 姜培庆.非线性数值分析[M], 上海交通大学出版社, 1984. 1-6[3] 袁东锦, 计算方法（第二版）[M], 南京师范大学出版社,2007. 183-209.[4] 李庆扬, 王能超，易大义.数值分析[M], 清华大学出版社,1995.863-1003[5] 姜波, 徐家旺. 非线性方程组的数值解法比较[J],沈阳航空工业学院报,2002(29):195-203.[6]邓建中, 葛仁杰,程正兴.计算方法[M], 西安交通大学出版社,2003(30): 1255-1258.[7]吴淦洲.求解非线性方程组的改进牛顿法[J], 茂民学院学报,2004, 8(2): 88-96.[8]田巧玉,古钟壁,周新志.基于混合遗传算法求解非线性方程组[J], 计算机技术与发展, 1999, 292: 99-125.[9]罗亚中,袁端才,唐国金.求解非线性方程组的混合遗传算法[J]. 计算力学学报,1979, 244(5): 1093-1096.[10] Gill P E, Murray W, Saunders M A, Tomlia J A, Wright M H. On projected Newton barrier methods for linear programming and an equivalence to Karmarkar’s projective method[J]. Mathematical Programming, 1986, 36: 183-209.。

2020年第6卷第6期PETROLEUM TUBULAR GOODS & INSTRUMENTS• 25 •-开发设计-动圈式检波器芯体极性的一种智能测试方法雷宇，余正杰，焦新程，王云琦，井乔（中国石油东方地球物理公司西安物探装备分公司 陕西 西安710077 $摘 要:动圈式检波器在其制造过程中存在芯体极性反接的情况，必须对每一个检波器芯体进行极性校验。

介绍了一种对动圈式检波器芯体极性的智能测试方法，利用单片机驱动气缸作为检波器的激励信号，同时采集芯体输出电压值来判断检波器芯体极 性，有效避免了检波器性 为因素干扰而造成的判断失误。

该 建检波器自动化生产线的重要一环，具有易于实现、成本低、准确性高的优点。

关 键 词：动圈式检波器;极性测试；自动激励；自动检测中图法分类号:P631.4 + 36 文献标识码:A 文章编号:2096 - 0077( 2020) 06 - 0025 - 04DOI ：10.19459/j. cnki. 61 - 1500/te. 2020. 06. 006A Intelligent Method to Test the Polarity of Moving-coil Detector CorrLE 【Yu, YU Zhengjie, JIAO Xincheng# WANG Yunqi# JING Qiao(Xian Geophysical Prospecting Equipmeni Company O BGP , CNPC,Xian,Shaanxi 710077 , China)Abstract : The phenomenon of the inversed grafting on the polaOty of moving-coii detector core usuaHy occurr when producing moving-coiidetector. As a result , every core of detector needs polaOty test. In this paper , an intellioent method is introduced for the polaOty testing ofmoving-coii detector core. We applied the air cylinder driven by singOe chip as the activating siynal of detector and utilized the output vo V-age value of detector core to determine the core polarity. In this way , we can aveid the user bias when testing the polarity of detector core.Polarity test is a sionificant step when we create the automatic production line. Our method can provide an easy-implemented , low-cost andhigh-cccurate way to achieve polarity test.Key word ： moving-coii detector ；polarity test ；automatic excitation ；automatic detection自现代石油工业诞生以来， 技术是 油气 有效的方法。

解剖科学进展Progress of Anatomicai Sciences 2004，10（2）：106~108，三三三三三三三三三三三三三三三三三三三三三三三三三三三三三三三三三三三三三三三三三三三三三三三111脱细胞同种异体神经移植物的制备及成分分析孙慧哲，佟晓杰，曹德寿，王振宁（中国医科大学解剖学教研室，组织工程学研究室，辽宁沈阳110001）【摘要】目的探讨脱细胞同种异体神经移植的制备方法及其含有的成分。

方法采用组织工程化低渗脱细胞方法制备神经异体移植物，光电镜观察其结构特征；用免疫组织化学法和聚丙烯酰胺凝胶电泳法分析神经异体移植物的成分。

结果正常的（天然的）周围神经经脱细胞处理后，清除了雪旺细胞、神经外膜和束膜的细胞，以及神经纤维的髓鞘和轴突，保存了由雪旺细胞基底膜管以及神经外膜和束膜的细胞外基质构成的三维支架结构。

成分分析结果显示，含有LN、FN以及生长相关蛋白GAP-43和65kda蛋白等促进和诱导受体损伤神经再生的重要成分。

结论脱细胞异体神经移植物含有诱导和促进神经再生的相关蛋白，有利于受损神经的再生。

【关键词】神经组织工程；脱细胞神经异体移植物；神经再生；大鼠【中图分类号】R322.85 【文献标识码】A 【文章编号】1006-2947（2004）02-0106-03Preparation of Acellular Nerve Allografts and Analysis of Their ComponentsSUN Hui-zhe，TONG Xiao-jie，CAO de-shou，WANG Zhen-yu（department of Anatomy，Institute of Tissue Engineering，China Medicai University，Liaoning Shenyang110001China）【Abstract】Objective To study the preparation of the aceiiuiar nerve aiiografts and anaiyze the components.Methods The aceiiuiar nerve aiiografts were prepared with the hypotonic aceiiuiar method and the characters of thestructure were observed under iight and eiectron microscope，and the components were anaiyzed by immunohistochem-istry and poiyacryiamidedei eiectrophoresis（PAGE）.Results Schwann ceiis，the ceiis of epineurium or nerve fas-cicies，myeiin sheath and axon of nerve fibers were eiiminated，but the basement membrane tube of Schwann ceiisand three-dimensionai supports of extraceiiuiar matrix of epineurium and nerve fascicies were reserved for normai pe-ripherai nerves treated aceiiuiariy.The component anaiyticai resuits of the aceiiuiar nerve aiiografts indicate that theycontain iaminin，fbronectin，growth associated protein GAO-43and65kda protein those can faciiitate and induce theregeneration of the injured nerves.Conclusion The aceiiuiar nerve aiiografts contain severai，proteins those induceand faciiitate the regeneration of the injured nerves.【Key words】nerve tissue-engineering；aceiiuiar nerve aiiografts；nerve regeneration；rat周围神经缺损的修复是创伤外科的难题之一，筛选促进神经再生理想的移植物是解决这一难题的关键。

