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.     

四川盆地中三叠世盐盆分布与演化特征研究

四川盆地卤水分布广泛，资源丰富, 早为世人所瞩目，中三叠统雷口坡组是四川盆地最早开采天然气和盐卤的层系之一。根据盆地雷口坡组大量钻井资料研究表明，盆地雷口坡组主要成盐期分别为雷三2 期和雷四2 期，在盆地东部和南部由于受到古隆起的影响，盐岩地层已被剥蚀难以保存，现今盐岩主要分布在开江古隆起和泸州古隆起的西部以及天井山古隆起的南部（华蓥山以西—龙门山以东的川西—川中地区）。印支运动对盆地雷口坡组盐岩在平面的迁移起着十分重要的作用，根据不同时期盐岩统计数据看，两期盐盆在平面上具有向西迁移、叠加的特征，其中川中的GS1 井就钻遇了这两期的盐岩。     

川东北地区须家河组二段储层差异性分析

随着勘探程度的深入，为了扩大四川盆地天然气勘探领域、寻找新的勘探接替区，四川盆地的勘探区域由盆内逐渐向盆缘延伸。在这种背景下，川东北地区须家河组二段相继取得新的勘探成果，但在勘探开发过程中逐渐暴露了诸如区块之间气井产能差异大，储层的碎屑组分、结构及厚度、物性、孔隙结构差异明显等问题，制约了勘探开发的步伐。针对上述问题，根据岩芯观察描述、分析化验资料及测井解释成果，从沉积、成岩方面入手，对比剖析了川东北龙岗和营山地区须二段储层特征的差异性及其成因，认为远离物源区和高能水体的反复淘洗是造成储层差异性的首要因素，强烈的压实作用和多期石英加大对储层差异性起关键作用，自生绿泥石衬边胶结和长石选择性溶蚀最终决定了储层差异性。从而解释了气藏勘探开发中遇到的诸多问题，对有效指导该区的下一步勘探开发工作具有重要意义。     

指数函数和对数函数的高精度快速算法

指数、对数函数的数值计算是一种最基本的算术操作,研究其快速算法,对于科学计算、数据处理,尤其是要求运算精度很高的实时控制系统,有着非常重要的意义.通过数值分析,考虑到精度要求较高,计算量和数表的存储空间这三个因素,结合计算和查表的方法给出了一些基本函数的快速算法.另外,还介绍了改进的高精度快速算法并演示了详细的计算过程.得出精度越高,内存越大,相应的操作速度却没有下降。     

Feminized Forestry: The Promises and Pitfalls of Change in a Masculine Organization

The gendering of forestry as a distinctly masculine profession has led to a wide range of negative outcomes, including legal actions concerning discrimination, poor public perceptions and poor environmental records. Forestry organizations have addressed these concerns by attempting to increase the number of women in the profession. These efforts have been largely ineffective. Using the case of community‐based forestry, I argue that when we begin to consider not only women but also normatively feminine values as agents of change, our understanding of the profession of forestry may be rejuvenated.     

大豆生产者补贴政策改革促进农户种植结构调整了吗?——基于全国446个县的准自然实验

以大豆生产者补贴政策改革作为切入点,将政策实施地区和非实施地区视为一次部分省份优化大豆、玉米种植结构的准自然实验,通过构建PSM-DID模型,考察大豆生产者补贴政策改革对农户种植结构调整的影响及其作用机制.结果表明:(1)大豆生产者补贴政策改革对农户种植结构调整具有显著的时间异质性,政策改革初年(2017年)大豆与玉米的面积之比显著提高6.5％,虽然2018年大豆与玉米的相对补贴额度出现大幅度增长,但大豆与玉米的面积之比仅显著提高3.4％;(2)预期相对收益的提升是2017年农户调整种植结构的重要原因,然而,经过对新制度一年的适应后,农户对种植玉米的路径依赖、补贴错位、地租的不合理增长以及大豆、玉米相对价格不理想等问题逐渐凸显,2018年农户重新调整种植决策,补贴的激励作用被迫减弱.基于此,研究结论为统筹大豆、玉米补贴机制可以促进农户种植结构调整提供经验性证据,也为在农业供给侧结构性改革和大豆振兴压力下如何持续改善种植结构提供政策启示.