基于百度指数的长江中游城市群城市网络特征研究

随着地理研究迈入大数据时代,运用互联网展开城市网络结构研究逐渐成为经理地理研究的新思路。以长江中游城市群为研究对象,借助百度指数,获取2011年~2014年百度用户关注度,构建百度指数城市信息流网络,从大城市群视角和所辖三大子城市群视角,分别探讨城市网络格局的时空变化。研究发现:长江中游城市群在一体化进程中城市等级日益明晰,差距拉大。三大子城市群表现出极化效应与扩散效应并存,区域非均衡性突显;互联网的普及在一定程度上重塑和改造着城市群,但非完全颠覆传统城市网络格局,地理区位对城市网络格局和城市影响力的影响仍不容忽视。     

A new pre-assessment model for failure-probability-based-planning by neural network

At present, the prediction of failure probability is based on the operation period for laid pipelines, and the method is complicated and time-consuming. If the failure probability can be predicted in the planning stage, the risk assessment system of gas pipeline will be greatly improved. In this paper, the pre-laying assessment model is established to minimize risk of leakage due to piping layout. Firstly, Fault Tree Analysis (FTA) modeling is carried out for urban natural gas pipeline network. According to expert evaluation, 84 failure factors, which can be determined in the planning stage, are selected as the input variables of the training network. Then the FTA model is used to calculate the theoretical failure probability value, and the failure probability prediction model is determined through repeated trial calculation based on BP (Back Propagation Neural Network) and RBF (Radial Basis Function), for obtaining the optimal network parameter combination. Finally, two prediction models are used to calculate the same example. By comparing our pre-assessment model with the theoretical prediction consequences of the fault tree, the results show that the error of RBF prediction model can be close to 3%, which proves the validity and correctness of the method.     

An integrated EDIB model for probabilistic risk analysis of natural gas pipeline leakage accidents

Natural gas pipeline construction is developing rapidly worldwide to meet the needs of international and domestic energy transportation. Meanwhile, leakage accidents occur to natural gas pipelines frequently due to mechanical failure, personal operation errors, etc., and induce huge economic property loss, environmental damages, and even casualties. However, few models have been developed to describe the evolution process of natural gas pipeline leakage accidents (NGPLA) and assess their corresponding consequences and influencing factors quantitatively. Therefore, this study aims to propose a comprehensive risk analysis model, named EDIB (ET-DEMATEL-ISM-BN) model, which can be employed to analyze the accident evolution process of NGPLA and conduct probabilistic risk assessments of NGPLA with the consideration of multiple influencing factors. In the proposed integrated model, event tree analysis (ET) is employed to analyze the evolution process of NGPLA before the influencing factors of accident evolution can be identified with the help of accident reports. Then, the combination of DEMATEL (Decision-making Trial and Evaluation Laboratory) and ISM (Interpretative Structural Modeling) is used to determine the relationship among accident evolution events of NGPLA and obtain a hierarchical network, which can be employed to support the construction of a Bayesian network (BN) model. The prior conditional probabilities of the BN model were determined based on the data analysis of 773 accident reports or expert judgment with the help of the Dempster-Shafer evidence theory. Finally, the developed BN model was used to conduct accident evolution scenario analysis and influencing factor sensitivity analysis with respect to secondary accidents (fire, vapor cloud explosion, and asphyxia or poisoning). The results show that ignition is the most critical influencing factor leading to secondary accidents. The occurrence time and occurrence location of NGPLA mainly affect the efficiency of emergency response and further influence the accident consequence. Meanwhile, the weight ranking of economic loss, environmental influence, and casualties on social influence is determined with respect to NGPLAs.     

Risk assessment of safety level in university laboratories using questionnaire and Bayesian network

Accidents in university laboratories not only create a great threat to students’ safety but bring significant negative social impact. This paper investigates the university laboratory safety in China using questionnaire and Bayesian network (BN) analysis. Sixteen influencing factors for building the Bayesian net were firstly identified. A questionnaire was distributed to graduate students at 60 universities in China to acquire the probability of safe/unsafe conditions for sixteen influencing factors, based on which the conditional probability of four key factors (human, equipment and material, environment, and management) was calculated using the fuzzy triangular theory and expert judgment. The determined conditional probability was used to develop a Bayesian network model for the risk analysis of university laboratory safety and identification of the main reasons behind the accidents. Questionnaire results showed that management problems are prominent due to insufficient safety education training and weak management level of management personnel. The calculated unsafe state probability was found to be 65.2%. In the BN analysis, the human factor was found to play the most important role, followed by equipment and material factor. Sensitive and inferential analysis showed that the most sensitive factors are personnel incorrect operation, illegal operation, and experiment equipment failure. Based on the analysis, countermeasures were proposed to improve the safe management and operation of university laboratories.     

