隐变量交互效应分析方法的比较与评价

本文通过一个实例,比较了分析隐变量交互效应的主要方法,包括用隐变量的因子得分做回归分析、分组线性结构方程模型分析、加入乘积项的结构方程模型分析和两步最小二乘回归分析,并评价了这些方法的优缺点。     

非交换非结合的多复变

多复变在非交换非结合领域的推广近年来取得了迅猛的发展.本文简单介绍这方面的最新进展,其中包括切片Clifford分析、离散Clifford分析、Hermitian Clifford分析、Dunkl Clifford分析、四元数分析、八元数分析,离散复分析在统计物理中Ising模型的应用,以及与切片Clifford分析相关的S-谱理论在量子物理的应用.     

中国建筑产业竞争力形成机理分析——基于PLS结构方程模型的实证研究

在对产业竞争力研究现状简单分析的基础上,述评了产业竞争力经典分析范式,提出了一个新的产业竞争力分析框架。在此基础上借鉴因果思想,总结建筑产业竞争力影响因素和度量指标,建立了中国建筑产业竞争力形成机理概念模型;采用PLS结构方程模型对中国2006、2007年各省份(除西藏外)建筑产业的60组基本数据进行了实证分析,验证了模型合理性。研究结果显示区域环境、生产要素、产业组织三个潜变量对当地建筑产业竞争力产生直接的影响作用。最后,进行了不足分析与研究展望。     

微生物组学的大数据研究

微生物组学大数据在生态环境、人类健康和疾病研究方面都起到了重要作用。通过数学、统计等数据挖掘方法,从高维复杂数据中提取有用信息,是微生物组学大数据建模和分析的关键问题。本文分析了微生物组学大数据的特点,对当前数据分析和计算研究中存在的热点和难点进行了探讨分析,并综述了当前微生物组学大数据模式挖掘、网络重建与分析的研究概况。     

基于物元分析的科技人才考评研究

利用可拓学中的物元分析理论,对科技人才考评问题进行了研究.在分析多种相关因素的基础上,从基本素质、学术素质、业务素质和业绩贡献等方面构建了科技人才考评指标体系.对考评指标实行规范化、科学化、定量化的基础上,建立了科技人才考评的物元分析模型.最后应用结果表明,把物元分析理论应用于科技人才考评是可行的,同时,物元分析拓展科技人才考评的研究途径.     

中国房地产网络舆情分析

随着互联网技术的运用,基于文本挖掘的网络舆情分析,近年来也越来越受到学术界相关人士的关注。本文结合词云、关联规则、文本倾向性分析等数据挖掘与可视化技术,对2015年第二季度中国房地产网络舆情进行了实证研究与分析,在论证相关方法可行性的基础上,构建了网络舆情分析的基本框架。基于分析本文发现了该季度热点话题的分布与关联,并对各个话题的总体倾向性水平进行了评述,给出了相关政策建议。     

一道新定义期末考试题的解法及推广

北京市海淀区2022届高三上学期期末考试压轴题是一道新定义试题,体现了北京高考压轴题考查数学核心素养的特点,重思维轻运算,解题方法灵活,可以从极端分析、反证法、整体分析算两次、分块分析等多种角度进行突破,本文给出了这道题目的四种方法,并将其做了进一步的推广.     

什么是随机动力系统

微生物组学大数据在生态环境、人类健康和疾病研究方面都起到了重要作用。通过数学、统计等数据挖掘方法,从高维复杂数据中提取有用信息,是微生物组学大数据建模和分析的关键问题。本文分析了微生物组学大数据的特点,对当前数据分析和计算研究中存在的热点和难点进行了探讨分析,并综述了当前微生物组学大数据模式挖掘、网络重建与分析的研究概况。     

数值分析实践教学实验设计

简述了数值分析课程实验设计的总体思路,设计了三个新的探索性实验,包括递归方程拟合、非光滑函数插值和奇异积分计算.这些实验均从最简单的情形出发,通过观察、比较、分析和联想得出了新的结论,有助于培养学生的创新思维能力,为更好地开展数值分析实践教学提供指导.     

影响我国人寿保险保费收入的因素实证分析

本文对影响我国人寿保险需求的因素进行了分析,并建立了非线性回归模型;同时,采用数学软件MATLAB对此模型进行了分析、改进、检验以及预测.     

Financial Risk Modelling and Econometric Inference

In finance, the explicit modelling of uncertainty takes on a particularly important role. The values of financial derivatives increase in the return volatility of the underlying security. This notion requires a concept of volatility and hence uncertainty. In addition, the choice between modelling in discrete and continuous time is not arbitrary, since it corresponds to a distinction between incomplete and complete markets, respectively, and this distinction matters for asset pricing, financial risk modelling, and inference. Risk and volatility are closely connected, and implied volatility, volatility forecasting, volatility in term structure models, stochastic volatility, and portfolio analysis are considered and related to a more general interplay between cross-sectional and dynamic aspects in finance. Stocks, bonds, and options are considered and placed in the context of efficiency and separation in inference.     

