火工品可靠性(或安全性)评估的Bayes方法

对火工品可靠性（或安全性）评估中通常采用的经典方法进行了剖析。介绍了数理统计方法学中近年来兴起的Ｂａｙｅｓ统计推断方法，并给出了此法在火工品二项分布、正态分布情况下，对可靠度评估的应用实例。     

生命价值的实证研究

笔者所研究的生命特指人的生命 ,所讨论的价值 ,是经济学意义上的价值。论文首先从理论与实践两个角度提出了测定生命价值的必要性 ,在深入探讨与本论文相关的人、生命、价值等概念后 ,对生命价值概念分 3个层次进行了界定。通过对价值理论的比较 ,选择马克思的劳动价值论及其最新研究成果与现代人力资本理论作为立论的基础 ,重点探讨了生命价值的决定理论 ,包括生命价值形成过程、人力资源和人力资本与生命价值的关系、生命价值的变化规律等。在借鉴国内外计算方法的基础上 ,分别就青少年、成年人、老年人设计了生命价值计算模型 ,并重点对成年人生命价值计算模型进行了检验与应用 ,对具有代表性的成年体力劳动者的生命价值进行了测算 ,结果比较合理 ,且与世界银行的估计值接近。论文在宏观方面为完善社会主义市场经济体制 ,充分发挥市场在资源配置中的基础性作用 ,实现人的全面发展将起到积极作用 ,并为国家制定安全、环保、社保政策提供了科学依据 ;在微观方面为个人在各种事故中的赔偿和涉及人身的各种民事诉讼提供仲裁或判决依据。     

基于可靠性统计分析法的制导弹药储存寿命评估

针对制导弹药价格昂贵而无法采用大规模性能试验和实弹射击方法进行可靠性研究的问题,采用可靠性统计分析法对制导弹药储存寿命进行评估。通过建立制导弹药可靠储存寿命模型,并以制导弹药火工品中的待发程控装置为研究对象,运用Bayes法对试验数据出现的＂倒挂＂现象进行了修正。依据修正后的试验数据进行计算,得到了在一定置信度下待发程控装置的储存寿命。     

The hybrid model of empirical wavelet transform and relevance vector regression for monthly wind speed prediction

ABSTRACT

In order to improve the prediction ability for the monthly wind speed of RVR, the hybrid model of empirical wavelet transform and relevance vector regression (EWT-RVR) is proposed for monthly wind speed prediction in this study. Compared with empirical mode decomposition (EMD), empirical wavelet transform (EWT) can obtain a more consistent decomposition and have a mathematical theory. In order to testify the superiority of EWT-RVR, several traditional RVR models are used to compare with the proposed EWT-RVR method under the situation of the same embedding dimensions. The experimental results show that the proposed EWT-RVR method has a better prediction ability for monthly wind speed than RVR. It can be concluded that the proposed EWT-RVR method for monthly wind speed is effective.     

长江上游地区泥石流灾害敏感性量化评价研究

泥石流敏感性分析对灾害分析评价预测具有重要作用。文章以长江上游为研究区,选择坡度、相对高差、地层岩性、年降雨量、地震烈度等5方面因素为评价因子,探讨运用GIS技术管理分析泥石流灾害信息和区域泥石流敏感性分析的方法。量化评价因子的作用分值大小,有效表达泥石流敏感性指数(DFSI),进而,结合空间分析与条件概率模型,实现了区域泥石流敏感性分析并制作了泥石流敏感性专题图。长江上游地区泥石流敏感性区域具有一定的规律性,高度敏感区主要分布在第一级阶梯与第二级阶梯的过渡地带,比较集中的区域有雅砻江中下游、安宁河、小江、普渡河、大渡河中下游、理塘河、白龙江、岷江上游、涪江上游等干支流的5～10 km范围内;中度敏感区主要分布在高度敏感区的外围约10～30 km范围内。利用历史泥石流资料验证,表明已经发生的泥石流主要分布在泥石流敏感性指数较高地区,说明敏感性分析的结果基本反映出研究区泥石流敏感性特性。     

