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Shaping safe and hygienic working conditions is the basic obligation of the employer. Employers are still looking for methods, tools, and solutions to improve work safety. The study reviews the literature on solutions aimed at improving occupational safety in construction. For this purpose, bibliometric analysis was used, consisting of the exploration and analysis of scientific studies in the field of occupational health and safety, including the possibility of using the LM tool to improve working conditions on construction sites. The study presents the possibility of implementing Lean Manufacturing (LM) tools in the construction industry. Their strengths and weaknesses were identified, and the benefits and possible barriers related to their effective implementation/use were identified. The result of the analyses was a summary of benefits for the construction industry (strengths and opportunities) as well as areas requiring improvement (weaknesses and hazards) regarding the LM tools used. Based on the conducted analyses, it was found that it is possible to use the tools of the LM concept to improve work safety and organize tasks performed on construction sites. The implementation of LM tools, such as 5S/6S, Poka-Yoke, and Standardization, enables risk reduction through a direct impact on the area being analyzed, in which occupational hazards have been identified. It is also noted that there is a need to simultaneously use LM tools (VM, Gemba-Walk, DHM) as solutions aimed at reducing risk. [ABSTRACT FROM AUTHOR]

This study innovatively derives a risk prediction model for hydrogen leakage accidents based on existing case evidence by combining MORT (Management Oversight and Risk Tree) with machine learning algorithms. 30 hydrogen leakage accidents causative factors are derived by analyzing 23 accident cases through MORT, and constructed a dataset of hydrogen leakage accident causative factors through data quantification. Using feature selection to optimize the risk indicators, 14 salient feature indicators are selected from the 30 full feature indicators. Two cross-validation strategies are introduced and combined with six machine learning models to establish 24 working conditions for numerical comparison experiments. The results show that the support vector machine model based on stratified K-fold cross-validation strategy has the best prediction performance, with the model Accuracy of 0.939, Precision of 0.848, Recall of 0.848, F1-score of 0.848, and AUC score of 0.98. This will effectively solve the problem of hydrogen leakage risk prediction and accident prevention in industrial systems. • Hydrogen leakage accident causative factors were derived through MORT. • A dataset of causal factors for hydrogen leakage accidents was constructed. • 24 risk prediction models were built for numerical comparison experiments. • A well-performing hydrogen leakage accident risk prediction model was screened. [ABSTRACT FROM AUTHOR]

For the cost-benefit analysis of road safety measures, it is essential to estimate the national value of statistical life. By calculating the updated values, it is possible to assess the aggregate national value of statistical life for road traffic crashes, thereby also characterizing the road safety situation in the country. It is important that the values set and the methods used are compatible with the practices of the European Member States. It must be stressed that updating the values is of major importance both for the cost benefit analysis of the various road safety measures and for raising public and decision-makers' awareness of the huge losses. The full identification and use of loss figures is an important element of road safety. In this article we present possible methods for estimating the value of statistical life. [ABSTRACT FROM AUTHOR]

To ensure precise and real-time perception of high-speed roadway conditions and minimize the potential threats to traffic safety posed by road debris and defects, this study designed a real-time monitoring and early warning system for high-speed road surface anomalies. Initially, an autonomous mobile intelligent road inspection robot, mountable on highway guardrails, along with a corresponding cloud-based warning platform, was developed. Subsequently, an enhanced target detection algorithm, YOLOv5s-L-OTA, was proposed. Incorporating GSConv for lightweight improvements to standard convolutions and employing the optimal transport assignment for object detection (OTA) strategy, the algorithm's robustness in multi-object label assignment was enhanced, significantly improving both model accuracy and processing speed. Ultimately, this refined algorithm was deployed on the intelligent inspection robot and validated in real-road environments. The experimental results demonstrated the algorithm's effectiveness, significantly boosting the capability for real-time, precise detection of high-speed road surface anomalies, thereby ensuring highway safety and substantially reducing the risk of liability disputes and personal injuries. [ABSTRACT FROM AUTHOR]

Intersections are crucial and high-risk areas in urban road networks due to dense traffic and complex scenarios. Deploying Roadside Units (RSUs) can enhance safety and efficiency by providing real-time traffic information. However, the impact of traffic accident risks on RSU deployment is largely ignored. This study introduces an innovative RSU deployment strategy that prioritizes the risk of traffic accidents at intersections. The approach begins with analyzing environmental conditions, traffic patterns, and historical accident data at target intersections to identify key risk dimensions: road, accident, and environmental. The Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) are used to weigh the indicators to evaluate their importance in accident risk assessment. Then, construct an objective function based on the accident risk value of the intersection. To overcome the redundancy problem in risk assessment, this study proposes an improved 0-1 knapsack algorithm that considers the redundancy of intersection accident risk to determine the optimal deployment location of RSUs. Simulations with SUMO, TraCI, Veins, and OMNeT++ demonstrate the algorithm’s superiority over traditional methods in all metrics. The results show that the vehicle coverage of this strategy is on average 2.63% and 2.86% higher than that of the IIA-ORD and UDA algorithms, respectively. It also leads by about 5.04% in traffic accident coverage and 5.72% in accident risk coverage. This intersection-focused RSU deployment method ensures timely information dissemination after incidents, providing valuable insights and practical guidelines for improving urban intersection safety and efficiency.