第30卷第2期地球科学)))中国地质大学学报Vol.30No.2 2005年3月Earth Science)Jour nal of China U niversit y of G eosciences M ar.2005前陆盆地二级层序内可容纳空间发育演化及三级层序对比王家豪,陈红汉,王华,严德天,赵忠新中国地质大学资源学院,湖北武汉430074摘要:强烈不对称的楔型地层是前陆盆地的典型特点,前隆带地层大量减薄或缺失、前隆带与前渊带三级层序的细分对比是建立前陆盆地层序地层格架的关键.结合前人对前陆盆地岩石圈挠曲变形模拟的认识,经过对库车前陆盆地的实例分析表明,前陆盆地挤压构造活动引起前渊带沉降、而前隆带隆升,导致可容纳空间发育在横向上不协调.可容纳空间的不协调发育与前隆的产生和迁移的动态演化过程相伴随:在构造的活动期,前隆隆升并向冲断带迁移,盆地变窄变深,可容纳空间发育的不协调性逐渐增强;在构造宁静期,盆地变宽变浅,可容纳空间整体性发育.因此,前陆盆地二级层序在地震剖面上具双层结构(如库车盆地侏罗系、白垩系卡普沙良群),其下层为一组楔状、向冲断带收缩的退积反射;上层反射呈带状、延续范围广.层序的对比模式为:在二级层序的底部,三级层序向克拉通渐次超覆;在二级层序的中部,三级层序的分布向冲断带渐次收缩;在二级层序的上部,三级层序分布广泛,可对比性强(如库车盆地下第三系).关键词:前陆盆地;可容纳空间;层序对比;库车前陆盆地.中图分类号:P53文章编号:1000-2383(2005)02-0140-07收稿日期:2004-07-23Accommodation Sp ace Evolu tion and Third-O rd er S equence Correlation inthe S econd-Ord er S equence of a Foreland BasinWANG Jia-hao,CH EN H o ng-han,WANG H ua,YAN De-tian,ZH AO Zho ng-x inFacu lty of Ear th R esour ces,Ch ina Unive rsity of Ge oscience s,Wu han430074,ChinaAbstract:Fr om the fo rebulge to the fo redeep of a fo reland basin,the str atig raphic framew or k appea rs asymmetrical and wedge-shaped.T he str at ig raphic sequence at the for ebulg e is thin o r absent.Resear ch on the K uqa for eland basin in N or th-er n T arim basin and pr ev io us studies o n litho spheric flexur al simulat ions of fo reland basins show that acco mmodation space from the fo rebulg e to the foredeep dev elo ps inco nsistently due to co mpr essio na l tecto nism,which results in the fo rebulge r is-ing while the for edeep subsides.T his inco nsistency is dynamically associat ed w ith t he fo rmation and mig ration of the for e-bulg e.I n an active tecto nic phase t he for ebulg e r ises and mig rates g radually tow ard the thrust-fault belt,w hich r esults in t he basin narr ow ing and deepening,and the dev elo pment o f acco mmodation space beco ming mo re and mor e inconsistent.In an inact ive tectonic phase the basin is w ide and shallow and acco mmodation space develo ps consistent ly.T herefo re,the r ef lec-t ion co nfig uration o f a seco nd-or der sequence is tw o-layer ed in seismic sect ions such as the Jurassic and the Kapushaliang Gro up,Cretaceous o f the K uqa basin:the lo wer layer is a g roup o f w edg e-sha ped,reg ressive reflect ions shr inking g radually tow ard the t hr ust-fault belt;the upper layer is a g ro up of strap-shaped,w idespread r eflectio ns.F urther,the cor relatio n model show ed that the third-o rder sequences in the low er par t of a second-o rder sequence onlapped gr adually to war d the cr a-to n,w hich indicat es the init ial tectonism and the fo rmation of the fo rebulge;that the thir d-o rder sequences in the middle part gr adually decrease in size to war d the thrust-fault belt,co rr espo nding to intense tectonism;that the third-or der sequences in the upper par t are widely dist ributed,co rr esponding to an inactiv e tectonic phase such as the Lower Tertiary of the Kuqa basin. Key words:fo reland basin;acco mmodation space;sequence co rr elation;K uqa for eland basin.基金项目:中国地质大学(武汉)优秀青年教师基金.作者简介:王家豪(1968-),男,讲师,博士在读,主要从事层序地层学、油气储层地质学.第2期 王家豪等:前陆盆地二级层序内可容纳空间发育演化及三级层序对比0 引言自20世纪80年代层序地层学产生以来,我国学者将该理论应用于东部广泛发育的断陷盆地,以/可容纳空间0、/坡折带0等概念为理论指导,在东部断陷盆地发现了丰富的岩性圈闭,使一些老油田获得了新生.相比之下,前陆盆地层序地层学研究至今还存在较多分歧(刘贻军,1988),诸多问题有待进一步深入.其中,强烈不对称的楔型地层是前陆盆地的典型特点,前隆带地层大量减薄或缺失,前隆带与前渊带三级层序的细分对比是建立前陆盆地层序地层格架的关键问题.1 前陆盆地二级层序内可容纳空间发育演化和三级层序对比前陆盆地二级层序与幕式构造活动对应,一个二级层序是一次成盆期的产物,其内部三级层序对比有赖于二级层序内可容纳空间演化过程的分析.图1 可容纳空间与岩石圈沉降关系示意(据Giles and Dickin -son,1995)F ig.1Schematic diagr am display ing the r elatio nship of the litho spheric flexur e to acco mmodation space1.1 二级层序内可容纳空间发育特点前陆盆地是挤压体制的产物,挤压应力既可使盆地边缘差异性隆起,又可使盆地中心差异性沉降(Posamentier and Allen,1993).Giles and Dickin -son(1995)对前陆盆地的动力学作用与可容空间变化关系进行了归纳(图1):伴随着前陆盆地的演化,前渊带强烈沉降、可容纳空间增加;同时,前隆带隆升、可容纳空间减少.