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Occupational safety and health is among the most challenging issues in many industrial workplaces, in that various factors can cause occupational illness and injury. Robotics, automation, and other state-of-the-art technologies represent risks that can cause further injuries and accidents. However, the tools currently used to assess risks in workplaces require manual work and are highly subjective. These tools include checklists and work assessments conducted by experts. Modern Industry 4.0 technologies such as a digital twin, a computerized representation in the digital world of a physical asset in the real world, can be used to provide a safe and healthy work environment to human workers and can reduce occupational injuries and accidents. These digital twins should be designed to collect, process, and analyze data about human workers. The problem is that building a human-focused digital twin is quite challenging and requires the integration of various modern hardware and software components. This paper aims to provide a brief survey of recent research papers on digital twins, focusing on occupational safety and health applications, which is considered an emerging research area. The authors focus on enabling technologies for human data acquisition and human representation in a virtual environment, on data processing procedures, and on the objectives of such applications. Additionally, this paper discusses the limitations of existing studies and proposes future research directions. [ABSTRACT FROM AUTHOR]

Agriculture, alongside construction and mining, is one of the three most hazardous industries, and is characterized by numerous risk factors for occupational accidents. Unlike other industries, agriculture faces significant safety concerns related to the natural environment. Determining the causes of accidents is therefore imperative for accident prevention. However, the difficulty in investigating accidents owing to inadequate reporting and management systems among self-employed farmers hampers the determination of their causes. This study aims to determine the factors influencing agricultural accidents through a literature review. A systematic literature search was conducted according to the PRISMA guidelines. The initial search yielded 165 articles of which 34 papers were selected after removing those by applying the selection criteria. The selected papers were categorized into three domains: work accidents, machinery accidents, and farmer safety promotion. Accident causes were classified using the 4M technique: machine, media, man, and management. The results were visualized with a heat map. The main causes of accidents thus identified were insufficient operation/management, inappropriate work situation, and insufficient education/training. The findings of this study can serve as foundational data for developing measures to improve working environments not only in agriculture but also in other high-hazard industries. [ABSTRACT FROM AUTHOR]

Management systems containing procedures, checklists, and descriptions for how various tasks should be conducted are often used in high-risk industries. Much has been written about the judicial and technological concerns of management systems, but less has been written on how to train staff in the use of them. Through a cognitive-constructionist perspective combined with social constructivism, this paper discusses how staff training can be designed to fit the characteristics of the workers. This paper focuses on how people learn in different ways, and how this is related to perspectives on knowledge. The method used is semi-structured interviews with twenty-seven workers in two different companies operating in the oil and gas-producing industry. The workers got only a short web-based theory course on the management system, with no practical exercises, repetitions, or other types of follow-ups. This is a signal that the management system is of less importance, and many of the workers thought they did not need it. Training must be designed to fit the workers, with practical exercises, repetition, and possibilities for on-the-job training. Accidents in this sector can cause human losses and great environmental harm, and this paper argues that better training of staff can prevent such accidents and reduce harm to the environment. [ABSTRACT FROM AUTHOR]

Complex risk factors make metro construction safety accidents prone to occur, and there are various types of accidents. Accident reports record detailed information about different types of accidents in text form. However, effectively utilizing such unstructured data presents a significant challenge. Text mining (TM) provides a viable foundation for addressing this challenge, but related studies have limitations in risk feature extraction and lack of in-depth analysis capability. To address the deficiencies of existing studies and provide a feasible strategy for identifying key risk factors in the metro construction domain, this paper proposes an integrated model combining TM and machine learning–based Bayesian networks. Firstly, the term frequency-inverse document frequency (TF-IDF) algorithm in TM was used to separately extract the direct and indirect cause factors from the accident reports, with the missing factors supplemented using the TextRank algorithm. Then, depending on the assumption of whether to consider the conditional independence between factors, an improved naive Bayesian network (NBN) and a tree-augmented naive Bayesian network (TAN) were built based on the extracted factors and the corresponding accident types, respectively, for further in-depth analysis. Finally, the training set was divided to train the two network models, and sensitivity analysis was used to identify the key risk factors. Using 162 accident reports from China as an application example, the results showed that TAN exhibited a higher average accuracy (79.62%) in the test set compared with the improved NBN (71.75%), and the importance of risk factors for different accident types was successfully ranked from multiple perspectives using TAN. Meanwhile, some new insights into metro accidents in China were obtained, which can support decision-making for accident prevention and control. In conclusion, this paper effectively addresses the relevant limitations of accident text utilization and presents a novel approach for metro construction safety management. Analyzing accident texts can help gain insights from objective historical data to support safety management efforts. However, accident texts are often unstructured and contain a lot of irrelevant content. How to quickly extract valid information from accident text and use it to analyze accidents in depth is of continuous interest to safety managers. In particular, those models that have real-time decision support capabilities in addition to theoretical insights. This paper proposes an integrated model that combines text mining and machine-learning Bayesian networks. This model achieves comprehensive textual feature extraction, multifaceted accident causation analysis, and allows safety managers to input current accident information into the model to obtain real-time decision support for accident prevention and control. Although the proposed model is developed for metro construction, it can be slightly adapted by incorporating the characteristics of accident texts from similar domains to obtain an integrated model suitable for these domains, so as to effectively control the occurrence of safety accidents. [ABSTRACT FROM AUTHOR]

In order to reduce the accident risk in road construction and maintenance, this paper proposes a novel solution for road-worker safety based on an untethered real-time locating system (RTLS). This system tracks the location of workers in real time using ultra-wideband (UWB) technology and indicates if they are in a predefined danger zone or not, where the predefined safe zone is delimited by safety cones. Unlike previous works that focus on road-worker safety by detecting vehicles that enter into the working zone, our proposal solves the problem of distracted workers leaving the safe zone. This paper presents a simple-to-deploy safety system. Our UWB anchors do not need any cables for powering, synchronisation, or data transfer. The anchors are placed inside safety cones, which are already available in construction sites. Finally, there is no need to manually measure the positions of anchors and introduce them to the system thanks to a novel self-positioning approach. Our proposal, apart from automatically estimating the anchors' positions, also defines the limits of safe and danger zones. These features notably reduce the deployment time of the proposed safety system. Moreover, measurements show that all the proposed simplifications are obtained with an accuracy of 97%. [ABSTRACT FROM AUTHOR]

Safety management is a key issue in the railroad industry that needs to be continuously focused on. And it is essential to study causes of accidents for preventing accidents. However, there is a limited academic discussion on the systematic study of organizations and accidents, as well as their safety-related interactions and accidents, as opposed to human-caused disasters. Thus, the model of China's railway safety supervision and management system by sorting out the existing organizations involved in management in China is established in this paper. Firstly, social forces and auxiliary enterprises are specifically added to the model. And then, the relationship between organizations and accidents, as well as the relationship between safety interactions among organizations and accidents are explored by analyzing 224 accident reports, which led to 4 principles for accident prevention. Finally, based on these principles, measures to secure organizational nodes, as well as measures to promote safe interactions among organizations are proposed. The results showed that: (1) China Railway node is not only the most critical node in the safety supervision and management system but also the most vulnerable to the influence of other nodes. (2) The accident occurred due to the simultaneous occurrence of an accident at the China Railway node and the social force node. (3) When there are often safety risks in auxiliary enterprises and social forces simultaneously, the government's management is likely to be defective. The findings in this study can provide helpful references not only for improvement of safety management system structure and supervision and management mechanism but also for the formulation of safety supervision and management policies in China and other countries. [ABSTRACT FROM AUTHOR]

