Using experience sampling methodology in organizational behavior

Experience sampling methodology and daily diary (ESM/DD) research elicits repeated reports of immediate or very recent experiences from the same sample of people for several days or weeks. Experience sampling and diary methods were almost unheard of in organizational research 15 years ago, but the past decade has seen a rapid rise in their use. These methods are helpful in studying dynamic within‐person processes involving affect, behavior, interpersonal interactions, work events, and other transient workplace phenomena over time. Assessing cross‐level effects of traits or other stable features on within‐person processes and reactivity is also possible with ESM/DD data. We provide an introduction to issues in designing and carrying out an ESM/DD study, including data collection choices and schedules, measures, technology, training and motivation of participants, and analysis of multilevel data. We offer best practice recommendations and refer readers to further resources for additional detail on conducting and analyzing ESM/DD research. Copyright © 2012 John Wiley & Sons, Ltd.     

Research on flood risk analysis and evaluation method based on variable fuzzy sets and information diffusion

Floods have become increasingly alarming worldwide. Flood risk management in terms of assessing disaster risk properly is a great challenge that society faces today. Natural disaster risk analysis is typically beset with issues such as imprecision, uncertainty, and partial truth. There are two basic forms of uncertainty related to natural disaster risk assessment, namely, randomness caused by inherent stochastic variability and fuzziness due to macroscopic grad and incomplete knowledge sample. However, the traditional probability statistical method ignores the fuzziness of risk assessment with incomplete data sets and requires a large sample size of data. The fuzzy set methodology is introduced in the area of disaster risk assessment to improve probability estimation. The purpose of the current study is to establish a fuzzy model to evaluate flood risk with incomplete data sets. The present paper puts forward a composite method based on variable fuzzy sets and information diffusion method for disaster risk assessment. The results indicate that the methodology is effective and practical; thus, it has the potential to forecast the flood risk in flood risk management. We hope that by conducting such risk analysis, the impact of flood disasters can be mitigated in the future.     

吸气式火灾探测系统采样管网优化设计

对吸气式火灾探测报警系统中采样管网进行优化设计是提高其探测性能的重要手段。基于流体动力学原理,建立管网内气体运动数学模型,给出各状态参数之间的关系式。分析常见管网布置方式的优缺点,提出吸气式采样管网的优化设计方案:视每个采样孔为1个点型感烟探测器,采样孔间距基本一致,调整孔径使各采样孔进气量相同,孔数较多时,可增设末端孔,以缩短气体样本传输时间。从理论上分析管网参数对探测性能的影响,并利用Fluent 6.3进行相应的数值模拟。结果表明,样本传输时间与管长、管径、开孔数成正比,与总流量和末端孔孔径成反比。     

三峡库区典型排污口河段污染物扩散降解特性研究

为减轻三峡库区长寿—涪陵段水环境污染,改善区域水质,建立了以水动力-水质模型为基础的区域关键排污口的筛选分析方法。采用三维数值模型EFDC对研究区域内污染物的超标面积进行模拟计算,并对污染物扩散降解特性进行分析,利用单位污染物负荷超标面积分析不同排污口污染物迁移扩散的差异,进行流域重点排污口的分析筛选。从改善整体河流水质出发,分析了各排污口位置的合理性。结果表明,支流回水区内排污口的混合区面积比干流河段处排污口要大,顺直江段处的排污口的混合区面积较弯段内侧处排污口要小。排污口应选在流速和水深较大的顺直河段,避免选择支流回水区。     

平地型铀尾矿库氡大气扩散数值模拟及环境效应分析

以平地型铀尾矿库为研究对象,采用数值模拟方法,分析了铀尾矿库下风向的氡扩散和浓度分布,并预测了该地区常年主导风向下氡对公众所致的年有效剂量。结果表明,风速从0.5 m/s变化到2.0m/s时,氡在尾矿库下风向的积聚范围由5 000 m缩减为2 500 m,尾矿库下风向2 500 m距离以内的氡浓度降低较快,随着距离的增大,近地面区域氡浓度不断降低,5 000 m外氡浓度变化渐趋平缓。U10=0.5m/s时尾矿库下风向地区的氡浓度比其他风速下最高高出近43%。对照公众个人的年有效剂量标准,考虑风频风速影响,对氡的环境效应分析表明,低风速下现行标准中铀尾矿库防护距离的规定值偏小,应进行适当调整。     