Arctic navigation faces numerous challenges, including uncertain ice conditions, rapid weather changes, limited communication capabilities, and lack of search and rescue infrastructure, all of which increase the risks involved. According to an Arctic Council statistical report, a remarkable 2638 maritime accidents were recorded in Arctic waters between 2005 and 2017, showing a fluctuating upward trend. This study collected and analyzed ship accident data in Arctic waters to identify the various accident scenarios and primary risk factors that impact Arctic navigation safety. By utilizing data-driven algorithms, a model for predicting ship navigation accidents in Arctic waters was constructed, providing an in-depth understanding of the risk factors that make accidents more likely. The research findings are of practical significance for enhancing quantitative risk assessment, specifically focusing on the navigational risks in Arctic waters. The results of this study can assist maritime authorities and shipping companies in conducting risk analysis and implementing accident prevention measures for safe navigation in Arctic waters. [ABSTRACT FROM AUTHOR]

Shaping safe and hygienic working conditions is the basic obligation of the employer. Employers are still looking for methods, tools, and solutions to improve work safety. The study reviews the literature on solutions aimed at improving occupational safety in construction. For this purpose, bibliometric analysis was used, consisting of the exploration and analysis of scientific studies in the field of occupational health and safety, including the possibility of using the LM tool to improve working conditions on construction sites. The study presents the possibility of implementing Lean Manufacturing (LM) tools in the construction industry. Their strengths and weaknesses were identified, and the benefits and possible barriers related to their effective implementation/use were identified. The result of the analyses was a summary of benefits for the construction industry (strengths and opportunities) as well as areas requiring improvement (weaknesses and hazards) regarding the LM tools used. Based on the conducted analyses, it was found that it is possible to use the tools of the LM concept to improve work safety and organize tasks performed on construction sites. The implementation of LM tools, such as 5S/6S, Poka-Yoke, and Standardization, enables risk reduction through a direct impact on the area being analyzed, in which occupational hazards have been identified. It is also noted that there is a need to simultaneously use LM tools (VM, Gemba-Walk, DHM) as solutions aimed at reducing risk.

All over the world, construction accidents and respiratory diseases are among the most important problems. The prefabricated system is an introduction to reduce emissions and accidents during the construction phase. However, there is a lack of research that proves the superiority of the prefabricated system in terms of both dust health impacts and accident risks simultaneously. Therefore, this study conducts an assessment in terms of dust health impacts and accident risks to compare the prefabricated system and the conventional system. This research is carried out in the following steps. (i) collection of data, (ii) calculation of dust emission, (iii) health impact assessment, and (iv) calculation of accident risk. The conventional system produced 5,454,527.43 kg of dust, had a willingness to pay $12,631.40, and caused 4.87 × 102 injuries and 8.05 × 102 fatalities, while the prefabricated system produced 2,711,423.72 kg of dust, had a willingness to pay $6282.02, and caused 3.45 × 102 injuries and 5.69 × 102 fatalities. The results show that when the prefabricated system is applied instead of the conventional system, the dust emission, health impact, and risk level can be reduced by 38.59%, 43.04%, and 29.03%, respectively. This study contributes to previous knowledge when decision-makers select prefabricated systems over conventional systems. Furthermore, it provides insights into the health and safety impacts of both construction methods and the necessary measures to mitigate these impacts. [ABSTRACT FROM AUTHOR]

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Abstract Background Job stress is a probable mediator of the relationship between safety climate and accident occurrence. To demonstrate this, this study investigates the relationship between safety climate, job stress, and accident risk using a large number of surveys. The study will use structural equation modeling (SEM) to analyze the data collected from the surveys to examine the effect of safety climate on accident risk through job stress. Methods The study is a cross-sectional study that was conducted on 1,530 male workers of a petrochemical company. The subjects were asked to complete several questionnaires during rest periods, which included demographic information, the Nordic safety climate questionnaire (NOSACQ-50), and the generic job stress questionnaire (GJSQ). Additionally, data on the frequency and intensity of accidents among participants were gathered from the health unit of the company. Path analysis was conducted by structural equation modeling (SEM) in Analysis of Moment Structures (AMOS) software. Results The results revealed that the latent variable of safety climate with an effect coefficient of – 0.112 did not have a direct effect on accident risk (P = 0.343). However, safety climate with an effect coefficient of − 0.633 had an indirect effect on accident risk through job stress (P