Construction collapse accidents are one of the most serious accidents. A variety of causal factors interact with each other, leading to the occurrence of accidents. Some causal factors may cause other causal factors to happen one after another, eventually leading to the occurrence of accidents. The propagation process of causal factors that can lead to construction collapse accident must be clarified. Therefore, this study aims to analyze the propagation path of construction collapse causal factors based on network theory and cascading failure model. First, this study collected and screened the cases of construction collapse accidents, and conducted a statistical analysis of their basic information. Second, a construction collapse accident causation (CCAC) framework was established to identify causal factors in accident cases. Then, a CCAC network model was established. Based on multiple measurement criteria in complex network theory, the causal factors were ranked. After that, through the cascading failure analysis of the causal factors, the critical causal factor-accident propagation path was obtained. It is found that construction scheme and organization is at the beginning and is the source of other causal factors. Finally, five cases were analyzed to verify the rationality of the results. This paper provides a solution to study the propagation path of causal factors and find out the characteristics of the CCAC mechanism through a large number of accident cases, which has theoretical and practical significance for the prevention of construction collapse accidents. [ABSTRACT FROM AUTHOR]

In today's world, where environmental protection and sustainability are increasingly important, it is essential to pay attention to the environmental impact of different industries. One of these industries with a potentially significant impact on life, human health, the environment, and property is gas stations, which are essential links in the fuel supply chain. This article focuses on the topic of assessing the impact of gas stations on surrounding environments and will examine the potential negative impacts that these operations can have on society and the environment. The aim of the paper is to analyze how gas stations affect their surroundings in the event of an incident involving a spill of hazardous substances. The scope of the paper is to assess the impacts of a spill of hazardous substances from a gas station, with an emphasis on assessing the risks and consequences on the life and health of the people in the immediate vicinity of the gas station. The selected gas station's location in the High Tatras National Park enhances the study's significance due to the unique environmental context, heightened environmental sensitivity, and potential legislative implications. ALOHA software version 5.4.7 was chosen for simulating the release of hazardous substance due to its extensive substance database, mathematical models, support for various release sources, internet availability, and graphical result representation. This manuscript argues for risk assessment beyond current legislation, addressing unclassified sources of risk. This research contributes by utilizing predictive modeling, recognizing environmental contexts, and emphasizing legislative attention. It discusses the consequences of emergency scenarios involving gasoline and LPG, addresses potential limitations and uncertainties, and advocates for accident prevention and risk assessment, especially in environmentally sensitive areas. The conclusion suggests improvements in predictive modeling, legislative adaptation, collaboration, and an expanded scope of analysis for future research. The aim of the paper is also to discuss measures that can be taken to minimize these impacts and ensure sustainable and safe operation. Assessing the risks arising from the operation of gas stations contributes to the development of measures to protect and preserve our environment for future generations. [ABSTRACT FROM AUTHOR]

High-strength bolts play a crucial role in ultra-high-pressure equipment such as bridges and railway tracks. Effective monitoring of bolt conditions is of paramount importance for common fault repair and accident prevention. This paper aims to detect and classify bolt corrosion levels accurately. We design and implement a bolt corrosion classification system based on a Wireless Acoustic Emission Sensor Network (WASN). Initially, WASN nodes collect high-speed acoustic emission (AE) signals from bolts. Then, the ReliefF feature selection algorithm is applied to identify the optimal feature combination. Subsequently, the Extreme Learning Machine (ELM) model is utilized for bolt corrosion classification. Additionally, to achieve high prediction accuracy, an improved goose algorithm (GOOSE) is employed to ensure the most suitable parameter combination for the ELM model. Experimental measurements were conducted on five classes of bolt corrosion levels: 0%, 25%, 50%, 75%, and 100%. The classification accuracy obtained using the proposed method was at least 98.04%. Compared to state-of-the-art classification diagnostic models, our approach exhibits superior AE signal recognition performance and stronger generalization ability to adapt to variations in working conditions. [ABSTRACT FROM AUTHOR]

The ever-increasing needs of the working population have led to the development of various branches of industry, an increase in the number of employees, and a rise in the number of work-related accidents. The welder occupation is one of the most sought after occupations in Europe, according to the EURopean Employment Services (EURES) statistics. Taking into account the work system in which welders conduct their activity (uncomfortable working positions, splashes, high temperatures, mechanical factors, gases and fumes, magnetic fields due to electric current), the paper presents the risk factors identified for the welder occupation, based on the occupational injury and illness risk assessments. Following the analysis of 25 risk assessments, carried out by the assessment team that must include qualified evaluators, process specialists, the workers' representative, occupational health and safety responsible at various industrial economic agents, a total of 70 main risk factors of occupational accidents and diseases were identified for the welder occupation. Risk factors were classified according to four main work components: worker, workload, work environment, and means of production. To reflect the importance of the identified risk factor, the number of organizations that considered that the risk was likely to occur but also the possibility that the risk was above the acceptable limit, calculated using the National Institute for Research and Development for Labor Protection "Alexandru Darabont" (INCDPM) method, a method often used in Romania, was identified from the analyzed assessments. Finally, a prevention and protection plan was drawn up with regard to the risks identified for the welder occupation, the final aim of which was to respectively reduce the probability of occurrence with the severity of the risks identified. [ABSTRACT FROM AUTHOR]

This study focuses on the potential of connected and automated vehicles (CAVs) to enhance road traffic safety through the provision of rich physical motion state information. Real‐time risk indicators are crucial for improving driving safety and must be tailored to the specific characteristics of the CAV environment. To this end, this paper introduces the concept of "risk pair" to decompose the joint actions of multiple traffic participants into pairwise interactions. An omnidirectional risk indicator (ORI) is developed to describe the effect of "risk pairs", and the superposition of ORI based on quality is proposed as the mass‐based ORI (MORI). A comparison between MORI and existing risk indicators shows that MORI has good performance in quantifying one‐dimensional scenarios. In two‐dimensional scenarios involving multiple participating entities, MORI provides two quantitative results: vector summation and scalar summation. The trajectory data from the next generation simulation database is used to validate the MORI model. The results show that both vector summation and scalar summation of MORI demonstrate strong risk quantification capabilities with different directions. Although some limitations of MORI still exist, this approach will provide a scientifically effective means of risk quantification for two‐dimensional complex scenarios. [ABSTRACT FROM AUTHOR]

Fault prediction is an important part of the safe operation and maintenance of pure electric vehicles, and it is crucial to predict faults efficiently and accurately. To address the problems that the pure electric vehicle fault prediction model has less than ideal prediction effect and classification bias to most classes due to unbalanced data set categories and high dimensionality of features, this paper firstly obtains a low dimensional balanced dataset based on hybrid sampling and joint feature selection. Then the classification ability of the model is improved through ensemble learning. Finally, a new hybrid model STLRF‐Stack is proposed to realize efficient fault prediction of pure electric vehicles. This paper is validated on a real operating dataset of pure electric vehicles. The experimental results show that the prediction effect of the hybrid model proposed in this paper is better than that of the single model and related literature. In addition, this model can successfully predict the fault 5 min before the actual fault of electric vehicles occurs, which provides the drivers with the opportunity of accident prevention and early treatment. Therefore, the model has specific feasibility and practical application value. [ABSTRACT FROM AUTHOR]