因此,在前陆盆地不同沉积单元(前渊、前隆和隆后盆地),可容纳空间发育极不协调,前隆至前渊带强烈不对称楔型地层正是可容纳空间不协调发育的结果.1.2 二级层序内可容纳空间的演化由于海(湖)平面变化产生的可容纳空间在全盆一致,前陆盆地可容纳空间发育的不协调性与构造活动密切相关.构造活动导致前渊与前隆带可容纳空间发育的方向相反;并且,前隆向上挠曲的幅度与前渊沉降中心下沉的幅度呈正比(Quinlan and Beaum ont,1984),从而造成可容纳空间发育的不协调性在前渊至前隆部位表现突出.大量的研究表明,前隆既不是先存的,也不是固定不变,它与构造活动相伴随,前隆产生和迁移指示着可容纳空间不协调发育的动态演化过程.关于前隆的产生,前人研究认为:在盆地发育早期,推覆载荷引起紧邻造山带的岩石圈发生挠曲沉降形成前渊;随着沉积物的大量注入,沉积物和水体的载荷作用开始共同对盆地的发展起影响,使岩石圈的挠曲向克拉通方向发展(Fleming s and Jordan,1989),由于地壳的均衡作用,在远离推覆体的前隆带抬升,将前渊带与隆后盆地分隔开来(Crampto n and Allen,1995).由此可见,伴随着前隆的产生,盆地范围和可容纳空间向克拉通方向扩展.前陆盆地动力学研究的弹性流变模型和粘弹性流变模型对前隆的迁移规律进行了分析.粘弹性流变模型中(Quinlan and Beaumo nt,1984;Beaum ont et al .,1988),岩石圈挠曲过程表现为(图2):冲断负载期间,前隆向逆冲断裂迁移,盆地变窄变深(曲线1至3);冲断负载之后,岩石圈发生应力松弛以及侵蚀卸载作用,临近逆冲断裂处将发生回弹上升图2 粘弹性岩石圈在逆冲负载和侵蚀卸载作用下的挠曲响应(据Beaum on t et al .,1988)Fig.2F lex ur al response of the v iscoelastic lithospher e o f thethrust loading and er osional unlo ading a.逆冲负载;b.侵蚀卸载141地球科学)))中国地质大学学报第30卷(曲线4至6).在弹性流变模型中(Flemings and Jordan,1989,1990),变形开始时,盆地变窄,前隆向冲断断裂迁移;在变形停止后,构造作用居于次要地位,由于造山带遭受剥蚀,岩石圈均衡补偿,导致弹性抬升,盆地变宽变浅,前缘隆起远离冲断带移动,此时的沉降主要与沉积负载有关.总体上,虽然前陆盆地的动力学模型在岩石圈的力学性质、盆地的负载和作用力方面存在不同认识(刘少峰和李思田,1995),但都将前陆盆地的构造活动划分为活动期和宁静期2个阶段;在构造活动期,前隆向冲断带迁移,盆地变窄变深,导致可容纳空间发育的局限性和不协调性增强;在构造宁静期,盆地宽而浅,可容纳空间发育的不协调性减弱、整体性增强.1.3 二级层序内三级层序对比前人在前陆盆地的层序研究中,大多强调构造活动对三级层序的控制作用,认为三级层序界面是构造活动或构造活动与海平面变化叠加作用的结果,即三级相对海平面变化是地方性的、区域性的,因而可能导出前陆盆地不同的构造活动单元(如前隆与前渊)的三级层序不等时的结论.对此,Giles and Dickinson(1995)的研究做了回答,他们对Ne -v ada 和Utah 的Antler 前陆盆地晚泥盆世至早石炭世各沉积区(前渊、前隆和隆后盆地)剖面进行详细对比研究,用生物地层和年代地层等对各层序界面的时限进行了精细标定,以不整合面或与之对应的整合面为标志,识别出8个时限约1~4.5M a 的层序(与Vail 的三级层序时限0.5~3M a 相当),各层序界面都与全球海平面下降期接近,印证了前陆盆地三级层序的等时性和可对比性,而构造作用只能加强层序界面的反应(Vail et al .,1991).关于前陆盆地的地层对比,普遍认为:构造活动期可容纳空间变化主要由前陆挠曲作用和全球海平面变化控制,受构造作用的影响,沉积厚度、沉积相及沉积体堆积样式各沉积区差别很大;构造宁静期,前陆盆地和隆后盆地表现为被动充填,地层展布样式主要受控于海(湖)平面变化和沉积物补给速率的变化,地层在各沉积区可对比性强(刘贻军,1998).进一步结合上文对可容纳空间发育演化过程的分析,可建立图3对比模式:在构造活动初期,可容纳空间向克拉通扩张,三级层序向克拉通方向渐进超覆(SQ 1-SQ 2);随后,前渊带迅速沉降,前隆隆升并向冲断带迁移、甚至暴露而遭受剥蚀,盆地的不对称性增强,层序分布向冲断带渐次收缩(SQ 3-SQ 7);图3 前陆盆地前隆-前渊带三级层序对比模式Fig.3Cor relat ion mo del o f thir d-o rder sequences fro m the for ebug le to foredeep o f a foreland basin在构造宁静期,盆地变宽变浅,可容纳空间发育的整体性增强,三级层序在前隆-前渊带发育较协调(SQ 8-SQ 9).2 库车前陆盆地三级层序对比分析我国中西部广泛发育的前陆盆地是我国21世纪油气勘探的希望所在(翟光明,2002).库车前陆盆地位于塔里木盆地北部,北缘是南天山造山带,南面是塔北隆起,呈北东东向展布,东西长550km,南北宽50~90km,面积42700km 2.经过/八五0和/九五0科技攻关,对库车盆地的构造、沉积和石油地质等研究已经积累了大量资料.近年来,通过盆地沉降史(杨庚和钱祥麟,1995)、层序地层学(肖建新等,2002;林畅松等,2002)、碎屑岩岩石学(张希明等,1996)等多方面的研究证实,库车盆地侏罗纪-老第三纪都遭受了不同程度的构造挤压,具有前陆盆地性质.库车盆地侏罗系-下第三系总体为一套巨厚的陆相碎屑岩沉积,夹薄层海侵地层.其中,侏罗系煤系地层发育;白垩系-下第三系湖相泥岩普遍呈褐红色调,表明研究区侏罗纪-老第三纪湖平面呈低幅度振荡变化,从而使构造活动对可容纳空间的作用更加突出.2.1 地震反射波组结构-层序对比的证据地震反射波组追踪是揭示宏观地层格架的有效途径.本次研究对塔北隆起(前隆)北斜坡带二维地震剖面反射波组结构进行了分析.在P 1剖面上(剖面位置见图6),T 32-T 40反射对应于白垩系下统卡普沙良群.T 40为侏罗系顶面反射,之下削截现象普遍;T 32反射轴之上见上超反射.142第2期 王家豪等:前陆盆地二级层序内可容纳空间发育演化及三级层序对比图4 P 1地震剖面白垩系卡普沙良群反射波组结构(剖面位置见图6)F ig.4Reflect ion co nf igurat ion of K apusha liangG ro up,Cretaceous in seismic pro file P 1(th e profile locationseein g in Fig.6)图5 P 2地震剖面侏罗系反射波组结构(剖面位置见图6)Fig.5R ef lection co nfigurat ion o f the Jur assic in seismicpr ofile P 2(th e profile location s eeing in Fig.6)剖面上,卡普沙良群地层明显呈楔型,可识别出7个反射波组.其中,波组1~6为一系列上超退积反射,波组7为一组前积反射.波组1~5总体呈发散状,波组2、3、4、5依次尖灭于下伏波组之上,尤其以波组4、5特征清晰;波组6、7延续范围广,可追踪性好(图4).在P 2剖面上(剖面位置见图6),侏罗系顶面T 40上超尖灭于前侏罗系T 50反射轴上.侏罗系属煤系地层,内部反射强,结构清晰,可识别为5个波组.其中,波组1呈双超的丘形;波组2~5都呈上超退积反射;波组2、3、4依次尖灭于下伏波组之上;T 40反射面之下未见削截现象,其下伏波组5延续范围最大(图5).以上分析表明,2条剖面反射波组都显示明显的双层结构:下层波组(P 1剖面波组1~5和P 2剖面波组2~4)为楔状、向北收缩的退积反射,反映相对湖平面持续上升,但湖盆范围和地层分布向北收缩,与图3所示构造活动期地层分布特点相同.上层波组(P 1剖面波组6和7、P 2剖面波组5)为带状、延续范围广的退积或前积反射,其特征反映前陆盆地进入构造宁静期,可容纳空间发育的整体性强.2.2 库车前陆盆地下第三系层序横向对比库车盆地下第三系由库姆格列木群(包括底砂岩段和上部的砂泥岩段)和苏维依组组成,与下白垩统平行或角度不整合接触,上白垩统缺失.由于库车盆地老第三纪早期发生了一次短暂海侵(贾进华,2000),在库姆格列木群底部形成一层泥晶灰岩或白云岩的区域标志层,并导致盆地水体咸化,地层含石膏,区别于下伏地层.在北部露头LT1,下第三系厚度大,地层发育保存完整(图6).库姆格列木群底砂岩段厚层混杂块状砾岩发育,夹大型交错层理粗砂岩-含砾粗砂岩,为扇三角洲平原泥石流和辫状分流河道微相沉积;内部夹一套海侵形成的泥晶灰岩.