To ensure precise and real-time perception of high-speed roadway conditions and minimize the potential threats to traffic safety posed by road debris and defects, this study designed a real-time monitoring and early warning system for high-speed road surface anomalies. Initially, an autonomous mobile intelligent road inspection robot, mountable on highway guardrails, along with a corresponding cloud-based warning platform, was developed. Subsequently, an enhanced target detection algorithm, YOLOv5s-L-OTA, was proposed. Incorporating GSConv for lightweight improvements to standard convolutions and employing the optimal transport assignment for object detection (OTA) strategy, the algorithm’s robustness in multi-object label assignment was enhanced, significantly improving both model accuracy and processing speed. Ultimately, this refined algorithm was deployed on the intelligent inspection robot and validated in real-road environments. The experimental results demonstrated the algorithm’s effectiveness, significantly boosting the capability for real-time, precise detection of high-speed road surface anomalies, thereby ensuring highway safety and substantially reducing the risk of liability disputes and personal injuries.

A method for assessing technical risks arising during the operation of heat engineering units is presented. Risk assessment is based on the data on the residual life of the unit's lining and an evaluation of the severity of the consequences of an accident. To obtain primary data when assessing the residual life, an instrument method is used that allows continuous monitoring of the thermal state of the lining. Thermomechanical stresses exceeding the ultimate strength of the material are shown to be the determining destruction factor. These stresses are calculated according to the developed scheme based on numerical methods. [ABSTRACT FROM AUTHOR]

Anytime, anywhere, anyone can collect not only personal information but also surrounding situation information from home or a vehicle on the move without being restricted by time and space. To this end, the need for various devices such as biosensors, wearable devices, and temperature sensors is increasing. In this circumstance, there are many problems with the interaction of multiple devices and data processes, and there is a lack of structured or unstructured emerging contextual data. Accordingly, it is necessary to come up with a data collection method to obtain a variety of information efficiently. Such a method can obtain a diversity of information consistently with the use of data convergence. Since the method makes possible decision making, entity identification, and context prediction, it is applicable to diverse areas, including object detection and context awareness. The data generated through convergence makes it possible to the improve performance of data analysis through machine learning and deep learning. For this reason, a variety of modal types are applied to data analysis. Modal convergence for analysis can improve analysis performance more than unimodal. In this study, propose the accident risk prediction model based on attention-mechanism LSTM using modality convergence in multi-modal. In order to predict the risk of an accident, the proposed method makes the convergence of structured and unstructured modality data on the basis of judgmental fusion and statistical fusion and generates data sets. It is capable of solving the problem of data shortage and obtaining a variety of information consistently. In the proposed method, firstly, preprocessing is performed, and data sets of accident risk information are generated with the uses of the road risk equation and accident risk equation. Next, the correlation coefficient and regression coefficient for each variable is calculated through regression analysis modal and correlation coefficient analysis modal. A correlation coefficient is used to judge which variable needs to be learned intensively in order for the attention-mechanism of the accident risk prediction model. A regression coefficient is used as attention-weight to adjust a data weighting factor in attention-mechanism LSTM. Accordingly, whether there is the risk of an accident is judged. Performance evaluation is conducted in two ways. Firstly, epoch-based loss is evaluated in the comparison between the attention-mechanism LSTM-based accident risk prediction model with convergence modal and the model without it. In short, their RMSE is evaluated and compared. Secondly, the proposed model is compared with conventional models in terms of RMSE. [ABSTRACT FROM AUTHOR]

Background: Job stress is a probable mediator of the relationship between safety climate and accident occurrence. To demonstrate this, this study investigates the relationship between safety climate, job stress, and accident risk using a large number of surveys. The study will use structural equation modeling (SEM) to analyze the data collected from the surveys to examine the effect of safety climate on accident risk through job stress. Methods: The study is a cross-sectional study that was conducted on 1,530 male workers of a petrochemical company. The subjects were asked to complete several questionnaires during rest periods, which included demographic information, the Nordic safety climate questionnaire (NOSACQ-50), and the generic job stress questionnaire (GJSQ). Additionally, data on the frequency and intensity of accidents among participants were gathered from the health unit of the company. Path analysis was conducted by structural equation modeling (SEM) in Analysis of Moment Structures (AMOS) software. Results: The results revealed that the latent variable of safety climate with an effect coefficient of – 0.112 did not have a direct effect on accident risk (P = 0.343). However, safety climate with an effect coefficient of − 0.633 had an indirect effect on accident risk through job stress (P < 0.001). The total score of job stress had a significant direct effect (0.649) on accident risk (P < 0.001). Among the dimensions of safety climate, the variables of management's safety priority, commitment, and competence (− 0.108) and workers' safety commitment (− 0.107) had the highest indirect effect coefficients on accident risk. Among the dimensions of job stress, the highest indirect effects belonged to the variables of conflict at work (0.636), physical environment (0.631), and workload and responsibility (0.631), respectively. Conclusion: The results of the study revealed that job stress mediates the relationship between safety climate and accident risk. This finding suggests that organizations can potentially decrease accidents in industries by addressing and managing job stress in the workplace. [ABSTRACT FROM AUTHOR]