In this paper, we present a pedestrian detection and avoidance scheme utilizing multi-sensor data collection and machine learning for intelligent transportation systems (ITSs). The system integrates a video camera, an infrared (IR) camera, and a micro-Doppler radar for data acquisition and training. A deep convolutional neural network (DCNN) is employed to process RGB and IR images. The RGB dataset comprises 1200 images (600 with pedestrians and 600 without), while the IR dataset includes 1000 images (500 with pedestrians and 500 without), 85% of which were captured at night. Two distinct DCNNs were trained using these datasets, achieving a validation accuracy of 99.6% with the RGB camera and 97.3% with the IR camera. The radar sensor determines the pedestrian's range and direction of travel. Experimental evaluations conducted in a vehicle demonstrated that the multi-sensor detection scheme effectively triggers a warning signal to a vibrating motor on the steering wheel and displays a warning message on the passenger's touchscreen computer when a pedestrian is detected in potential danger. This system operates efficiently both during the day and at night. [ABSTRACT FROM AUTHOR]

Forestry work, which is considered one of the most demanding and dangerous professions in the world, is claiming more and more lives. In a country as small as Austria, more than 50 forestry workers are killed in accidents every year, and the number is increasing rapidly. This serves as a catalyst for us to implement more stringent measures for workplace safety in order to achieve the sustainability objective of SDG 3, which focuses on health and well-being. This study contributes to the analysis of occupational accidents and focuses on two large real-world datasets from both the Austrian Federal Forests (ÖBf) and the Austrian Workers' Compensation Board (AUVA). Decision trees, random forests, and fully connected neural networks are used for the analysis. By exploring different interpretation methods, this study sheds light on the decision-making processes ranging from basic association to causal inference and emphasizes the importance of causal inference in providing actionable insights for accident prevention. This paper contributes to the topic of explainable AI, specifically in its application to occupational safety in forestry. As a result, it introduces novel aspects to decision support systems in this application domain. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to conduct a scoping review of studies on interventions for the prevention of safety accidents involving infants. Methods: The scoping review method by Arksey and O'Malley was used to conduct an overview based on information spanning a wide range of fields. Multiple electronic databases, PubMed, CINAHL, RISS, and KISS, were searched for articles written in English or Korean published from 2012 to the present on safety accident prevention interventions. A total of 2,137 papers were found, and 20 papers were ultimately analyzed. Results: Most studies were conducted in the United States (55.0%) and in the medical field (45.0%), and most were experimental studies (35.0%). The results were organized across five categories: 1) preventive precautions, 2) characteristics of children's developmental stages, 3) encouraging voluntary participation, 4) continuity of interventions, and 5) teaching methods. Conclusion: Safety accident prevention interventions should cover the establishment of a safe home environment, include voluntary participation, and provide routine follow-up interventions. Additionally, practical training and teaching methods that incorporate feedback rather than a lecture-oriented approach should be adopted. [ABSTRACT FROM AUTHOR]

To address the issues of insufficient danger excavation and long data collection period in traditional traffic risk assessment methods, this paper proposes a risk assessment method based on driver's improper driving behavior and abnormal vehicle state warning data. Meanwhile, this paper analyses the built environment's impact on traffic risk using the spatial econometric model. Firstly, a risk assessment system with the relative incidence of driver's improper driving behavior (eye closure, yawn, and looking away) and abnormal vehicle state (rapid acceleration, rapid deceleration, and lane departure) warnings as assessment indicators is constructed. Then, the risk responsibility weights of each warning type were determined using the entropy weight method. The risk classification thresholds were determined based on the Gaussian Mixture Model algorithm. Finally, a spatial econometric model was used to quantify the impact of built environment factors characterized by Point of Interest (POI) data on regional traffic risk, with the results of risk class classification as the dependent variable. The data of bus vehicle warnings in Zhenjiang, Jiangsu Province, are employed as an example for validation. The geographic cell of 1 km × 1 km scale is applied as the basic risk assessment unit. The results show that the optimal risk classification threshold for road traffic risk levels I and II is 1.92, the accuracy rate of class classification is 79.3%; the optimal risk classification threshold for levels II and III is 0.75, and the accuracy rate of class classification is 83.4%. The number of residential areas, Point of Interest (POI) mixing degree, and bus stops were significantly and positively correlated with transit traffic risk. The study results provide references for developing customized accident prevention measures and the appropriate setting of urban supporting facilities. [ABSTRACT FROM AUTHOR]

This paper introduces a cutting-edge approach that combines facial recognition and drowsiness detection technologies with Internet of Things capabilities, including 5G/6G connectivity, aimed at bolstering vehicle security and driver safety. The delineated two-phase project is tailored to strengthen security measures and address accidents stemming from driver distraction and fatigue. The initial phase is centered on facial recognition for driver authentication before vehicle initiation. Following successful authentication, the subsequent phase harnesses continuous eye monitoring features, leveraging edge computing for real-time processing to identify signs of drowsiness during the journey. Emphasis is placed on video-based identification and analysis to ensure robust drowsiness detection. Finally, the study highlights the potential of these innovations to revolutionize automotive security and accident prevention within the context of intelligent transport systems. [ABSTRACT FROM AUTHOR]

The paper presented introduces an innovative accident prevention system that employs deep learning models and sensors to identify and alert drivers who lose concentration while driving for a variety of reasons. The system targets drowsiness, cell phone use, and alcohol consumption, which are among the leading causes of automobile accidents. The proposed solution employs neural networks to identify specific patterns associated with driver inattention and generates an audible alert to refocus the driver's attention on the road. In addition, the system utilizes alcohol-detection sensors, as alcohol consumption is another significant cause of accidents. This system seeks to reduce the number of casualties by enhancing road safety considering the high number of daily collisions, which are primarily caused by speeding. The paper describes the design, architecture, and implementation of the system on a modest scale to provide a lifesaving, cost-effective solution. [ABSTRACT FROM AUTHOR]

Railway accidents, particularly serious derailments, can lead to catastrophic consequences. Therefore, it is essential to prevent derailment escalation to reduce the likelihood of severe derailments. Train post-derailment behaviours and containment methods play a critical role in preventing derailment escalation and providing passive safety protection and accident prevention in the event of a derailment. However, despite the increasing attention on this field from academia and industry in recent years, there is a lack of systematic exploration and summarization of emerging applications and containment methods in train post-derailment research. For this reason, this paper presents a comprehensive review of existing studies on train post-derailment behaviours, encompassing various topics such as post-derailment contact–impact models, dynamic modelling and simulation techniques, and the primary factors influencing post-derailment behaviours. Significantly, this review introduces and elucidates substitute guidance mechanisms (SGMs), which serve as railway-specific passive safety protection and accident prevention measures. The various types of SGMs are depicted, and their ongoing developments and applications are explored in depth. The review additionally points out several unresolved challenges including the adverse effects of SGMs, and proposes future research directions to advance the theoretical understanding and practical application of train post-derailment behaviours and containment methods. This review seeks to be a valuable reference for railway industry professionals in preventing catastrophic derailment consequences through post-derailment containment methods. [ABSTRACT FROM AUTHOR]

