癫痫的诊治与人文思想

癫痫是神经科常见的疾病之一,对于患者的学习、生活和工作均有不同程度影响。它的发病机制尚不十分清楚,主要是以药物治疗为主。在癫痫的诊断中要根据病情和患者的实际情况选择合适的检查;在癫痫的治疗中须按照最优化的医疗原则选择最佳的治疗方案。在治疗中要注意结合人文思想来提高癫痫的治疗效果。     

动态文本最优化呈现的眼动研究

采用2（呈现方式：平滑滚动引导式、RSVP）×5（呈现速度：120、240、360、480、600字/分）×3（窗口大小：5、10、15字）的多因素混合实验设计,运用EyelinkⅡ眼动仪对汉语动态文本阅读过程进行研究。结果发现,呈现速度较快（≥360字/分）时,与RSVP条件相比,被试在平滑滚动引导式条件下的平均注视时间短、注视次数多、眼跳距离小,阅读理解率高,且被试更偏爱此种呈现方式;呈现速度较慢（≤240字/分）时,两种呈现方式在平均注视时间和注视次数上没有差异。在平滑滚动引导式下,被试在三种窗口大小中各项指标上没有差异;在RSVP条件下,与5字窗口相比,大窗口（10字或15字）条件下平均注视时间更短。     

Undecidable Decisions: Rationality Limits and Decision-making Heuristics

In this article the theoretic evolution and the empirical-experimental efforts that have led to the affirmation of the bounded/procedural rationality paradigm are discussed. Moreover, the debate on supporters of the “optimization” approach and supporters of the “bounded/procedural rationality” approach is traced, highlighting the irreconcilability of these two approaches and, in retort, a solid defense against a merely “reductionist” attempt of the innovative context of the Simonian theory. Critically going over the debate on decision dynamics, it becomes clear how, due to the uncertain nature of rational processes, it is impossible to establish the decision-making best way. The imperfect character of individual choice is explained by how the decider identifies a solution that appears satisfying in that moment due to cognitive and temporal limits.     

Trends in optimization models of sales force management

In the last half-century, significant advances have been made in directing sales force behavior with the use of optimization and decision models. The present paper both presents the current state-of-the art in sales force decision modeling, and also discusses key issues and trends in contemporary modeling of relevance to sales force researchers. The paper begins by exploring critical concepts regarding the estimation of the sales response function, and then discusses critical problems of endogeneity, heterogeneity, and temporal variation that are faced by modelers in this task. Modern approaches to dealing with these issues are presented. We then discuss areas of importance concerning finding model solutions, including closed form versus simulation, and optimization versus heuristic solutions. The paper next moves to areas of practical importance where models can help, including call planning, sales force size, territory allocation, and compensation design. Finally, we discuss trends that will likely impact on sales force modeling in coming years, including the use of big data and data mining, the possible breakdown of rationality, the rise of the Internet and social media, and the potential of agent-based modeling.     

A New VNS Metaheuristic Using MADS as a Local Optimizer

We propose a new hybrid metaheuristic VNSMADS that is an implementation of the Variable Neighbourhood Search (VNS) algorithm with the Mesh Adaptive Direct Search (MADS) as the local search. Because MADS is a Derivative‐Free Optimization method, the targeted problems may be non‐smooth on black box functions possessing several local optima. Constraints other than bounds on the variables are not considered. VNSMADS is applied on a real‐world problem from Finance: the portfolio selection problem using the investor preferences. Numerical results show the efficiency of our approach. Copyright © 2012 John Wiley & Sons, Ltd.     

Government accounting optimization based on computational linguistics

The level of moral development and moral intensity in cognitive psychology will not only affect the ethical behavior of accountants, but also have a direct impact on the quality and level of accounting work. Therefore, in this paper, the ethical behavior of accountants was analyzed from the perspective of cognitive psychology. Computer-aided data mining techniques were introduced, and government accounting risk assessment management of financial accountants was studied. In this paper, the principle of cognitive psychology to measure the ethical level of accountants was first described. The predicament of moral judgments was analyzed and an optimization plan to improve the ethical intention of accountants was proposed. Support Vector Machine classification technology in data mining was studied to explore how to conduct effective and reliable evaluation, so as to provide a scientific basis for decision-making in improving accounting management. After the simulation experiment, it is proved that continuously improving the ethical standards of accountants and strengthening the forecast of accounting risks can continue to optimize the accounting office management.     

Enhancing Interpretability in Factor Analysis by Means of Mathematical Optimization

Abstract

Exploratory Factor Analysis (EFA) is a widely used statistical technique to discover the structure of latent unobserved variables, called factors, from a set of observed variables. EFA exploits the property of rotation invariance of the factor model to enhance factors’ interpretability by building a sparse loading matrix. In this paper, we propose an optimization-based procedure to give meaning to the factors arising in EFA by means of an additional set of variables, called explanatory variables, which may include in particular the set of observed variables. A goodness-of-fit criterion is introduced which quantifies the quality of the interpretation given this way. Our methodology also exploits the rotational invariance of EFA to obtain the best orthogonal rotation of the factors, in terms of the goodness-of-fit, but making them match to some of the explanatory variables, thus going beyond traditional rotation methods. Therefore, our approach allows the analyst to interpret the factors not only in terms of the observed variables, but in terms of a broader set of variables. Our experimental results demonstrate how our approach enhances interpretability in EFA, first in an empirical dataset, concerning volumes of reservoirs in California, and second in a synthetic data example.     

Some Remarks on Dualization in Vector Optimization

In recent years there have been several reports on duality in vector optimization. However, there seems to be no unified approach to dualization. In a previous paper by the author a geometric consideration was given to duality in non-linear vector optimization. In this paper a relationship between duality, stability (normality) and condition of the alternative will be reported on the basis of some geometric consideration. In addition, Iserman's duality in linear cases will be derived from the stated geometric approach.     

Group decision as a twice multicriteria optimum problem

This paper studies the problem of aggregation of preferences, preserving the multicriteria nature of the individual decision process. The resulting group decision problem is in effect a twice multicriteria optimum problem. We give a structural theory for the efficient set of this optimum problem. Moreover, on the basis of this theory we study the relationships between optimality, consensus and evaluation. We confirm the pathological nature of aggregation of preferences, but in a new setting and with a different meaning.     

异步异构分层并行演化算法及其在串并系统可靠性优化中的应用(英文)

本文提出了一种新的并行演化算法――异步异构分层并行演化算法(AHHPGA),用于求解串并系统可靠性分配的多目标优化问题(RAP)。AHHPGA采用分层结构,上层由粗粒度模型构成,下层由细粒度模型构成。在AHHPGA的每个子种群中,采用不同的全局/局部搜索度和拓扑结构,构成异构模型。AHHPGA的迁移方式包括异步接收和异步迁出。采用基于模型的复杂系统可靠性评价工具(HiP-HOPS),以克服可靠性框图方法(RBD)的缺点。仿真结果表明,基于异步异构分层并行演化算法和HiP-HOPS工具的串并系统可靠性分配的多目标优化方法,优于传统的基于遗传算法的优化方法。