Evacuation Risks: A Tentative Approach for Quantification

This study tries to assess the risk of deaths and injuries from motor vehicle accidents associated with an evacuation of population groups in case of nuclear plant accidents. The risk per person–km is evaluated using: (a) data from previous evacuation: information from Soufriere evacuation (Guadeloupe Island 1976) and Mississauga (1979), added to Hans and Sell's data: no road accident occurred for a sample of 1,500,000 persons; (b) national recording system for motor vehicle accident: the rates of 2.2 10 ^-8 deaths per person–km and 32 10^-8 injuries per person–km is calculated as an average. These last rates in France overestimate the number of casualties. A reasonable hypothesis is to assume that the probability of road accident occurrence follows a Poisson distribution, as these events are independent and unfrequent, as no accident was observed in a sample of 1,500,000 persons the probability is between 0 and an upper value of 0.24 10^-8 deaths per person-km and 3.29 10^-8 injuries per person–km. The average and maximum population involved within different radii around French and U.S. Nuclear power sites are taken as a sample size in order to study the total risk of deaths and injuries in the hypothesis of an evacuation being necessary to protect the populations.     

Modeling Ship Transportation Risk

This article presents results from the Commission of the European Communities (CEC) project 'Safety of Shipping in Coastal Waters' (SAFECO). The project was performed by ten European partners during the period 1995-1998. The principal aim of the SAFECO project was to determine the influences that could increase the safety of shipping in coastal waters by analyzing the underlying factors contributing to the marine accident risk level. The work reported here focuses on the Marine Accident Risk Calculation System (MARCS) that was further developed during the SAFECO project. This paper presents the methods used by MARCS, as well as data and results from a 'demonstration of concept' case study covering the North Sea area. The estimated accident frequencies (number of accidents per year) were compared with historical accident data, to demonstrate the validity of the modeling approach. Reasonable (within a factor of 5) to good (within a factor of 2) agreement between calculated accident frequencies and observed accident statistics was generally obtained. However, significant discrepancies were identified for some ship types and accident categories. The risk model has particular problems with estimating the accident frequency for drift grounding in general and powered grounding for ferries. It was concluded that these discrepancies are related to uncertainties in several areas, specifically in the risk model algorithms, the traffic data, the error and failure probability data, and the historical accident statistics.     

一个新的银行信用风险评估方法

本文以银行信用风险管理为例,将粗糙集和决策树两种具有互补优势的数据挖掘方法相结合,对客户信用做出归类分析判断,最后利用决策树生成决策规则.实践证明,这种方法忠于原始数据,提高了分类准确度,减小了决策树规模,具有良好的性能.     

A Fuzzy Decision Tree for Fault Classification

In plant accident management, the control room operators are required to identify the causes of the accident, based on the different patterns of evolution of the monitored process variables thereby developing. This task is often quite challenging, given the large number of process parameters monitored and the intense emotional states under which it is performed. To aid the operators, various techniques of fault classification have been engineered. An important requirement for their practical application is the physical interpretability of the relationships among the process variables underpinning the fault classification. In this view, the present work propounds a fuzzy approach to fault classification, which relies on fuzzy if-then rules inferred from the clustering of available preclassified signal data, which are then organized in a logical and transparent decision tree structure. The advantages offered by the proposed approach are precisely that a transparent fault classification model is mined out of the signal data and that the underlying physical relationships among the process variables are easily interpretable as linguistic if-then rules that can be explicitly visualized in the decision tree structure. The approach is applied to a case study regarding the classification of simulated faults in the feedwater system of a boiling water reactor.     

油田开发污染源危险度评价模型及实证研究

针对油田开发污染源存在危险度,提出一种将灰关联度分析与故障树分析相结合的危险度评价模型.该模型运用灰关联度分析方法进行事故模式的识别,并根据某一具体的故障树,分析和导出顶事件发生的各种故障模式的可能性大小.然后,选取大庆油田工程有限公司典型案例进行实证研究,结果表明该方法对油田开发污染源危险度评价具备可行性和操作性.     