库姆格列木群砂泥岩段主要由泥岩、砂质泥岩与薄层粉砂岩、细砂岩互层组成,波状层理、波状交错层理、波痕构造发育,局部夹混杂块状细-中砾岩和大型交错层理中-粗砂岩,总体以滨浅湖沉积为主体、扇三角洲体系间歇性发育为特征.苏维依组由发育小型交错层理、波痕构造和常见虫迹化石的粉砂岩-砂质泥岩-泥岩与混杂块状细-中砾岩、大型交错层理细砂岩-含砾砂岩组成,为一套扇三角洲体系平原相、前缘相与滨浅湖相交替沉积.LT 1露头下第三系岩性至下而上呈粗-细-粗的变化,沉积体系由扇三角洲体系-滨浅湖体系为主-扇三角洲体系演化,总体构成一个完整二级层序(林畅松等,2002),库姆格列木群底部大套粗碎屑沉积是构造重新活动的标志(李勇等,2000;林畅松等,2002).老第三纪时间跨度41M a(起止时间65~24M a)(杨庚和钱祥麟,1995),与V ail et al .(1991)二级层序的持续时间(3~50Ma)一致.根据沉积体系垂相演化特征,下第三系可进一步识别为8个三级层序SQ 1-SQ 8,库姆格列木群、苏维依组分别包括6个和2个三级层序.三级层序的低水位体系域普遍为扇三角洲体系粗碎屑沉积.除SQ 1为海侵形成的局限海沉积之外,三级层序的湖侵体系域以滨浅湖、浅湖沉积为主,SQ 5、SQ 6还发育扇三角洲前缘相沉积.三级层序的高位体系域沉积沉积相或微相包括扇三角洲平原相(SQ 1、SQ 8)、扇三角洲前缘相(SQ 2、SQ 6、SQ 7)、滨浅湖(SQ 3、SQ 4、SQ 5).下第三系泥晶灰岩或白云岩标志层在盆地腹部的D1井和塔北隆起上的D5井也被揭示(图5).在D5井为一套灰色厚5.0m 微晶白云岩、砂屑白143地球科学)))中国地质大学学报第30卷图6 库车前陆盆地东部下第三系北北西向层序地层对比剖面Fig.6N N W sequence str atig raphic co rr elation pr ofile of L ow er T ertiar y in the east of K uqa for eland basin云岩,造成了钻进过程中泥浆大量漏失,其发育表明老第三纪初期的海侵波及范围大、盆地形态宽缓、南北地形高差较小,由此可认为库姆格列木群底部地层在塔北隆起上分布广泛.杨庚和钱祥麟(1995)研究认为,库车盆地老第三纪的粗碎屑沉积在塔北隆起上分布广泛;粗碎屑沉积时期,库车盆地的构造活动较弱,其强烈沉降期对应于粗碎屑之上的细碎屑沉积,这与Blair and Bilo deau (1988)、Parkash and Kum ar(1991)的观点相同.由此看来,前陆盆地构造活动呈现由弱增强的过程;在构造活动之初,活动性弱,可容纳空间主要受湖平面变化控制,相应的地层整个盆地分布广泛.在垂直于构造走向的北北西向露头-钻井剖面上,下第三系呈北厚南薄的强烈不对称楔型,显示了前陆盆地前渊至前隆带的地层分布特点(图6).D1井、D2井、D3井、D4井的岩性组成垂向上都呈粗-细-粗变化,库姆格列木群底砂岩段为一套厚层粉砂岩、细砂岩、含砾细砂岩,自然伽玛曲线呈箱型.库姆格列木群砂泥岩段由粉砂岩、泥岩、膏盐薄互层组成,自然伽玛曲线呈细锯齿型,为滨浅湖相全面发育期.D4井苏维依组以厚层细砂岩夹薄层泥岩为主,自然伽玛曲线呈箱型,为扇三角洲前缘相沉积;D3井、D1井苏维依组发育厚层粉砂岩,自然伽玛曲线以箱型为主;D2井苏维依组由粉砂岩、泥岩薄互层组成,自然伽玛呈细锯齿型,属滨浅湖相沉积.在层序的结构上,与LT 1剖面三级层序三个体系域发育齐全不同,D1井、D2井、D3井、D4井三级层序的低水位体系域普遍缺乏.层序对比表明,除了单个三级层序厚度减薄之外,楔型地层的成因还与D2井、D3井、D4井三级层序缺失有关.其中,D4井、D3井缺失SQ 1、SQ 5、SQ 6层序;D2井缺失SQ 1、SQ 6层序.从SQ 1至144第2期王家豪等:前陆盆地二级层序内可容纳空间发育演化及三级层序对比SQ2,显示由LT1向D4井超覆特征;从SQ3至SQ6,层序的分布和浅湖相区渐次向北收缩.SQ7、SQ8层序全区分布,浅湖相的厚度薄,浅湖相区由之前的LT1部位南迁至D1井部位.根据上文分析, SQ1-SQ2层序对应于前隆形成期;SQ3-SQ6层序对应于构造强烈活动期;SQ7-SQ8层序对应于构造宁静期.3结论前陆盆地的挤压体制既可使盆地边缘差异性隆起,又可使盆地中心差异性沉降,导致可容纳空间在横向上(前隆-前渊带)不协调发育.可容纳空间的不协调发育是与前隆的产生和迁移密切伴随的动态演化过程:在构造的活动期,前隆向冲断带迁移,盆地变窄变深,可容纳空间发育的不协调性逐渐增强;在构造宁静期,盆地变宽变浅,可容纳空间整体性发育.其层序记录为:在二级层序的底部(对应于构造初始活动和前隆的产生),三级层序向克拉通超覆;在二级层序的中部(对应于构造强烈活动期),三级层序和沉积相的分布向冲断带收缩;在二级层序的上部(对应于构造宁静期),三级层序分布广泛,可对比性强.ReferencesBeaumont,C.,Q uinlan,G.M.,H amilton,J.,1988.O ro geny and str atig raphy:Numeral model o f the P aleo zo ic in theeastern interior o f No rth A merican.T ectonics,7(3):389-416.Blair,T.C.,Bilodeau,W.,L.,1988.Development o f tectonic cyclothems in rift,pul-l apart,and for eland basins:Sed-iment ary r esponse t o episo de tecto nism.Geology,16:517-520(in Chinese w ith Eng lish abstract).Cr ampt on,S.L.,A llen,P.A.,1995.Recog nition of fo rebulg e unco nfo rmities associated with early stage for eland ba-sin 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Zhang,X.M.,L iu,Q.F.,W ang,G.Q.,1996.P late str uctur-al setting of the T riassic-Jurassic pr ovenance and it s r e-lat ionship w it h sedimentar y basin type in the no rth of the T a rim basin.Ex p er imental Petr oleum Geology,18(3):252-258(in Chinese w ith English abstr act).附中文参考文献贾进华,2000.库车前陆盆地白垩纪巴什基奇克组沉积层序与储层研究.地学前缘,7(3):133-143.李勇,王成善,曾允孚,2000.造山作用与沉积响应.矿物岩石,20(2):49-56.林畅松,刘景彦,张燕梅,等,2002.库车坳陷第三系构造层序的构成特征及其对前陆构造作用的响应.中国科学(D 辑),32(3):177-183.刘少峰,李思田,1995.前陆盆地挠曲过程模拟的理论模型.地学前缘,2(3):69-77.刘贻军,1998.前陆盆地层序地层学研究中的几个问题.地球学报,19(1):90-96.肖建新,林畅松,刘景颜,2002.塔里木盆地北部库车坳陷白垩系层序地层与体系域特征.地球学报,23(5):453-458.杨庚,钱祥麟,1995.库车坳陷沉降与天山中新生代构造活动.新疆地质,13(3):264-274.翟光明,2002.21世纪中国油气资源远景.新疆石油地质,23(4):271-279.张希明,刘青芳,王贵全,1996.塔里木盆地北部三叠-侏罗纪物源区板块构造背景与沉积盆地类型关系的研究.石油实验地质,18(3):252-258.*********************************************《地球科学)))中国地质大学学报》2005年第30卷第3期要目预告华北克拉通南缘太古-元古宙界线安沟群火山岩年龄及地球化学高山等………………………………北喜马拉雅淡色花岗岩地球化学:区域对比、岩石成因及其构造意义张宏飞等……………………………大别造山带东段扬子陆块和华北陆块缝合带的位置江来利等………………………………………………从断裂带内部结构出发评价断层垂向封闭性的方法付晓飞等………………………………………………大气降水量对成矿流体热场的影响)))以锡矿山锑矿床成矿流体为例杨瑞琰等…………………………高精度重力测量探测秦始皇帝陵地宫袁炳强等………………………………………………………………146。

