新冠肺炎疫情常态化背景下高职院校体育课教学现状调查与对策研究——以吉林省7所高职院校为例

新冠肺炎疫情常态化使高职院校的体育课教学面临新的挑战。研究组通过问卷调查线上体育课的教学情况,并根据调查结果提出新冠肺炎疫情常态化背景下高职院校体育课教学的对策。     

核技术应用企业研发骨干激励机制探究

核技术应用一般是指射线技术与射线装置在医疗、工农业、地质调查、研究与教育中的应用。我国核技术应用企业研发骨干激励存在着激励的创新导向不足、薪酬激励机制不合理、组织文化有欠缺等问题,需结合行业特点及核技术应用企业研发骨干激励的特殊要求,采取相应的激励对策,包括从核心能力出发研究激励问题、建立适于激励研发人员的能力工资制、注重职业发展激励、注重责任激励、建立激发创新活力的组织文化等。     

Non-parametric estimation of reciprocal coordinate subtangent for right censored dependent scheme

The concept of reciprocal coordinate subtangent (RCST) has been used as a useful tool to study the monotone behavior of a continuous density function and for characterizing probability distributions. In this paper, we propose a non-parametric estimator for RCST based on the censored dependent data. Asymptotic properties of the estimator are established under suitable regularity conditions. A simulation study is carried out to examine the performance of the estimator. The usefulness of the estimator is also examined through a real data.     

丑角、看客与孤独者的悲喜剧 ——鲁迅与穆时英笔下的丑角形象之比较

鲁迅与穆时英笔下都塑造过一系列具有丑角特质的形象。不同的是,穆时英笔下的丑角具有自觉性,以小丑身份为面具,掩盖自己的孤独与恐惧；鲁迅笔下的丑角多是非自觉的,他们认真地践行着自己的人生理念,却因其与周遭环境格格不入,被人当作小丑、疯子。从中折射出两位作家迥异的生命观,穆时英从个体角度出发,写出了都市人在悲哀的脸上戴了快乐面具的无奈,对丑角表演持拥抱态度；鲁迅则从批判国民性的宏观角度出发,敏锐地意识到了丑角表演背后的隐忧,对其持拒斥的姿态。     

双循环新发展格局下中国双向直接投资研究

构建以国内大循环为主体、国内国际双循环相互促进的新发展格局,是对未来中国经济发展路径做出的重大战略部署,作为连接国内国际双循环的纽带,双向直接投资的发展战略和路径面临重塑和调整。研究认为,应当以推动新时代科技创新和产业升级为目标,提高双向直接投资的质量,畅通供应链和产业链; 以“一带一路”倡议为依托,通过更高水平的双向投资带动中国与沿线国家贸易水平的提高; 建设高水平自贸区和自贸港,提高对外开放标准,更高质量吸引外资,提升参与国际大循环的技术含量和自主性; 在畅通要素流动领域,建立更加完善的现代化物流体系,建设更高标准的要素流通市场体系,降低制度性交易费用,提高生产率和对高质量外资的吸引能力。     

Does geographical proximity matter in determining the profitability of banks?

Despite the existence of a burgeoning literature on bank profitability, yet, none of them gave due consideration to geographical proximity. I fulfill such a gap by analyzing the effects of COVID-19 on the profitability of top-rated banks. Findings confirm the prevalence of spatial dependence at both the global and sub-global with feedback effects being systematically higher than spillover effects. My study uncovers evidence of a COVID-19 induced decline in asset utilization. Findings advocate sharing economy as a potential tool to banks in combating any future pandemic risk with regionalized approach to supervision being deemed better than its globalized counterpart.     

疫情防控期间大学生焦虑水平及其影响因素研究

为了解大学生在新型冠状病毒肺炎疫情防控期间的焦虑状况，采用汉密顿焦虑量表（HAMA），对四川、云南、重庆三省（直辖市）的3 178名大学生进行在线问卷测试。结果发现：新冠肺炎疫情防控期间大学生焦虑水平高于常模，并且与大学生的性别、就读学历层次、就读学科、每日上网时间等存在显著相关性。其中，28.48%的大学生还可能有严重焦虑；女性、专科层次、尚不清楚就读学科的大学生群体焦虑水平更为明显；精神性焦虑是新冠肺炎疫情防控期间大学生焦虑的主要特质；每日上网时间与大学生精神性焦虑水平呈显著正相关。在公共卫生事件突发时期，应积极关注大学生的心理健康状态，采取针对性措施加以有效疏导和干预，促进其心理健康发展。     

Recognizing Structural Nonidentifiability: When Experiments Do Not Provide Information About Important Parameters and Misleading Models Can Still Have Great Fit

In the quest to model various phenomena, the foundational importance of parameter identifiability to sound statistical modeling may be less well appreciated than goodness of fit. Identifiability concerns the quality of objective information in data to facilitate estimation of a parameter, while nonidentifiability means there are parameters in a model about which the data provide little or no information. In purely empirical models where parsimonious good fit is the chief concern, nonidentifiability (or parameter redundancy) implies overparameterization of the model. In contrast, nonidentifiability implies underinformativeness of available data in mechanistically derived models where parameters are interpreted as having strong practical meaning. This study explores illustrative examples of structural nonidentifiability and its implications using mechanistically derived models (for repeated presence/absence analyses and dose–response of Escherichia coli O157:H7 and norovirus) drawn from quantitative microbial risk assessment. Following algebraic proof of nonidentifiability in these examples, profile likelihood analysis and Bayesian Markov Chain Monte Carlo with uniform priors are illustrated as tools to help detect model parameters that are not strongly identifiable. It is shown that identifiability should be considered during experimental design and ethics approval to ensure generated data can yield strong objective information about all mechanistic parameters of interest. When Bayesian methods are applied to a nonidentifiable model, the subjective prior effectively fabricates information about any parameters about which the data carry no objective information. Finally, structural nonidentifiability can lead to spurious models that fit data well but can yield severely flawed inferences and predictions when they are interpreted or used inappropriately.