Summary: Obstructive sleep apnea leads to excessive daytime sleepiness and cognitive dysfunction, which are risk factors for motor vehicle collisions. We aimed to clarify if vehicles with an advanced emergency braking system could reduce motor vehicle collisions caused by falling asleep while driving among patients with untreated obstructive sleep apnea. We enrolled patients with untreated obstructive sleep apnea who underwent polysomnography. The questionnaires included the Epworth Sleepiness Scale, Pittsburgh Sleep Quality Index, history of drowsy driving accidents, and use of an advanced emergency braking system. Multivariate analysis was performed, and odds ratios and 95% confidence intervals were calculated. This study included 1097 patients (mean age, 51.2 ± 12.9 years). Collisions caused by falling asleep while driving were recorded in 59 (5.4%) patients, and were more frequently observed in vehicles without an advanced emergency braking system (p = 0.045). Multivariate analysis showed that these collisions were associated with use of an advanced emergency braking system (odds ratio [95% confidence interval]: 0.39 [0.16–0.97], p = 0.04), length of driving (2.79 [1.19–6.50], p = 0.02), total sleep time (2.40 [1.62–3.55], p < 0.0001), sleep efficiency (0.94 [0.90–0.98], p = 0.003) and periodic limb movement index (1.02 [1.01–1.03], p = 0.004). The collision risk caused by falling asleep while driving in vehicles with an advanced emergency braking system was significantly lower. This study indicates that advanced emergency braking systems may be a preventive measure to reduce motor vehicle collisions among patients with untreated obstructive sleep apnea. [ABSTRACT FROM AUTHOR]

The traffic safety of mountain highway has always been one of the taking point. This study aims to collect road design data in large-scale research and analyzes the accident risk of highway geometric alignment. Accordingly, a method based on satellite maps and clustering algorithms is proposed to calculate the geometric alignment of the highway plane and its longitudinal section. The reliability of the method was verified on Nanfu highway in Chongqing, China. The planar and longitudinal sectional geometries of the four highways in Chongqing were obtained by the above method, and the corresponding 36,439 traffic accidents which occurred from 2010 to 2016 were used as the research objects. The accident risk of the highway geometry was analyzed based on the SHAP and MLP theories. The results show that the fitting and prediction abilities of the MLP model are better than those of the negative binomial model, and its correlation coefficient is improved by 33.2%. In addition, compared with the negative binomial model, the MLP model can estimate more accurately and flexibly the complex nonlinear relationship between the independent and the dependent variables. [ABSTRACT FROM AUTHOR]

Arctic navigation faces numerous challenges, including uncertain ice conditions, rapid weather changes, limited communication capabilities, and lack of search and rescue infrastructure, all of which increase the risks involved. According to an Arctic Council statistical report, a remarkable 2638 maritime accidents were recorded in Arctic waters between 2005 and 2017, showing a fluctuating upward trend. This study collected and analyzed ship accident data in Arctic waters to identify the various accident scenarios and primary risk factors that impact Arctic navigation safety. By utilizing data-driven algorithms, a model for predicting ship navigation accidents in Arctic waters was constructed, providing an in-depth understanding of the risk factors that make accidents more likely. The research findings are of practical significance for enhancing quantitative risk assessment, specifically focusing on the navigational risks in Arctic waters. The results of this study can assist maritime authorities and shipping companies in conducting risk analysis and implementing accident prevention measures for safe navigation in Arctic waters.

The goal of the paper is to investigate the impact of tire tread depth on road accident risk and to develop an accident rate prediction model. The state of 4288 vehicle tires using tread depth gauge was inspected and processed statistically. The tread depth of the most worn tire from each vehicle was registered for further analy-sis. Based on the collected data, a statistical tire tread depth model for an insurance company vehicle fleet had been developed. The conformity of the gamma distribu-tion to the data was verified upon applying the Pearson compatibility criterion. The paper provides the histo-grams of the frequencies of tire tread depths and the theoretical curves of the distribution density. The prob-ability of the accident risk depending on the tire tread depth (adaptive risk index) was calculated applying the formed distributions and risk index dependence on the tire tread depth for the inspected vehicle fleet. Accord-ing to the developed prediction model, an upgrade of the regulation for the minimum allowed tire tread depth by 2 mm (up to 3.6 mm) could reduce road accident risk (caused by poor adhesion to road surface) to 19.3% for the chosen vehicle fleet. Such models are useful for road safety experts, insurance companies and accident cost evaluation specialists by predicting expenses related to insurance events.

