Risk Of Accidents Research Articles

Zakłady niesevesowskie – stwarzające zagrożenie wystąpienia poważnej awarii przemysłowej

Non-Seveso establishments – plants posing a risk of a major industrial accident Various definitions and approaches used by organizations and control and supervisory institutions are reflected in the list of establishments posing a risk of a major industrial accident, which are not upper- and lower-tier establishments. The article proposes a definition that includes both sub-threshold establishments with smaller quantities of non-hazardous substances than qualify an establishment as a lower tier establishment and establishments containing other chemical substances that could be the source of a major accident. The results of analyses of databases of chemical substances present in non-Seveso establishments, the correlation of the definition of a major accident and chemical substances, and the failure rate for non-Seveso establishments are presented. Keywords: non-Seveso establishments, dangerous substances, major accidents

The Sight Distance Planning for Road User Safety : A Case of Jalan Pasukan Sindangkasih-Majalengka

This study aims to provide practical and applicable solutions to improve road safety on the Troop-Sindangkasih Road section in Majalengka Regency, West Java, by analyzing road visibility using empirical data and the Bina Marga method. The primary data consisted of looking for a literature review on the classification of road classes based on their functions, calculation of visibility, while for secondary data collected about the location map of this study. The results show that visibility planning can be improved to improve traffic efficiency and reduce the risk of accidents. The research could also consider the influence of different vehicle types and weather conditions on visibility requirements to refine the standards set by the Geometric Planning Procedures for Intercity Roads. It is also hoped that this research will increase awareness of the importance of visibility planning for overall road safety to avoid accidents.

Loading Round Wood in Forestry Trucks and Forestry Platforms: A Case Study for Romania

Wood, as the main product of the forest, must be brought from the forest to various beneficiaries by various vehicles. The choice of a particular type of vehicle should consider the characteristics of the area from which the wood is loaded, the wood-storage yard, and the availability of a certain model. Besides this, the assortments and quantities of wood to be transported influence, to a large extent, the type of vehicle, which has consequences for transport efficiency and productivity. The aim of the paper was to evaluate the wood-loading process in various types of transport vehicles in terms of time consumption for each working phase and the factors that can affect productivity. The research was carried out in four counties of Romania and addressed the loading of wood in different forestry trucks and platforms, with or without trailers. Transport distances were documented in Brasov County based on 200 transport documents. To evaluate the transport distances in the case of wood intended for contractors, field measurements were carried out in three different are as managed by the private Lignum Forest District (Bacău County). The study of the loading process and productivity estimation at loading was carried out in three locations, namely Bacău County, Sibiu County, and Caraș-Sever in County. To observe whether there are differences in the loading of tree lengths from the harvesting area in various types of vehicles (forestry trucks—ATF—and forestry platforms—APF), research was carried out in Caraș-Sever in County. After processing the data, it was found that in the forests managed by the state and in the forests owned by the local public administrations, the fuelwood reaches mainly locals, and the working wood reaches some wood processing companies. For forestry trucks (ATFs), the loading time was somewhat constant, while for forestry platforms (APF), it varied, with differences observed from the first to the last loading. Following the evaluation of the work-element phases specific to the operation of loading round wood into vehicles, it was found that, sometimes, significant importance is given to the working elements as phases of preparation of the wooden material that can be carried out before the loading process, in order to load it. To eliminate delays in the loading process, it is recommended that logs are prepared before loading, which will increase productivity and it will decrease the risk of accidents for workers who perform these tasks during the loading process.