In recent years, the global upswing in vessel-bridge collisions underscores the vital need for robust vessel track identification in accident prevention. Contemporary vessel trajectory identification strategies often integrate target detection with trajectory tracking algorithms, employing models like YOLO integrated with DeepSORT or Bytetrack algorithms. However, the accuracy of these methods relies on target detection outcomes and the imprecise boundary acquisition method results in erroneous vessel trajectory identification and tracking, leading to both false positives and missed detections. This paper introduces a novel vessel trajectory identification framework. The Co-tracker, a long-term sequence multi-feature-point tracking method, accurately tracks vessel trajectories by statistically calculating the translation and heading angle transformation of feature point clusters, mitigating the impact of inaccurate vessel target detection. Subsequently, vessel trajectories are predicted using a combination of Long Short-Term Memory (LSTM) and a Graph Attention Neural Network (GAT) to facilitate anomaly vessel trajectory warnings, ensuring precise predictions for vessel groups. Compared to prevalent algorithms like YOLO integrated with DeepSORT, our proposed method exhibits superior accuracy and captures crucial heading angle features. Importantly, it effectively mitigates the common issues of false positives and false negatives in detection and tracking tasks. Applied in the Three Rivers area of Ningbo, this research provides real-time vessel group trajectories and trajectory predictions. When the predicted trajectory suggests potential entry into a restricted zone, the system issues timely audiovisual warnings, enhancing real-time alert functionality. This framework markedly improves vessel traffic management efficiency, diminishes collision risks, and ensures secure navigation in multi-target and wide-area vessel scenarios. [ABSTRACT FROM AUTHOR]

To find the most‐likely‐failure scenarios given a certain operation domain, a critical‐scenario‐based test is supposed as an effective method. However, for the state of the art, critical‐scenario‐generation approaches commonly based on random‐search and take amounts of computing resource, some of them are also inapplicable in real time. Moreover, the approaches sometimes fail to obtain critical results, which are strongly relevant to the choice of initial condition. In order to address the above challenges, the authors proposed a Real‐time Critical‐scenario‐generation framework in this paper. The authors proposed an aggressive‐driving algorithm based on the model predictive control method to lead the agent vehicle. The agent vehicle will be controlled to directly create critical scenarios for a black‐box target under test, and the real‐time critical‐scenario test can be brought into reality. A specially designed cost function is presented that guides scenarios to evolve towards the interested conditions, and a self‐adaptive coefficient iteration is designed that enables the approach to be applied within a wider range of initial conditions. The authors carried out both simulation and Vehicle‐in‐the‐Loop (VIL) test; in the VIL test, the authors' approach improves 15.45% criticality of scenarios with around 9.7 times of efficiency, or improves 38.67% criticality with still around 1.7 times of efficiency with further iterations. [ABSTRACT FROM AUTHOR]

Monitoring workers' safety compliance is critical to construction accident prevention. However, most of the previous research focuses on checking the entry of workers wearing safety belts and the monitoring of the fastening action of safety rope screw buckles has not yet been considered. In this paper, a reconstruction method for the safety rope fastening behaviour of workers is proposed based on inertial measurement units (IMU) and a time series approach to monitor safety belt use. The proposed method was applied and evaluated through on-site construction experiments. The experimental results show that the acceleration, angular velocity, and magnetic induction intensity data obtained by the inertial measurement unit exhibit clear behavioural characteristics during safety rope fastening. The trajectory of the safety rope can be reconstructed and monitored through inertial measurement units and a time series approach. The results of this study will contribute to the reconstruction and monitoring of safety rope attachment trajectories for scaffolding workers working at heights in order to prevent falls at construction sites. [ABSTRACT FROM AUTHOR]

Several cameras are mounted on navigation aid buoys and these cameras can be used for accident prevention systems by processing the images captured. The currently existing image processing algorithms were originally designed for accident prevention on land—for example, CCTV (closed-circuit television)—which are performance oriented. However, when it comes to ocean-based images, navigation aids are usually located at sea and the cameras must be battery operated, and consequently, the energy efficiency of image processing is a major concern. Therefore, this paper proposed a novel approach to the detection of images in an ocean environment with a significantly lower computation. The new algorithm clustered pixels to grids and dealt with grids using greyscale rather than the particular color values of each pixel. Simulation-based experiments demonstrated that the grid-based algorithm provided five-times faster image processing in order to detect an object and achieved an up to 2.5 higher detection rate when compared with existing algorithms using ocean images. [ABSTRACT FROM AUTHOR]

While developing traffic-based cognitive enhancement technology (CET), such as bike accident prevention systems, it can be challenging to test and evaluate them properly. After all, the real-world scenario could endanger the subjects' health and safety. Therefore, a simulator is needed, preferably one that is realistic yet low cost. This paper introduces a way to use the video game Grand Theft Auto V (GTA V) and its sophisticated traffic system as a base to create such a simulator, allowing for the safe and realistic testing of dangerous traffic situations involving cyclists, cars, and trucks. The open world of GTA V, which can be explored on foot and via various vehicles, serves as an immersive stand-in for the real world. Custom modification scripts of the game give the researchers control over the experiment scenario and the output data to be evaluated. An off-the-shelf bicycle equipped with three sensors serves as a realistic input device for the subject's movement direction and speed. The simulator was used to test two early-stage CET concepts enabling cyclists to sense dangerous traffic situations, such as trucks approaching from behind the cyclist. Thus, this paper also presents the user evaluation of the cycling simulator and the CET used by the subjects to sense dangerous traffic situations. With the knowledge of the first iteration of the user-centered design (UCD) process, this paper concludes by naming improvements for the cycling simulator and discussing further research directions for CET that enable users to sense dangerous situations better. [ABSTRACT FROM AUTHOR]

Exploring spatiotemporal patterns of traffic accidents from historic crash databases is one essential prerequisite for road safety management and traffic risk prevention. Presently, with the emergence of GIS and data mining technologies, numerous geospatial analysis methods have been successfully adopted for traffic accident analysis. As characterized by high driving speeds, diverse vehicle types, and isolated traffic environments, expressways are confronted with more serious accident risks than urban roads. In this paper, we propose a combined method based on improved density clustering and the Bayesian inference network to explore spatiotemporal patterns of expressway accidents. Firstly, the spatiotemporal accident neighborhood is integrated into the DBSCAN clustering algorithm to discover multi-scale expressway black spots. Secondly, the Bayesian network model is separately employed in both local-scale black spots and regional-scale expressway networks to fully explore spatially heterogenous accident factors in various black spots and expressways. The experimental results show that the proposed method can correctly extract spatiotemporal aggregation patterns of multi-scale expressway black spots and meanwhile efficiently discover diverse causal factors for various black spots and expressways, providing a comprehensive analysis of accident prevention and safety management. [ABSTRACT FROM AUTHOR]

The goal of the paper was to assess safety of passenger cars sold in the Czech Republic in terms of the development of both active and passive safety features of cars sold between 2020-2022. Using content analysis aimed at collecting secondary data, the sales of passenger cars and their safety ratings were examined. Cluster analysis and neural networks were subsequently used to classify vehicles into selforganizing Kohonen maps, within which the movement between individual clusters was monitored. It was found that more than 25 % of vehicles sold between 2021 and 2022 changed their position compared to the year 2020. When taking into account vehicles newly introduced to the market, the average level of safety of vehicles sold compared to the year 2020. Further research could focus on a more detailed analysis of factors affecting safety on roads and their quantification for making better predictions and prevention of road accidents. It should be considered that vehicle safety ratings are based on a specific methodology and criteria and can vary significantly. [ABSTRACT FROM AUTHOR]