Drowsiness is the inactivated state of the brain and observed during the transition from awaken state to sleepy state. This inactive state diminishes an individual's attention and leads to accidents during professional or personal activities. The prediction of this inactive (drowsiness) state using AI plays a substantial role in the avoidance of accidents. The advancements in the field of Artificial Intelligence and Neuroscience approaches are used for the prediction of this inactive drowsy state. In order to prevent these devastating accidents, the state of drowsiness of the driver has to be monitored. Electroencephalogram (EEG) is a predominant tool adopted to examine various states of the brain effectually. It is generally known as Brain- Computer Interface System. The EEG channels are used for predicting the inactive state while implementing the real-time applications. However, the researchers face various challenges during execution based on the classification and channel selection process. This research concentrates on modelling and efficient drowsiness prediction methods and intends to bridge the gap encountered in the existing approaches. A novel stacked Long Short-Term Memory(s -- LSTM) with Deep Fully Connected- Convolutional Neural Network (DFC -- CNN) is used to learn and memorize the long-term feature dependencies and attains essential information based on time-series prediction. Single and multi-channel EEG data is considered to measure the statistical characteristics of available EEG signals. The online available Open BCI sleep analysis data is used for performing the experimentation, and run in GoogleColab environment. The proposed s -- LSTM model provides a better trade-off compared to existing approaches. The model generalization is improved with the validation of combined feature subjects. Here, metrics like prediction accuracy, RMSE, false positives, scaling coefficients related to false positives are measured to show the significance of the model. [ABSTRACT FROM AUTHOR]

Road traffic accidents have emerged as an important public health issue. In 2019, the 27 EU Member States reported more than 22,800 road fatalities. In Italian context, 3,173 people were killed in road accidents, whose 13.89% were under 25. Driving distraction is the leading cause of driving accidents (ISTAT in Road Accident 2019, 2020), particularly due to the driver engagement in secondary tasks, such as smartphone usage. Although many studies have investigated the effects of mobile phone use when driving, few have studied the psychological variables underlying such behaviors. Therefore, the reasons why people take these risk behaviors, in particular young drivers, are still unclear. This study attempts to investigate the effects of certain variables—such as attitude toward multitasking, risk perception, self-efficacy, sensation seeking—on using a mobile phone while driving. Data for this study were collected from different Italian High Schools; 1498 young drivers. (43.3% females) completed a driving survey (age 14–21; M = 17.11 and DS = 1.52). We found the aptitude for using a mobile phone while driving is largely explained by the variables that we have considered. Furthermore, multi-group analyses showed that, although multitasking while driving is common for all teenagers. [ABSTRACT FROM AUTHOR]

Occupational safety is generally known to be low in forestry work. A similar situation may be found in Southeast Asian countries, where health and safety aspects are not commonly taken care of so rigorously. However, there is also a lack of primary data which could be suitable for evaluating such issues. The auditing reports of FCS certification are a source of useful information to evaluate and analyze health and safety concerns in forestry work. This paper addressed the coverage of available information, classified the risk factors uniformly from different certifying body criteria, compared occurrence of risk factors in groups and checked for dependencies in data. The key findings are that the main issues were those related to the organizations' failures to protect the workers and to the lack of awareness of safety. In turn, these may explain the high incidence of forestry-related work accidents in Southeast Asia. [ABSTRACT FROM AUTHOR]

The definition of pedestrian behavior when crossing the street and facing potential collision situations is crucial for the design of new Autonomous Emergency Braking systems (AEB) in commercial vehicles. To this end, this article proposes the generation of classification models through the deployment of machine learning techniques that can predict whether there will be a collision depending on the type of reaction, the lane where it occurs, the visual acuity the level of attention, and consider the most relevant factors that determine the cognitive and movement characteristics of pedestrians. Thereby, the inclusion of this type of model in the decision-making algorithm of the AEB system allows for modulating its response. For this purpose, relevant information on pedestrian behavior is obtained through experiments made in an ad-hoc, Virtual Reality (VR) environment, using a portable backpack system in three urban scenarios with different characteristics. Database generation, feature selection, and k-fold cross-validation generate the inputs to the supervised learning models. A subsequent analysis of the accuracy, optimization, error measurement, variable importance, and classification capability is conducted. The tree-based models provide more balanced results for the performance metrics (with higher accuracy for the single decision tree case) and are more easily interpretable and adaptable to the algorithm. From them it is deduced the high importance of the reaction type and the relative position where it occurs, coinciding with the high significance of these factors in the analyzed collisions. [ABSTRACT FROM AUTHOR]

All over the world, construction accidents and respiratory diseases are among the most important problems. The prefabricated system is an introduction to reduce emissions and accidents during the construction phase. However, there is a lack of research that proves the superiority of the prefabricated system in terms of both dust health impacts and accident risks simultaneously. Therefore, this study conducts an assessment in terms of dust health impacts and accident risks to compare the prefabricated system and the conventional system. This research is carried out in the following steps. (i) collection of data, (ii) calculation of dust emission, (iii) health impact assessment, and (iv) calculation of accident risk. The conventional system produced 5,454,527.43 kg of dust, had a willingness to pay $12,631.40, and caused 4.87 × 102 injuries and 8.05 × 102 fatalities, while the prefabricated system produced 2,711,423.72 kg of dust, had a willingness to pay $6282.02, and caused 3.45 × 102 injuries and 5.69 × 102 fatalities. The results show that when the prefabricated system is applied instead of the conventional system, the dust emission, health impact, and risk level can be reduced by 38.59%, 43.04%, and 29.03%, respectively. This study contributes to previous knowledge when decision-makers select prefabricated systems over conventional systems. Furthermore, it provides insights into the health and safety impacts of both construction methods and the necessary measures to mitigate these impacts.