密闭空间燃气泄漏爆炸危险区域迁移规律

为了给室内燃气泄漏爆炸事故的预防和事故后果评价提供理论依据,借助计算流体力学技术,对密闭空间内燃气泄漏扩散的非稳态流场进行了数值模拟,着重考察了燃气爆炸危险区域随时间和空间的分布特征。研究结果表明,在泄漏初始阶段,爆炸危险区域位于泄漏源上部。随着泄漏和扩散的持续发展,爆炸危险区域整体下移,最终迁移至地面附近。爆炸危险区域范围随时间由小变大,再由大变小。爆炸危险区域在房间下部的持续时间明显长于房间中上部。     

福州市行道树对交通污染物扩散的影响

选择福州市中心城区3种典型的行道树结构作为研究对象,以CO为交通污染物的示踪气体,分别对绿化带两侧,即道路中央和人行道上CO浓度的时空变化进行了测定,分析了道路中央CO浓度与人行道上CO浓度的差值ΔC。研究结果表明:不同结构的行道树对交通污染物扩散有显著影响。主干道低覆盖度结构,最有利于交通污染物扩散;主干道高覆盖度结构和支路结构,则不利于交通污染物的扩散,使大量污染物涌入人行道,对行人健康构成危害。交通污染物的扩散效果取决于树冠结构,通过对各道路树冠结构的研究发现,疏透度与覆盖度均较小时,扩散效果最好;疏透度较大、覆盖度也较大时,扩散效果最差。     

生物质燃烧源排放烟尘罩稀释采样系统设计与应用

为研究生物质燃烧源颗粒物与气态污染物的排放特征,设计了可用于生物质燃烧排放实验室模拟研究的烟尘罩稀释采样系统.该系统可模拟生物质燃烧释放的高温高浓烟气排放到大气中的冷却、稀释、生长等理化过程,捕集燃烧排放的细颗粒物和多种痕量气态污染物.通过更换不同内径采样嘴等方法,系统稀释倍数可在10~70倍之间调节.详细介绍了系统的设计原理及结构,并对系统可靠性进行了检验.整个采样系统通过了气密性测试,分级采样系统平行性良好,细颗粒物损失在可接受范围,对燃烧状态扰动低,燃烧状态可实时评估.应用该系统测定了我国南方地区典型生物质燃烧源含碳污染物种的排放特征,结果表明其适于模拟各种类型的生物质燃烧.     

有害物质泄漏扩散的数值模拟

有害物质泄漏是一种常见事故.利用高斯公式和三维有限元建立有害物质泄漏扩散数学模型,估测有害物质泄漏扩散的危害范围和泄漏物质扩散过程中浓度的大小,相应的数学模型可作为泄漏事故安全保障工作中预防为主的科学依据,从而为可能发生的事故进行预测预警.     

Automated conservation assessment of the orchid family with deep learning

International Union for Conservation of Nature (IUCN) Red List assessments are essential for prioritizing conservation needs but are resource intensive and therefore available only for a fraction of global species richness. Automated conservation assessments based on digitally available geographic occurrence records can be a rapid alternative, but it is unclear how reliable these assessments are. We conducted automated conservation assessments for 13,910 species (47.3% of the known species in the family) of the diverse and globally distributed orchid family (Orchidaceae), for which most species (13,049) were previously unassessed by IUCN. We used a novel method based on a deep neural network (IUC-NN). We identified 4,342 orchid species (31.2% of the evaluated species) as possibly threatened with extinction (equivalent to IUCN categories critically endangered [CR], endangered [EN], or vulnerable [VU]) and Madagascar, East Africa, Southeast Asia, and several oceanic islands as priority areas for orchid conservation. Orchidaceae provided a model with which to test the sensitivity of automated assessment methods to problems with data availability, data quality, and geographic sampling bias. The IUC-NN identified possibly threatened species with an accuracy of 84.3%, with significantly lower geographic evaluation bias relative to the IUCN Red List and was robust even when data availability was low and there were geographic errors in the input data. Overall, our results demonstrate that automated assessments have an important role to play in identifying species at the greatest risk of extinction.