EQUILIBRIUM ALGORITHMS WITH NONMONOTONE LINE SEARCH TECHNIQUE FOR SOLVING THE TRAFFIC ASSIGNMENT PROBLEMS

This paper presents a unified bination algorithms （such as FrankWolfe problems. Global convergence results are framework of the nonmonotone convex comAlgorithm） for solving the traffic assignment established under mild conditions. The line search procedure used in our algorithm includes the nonmonotone Armijo rule, the non- monotone Goldstein rule and the nonmonotone Wolfe rule as special cases. So, the new algorithm can be viewed as a generalization of the regular convex combination algorithm.     

可变分区存储管理方式的模拟

论述了如何用C语言模拟可变分区存储管理方式的过程。形象地展示了操作系统中可变分区存储管理中采用的最佳适应算法的分配与回收过程，达到了理论与实践相联系的目的。     

Seismic 3D Prestack Time Migration on Parallel Computers

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系统结构模型分部的底层要素法

本文从系统的底层要素出发，提出并证明了底层要素存在性、底层要素后果集并集的完全性以及它们在同一部分的充分必要条件，由此导出了系统结构模型分部的算法，实际应用进一步说明了这一方法的有效性。     

一类自适应松弛算法的算法模型

对于一类自适应松弛算法,本文提出了一个算法模型。在一些通常的假定下,证明了该模型生成的序列的收敛性定理。     

Using genetically-defined rodent strains for the identification of hippocampal traits relevant for two-way avoidance behavior: a non-invasive approach

Summary Genetically-defined rodent strains permit the identification of hippocampal traits which are of functional relevance for the performance of two-way avoidance behavior. This is exemplified here by analyzing the relationship between infrapyramidal mossy fibers (a tiny projection terminating upon the basal dendrites of hippocampal pyramidal neurons) and two-way avoidance learning in about 800 animals. The necessary steps include 1) identification of structural traits sensitive to selective breeding for extremes in two-way avoidance, 2) testing the robustness of the associations found by studying individual and genetical correlations between hippocampal traits and behavior, 3) establishing causal relationships by Mendelian crossing of strains with extreme structural traits and studying the behavioral consequences of such structural randomization, 4) confirming causal relationships by manipulating the structural variable in inbred (isogenic) strains, thereby eliminating the possibility of genetic linkage, and 5) ruling out the possibility of spurious associations by studying the correlations between the hippocampal trait and other behaviors known to depend on hippocampal functioning.In comparison with the classical lesion approach for identifying relationships between brain and behavior, the present procedure appears to be superior in two aspects: it is non-invasive, and it focuses automatically on those brain traits which are used by natural selection to shape behaviorally-defined animal populations, i.e., it reveals the natural regulators of behavior.     

The Young-Householder algorithm and the least squares multidimensional scaling of squared distances

It is shown that replacement of the zero diagonal elements of the symmetric data matrix of approximate squared distances by certain other quantities in the Young-Householder algorithm will yield a least squares fit to squared distances instead of to scalar products. Iterative algorithms for obtaining these replacement diagonal elements are described and relationships with the ELEGANT algorithm (de Leeuw 1975; Takane 1977) are discussed. In large residual situations a penalty function approach, motivated by the ELEGANT algorithm, is adopted. Empirical comparisons of the algorithms are given.An early version of this paper was presented at the Multidimensional Data Analysis Workshop, Pembroke College, Cambridge, July 1985. I want to thank Jan de Leeuw and Yoshio Takane for bringing the ELEGANT algorithm to my attention and for clarifying its rationale and notation. My thanks go also to Stephen du Toit for help with the ALSCAL computations reported in Section 7.     

