Not in Control,but Liable?Attributing Human Responsibility for Fully Automated Vehicle Accidents

Human agency has become increasingly limited in complex systems with increasingly automated decision-making capabilities.For instance,human occupants are passengers and do not have direct vehicle control in fully automated cars(i.e.,driverless cars).An interesting question is whether users are responsible for the accidents of these cars.Normative ethical and legal analyses frequently argue that individuals should not bear responsibility for harm beyond their control.Here,we consider human judgment of responsibility for accidents involving fully automated cars through three studies with seven experiments(N=2668).We compared the responsibility attributed to the occupants in three conditions:an owner in his private fully automated car,a passenger in a driverless robotaxi,and a passenger in a conventional taxi,where none of these three occupants have direct vehicle control over the involved vehicles that cause identical pedestrian injury.In contrast to normative analyses,we show that the occupants of driverless cars(private cars and robotaxis)are attributed more responsibility than conventional taxi passengers.This dilemma is robust across different contexts(e.g.,participants from China vs the Republic of Korea,participants with first-vs third-person perspectives,and occupant presence vs absence).Furthermore,we observe that this is not due to the perception that these occupants have greater control over driving but because they are more expected to foresee the potential consequences of using driverless cars.Our findings suggest that when driverless vehicles(private cars and taxis)cause harm,their users may face more social pressure,which public discourse and legal regulations should manage appropriately.

Exploring the Factors Associated with 12-Month Non-Return to Work among Motorcyclists Involved in Road Accidents

Introduction: Motorcyclists bear a disproportionate burden of morbidity and mortality from road accidents. In addition, the consequences of these accidents affect the ability of victims to return to work. This study aimed to determine the prevalence and factors associated with non-return to work among surviving motorcyclists involved in road accidents 12 months after the event. Materials and Methods: It was a cross-sectional study conducted using data from a cohort of motorcyclists involved in accidents and recruited in five hospitals in Benin from July 2019 to January 2020. The dependent variable was non-return to work 12 months after the accident (yes vs no). The independent variables were categorized into two groups: baseline and 12-month follow-up variables. Logistic regression was used to determine the factors associated with non-return to work at 12 months among the participants. Results: Among the 362 participants, 55 (15.19%, 95% CI = 11.84 - 19.29) had not returned to work 12 months after the accident. Risk factors for non-return to work identified were: smoking (aOR = 4.41, 95% CI = 1.44 - 13.56, p = 0.010), hospitalization (aOR = 2.87, 95% CI = 1.14 - 7.24, p Conclusion: The prevalence of non-return to work at 12 months was high among surviving motorcyclists involved in road accidents in Benin. Integrated support for patients based on identified risk factors should effectively improve their return to work.

Assessing the Variability of Extreme Weather Events and Its Influence on Marine Accidents along the Northern Coast of Tanzania