环境治理政策的减排、增效与结构优化效果动态评价

为测度环境治理政策波动对产出、减排、要素配置、消费与产业结构的影响及其传导机制,将异质性消费偏好、产出差异性、环境效用和环境损失函数引入E-DSGE模型。政策强度分析发现,技术进步、排污税和消费税政策效果较强,环境控制和治污支出冲击效果较弱。政策效应分析发现,技术进步政策效应最优,能实现增产和减排双重目标,促进要素配置和居民消费、产出与消费结构改进;排污税和政府治污支出政策次优,能实现增产和减排的双赢目标,促进要素供给和产出结构清洁化,不利于消费提升和消费结构优化;环境控制与消费税政策效果最差,以牺牲产出实现减排,不利于要素配置,产出与消费结构改进效果较弱,消费税会抑制居民消费,但消费税政策效果随环境友好型家庭比例提升而加强。     

On effectiveness of wiretap programs in mapping social networks

Snowball sampling methods are known to be a biased toward highly connected actors and consequently produce core-periphery networks when these may not necessarily be present. This leads to a biased perception of the underlying network which can have negative policy consequences, as in the identification of terrorist networks. When snowball sampling is used, the potential overload of the information collection system is a distinct problem due to the exponential growth of the number of suspects to be monitored. In this paper, we focus on evaluating the effectiveness of a wiretapping program in terms of its ability to map the rapidly evolving networks within a covert organization. By running a series of simulation-based experiments, we are able to evaluate a broad spectrum of information gathering regimes based on a consistent set of criteria. We conclude by proposing a set of information gathering programs that achieve higher effectiveness then snowball sampling, and at a lower cost. Maksim Tsvetovat is an Assistant Professor at the Center for Social Complexity and department of Public and International Affairs at George Mason University, Fairfax, VA. He received his Ph.D. from the Computation, Organizations and Society program in the School of Computer Science, Carnegie Mellon University. His dissertation was centered on use of artificial intelligence techniques such as planning and semantic reasoning as a means of studying behavior and evolution of complex social networks, such as these of terrorist organizations. He received a Master of Science degree from University of Minnesota with a specialization in Artificial Intelligence and design of Multi-Agent Systems, and has also extensively studied organization theory and social science research methods. His research is centered on building high-fidelity simulations of social and organizational systems using concepts from distributed artificial intelligence and multi-agent systems. Other projects focus on social network analysis for mapping of internal corporate networks or study of covert and terrorist orgnaizations. Maksim’s vita and publications can be found on Kathleen M. Carley is a professor in the School of Computer Science at Carnegie Mellon University and the director of the center for Compuational Analysis of Social and Organizational Systems (CASOS) which has over 25 members, both students and research staff. Her research combines cognitive science, social networks and computer science to address complex social and organizational problems. Her specific research areas are dynamic network analysis, computational social and organization theory, adaptation and evolution, text mining, and the impact of telecommunication technologies and policy on communication, information diffusion, disease contagion and response within and among groups particularly in disaster or crisis situations. She and her lab have developed infrastructure tools for analyzing large scale dynamic networks and various multi-agent simulation systems. The infrastructure tools include ORA, a statistical toolkit for analyzing and visualizing multi-dimensional networks. ORA results are organized into reports that meet various needs such as the management report, the mental model report, and the intelligence report. Another tool is AutoMap, a text-mining systems for extracting semantic networks from texts and then cross-classifying them using an organizational ontology into the underlying social, knowledge, resource and task networks. Her simulation models meld multi-agent technology with network dynamics and empirical data. Three of the large-scale multi-agent network models she and the CASOS group have developed in the counter-terrorism area are: BioWar a city-scale dynamic-network agent-based model for understanding the spread of disease and illness due to natural epidemics, chemical spills, and weaponized biological attacks; DyNet a model of the change in covert networks, naturally and in response to attacks, under varying levels of information uncertainty; and RTE a model for examining state failure and the escalation of conflict at the city, state, nation, and international as changes occur within and among red, blue, and green forces. She is the founding co-editor with Al. Wallace of the journal Computational Organization Theory and has co-edited several books and written over 100 articles in the computational organizations and dynamic network area. Her publications can be found at: http://www.casos.cs.cmu.edu/bios/carley/publications.php     

Uniform random number generation

In typical stochastic simulations, randomness is produced by generating a sequence of independent uniform variates (usually real-valued between 0 and 1, or integer-valued in some interval) and transforming them in an appropriate way. In this paper, we examine practical ways of generating (deterministic approximations to) such uniform variates on a computer. We compare them in terms of ease of implementation, efficiency, theoretical support, and statistical robustness. We look in particular at several classes of generators, such as linear congruential, multiple recursive, digital multistep, Tausworthe, lagged-Fibonacci, generalized feedback shift register, matrix, linear congruential over fields of formal series, and combined generators, and show how all of them can be analyzed in terms of their lattice structure. We also mention other classes of generators, like non-linear generators, discuss other kinds of theoretical and empirical statistical tests, and give a bibliographic survey of recent papers on the subject.     