基于SSA-LSTM-GF的混凝土坝变形预测方法

针对混凝土坝变形分析预测的复杂性,应用相空间重构思想和融合建模理念,提出了一种基于SSA-LSTM-GF的混凝土坝变形分析预测方法。SSA-LSTM-GF方法利用奇异谱分析法(SSA)将变形实测数据序列分解为趋势分量、周期分量和剩余分量,并将剩余分量视为噪声分量予以剔除;采用长短期记忆神经网络(LSTM)模型和高斯拟合(GF)算法分别进行周期分量和趋势分量的分析预测,并将二者结果进行叠加重构,得到最终预测结果。实例验证结果表明,SSA可以达到较好的数据分解和消噪效果,LSTM模型针对周期分量的预测性能优越,GF算法能够很好地实现趋势分量的拟合预测和部分信息的挖掘提取,LSTM模型和GF算法的成果重构效果良好,SSA-LSTM-GF方法具有一定的可行性和应用价值。     

南京椴群体遗传多样性和遗传结构分析

【目的】 南京椴(Tilia miqueliana)为江苏省重要的乡土树种,野外资源稀缺。南京椴野外群体遗传多样性和遗传结构的探索,可为资源保护、品种选育及遗传改良提供依据。【方法】 以南京椴5个天然群体[江苏宝华山(P1)、牛首山(P2)、安徽皇藏峪(P3)、安徽蜀山(P4)和浙江天台山(P5)]93个体为实验材料,选用15对多态性EST-SSR引物,进行遗传多样性及群体遗传结构分析。【结果】 ①用15对引物共检测等位基因数(A)总和为96,平均值为6.4,四倍体基因型(G)和四倍体特异基因型(G_i)总和分别为441和251,特异基因型比率(R₁)和种质鉴别率(R₂)均值分别为45.73%和17.99%。②在5个群体中,每个位点等位基因数(A_loc)和四倍体基因型丰富度均值(G_loc)分别为5.50±2.43和9.41±4.29;平均观测杂合度(H_o)和平均期望杂合度(H_e)为0.61±1.43和0.62±0.14。参考各群体G_loc和H_e值,确定遗传多样性较高的群体为P1和P3。③群体间遗传分化较小,遗传分化系数(G_st)仅为0.030;AMOVA分子变异分析显示,群体多样性水平变异来自于群体内(96%)。④聚类和遗传结构Structure分析显示,5个群体可划分为2组(组1包括P1、P2和P5;组2包括P3和P4)。Mental检验结果表明遗传距离与地理距离之间无显著相关。【结论】 南京椴群体均具有丰富的遗传多样性,其中宝华山群体和皇藏峪群体多样性较高,群体扩张可能是以这两个种群为中心,经人类活动迁移至其他区域。但南京椴群体间未形成明显分化,主要是由于植株寿命长,群体缺乏自然更新,加之群体间存在人为种子传播。因此,本研究提出通过建立隔离区,明确优先保护群体、加大植株异交,并采用人工繁育及种质回迁的方式保护南京椴野外群体。     

两地点8年生柏木生长性状家系变异及选择

【目的】揭示柏木生长性状的遗传变异规律,为高阶遗传改良筛选优良种质,选择优良家系,及其区域推广奠定基础。【方法】以营建在浙江省开化县林场(KH)和湖北省太子山林场(TZS)两个试验点的8年生柏木优树子代家系为材料,分析其生长性状的遗传变异,估算其育种值、遗传力等参数。【结果】柏木生长性状(树高、胸径和材积)的遗传变异系数为3.08%～13.93%,其中材积的变异最大。子代家系的树高、胸径和材积在地点间差异显著,TZS点的单株材积高于KH点315.79%,且家系与地点间的交互效应显著。胸径、树高和材积具有显著的家系效应,KH和TZS试验点的家系遗传力变幅分别为0.42～0.61和0.58～0.74。KH和TZS试验点分别以单株材积育种值大于CK的10%和45%为标准,各选出6个优良家系,现实增益分别为33.51%和67.95%。利用独立淘汰法,在KH点和TZS点各筛选优良单株17和12株,材积的遗传增益分别为41.38%和40.92%。【结论】8年生柏木优树子代家系生长性状具有较大的遗传改良潜力,但家系与环境的互作效应显著;通过子代家系的遗传评价有助于种子园建园亲本的再选择和现有种子园的...