新的研究方法英语作文Title: Innovative Research Methods。

In the realm of academic inquiry, the pursuit of novel research methods is essential for advancing knowledge and addressing complex questions. In this essay, we will explore several innovative research methodologies that have emerged in recent years, highlighting their significance and potential impact on various fields of study.1. Mixed-Methods Research: This approach involves the integration of qualitative and quantitative research methods within a single study. By combining the strengths of both approaches, researchers can gain a more comprehensive understanding of their research topic. For example, in social sciences, mixed-methods research allows for the exploration of both the depth of individual experiences through qualitative interviews and the breadth of trends through quantitative surveys or analysis.2. Big Data Analytics: With the exponential growth of digital data, big data analytics has become increasingly important across disciplines. This approach involves the analysis of large and complex datasets to uncover patterns, trends, and correlations that may not be apparent through traditional methods. In fields such as healthcare, finance, and marketing, big data analytics enables researchers to extract valuable insights for decision-making and prediction.3. Machine Learning and Artificial Intelligence: Machine learning and artificial intelligence (AI) techniques are revolutionizing research methodologies by enabling computers to learn from data and make predictions or decisions without explicit programming. In areas like natural language processing, image recognition, and predictive modeling, machine learning algorithms are being employed to automate tasks, identify patterns, and generate new hypotheses.4. Participatory Action Research: Participatory action research (PAR) is an approach that involves collaborationbetween researchers and community members to address issues of mutual concern. Through a cyclical process of planning, action, reflection, and evaluation, PAR aims to empower participants and effect positive social change. This methodology is particularly prevalent in fields such as education, community development, and environmental sustainability.5. Virtual Reality and Immersive Technologies: Virtual reality (VR) and immersive technologies offer new possibilities for conducting research in controlled yet realistic environments. Researchers can create virtual simulations to study human behavior, test hypotheses, and explore scenarios that may be difficult or unethical to replicate in the real world. In psychology, medicine, and engineering, VR technology is being utilized to investigate phenomena and develop innovative solutions.6. Longitudinal Studies: Longitudinal studies involve the collection of data from the same subjects over an extended period to observe changes or developments over time. By tracking individuals or cohorts over months, years,or even decades, researchers can gain insights into the effects of various factors on human behavior, health outcomes, and social dynamics. Longitudinal studies are invaluable for uncovering causal relationships and informing policy decisions.7. Crowdsourcing and Citizen Science: Crowdsourcing and citizen science initiatives engage the public in the research process, leveraging the collective intelligence and resources of a diverse group of participants. Whether through crowdsourced data collection, collaborative problem-solving, or distributed computing, these approaches enable researchers to tackle large-scale projects and harness the expertise of non-experts. From astronomy to ecology, crowdsourcing has democratized research and facilitated innovative discoveries.In conclusion, the adoption of innovative research methods is essential for advancing knowledge, solving complex problems, and addressing pressing societal challenges. By embracing approaches such as mixed-methods research, big data analytics, machine learning,participatory action research, virtual reality, longitudinal studies, crowdsourcing, and citizen science, researchers can expand the frontiers of their respective fields and make meaningful contributions to society. As technology continues to evolve and interdisciplinary collaborations flourish, the possibilities for innovation in research methodologies are limitless.。