The marine accidents are among the main components of the Zanzibar Disaster Management Policy (2011) and the Zanzibar Blue Economy Policy (2020). These policies aimed to institute legal frame works and procedures for reducing both the frequency of marine accidents and their associated fatalities. These fatalities include deaths, permanent disabilities and loss of properties which may result into increased poverty levels as per the sustainable development goal one (SDG1) which stipulates on ending the poverty in all its forms everywhere. Thus, in the way to support these Government efforts, the influence of climate and weather on marine accidents along Zanzibar and Pemba Channels was investigated. The study used the 10 years (2013-2022) records of daily rainfall and hourly wind speed acquired from Tanzania Meteorological Authority (TMA) (for the observation stations of Zanzibar, Pemba, Dares Salaam and Tanga), and the significant wave heights data, which was freely downloaded from Globally Forecasting System (GFS-World model of 13 km resolution). The marine accident records were collected from TASAC and Zanzibar Maritime Authority (ZMA), and the anecdotal information was collected from heads of quay and boat captains in different areas of Zanzibar. The Mann Kendal test, was used to determine the slopes and trends direction of used weather parameters, while the Pearson correlations analysis and t-tests were used to understand the significance of the underlying relationship between the weather and marine accidents. The paired t-test was used to evaluate the extent to which weather parameters affect the marine accidents. Results revealed that the variability of extreme weather events (rainfall, ocean waves and wind speed) was seen to be among the key factors for most of the recorded marine accidents. For instance, in Pemba high rainfall showed an increasing trend of extreme rainfall events, while Zanzibar has shown a decreasing trend of these events. As for extreme wind events, results show that Dar es Salaam and Tanga had an increasing trend, while Zanzibar and Pemba had shown a decreasing trend. As for the monthly variability of frequencies of extreme rainfall events, March to May (MAM) season was shown to have the highest frequencies over all stations with the peaks at Zanzibar and Pemba. On the other hand, high frequency of extreme wind speed was observed from May to September with peaks in June to July, and the highest strength was observed during 09:00 to 15:00 GMT. Moreover, results revealed an increasing trend of marine accidents caused by bad weather except during November. Also, results showed that bad weather conditions contributed to 48 (32%) of all 150 recorded accidents. Further results revealed significant correlation between the extreme wind and marine accidents, with the highest strong correlation of r = 0.71 (at p ≤ 0.007) and r = 0.75 (at p ≤ 0.009) at Tanga and Pemba, indicating the occurrence of more marine accidents at the Pemba channel. Indeed, strong correlation of r = 0.6 between extreme rainfall events and marine accidents was shown in Pemba, while the correlations between extremely significant wave heights and marine accidents were r = 0.41 (at p ≤ 0.006) and r = 0.34 (p ≤ 0.0006) for Pemba and Zanzibar Channel, respectively. In conclusion, the study has shown high influence between marine accidents and bad weather events with more impacts in Pemba and Zanzibar. Thus, the study calls for more work to be undertaken to raise the awareness on marine accidents as a way to alleviate the poverty and enhance the sustainable blue economy.

Conceptual Strategy for Mitigating the Risk of Hydrogen as an Internal Hazard in Case of Severe Accidents at Nuclear Power Plant Considering Existing Risks and Uncertainties Associated with the Use of Traditional Strategies

Hydrogen challenge mitigation stands as one of the main objectives in the management of severe accidents at Nuclear Power Plants (NPPs). Key strategies for hydrogen control include atmospheric inertization and hydrogen removal with Passive Autocatalytic Recombiners (PARs) being a commonly accepted approach. However, an examination of PAR operation specificity reveals potential inefficiencies and reliability issues in certain severe accident scenarios. Moreover, during the in-vessel stage of severe accident development, in some severe accident scenarios PARs can unexpectedly become a source of hydrogen detonation. The effectiveness of hydrogen removal systems depends on various factors, including the chosen strategies, severe accident scenarios, reactor building design, and other influencing factors. Consequently, a comprehensive hydrogen mitigation strategy must effectively incorporate a combination of strategies rather than be based on one strategy, taking into consideration the probabilistic risks and uncertainties associated with the implementation of PARs or other traditional methods. In response to these considerations, within the framework of this research it has been suggested a conceptual strategy to mitigate the hydrogen challenge during the in-vessel stage of severe accident development.

Child Victims of Road Traffic Accidents: Epidemiological and Clinical Aspects in the Medical and Surgical Emergency Department of the Donka National Hospital

Introduction: Children involved in road accidents most often suffer head or limb injuries. The severity of these injuries varies according to the age of the child, the type of road user and whether or not protective devices are used. The aim of this study was to carry out a census of admissions and to identify the epidemiological and clinical profile of children involved in road traffic accidents at Donka National Hospital. Methods: This is a descriptive cross-sectional study of children involved in traffic accidents over a 3-month period from September 1 to November 30, 2021. All children admitted to the emergency department for a traffic accident were included in the study. Results: During the study period, 435/530 children (82.1%) were victims of road accidents. Boys accounted for 54.94% of cases, compared with 45.06% for girls. The age group most affected was 12-17 years old (48.97%). October had the highest accident rate (36.4%). Pedestrians are most affected (52.87%). Bicycles and mopeds were involved in 59.77% of accidents. Head injury was the most frequent pathology (33.33%). The study showed that 12.64% of victims were referred to intensive care, pediatric surgery, etc. We recorded 8 cases of death (1.84%). Conclusion: This study highlighted the characteristics, incidence and risk factors for accidents in children admitted to emergency departments. Accident prevention, particularly serious accidents in children, requires more precise knowledge of the factors and circumstances leading to their occurrence.

