A hybrid model for human factor analysis in process accidents: FBN-HFACS

Human factors are the largest contributing factors to unsafe operation of the chemical process systems. Conventional methods of human factor assessment are often static, unable to deal with data and model uncertainty, and to consider independencies among failure modes. To overcome the above limitations, this paper presents a hybrid dynamic human factor model considering Human Factor Analysis and Classification System (HFACS), intuitionistic fuzzy set theory, and Bayesian network. The model is tested on accident scenarios which have occurred in a hot tapping operation of a natural gas pipeline. The results demonstrate that poor occupational safety training, failure to implement risk management principles, and ignoring reporting unsafe conditions were the factors that contributed most failures causing accident. The potential risk-based safety measures for preventing similar accidents are discussed. The application of the model confirms its robustness in estimating impact rate (degree) of human factor induced failures, consideration of the conditional dependency, and a dynamic and flexible modelling structure.     

Fishing‐gear restrictions and biomass gains for coral reef fishes in marine protected areas

Considerable empirical evidence supports recovery of reef fish populations with fishery closures. In countries where full exclusion of people from fishing may be perceived as inequitable, fishing‐gear restrictions on nonselective and destructive gears may offer socially relevant management alternatives to build recovery of fish biomass. Even so, few researchers have statistically compared the responses of tropical reef fisheries to alternative management strategies. We tested for the effects of fishery closures and fishing gear restrictions on tropical reef fish biomass at the community and family level. We conducted 1,396 underwater surveys at 617 unique sites across a spatial hierarchy within 22 global marine ecoregions that represented 5 realms. We compared total biomass across local fish assemblages and among 20 families of reef fishes inside marine protected areas (MPAs) with different fishing restrictions: no‐take, hook‐and‐line fishing only, several fishing gears allowed, and sites open to all fishing gears. We included a further category representing remote sites, where fishing pressure is low. As expected, full fishery closures, (i.e., no‐take zones) most benefited community‐ and family‐level fish biomass in comparison with restrictions on fishing gears and openly fished sites. Although biomass responses to fishery closures were highly variable across families, some fishery targets (e.g., Carcharhinidae and Lutjanidae) responded positively to multiple restrictions on fishing gears (i.e., where gears other than hook and line were not permitted). Remoteness also positively affected the response of community‐level fish biomass and many fish families. Our findings provide strong support for the role of fishing restrictions in building recovery of fish biomass and indicate important interactions among fishing‐gear types that affect biomass of a diverse set of reef fish families.     

Modeling of a sequencing batch reactor treating municipal wastewater using multi-layer perceptron and radial basis function artificial neural networks

A sequencing batch reactor was modeled using multi-layer perceptron and radial basis function artificial neural networks (MLPANN and RBFANN). Then, the effects of influent concentration (IC), filling time (FT), reaction time (RT), aeration intensity (AI), SRT and MLVSS concentration were examined on the effluent concentrations of TSS, TP, COD and NH₄⁺-N. The results showed that the optimal removal efficiencies would be obtained at FT of 1 h, RT of 6 h, aeration intensity of 0.88 m³/min and SRT of 30 days. In addition, COD and TSS removal efficiencies decreased and TP and NH₄⁺-N removal efficiencies did not change significantly with increases of influent concentration. The TSS, TP, COD and NH₄⁺-N removal efficiencies were 86%, 79%, 94% and 93%, respectively. The training procedures of all contaminants were highly collaborated for both RBFANN and MLPANN models. The results of training and testing data sets showed an almost perfect match between the experimental and the simulated effluent of TSS, TP, COD and NH₄⁺-N. The results indicated that with low experimental values of input data to train ANNs the MLPANN models compared to RBFANN models are more precise due to their higher coefficient of determination (R²) and lower root mean squared errors (RMSE) values.     

基于复杂系统建模的水管理政策研究进展

基于Agent的建模方法（ABSS）通过自下而上的方式，考察系统中主体的不同状态和行为特征、主体与环境的作用、主体之间的作用，来表征系统的宏观特征，是进行复杂社会环境系统分析和政策研究的重要手段，在水资源、水环境管理系统中得到了有效的应用。本文在大量文献调研的基础上，讨论了ABSS建模方法的基本原理、主要特征、系统构成、分类方法和开发工具等特征，总结了其供水系统发展、水资源分配和流域水资源管理、农业土地和水资源利用、居民用水行为、农业污染控制及人类活动的水环境影响等方面的应用，分析了其在研究内容、研究方法、研究结果等方面的发展趋势，讨论了利用该方法研究我国水资源管理和水污染控制政策的必要性和重要性。     

长江三峡区间数字流域水系的构建

基于全球陆地一公里基础高程GLOBE数据 ,采用Martz和Garbrecht研制的数字高程流域水系模型自动提取长江三峡万县～宜昌区间的河网水系、各子流域分水线、河网与子流域编码及河网结构拓扑关系 ,从而构成该区域的数字流域和数字水系。结果表明该模型生成的水系是可以接受的 ,与 1:10万地形图上长江三峡万县～宜昌区间的水系一致 ,这为空间分布式模型的建立提供了必备的空间信息数据。最后 ,探讨了数字流域水系在流域生态学中的应用前景 ,构建的数字水系可为中尺度以上流域水体和水生生物的资源保护与合理利用提供决策平台 ,服务于社会经济的可持续发展。     

Phosphate recovery with granular acid-activated neutralized red mud: Fixed-bed column performance and breakthrough curve modelling