This paper presents a novel machine learning-based approach for detecting abnormal ship movements using CCTV videos. Our method utilizes graph-based algorithms to analyze ship trajectories and identify anomalies, with a focus on enhancing maritime safety and accident prevention. Unlike conventional AIS data-dependent methods, our approach directly detects and visualizes abnormal movements from CCTV videos, particularly in narrow coastal areas. We evaluate the proposed method using real-world CCTV video data and demonstrate its effectiveness in detecting abnormal ship movements, offering promising results in real-world scenarios. The findings of this study have important implications to improve maritime safety and prevent accidents. [ABSTRACT FROM AUTHOR]

Railways speedily transport many people and goods nationwide, so railway accidents can pose immense damage. However, the infrastructure of railways is so complex that its maintenance is challenging and expensive. Therefore, using artificial intelligence for railway safety has attracted many researchers. This paper examines artificial intelligence applications for railway safety, mainly focusing on deep learning approaches. This paper first introduces deep learning methods widely used for railway safety. Then, we investigated and classified earlier studies into four representative application areas: (1) railway infrastructure (catenary, surface, components, and geometry), (2) train body and bogie (door, wheel, suspension, bearing, etc.), (3) operation (railway detection, railroad trespassing, wind risk, train running safety, etc.), and (4) station (air quality control, accident prevention, etc.). We present fundamental problems and popular approaches for each application area. Finally, based on the literature reviews, we discuss the opportunities and challenges of artificial intelligence for railway safety. [ABSTRACT FROM AUTHOR]

The identification of hazards and risk assessment are key factors in the safety of the industries, including power plants. This paper contains an original risk analysis method that increases the level of safety in commissioning and start-up operations. With the proposed method, which has been tested in real industrial facilities, it has been possible to increase the safety of the system and reduce the likelihood of incidents and accidents in one of the most dangerous stages of project construction activities. This paper also gives an overview of the processes and procedures used in the construction of power plants compared to other industry sectors, proposing some areas for potential improvement. It has been verified that temporary systems used during commissioning apply risk evaluation techniques that do not consider some aspects related to process hazards, something that can be important for the prevention of accidents that historically happen at this stage. Analysis of the data has determined that, in these stages, there have been incidents and accidents, some of them severe. Thus, in this paper, a methodology is proposed. The method allows addressing the particularities of the execution of temporary systems in a safe way by putting into practice an agile and flexible method that can be applied to these particular systems, so that the risk levels can be reduced. The method was applied to one real application representative of this kind of system and yielded excellent results. The proposed methodology is highly recommended as an improvement for the power industry. [ABSTRACT FROM AUTHOR]

Accident prevention is of great significance in avoiding or reducing all kinds of casualties and economic losses, and is one of the main challenges for social sustainable development. Hence, it has been an active research field for many decades around the world. To master the research status of accident prevention, and explore the knowledge base and hot trends, 1294 papers from the WOS retrieval platform SCIE and SSCI databases from 1990 to 2021 were selected as data samples. Co-occurrence analysis, co-citation analysis, co-authorship analysis, and keyword analysis were performed on the literature on accident prevention research with bibliometric analysis methods. The study showed that the United States ranked first in the number of publications of any country/region and Georgia Inst Technol ranked first in the number of institutional publications. System analysis and accident model establishment, analysis of construction accidents, road accident prevention, and safety culture and safety climate are the knowledge base in the accident prevention studies and the core journals in this field are Safety Science, Accident Analysis and Prevention, Pediatrics, and Reliability Engineering & System Safety. There are four major research hotspots in accident prevention studies: routine accident prevention, model-based research, systems analysis and accident prediction, and occupational safety and public health research. At present, the basic theory and structural system of accident prevention research have been basically established, with many research directions and a wide range of frontier branches. Safety management, public safety, Bayesian networks, and simulation are the research frontiers of accident prevention. [ABSTRACT FROM AUTHOR]

The movement of a motorcycle is one of the critical factors that influences the stability of the ride. It has been established that the gait patterns of drunk and sober people are distinct. However, drunk motorcycle (MC) drivers' balance has not been investigated as a predictor of intoxication. This paper characterized and used MC and head movements, such as pitch and roll, to predict intoxication while riding. Two separate experiments were conducted to monitor MC and head movement. Male participants were recruited between the ages of 23 and 50 to participate in the study. Participants used alcohol intoxication goggles (AIG) to simulate blood alcohol content (BAC) while driving on a straight path. Placebo goggles were used for control. Results showed that pitch and roll amplitudes of the MC could distinguish drivers wearing placebo and AIGs, as well as the pitch and roll frequency of the head. Deep learning can be used to predict the intoxication of MC riders. The predictive accuracy of the algorithm shows a viable opportunity for the use of movement to monitor drunk riders on the road. [ABSTRACT FROM AUTHOR]

Vehicle accidents have suddenly increased over the years, and several technologies are also being researched to prevent them. The above research work offers ways of an accident prevention system employing an eye blink sensor as well as an automated braking technique to make sure that when fatigue is detected and the driver doesn't respond to the buzzer's warning bell within the permitted time, the Vehicle comes to a slow stop. During the allocated time before circuit design, the vehicle's hazard, warning lights are turned on to warn other drivers, particularly those driving ahead. Thus, the results show that the eye blink sensor is used by the car's accident prevention system once the car has stopped. The Proteus software package and the C++ programming language were utilized to verify that the automated braking system is an effective method for reducing accidents caused by fatigued driving. The design and development of a car collision and accident prevention system, when drowsiness is identified using eye blink sensors is proposed in this research paper. This research work is essential because incorporating this technology into automobiles prevents accidents caused by driving when fatigued. This research work might be improved by more research to increase driver attention by employing wireless technology to inform other cars when the driver is tired rather than using the vehicle's danger warning lights. [ABSTRACT FROM AUTHOR]

This paper focuses on the safety issue for cyclists and pedestrians at unsignalized intersections. The cycling speed needs to be calmed when approaching the intersection. This study proposes and deploys an integrated portable ultra-wideband/inertial navigation system (UWB/INS) to extract cycling trajectories for a cycling safety study. The system is based on open-source hardware and delivers an open-source code for an adaptive Kalman filter to enhance positioning precision for data quality assurance at an outdoor experimental site. The results demonstrate that the system can deliver reliable trajectories for low-mobility objects. To mitigate accident risk and severity, varied cycling speed calming measures are tested at an experimental site. Based on the trajectory data, the statistical features of cycling velocities are evaluated and compared. A new proposed geometric design is found to be most effective when compared with conventional traffic signs. [ABSTRACT FROM AUTHOR]

With the rapid development of high-pressure combustible gas pipelines, it brings convenience and also buries potential safety hazards. This paper presents an in-depth exploration of the thermal radiation hazards of fireball accidents caused by leakage and provides a reference for the prevention and control of this type of accident and on-site rescue. Based on the basic principle of fluid mechanics and the calculation model of the leakage rate, a three-dimensional pipeline model was constructed by FDS software to simulate the fireballs with different positions of low, middle and high. The simulation shows that the ground temperature field of the low and middle fireballs is quite different from that of the high fireball, and the temperature level is: low position > middle position > high position. On this basis, the observation elevation angle is introduced to improve the classical fireball thermal radiation model formula, the model calculation value is compared with the numerical simulation value and the optimal threshold is determined by combining the thermal radiation flux criterion. The results show that the numerical simulation is basically consistent with the calculation results of the improved model. The smaller the observation elevation angle, the closer the target receives the thermal radiation flux to the optimal threshold and the calculated hazard range is more reliable. [ABSTRACT FROM AUTHOR]

