On April 26, 1986, Unit Four of the Chernobyl nuclear reactor exploded in then Soviet Ukraine. More than 3.5 million people in Ukraine alone, not to mention many citizens of surrounding countries, ...are still suffering the effects.Life Exposedis the first book to comprehensively examine the vexed political, scientific, and social circumstances that followed the disaster. Tracing the story from an initial lack of disclosure to post-Soviet democratizing attempts to compensate sufferers, Adriana Petryna uses anthropological tools to take us into a world whose social realities are far more immediate and stark than those described by policymakers and scientists. She asks: What happens to politics when state officials fail to inform their fellow citizens of real threats to life? What are the moral and political consequences of remedies available in the wake of technological disasters?
Through extensive research in state institutions, clinics, laboratories, and with affected families and workers of the so-called Zone, Petryna illustrates how the event and its aftermath have not only shaped the course of an independent nation but have made health a negotiated realm of entitlement. She tracks the emergence of a "biological citizenship" in which assaults on health become the coinage through which sufferers stake claims for biomedical resources, social equity, and human rights.Life Exposedprovides an anthropological framework for understanding the politics of emergent democracies, the nature of citizenship claims, and everyday forms of survival as they are interwoven with the profound changes that accompanied the collapse of the Soviet Union.
It is necessary to clearly understand construction accidents for preventing a rise in Chinese construction accidents and deaths. Better analysis methods are required for Chinese construction sector ...accidents.
Choosing and analyzing a typical construction accident based on four popular contemporary accident causation models: STAMP, AcciMap, HFACS, and the 2-4 Model. Then we evaluated the models' applicability to construction accidents, including their usability, reliability, and validity.
STAMP addressed how complexity within the accident system influenced the accident development, and its output makes the responsibilities clearer for the accident. AcciMap described the entire system's failure, the entire accident's trajectory, and the relationship between them. AcciMap showed that the accident was a dynamic developing process, and this method has a high usability. The taxonomic nature of HFACS is an important feature that provides it with a high reliability. In the accident reviewed here, we found that poor management was a critical factor rather than the individual factor in the accident. The 2-4 Model provided detailed causes of the accident and established the relationship among the accident causes, the safety management system, and the safety culture. It also avoided capturing all of the complexity in the large sociotechnical system and revealed a dynamic analysis and developing process. We confirmed that it has a high usability and validity. Therefore, the 2-4Model is recommended for future Chinese construction accident analysis efforts.
The study provides a useful, reliable, and effective analysis method for Chinese construction accidents.
•STAMP, AcciMap, HFACS, and the 2-4Model, were comprehensively analyzed and applied to a typical construction accident.•We evaluated the four models' applicability to construction accidents, including their usability, reliability, and validity.•The 2-4 Model is recommended for future construction accident analysis efforts.
The March 11, 2011, Great East Japan Earthquake and tsunami sparked a humanitarian disaster in northeastern Japan. They were responsible for more than 15,900 deaths and 2,600 missing persons as well ...as physical infrastructure damages exceeding $200 billion. The earthquake and tsunami also initiated a severe nuclear accident at the Fukushima Daiichi Nuclear Power Station. Three of the six reactors at the plant sustained severe core damage and released hydrogen and radioactive materials. Explosion of the released hydrogen damaged three reactor buildings and impeded onsite emergency response efforts. The accident prompted widespread evacuations of local populations, large economic losses, and the eventual shutdown of all nuclear power plants in Japan.
Lessons Learned from the Fukushima Nuclear Accident for Improving Safety and Security of U.S. Nuclear Plants is a study of the Fukushima Daiichi accident. This report examines the causes of the crisis, the performance of safety systems at the plant, and the responses of its operators following the earthquake and tsunami. The report then considers the lessons that can be learned and their implications for U.S. safety and storage of spent nuclear fuel and high-level waste, commercial nuclear reactor safety and security regulations, and design improvements. Lessons Learned makes recommendations to improve plant systems, resources, and operator training to enable effective ad hoc responses to severe accidents. This report's recommendations to incorporate modern risk concepts into safety regulations and improve the nuclear safety culture will help the industry prepare for events that could challenge the design of plant structures and lead to a loss of critical safety functions.
In providing a broad-scope, high-level examination of the accident, Lessons Learned is meant to complement earlier evaluations by industry and regulators. This in-depth review will be an essential resource for the nuclear power industry, policy makers, and anyone interested in the state of U.S. preparedness and response in the face of crisis situations.