The increase in the number of traffic accidents, deaths, and injuries is a major concern for traffic and safety professionals. Distraction from the road is common, but increases safety concerns. Drivers engage in many behaviors that are a distraction from the task of driving, and these sources may be inside or outside the vehicle. The driver may not have a clear idea of the negative impact of such activities on achieving safe driving. The paper focused on one type of distraction that occurs inside the car, the factor of eating while driving in order to address the question of whether such activities cause anxiety. The effect of eating while driving as an additional task (dual task) compared to driving baseline (single task) on each driver's performance and safety was studied. This research was conducted at the University of Technology-Iraq uses a fixed and medium accuracy driving simulator. To measure driver performance, the driver's ability to maintain a set speed limit was tested. For safety, the driver's ability to avoid accidents was measured. The highway environment scenario was adopted to perform driving experiences for the 42 participants, the length of the road was 15 km, and the driving experience took (30) minutes at a rate of (10-15) minutes for each driving task. The results revealed a decrease in the mean journey speed of all participants during the (dual driving) experiment compared to the results of the mean journey speed (single driving). The speed of females decreased more than males. The youngest age group (19-29) years led at a faster rate than the age groups (30-39), (40-49), and (50-55). No accidents were recorded during the baseline driving task. In the dual driving task, females recorded a higher number of accidents than males. The youth category (19-29) was characterized by recording the largest number of accidents.

Ammonia is a colorless gas, corrosive, and highly toxic. This compound can cause irreversible consequences for humans and the environment depending on the concentration. Currently, accidents involving ammonia leakage in industrial facilities are recurring. Thus, outlining contingency and mitigation strategies is crucial for the industry. In this work, we performed several simulations of ammonia leakage scenarios using the ALOHA® 5.4.7 software. Moreover, we propose a well-validated model with significant parameters to estimate the gas cloud volume for a range of environmental and leak conditions. The simplicity of this model facilitates its utilization and may enrich risk analysis and help in proposing adequate safety measures for specific atmospheric and leakage conditions.

Active safety system components, such as autonomous emergency braking (AEB) systems, are designed to play a key role in preventing serious car accidents. We investigate the extent to which vehicles with an AEB system are safer than those without it. Empirical analysis shows that the relative risk of accident when driving a car equipped with AEB is not lower than driving one without AEB. However, when we focus on drivers aged 70 years or older, those driving with AEB are safer than they otherwise would be: their risk is approximately two-third lower. Thus, an AEB system is effective for drivers who need the most assistance. [ABSTRACT FROM AUTHOR]

The geometric design of the road should be based on the road design criteria and requirements contained in the design guidelines. However, the limitations of natural conditions and considerations during the design make the road design not ideal according to the guidelines. Deviations from the guidelines may indicate that the road is not standard and has potential accident risk. This study intends to analyze the risk of accidents from the geometric aspect of the road. The purpose of this study is to find out how the level of accident risk is due to the geometric conditions of the road. The approach used in this analysis is functional worthiness where each road component is compared with field data and design guidelines. The data collection method was carried out by measuring the geometric components of the road, which included horizontal and vertical alignments. The location of data collection was national roads in the Jambi Province area with a total road length of 1.095 km. For analysis purposes, the road data is divided into 82 segments. The analytical method used is multiple regression with various statistical parameters related to these results. The modeling results show that the regression coefficients for each variable horizontal alignment, vertical alignment, and combination of horizontal and vertical alignment are 0.248, 0.349, and 1.170, respectively. While the constant in this regression equation model is 3.366. The greatest value of the accident risk contribution is in the horizontal and vertical alignment variables with a coefficient of 1.170. The results of statistical tests show that the relationship between accident risk and the geometric aspect of the road has a strong linear relationship. [ABSTRACT FROM AUTHOR]

Identifying risk factors and improving safety "immunity" is beneficial to chemical enterprises to reduce the occurrence of production accidents. In this paper, an immune system model has been built according to the bionics principle, and the corresponding antigen–antibody index system based on hidden dangers and treatment measures has been established, including 8 factor layers and 33 index layers. Then, the immune evaluation model of production safety was built using index weight, and an evaluation model of immune response ability for production safety was constructed based on grey system theory. Finally, aiming to identify immune deficiency through numerical analysis, a safety-management platform focused on people management, visual monitoring, and danger warning was constructed. The results verify the suitability of the immune correlation principle applied in chemical enterprises, which makes an instructive contribution to the construction of information management platforms for chemical enterprise safety production. [ABSTRACT FROM AUTHOR]

