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。     

深埋水平岩层隧道开挖稳定性分析及控制*

针对深埋高地应力水平岩层掌子面开挖稳定性及支护结构失效问题,以大峡谷隧道为工程背景,通过现场测试、室内试验、数值模拟等方法,探究深埋高地应力水平岩层失稳机理及控制措施。研究结果表明:坚硬岩体被节理面切割后,在高地应力作用下容易发生挤压破碎,破碎岩体遇水发生软化,导致掌子面发生大范围塌方,初支和超前支护失效;隧道开挖后岩层发生不均匀沉降,浅部岩层最先发生弯折破坏,层内块体错动滑移,继而向上方岩层发展,并伴随层间分离和层内裂隙发育,最终形成宏观破裂面;提出的台阶法、2 m开挖进尺、砼喷层、双层小导管、提高初支强度的整体优化控制措施,可有效提高现场支护效果。     

基于无人机影像的城市植被精细分类

针对传统植被资源调查方法工作量大、成本高、效率低的问题,利用高分辨率无人机遥感影像,联合地物光谱-纹理-空间信息,构建了一种适用于描述城市不同植被种类的多维特征空间,在此基础上对三种应用广泛的分类算法(基于像素的、面向对象的支持向量机及深度学习Mobile-Unet语义分割模型)开展了对比分析研究.结果表明:本文提出的联合地物光谱-纹理-空间信息的特征空间构建方法能够有效地描述城市不同类型植被的特征差异,提升影像分割、植被分类的精度;在分类精度上,基于像素和面向对象的支持向量机分类结果的总体精度均超过90%,深度学习方法的总体分类精度为84%;在算法效率上,传统机器学习方法也优于深度学习方法.因此,得出结论针对城市小区域、小样本的植被精细分类,传统机器学习分类方法比深度学习方法效果更好.     

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.     

西藏环境信息化基础建设设计实践

描述了西藏环保厅“西藏自治区环境保护厅环境信息化能力建设项目”前期调研、设计的过程,总结在西藏信息化发展基础差、技术人才少、资金投入紧张、稳定性要求高、信息技术发展迅速等现实情况下基础信息化项目建设前期设计特点,为西藏或其他欠发达地区信息化基础建设项目设计提供参考。