应用判别函数和方差分析对蓝圆鲹分群问题的探讨

<正> 蓝圆鲹 Decapterus Maruadsi(T.& S.)属暖水性中上层鱼类.它广泛分布于南海,是南海数量大宗的主要经济鱼类之一.尤其在南海北部,蓝圆鲹是广东,广西,福建灯光围网渔业和底拖网渔业的重要捕捞对象.对蓝圆鲹生物学的研究表明,南海北部的蓝圆鲹可能存在不同的种群.渔民群众在生产实践中也认为粤东春汛的蓝圆鲹是分群分批进入渔场的.随着渔业生产不断发展的要求,必须更好地研究蓝圆鲹资源状况和分布移动规律,从而为今后利用蓝圆鲹资源,不     

含有保护区的两斑块鱼类生物经济模型

研究了同一鱼类种群同时分布于保护区斑块和非保护区斑块,且鱼群密度服从Gompertz增长的生物经济系统.首先,证明了系统内存在唯一的非平凡奇点并且局部稳定,运用Bendixson-Dulac定理得到该奇点的全局稳定性;然后,通过Pontryagin最大值原理获得该鱼类种群的最优捕捞路径;最后,通过数值模拟来说明部分结论.     

创新企业种群成长动力学分析

以生物系统中种群动力学为主要理论依据,开发了创新种群成长动力学模型.方程组推导结果表明:创新种群的成长过程中存在一定的均衡条件.科研种群是创新创群的主要互动种群.创新种群和科研种群之间存在显著地激励机制,两类种群对创新资源的占用存在一定的比例关系.创新种群的增长收到多种环境因素的影响,政府部门、服务机构、科研种群的影响均不同.创新种群的总量平衡点受总体环境制约.创新种群的增量平衡点主要受科研种群影响.     

基于Leslie种群模型的城市流浪猫数量模型研究

城市中日益增多的流浪猫正在成为一个人们关注的问题.其种群个体数量有不同的模型可用于描述,这里应用Leslie种群模型建立了城市雌性流浪猫数量模型,并在此基础上推导了整体数量模型;分析了随时间发展流浪猫数量的性态.根据有关资料数据,给出模型算例,预测了发展趋势.     

生物种群在污染环境中的生灭过程

建立了生物种群在污染环境中的一个线性生灭过程模型.利用马尔可夫过程的理论和方法,得到生物种群数量变化的概率分布,最后讨论了各模型参数的变化对生物种群生存的影响.     

种群生态系统中的模糊软测量方法

经典的生物模型中,关键参数的难以测量使整个动力系统具有较大的不确定性.本文引入模糊软测量技术,以湿地环境中三种群生态系统为例,将软洲量技术与模糊系统结合应用于种群动力学模型,以种群数量的初始值、时间t及种群变化率为辅助参数进行种群数量的预测.运用软测量技术估计微分方程中关键参数(如竞争系数、最大捕获率、能量转换系数),建立确定的动力学微分方程模型.仿真结果显示此方法的可行性、有效性.     

马尔可夫骨架过程在种群动态学中的应用

本文借助于马尔可夫骨架过程（舱妒）方法研究了种群动态学中单种群种群数量的瞬时分布，并证明单种群种群数量在时刻t的瞬时分布是某一非负线性方程的最小非负解。     

变捕捞努力量收获模型控制

利用微分包含给出了努力量可变的收获模型,基于生存理论和求解线性规划给出了将单种群数量控制在某范围的方法.最后对于常用的Logistic模型,证明只要控制努力量就可以将种群数量控制在指定范围内.     

一类阻滞增长的生灭过程模型

应用生灭过程理论,考虑环境容纳量对种群增长的阻滞作用,引入带阻滞的增长率和常数的死亡率,建立具有阻滞增长的生灭过程模型.通过求解所构建的一阶矩及二阶矩的方程,计算出种群数量的期望和方差.计算机模拟结果表明种群数量不会无限增长,这也符合生态实际,进一步说明模型构建的合理性.最后得到的结果是对生灭过程理论的有益扩展.     

具有分布时滞的脉冲种群系统的动力学行为

考虑的是带脉冲毒物输入和时滞的单种群模型的动力学行为,特别地,这里时滞项包含常时滞和分布成熟时滞.通过控制成熟个体的收获率,不仅得到了种群灭绝的充分条件,而且得到了种群灭绝周期解的指数渐近稳定和种群持久性的充分条件.这样的话,通过控制收获率,脉冲周期及脉冲毒物的输入量就能保护物种的数量,从而,结果对生物资源的管理具有一定的意义.     

控藻鲢鱼的捕捞策略

鲢鱼是一种以水中浮游动植物为食的滤食性鱼类,利用鲢鱼控制蓝藻数量是一种有效的纯生物手段.分析蠡湖鲢鱼资源稳定的条件,建立鱼量的基本方程式及利润模型,给出利润与捕捞强度的关系和鱼群数量的变化趋势.最后得到了鲢鱼的放流与回捕策略.     

