Entrainment mortality of Ichthyoplankton: Detectability and precision of estimates

The ability to detect and to develop a precise and accurate estimate of the entrainment mortality fraction is an important step in projecting power plant impacts on future fish population levels. Recent work indicates that these mortailities may be considerably less than 100% for some fish species in the early life stages. Point estimates of the entrainment mortality fraction have been developed based on probabilistic arguments, but the precision of these estimates has not been studied beyond the simple statistical test of the null hypothesis that no entrainment mortaility exists.The ability to detect entrainment mortality is explored as a function of the sample sizes (numbers of organisms collected) at the intake and discharge sampling stations of a power plant and of the proportion of organisms found alive in the intake samples (intake survival). Minimum detectable entrainment mortality, confidence interval width, and type II error (probability of accepting the null hypothesis of no entrainment mortality when there is mortality) are considered. Increasing sample size and/or decreasing sampling mortality will decrease the minimum detectable entrainment mortality, confidence interval width, and type II error for a given level of type I error.The results of this study are considered in the context of designing useful monitoring programs for determining the entrainment mortality fraction. Preliminary estimates of intake survival and the entrainment mortality fraction can be used to obtain estimates of the sample size needed for a specified level of confidence interval width or type II error. Final estimates of the intake survival and the entrainment mortality fraction can be used to determine the minimum detectable entrainment mortality and the type II error.     

Rethinking receiver operating characteristic analysis applications in ecological niche modeling

The area under the curve (AUC) of the receiver operating characteristic (ROC) has become a dominant tool in evaluating the accuracy of models predicting distributions of species. ROC has the advantage of being threshold-independent, and as such does not require decisions regarding thresholds of what constitutes a prediction of presence versus a prediction of absence. However, we show that, comparing two ROCs, using the AUC systematically undervalues models that do not provide predictions across the entire spectrum of proportional areas in the study area. Current ROC approaches in ecological niche modeling applications are also inappropriate because the two error components are weighted equally. We recommend a modification of ROC that remedies these problems, using partial-area ROC approaches to provide a firmer foundation for evaluation of predictions from ecological niche models. A worked example demonstrates that models that are evaluated favorably by traditional ROC AUCs are not necessarily the best when niche modeling considerations are incorporated into the design of the test.     

The impact of team familiarity and team leader experience on team coordination errors: A panel analysis of professional basketball teams

To explore the dynamics involved in team coordination, we examine the impact of team familiarity and team leader experience on team coordination errors (TCEs). We argue that team familiarity has a U‐shaped effect on TCEs. We study the moderating effects of team leader prior experience and team leader team‐specific experience on the association between team familiarity and TCEs. We use panel data on teams from the National Basketball Association to test the hypotheses. Our findings support the U‐shaped relationship between team familiarity and TCEs and the moderating effect of team leader team‐specific experience on this relationship. The paper advances research on errors in organizations by analyzing the antecedents of TCEs, so far an underexplored empirical phenomenon. Moreover, it contributes to research on coordination in teams by empirically examining the interplay between formal and informal coordination mechanisms. Copyright © 2015 John Wiley & Sons, Ltd.     

基于改进的HFACS和SPA的矿山安全人因分析

为了更好地服务于矿山安全管理,同时解决矿山人为因素调查、分析和评价方面存在的缺失,对部分矿山事故进行了统计分析,研究了诱发事故发生的人因因素,并分析了各因素发生的原因。在此基础上,结合对部分矿山安全管理现状的研究,建立了矿山HFACS分析模型。该模型包括5类事故致因、24个人因因素。应用该模型,结合集对分析方法,可实现矿山人因因素定量与定性分析。以陕西省某矿山为例,建立了该矿山的HFACS模型。采用专家评分法、层次分析法确定各因素的权重,计算出系统的联系度表达式。利用该表达式对该矿山的集对势、联系度、不确定性和悲观势进行了分析。结果表明,该矿山人因安全状况良好,个别人因因素指标需要改善和提高。矿山HFACS模型可以很好地涵盖矿山事故中的人因因素,通过对矿山人因现状的集对分析,可以有效提高矿山安全性。     

高寒条件下振弦式传感器在线测量尾矿坝浸润线的误差分析及对策

为减少和消除高寒条件下振弦式传感器在线测量浸润线的误差,通过理论分析和现场试验,对造成误差的因素及其影响情况进行了深入研究。结果表明,振弦式传感器产生测量误差的主要来源为设备安装深度的测量误差与尾矿水密度及重力加速度的取值误差,并提出了减少或消除误差的方法和工程技术措施,为高寒地区的浸润线在线精确测量提供了重要指导意义。     