Otorhinolaryngological (ORL) and Cervicofacial Injuries Resulting from Road Accidents within the Otorhinolaryngology (ORL) Department of the Regional Hospital of Mamou

Introduction: Otorhinolaryngological (ORL) and cervicofacial traumas encompass injuries affecting the neck, face, and ORL region, resulting from external influence. The objective of this study was to conduct an epidemiological and clinical analysis of ORL and cervicofacial injuries observed in victims of road accidents treated at the Otorhinolaryngology (ORL) department of the Regional Hospital of Mamou. Materials and Methods: This prospective and descriptive study was conducted over a period of 12 months, from January 1st to December 31st, 2021, within the Otorhinolaryngology (ORL) department of the Regional Hospital of Mamou. It included patients hospitalized for ORL and/or cervicofacial traumas resulting from road accidents during the study period. Results: A total of 348 cases were included, representing 36.22% of all traumas related to road accidents (AVP) within the Regional Hospital of Mamou. The average age of the patients was 26.44 years, with a sex ratio of 3.83, favoring males. The dominant socio-professional category was students, encompassing 32.47% of cases. Road accidents were mainly associated with motorcycle use (31.32% motorcycle-to-motorcycle, 24.43% motorcycle skidding, 13.51% motorcycle-pedestrian). Facial region injuries were the most common, accounting for 69.03% of cases, followed by rhinological injuries (17.28%) and otological injuries (13.69%). Associated injuries were predominantly closed head traumas (52.55%) and lower limb fractures (20.41%). Imaging was underutilized. Regarding surgical interventions, reconstructive surgery was the most predominant, accounting for 52.51% of cases, with a management timeframe of 30 minutes to 12 hours for 99.72% of patients. Conclusion: Road traffic accidents remain a serious public health issue, primarily affecting young males. Emphasis should be placed on road safety measures to reduce the frequency of these accidents.

Analysis of Traffic Accidents in Vietnam

Understanding the causes and solutions of road traffic accidents is important for developing road and action plans in a country. In Vietnam, awareness of traffic participants is the main cause of serious traffic accidents. In recent years, the number of road traffic accidents in Tuyen Quang province with deaths has decreased, but the number of accidents has increased significantly. The article uses data on traffic accidents in Tuyen Quang over the (2016-2023) has been analytically reviewed. From there, analyze accident characteristics and causes of traffic accidents in Tuyen Quang province, and propose solutions to improve traffic safety in Tuyen Quang, Vietnam. The findings can be information for managers and researchers interested in studying the province of Tuyen Quang, Vietnam road traffic safety. Additionally, the findings have led the government to achieve national targets in reducing the number of accidents and serious injuries.

A Workable Solution for Reducing the Large Number of Vehicle and Pedestrian Accidents Occurring on a Yellow Light

Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada, during 2019, the National Collision Database shows that 28% of traffic fatalities and 42% of serious injuries occurred at intersections. Likewise, the U.S. National Highway Traffic Administration (NHTSA) found that about 40% of the estimated 5,811,000 accidents in the U.S. during the year studied were intersection-related crashes. In fact, a major survey by the car insurance industry found that nearly 85% of drivers could not identify the correct action to take when approaching a yellow traffic light at an intersection. One major reason for these accidents is the “yellow light dilemma,” the ambiguous situation where a driver should stop or proceed forward when unexpectedly faced with a yellow light. This situation is even further exacerbated by the tendency of aggressive drivers to inappropriately speed up on the yellow just to get through the traffic light. A survey of Canadian drivers conducted by the Traffic Injury Research Foundation found that 9% of drivers admitted to speeding up to get through a traffic light. Another reason for these accidents is the increased danger of making a left-hand turn on yellow. According to the National Highway Traffic Safety Association (NHTSA), left turns occur in approximately 22.2% of collisions—as opposed to just 1.2% for right turns. Moreover, a study by CNN found left turns are three times as likely to kill pedestrians than right turns. The reason left turns are so much more likely to cause an accident is because they take a driver against traffic and in the path of oncoming cars. Additionally, most of these left turns occur at the driver’s discretion—as opposed to the distressingly brief left-hand arrow at busy intersections. Drive Safe Now proposes a workable solution for reducing the number of accidents occurring during a yellow light at intersections. We believe this fairly simple solution will save lives, prevent injuries, reduce damage to public and private property, and decrease insurance costs.

Analysis of the Necessity of Stellate Ganglion Block after Anesthesia to Reduce the Risk of Cardiovascular Accidents in Coronary Heart Disease

Objective:To adjust the sympathetic nervous system through preoperative ultrasound-guided stellate ganglion block and to explore and analyze the situation and necessity of postanesthesia stellate ganglion block to reduce the risk of cardiovascular accidents in coronary heart disease.Methods:80 patients with cardiovascular risk factors in Songshan Hospital of Chifeng were selected,and the time span would cover from October 2022 to June 2024,with 80 cases of elective surgery and combined coronary heart disease.They will be randomly divided into blocked groups and conventional groups,40 cases each.Conventional block was performed after anesthesia in patients in the conventional group,and planetary ganglion block was performed after anesthesia in patients in the blocked group.The cardiovascular responses of patients in the two groups were observed,and the number of intraoperative cardiovascular active drugs and the occurrence of cardiovascular adverse events were recorded in patients in the two groups.Results:Analysis of the mean arterial pressure(MAP)and heart rate(HR)values of the two groups of patients at the time points before anesthesia(T0),the moment of tracheal intubation(T1),the moment of surgical skin cutting(T2),the end of the operation(T3),and the 6h postoperative period(T4)showed that the MAP and HR of the blocked group were lower than those of the conventional group at the time points of T1 to T4,and the differences in MAP and HR values of the two groups in different time points compared with the T0 time point were statistically significant(P<0.05)and the differences in cardiovascular response(P<0.05)were statistically significant(P<0.05).significance(P<0.05).The number of intraoperative cardiovascular active drugs used in the blocked group was shorter than that in the conventional group,and the incidence of cardiovascular adverse events was lower than that in the conventional group,with a statistically significant difference(P<0.05).Conclusion:By implementing preoperative ultrasound-guided stellate ganglion block in patients with cardiovascular risk factors,the frequency and degree of coronary heart disease symptoms will be reduced,thus reducing the risk of patients,which is worth promoting.

Characteristics of coal resources in China and statistical analysis and preventive measures for coal mine accidents

In the process of green and smart mine construction under the context of carbon neutrality,China's coal safety situation has been continuously improved in recent years.In order to recognize the development of coal production in China and prepare for future monitoring and prevention of safety incidents,this study mainly elaborated on the basic situation of coal resources and national mining accidents over the past five years(2017-2021),from four dimensions(accident level,type,region,and time),and then proposed the preventive measures based on accident statistical laws.The results show that the storage of coal resources has obvious geographic characteristics,mainly concentrated in the Midwest,with coal resources in Shanxi and Shaanxi accounting for about 49.4%.The proportion of coal consumption has dropped from 70.2%to 56%between 2011 and 2021,but still accounts for more than half of the all.Meanwhile,the accident-prone areas are positively correlated with the amount of coal production.Among different levels of coal mine accidents,general accidents had the highest number of accidents and deaths,with 692 accidents and 783 deaths,accounting for 87.6%and 54.64%respectively.The frequency of roof,gas,and transportation accidents is relatively high,and the number of single fatalities caused by gas accidents is the largest,about 4.18.In terms of geographical distribution of accidents,the safety situation in Shanxi Province is the most severe.From the time distribution of coal mine accidents,the accidents mainly occurred in July and August,and rarely occurred in February and December.Finally,the"4+4"safety management model is proposed,combining the statistical results with coal production in China.Based on the existing health and safety management systems,the manage-ments are divided into four sub-categories,and more specific measures are suggested.