慢速饮酒血液中酒精含量的数学模型

描述的是在一定时间内慢慢地匀速喝完一定数量酒的问题,并建立了相应的数学模型.同时也建立了快速饮酒的数学模型,以及证明了在各次饮酒后的某时刻酒精含量的可叠加性.     

关于生成树数目公式的概率求法

本文通过一个数学模型，采用古典概率的方法给出了k_n生成树数目的计算公式。     

Distribution of the inter and intra inertia in conditional MCA

Conditional Multiple Correspondence Analysis (MCA), where a control variable plays the role of a partition model, allows us to decompose global inertia into between inertia and within inertia. The problem is to assess when the conditioning variable gives different results with respect to the unconditional analysis. In this paper, we study the asymptotic distribution function of these inertias, which can allow us to determine whether conditioning is significant. Some simulations were performed to corroborate the established results.     

我国市场机制条件下工资率与工作时间的关系

本文根据社会主义市场机制的特点，建立了一类描述社会总福利最大的模型，给出了所有消费者处于均衡状态时实现帕累托最优的条件．研究了当消费者处于均衡状态时，工资率与工作时间之间的变化关系．     

Parametric aggregation in ordered weighted averaging

Incorporating further information into the ordered weighted averaging (OWA) operator weights is investigated in this paper. We first prove that for a constant orness the minimax disparity model [13] has unique optimal solution while the modified minimax disparity model [16] has alternative optimal OWA weights. Multiple optimal solutions in modified minimax disparity model provide us opportunity to define a parametric aggregation OWA which gives flexibility to decision makers in the process of aggregation and selecting the best alternative. Finally, the usefulness of the proposed parametric aggregation method is illustrated with an application in metasearch engine.     

On the poset of conjugacy classes of subgroups of finite p-groups

This paper is a continuation of the paper “On the Deskins completions, theta completions and theta pairs for maximal subgroups ”.In the former paper, Zhao Yaoqing introduced the concept of θ-completions associated to a maximal subgroup of a finite group. The concept offers a convenience for us to study the completions introduced by Deskins and gives us a way to reveal the relationship between the concepts of completions and θ-pairs, the latter concept is introduced by Mukherjee and Bhattacharya. The present paper is devoted to discussing the π-solvability, π-supersolvability and π-nilpotency of a finite group by using the θ-completions. Moreover, a new proof on the Deskins conjecture concerning the supersolvability is included.     

Nonnegative non-redundant tensor decomposition

Nonnegative tensor decomposition allows us to analyze data in their ‘native’ form and to present results in the form of the sum of rank-1 tensors that does not nullify any parts of the factors. In this paper, we propose the geometrical structure of a basis vector frame for sum-of-rank-1 type decomposition of real-valued nonnegative tensors. The decomposition we propose reinterprets the orthogonality property of the singularvectors of matrices as a geometric constraint on the rank-1 matrix bases which leads to a geometrically constrained singularvector frame. Relaxing the orthogonality requirement, we developed a set of structured-bases that can be utilized to decompose any tensor into a similar constrained sum-of-rank-1 decomposition. The proposed approach is essentially a reparametrization and gives us an upper bound of the rank for tensors. At first, we describe the general case of tensor decomposition and then extend it to its nonnegative form. At the end of this paper, we show numerical results which conform to the proposed tensor model and utilize it for nonnegative data decomposition.     

投资环境地区横向比较

本文以各地区经济数据为基础,通过建立统计分析的数学模型,给出了度量、评测投资环境的客观标准,并应该标准对几个典型城投资环境进行评价和分类,从一个新的角度,就客观、定量的研究投资环境进行了探索.     

Estimating copula densities, using model selection techniques

Recently a new way of modeling dependence has been introduced considering a sequence of parametric copula models, covering more and more dependency aspects and thus giving a closer approximation to the true copula density. The method uses contamination families based on Legendre polynomials. It has been shown that in general after a few steps accurate approximations are obtained. In this paper selection of the adequate number of steps is considered, and estimation of the unknown parameters within the chosen contamination family is established, thus obtaining an estimator of the unknown copula density. There should be a balance between the complexity of the model and the number of parameters to be estimated. High complexity gives a low model error, but a large stochastic or estimation error, while a very simple model gives a small stochastic error, but a large model error. Techniques from model selection are applied, thus letting the data tell us which aspects are important enough to capture into the model. Natural and simple estimators of the involved Fourier coefficients complete the procedure. Theoretical results show that the expected quadratic error is reduced by the selection rule to the same order of magnitude as in a classical parametric problem. The method is applied on a real data set, illustrating that the new method describes the data set very well: the error involved in the classical Gaussian copula density is reduced with no fewer than 50%.