Granular acid-activated neutralized red mud (AaN-RM) has been successfully prepared with good chemical stability and physical strength. However, its potential for industrial application remains unknown. Therefore, the performance of granular AaN-RM for phosphate recovery in a fixed-bed column was investigated. The results demonstrated that the phosphate adsorption performance of granular AaN-RM in a fixed-bed column was affected by various operational parameters, such as the bed depth, flow rate, initial solution pH and initial phosphate concentration. With the optimal empty-bed contact time (EBCT) of 24.27 min, the number of processed bed volumes and the phosphate adsorption capacity reached 496.95 and 84.80 mg/g, respectively. Then, the saturated fixed-bed column could be effectively regenerated with a 0.5 mol/L HCl solution. The desorption efficiency remained as high as 83.45% with a low weight loss of 3.57% in the fifth regeneration cycle. In addition, breakthrough curve modelling showed that a 5-9-1 feed-forward artificial neural network (ANN) could be effectively applied for the optimization of the fixed-bed adsorption system; the coefficient of determination (R²) and the root mean square error (RMSE) evaluated on the validation-testing data were 0.9987 and 0.0183, respectively. Therefore, granular AaN-RM fixed-bed adsorption exhibits promising potential for phosphate removal and recovery from polluted water.     

旅游发展背景下乡村适应性演化理论框架与实证

乡村转型及适应演化路径是乡村可持续发展领域重要研究内容,对实施乡村振兴战略及其多元目标的实现具有重要理论和现实意义。通过国家制度（战略）变迁关键时间节点,梳理典型案例乡村发展阶段,明确案例乡村转型适应的体制演化特征。在乡村适应性演化理论分析框架构建基础上,以体制转换明显的乡村为实证案例,结合农户调查与深度访谈数据,评估乡村适应演化下系统适应能力（结果）及农户生计响应效果,并基于归纳法和灰色关联分析,揭示乡村体制转换与微观农户生计关联的适应演化路径及机制。研究结果表明:（1）传统农业体制乡村向旅游体制转换后出现乡村经济发展和环境破坏、社会矛盾风险并存局面,农户生计适应选择对乡村转型响应呈现多样化。（2）不同适应路径农户生计恢复力存在显著差异,升级型、扩张型生计恢复力相对较高,维持型和更替型较低。（3）乡村体制转换（R₁到R_n）与农户生计适应（P₁到P₅）呈现协同且分化的演化路径,基础设施、政策扶持以及生态环境因子是乡村转型与农户生计关联适应演化的关键影响变量。     

旅游目的地网络空间结构及其复杂性研究——野三坡旅游地案例实证

复杂性是旅游目的地网络化发展的必然趋势,是认识旅游地网络空间结构与组织特征的重要研究手段。论文以河北省野三坡旅游地为例,通过构建旅游联系强度模型,建立旅游地空间网络,并运用TOP网络空间分析以及复杂网络理论,揭示旅游地网络空间结构与组织的复杂性特征。研究发现：1）旅游地网络具有空间集聚性,形成了以核心景区为中心,由中心城镇、乡村社区和特色园区共同构成的空间集聚单元,并体现出“邻近连接”与“择优连接”的双重特性;2）旅游地网络服从幂律分布,具有无标度网络特性;3）每个节点的前三名和前五名旅游联系强度网络（Top3和Top5网络）具有较小的平均路径长度、较大的集聚系数,呈现出典型的“小世界网络”特征;4）旅游地网络是典型的异配型网络,度-度呈负相关,而网络节点的集聚系数与其度值之间存在近似的倒数关系,因此具有层次性。     

基于SOM-SVM组合分类器的涂层防护性能研究

目的为避免EIS,EN技术可能出现的问题,建立一个准确、高效的评价模型,以探究现役军用有机涂层防护性能。方法利用电化学阻抗谱(EIS)、电化学噪声(EN)技术分析了两种军车有机涂层在循环暴露试验中的腐蚀行为,提取低频阻抗模值|Z|_(0.1 Hz)与涂层噪声电阻R_n两种电化学评价参数作为自组织神经网络(SOM)的输入训练样本,同时结合支持向量机(SVM)方法建立涂层防护性能组合分类器。结果将涂层失效过程自适应地分为涂层防护性能良好、防护性能下降、基本失效三个阶段。结论所建立的SOM-SVM组合分类器对于辅助分析涂层防护性能具有可行性。     

Monitoring Air Quality in Mountains: Designing an Effective Network

A quantitatively robust yet parsimonious air-quality monitoring network in mountainous regions requires special attention to relevant spatial and temporal scales of measurement and inference. The design of monitoring networks should focus on the objectives required by public agencies, namely: 1) determine if some threshold has been exceeded (e.g., for regulatory purposes), and 2) identify spatial patterns and temporal trends (e.g., to protect natural resources). A short-term, multi-scale assessment to quantify spatial variability in air quality is a valuable asset in designing a network, in conjunction with an evaluation of existing data and simulation-model output. A recent assessment in Washington state (USA) quantified spatial variability in tropospheric ozone distribution ranging from a single watershed to the western third of the state. Spatial and temporal coherence in ozone exposure modified by predictable elevational relationships ( 1.3 ppbv ozone per 100 m elevation gain) extends from urban areas to the crest of the Cascade Range. This suggests that a sparse network of permanent analyzers is sufficient at all spatial scales, with the option of periodic intensive measurements to validate network design. It is imperative that agencies cooperate in the design of monitoring networks in mountainous regions to optimize data collection and financial efficiencies.