galvanic replacement method -回复Galvanic Replacement Method: Unlocking the Secrets of NanotechnologyIntroduction:Nanotechnology is a field that has gained immense popularity and significance in recent years. Its applications span a wide range of industries, including electronics, medicine, energy, and materials science. One of the key techniques used in nanotechnology is the galvanic replacement method. This method has revolutionized the synthesis and fabrication of nanomaterials, leading to breakthroughs in various research areas. In this article, we will delve into the intricacies of the galvanic replacement method, exploring its step-by-step process and its impact on nanoscale science.Step 1: Understanding the BasicsTo comprehend the galvanic replacement method, we first need to understand the fundamentals. This method involves the chemical transformation of metal structures through a redox reaction. It typically occurs between two different metals, where one metal isoxidized, and the other metal is reduced. This transformation process leads to the formation of a new nanoscale structure, exhibiting unique properties that differ from those of the starting materials.Step 2: Preparation of Reactant MetalsThe first step in the galvanic replacement method is acquiring the reactant metals. These metals should have diverse properties to facilitate the redox reaction. For example, one metal could be chosen for its high reactivity towards a particular ion, while the other metal acts as a template to control the shape and size of the resulting nanomaterial. The selection of suitable reactant metals forms the foundation for a successful galvanic replacement process.Step 3: Immersion in Ionic SolutionFollowing the acquisition of reactant metals, they are immersed in a solution containing an appropriate ionic compound. The choice of this compound depends on the desired final nanomaterial and its specific properties. For instance, if one wishes to create a magneticnanomaterial, an iron salt solution may be suitable. The immersion of metals in the ionic solution initiates the redox reaction, leading to the replacement of one metal ions with the other.Step 4: Manipulating Reaction ParametersThe galvanic replacement reaction is influenced by various parameters, including temperature, pH, concentration, and reaction time. Manipulating these parameters provides control over the reaction kinetics and morphology of the resulting nanomaterial. Higher temperatures typically accelerate the reaction, while pH and concentration affect the deposition rate and the distribution of the new metal ions. These parameters need to be carefully optimized to achieve the desired nanomaterial properties.Step 5: Characterization and AnalysisOnce the galvanic replacement reaction is complete, the resulting nanomaterial needs to be characterized and analyzed. Various techniques are employed to examine its structure, composition, and properties. Electron microscopy techniques such as transmission electron microscopy (TEM) and scanning electronmicroscopy (SEM) provide high-resolution images, enabling researchers to observe the nanomaterial's morphology and size.X-ray diffraction (XRD) and energy-dispersive X-ray spectroscopy (EDS) are used to analyze the crystal structure and element composition of the nanomaterial. These characterization techniques shed light on the success of the galvanic replacement method and offer insights into potential applications.Conclusion:The galvanic replacement method is a powerful tool in the realm of nanotechnology. By harnessing the redox reaction between metals, researchers can synthesize nanomaterials with unique properties. Understanding the step-by-step process of this method, from selecting suitable reactant metals to characterizing the resulting nanomaterial, empowers scientists to unlock the secrets of nanoscale science. As advancements in nanotechnology continue to reshape various industries, the galvanic replacement method is poised to play a crucial role in the discovery and development of novel materials and technologies.。

第8卷　第3期2010年6月实验科学与技术Experi m ent Science and Technol ogyVol 18No 13Jun 12010收稿日期:2010-01-19基金项目:深圳大学精品课程建设项目。

作者简介:郭小勤(1960-),女,硕士,副教授,主要从事控制理论与控制工程领域的教学与科研工作。

基于项目的C D IO 理念在课程教学中的应用郭小勤,曹广忠(深圳大学机电与控制工程学院,广东深圳　518060)摘要:根据C D I O 工程教育理念,结合线性系统理论课程特点,设计了模拟真实工程环境的龙门吊车控制实验项目。

该项目以设计为导向、工程能力培养为目标,构思了以吊运过程平稳性和快速性为总体控制目标的实验教学内容。

在项目实施中,以问题为导向,引导学生主动学习,并在完成项目的整个过程中主动探寻学科知识。

教学实践证明基于项目的教学模式极大地提高了学生学习的积极性和主动性,提高了科学研究能力和工程实践能力。

关　键　词:C D I O 工程教育;线性系统理论;设计导向学习;龙门吊车控制中图分类号:G642·423;TP27114 文献标志码:B 文章编号:1672-4550(2010)03-0083-03Appli cati on of the Project 2based CD I O I dea i n Course Teachi n gG UO Xiao 2qin,CAO Guang 2zhong(College of Electr omechanical and Contr ol Engineering,Shenzhen University,Shenzhen 518060,China )Abstract:The experi m ent p r oject of gantry crane contr ol is designed t o si m ulate p ractical engineering according t o the CD I O engineer 2ing educati on initiative and peculiarity of linear syste m theory course .The p r oject is design 2oriented f or engineering ability training,and its final contr ol goal t o achieve a s mooth and rap id lifting and moving p r ocess was conceived f or the contents of experi m ent teach 2ing .I n the p r oject i m p le mentati on,students are guided t o learning actively by p r oble m s 2oriented method and t o exp l ore acade m icknowledge in p ractice .Teaching p ractice p r oved that the p r oject 2based teaching model greatly enhance students πlearning enthusias mand initiative and i m p r ove the capacity of scientific research and engineering p ractice 1Key words:C D I O engineering educati on;linear syste m theory;design 2oriented learning;gantry crane contr ol1　引　言2000年10月,由美国麻省理工学院和瑞典皇家理工学院等4所大学组成的工程教育改革研究团队提出、并持续发展和倡导了全新的CD I O (Con 2ceiving -Designing -I m p le menting -Operati on )工程教育理念即构思—设计—实现—运行。