The Effectiveness of Pedestrian Crossing Bridges for Urban Areas in Indonesia Based on Crossing Times and Pedestrian Perspective

The use of pedestrian bridges is often ineffective as pedestrians tend to prefer crossing directly on the roadway, even though heavy traffic conditions increase the risk of accidents. This lowers the safety level of crossers and causes traffic problems. This study aims to evaluate the efficient use of pedestrian bridges and determine public perceptions of road crossing facilities. The research was conducted using geometric assessment and road performance evaluation methods that refer to the 2023 Indonesian Road Capacity Guidelines. In addition, crossing times were analyzed based on the traffic TCT distribution histogram, as well as pedestrian perceptions of crossing facilities. The evaluation results show that the effectiveness of road services falls into category A. Based on pedestrian perceptions, 60% of respondents prefer to cross directly on the roadway, with an average TCT of 1.2 m/sec. From the results of the CSI analysis, the level of respondents' satisfaction with the condition of the pedestrian bridge is 71.373%. Based on the results of the study, the use of pedestrian bridges will be effective in the next 15 years. Recommendations proposed to increase the effectiveness of the use of pedestrian bridges and the level of pedestrian safety is the use of guardrails.

Qualitative prioritization of accident risks in the mining industry

Qualitative prioritization of accident risks in the mining industry

Thickness and Structure of Permafrost in Oil and Gas Fields of the Yamal Peninsula: Evidence from Shallow Transient Electromagnetic (sTEM) Survey

The Yamal-Nenets Autonomous District, especially the Yamal Peninsula located in the permafrost zone, stores Russia’s largest oil and gas resources. However, development in the area is challenging because of its harsh climate and engineering–geological features. Drilling in oil and gas fields in permafrost faces problems that are fraught with serious accident risks: soil heaving leading to the collapse of wellheads and hole walls, deformation and breakage of casing strings, gas seeps or explosive emissions, etc. In this respect, knowledge of the permafrost’s structure is indispensable to ensure safe geological exploration and petroleum production in high-latitude regions. The extent and structure of permafrost in West Siberia, especially in its northern part (Yamal and Gydan Peninsulas), remain poorly studied. More insights into the permafrost’s structure have been obtained by a precise sTEM survey in the northern Yamal Peninsula. The sTEM soundings were performed in a large oil and gas field where permafrost is subject to natural and anthropogenic impacts, and its degradation, with freezing–thawing fluctuations and frost deformation, poses risks to exploration and development operations, as well as to production infrastructure. The results show that permafrost in the western part of the Yamal geocryological province is continuous laterally but encloses subriver and sublake unfrozen zones (taliks) and lenses of saline liquid material (cryopegs). The total thickness of perennially frozen rocks is 200 m. The rocks below 200 m have negative temperatures but are free from pore ice. Conductive features (<10 Ohm﮲m) traceable to the permafrost base may represent faults that act as pathways for water and gas fluids and, thus, can cause a geohazard in the oil and gas fields (explosion of frost mounds, gas blow during shallow drilling, etc.).

Complex network analysis for accident causes modelling to enhance process safety in chemical enterprises

AbstractHazardous chemicals often cause catastrophic accidents, and accidents often result from intricate interactions among various causes. Due to the varying risk factors in different areas of chemical enterprises, to achieve more precise prevention, a more detailed study of the accident risk factors in each area is necessary. Therefore, this study focuses on analyzing critical accident causes and their interrelationships in different functional areas of chemical enterprises to enhance process safety by using a complex network model. Based on 90 accident information, complex network models are constructed for hazardous chemical warehouse areas (HCWAs), tank farm areas (TFAs), and production areas (PAs). Subsequently, a topological analysis of the complex network models is conducted. Based on the PageRank algorithm, 13 critical nodes are identified for HCWAs, while 14 for TFAs and 13 for PAs. Node degree analysis with confidence quantifies mutual influences, forming critical accident causal links for each area. The research results offer decision support for precise accident risk control, aiding in reducing future accidents and improving process system safety in chemical enterprises.

