2种侧围加强内框总成检具结构对比分析

概述了侧围加强内框总成与周边零件的匹配关系,从检具操作的人机工程出发,描述了侧围加强内框总成检具的2种定位方式和结构设计,对比了2种定位方式对于检测结果的影响,总结了其优点和弊端,结果表明侧围加强内框总成检具采用车内方向朝下的定位方式效果更好。     

疫情下电磁场理论课程的网络教学

本文主要总结了新冠疫情期间作者的电磁场理论课程在线教学经验。对比分析了录播和直播的优缺点后,选择录播教学方式。基于超星网络教学平台,展示了录播网络教学的具体措施,包括网上答疑和学习效果检查以及在线批改作业等。给出了网络教学可以为线下教学继续使用的方法和手段,为疫情结束后的正常教学提供了新的网络教学补充措施。     

纸浆浓度在线测量技术的发展

本文介绍了几种纸浆浓度在线测量技术的常用控制方式、测量原理及最新发展状况，并从技术成长角度探讨了该技术的制约因素及发展趋势。     

药用辅料对CYP3A活性的影响及其对仿制药质量和疗效一致性评价的指导作用

药物制剂的处方和工艺是保证药物质量和疗效的基础，药用辅料是药物制剂处方的重要组成部分。部分药用辅料可影响CYP3A的活性，继而可能影响其底物在体内的代谢和生物利用度。在中国和美国，相对生物利用度是仿制药研究的关键内容。我国正在推进仿制药质量和疗效一致性评价。因此，了解药用辅料对CYP3A的影响及其对一致性评价的指导作用具有十分重要的现实意义。     

快速灵敏的LC-MS/MS法测定人血浆中阿莫西林并应用于两种制剂的一致性评价

目的: 建立快速灵敏的LC-MS/MS法测定人血浆中阿莫西林浓度，并用于两种阿莫西林胶囊的一致性评价。方法: 采用岛津公司LCMS-8060型LC-MS/MS仪，以MRM模式测定阿莫西林（m/z 366.00/114.00）的浓度，d4-阿莫西林作内标（m/z 370.10/114.05），离子源为ESI源。色谱柱选用Waters ACQUITY BEH C18（2.1×50 mm，1.7 μm），梯度洗脱。血浆样本加入内标，经甲醇沉淀蛋白后取上清液进样检测。结果: 所建方法经验证，其线性、准确度、精密度、最低定量限、提取回收率、特异性、基质效应、稳定性等各项指标均符合CFDA的指导原则及最新核查标准要求，并较文献报道中的方法有处理简单、灵敏度高、色谱峰形好的优点。结论: 所建方法快速、灵敏，适用于人血浆中阿莫西林浓度的检测。用于一致性评价的样本实测，两种制剂生物等效。     

Safety performance functions incorporating design consistency variables

Highway design which ensures that successive elements are coordinated in such a way as to produce harmonious and homogeneous driver performances along the road is considered consistent and safe. On the other hand, an alignment which requires drivers to handle high speed gradients and does not meet drivers' expectancy is considered inconsistent and produces higher crash frequency.     

企业社会责任对跨国并购绩效的影响——基于沪深A股上市企业的实证研究

为了帮助我国企业在跨国并购交易中更好地应对因企业社会责任表现所引起的跨国并购风险，平衡好经济利益诉求与社会责任的履行，论文以2009—2017年沪深A股上市公司109起成功的跨国并购事件为研究对象，基于利益相关者理论、社会契约理论与委托代理理论，研究了企业社会责任对于跨国并购绩效的影响，同时考虑了企业所有制、行业一致性和跨国并购经验的调节作用。采用多元回归模型的实证结果表明：1）企业社会责任对跨国并购绩效存在显著的正向影响；2）企业社会责任与跨国并购之间的正向关系受到国有属性与跨国并购经验的负向调节；3）当并购双方属于同一行业时，行业一致性会显著促进企业社会责任对跨国并购绩效的正向影响。建议企业主动披露企业社会责任履行状况并引入国际通行标准，也可借助国际公关公司制定企业社会责任战略。以上研究结论对中国企业履行社会责任方面具有一定的指导意义，同时也为跨国并购绩效的影响因素研究提供了新的理论视角。     

