我国儿童动物文学及其翻译研究

中外儿童文学充满了“动物”色彩，动物文学因其独特的内涵和异质的美学魅力成为了对少年儿童大自然教育和生命教育的特殊题材。通过论述动物文学与儿童文学交错重叠的关系，回顾了儿童动物文学的兴起，并从政治、社会、科技发展等方面分析其发展动力，综述我国儿童动物文学及其翻译研究较为匮乏的现状，挖掘其研究意义，肯定其对儿童成长的积极作用与影响，旨在为我国儿童文学翻译和儿童文学事业发展提供借鉴与新动力。     

应用型本科高校教学督导工作实践研究

教学质量是高校生存与发展的基本保障.对应用型本科高校教学来说,教学督导具有非常重要的作用,做好教学督导工作,能够使教学质量和教学水平大幅度提升,为现代社会培育出更多的优质人才.概述教学督导,明确教学督导的职能,分析应用型本科高校教学督导工作存在的问题,并提出具体的工作实践路径,可供高校参考.     

信息社会个人信息法律关系的构造

信息社会的本质是能够产生个人信息法律关系,信息流是解构个人信息法律关系的关键,也是个人信息法律关系产生的直接动力.个人信息法律关系的主流构造分别以信息自决、隐私和人格权益为基础,其中,国家利益成为个人信息法律关系构造中不可忽视的力量.三种构造模式分别体现出个人与企业,个人与国家,个人、企业和国家三者之间的利益平衡.由于利益平衡体现出的幸存者偏差效应,个人信息法律关系的构造以主体场域信息自治为中心,具有合理性和现实性.     

周人之“德”为“明德”论——兼论殷周之“德”的区别

"德"的原始观念来源于人们对"效能"和"能力"崇拜,是古代君王通过"巡省"方式树立自己的权威和影响力的反映。"德"字之初义就是"政治控驭能力和权威影响力",这种观念大概产生于部族联合体的尧舜禹时代,所以"德"不是周人的首创,周人只是提出了不同于商人的"明德"观念,以"帝迁明德"为自己的政治合法性依据。"德"作为政治控驭能力的代称包括"刚克"和"柔克"两种手段,作为"刚克"手段的刑罚也是原始"德"含义的一个方面。与周人的"明德"观念相一致,"明刑"、"明罚"思想也是周人"明德"观念的衍生思想。     

创业文化的道德哲学基础

创业文化做为属人的文化必须建立在生命道德基础之上,以生命为中心指导创业实践。而在生命的实践过程中,生命道德基础必然外显为实践道德,最终决定了做为其结果的功业道德。从而创业文化具有由生命道德、实践道德、功业道德三个不同层级构成的道德基础,而西方文化没有自己的生命道德基础,从而无法实现三个道德基础的贯通,只有回到中国传统文化的生命哲学,才能实现创业文化三个道德基础的贯通。     

国家级教学名师的优秀品质及其养成

通过对5位国家级教学名师的自述报告进行三级编码和主题凝练，研究发现国家级教学名师作为教师队伍的杰出代表，在其专业发展过程中养成了许多优秀品质。例如：教学方面注重实践、考虑周全，师德方面以德施教、追求卓越，科研方面以教带研、实现创新。国家级教学名师优秀品质的养成通常经历了漫长而曲折的过程，不仅需要精神与物质力量的共同推动，还需要家庭、学校和社会各环境要素的相互促进，以及社会取向养成方式和个人取向养成方式的互相配合。深入探究国家级教学名师的优秀品质及其养成之道，可以为教师专业发展提供借鉴与启示。     

Integrating Operational and Organizational Aspects in Interdependent Infrastructure Network Recovery

Managing risk in infrastructure systems implies dealing with interdependent physical networks and their relationships with the natural and societal contexts. Computational tools are often used to support operational decisions aimed at improving resilience, whereas economics‐related tools tend to be used to address broader societal and policy issues in infrastructure management. We propose an optimization‐based framework for infrastructure resilience analysis that incorporates organizational and socioeconomic aspects into operational problems, allowing to understand relationships between decisions at the policy level (e.g., regulation) and the technical level (e.g., optimal infrastructure restoration). We focus on three issues that arise when integrating such levels. First, optimal restoration strategies driven by financial and operational factors evolve differently compared to those driven by socioeconomic and humanitarian factors. Second, regulatory aspects have a significant impact on recovery dynamics (e.g., effective recovery is most challenging in societies with weak institutions and regulation, where individual interests may compromise societal well‐being). And third, the decision space (i.e., available actions) in postdisaster phases is strongly determined by predisaster decisions (e.g., resource allocation). The proposed optimization framework addresses these issues by using: (1) parametric analyses to test the influence of operational and socioeconomic factors on optimization outcomes, (2) regulatory constraints to model and assess the cost and benefit (for a variety of actors) of enforcing specific policy‐related conditions for the recovery process, and (3) sensitivity analyses to capture the effect of predisaster decisions on recovery. We illustrate our methodology with an example regarding the recovery of interdependent water, power, and gas networks in Shelby County, TN (USA), with exposure to natural hazards.     

Cross‐Sectional Psychological and Demographic Associations of Zika Knowledge and Conspiracy Beliefs Before and After Local Zika Transmission

Perceptions of infectious diseases are important predictors of whether people engage in disease‐specific preventive behaviors. Having accurate beliefs about a given infectious disease has been found to be a necessary condition for engaging in appropriate preventive behaviors during an infectious disease outbreak, while endorsing conspiracy beliefs can inhibit preventive behaviors. Despite their seemingly opposing natures, knowledge and conspiracy beliefs may share some of the same psychological motivations, including a relationship with perceived risk and self‐efficacy (i.e., control). The 2015–2016 Zika epidemic provided an opportunity to explore this. The current research provides some exploratory tests of this topic derived from two studies with similar measures, but different primary outcomes: one study that included knowledge of Zika as a key outcome and one that included conspiracy beliefs about Zika as a key outcome. Both studies involved cross‐sectional data collections that occurred during the same two periods of the Zika outbreak: one data collection prior to the first cases of local Zika transmission in the United States (March–May 2016) and one just after the first cases of local transmission (July–August). Using ordinal logistic and linear regression analyses of data from two time points in both studies, the authors show an increase in relationship strength between greater perceived risk and self‐efficacy with both increased knowledge and increased conspiracy beliefs after local Zika transmission in the United States. Although these results highlight that similar psychological motivations may lead to Zika knowledge and conspiracy beliefs, there was a divergence in demographic association.     

Towards Uniformly Efficient Trend Estimation Under Weak/Strong Correlation and Non‐stationary Volatility

In this paper, we consider the deterministic trend model where the error process is allowed to be weakly or strongly correlated and subject to non‐stationary volatility. Extant estimators of the trend coefficient are analysed. We find that under heteroskedasticity, the Cochrane–Orcutt‐type estimator (with some initial condition) could be less efficient than Ordinary Least Squares (OLS) when the process is highly persistent, whereas it is asymptotically equivalent to OLS when the process is less persistent. An efficient non‐parametrically weighted Cochrane–Orcutt‐type estimator is then proposed. The efficiency is uniform over weak or strong serial correlation and non‐stationary volatility of unknown form. The feasible estimator relies on non‐parametric estimation of the volatility function, and the asymptotic theory is provided. We use the data‐dependent smoothing bandwidth that can automatically adjust for the strength of non‐stationarity in volatilities. The implementation does not require pretesting persistence of the process or specification of non‐stationary volatility. Finite‐sample evaluation via simulations and an empirical application demonstrates the good performance of proposed estimators.