基于储量结构变化的油田开发指标预测与优化数学模型研究

大型油田开发过程中,随着不同类型新储量的不断投入,地质储量结构发生变化,表征油田整体开发技术与经济效果的关键性指标,如产量、可采储量、采收率、储采比、开发成本与操作成本等等也随之改变,其变化规律可用一套数学模型来描述。在开发指标预测基础上,运用多属性决策方法如TOPSIS,可以实现新投产区块组合方案的优选。应用实例表明,本文方法实用、操作性强,可以为油区开发决策提供重要方法与手段。     

乡村绿色生态旅游适宜等级VIKOR评估方法

针对乡村绿色生态旅游特征,并考虑旅游者个体感受与群体效益,本文从乡村绿色生态旅游开发与适宜评级概念出发,提出乡村绿色生态旅游适宜等级VIKOR评估模型与方法。首先,从等级评估的概念与内涵出发,刻画乡村绿色生态旅游适宜评级问题,避免混淆旅游适宜评级和排序两个本质不同的问题。然后,构建乡村绿色生态旅游适宜等级评估指标及隶属函数,提出评估指标的熵权确定方法。其次,提出乡村绿色生态旅游适宜等级VIKOR评估模型与二元语义等级评估方法。最后,通过案例研究和比较分析,说明本文所提模型与方法的合理性、可行性与实用性。     

运筹学在整车物流智能调度决策支持系统中的研究与应用

本文基于整车物流智能调度决策支持系统的研发、实施与运维的成功应用,论述运筹学在智能化上的应用路径以及实践驱动的学术路径。该系统是国内较早在汽车物流企业实现落地的智能化调度系统,其所形成的思想理论与方法技术揭示了运筹学在智能化应用上的核心价值,以及实践驱动的学术价值,对解决“卡脖子”难题提供示范性思路。本文提出运筹学在智能化研发上“三环七步”的整体研发框架。首先,分析智能化需求的运筹学特征,详细介绍汽车整车物流的发展趋势、瓶颈及智能调度需求;其次,论述运筹学系统模型的作用与建模方法,分析汽车整车物流系统模型的决策要素、目标及约束,提出汽车整车物流智能调度的运筹学应用问题。然后,提出“模式装箱”的新装箱理论问题,明确问题的计算难解性、可解性及核心科学特征。进一步,建立汽车整车物流调度应用问题与科学问题的混合整数线性规划模型;提出求解汽车整车物流调度问题的分支定界算法,以及大规模问题求解的时空分解及滚动求解方法与技术;提出面向运筹应用的生产测试及压力测试方法,给出汽车整车物流调度的测试分析的流程与结果。此外,提出深度集成整车运输管理系统与仓库管理系统、优化算法引擎驱动的分布式、多视图、多系统融合的智能调度决策支持系统。最后,论述该系统在实施过程中的推广使用和运维情况,并对运筹学应用及实践驱动的科学研究进行总结与展望。     

“Do we teach subjects or students?” Analyzing science and mathematics teacher conversations about issues of equity in the classroom

Teachers involved in a Master's level course in diversity participated in virtual, synchronous, anonymized discussions around issues of ethnic and racial diversity, gender, and stereotypes that could impact their students’ participation in fields related to science, technology, engineering, and mathematics (STEM). Guided by theoretical frameworks from Social Cognitive Career Theory (SCCT) and Critical Race Theory (CRT), a convenience sample of 14 science and mathematics teachers participated in a series of virtual chats using open‐ended questioning and facilitated by two university instructors. Using conversation and critical discourse analyses, three primary themes emerged: understanding of issues related to stereotypes, encouragement of females and minorities to pursue careers in STEM, and the place for diversity discussions in science and mathematics classrooms. The teachers felt burdened by curricular and administrative constraints that inhibit their ability to participate in thought‐provoking critical conversations. The paper concludes with a discussion of ways teachers can assist in the STEM career identity development of their underrepresented females and students of color and calls for research that combines the key findings in SCCT and CRT to build confidence and capacity for teachers to effectively confront issues of racism, sexism, and stereotyping in science and mathematics classrooms.     

A review of the recent contribution of systems thinking to operational research and management science

The systems approach, or systems thinking, has been intimately connected with the development of OR and management science initially through the work of founders such as Churchman and Ackoff and latterly through innovations such as soft systems. In this paper we have undertaken a review of the contribution that systems thinking has been making more recently, especially to the practice of OR. Systems thinking is a discipline in its own right, with many theoretical and methodological developments, but it is also applicable to almost any problem area because of its generality, and so such a review must always be selective. We have looked at the literature from both a theoretical and an applications orientation. In the first part we consider the main systems theories and methodologies in terms of their recent developments and also their applications. This covers: the systems approach, complexity theory, cybernetics, system dynamics, soft OR and PSMs, critical systems and multimethodology. In the second part we review the main domains of application: strategy, information systems, organisations, production and operations, ecology and agriculture, and medicine and health. Our overall conclusion is that while systems may not be well established institutionally, in terms of academic departments, it is incredibly healthy in terms of the quantity and variety of its applications.