•Creating a reference list or bibliographyA numbered list of references must be provided at the end of thepaper. The list should be arranged in the order of citation in the text of the assignment or essay, not in alphabetical order. List only one reference per reference number. Footnotes or otherinformation that are not part of the referencing format should not be included in the reference list.The following examples demonstrate the format for a variety of types of references. Included are some examples of citing electronic documents. Such items come in many forms, so only some examples have been listed here.Print DocumentsBooksNote: Every (important) word in the title of a book or conference must be capitalised. Only the first word of a subtitle should be capitalised. Capitalise the "v" in Volume for a book title.Punctuation goes inside the quotation marks.Standard formatSingle author[1] W.-K. Chen, Linear Networks and Systems. Belmont, CA: Wadsworth,1993, pp. 123-135.[2] S. M. Hemmington, Soft Science. Saskatoon: University ofSaskatchewan Press, 1997.Edited work[3] D. Sarunyagate, Ed., Lasers. New York: McGraw-Hill, 1996.Later edition[4] K. Schwalbe, Information Technology Project Management, 3rd ed.Boston: Course Technology, 2004.[5] M. N. DeMers, Fundamentals of Geographic Information Systems,3rd ed. New York : John Wiley, 2005.More than one author[6] T. Jordan and P. A. Taylor, Hacktivism and Cyberwars: Rebelswith a cause? London: Routledge, 2004.[7] U. J. Gelinas, Jr., S. G. Sutton, and J. Fedorowicz, Businessprocesses and information technology. Cincinnati:South-Western/Thomson Learning, 2004.Three or more authorsNote: The names of all authors should be given in the references unless the number of authors is greater than six. If there are more than six authors, you may use et al. after the name of the first author.[8] R. Hayes, G. Pisano, D. Upton, and S. Wheelwright, Operations,Strategy, and Technology: Pursuing the competitive edge.Hoboken, NJ : Wiley, 2005.Series[9] M. Bell, et al., Universities Online: A survey of onlineeducation and services in Australia, Occasional Paper Series 02-A. Canberra: Department of Education, Science andTraining, 2002.Corporate author (ie: a company or organisation)[10] World Bank, Information and Communication Technologies: AWorld Bank group strategy. Washington, DC : World Bank, 2002.Conference (complete conference proceedings)[11] T. J. van Weert and R. K. Munro, Eds., Informatics and theDigital Society: Social, ethical and cognitive issues: IFIP TC3/WG3.1&3.2 Open Conference on Social, Ethical andCognitive Issues of Informatics and ICT, July 22-26, 2002, Dortmund, Germany. Boston: Kluwer Academic, 2003.Government publication[12] Australia. Attorney-Generals Department. Digital AgendaReview, 4 Vols. Canberra: Attorney- General's Department,2003.Manual[13] Bell Telephone Laboratories Technical Staff, TransmissionSystem for Communications, Bell Telephone Laboratories,1995.Catalogue[14] Catalog No. MWM-1, Microwave Components, M. W. Microwave Corp.,Brooklyn, NY.Application notes[15] Hewlett-Packard, Appl. Note 935, pp. 25-29.Note:Titles of unpublished works are not italicised or capitalised. Capitalise only the first word of a paper or thesis.Technical report[16] K. E. Elliott and C.M. Greene, "A local adaptive protocol,"Argonne National Laboratory, Argonne, France, Tech. Rep.916-1010-BB, 1997.Patent / Standard[17] K. Kimura and A. Lipeles, "Fuzzy controller component, " U.S. Patent 14,860,040, December 14, 1996.Papers presented at conferences (unpublished)[18] H. A. Nimr, "Defuzzification of the outputs of fuzzycontrollers," presented at 5th International Conference onFuzzy Systems, Cairo, Egypt, 1996.Thesis or dissertation[19] H. Zhang, "Delay-insensitive networks," M.S. thesis,University of Waterloo, Waterloo, ON, Canada, 1997.[20] M. W. Dixon, "Application of neural networks to solve therouting problem in communication networks," Ph.D.dissertation, Murdoch University, Murdoch, WA, Australia, 1999.Parts of a BookNote: These examples are for chapters or parts of edited works in which the chapters or parts have individual title and author/s, but are included in collections or textbooks edited by others. If the editors of a work are also the authors of all of the included chapters then it should be cited as a whole book using the examples given above (Books).Capitalise only the first word of a paper or book chapter.Single chapter from an edited work[1] A. Rezi and M. Allam, "Techniques in array processing by meansof transformations, " in Control and Dynamic Systems, Vol.69, Multidemsional Systems, C. T. Leondes, Ed. San Diego: Academic Press, 1995, pp. 133-180.[2] G. O. Young, "Synthetic structure of industrial plastics," inPlastics, 2nd ed., vol. 3, J. Peters, Ed. New York:McGraw-Hill, 1964, pp. 15-64.Conference or seminar paper (one paper from a published conference proceedings)[3] N. Osifchin and G. Vau, "Power considerations for themodernization of telecommunications in Central and Eastern European and former Soviet Union (CEE/FSU) countries," in Second International Telecommunications Energy SpecialConference, 1997, pp. 9-16.[4] S. Al Kuran, "The prospects for GaAs MESFET technology in dc-acvoltage conversion," in Proceedings of the Fourth AnnualPortable Design Conference, 1997, pp. 137-142.Article in an encyclopaedia, signed[5] O. B. R. Strimpel, "Computer graphics," in McGraw-HillEncyclopedia of Science and Technology, 8th ed., Vol. 4. New York: McGraw-Hill, 1997, pp. 279-283.Study Guides and Unit ReadersNote: You should not cite from Unit Readers, Study Guides, or lecture notes, but where possible you should go to the original source of the information. If you do need to cite articles from the Unit Reader, treat the Reader articles as if they were book or journal articles. In the reference list or bibliography use the bibliographical details as quoted in the Reader and refer to the page numbers from the Reader, not the original page numbers (unless you have independently consulted the original).[6] L. Vertelney, M. Arent, and H. Lieberman, "Two disciplines insearch of an interface: Reflections on a design problem," in The Art of Human-Computer Interface Design, B. Laurel, Ed.Reading, MA: Addison-Wesley, 1990. Reprinted inHuman-Computer Interaction (ICT 235) Readings and Lecture Notes, Vol. 1. Murdoch: Murdoch University, 2005, pp. 32-37. Journal ArticlesNote: Capitalise only the first word of an article title, except for proper nouns or acronyms. Every (important) word in the title of a journal must be capitalised. Do not capitalise the "v" in volume for a journal article.You must either spell out the entire name of each journal that you reference or use accepted abbreviations. You must consistently do one or the other. Staff at the Reference Desk can suggest sources of accepted journal abbreviations.You may spell out words such as volume or December, but you must either spell out all such occurrences or abbreviate all. You do not need to abbreviate March, April, May, June or July.To indicate a page range use pp. 111-222. If you refer to only one page, use only p. 111.Standard formatJournal articles[1] E. P. Wigner, "Theory of traveling wave optical laser," Phys.Rev., vol. 134, pp. A635-A646, Dec. 1965.[2] J. U. Duncombe, "Infrared navigation - Part I: An assessmentof feasability," IEEE Trans. Electron. Devices, vol. ED-11, pp. 34-39, Jan. 1959.[3] G. Liu, K. Y. Lee, and H. F. Jordan, "TDM and TWDM de Bruijnnetworks and shufflenets for optical communications," IEEE Trans. Comp., vol. 46, pp. 695-701, June 1997.OR[4] J. R. Beveridge and E. M. Riseman, "How easy is matching 2D linemodels using local search?" IEEE Transactions on PatternAnalysis and Machine Intelligence, vol. 19, pp. 564-579, June 1997.[5] I. S. Qamber, "Flow graph development method," MicroelectronicsReliability, vol. 33, no. 9, pp. 1387-1395, Dec. 1993.[6] E. H. Miller, "A note on reflector arrays," IEEE Transactionson Antennas and Propagation, to be published.Electronic documentsNote:When you cite an electronic source try to describe it in the same way you would describe a similar printed publication. If possible, give sufficient information for your readers to retrieve the source themselves.If only the first page number is given, a plus sign indicates following pages, eg. 26+. If page numbers are not given, use paragraph or other section numbers if you need to be specific. An electronic source may not always contain clear author or publisher details.The access information will usually be just the URL of the source. As well as a publication/revision date (if there is one), the date of access is included since an electronic source may change between the time you cite it and the time it is accessed by a reader.E-BooksStandard format[1] L. Bass, P. Clements, and R. Kazman. Software Architecture inPractice, 2nd ed. Reading, MA: Addison Wesley, 2003. [E-book] Available: Safari e-book.[2] T. Eckes, The Developmental Social Psychology of Gender. MahwahNJ: Lawrence Erlbaum, 2000. [E-book] Available: netLibrary e-book.Article in online encyclopaedia[3] D. Ince, "Acoustic coupler," in A Dictionary of the Internet.Oxford: Oxford University Press, 2001. [Online]. Available: Oxford Reference Online, .[Accessed: May 24, 2005].[4] W. D. Nance, "Management information system," in The BlackwellEncyclopedic Dictionary of Management Information Systems,G.B. Davis, Ed. Malden MA: Blackwell, 1999, pp. 138-144.[E-book]. Available: NetLibrary e-book.E-JournalsStandard formatJournal article abstract accessed from online database[1] M. T. Kimour and D. Meslati, "Deriving objects from use casesin real-time embedded systems," Information and SoftwareTechnology, vol. 47, no. 8, p. 533, June 2005. [Abstract].Available: ProQuest, /proquest/.[Accessed May 12, 2005].Note: Abstract citations are only included in a reference list if the abstract is substantial or if the full-text of the article could not be accessed.Journal article from online full-text databaseNote: When including the internet address of articles retrieved from searches in full-text databases, please use the Recommended URLs for Full-text Databases, which are the URLs for the main entrance to the service and are easier to reproduce.[2] H. K. Edwards and V. Sridhar, "Analysis of software requirementsengineering exercises in a global virtual team setup,"Journal of Global Information Management, vol. 13, no. 2, p.21+, April-June 2005. [Online]. Available: Academic OneFile, . [Accessed May 31, 2005].[3] A. Holub, "Is software engineering an oxymoron?" SoftwareDevelopment Times, p. 28+, March 2005. [Online]. Available: ProQuest, . [Accessed May 23, 2005].Journal article in a scholarly journal (published free of charge on the internet)[4] A. Altun, "Understanding hypertext in the context of readingon the web: Language learners' experience," Current Issues in Education, vol. 6, no. 12, July 2003. [Online]. Available: /volume6/number12/. [Accessed Dec. 2, 2004].Journal article in electronic journal subscription[5] P. H. C. Eilers and J. J. Goeman, "Enhancing scatterplots withsmoothed densities," Bioinformatics, vol. 20, no. 5, pp.623-628, March 2004. [Online]. Available:. [Accessed Sept. 18, 2004].Newspaper article from online database[6] J. Riley, "Call for new look at skilled migrants," TheAustralian, p. 35, May 31, 2005. Available: Factiva,. [Accessed May 31, 2005].Newspaper article from the Internet[7] C. Wilson-Clark, "Computers ranked as key literacy," The WestAustralian, para. 3, March 29, 2004. [Online]. Available:.au. [Accessed Sept. 18, 2004].Internet DocumentsStandard formatProfessional Internet site[1] European Telecommunications Standards Institute, 揇igitalVideo Broadcasting (DVB): Implementation guidelines for DVBterrestrial services; transmission aspects,?EuropeanTelecommunications Standards Institute, ETSI TR-101-190,1997. [Online]. Available: . [Accessed:Aug. 17, 1998].Personal Internet site[2] G. Sussman, "Home page - Dr. Gerald Sussman," July 2002.[Online]. Available:/faculty/Sussman/sussmanpage.htm[Accessed: Sept. 12, 2004].General Internet site[3] J. Geralds, "Sega Ends Production of Dreamcast," ,para. 2, Jan. 31, 2001. [Online]. Available:/news/1116995. [Accessed: Sept. 12,2004].Internet document, no author given[4] 揂憀ayman抯?explanation of Ultra Narrow Band technology,?Oct.3, 2003. [Online]. Available:/Layman.pdf. [Accessed: Dec. 3, 2003].Non-Book FormatsPodcasts[1] W. Brown and K. Brodie, Presenters, and P. George, Producer, 揊rom Lake Baikal to the Halfway Mark, Yekaterinburg? Peking to Paris: Episode 3, Jun. 4, 2007. [Podcast television programme]. Sydney: ABC Television. Available:.au/tv/pekingtoparis/podcast/pekingtoparis.xm l. [Accessed Feb. 4, 2008].[2] S. Gary, Presenter, 揃lack Hole Death Ray? StarStuff, Dec. 23, 2007. [Podcast radio programme]. Sydney: ABC News Radio. Available: .au/newsradio/podcast/STARSTUFF.xml. [Accessed Feb. 4, 2008].Other FormatsMicroform[3] W. D. Scott & Co, Information Technology in Australia:Capacities and opportunities: A report to the Department ofScience and Technology. [Microform]. W. D. Scott & CompanyPty. Ltd. in association with Arthur D. Little Inc. Canberra:Department of Science and Technology, 1984.Computer game[4] The Hobbit: The prelude to the Lord of the Rings. [CD-ROM].United Kingdom: Vivendi Universal Games, 2003.Software[5] Thomson ISI, EndNote 7. [CD-ROM]. Berkeley, Ca.: ISIResearchSoft, 2003.Video recording[6] C. Rogers, Writer and Director, Grrls in IT. [Videorecording].Bendigo, Vic. : Video Education Australasia, 1999.A reference list: what should it look like?The reference list should appear at the end of your paper. Begin the list on a new page. The title References should be either left justified or centered on the page. The entries should appear as one numerical sequence in the order that the material is cited in the text of your assignment.Note: The hanging indent for each reference makes the numerical sequence more obvious.[1] A. Rezi and M. Allam, "Techniques in array processing by meansof transformations, " in Control and Dynamic Systems, Vol.69, Multidemsional Systems, C. T. Leondes, Ed. 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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.*。