Combination of two-fluid model and delayed equilibrium model for the critical flow in a slit

Accurately predicting the mass flux, pressure profile, and velocity profile of the critical flow in a slit is essential for analyzing the breaking process of the liquid phase and calculating the aerosol source term for leak-before-break (LBB) monitoring and Loss of Coolant Accident (LOCA) risk analysis. A new critical flow model combining Two-fluid Model (TFM) and Delayed Equilibrium Model (DEM) is built to get accurate profiles while avoiding the same phase velocity in DEM and the arbitrary critical flow criterion in TFM. The new model is verified using past experiments of the critical flow in a slit. It proves to be accurate in mass flux but not in critical pressure, with maximum relative errors of around 25% in mass flux and around 80% in critical pressure. The new model is optimized for higher accuracy in critical pressure. The empirical equation of saturated phase mass flow rate fraction gradient is optimized by conducting approximate pressure profile calculation and regression analysis. The maximum relative error decreases little while the ratio of critical pressure relative errors lying in the range of ±40% increases after optimization. In contrast, the difference in the average abstract relative error of pressure between original TFM-DEM and DEM is much larger, for the maximum relative error of mass flux and critical pressure are around 25% and 110%. The comparison between the original and optimized TFM-DEM proves that the new critical flow model is accurate in mass flux and can be optimized to raise pressure calculation accuracy. The comparison between the original TFM-DEM and DEM proves that the phase velocity difference is the major source of accuracy improvement in the pressure profile.

Laser Direct Writing of Setaria Virids-Inspired Hierarchical Surface with TiO2 Coating for Anti-Sticking of Soft Tissue.

In minimally invasive surgery, the tendency for human tissue to adhere to the electrosurgical scalpel can complicate procedures and elevate the risk of medical accidents. Consequently, the development of an electrosurgical scalpel with an anti-sticking coating is critically important. Drawing inspiration from nature, we identified that the leaves of Setaria Virids exhibit exceptional non-stick properties. Utilizing this natural surface texture as a model, we designed and fabricated a specialized anti-sticking surface for electrosurgical scalpels. Employing nanosecond laser direct writing ablation technology, we created a micro-nano textured surface on the high-frequency electrosurgical scalpel that mimics the structure found on Setaria Virids leaves. Subsequently, a TiO2 coating was deposited onto the ablated scalpel surface via magnetron sputtering, followed by plasma-induced hydrophobic modification and treatment with octadecyltrichlorosilane (OTS) to enhance the surface's affinity for silicone oil, thereby constructing a self-lubricating and anti-sticking surface. The spreading behavior of deionized water, absolute ethanol, and dimethyl silicone oil on this textured surface is investigated to confirm the effectiveness of the self-lubrication mechanism. Furthermore, the sticking force and quality are compared between the anti-sticking electrosurgical scalpel and a standard high-frequency electrosurgical scalpel to demonstrate the efficacy of the nanosecond laser-ablated micro-nano texture in preventing sticking. The findings indicate that the self-lubricating anti-sticking surface fabricated using this texture exhibits superior anti-sticking properties.

Road accident risk analysis of at- and near-port areas reflecting regional heterogeneous characteristics

ABSTRACT In port cities, road traffic safety management should consider various types of cargo that will shift in the trucks and containers. This applies not only in the case of ‘at port’ areas within a 2-km radius but also ‘near-port’ areas within a 4–8-km radius. This is important for traffic safety because the crash severity can be high as there are many trucks to the ports that can cause large-scale crashes. Therefore, to prepare traffic safety management strategies for port city, it is necessary to identify crash risk factors that affect the crash severity. This study developed a two-stage road crash severity model of ‘at-port’ and ‘near-port’ regions using a support vector machine and a Bayesian multinomial model. Additionally, a severity model suitable for Korean ports was proposed that considered spatial heterogeneity in the Bayesian multilevel multinomial model. Findings derived from this study can be used for policy development to increase road traffic safety in port cities. ‘This article is a revised and expanded version of a paper entitled [Accident Risk Analysis of At and Near-Port Areas Reflecting Regional Heterogeneous Characteristics] presented at [The 11th International Conference on Logistics and Maritime Systems, Korea, 4-7 September 2023]’.