Is Eradication of the Pinewood Nematode (Bursaphelenchus xylophilus) Likely? An Evaluation of Current Contingency Plans

The pinewood nematode (PWN) is one of the worst tree‐killing exotic pests in East‐Asian countries. The first European record of establishment in Portugal in 1999 triggered extensive surveys and contingency plans for eradication in European countries, including immediate removal of large areas of conifer host trees. Using Norway as an example, we applied a simulation model to evaluate the chance of successful eradication of a hypothetical introduction by the current contingency plan in a northern area where wilting symptoms are not expected to occur. Despite a highly variable spread of nematode infestations in space and time, the probability of successful eradication in 20 years was consistently low (mean 0.035, SE 0.02). The low success did not change significantly by varying the biological parameters in sensitivity analyses (SA), probably due to the late detection of infestations by the survey (mean 14.3 years). SA revealed a strong influence of management parameters. However, a high probability of eradication required unrealistic measures: achieving an eradication probability of 0.99 in 20 years required 10,000 survey samples per year and a host tree removal radius of 8,000 m around each detection point.     

Foreign Languages and their Impact on Unemployment

Using a large European data set, I investigate the impact of knowing foreign languages on unemployment for the first time. The focus is on natives (not on immigrants). I find that (1) knowing a foreign language reduces the probability of being unemployed by at least 3.4 percentage points; (2) females benefit more than males from learning foreign languages; (3) English and German tend to have a larger and more robust impact on unemployment than French, Spanish, and Italian; (4) but the impact of all these five languages varies considerably across countries.     

Human Factors Analysis for Maritime Accidents Based on a Dynamic Fuzzy Bayesian Network

Human factors are widely regarded to be highly contributing factors to maritime accident prevention system failures. The conventional methods for human factor assessment, especially quantitative techniques, such as fault trees and bow-ties, are static and cannot deal with models with uncertainty, which limits their application to human factors risk analysis. To alleviate these drawbacks, in the present study, a new human factor analysis framework called multidimensional analysis model of accident causes (MAMAC) is introduced. MAMAC combines the human factors analysis and classification system and business process management. In addition, intuitionistic fuzzy set theory and Bayesian Network are integrated into MAMAC to form a comprehensive dynamic human factors analysis model characterized by flexibility and uncertainty handling. The proposed model is tested on maritime accident scenarios from a sand carrier accident database in China to investigate the human factors involved, and the top 10 most highly contributing primary events associated with the human factors leading to sand carrier accidents are identified. According to the results of this study, direct human factors, classified as unsafe acts, are not a focus for maritime investigators and scholars. Meanwhile, unsafe preconditions and unsafe supervision are listed as the top two considerations for human factors analysis, especially for supervision failures of shipping companies and ship owners. Moreover, potential safety countermeasures for the most highly contributing human factors are proposed in this article. Finally, an application of the proposed model verifies its advantages in calculating the failure probability of accidents induced by human factors.     

Exposure to Traffic and Risk of Hospitalization Due to Injuries

Research on the risk of motor vehicle injuries and their relationship with the amount of travel has been only partially analyzed. The few individual exposure assessments are related to very specific subsets of the driving and traveling populations. This study analyzes the relationship between kilometers traveled and hospitalization due to motor vehicle injuries. Twelve thousand three hundred and sixty nine Spanish university graduates from the Seguimiento Universidad de Navarra multipurpose cohort study were evaluated. They had not been hospitalized due to motor vehicle injuries at baseline and were followed up to eight years. Biannual questionnaires allowed for self‐reporting of kilometers traveled in motor vehicles, together with incidence of hospitalization. Covariates in the Cox regression models included age and gender and baseline use of safety belt while driving, driving a vehicle with driver‐side airbag, driving a motorcycle, and drinking and driving. There were 49,766 participant‐years with an average yearly travel of 7,828 km per person‐year. Thirty‐six subjects reported a first hospitalization event during this time. The adjusted hazard ratio per additional kilometer traveled was 1.00005 (95% confidence interval 1.000013 to 1.000086). Even the smallest of reductions in the amount of kilometers traveled (from an average of 3,250 km per year to 1,000) has a statistically significant protective effect on the likelihood of sustaining hospitalization due to motor vehicle injury (aHR 0.9, 95% CI 0.78 to 0.98). In light of current policies aimed to reduce motorized traffic due to environmental concerns, it may be appropriate to consider the additional health benefit related to reductions in injuries.     

