On portfolio management with value at risk and uncertain returns via an artificial neural network scheme

This paper focuses on the computation issue of portfolio optimization with scenario-based Value-at-Risk. The main idea is to replace the portfolio selection models with linear programming problems. According to the convex optimization theory and some concepts of ordinary differential equations, a neural network model for solving linear programming problems is presented. The equilibrium point of the proposed model is proved to be equivalent to the optimal solution of the original problem. It is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the portfolio selection problem with uncertain returns. Several illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper.     

Proposal of a driver profile classification in relation to risk level in overtaking maneuvers

Traffic crashes are a worldwide problem, and records have indicated frontal collisions have resulted in the most significant number of fatalities. Such a type of crash is frequently caused by improper overtaking of vehicles, which highlights the interference of human factors. Therefore, investigations on driver's risk perception are necessary. This study proposes a classification of driver's risk level through a decision tree using the Classification and Regression Tree (CART) algorithm from data collected from the overtaking maneuvers in a driving simulator. The model obtained by CART algorithm indicated young male drivers are more likely to take risks in overtaking maneuvers. The results were correlated with governmental records and similar studies. In addition, the results showed the potential of the tool for used as a risk level classifier, as well as the validation of the driving simulator in studies associated with human factor behaviours, accident analysis and investigation.     

基于脑结构像的精神分裂症机器学习分类

将机器学习应用于精神疾患的临床和基础研究是近年来的趋势。研究者将机器学习应用于精神分裂症患者及高危人群的T1加权像和弥散张量成像的脑影像数据中, 为了解疾病的生理病理学机制提供帮助。回顾以往研究发现额叶及颞叶的脑结构特征具有较高的区分能力, 行为数据和脑影像数据结合的分类效果优于单模态数据。现阶段研究存在样本量不足和泛化能力欠缺的局限, 未来研究应注意扩大样本量、制定标准化的分类方法, 从而进一步探究机器学习在精神疾患中的作用。     

累积生态风险对农村初中生攻击行为的影响：有调节的中介模型

本研究旨在探讨累积生态风险与农村初中生攻击行为之间的关系,以及道德推脱的中介作用与共情的调节作用。通过对845名农村初中生进行问卷调查,结果发现：（1）农村初中生累积生态风险、道德推脱与攻击行为两两之间均存在显著正相关;（2）道德推脱在累积生态风险与农村初中生攻击行为之间起部分中介作用;（3）共情调节道德推脱在累积生态风险与攻击行为之间的中介效应,相对于共情水平较高的农村初中生,中介效应在共情水平较低的农村初中生中更显著。     

早期累积家庭风险对青少年内化问题的影响：弹性双重机制

为了解早期累积家庭风险因素对青少年内化问题的影响及其心理机制,采用累积家庭风险问卷、个人成长主动性问卷、领悟社会支持问卷以及青少年内化问题问卷对780名青少年进行调查。结果表明：（1）累积家庭风险对青少年内化问题具有显著的正向预测作用;（2）累积家庭风险因素还通过个人成长主动性和领悟社会支持的中介作用影响青少年内化问题。研究结果有助于揭示累积家庭风险对青少年内化问题的影响机制,对预防以及减少青少年内化问题行为具有一定的启示作用。     

高技术的伦理风险及其应对

当代高技术的迅速崛起正日益改变世界,高技术的发展更增加了高技术伦理风险负效应的不确定性,既提供了更多的道德选择的可能性,又提出了严峻的挑战,进而使高技术的道德选择成为高技术活动主体不可回避的问题。为了在高技术的道德选择中尽可能减少高技术伦理风险负效应,就必须坚持必仁且智的价值取向、德得相通的运作方略、内圣外王的探究智慧,进而达到天人合一的澄明之境———使人-社会-自然系统协调发展。     

工作情景中员工风险认知研究

本研究是风险认知系列研究之一 ,目的是探讨和比较企业中员工及管理人员的风险认知状况 ,以及员工与管理人员认知上的异同。研究采用问卷设计的方法。结果表明 :1员工对 2 0个风险因素的风险程度认知均超过了中性水平 ,“企业效益”仍然被知觉为最高风险点 ;2风险因素空间结构分析 ,进一步证实了风险特征变量间稳定的关系 ,同时也说明本系列研究方法上的合理性 ;3各风险特征变量间的相关 ,反映了员工风险认知中的各种特征 ;4(本研究经过信度和效度检验均达到可接受水平。     

“风险”性质的探讨──一项联想测验

该实验是为探讨“风险”的性质而进行的词联想测验。实验以“风险”一词作为刺激词，让被试连续进行三次自由式联想。其结果得到了三大类不同性质的联想词及其它相关结论。     

Taking Care of Each Other: How Can We Increase Compliance with Personal Protective Measures During the COVID-19 Pandemic in Chile?

In the context of the COVID-19 pandemic, personal protective measures (e.g., social distancing, handwashing, and mask wearing) have been adopted as a cornerstone to limit the spread of the disease. Yet, the effectiveness of these measures depends on people's levels of adherence. In this article, we apply social-psychological research to the study of compliance with personal protective measures during the COVID-19 pandemic in Chile. We consider three possible models underlying adherence: (1) sociodemographic and socioeconomic factors, (2) instrumental factors, and (3) normative factors. We draw on data from a longitudinal nonrepresentative panel study (Study 1, n = 32,304) and a cross-sectional representative survey (Study 2, n = 1,078) to explore the impact of these different factors on personal protective measures compliance. Findings show the strongest support for the role of instrumental and normative factors, in that people who comply with protective measures report to a greater extent that relatives and friends comply too and tend to perceive high risk of COVID-19. We finish by proposing policy recommendations to promote effective strategies to contain the spread of the virus.     

累积情境风险与流动儿童主观幸福感的关系:个体保护因子的补偿效应和保护效应

对511名初二年级的流动儿童（平均年龄：14.37±0.78岁）及其家长进行问卷调查,考察累积情境风险与流动儿童主观幸福感的关系,及自尊、心理弹性和情绪调节等个体保护因子的作用方式差异及累积效应。结果发现：（1）累积情境风险负向预测流动儿童主观幸福感,各领域风险预测作用由大到小依次是家庭关系风险、同伴风险和学校风险,家庭社会经济地位风险预测作用不显著;（2）自尊在各领域风险与主观幸福感的关系中均发挥补偿效应;心理弹性在同伴、学校领域风险中发挥补偿效应,在家庭关系领域风险中发挥保护效应;情绪调节仅在同伴领域风险中发挥保护效应;（3）个体面临的风险数越多,具备的保护因子数越少,流动儿童主观幸福感越低。