Dynamic risk assessment for underground gas storage facilities based on Bayesian network

Loss of the underground gas storage process can have significant effects, and risk analysis is critical for maintaining the integrity of the underground gas storage process and reducing potential accidents. This paper focuses on the dynamic risk assessment method for the underground gas storage process. First, the underground gas storage process data is combined to create a database, and the fault tree of the underground gas storage facility is built by identifying the risk factors of the underground gas storage facility and mapping them into a Bayesian network. To eliminate the subjectivity in the process of determining the failure probability level of basic events, fuzzy numbers are introduced to determine the prior probability of the Bayesian network. Then, causal and diagnostic reasoning is performed on the Bayesian network to determine the failure level of the underground gas storage facilities. Based on the rate of change of prior and posterior probabilities, sensitivity and impact analysis are combined to determine the significant risk factors and possible failure paths. In addition, the time factor is introduced to build a dynamic Bayesian network to perform dynamic assessment and analysis of underground gas storage facilities. Finally, the dynamic risk assessment method is applied to underground gas storage facilities in depleted oil and gas reservoirs. A dynamic risk evaluation model for underground gas storage facilities is built to simulate and validate the dynamic risk evaluation method based on the Bayesian network. The results show that the proposed method has practical value for improving underground gas storage process safety.     

A bayesian network-based safety assessment method for solid propellant granule-casting molding process

The safety of the solid propellant molding process is vital for the stable production of high-quality propellants. Failure events caused by abnormal parameters in the molding process may have catastrophic consequences. In this paper, a Bayesian network (BN) model is proposed to assess the safety of the solid propellant granule-casting molding process. Fault tree analysis (FTA) is developed to construct a causal link between process variables and process failures. Subsequently, expert experience and fuzzy set theory (FST) are used to obtain failure probabilities of the basic events (BEs). Based on the mapping rules, FTA provides BN with reliable prior knowledge and a network structure with interpretability. Finally, when new evidence is obtained, the probability is updated with the diagnostic reasoning capability of BN. The results of the sensitivity analysis and diagnostic inference were combined to identify key parameters in the granule-casting molding process, including curing temperature, vacuum degree, extrusion, calendering roll distance, length setting value, holding time, and polish time. The results of this paper can provide effective supporting information for managers to conduct process safety analysis.     

基于BP神经网络的矿车运行时矿井摩擦阻力的预测*

为了快速有效地确定矿车等运输设备在巷道内运行时矿井摩擦阻力的变化情况,克服模拟软件计算量和现场实测工作量大的问题,以巷道风流速度、矿车运行速度、阻塞比、矿车长度4个矿车运行时巷道摩擦阻力的影响因素作为切入点,采用动网格技术模拟得到矿车在巷道内运行时有关矿井摩擦阻力的数据,以此为样本构建基于BP神经网络的矿井摩擦阻力预测模型,运用MATLAB软件进行网络训练,并将BP神经网络预测值与FLUENT模拟值进行对比。研究结果表明:BP神经网络结构比较简单,能以较快速度收敛,预测值与模拟值最大误差在7%以内,该神经网络模型用于求解矿车等运输设备在巷道内运行时摩擦阻力的变化情况是可行的。     

Predicting geogenic groundwater fluoride contamination throughout China

Endemic fluorosis exists in almost all provinces of China. The long-term ingestion of groundwater containing high concentrations of fluoride is one of the main causes of fluorosis. We used artificial neural network to model the relationship between groundwater fluoride concentrations from throughout China and environmental variables such as climatic, geological. and soil parameters as proxy predictors. The results show that the accuracy and area under the receiver operating characteristic curve of the model in the test dataset are 80.5% and 0.86%, respectively, and climatic variables are the most effective predictors. Based on the artificial neural network model, a nationwide prediction risk map of fluoride concentrations exceeding 1.5 mg/L with a 0.5 × 0.5 arc minutes resolution was generated. The high risk areas are mainly located in western provinces of Xinjiang, Tibet, Qinghai, and Sichuan, and the northern provinces of Inner Mongolia, Hebei and Shandong. The total number of people estimated to be potentially at risk of fluorosis due to the use of untreated high fluoride groundwater as drinking water is about 89 million, or 6% of the population. The high fluoride groundwater risk map helps the authorities to prioritize areas requiring mitigation measures and thus facilitates the implementation of water improvement and defluoridation projects.     

