Hierarchical Bayesian Modeling of Post-Earthquake Ignition Probabilities Considering Inter-Earthquake Heterogeneity

Post-earthquake fires are high-consequence events with extensive damage potential. They are also low-frequency events, so their nature remains underinvestigated. One difficulty in modeling post-earthquake ignition probabilities is reducing the model uncertainty attributed to the scarce source data. The data scarcity problem has been resolved by pooling the data indiscriminately collected from multiple earthquakes. However, this approach neglects the inter-earthquake heterogeneity in the regional and seasonal characteristics, which is indispensable for risk assessment of future post-earthquake fires. Thus, the present study analyzes the post-earthquake ignition probabilities of five major earthquakes in Japan from 1995 to 2016 (1995 Kobe, 2003 Tokachi-oki, 2004 Niigata–Chuetsu, 2011 Tohoku, and 2016 Kumamoto earthquakes) by a hierarchical Bayesian approach. As the ignition causes of earthquakes share a certain commonality, common prior distributions were assigned to the parameters, and samples were drawn from the target posterior distribution of the parameters by a Markov chain Monte Carlo simulation. The results of the hierarchical model were comparatively analyzed with those of pooled and independent models. Although the pooled and hierarchical models were both robust in comparison with the independent model, the pooled model underestimated the ignition probabilities of earthquakes with few data samples. Among the tested models, the hierarchical model was least affected by the source-to-source variability in the data. The heterogeneity of post-earthquake ignitions with different regional and seasonal characteristics has long been desired in the modeling of post-earthquake ignition probabilities but has not been properly considered in the existing approaches. The presented hierarchical Bayesian approach provides a systematic and rational framework to effectively cope with this problem, which consequently enhances the statistical reliability and stability of estimating post-earthquake ignition probabilities.     

一种嵌入式GUI软件结构实现方案

综合比较了嵌入式GUI的几种实现方式,结合嵌入式系统的特点,研究了嵌入式GUI中的关键技术,分析了嵌入式GUI与普通GUI系统的不同之处,提出一种通用的嵌入式图形用户界面系统的设计思想和体系结构,这种嵌入式GUI实现方案具有轻型、占用资源少、可剪裁等特点。     

诚信是精神文明的重要尺度

社会的发展要求信誉的提高与之相适应,社会信誉的建设是中国特色社会主义政治文明建设的一项重要内容。由于受主客观因素的影响,信誉缺失已成为社会转型时期的一种道德病态。文章从社会信誉的作用和特征入手,提出相应的对策:构建社会信誉制度,加强社会信誉的立法,加大对失信行为的处罚力度,打造诚信政府,等等。     

试论建设节约型社会的重要意义

建设节约型社会,可以使我们正确认识我国现阶段的发展水平,避免盲目的高消费;有利于提高我国的科学技术水平和生产力;有利于进一步加强政治体制改革、反腐防腐;有利于我国社会的可持续发展和建设和谐社会;有利于教育广大人民群众、尤其是年青一代形成良好的社会风气,使我国勤俭节约、艰苦朴素的光荣传统得到发扬光大。     

重要数据跨境流动背景下风险路径的识别与分级

重要数据的跨境流动引发了数据安全、国家安全等风险挑战。风险路径的识别和分级是对重要数据跨境流动进行预警管理的重要内容。本文基于复杂网络中的二分网络模型,对重要数据的跨境流动进行研究。首先,通过重要数据跨境流动的二分网络和关联网络识别风险路径;其次,构建基于网络结构和接收节点属性的目标风险路径方法以计算其风险值;最后,对我国某重要行业跨境流动的数据开展实证分析,验证算法的有效性和精准度。本文旨在为重要数据跨境流动的预警管理提供量化方法,有效预防重要数据跨境流动带来的风险,提升我国数据治理能力。     

动结式的事件结构和句法生成

在语言使用过程中,动结式逐渐被语法化成表示致使—结果的特定语法形式,被语法化了的不是具体的词汇单位,而是一种语法结构或构式,即任何两个没有语义关联的动词只要并列出现在一个句子里就会形成致使—结果的语义关系。动结式里的两个动词各自表示一个事件,每个事件都能表述为一个独立的句子,通过移位和融合等句法操作合并在一起,临时构成独立的动词单位,从而在句法上把分别表示不同事件的两个句子合并为一个,实现表达的简约性。因为题元指派首先在底层结构完成,而动结式是表层句法操作的结果,所以动结式的语义关系仍需回到合并前的两个句子里确认。这种追根溯源的底层结构解释,清晰地揭示了动结式表层所呈现的复杂的句法和语义关系,是一种对于动结式的句法派生和形成机制提出的全新看法。     