Accident risk management in mining facilities includes measures to eliminate the possibility of accidents, which ensures that the risk of hazardous activities and hazardous substances is acceptable. Accident risk reduction takes place through accident prevention phases that are elaborated in detail separately for each mine and for each type of mining activity. In this paper, a multi-criteria analysis of the most important prevention measures is done in order to reduce the risk of accidents in mines with surface exploitation. The analysis was performed by AHP method. The results are used to develop an optimal plan for mine prevention from accidents, and the most important measures are the selection of those technologies and techniques that pollute the environment less and the determination of endangered zones and adequate spatial planning for new facilities, settlements and excavation dynamics. [ABSTRACT FROM AUTHOR]

In vehicular ad hoc networks (VANETs), efficient data dissemination to a specified number of vehicles with minimum collisions and limited access delay is critical for accident prevention in road safety. However, packet collisions have a significant impact on access delay, and they may lead to unanticipated link failure when a range of diversified collisions are combined due to complex traffic conditions and rapid changes in network topology. In this paper, we propose a distributed contention-free cooperative medium access control (CFC-MAC) protocol to reduce heterogenous collisions and unintended access delay in stochastic traffic scenarios. Firstly, we develop a cooperative communication system model and cooperative forwarding mechanism to explore the optimum road path between the source and destination by identifying the potential cooperative vehicles. Secondly, we propose a vectorized trajectory estimation mechanism to suppress merging collisions by identifying the relative velocity of vehicles with different speeds in a specific time interval. Based on the case study, typical heterogeneous collisions and aggregated heterogeneous collisions at dissociated positions and associated positions are investigated. In both cases, we propose the corresponding collision-resolving mechanisms by methodically recapturing the colliding time slot or acquiring the available free time slots after identifying the access vehicles and comparing the received signal strengths. Performance analysis for collision probability and access delay is conducted. Finally, the simulation results show that the proposed protocol can achieve deterministic access delay and a minimal collision rate, substantially outperforming the existing solutions. [ABSTRACT FROM AUTHOR]

In order to investigate the explosion mechanism of sucrose in the air atmosphere, the explosion intensity under different ignition delay times (IDT), powder input pressures (PIP), and concentrations were studied using a 20L-sphere. The sucrose particles were analyzed in a synchronized thermal analyzer (STA) and scanning electron microscope (SEM). The results are as follows: 1. The DSC curve has two endothermic peaks and one exothermic peak, respectively at T = 180.5 ℃, 510.2 ℃ and 582.6 ℃. 2. The explosion intensity varies with the experiment conditions. The maximum explosion pressure (Pmax) appears when IDT = 90 ms, PIP = 1.5 MPa and concentration = 625 g/m3. 3. The explosive mechanism is a homogeneous combustion mechanism based on particle surface pyrolysis and volatilization. Because of the decomposition, H2, CO, furfural, and other flammable gas-phase products are released, then surface burn appears, which leads to the crystal rupture on account of thermal imbalance, resulting in multiple flame points and a chain explosion. As the temperature of the 20L-sphere rises, more explosive products are released, which causes a rapidly expanding explosion and eventually forms the explosion. This paper can be used as a reference for the prevention of explosion accidents in sucrose production processing. [ABSTRACT FROM AUTHOR]

Background: In the appraisal of clinical interventions, complex evidence synthesis methods, such as network meta-analysis (NMA), are commonly used to investigate the effectiveness of multiple interventions in a single analysis. The results from a NMA can inform clinical guidelines directly or be used as inputs into a decision-analytic model assessing the cost-effectiveness of the interventions. However, there is hesitancy in using complex evidence synthesis methods when evaluating public health interventions. This is due to significant heterogeneity across studies investigating such interventions and concerns about their quality. Threshold analysis has been developed to help assess and quantify the robustness of recommendations made based on results obtained from NMAs to potential limitations of the data. Developed in the context of clinical guidelines, the method may prove useful also in the context of public health interventions. In this paper, we illustrate the use of the method in public health, investigating the effectiveness of interventions aiming to increase the uptake of accident prevention behaviours in homes with children aged 0-5., Methods: Two published random effects NMAs were replicated and carried out to assess the effectiveness of several interventions for increasing the uptake of accident prevention behaviours, focusing on the safe storage of other household products and stair gates outcomes. Threshold analysis was then applied to the NMAs to assess the robustness of the intervention recommendations made based on the results from the NMAs., Results: The results of the NMAs indicated that complex intervention, including Education, Free/low-cost equipment, Fitting equipment and Home safety inspection, was the most effective intervention at promoting accident prevention behaviours for both outcomes. However, the threshold analyses highlighted that the intervention recommendation was robust for the stair gate outcome, but not robust for the safe storage of other household items outcome., Conclusions: In our case study, threshold analysis allowed us to demonstrate that there was some discrepancy in the intervention recommendation for promoting accident prevention behaviours as the recommendation was robust for one outcome but not the other. Therefore, caution should be taken when considering such interventions in practice for the prevention of poisonings in homes with children aged 0-5. However, there can be some confidence in the use of this intervention in practice to promote the possession of stair gates to prevent falls in homes with children under 5. We have illustrated the potential benefit of threshold analysis in the context of public health and, therefore, encourage the use of the method in practice as a sensitivity analysis for NMA of public health interventions., (© 2022. The Author(s).)

This paper presents a novel approach aimed at bolstering network resilience against traffic accidents, particularly when users possess complete information about accident locations. Two new measures are introduced to evaluate the performance of the network and the importance of a link within the network. In addition, an objective function is designed to quantify optimal trip scheduling following an accident that guides investment decisions in network infrastructure. Additionally, we propose a model for addressing the Accident Improvement Problem and put forth a heuristic algorithm to solve this model. To illustrate the feasibility and effectiveness of the proposed methodologies, we present and analyze two illustrative examples, one at a small scale and the other at a medium scale. The findings underscore how the occurrence of accidents can markedly alter the importance of a link within the network during time. Unlike the prevailing trend in existing studies, which often overlook the repercussions of accidents on traffic flow along other links, our research highlights the importance of considering the impact of newcomers on the routes of existing travelers within the network. These findings demonstrate that such considerations can significantly influence the overall performance of the network in the event of an accident. [ABSTRACT FROM AUTHOR]

The visibility available to an equipment operator on a dynamic construction site can often be blocked by various obstacles such as materials, temporary or permanent facilities, other equipment, and workers. Equipment monitoring in real-time digital twins can thus play a crucial role in accident prevention. This paper develops a scalable technical approach and presents a prototype application framework for transmitting real world sensor data to update 3D equipment models inside a graphical digital twin for concurrent visualization of a monitored construction operation. The developed framework and workflow can be extended to visualize any construction operation, as it occurs, inside a dynamic 3D world simply by outfitting the real equipment with appropriate sensors and connecting them to their virtual counterparts. The implemented proof-of-concept interface is described in the context of a real-time 3D digital twin for assisting excavator operators prevent unintended strikes with underground utilities. Experiments to validate the proposed technical approach by simulating the real-time motion of a backhoe loader's articulated arm using orientation sensors installed on its boom, stick, and bucket are described. The experimental results characterize the scope and potential reasons for spatio-temporal discrepancies that can occur between a monitored real operation and its replicated digital twin. The effect of an operator warning mechanism based on preset safety thresholds is also investigated and described. [ABSTRACT FROM AUTHOR]