第35卷第6期2023年11月岩性油气藏LITHOLOGIC RESERVOIRSV ol.35No.6Nov.2023收稿日期：2023-06-08；修回日期：2023-07-03；网络发表日期：2023-08-11基金项目：国家自然科学基金项目“特提斯演化控制下的油气差异富集机理与勘探领域”（编号：92255302）和中国石油天然气股份有限公司科学研究与技术开发项目“海外复杂碳酸盐岩精细勘探关键技术研究”（编号：2021DJ3104）联合资助。

第一作者：罗贝维（1986—），男，博士，高级工程师，主要从事中东地区石油地质综合研究和油气勘探研究方面的工作。

地址：（100083）北京市海淀区学院路20号910信箱。

Email ：************************.cn 。

文章编号：1673-8926（2023）06-0063-09DOI ：10.12108/yxyqc.20230608引用：罗贝维，尹继全，胡广成，等.阿联酋西部地区白垩系森诺曼阶高孔渗灰岩储层特征及控制因素［J ］.岩性油气藏，2023，35（6）：63-71.Cite ：LUO Beiwei ，YIN Jiquan ，HU Guangcheng ，et al.Characteristics and controlling factors of high porosity and permeability lime ‐stone reservoirs of Cretaceous Cenomanian in the western United Arab Emirates ［J ］.Lithologic Reservoirs ，2023，35（6）：63-71.阿联酋西部地区白垩系森诺曼阶高孔渗灰岩储层特征及控制因素罗贝维1，尹继全1，胡广成2，陈华1，康敬程1，肖萌1，朱秋影1，段海岗1（1.中国石油勘探开发研究院，北京100083；2.中国石油国际勘探开发有限公司，北京100034）摘要：利用岩心及薄片分析、核磁共振、微米CT 测试、层序格架下的等时追踪及古地貌恢复等方法，对阿联酋西部地区白垩系森诺曼阶高孔渗灰岩的沉积特征、层序及沉积演化特征和成岩作用进行了系统剖析，从构造-沉积-成岩多维度对高孔渗储层的控制因素进行了研究。