Pharmacovigilance in Clinical Trials: Current Practice and Challenges

In view of the MENA increasing participation in multinational trials and the increasing number of national/regional trials, this article explores potential areas of pharmacovigilance, requiring reform and provides recommendations for building a robust safety reporting system. Regulatory silence on expedited reporting requirements creates confusion for local sites that are part of multinational trials. Not allowing waiver for serious adverse events that are protocol specified or are study endpoints, along with lack of emphasis on causality as reporting criteria, adds substantial burden of uninformative cases for regulatory review. Despite global focus on Development Safety Update Report, local regulators are not yet insistent on real-time update of a drug’s cumulative safety profile. Issues like reporting requirements for generic trials, pregnancy reporting and lenient timeline for death/life-threatening events need attention. Finally, the need to formulate an all-encompassing local pharmacovigilance guideline, in sync with global practice cannot be overemphasized.     

套管的失效分析

通过收集石油套管现场失效数据资料,以套管失效为顶事件建立了石油套管的故障树,该故障树共考虑了47个不同的基本事件。通过对套管故障树的分析,得到了套管失效故障树的一阶最小割集27个,二阶最小割集29个,四阶最小割集1个,确立了套管的主要失效形式为潜在损坏、套管挤毁、套管断裂及严重腐蚀,并提出了提高套管可靠性的措施。     

Understanding Community Resilience from a PRA Perspective Using Binary Decision Diagrams

Probabilistic risk assessment (PRA) is a useful tool to assess complex interconnected systems. This article leverages the capabilities of PRA tools developed for industrial and nuclear risk analysis in community resilience evaluations by modeling the food security of a community in terms of its built environment as an integrated system. To this end, we model the performance of Gilroy, CA, a moderate‐size town, with regard to disruptions in its food supply caused by a severe earthquake. The food retailers of Gilroy, along with the electrical power network, water network elements, and bridges are considered as components of a system. Fault and event trees are constructed to model the requirements for continuous food supply to community residents and are analyzed efficiently using binary decision diagrams (BDDs). The study also identifies shortcomings in approximate classical system analysis methods in assessing community resilience. Importance factors are utilized to rank the importance of various factors to the overall risk of food insecurity. Finally, the study considers the impact of various sources of uncertainties in the hazard modeling and performance of infrastructure on food security measures. The methodology can be applicable for any existing critical infrastructure system and has potential extensions to other hazards.     

Bayesian adaptive dose‐escalation designs for simultaneously estimating the optimal and maximum safe dose based on safety and efficacy

The main purpose of dose‐escalation trials is to identify the dose(s) that is/are safe and efficacious for further investigations in later studies. In this paper, we introduce dose‐escalation designs that incorporate both the dose‐limiting events and dose‐limiting toxicities (DLTs) and indicative responses of efficacy into the procedure. A flexible nonparametric model is used for modelling the continuous efficacy responses while a logistic model is used for the binary DLTs. Escalation decisions are based on the combination of the probabilities of DLTs and expected efficacy through a gain function. On the basis of this setup, we then introduce 2 types of Bayesian adaptive dose‐escalation strategies. The first type of procedures, called “single objective,” aims to identify and recommend a single dose, either the maximum tolerated dose, the highest dose that is considered as safe, or the optimal dose, a safe dose that gives optimum benefit risk. The second type, called “dual objective,” aims to jointly estimate both the maximum tolerated dose and the optimal dose accurately. The recommended doses obtained under these dose‐escalation procedures provide information about the safety and efficacy profile of the novel drug to facilitate later studies. We evaluate different strategies via simulations based on an example constructed from a real trial on patients with type 2 diabetes, and the use of stopping rules is assessed. We find that the nonparametric model estimates the efficacy responses well for different underlying true shapes. The dual‐objective designs give better results in terms of identifying the 2 real target doses compared to the single‐objective designs.