A multilevel investigation of predictors and outcomes of shared leadership

For modern organizations, shared leadership becomes increasingly important. Knowledge on shared leadership may be limited, as past research often relies on cross-sectional data or student samples, and most studies neglect the multilevel nature of shared leadership. Our research model includes transformational leadership, trust, and organizational support as predictors of shared leadership. Furthermore, we analyze the influence of shared leadership on team performance and team creativity. In total, 160 teams with 697 employees participated in our field study. Data collection took place at three time points. To test our hypotheses, we used multilevel modeling with a Bayesian estimator. We found relationships of transformational leadership and trust with shared leadership at the team level and of transformational leadership, trust, and organizational support with shared leadership at the individual level. Furthermore, shared leadership fully mediated the effect of the three input factors on team performance and team creativity. This study contributes to the understanding of the antecedents and outcomes of shared leadership. Furthermore, the dynamic development of team processes based on an input–mediator–output model is explored. On the basis of the results, organizations can increase shared leadership behavior by focusing on transformational leadership and trust building.     

Shifts in the spatio-temporal growth dynamics of shortleaf pine

Previous studies focusing on the growth history of Pinus echinata at the edge of its geographical range have suggested that changes in growth correspond to climatic and non-climatic (e.g., anthropogenic) factors. We employ a regime-dependent state-space model that allows us to detect and characterize the changes in tree growth dynamics over space and time using readily available dendrochronological and climatic data in the presence of various sources of uncertainty. We utilize methods common in atmospheric sciences but relatively unknown in ecology and forestry to develop a hierarchical model for tree growth and describe the growth dynamics. The utility of such methods for addressing ecological problems will grow as more high dimensional spatio-temporal processes are considered and datasets become more readily available.     

Cluster detection using Bayes factors from overparameterized cluster models

In this paper, we consider the use of a partition model to estimate regional disease rates and to detect spatial clusters. Formal inference regarding the number of partitions (or clusters) can be obtained with a reversible jump Markov chain Monte Carlo algorithm. As an alternative, we consider the ability of models with a fixed, but overly large, number of partitions to estimate regional disease rates and to provide informal inferences about the number and locations of clusters using local Bayes factors. We illustrate and compare these two approaches using data on leukemia incidence in upstate New York and data on breast cancer incidence in Wisconsin.     

Restrictions and options: A heuristic tool to integrate knowledge for strategies towards a sustainable development

SUMMARY

This article presents the heuristic ‘Restrictions and Options’ that was created in an interdisciplinary research team to identify restricting and supporting factors regarding sustainable ways of actions for different institutional and individual actors in the need-field of nutrition. The heuristic format is first presented on a general level. Then it is combined with two different approaches: first, with a systemic constructivist approach to organization theory, and second, with the ipsative theory of action (psychology). Its abilities are illustrated by examples from two research projects.

The heuristic tool was developed to help identify relevant factors that restrict or support sustainable development in two empirical settings and also could be used to compare the findings. Combining the two approaches remains for future research.     

公路隧道水害倾向性分级的Bayes判别法及应用

为提高公路隧道整体安全性能,保障人员安全,减少财产损失,避免公路隧道水害事故的发生,将Bayes判别理论应用于公路隧道水害倾向性判别和分级中。采用影响隧道水害发生的隧道区渗透系数、降水情况、单位涌水量、构造断裂带类型、围岩分级、隧道施工情况、防排水措施情况等7项指标作为基本判别因子;将公路隧道水害倾向性分为4个等级作为Bayes判别分析的4个正态总体。以采自典型的20组公路隧道的实测数据为训练样本,建立公路隧道水害倾向性分级的Bayes判别函数。对训练后的模型运用交叉确认估计法进行验证,然后运用该模型对6条待检验的公路隧道样本的水害倾向性进行分级。研究结果表明:构建的Bayes判别分析模型误判率极低,分级效果合理有效,可以运用于公路隧道水害倾向性的分级中,有利于公路隧道水害的预防和治理。