Profile of Declared Work Accidents at the Parakou Agency of the National Social Security Fund, 2015-2021, Benin

Introduction: Work accidents (WA) have consequences for both the individual and the company. The objective of this study is to study the profile of work accidents declared to the Parakou agency of the National Social Security Fund (CNSS) from 2015 to 2021. Method: This was a descriptive cross-sectional study with retrospective data collection. It concerned workers affiliated with the CNSS in Parakou, victims of declared WA whose file was available and exploitable. The sampling was exhaustive. Data was collected from registers and worker records. The variables studied were socio-demographic and related to the accident. They were analyzed using R version 4.1 software. Results: The annual incidence rate of WA varied from 5.8% in 2015 to 1.7% in 2021. The victims were 90.6% male. There were more subjects aged 28 to 37 years (37.1%). Workers and laborers were the most concerned. Fractures (22.9%) were the major lesions. At 38.5% they sat in the thoracic limbs and at 22.5% in the pelvic limbs. The majority (63.6%) of these accidents occurred in the workplace. The processing industry sector was mainly (52.1%) the place of accidents. Conclusion: The prevalence of WA declared to the agency of the National Social Security Fund of Parakou from 2015 to 2021 is decreasing. The most affected were young people. It is important to assess their socio-economic impacts.

A Deep Learning Based Sentiment Analytic Model for the Prediction of Traffic Accidents

The severity of traffic accidents is a serious global concern,particularly in developing nations.Knowing the main causes and contributing circumstances may reduce the severity of traffic accidents.There exist many machine learning models and decision support systems to predict road accidents by using datasets from different social media forums such as Twitter,blogs and Facebook.Although such approaches are popular,there exists an issue of data management and low prediction accuracy.This article presented a deep learning-based sentiment analytic model known as Extra-large Network Bi-directional long short term memory(XLNet-Bi-LSTM)to predict traffic collisions based on data collected from social media.Initially,a Tweet dataset has been formed by using an exhaustive keyword-based searching strategy.In the next phase,two different types of features named as individual tokens and pair tokens have been obtained by using POS tagging and association rule mining.The output of this phase has been forwarded to a three-layer deep learning model for final prediction.Numerous experiment has been performed to test the efficiency of the proposed XLNet-Bi-LSTM model.It has been shown that the proposed model achieved 94.2%prediction accuracy.

Epidemioclinical Aspects of Childhood Domestic Accidents in the Emergency Department of Boffa Hospital

Introduction: Domestic accidents are defined as accidents that occur in the home or its immediate surroundings. Objective: To describe the epidemioclinical and therapeutic aspects. Patients and Methods: Prospective, descriptive study from July 1, 2019 to June 30, 2020 in the emergency department of Boffa prefectural hospital included all children aged 0 to 15 years admitted for domestic accidents. Results: Of 120 cases of accidental trauma, 56 were domestic accidents. The mean age was 7.08 years. The 0 - 5 and 11 - 15 age groups were the most represented (35.71%). The sex ratio was 1.55 in favor of boys. Domestic accidents were represented by falls (26.78%), thermal burns (21.43%), snakebite wounds (17.86%) and dog bites (10.71%), drowning (5.36%), and electrification by lightning (3.60%), one case or 1.78% of each of the following: esophageal foreign body by palm nut, firearm, wall collapse, stretching of upper limb. Paracetamol was the molecule most frequently used (89.28%). The outcome was favorable in 92.86% of cases. However, four (4) deaths were recorded. Conclusion: Domestic accidents remain a significant pathology at Boffa prefectural hospital.

