Evaluating outcomes of conservation with multidimensional indicators of well-being

Many conservation interventions are hypothesized to be beneficial for both the environment and people's well-being, but this has rarely been tested rigorously. We examined the effects of adoption or nonadoption of a conservation intervention on 3 dimensions of people's well-being (material, relational, and subjective) over time. We focused on a fisheries bycatch management initiative intended to reduce environmental externalities associated with resource extraction. We collected panel data from fishers (n = 250) in villages with (adopters and nonadopters) and without (control) the conservation intervention 3 times over 2 years. We found no evidence that adoption reduced any of the 3 dimensions of well-being in the local populations affected by the intervention. There were modest improvements in material (t = –1.58) and subjective livelihood well-being (p = 0.04) for adopters relative to nonadopters over time. The variations in well-being experiences (in terms of magnitude of change) among adopters, nonadopters, and controls across the different domains over time affirmed the dynamic and social nature of well-being.     

Pulling force prediction using neural networks

Purpose. In ergonomics and human factors investigations, pulling force (PF) estimation has usually been achieved using various types of biomechanical models, and independent approximation of PF was done with the help of upper extremity joints. Recently, multiple regression methods have gained importance for task-relevant inputs in predicting PF. Artificial neural networks (ANNs) also play a vital role in fitting the data; however, their use in work-related biomechanics and ergonomics is inadequate. Therefore, the current research aimed to accomplish comparative investigation of ANN and regression models by assessing their capacity to predict PF values. Methods. Multipositional PF data were acquired from 200 subjects at three different handle heights and body locations. ANN and regression models were formed using a random sample of three subsets (75% training, 15% selection, 10% validation) for proving the outcomes. Results. The comparison of ANN and regression models shows that the predictions of ANN models had a profoundly explained variance and lower root mean square difference values for the PF data at three handle heights. Conclusions. These outcomes advise that ANNs offer a precise and robust substitute for regression methods, and should be considered a useful method in biomechanics and ergonomics task assessments.     

Rapid estimation of compost enzymatic activity by spectral analysis method combined with machine learning

The aim of this study was to investigate the feasibility of using visible near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) as an easy, inexpensive, and rapid method to predict compost enzymatic activity, which traditionally measured by fluorescein diacetate hydrolysis (FDA-HR) assay. Compost samples representative of five different compost facilities were scanned by DRS, and the raw reflectance spectra were preprocessed using seven spectral transformations for predicting compost FDA-HR with six multivariate algorithms. Although principal component analysis for all spectral pretreatments satisfactorily identified the clusters by compost types, it could not separate different FDA contents. Furthermore, the artificial neural network multilayer perceptron (residual prediction deviation = 3.2, validation r² = 0.91 and RMSE = 13.38 μg g^?1 h^?1) outperformed other multivariate models to capture the highly non-linear relationships between compost enzymatic activity and VisNIR reflectance spectra after Savitzky–Golay first derivative pretreatment. This work demonstrates the efficiency of VisNIR DRS for predicting compost enzymatic as well as microbial activity.     

Engaging communities in municipal sustainability planning: the use of communication strategies and social networks in Alberta

In this paper we examine and compare communication processes aimed at building social networks and increasing citizen engagement in two communities participating in a municipal sustainability planning (MSP) pilot programme in Alberta, Canada, initiated by the Alberta Urban Municipalities Association (AUMA). Data were gathered through qualitative semi-structured interviews and documentary review. In soliciting citizen engagement, each community utilised a variety of communication methods, based on available resources and on-going evaluation of community responses. In both cases, citizens developed a shared vision of a preferred future for their community, aided by a reconfiguration of their internal and external social networks. In addition to drawing upon and strengthening bonding relationships within the community, bridging relationships with external actors and agencies promoted knowledge mobilisation that aided in planning and transitioning towards sustainability. Both communities faced challenges as well, which the authors discuss in relation to the sustainability of MSP planning.     