It is challenging to determine which patients with obstructive sleep apnea (OSA) have impaired driving ability. Vulnerability to this neurobehavioral impairment may be explained by lower brain metabolites levels involved in mitochondrial metabolism. This study compared markers of brain energy metabolism in OSA patients identified as vulnerable vs resistant to driving impairment following extended wakefulness. 44 patients with moderate‐severe OSA underwent 28hr extended wakefulness with three 90min driving simulation assessments. Using a two‐step cluster analysis, objective driving data (steering deviation and crashes) from the 2nd driving assessment (22.5 h awake) was used to categorise patients into vulnerable (poor driving, n = 21) or resistant groups (good driving, n = 23). 1H magnetic resonance spectra were acquired at baseline using two scan sequences (short echo PRESS and longer echo‐time asymmetric PRESS), focusing on key metabolites, creatine, glutamate, N‐acetylaspartate (NAA) in the hippocampus, anterior cingulate cortex and left orbito‐frontal cortex. Based on cluster analysis, the vulnerable group had impaired driving performance compared with the resistant group and had lower levels of creatine (PRESS p = ns, APRESS p = 0.039), glutamate, (PRESS p < 0.01, APRESS p < 0.01), NAA (PRESS p = 0.038, APRESS p = 0.035) exclusively in the left orbito‐frontal cortex. Adjusted analysis, higher glutamate was associated with a 21% (PRESS) and 36% (APRESS) reduced risk of vulnerable classification. Brain mitochondrial bioenergetics in the frontal brain regions are impaired in OSA patients who are vulnerable to driving impairment following sleep loss. These findings provide a potential way to identify at risk OSA phenotype when assessing fitness to drive, but this requires confirmation in larger future studies. [ABSTRACT FROM AUTHOR]

The article presents the history of the development, and the reasons for the creation of the SEVESO Directive, with particular emphasis on the latest Directive 2012/18 / EU of the European Parliament and the Council. The SEVESO III directive imposes detailed requirements on the plant operators regarding their control against major accident hazards related to dangerous substances. Individual countries of the European Union apply various requirements (following SEVESO III or more rigorous ones) for industrial plants. In Poland, the requirements of the SEVESO III Directive have been implemented in the Environmental Protection Act, and its executive acts. The SEVESO III Directive requires that the citizens of a given country have access to information about high and increased-risk establishments. In Poland, this requirement is met through the website www.mapy.geoportal.gov.pl. People can find information on establishments posing a risk of a serious industrial accident. The article presents how to use the portal and what information can be obtained there. [ABSTRACT FROM AUTHOR]

Forklift drivers in in-house logistics are at a high risk of occupational injuries. This study piloted a video-based learning game for forklift drivers, designed to increase their safety awareness in their highly dynamic operating environment. The game consisted of video clips filmed from the driver's perspective. Each video clip was stopped and masked at a point where a safety-relevant item or hazard appeared. Two locations were marked on the screen, one with the hazard and the other with no hazard. The players had to choose the location with the hazard while the screen was masked and received feedback on their choice. Eighty-one professional forklift drivers played the game at their workplaces during the 3–4 month intervention. In addition, forklift registered hard shocks were recorded and compared to the game performance of 20 participants. Higher accuracy in the game correlated with a lower shock rate, suggesting that real-world forklift driving skills transferred to the game. However, accuracy improvement in the game did not decrease the vehicle registered hard shocks. During the intervention, 40% of the participants reported that they began to pay more attention to the hazards of their work environment. Overall, the results suggest that a gamified approach could be useful for demonstrating safety-relevant features at the workplace and supporting drivers' safety-related self-reflection. [ABSTRACT FROM AUTHOR]

Occupational safety is generally known to be low in forestry work. A similar situation may be found in Southeast Asian countries, where health and safety aspects are not commonly taken care of so rigorously. However, there is also a lack of primary data which could be suitable for evaluating such issues. The auditing reports of FCS certification are a source of useful information to evaluate and analyze health and safety concerns in forestry work. This paper addressed the coverage of available information, classified the risk factors uniformly from different certifying body criteria, compared occurrence of risk factors in groups and checked for dependencies in data. The key findings are that the main issues were those related to the organizations’ failures to protect the workers and to the lack of awareness of safety. In turn, these may explain the high incidence of forestry-related work accidents in Southeast Asia.

The definition of pedestrian behavior when crossing the street and facing potential collision situations is crucial for the design of new Autonomous Emergency Braking systems (AEB) in commercial vehicles. To this end, this article proposes the generation of classification models through the deployment of machine learning techniques that can predict whether there will be a collision depending on the type of reaction, the lane where it occurs, the visual acuity the level of attention, and consider the most relevant factors that determine the cognitive and movement characteristics of pedestrians. Thereby, the inclusion of this type of model in the decision-making algorithm of the AEB system allows for modulating its response. For this purpose, relevant information on pedestrian behavior is obtained through experiments made in an ad-hoc, Virtual Reality (VR) environment, using a portable backpack system in three urban scenarios with different characteristics. Database generation, feature selection, and k-fold cross-validation generate the inputs to the supervised learning models. A subsequent analysis of the accuracy, optimization, error measurement, variable importance, and classification capability is conducted. The tree-based models provide more balanced results for the performance metrics (with higher accuracy for the single decision tree case) and are more easily interpretable and adaptable to the algorithm. From them it is deduced the high importance of the reaction type and the relative position where it occurs, coinciding with the high significance of these factors in the analyzed collisions.