Prognostic Factors for the Occurrence of Blood Exposure Accidents among Health Personnel at the Kaolack Regional Hospital (Senegal)

Caregivers are highly exposed to blood exposure accidents (BEA). The objective of our study is to determine the frequency of BEA and to identify the predictive factors for the occurrence of BEAs among caregivers at the Kaolack Regional Hospital (KRH). It is a descriptive and analytical cross-sectional study of KRH’s caregivers. The data were collected during the period from 16 to 26 April 2018. The multivariate analysis was carried out using the binary logistic regression model with a dependent variable (occurrence of blood exposure accidents) and 5 explanatory variables (sex, age, service of belonging, professional category and seniority in the profession). A total of 115 caregivers were surveyed out of 144. It is noted that 68 caregivers (59.1%) have had at least one BEA in the previous 12 months. The age of caregivers significantly reduces the risk of developing a BEA (p = 0.004, CI [0.04 - 0.21]);belonging to the age group [20 - 40 years] increases the risk of a BEA by 6.66. Sex significantly influences the occurrence of BEA with a (p = 0.013, CI [1.47 - 19.4]);men are 5 times more likely to develop BEA. The risk of a BEA occurring varies according to the professional category. Senior health technicians have a significantly 50 times lower risk of occurrence of a BEA (p = 0.007, CI [0.00 - 0.24)]. Nurses and midwives have a 5.8 lower risk of developing BEAs (p = 0.031, CI [0.03 - 0.75]). The risk of occurrence of BEAs varies according to the service of belonging;Medicine service caregivers have a significantly 50-fold lower risk of developing BEAs (p = 0.004, CI [0.00 - 0.17]). The identified risk factors will be used to better guide our BEAs prevention interventions.

Prognostic Factors for the Occurrence of Blood Exposure Accidents among Health Personnel at the Kaolack Regional Hospital (Senegal)

Caregivers are highly exposed to blood exposure accidents (BEA). The objective of our study is to determine the frequency of BEA and to identify the predictive factors for the occurrence of BEAs among caregivers at the Kaolack Regional Hospital (KRH). It is a descriptive and analytical cross-sectional study of KRH’s caregivers. The data were collected during the period from 16 to 26 April 2018. The multivariate analysis was carried out using the binary logistic regression model with a dependent variable (occurrence of blood exposure accidents) and 5 explanatory variables (sex, age, service of belonging, professional category and seniority in the profession). A total of 115 caregivers were surveyed out of 144. It is noted that 68 caregivers (59.1%) have had at least one BEA in the previous 12 months. The age of caregivers significantly reduces the risk of developing a BEA (p = 0.004, CI [0.04 - 0.21]);belonging to the age group [20 - 40 years] increases the risk of a BEA by 6.66. Sex significantly influences the occurrence of BEA with a (p = 0.013, CI [1.47 - 19.4]);men are 5 times more likely to develop BEA. The risk of a BEA occurring varies according to the professional category. Senior health technicians have a significantly 50 times lower risk of occurrence of a BEA (p = 0.007, CI [0.00 - 0.24)]. Nurses and midwives have a 5.8 lower risk of developing BEAs (p = 0.031, CI [0.03 - 0.75]). The risk of occurrence of BEAs varies according to the service of belonging;Medicine service caregivers have a significantly 50-fold lower risk of developing BEAs (p = 0.004, CI [0.00 - 0.17]). The identified risk factors will be used to better guide our BEAs prevention interventions.