The Seveso Directive: An Italian Pilot Study in Enabling Communication

In 1982 the Council of the European Communities issued a Directive on the Major Accident Hazards of Certain Industrial Activities, commonly known as the Seveso Directive. Besides other requirements, the Directive specifies that members of the public liable to be affected by a major accident from certain industrial activities must be informed of safety measures and how they should behave in the event of an accident. The Directive was subsequently amended in an extensive way. Upon the transposition of the Seveso Directive and its amendments into national legislations, the 12 European Member States are faced with the task of "appropriately and effectively" informing the public about major accident hazards. Delays are observed throughout the community, both in transposing the Directives and implementing public information. A pilot study on "Risk Information Needs" was commissioned and carried out at two Seveso sites in Italy in order to begin to resolve some of the problems involved in the provision of public information and to suggest appropriate guidelines. One hundred subjects answered an experimental protocol designed to assess their "information needs." This article presents the theoretical background, the pragmatic aims, and the design of the study. It also summarizes its most relevant findings and suggests some recommendations for the provision of information to the public.     

Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously.

In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems.     

Psychosocial variables related to transit safety: The application of catastrophe theory

This research applied a paradigm for the study of occupational safety and health risks in manufacturing settings to the risks associated with urban and regional transit operation. Subjects were 290 transit operators who completed a questionnaire measuring safety management, danger ratings, beliefs about accident control, work experience, physical stress, social stress, anxiety, environmental hazards, transit hazards (assaults and insults upon the operator, the need to reprimand passengers for various infractions, etc.), vehicle crashes and operator injuries, and alcohol and drug use. Principal findings were that operator injuries were most closely associated with transit hazards, which were more frequent at night and on the least desirable routes. Injuries occurred in a climate of elevated stress and anxiety which may have hampered the operators' ability to respond effectively to potentially dangerous situations. The cusp catastrophe model provided a non-linear dynamic explanation for accident occurrence and was more accurate (R = 0.71) for modelling accident incidence than the next best linear alternative (R = 0.51).     

A Combined Monte Carlo and Possibilistic Approach to Uncertainty Propagation in Event Tree Analysis

In risk analysis, the treatment of the epistemic uncertainty associated to the probability of occurrence of an event is fundamental. Traditionally, probabilistic distributions have been used to characterize the epistemic uncertainty due to imprecise knowledge of the parameters in risk models. On the other hand, it has been argued that in certain instances such uncertainty may be best accounted for by fuzzy or possibilistic distributions. This seems the case in particular for parameters for which the information available is scarce and of qualitative nature. In practice, it is to be expected that a risk model contains some parameters affected by uncertainties that may be best represented by probability distributions and some other parameters that may be more properly described in terms of fuzzy or possibilistic distributions. In this article, a hybrid method that jointly propagates probabilistic and possibilistic uncertainties is considered and compared with pure probabilistic and pure fuzzy methods for uncertainty propagation. The analyses are carried out on a case study concerning the uncertainties in the probabilities of occurrence of accident sequences in an event tree analysis of a nuclear power plant.     

A Model of Hygiene Practices and Consumption Patterns in the Consumer Phase

A mathematical model is presented, which addresses individual hygiene practices during food preparation and consumption patterns in private homes. Further, the model links food preparers and consumers based on their relationship to household types. For different age and gender groups, the model estimates (i) the probability of ingesting a meal where precautions have not been taken to avoid the transfer of microorganisms from raw food to final meal (a risk meal), exemplified by the event that the cutting board was not washed during food preparation, and (ii) the probability of ingesting a risk meal in a private home, where chicken was the prepared food item (a chicken risk meal). Chicken was included in the model, as chickens are believed to be the major source of human exposure to the foodborne pathogen Campylobacter. Monte Carlo simulations showed that the probability of ingesting a risk meal was highest for young males (aged 18-29 years) and lowest for the elderly above 60 years of age. Children aged 0-4 years had a higher probability of ingesting a risk meal than children aged 5-17 years. This difference between age and gender groups was ascribed to the variations in the hygiene levels of food preparers. By including the probability of ingesting a chicken meal at home, simulations revealed that all age groups, except the group above 60 years of age, had approximately the same probability of ingesting a chicken risk meal, the probability of females being slightly higher than that of males. The simulated results show that the probability of ingesting a chicken risk meal at home does not only depend on the hygiene practices of the persons preparing the food, but also on the consumption patterns of consumers, and the relationship between people preparing and ingesting food. This finding supports the need of including information on consumer behavior and preparation hygiene in the consumer phase of exposure assessments.     

