A proposed methodology for the prediction of mental workload, based on engineering system parameters

In the design of engineering systems, mental workload is one of the most important factors in the allocation of cognitive tasks. Current methods of task allocation have criteria that are defined in only general terms and are thus not very useful in aiding detailed decision-making in system design. Whilst there are many quantitative criteria available to determine the physical space in human-machine interaction, system designers really require an explicit model and specific criteria for the following identification of the mental workload imposed by the system; prediction of both human and system performance; evaluation of the alternatives of system design; and the design of system components. It is argued that the available methods of workload or performance are either too domain-dependent to apply to the design of other systems, or subject-dependent and thus do not reflect the objective workload imposed by the system. The presented research adopts a new approach to cognitive task analysis in dynamic decision-making systems. Based on the characteristics derived from task analysis, a general conceptual model of the prediction of mental workload in system design is proposed. In the new model, workload is represented by a set of system parameters—task arrival rate, task complexity, task uncertainty, and performance requirements—which are considered to be the main sources of workload. In this context, workload becomes an objective demand of engineering systems, independent of any subjective factors. Whether an individual or population is overloaded depends upon their workload threshold with respect to the specified task and environment. It is hoped that this new model, after both laboratory and industrial validation, could be used by system designers to predict the workload imposed on people by systems.     

Aggressive Behaviors in Social Interaction and Developmental Adaptation: A Narrative Analysis of Interpersonal Conflicts During Early Adolescence

Using conflict narratives reported by adolescents in grade 7 (mean age = 13.4 years), this study investigated the interactional properties and developmental functions of four types of aggressive behaviors: social aggression, direct relational aggression, physical aggression, and verbal aggression. A total of 475 participants from the Carolina Longitudinal Study ( Cairns & Cairns, 1994 ) were included. Results showed that the majority of conflict interactions involved more than a dyad. The use of social aggression (e.g., concealed social attack) was associated with more individuals involved in the conflict. Social aggression was primarily reported as an initiating behavior for interpersonal conflicts, while direct relational aggression was a responding behavior. Medium to high levels of reciprocity were found for physical, verbal, and direct relational aggression, whereas a low level of reciprocity was reported for social aggression. School authorities were most likely to intervene in physical aggression. The use of social aggression was associated with higher network centrality among adolescents. Developmental maladjustment in late adolescence and early adulthood was primarily predicted by physical aggression.     

广告创意的逆向思维技巧

逆向思维是一种超常思维，是广告创意的基础思维之一。文章从逆向思维的内涵实质、常用技巧等角度探讨了逆向思维和广告创意的关系。认为：逆向思维就其实质是事物对立统一规律在思维上的反映，所以找准事物对立面并以此为基点展开构思，是逆向思维创意的基本思路。文章用丰富的案例阐述分析了反弹琵琶、有意示短、反向变体、逆向假设、倒置形象等从广告诉求主题相反方向开辟表现蹊径的反常技巧．指出：逆向思维和多向思维、横向思维在广告创意中是融合交叉、相辅相成的，应同样为广告人重视。     

重庆直辖市优势工业选择研究

分别运用层次分析模型和最优动态组合预测模型对重庆市工业结构进行多指标静态分析与单指标动态预测,科学地得出重庆市优势工业选择的结论。     

基于路径的多元标准回归模型及其应用

在多元回归模型建立的过程中,由于各个变量的测量单位不相同,多元回归模型无法正确地评估出各个分量对预测值直接的和间接的影响.为此,引入路径系数,将多元回归模型转化为多元标准回归模型,通过变量间的相关系数和多元标准回归模型,分析预测变量对响应变量的直接和间接影响.     

不能混淆同源字和通假字的界限──从两篇文章对两种文字现象的混淆谈起

2001年第10期《语文建设》的《“无须”和“无需”》主张胡须的“须”和遇雨止需不进的“需”是同源字,另一篇文章《说与》又主张有缺口环形玉的“”和器皿有缺口的“缺”是通假字。可是,考之于两对词的语源义,却发现前者的“胡须”义和遇雨不能前进的“止需”义,意义上并无联系,是典型的通假字,而后者两个字都有“有缺口”这一共同义素,是典型的同源词,这两篇文章恰恰把这两种文字现象的关系弄颠倒了。     

中国数字经济发展综合评价与预测

文章结合数字经济的内涵和外延,分别从数字基础设施、数字产业化、产业数字化等方面构建了数字经济发展水平量化指标体系,基于熵值法测度了中国2010—2018年数字经济发展趋势,并建立灰色预测模型对2019—2028年的数字经济发展走向进行预测。研究结果表明:2010—2018年,中国数字经济发展水平增长了4.4倍,年均增速达到50%,2019—2028年,中国数字经济发展水平增速将不断提高,预计2028年数字经济发展水平将是2018年的5倍。因此,我国要进一步推动新型数字经济基础设施建设,加快传统产业的数字化转型,积极发展电信业等数字产业,以保障我国数字经济快速发展。     

社会救援资源动员联盟伙伴选择

社会救援资源是应急救援工作的重要资源支撑,平时被企业、非政府组织和社会公众等各类社会主体所掌控。动员联盟则是各类主体所组建的特殊虚拟企业。动员联盟的特殊性表现为无准备期、联盟盟主往往多线程、弱经济性以及时效性和高动态性,其组织形式应为星型联邦模式。伙伴选择则是组建动员联盟的关键问题,直接关系到救援成效。社会救援资源动员联盟伙伴选择包括正序选择与逆序选择两种方式,每种方式都适合不同的条件,并有不同的步骤且各有其合理算法。     

基于LSTM神经网络的金融时间序列预测

本文提出将小波分析与纳入时间序列依赖特征的长短期记忆(LSTM)神经网络相结合,构建金融时间序列数据预测模型,以克服现有模型对金融时间序列数据非平稳、非线性、序列相关等复杂特征以及数据间非线性交互关系无法反映的缺陷。同时,以道琼斯工业指数日收盘价为例,探究LSTM神经网络对实际金融时间序列数据的预测能力,比较其与多层感知机、支持向量机、K近邻、GARCH四种模型的预测效果。实证结果表明LSTM神经网络具有更高的预测精度,能够有效预测金融时间序列数据的长短期动态变化趋势,说明了其对金融时间序列数据预测的适用性与有效性。此外,对金融时间序列数据进行小波分解与重构,可有效提高LSTM预测模型的泛化能力,以及对长短期动态趋势的预测精度。     

基于灰色系统建模技术的人体疾病早期预测预警研究

传统健康体检主要通过对单次体检指标进行横向比较和静态分析,忽略个体差异,纯粹从指标高低来判断体检者健康状况与身体状态,难以及时发现体检者可能存在的疾病隐患。由于个体体检指标具有样本量小、信息不确定、数据类型异构、影响因素构成复杂等特征,传统以大样本为基础的数学模型均难以适应此类小数据系统的建模要求。为此,通过建立适用于人体主要指标趋势预测的灰色系统模型(简称HIGM(1,1)模型),实现对人体主要健康指标的动态建模与趋势分析,从而可以掌握体检者未来一段时间身体指标的变化趋势及可能存在的疾病隐患。研究成果对提升体检效果、强化体检意义,促进灰色理论与现实问题的有效对接等方面,均具有积极意义。