基于情景构建的高校实验室爆炸事故应急演练与实践

应急演练在提高实验室应对突发事件能力方面发挥着重要作用,应急演练脚本是实施应急演练的重要依据,该文提出利用情景构建技术开展应急演练脚本设计。结合对以往演练的梳理、经验总结和实际案例分析,基于情景构建技术提出高校实验室爆炸事故应急演练场景规划构思与构建设计的具体内容,梳理总结出高校实验室应急演练的组织架构和分工,具体描述了高校实验室爆炸事故的演变过程,生成了基于实际案例的实验室爆炸事故应急演练脚本。实验室应急演练脚本提供了实验室爆炸事故的应急场景,可用于实际模拟演练或虚拟演练,并可结合不同案例形成实验室应急演练脚本库,为实验室应急演练提供支持。

化工园区事故情景耦合演化的复杂网络分析

为进一步研究化工园区事故易因耦合效应引发事故升级等问题,首先,以化工园区储存环节为研究对象,从要素及关系2个角度构建涵盖人因层、物质层、环境层、管理层、组合层及事故层的典型事故情景要素框架体系。然后,基于拓扑理论及相关方法,构建化工园区典型事故情景复杂网络模型,分析复杂网络的统计特征,确定节点的网络贡献度和连接边的脆弱性,探究最大可能的致因路径,并结合事故的演化特征提出断链视角下的应急响应对策。最后,引入案例分析其最大致因路径,并提出相关事故的预防措施。研究结果表明:初期是化工园区事故控制的关键时期,加强员工培训是强化初期处置的重要手段之一,点火源是事故耦合升级的关键因素之一;本文构建的化工园区事故情景演化复杂网络模型具有较好的实用性,案例分析得到的最大致因路径与实际情况基本相符。研究结果可为化工园区的事故防控及演化控制提供一定参考。

基于Apriori算法的煤矿安全事故分析

为分析煤矿事故报告中的危险致因因素,统计分析了2018—2022年全国煤矿事故报告数据,采用Apriori关联规则算法,并利用Gephi进行关联规则可视化,探究各个致因之间的复杂关系。首先对数据进行预处理,计算词频-逆向文件频率(Term Frequency-Inverse Document Frequency, TF-IDF),提取了78个煤矿事故致因因素,其中人因层包括31个因素,设备层包括9个因素,管理层包括31个因素,环境层包括7个因素;然后,经过关联规则挖掘算法,得到了585条关联规则,绘制了其支持度、置信度和提升度的散点图;最后,根据Gephi生成的事故致因复杂网络图,分别分析了高支持度、高置信度和高提升度关联规则致因因素。结果表明:基于Apriori算法的煤矿事故致因分析,得到了人因层、管理层、设备层和环境层4个方面的关键致因因素;对煤矿关键致因因素进行直观、多视图的展现,有助于提高煤矿安全管理水平。

基于DEMATEL-ISM方法的水电站机组检修作业安全事故致因链分析

针对水电站机组检修作业种类和危险因素多、安全事故频发等问题,通过文献挖掘与专家访谈方式,对水电站机组检修作业安全影响因素类别进行了划分。基于DEMATEL-ISM方法,构建了安全事故致因多层次递阶ISM模型来确定事故致因因素层级,通过剖析事故致因因素的中心度和权重,挖掘关键致因因素和推求事故致因链的路径权重,厘定关键致因链。采用该模型对某水电站致因链进行了分析,结果表明,安全检查不到位为关键致因因素,“安全检查不到位→违规作业→设备的问题→粉尘、油漆等有害物质”为关键致因链。

基于事故表征和案例推理的煤矿瓦斯爆炸预测研究

为更好地通过案例预测煤矿瓦斯爆炸事故结果,在融合事故树分析和防御性悲观思想后,提出煤矿瓦斯爆炸事故表征方法,包括事故表征结构模型、表征规范和案例库编码规则;为提升案例推理精度,通过专家置信度改进层次分析法的主观影响,提出针对不同类型表征信息的相似度计算方法,引入时间衰退系数修正案例时效性。通过山西某煤矿瓦斯爆炸事故案例对该方法进行验证,结果表明,该方法的预测结果与实际结果相吻合,并能推荐事故预防措施。研究结果可为煤矿安全管理提供支持。