Accident detection in surveillance or dashcam videos is a common task in the field of traffic accident analysis by using videos. However, as accidents occur sparsely and randomly in the real world, ...the data records are more scarce than the training data for standard detection tasks such as object detection or instance detection. Moreover, the limited and diverse accident data makes it more difficult to model the accident pattern for fine-grained accident detection tasks analyzing the accident in detail. Extra prior information should be introduced in the tasks such as the common vision feature which could offer relatively effective information for many vision tasks. The big model could generate the common vision feature by training on abundant data and consuming a lot of computing time and resources. Even though the accident video data is special, the big model could also extract common vision features. Thus, in this paper, we propose to apply knowledge distillation to fine-grained accident detection which analyzes the spatial temporal existence and severity for solving the issues of complex computing (distillation to the small model) and keeping good performance under limited accident data. Knowledge distillation could offer extra general vision feature information from the pre-trained big model. Common knowledge distillation guides the student network to learn the same representations from the teacher network by logit mimicking or feature imitation. However, single-level distillation could only focus on one aspect of mimicking classification logit or deep features. Multiple tasks with different focuses are required for fine-grained accident detection, such as multiple accident classification, temporal-spatial accident region detection, and accident severity estimation. Thus in this paper, multiple-level distillation is proposed for the different modules to generate the unified video feature concerning all the tasks in fine-grained accident detection analysis. The various experimental results on a fine-grained accident detection dataset which provides more detailed annotations of accidents demonstrate that our method could effectively model the video feature for multiple tasks.
•Over 30 different methods used to produce accident investigation data.•17 examples of serious differences among data producing methods.•Over 150 differing foundational views in investigation ...knowledge base.•A dozen serious challenges the situation poses for users of reported data.•9 suggested responses to this situation.
The purpose of this study report is to raise awareness among accident investigation data users about the differences in investigation and analysis methods used to produce that data, and the challenges this situation poses for any users. Accident investigation practitioners may utilize any of over 30 methods to produce reported data about an accident. When descriptions of these methods and investigators’ work products are compared, each method can be seen to differ from all the others, usually in several ways. Numerous samples of the foundational thinking about what accidents and investigations are, investigation purposes, and why accidents happen, show the diversity in that thinking, and indicate why so many investigation and analysis methods exist today. This report documents an experienced investigation practitioner’s observations and analysis of the consequences of using these different methods to produce the data in accident investigation reports, and the challenges that situation poses for accident data users. Responses to the situation are suggested for users, practitioners and academics.
South Korea is ranked as 4th among 34 nations of the Organization for Economic Cooperation and Development with 102 deaths in road accidents per one million population. This paper aims to investigate ...the factors associated with road accidents in South Korea. The rainfall data of the Korea Meteorological Administration and road accidents data of Traffic Accident Analysis System of Korea Road Traffic Authority is analyzed for this purpose. In this connection, multivariate regression analysis and ratio analysis with the descriptive analysis are performed to uncover the catastrophic factors involved. In turn, the results reveal that traffic volume is the leading factor in road accidents. The limited road extension of 1.47% compared to the 4.14% per annum growth of the vehicles is resulting in road accidents at such a large scale. The increasing proportion of passenger cars accelerate road accidents as well. 56% of accidents occur by the infringement of safety driving violations. The drivers with higher driving experience tend to have a higher accident ratio. The collected data is analyzed in terms of gender, driver experience, type of violations and accidents as well as the associated time of the accidents when they happen. The results indicate that 36.29% and 53.01% of accidents happen by male drivers in the day and night time, respectively. 29.15% of crashes happen due to safety infringement and violations of 41 to 60 years old drivers. The results demonstrate that population density is associated with the accidents frequency and lower density results in an increased number of accidents. The necessity of the state-of-the-art regulations to govern the urban road traffic is beyond dispute, and it becomes even more crucial for citizens' relief since in our daily lives road accidents are getting more diverse.
The purpose of this study is to develop a prediction model that identifies the potential risk of fatality accidents at construction sites using machine learning based on industrial accident data ...collected by the Ministry of Employment and Labor (MOEL) of the Republic of Korea from 2011 to 2016. The data details 137,323 injuries and 2846 deaths, and includes age, sex, and length of service of each accident victim, as well as the type of construction, employer scale, and date of the accident. Upon describing the distribution of the dataset, machine learning methods, such as logistic regression, decision tree, random forest, and AdaBoost analyses were applied with the derivation of major variables influencing classification in each algorithm. A comparison of the performance of each model showed the area under the receiver operating characteristic (AUROC) curve to be highest for the random forest method, at 0.9198, which translates to a 91.98% successful predictive rate in terms of classifying workers who could face a high fatality risk. The random forest analysis of this study indicates that the month (season) and employment size are the most influential factors, followed by age, weekday, and service length based on mean decrease Gini values to predict the likelihood of a fatality accident. Moreover, this analysis generated ensemble predictions based on all the factors contained in the dataset. Hence, this study demonstrates the feasibility of machine learning in the construction safety management area. The results obtained can contribute to the prevention of accidents by raising awareness of potential safety risks, by quantitatively predicting fatal accidents and incorporating the findings with a manpower control system at a construction site.