There is high demand for techniques to estimate human mental workload during some activities for productivity enhancement or accident prevention. Most studies focus on a single physiological sensing modality and use univariate methods to analyse multi-channel electroencephalography (EEG) data. This paper proposes a new framework that relies on the features of hybrid EEG–functional near-infrared spectroscopy (EEG–fNIRS), supported by machine-learning features to deal with multi-level mental workload classification. Furthermore, instead of the well-used univariate power spectral density (PSD) for EEG recording, we propose using bivariate functional brain connectivity (FBC) features in the time and frequency domains of three bands: delta (0.5–4 Hz), theta (4–7 Hz) and alpha (8–15 Hz). With the assistance of the fNIRS oxyhemoglobin and deoxyhemoglobin (HbO and HbR) indicators, the FBC technique significantly improved classification performance at a 77% accuracy for 0-back vs. 2-back and 83% for 0-back vs. 3-back using a public dataset. Moreover, topographic and heat-map visualisation indicated that the distinguishing regions for EEG and fNIRS showed a difference among the 0-back, 2-back and 3-back test results. It was determined that the best region to assist the discrimination of the mental workload for EEG and fNIRS is different. Specifically, the posterior area performed the best for the posterior midline occipital (POz) EEG in the alpha band and fNIRS had superiority in the right frontal region (AF8). [ABSTRACT FROM AUTHOR]

This paper addressed safety risks in the construction industry, emphasizing the prevalent and fatal risk of falls from heights due to floor openings. Although advancements in computer vision and deep learning offer opportunities for automated safety monitoring, challenges such as inaccuracies in object localization and measuring distances to unsafe zones persist. To overcome these issues, a detection method employing convex quadrilateral bounding boxes was presented, taking into account perspective changes from the view of the camera. By leveraging a pretrained pose-estimation model and enhancing the YOLOv7 architecture, the new method precisely identified unsafe areas and generated virtual fences around floor openings. The presented approach resulted in an average precision of 80.55% and an F1-score of 86.49% in alerting dangers, outperforming existing techniques. This paper underscores the effective integration of state-of-the-art computer vision methodologies for practical safety monitoring in construction sites, highlighting its promise in accident prevention. • Uses convex quadrilateral boxes to boost construction site object detection accuracy. • Combines pose-estimation with opening detection to alert hazards via virtual fences. • Enhanced YOLOv7 architecture improves danger alert F1-score by 5.24% over prior methods. [ABSTRACT FROM AUTHOR]

Traffic accidents are a major concern worldwide, since they have a significant impact on people's safety, health, and well-being, and thus, they constitute an important field of research on the use of state-of-the-art techniques and algorithms to analyze and predict them. The study of traffic accidents has been conducted using the information published by traffic entities and road police forces, but thanks to the ubiquity and availability of social media platforms, it is possible to have detailed and real-time information about road accidents in a given region, which allows for detailed studies that include unrecorded road accident events. The focus of this paper is to propose a model to predict traffic accidents using information gathered from social media and open data, applying an ensemble Deep Learning Model, composed of Gated Recurrent Units and Convolutional Neural Networks. The results obtained are compared with baseline algorithms and results published by other researchers. The results show promising outcomes, indicating that in the context of the problem, the proposed ensemble Deep Learning model outperforms the baseline algorithms and other Deep Learning models reported by literature. The information provided by the model can be valuable for traffic control agencies to plan road accident prevention activities. [ABSTRACT FROM AUTHOR]

This paper presents the proposed measures to prevent traffic accidents from compressed natural gas (CNG) buses. The fire-gutted bus with a CNG system was investigated in detail of component and system defects as a case study, although many single bus accidents were recorded from and caused by errant bus drivers in Thailand. Thus, several measures were proposed to encourage safer road users from bus drivers in Thailand, such as drink-not-drive, GPS-monitored speed, and the readiness of rest areas, including driver fatigue prevention. While there are many existing vehicle standards for buses from United Nations (UN) regulation and Thailand, several buses have still erupted in flames. Therefore, the proposed measures from the investigation of the fire-gutted bus and the existing vehicle regulations can be used to enhance and accelerate actions in the approval of CNG bus and periodical technical inspection for roadworthiness. Through data information from the accident investigation and proposed technical inspection, the prevention of bus accidents with CNG systems can be continuously improved and enhanced under various traffic situations. Furthermore, the results from the investigation can be useful for forensic experts to support SDG Target 3.6 in reduction of deaths from road traffic accidents. [ABSTRACT FROM AUTHOR]

Various construction accidents are proven to be caused by multiple unsafe behaviors (e.g., wrong use of PPE), but the risk transmission among different behaviors remains unclear. This paper provides insight into risk transmission through behavioral risk chain that leads to accidents from a system safety perspective. To better understand the coupling mechanism of various unsafe behaviors, integrate different behavioral risk chains and present the risk transmission process, a directed-weighted complex network (DWCN) method was adopted. Historical urban railway construction accidents in China are investigated to extract behavioral risk chain. A DW-BRCNA is applied to integrated behavioral risk chain and the behavioral risk transmission characteristics are explored and clarified by the five network properties, including degree and degree distribution, node strength and node strength distribution, average path length and diameter, weighted clustering coefficient and betweenness centrality. The results show that DW-BRCNA has the characteristics of a small-world, scale-free and hierarchical network, indicating that some unsafe behaviors are of greater importance in the process of risk transmission through behavioral risk chains. In addition, risk transmission in critical behavioral risk chains is more potentially to lead to accidents. This study proposed a new perspective of accident causation analysis from risk transmission among unsafe behaviors. It explains the risk transmission characteristics by a DWCN method based on behavioral risk chains. The findings also provide a practical guidance for developing control strategies on sites to prevent risk transmission and reduce accidents. [ABSTRACT FROM AUTHOR]

Between 2010 and 2020 in the European Union, 30% of road accidents resulted in the death of a pedestrian or a cyclist. Accidents of unprotected pedestrians and cyclists are the reason why it is essential to introduce road safety measures. In our paper, we identify and rank black spots using an innovative reactive approach based on statistics. We elaborate on the mathematical methodological considerations through the processing of real-life empirical data in a Matlab environment. The applied black-spot analysis is based on a Kernel density estimate method, and the importance of the kernel functions and bandwidth are elaborated. Besides, special attention is devoted to the distorting effect of annual average daily traffic. The result of our research is a new methodology by which the real locations of the examined black spots can be determined. Furthermore, the boundaries of the critical sections and the extent of the formation of black spots can be determined by the introduced mathematical methods. With our innovative model, the black spots can be ranked, and the locations having the highest potential for improvement can be identified. Accordingly, optimal measures can be determined considering social-economic and sustainability aspects. [ABSTRACT FROM AUTHOR]

