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.     

因子回归和交互联合探索区域植被覆盖度的影响因素——以三江源地区为例

利用SPOT VEGETATION数据获取的归一化植被指数（NDVI）,分析三江源地区植被覆盖度（FVC）的空间异质性,围绕自然和人类活动因素,基于因子回归与交互作用联合探索自然因素和人为因素对三江源地区植被覆盖的影响.结果表明：（1）三江源地区植被覆盖度整体呈现明显的空间异质性;（2）总体上FVC空间分布的影响因素表现为自然环境因素>人类活动因素;（3）降水是影响三江源地区FVC的主要驱动因子,解释力达0.777;（4）因子交互发现：驱动解释系统呈现双因子增强,说明从系统的角度来看不存在独立起作用的因子,年降水量与其他因子的交互作用最强;（5）降水梯度影响了三江源地区FVC空间异质性的解释程度.随着降水增加,因子解释力趋稳,在降水量较多的三江源东部地区,FVC趋向于更易受高程和气温的影响;（6）数据结果亦验证了因子独立的全局最优筛选仅仅是模拟因变量特征的最优函数,其解释效果与因变量的驱动解释不能完全等同.     

氮和氧同位素示踪伊洛河河水硝酸盐来源及转化过程

河流硝酸盐(NO^-₃)浓度及氮和氧同位素组成(δ¹⁵N-NO^-₃和δ¹⁸O-NO^-₃)可以辨识河水NO^-₃受自然过程和人为输入的影响,但流域不同土地利用方式对河水NO^-₃来源及转化过程的影响尚不明确,特别是山区人为输入对河水NO^-₃的影响仍不清楚.选择土地利用空间异质性显著的伊河和洛河作为研究对象,借助水体水化学组成,氢氧同位素(δD-H₂O和δ¹⁸O-H₂O)、δ¹⁵N-NO^-₃和δ¹⁸O-NO^-₃,辨识不同土地利用方式影响下河水NO^-₃来源及...     

中国碳排放及影响因素的市域尺度分析

评估区域碳排放及其与社会经济状况的关系对于制定碳减排措施至关重要.以中国339个地级及以上城市(不含新疆部分城市和港澳台地区)为研究对象,探究了非化石能源占比、土地开发度、常住人口城镇化率、第二产业占比、人均GDP和人均建设用地面积对人均CO₂排放量的影响.通过构建模拟人均CO₂排放量的贝叶斯信念网络,识别各因素对人均CO₂排放量的全局影响;采用多尺度地理加权回归模型,分析各因素对人均CO₂排放量的局部影响.结果表明：(1)2020年,中国地级及以上城市人均CO₂排放量呈现出由南向北递增,东部沿海向内陆递减的格局.(2)从全局来看,人均CO₂排放量对各因素的敏感性从高到低依次为：人均建设用地面积>人均GDP>常住人口城镇化率>土地开发度>第二产业占比>非化石能源占比.(3)从局部来看,各因素与人均CO₂排放量的空间关系方向与全局关系一致,关系强度上存在空间异质性.(4)清洁能源、脱碳技术、土地节约集约利用...     

Estimation of the maximal population frequency of “alien” biotypes taking into account a priori data

A method is described for calculating the probability that the percentage of alien biotypes is higher than a specified threshold (e.g., 5%) in a population in which a certain number of alien biotypes has been found preliminarily. The method is based on the Bayesian approach; it assumes that the researcher has preliminary (a priori) information on the frequency of these biotypes. This a priori information makes it possible to estimate the aforementioned probability more accurately than is possible with the use of the standard binomial estimation. The method is illustrated by the results of the estimation of cultivar purity in batches of stock and foundation seeds of spring barley with the use of protein markers.Translated from Ekologiya, No. 2, 2005, pp. 106–109.Original Russian Text Copyright © 2005 by Zhivotovsky, Pomortsev, Lyalina, Kalabushkin, Pukhalskii.     

Effectiveness of Panama as an intercontinental land bridge for large mammals

Habitat fragmentation is a primary driver of wildlife loss, and establishment of biological corridors is a common strategy to mitigate this problem. A flagship example is the Mesoamerican Biological Corridor (MBC), which aims to connect protected forest areas between Mexico and Panama to allow dispersal and gene flow of forest organisms. Because forests across Central America have continued to degrade, the functioning of the MBC has been questioned, but reliable estimates of species occurrence were unavailable. Large mammals are suitable indicators of forest functioning, so we assessed their conservation status across the Isthmus of Panama, the narrowest section of the MBC. We used large-scale camera-trap surveys and hierarchical multispecies occupancy models in a Bayesian framework to estimate the occupancy of 9 medium to large mammals and developed an occupancy-weighted connectivity metric to evaluate species-specific functional connectivity. White-lipped peccary (Tayassu pecari), jaguar (Panthera onca), giant anteater (Myrmecophaga tridactyla), white-tailed deer (Odocoileus virginianus), and tapir (Tapirus bairdii) had low expected occupancy along the MBC in Panama. Puma (Puma concolor), red brocket deer (Mazama temama), ocelot (Leopardus pardalis), and collared peccary (Pecari tajacu), which are more adaptable, had higher occupancy, even in areas with low forest cover near infrastructure. However, the majority of species were subject to ≥1 gap that was larger than their known dispersal distances, suggesting poor connectivity along the MBC in Panama. Based on our results, forests in Darien, Donoso–Santa Fe, and La Amistad International Park are critical for survival of large terrestrial mammals in Panama and 2 areas need restoration.     

基于模糊贝叶斯网络的煤气化职业健康风险分析

针对煤气化行业职业健康风险影响因素不确定及模糊的特点,建立了职业健康风险计算模型。该模型将模糊数学与贝叶斯网络相耦合,模拟事件概率,找出导致风险的主要因素。通过分析煤气化行业中存在的多种风险因素,应用问卷调查法和模糊集理论模拟了根节点的发生概率,得出职业健康风险概率的预测值;应用贝叶斯网络反向推理的功能计算根节点后验概率并排序,确定了薄弱环节。该模型不仅能解决概率缺失情况下的风险量化推算问题,定量进行职业健康风险评估,还可以实现关键因素的识别,并能有针对性地提出改进措施,为职业健康风险预防提供决策依据。     

Spatial modeling and prediction under stationary non-geometric range anisotropy

For modeling spatial processes, we propose a rich parametric class of stationary range anisotropic covariance structures that, when applied in R², greatly increases the scope of variogram contors. Geometric anisotropy, which provides the most common generalization of isotropy within stationarity, is a special case. Our class is built from monotonic isotropic correlation functions and special cases include the Matérn and the general exponential functions. As a result, our range anisotropic correlation specification can be attached to a second order stationary spatial process model, unlike ad hoc approaches to range anisotropy in the literature. We adopt a Bayesian perspective to obtain full inference and demonstrate how to fit the resulting model using sampling-based methods. In the presence of measurement error/microscale effect, we can obtain both the usual predictive as well as the noiseless predictive distribution. We analyze a data set of scallop catches under the general exponential range anisotropic model, withholding ten sites to compare the accuracy and precision of the standard and noiseless predictive distributions.