斜坡稳定性评估神经网络专家系统N^2ES^3E

在于工程实列，采用神经网络理论，建立了斜坡稳定性评估分支结构专家系统，并针对斜坡评价的不同阶段，分别建立了以地质信息为３指标和斜坡的几何与力学参数为评价指标的两种评估系统。文中阐述了该系统的开发设想，介绍了系统的主要组成部分，同时给出了应用实例，应用结果表明该系统是有效的。     

网络重要基础设施脆弱性评价模型及其应用

为了评价网络重要基础设施的脆弱性,基于网络图论,构建一个数学规划模型。利用ArcGIS将网络重要基础设施的弧段和节点赋值,将网络拓扑结构以数字形式表示;利用数学规划软件对该模型进行优化求解,得出网络重要基础设施中的脆弱性组件(弧段或节点)。结果表明这些组件的破坏会使网络脆弱性达到最大值,需要对这些组件进行优先防护。将该模型应用于京津冀高速公路交通网络系统,得到该交通网络系统的脆弱性路段。     

震害下电-气关联生命线网络韧性研究

随着各类生命线网络间关联日益紧密,对关联生命线网络进行韧性研究刻不容缓。以地震灾害为背景,构建了城市电-气关联生命线网络韧性评估框架,并基于电力潮流分析和天然气稳态仿真分析,建立了考虑电力网络和燃气网络功能特性的性能评价指标。同时,以关联的IEEE30节点网络和20节点燃气网络为案例,结合评估框架分析网络韧性在不同恢复预算和恢复资源下的变化规律,为资源优化配置提供依据,为建设“韧性城市”提供支撑。     

辽东湾地区~(137)Cs大气沉降研究

利用137Cs示踪技术的前提条件是了解137Cs的大气沉降历史,从而准确求算区域环境中137Cs的背景值.本文利用日本秋田、东京及新泻地区137Cs年沉降数据,并结合相应的降水数据进行修正,对辽东湾地区137Cs的大气沉降进行了定量研究.结果表明,辽东湾地区137Cs的大气沉降趋势与北半球基本一致;1957—2008年辽东湾地区137Cs大气总沉降通量的模拟结果为1536 Bq·m-2(衰变校正至2009年),与该地区土壤中137Cs背景值较为接近;20世纪50年代末60年代初频繁的大气核试验和1986年的切尔诺贝利核事故是1963年和1986年两个137Cs沉降峰值产生的原因;1990年以后,沉降在地表的137Cs在风力侵蚀作用下,发生再悬浮,使得大气中137Cs沉降通量再次出现量级变化明显.福岛核事故产生的核素沉降对研究区的137Cs沉降通量的影响并不明显.     

成都市区夏季大气污染物浓度时空变化特征分析

为了解成都市区大气污染物浓度水平及其变化规律,统计分析了2013年6月1日—8月31日3个市区站点(十里店、梁家巷和草堂寺)SO2、NO2、O3、PM2.5、PM10和CO逐时观测资料.结果表明,观测期间O3污染严重,上述3个站点小时均值超标率分别达22%、37%和42%.大气颗粒物污染也较为严重,上述3个站点PM10日均浓度超标率分别为13%、8%和3%,而PM2.5日均值超标率分别高达34%、27%和26%.NO2和CO早晚的浓度高峰主要与机动车流量增加和混合层高度降低有关.由于紫外辐射影响,O3浓度在正午出现峰值.受机动车流量高峰和气象条件的影响,PM2.5和PM10最大值和最小值分别出现在上午和下午.通过对污染物"周末效应"的分析,发现周末O3、PM2.5和PM10的浓度显著高于工作日,SO2、NO2和CO反之.成都市区大气污染受局地排放和外源输送共同影响,其中PM10和NO2主要受局地源控制,而PM2.5、SO2和O3受外输送影响较大.     

矿井通风网路的线性解算法及其程序设计

针对“京大二试算法”存在的不足——当已知矿井总风量时,“京大二试算法”的迭代格式是错误的,解算出的风量分配不合理,提出了矿井通风网络线性算法的数学模型.该通风网络算法是将支路特性方程变换为线性关系,则风网回路方程被线性化,就可以应用线性逼近法求解.该算法弥补了“京大二试算法”的缺陷,当已知矿井总风量时,解算出的风量值更接近真值,结果更加精确.介绍了线性算法数学模型的迭代格式及迭代精度问题,并编制了该线性算法的Matlab程序,程序简单,节省计算时间,给出了算法原理,并以实例进行计算,得到了令人满意的结果.