Background: Limited knowledge regarding the relative effectiveness of workplace accident prevention approaches creates barriers to informed decision‐making by policy makers, public health practitioners, workplace, and worker advocates. Objectives: The objective of this review was to assess the effectiveness of broad categories of safety interventions in preventing accidents at work. The review aims to compare effects of safety interventions to no intervention, usual activities, or alternative intervention, and if possible, to examine which constituent components of safety intervention programs contribute more strongly to preventing accidents at work in a given setting or context. Date Sources: Studies were identified through electronic bibliographic searches, government policy databanks, and Internet search engines. The last search was carried out on July 9, 2015. Gray literature were identified by searching OSH ROM and Google. No language or date restrictions were applied. Searches done between February and July of 2015 included PubMed (1966), Embase (1980), CINAHL (1981), OSH ROM (NIOSHTIC 1977, HSELINE 1977, CIS‐DOC 1974), PsycINFO (1806), EconLit (1969), Web of Science (1969), and ProQuest (1861); dates represent initial availability of each database. Websites of pertinent institutions (NIOSH, Perosh) were also searched. Study Eligibility Criteria, Participants, and Interventions: Included studies had to focus on accidents at work, include an evaluation of a safety intervention, and have used injuries at work, or a relevant proxy, as an outcome measure. Experimental, quasi‐experimental, and observational study designs were utilized, including randomized controlled trials (RCTs), controlled before and after (CBA) studies, and observational designs using serial measures (interrupted time series, retrospective cohort designs, and before and after studies using multiple measures). Interventions were classified by approach at the individual or group level, and broad categories based on the prevention approach including modification of: Attitudes (through information and persuasive campaign messaging).Behaviors (through training, incentives, goal setting, feedback/coaching).Physiological condition (by physical training).Climate/norms/culture (by coaching, feedback, modification of safety management/leadership).Structural conditions (including physical environment, engineering, legislation and enforcement, sectorial‐level norms). When combined approaches were used, interventions were termed "multifaceted," and when an approach(es) is applied to more than one organizational level (e.g., individual, group, and/or organization), it is termed "across levels." Study Appraisal and Synthesis Methods: Narrative report review captured industry (NACE), work setting, participant characteristics, theoretical basis for approach, intervention fidelity, research design, risk of bias, contextual detail, outcomes measures and results. Additional items were extracted for studies with serial measures including approaches to improve internal validity, assessments of reasonable statistical approaches (Effective Practice of Organization of Care [EPOC] criteria) and overall inference. Random‐effects inverse variance weighted meta‐analytic methods were used to synthesize odds ratios, rate ratios, or standardized mean differences for the outcomes for RCT and CBA studies with low or moderate levels of heterogeneity. For studies with greater heterogeneity and those using serial measures, we relied on narrative analyses to synthesize findings. Results: In total 100 original studies were included for synthesis analysis, including 16 RCT study designs, 30 CBA study designs, and 54 studies using serial measures (ITS study designs). These studies represented 120 cases of safety interventions. The number of participants included 31,971,908 individuals in 59 safety interventions, 417,693 groups/firms in 35 safety interventions, and 15,505 injuries in 17 safety interventions. Out of the 59 safety interventions, two were evaluating national prevention measures, which alone accounted for 31,667,110 individuals. The remaining nine safety interventions used other types of measures, such as safety exposure, safety observations, gloves or claim rates. Strong evidence supports greater effects being achieved with safety interventions directed toward the group or organization level rather than individual behavior change. Engineering controls are more effective at reducing injuries than other approaches, particularly when engineered changes can be introduced without requiring "decision‐to‐use" by workplaces. Multifaceted approaches combining intervention elements on the organizational level, or across levels, provided moderate to strong effects, in particular when engineering controls were included. Interventions based on firm epidemiologic evidence of causality and a strong conceptual approach were more effective. Effects that are more modest were observed (in short follow‐up) for safety climate interventions, using techniques such as feedback or leadership training to improve safety communication. There was limited evidence for a strong effect at medium‐term with more intense counseling approaches. Evidence supports regulation/legislation as contributing to the prevention of accidents at work, but with lower effect sizes. Enforcement appears to work more consistently, but with smaller effects. In general, the results were consistent with previous systematic reviews of specific types of safety interventions, although the effectiveness of economic incentives to prevent accidents at work was not consistent with our results, and effectiveness of physiological safety intervention was only consistent to some extent. Limitations: Acute musculoskeletal injuries and injuries from more long‐time workplace exposures were not always clearly distinguished in research reports. In some studies acute and chronic exposures were mixed, resulting in inevitable misclassification. Of note, the classification of these events also remains problematic in clinical medicine. It was not possible to conduct meta‐analyses on all types of interventions (due to variability in approach, context, and participants). The findings presented for most intervention types are from limited sources, and assessment of publication bias was not possible. These issues are not surprising, given the breadth of the field of occupational safety. To incorporate studies using serial measures, which provide the only source of information for some safety interventions such as legislation, we took a systematic, grounded approach to their review. Rather than requiring more stringent, specific criteria for inclusion of ITS studies, we chose to assess how investigators justified their approach to design and analyses, based on the context in which they were working. We sought to identify measures taken to improve external validity of studies, reasonable statistical inference, as well as an overall appropriate inferential process. We found the process useful and enlightening. Given the new approach, we may have failed to extract points others may find relevant. Similarly, to facilitate the broad nature of this review, we used a novel categorization of safety interventions, which is likely to evolve with additional use. The broad scope of this review and the time and resources available did not allow for contacting authors of original papers or seeking translation of non‐English manuscripts, resulting in a few cases where we did not have sufficient information that may have been possible to obtain from the authors. Conclusions and Implications of Key Findings: Our synthesis of the relative effectiveness of workplace safety interventions is in accordance with the Public Health Hierarchy of Hazard Control. Specifically, more effective interventions eliminate risk at the source of the hazard through engineering solutions or the separation of workers from hazards; effects were greater when these control measures worked independently of worker "decision‐to‐use" at the worksite. Interventions based on firm epidemiological evidence of causality and clear theoretical bases for the intervention approach were more effective in preventing injuries. Less effective behavioral approaches were often directed at the prevention of all workplace injuries through a common pathway, such as introducing safety training, without explicitly addressing specific hazards. We caution that this does not mean that training does not play an essential function in worker safety, but rather that it is not effective in the absence of other efforts. Due to the potential to reach large groups of workers through regulation and enforcement, these interventions with relatively modest effects, could have large population‐based effects. [ABSTRACT FROM AUTHOR]

There is an urgent need to explore the current situation of China's water pollution incidents for policymaking, accident prevention, and risk mitigation. This study analyzed 1528 water pollution incidents in China from 2001 to 2020 and explored the spatiotemporal characteristics and causes of incidents and consequent damage. The frequency of water pollution accidents increased in 2004, peaked in 2006, and decreased thereafter with a significant decline in 2016. Due to the developed river networks, high population densities, and increasing environmental awareness, pollution incidents were mostly concentrated in China's relatively industrially developed eastern coastal regions. Illegal emission is the major cause, accounting for 51% of all incidents, but with pipeline construction gradually approaching a peak, pipeline leakage poses a noteworthy risk in the future. Although the severity of accidents has reduced recently, it is still necessary to strengthen the risk prevention strategies for general and major accidents. Furthermore, three key factors, including economic development, regulations and legal system, and public participation in pollution supervision and control, which affect the trends and characteristics of water pollution incidents in China, were discussed. This paper offers valuable insights and suggestions that may have useful implications for policymaking and the prevention of water pollution. [ABSTRACT FROM AUTHOR]