The concepts of games and toys have a very important role in children's lives. It contributes to the development of cognitive, motor, psychosocial, emotional, and linguistic skills. It also plays a key role in raising self-confident, creative, and happy children. Therefore, attention should be paid to the concepts of games and toys, which are so important for the child to be a part of society as a healthy individual at every stage of his development. On the other hand, providing playgrounds where children can play comfortably and safely are essential in reducing the risk of accidents related to toys. All health-care components, especially pediatricians and family physicians, should take an active role in ensuring that these play processes, which are the most beautiful parts of childhood, are healthy and safe. [ABSTRACT FROM AUTHOR]

• The main accident causes at the macro level are not applicable to specific scenes. • The distribution of accident causes concentrates with the refinement of the scene. • A data-driven Bayesian network construction approach is proposed. • The significant causes in various specific scenes can be accurately identified. As the freight railway system is a typical complex system, freight railway accidents have various and complex accident scenes. A Data-Driven Bayesian Network (DDBN) with random variables representing scene elements, accident causes, and accident consequences as nodes, and conditional dependencies between nodes as edges is proposed to identify the most significant accident causes in various and complex specific accident scenes. First, an unsupervised-supervised method is designed to define the states of nodes in the DDBN, considering the characteristics of the data involving both continuous and discrete states. Second, a greedy algorithm is proposed to mine the causal sequence between nodes, and the direction of edges in DDBN is established accordingly. Then, an NB-K2-MLE approach is proposed to generate the structure and parameters of the DDBN from data. Finally, a risk calculation function based on DDBN is proposed to calculate the risk of various accident causes in specific scenes. In empirical analysis based on real accident data, the inference accuracy of DDBN reached 87.92 %, with precision and recall exceeding 70 %. More importantly, the research results indicate that the distribution of accident causes will concentrate with the refinement and specific of the scene, and the main accident causes at the macro level cannot be fully applicable to accident prevention in specific scenes. The DDBN constructed in this study can provide data support for the determination of the significant accident causes and the development of targeted accident prevention strategies in various and complex specific scenes. [ABSTRACT FROM AUTHOR]

Identifying risk factors and improving safety “immunity” is beneficial to chemical enterprises to reduce the occurrence of production accidents. In this paper, an immune system model has been built according to the bionics principle, and the corresponding antigen–antibody index system based on hidden dangers and treatment measures has been established, including 8 factor layers and 33 index layers. Then, the immune evaluation model of production safety was built using index weight, and an evaluation model of immune response ability for production safety was constructed based on grey system theory. Finally, aiming to identify immune deficiency through numerical analysis, a safety-management platform focused on people management, visual monitoring, and danger warning was constructed. The results verify the suitability of the immune correlation principle applied in chemical enterprises, which makes an instructive contribution to the construction of information management platforms for chemical enterprise safety production.

The advent of autonomous vehicles (AVs) has complicated traffic flow and raised safety concerns, there is a pressing need to enhance traffic efficiency alongside ensuring robust safety. This paper proposes a dynamic longitudinal control strategy for AVs, based on real-time accident risk assessments. Data from traffic flow, road characteristics, and weather conditions are analyzed using a random forest algorithm, producing a real-time accident risk identification model through a support vector machine. In addition, the Perceived Risk Field Model (PRFM), a car-following model calibrated using the NGSIM dataset, considers vehicle distance, velocity, relative velocity, and acceleration. These models underpin our proposed longitudinal control strategies, tailored to variable accident risk levels. Under the normal risk control strategy, the PRFM outperforms the Intelligent Driver Model (IDM) and the Cooperative Adaptive Cruise Control Model (CACC) in stability and driving comfort. For high accident risk scenarios, our approach shows improved collision avoidance and traffic oscillation mitigation. Simulations indicate that the high accident risk control strategy offers better safety and stability than the normal risk strategy. Furthermore, traffic flow diagrams suggest an increase in road capacity as AV penetration rates rise, pointing to the promising efficiency of our proposed solution. This research provides a robust framework for AV operation, ensuring optimal traffic safety and efficiency. • A real-time accident risk identification model tailored for AVs using machine learning with high predictive accuracy. • Development of a car-following model based on safety potential field and validation using real-world datasets. • Adaptive longitudinal control strategies for AVs, capable of adjusting car-following spacing based on real-time risk. • Framework with risk identification, behavior modeling, and control strategy, providing an integrated approach to AV safety. [ABSTRACT FROM AUTHOR]