Model checking temporal knowledge and commitments in multi-agent systems using reduction

Though modeling and verifying Multi-Agent Systems (MASs) have long been under study, there are still challenges when many different aspects need to be considered simultaneously. In fact, various frameworks have been carried out for modeling and verifying MASs with respect to knowledge and social commitments independently. However, considering them under the same framework still needs further investigation, particularly from the verification perspective. In this article, we present a new technique for model checking the logic of knowledge and commitments (CTLKC⁺). The proposed technique is fully-automatic and reduction-based in which we transform the problem of model checking CTLKC⁺ into the problem of model checking an existing logic of action called ARCTL. Concretely, we construct a set of transformation rules to formally reduce the CTLKC⁺ model into an ARCTL model and CTLKC⁺ formulae into ARCTL formulae to get benefit from the extended version of NuSMV symbolic model checker of ARCTL. Compared to a recent approach that reduces the problem of model checking CTLKC⁺ to another logic of action called GCTL¹, our technique has better scalability and efficiency. We also analyze the complexity of the proposed model checking technique. The results of this analysis reveal that the complexity of our reduction-based procedure is PSPACE-complete for local concurrent programs with respect to the size of these programs and the length of the formula being checked. From the time perspective, we prove that the complexity of the proposed approach is P-complete with regard to the size of the model and length of the formula, which makes it efficient. Finally, we implement our model checking approach on top of extended NuSMV and report verification results for the verification of the NetBill protocol, taken from business domain, against some desirable properties. The obtained results show the effectiveness of our model checking approach when the system scales up.     

Model repair — aligning process models to reality

Process mining techniques relate observed behavior (i.e., event logs) to modeled behavior (e.g., a BPMN model or a Petri net). Process models can be discovered from event logs and conformance checking techniques can be used to detect and diagnose differences between observed and modeled behavior. Existing process mining techniques can only uncover these differences, but the actual repair of the model is left to the user and is not supported. In this paper we investigate the problem of repairing a process model w.r.t. a log such that the resulting model can replay the log (i.e., conforms to it) and is as similar as possible to the original model. To solve the problem, we use an existing conformance checker that aligns the runs of the given process model to the traces in the log. Based on this information, we decompose the log into several sublogs of non-fitting subtraces. For each sublog, either a loop is discovered that can replay the sublog or a subprocess is derived that is then added to the original model at the appropriate location. The approach is implemented in the process mining toolkit ProM and has been validated on logs and models from several Dutch municipalities.     

FibVID: Comprehensive fake news diffusion dataset during the COVID-19 period

As the SARS-CoV-2 (COVID-19) pandemic has run rampant worldwide, the dissemination of misinformation has sown confusion on a global scale. Thus, understanding the propagation of fake news and implementing countermeasures has become exceedingly important to the well-being of society. To assist this cause, we produce a valuable dataset called FibVID (Fake news information-broadcasting dataset of COVID-19), which addresses COVID-19 and non-COVID news from three key angles. First, we provide truth and falsehood (T/F) indicators of news items, as labeled and validated by several fact-checking platforms (e.g., Snopes and Politifact). Second, we collect spurious-claim-related tweets and retweets from Twitter, one of the world’s largest social networks. Third, we provide basic user information, including the terms and characteristics of “heavy fake news” user to present a better understanding of T/F claims in consideration of COVID-19. FibVID provides several significant contributions. It helps to uncover propagation patterns of news items and themes related to identifying their authenticity. It further helps catalog and identify the traits of users who engage in fake news diffusion. We also provide suggestions for future applications of FibVID with a few exploratory analyses to examine the effectiveness of the approaches used.