页岩气压裂作业人因失误结构化辨识方法研究

为提高页岩气压裂作业人因失误辨识结果的全面性和针对性,建立一种结构化的人因失误辨识方法。通过概括辨识阶段的作业流程,建立作业人员行为模型,概述作业人员的行为阶段。利用筛选出的引导词辨识行为阶段人因失误。将该方法应用于页岩气压裂过程的替液阶段。结果表明,用引导词能明确辨识方向,减少头脑风暴时间以及对专家知识的依赖;辨识过程的动态调整能改善认知可靠性,使更多人因失误模式(与传统CREAM方法相比)被辨识出来。     

Water quality modelling of the river Yamuna (India) using QUAL2E-UNCAS

This paper describes the utility of QUAL2E as a modelling package in the evaluation of a water quality improvement programme. In this study, QUAL2E was applied to determine the pollution loads in the river Yamuna during its course through the national capital territory of Delhi, India. The study aimed at examining the influence of different scenarios on river water quality. Four different pollution scenarios were analysed besides the 'business as usual' situation. The study revealed that it was necessary to treat the discharge from drains to the river Yamuna and diversion of a substantial load to the Agra canal for further treatment was also essential. It was also established through this study that maintaining a flow rate of more than 10 m(3)/s in the river could also help preserve the river's water quality. To clearly display the model outcomes and demarcate polluted zones in the river stretch, model results were interfaced with a Geographical Information System (GIS) to produce cartographic outputs. In addition, uncertainty analysis in the form of first-order error analysis and Monte Carlo analysis was performed, to realise the effect of each model parameter on DO and BOD predictions. The uncertainty analysis gave satisfactory results on simulated data.     

Uncertainty Models for Estimates of Physical Characteristics of River Segments Over Large Areas

Spatially comprehensive estimates of the physical characteristics of river segments over large areas are required in many large‐scale analyses of river systems and for the management of multiple basins. Remote sensing and modeling are often used to estimate river characteristics over large areas, but the uncertainties associated with these estimates and their dependence on the physical characteristics of the segments and their catchments are seldom quantified. Using test data with varying degrees of independence, we derived analytical models of the uncertainty associated with estimates of upstream catchment area (CA), segment slope, and mean annual discharge for all river segments of a digital representation of the hydrographic network of France. Although there were strong relationships between our test data and estimates at the scale of France, there were also large relative local uncertainties, which varied with the physical characteristics of the segments and their catchments. Discharge and CA were relatively uncertain where discharge was low and catchments were small. Discharge uncertainty also increased in catchments with large rainfall events and low minimum temperature. The uncertainty of segment slope was strongly related to segment length. Our uncertainty models were consistent across large regions of France, suggesting some degree of generality. Their analytical formulation should facilitate their use in large‐scale ecological studies and simulation models.     

基于边际机会成本理论的农地价格矫正研究——以江都市为例

当前我国农地非农化进程的加快使农地资源价值显化问题凸现出来，但在现行的政策与制度框架内，农地价格严重扭曲，不能完全表达农地的真正价值。社会经济的不断发展要求农地资源能合理配置并达到可持续利用，因此要实现这一目标，就必须建立一套完整的农地价值核算体系与农地价格体系，并对现行扭曲的农地价格进行矫正。本文通过理论与实证分析，利用边际成本定价的方法探讨体现农地真正价值的农地完全价格，并通过这一价格将农地的资源价值纳入整个绿色GDP核算体系中，这也是本文提出的政策性建议之一。     

基于分解模型与VEC模型的沈阳经济区经济与环境时空关系研究

选择GDP、工业废水排放量、工业废气排放量、工业废物产生量4个指标,从横向的空间作用关系及纵向的时间作用关系对沈阳经济区经济增长与环境演化关系进行了动态综合分析.对于空间作用关系,采用完全分解模型,利用沈阳经济区内8个城市的面板数据,将经济与环境关系分解为经济规模效应、空间结构效应和技术效应,判定了由于经济增长造成的工业废物排放的增减变化而带来的区域环境压力扰动程度.对于时间作用关系,选择协整与向量误差修正模型(VEC),依次对指标序列进行了单位根检验、VAR估计、Johansen协整检验及向量自回归的VEC修正,进而对沈阳经济区经济与环境指标前期与当期作用关系进行了长期修正与短期校正.结果表明:对于工业废物排放,技术效应发挥了最突出的减量作用,经济效应的增量效应凸显,而空间结构效应的作用不明显;GDP与工业废水排放量、工业废气排放量、工业废物产生量之间的作用关系均经历了由波动到平稳的过程,但在研究期内GDP与工业废水排放之间的稳定性相对较弱,保持GDP与工业废气排放、工业废物产生各自的均衡关系,对各变量的优化有促进作用,而保持GDP与工业废水排放的均衡关系,则对各变量的优化有抑制作用.