Assessing Crew Response Probability for a Sequence of Actions with an Overall Time Constraint

The following situation has been considered: during an accident sequence, a crew has to do a proper set of actions within a finite time window in order to achieve a specific goal. The operator actions are considered independent, they are lined up in series, and a single failure leads to mission failure. This paper proposes a general methodology for assessing the crew response (or success) probability, through a convolution integral formulation. This method is general and can be applied to any response probability model; an application to the Human Cognitive Reliability model is given.     

Sensitivity Analysis to Evaluate the Impact of Uncertain Factors in a Scenario Tree Model for Classical Swine Fever Introduction

Introduction of classical swine fever virus (CSFV) is a continuing threat to the pig production sector in the European Union. A scenario tree model was developed to obtain more insight into the main risk factors determining the probability of CSFV introduction (P(CSFV)). As this model contains many uncertain input parameters, sensitivity analysis was used to indicate which of these parameters influence model results most. Group screening combined with the statistical techniques of design of experiments and meta-modeling was applied to detect the most important uncertain input parameters among a total of 257 parameters. The response variable chosen was the annual P(CSFV) into the Netherlands. Only 128 scenario calculations were needed to specify the final meta-model. A consecutive one-at-a-time sensitivity analysis was performed with the main effects of this meta-model to explore their impact on the ranking of risk factors contributing most to the annual P(CSFV). The results indicated that model outcome is most sensitive to the uncertain input parameters concerning the expected number of classical swine fever epidemics in Germany, Belgium, and the United Kingdom and the probability that CSFV survives in an empty livestock truck traveling over a distance of 0-900 km.     

An Evidential Reasoning‐Based CREAM to Human Reliability Analysis in Maritime Accident Process

This article proposes a modified cognitive reliability and error analysis method (CREAM) for estimating the human error probability in the maritime accident process on the basis of an evidential reasoning approach. This modified CREAM is developed to precisely quantify the linguistic variables of the common performance conditions and to overcome the problem of ignoring the uncertainty caused by incomplete information in the existing CREAM models. Moreover, this article views maritime accident development from the sequential perspective, where a scenario‐ and barrier‐based framework is proposed to describe the maritime accident process. This evidential reasoning‐based CREAM approach together with the proposed accident development framework are applied to human reliability analysis of a ship capsizing accident. It will facilitate subjective human reliability analysis in different engineering systems where uncertainty exists in practice.     

Uncertainty Assessment in the Quantification of Risk Rates of Occupational Accidents

Occupational risk rates per hour of exposure have been quantified for 63 occupational accident types for the Dutch working population. Data were obtained from the analysis of more than 9,000 accidents that occurred over a period of six years in the Netherlands and resulted in three types of reportable consequences under Dutch law: (a) fatal injury, (b) permanent injury, and (c) serious recoverable injury requiring at least one day of hospitalization. A Bayesian uncertainty assessment on the value of the risk rates has been performed. Annual risks for each of the 63 occupational accident types have been calculated, including the variability in the annual exposure of the working population to the corresponding hazards. The suitability of three risk measures—individual risk rates, individual annual risk, and number of accidents—is examined and discussed.     

Bayesian Classifiers Applied to the Tennessee Eastman Process

Fault diagnosis includes the main task of classification. Bayesian networks (BNs) present several advantages in the classification task, and previous works have suggested their use as classifiers. Because a classifier is often only one part of a larger decision process, this article proposes, for industrial process diagnosis, the use of a Bayesian method called dynamic Markov blanket classifier that has as its main goal the induction of accurate Bayesian classifiers having dependable probability estimates and revealing actual relationships among the most relevant variables. In addition, a new method, named variable ordering multiple offspring sampling capable of inducing a BN to be used as a classifier, is presented. The performance of these methods is assessed on the data of a benchmark problem known as the Tennessee Eastman process. The obtained results are compared with naive Bayes and tree augmented network classifiers, and confirm that both proposed algorithms can provide good classification accuracies as well as knowledge about relevant variables.