历史抗生素胁迫改变磺胺甲唑和甲氧苄啶对活性污泥的影响:ARGs及其潜在宿主

抗生素共暴露对污水处理厂抗生素抗性基因（ARGs）及微生物群落聚集过程有着重要影响.然而,历史抗生素接触胁迫差异（遗留效应）是否能决定微生物和ARGs对抗生素复合污染的响应尚不清楚.通过选择高浓度（30 mg ·L^-1）磺胺甲唑（SMX）和甲氧苄啶（TMP）作为历史接触胁迫条件,探究SMX和TMP复合污染对ARGs、细菌群落及其交互作用的短期影响.基于高通量荧光定量PCR,共检测出13种ARGs,它们的绝对丰度介于2.21~5.42 copies ·μL^-1（对数值,以DNA计,下同）,其中sul2、ermB、mefA和tetM-01是样品中最主要的亚型,绝对丰度在2.95~5.40 copies ·μL^-1之间;SMX和TMP复合污染会引起ARGs和可移动基因元件（MGEs）的富集,但其对各亚型的影响不同,且SMX的历史遗留效应高于TMP;不同接触史的复合污染下,ARGs间的共现与互斥模式均存在;MGEs （尤其是intI-1）与磺胺类（sul1、sul2）、四环素类［tet （32）］和大环内酯-林可酰胺-链霉菌素类抗性基因（ermB）均呈显著正相关性;基于微生物全尺度分类发现各类群微生物群落结构对复合污染的响应不同,且条件丰富菌属（CAT）得到了显著富集;陶厄氏菌属（Thauera）、假黄单胞菌属（Pseudoxanthomonas）和副球菌属（Paracoccus）是优势的抗性菌属;网络分析共发现31个ARGs的潜在宿主,以条件稀有菌属（CRT）为主,尤其是Candidatus_Alysiosphaera和纺锤状菌属（Fusibacter）与多种ARGs呈现正相关,是多种ARGs的潜在公共细菌宿主;部分稀有菌属［RT,变异杆菌属（Variibacter）、气单胞菌属（Aeromonas）和管道杆菌属（Cloacibacterium）等］是转座子IS 613的潜在宿主,在ARGs的增殖传播中发挥重要作用.综上,研究揭示了污水处理系统中抗生素历史胁迫对ARGs赋存特征和及其宿主细胞的遗留效应,可为削减抗生素复合污染引起的ARGs污染提供新思路与理论依据.     

长期覆膜条件下农田土壤微生物群落的响应特征

地膜覆盖是农业生产中保障粮食增产增收的重要措施.为明确长期地膜覆盖对农田土壤微生物群落结构特征的影响,采集4个不同覆膜年限的农田土壤,利用高通量测序技术分析土壤细菌和真菌群落结构变化,探讨长期覆膜农田土壤中微生物群落的变化及其对微生物生态环境效应的影响.结果表明,长期覆膜对土壤细菌多样性无显著影响,但降低真菌多样性;长期覆膜使土壤细菌酸杆菌（Acidobacteriota）和真菌被孢霉菌（Mortierellomycetes）物种丰度降低,增加土壤放线菌（Actinobacteriota）物种丰度.长期覆膜可以使土壤富集细菌中的芽孢杆菌（Bacillus）和类诺卡氏菌（Nocardioidaceae）,及真菌中的肉座菌目（Hypocreales）和曲霉菌（Aspergillus）等有益微生物菌群.然而长期覆膜使土壤真菌共生网络变得简单而脆弱,其关键物种仅有子囊菌门中的粪壳菌目（Sordariales）中的未知菌属一种,因此对农田土壤生态环境带来潜在风险.本研究为深化了解长期覆膜对农田微生物生态环境效应的影响提供理论依据.