•A predictive model for the likelihood of a fatality accident was compared and developed using machine learning methods.•The random forest method showed a predictive rate of 91.98% success at classifying workers who might face a fatality risk.•The season and employment size are the most influential factors in the model, followed by age, weekday, and service length.•The prediction model is expected to prevent accidents by raising awareness of potential safety risks.
•Text mining can help identify safety risk factors from construction accident reports.•37 safety risk factors were extracted from 221 metro construction accident reports.•Domain-specific lexicon was ...compiled for the construction accident analysis.•High-frequency terms were selected based on a tailored threshold.
Workplace accidents in construction commonly cause fatal injury and fatality, resulting in economic loss and negative social impact. Analysing accident description reports helps identify typical construction safety risk factors, which then becomes part of the domain knowledge to guide safety management in the future. Currently, such practice relies on domain experts' judgment, which is subjective and time-consuming. This paper developed an improved approach to identify safety risk factors from a volume of construction accident reports using text mining (TM) technology. A TM framework was devised, and a workflow for building a tailored domain lexicon was established. An information entropy weighted term frequency (TF-H) was proposed for term-importance evaluation, and an accumulative TF-H was proposed for threshold division. A case study of metro construction projects in China was conducted. A list of 37 safety risk factors was extracted from 221 metro construction accident reports. The result shows that the proposed TF-H approach performs well to extract important factors from accident reports, solving the impact of different report lengths. Additionally, the obtained risk factors depict critical causes contributing most to metro construction accidents in China. Decision-makers and safety experts can use these factors and their importance degree while identifying safety factors for the project to be constructed.
•A statistical analysis of 1207 marine accidents worldwide from 2010 to 2019.•A critical literature review on previous studies on marine accident severity.•A study on the relationship between the ...influencing factors and accident severity.•Provision of recommendations for the improvement of maritime safety.
This study aims to explore the relationship between the severity of marine accidents and influencing factors. An ordered logistic regression model is used to reflect the relationship between these factors and the severity of marine accidents using the worldwide accident investigation reports in the period of 2010–2019. The obtained results show that the marine accident severity is positively associated with sinking accidents, far away from port, strong wind, heavy sea, strong current and/or good visibility. With respect to ship types, fishing vessels, yachts and sailing vessels, and other ship types are the ship types most involved in accidents of higher severity. The severity level is higher for ships having incomplete or invalid seafarers’ certificates, inadequate ship manning, incomplete or invalid ship certificates and/or over 30 years of age. Seafarers with poor theoretical knowledge and less sea experience are more likely to be involved in accidents of serious consequences. Small water depth and ship types such as chemical tankers, oil tankers, container ships and/or bulk carriers are negatively related to the accident severity. The results of this study can be used to assist the relevant maritime authorities in taking effective measures of preventing the occurrence of serious marine accidents.
The importance of accident investigations carried out in every field where operators play a vital role is increasingly recognised. Many researchers argue that understanding accident formation is the ...most important way to prevent future disasters. In this research, an analysis of the modified Human Factor Analysis and Classification System (HFACS) structures developed for use in the analysis of marine accidents was conducted. These structures include HFACS-PV (Passenger Vessels), HFACS-MA (Maritime Accidents), HFACS-Coll (Collisions), HFACS-SIBCI (ship collision accidents between assisted ships and icebreakers in ice-covered waters) and HFACS-Ground (Groundings). In this study, revisions in HFACS structures were examined. It was found that the accident factors were classified at different levels to facilitate the application of the original HFACS framework. The first of the remarkable differences among the basically developed methods is the level of external factors (first level), where the accident factors arising from national and international rules are classified. The second is the level of operational conditions (last level). It has been observed that the precondition for the unsafe acts level has been revised in all methods examined. This study will guide researchers in choosing an HFACS structure suitable for the area they will study, as well as revealing different aspects of the modified methods examined in marine accident analysis.
•In this study, a number of modified Human Factor Analysis and Classification System (HFACS) methods developed to analyse marine accidents were examined.•In the study, different and similar aspects of the classifications made in HFACS were revealed, and different perspectives in a number of applications were reviewed.•This study was conducted to provide researchers with the selection of a specific HFACS structure with the associated content in marine accident analysis.
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