Assessing head injury risks in electric scooter accidents: A multi-body simulation study with insights into sex differences

E-scooters have become increasingly popular for short-distance travel in urban areas, but this rise in usage also brings about an increased risk of accidents. Studies have shown that approximately 40% of electric scooter accident victims admitted to hospitals suffer head injuries. Therefore, it is crucial to implement safety measures and improve safety systems and equipment to mitigate these risks. One approach to gaining insights into the injuries users face is through simulations using the multi-body method. This method allows for the reconstruction of accidents by modeling and analyzing the dynamic behavior of interconnected bodies. This study aims to assess the impacts on the user’s head and the injuries they may sustain in electric scooter accidents using numerical methods. Initially, a reference scenario was established based on a YouTube video, with the assumption that the user was an average-height man. Simulations were conducted for various percentiles, including both males and females. Different velocities were simulated to determine the threshold velocity at which survival becomes practically impossible. Two scenarios were considered: one where the car braked for 0.333 s and another where the distance between the start the braking task and the collision was kept constant. The location of the first head impact on the vehicle was also examined. Injury assessment was conducted using two criteria: Head Injury Criterion (HIC) and Brain Injury Criterion (BrIC). The study found that smaller individuals are more vulnerable to severe injuries, and higher car velocities correlate with more severe user injuries. Furthermore, the location of the first impact varies between genders, with women more likely to experience impacts in the lower part of the windshield, while men tend to experience impacts in the central zone. This study highlights the importance of considering user characteristics and accident dynamics in assessing injury risks associated with e-scooters.

Work stressors and aggressive driving: The mediating roles of stress appraisals

While the association between work stressors and the risk of traffic accidents and driving lapses has been established, there is a lack of research exploring the relationship between work stressors and aggressive driving—a notable contributor to traffic incidents and injuries. To fill this gap, the present study utilized the challenge-hindrance stressor framework and the transactional theory of stress to investigate the effects of these stressors on aggressive driving behaviors, with a particular focus on the mediating roles of challenge and hindrance appraisals. We used time-lagged design and gathered self-reported data from 247 employees at a Chinese company over three distinct time points. The results of structural equation modeling indicated that both challenge and hindrance stressors are positively associated with aggressive driving, with their effects mediated by corresponding appraisals of challenge and hindrance. Notably, challenge appraisals were inversely related to aggressive driving. Our study is one of the first attempts to explore the relationship between work stressors and aggressive driving, underscoring the critical role of stress appraisals in understanding responses to stressors.

Risk identification model of aviation system based on text mining and risk propagation

Due to the aviation accident is rarely predictable and often irreversible, how to ensure aviation safety is of uttermost importance. Textual aviation accident reports contain the cause and process of the accident which could help people understand incidents. However, the cause of the accident always is summarized by the expert and the accident report would be incomplete, the identification of aviation safety accident risk is not timely and accurate. In this paper, a safety risk identification model is proposed, aiming to identify the correlation between aviation safety accident risk factors by machine learning from textual aviation accident reports. In detail, the feature of aviation accidents is extracted and classified by text mining technology, on this basis, the correlation coefficient matrix between different features is established. Finally, the correlation network of aviation safety risk is proposed, and the risk propagation process of accidents is developed based on the network to identify aviation safety accident risk.

Exploring the impacts of daytime sleepiness on gait, physical balance, and the occurrence of orofacial trauma: a narrative review

Daytime sleepiness (DS) often accompanies obstructive sleep apnea (OSA), exacerbating its effects. DS can impair cognitive function, attention, reaction time, and motor coordination, increasing the risk of accidents. This study aimed to explore the relationship between DS, changes in gait and balance, and the occurrence of orofacial fractures. A narrative review was conducted using PubMed, Scopus, and Web of Science publications. The analysis revealed the impact of DS and OSA on gait and balance control in older individuals, emphasizing the need for interventions to address these issues and identify associated pathologies. Continuous Positive Airway Pressure (CPAP) therapy was recognized as a potential treatment, although further research is needed to understand DS triggers and treatment options in older populations. The study underscores the importance of analyzing interactions between the central nervous system, medications, balance, and mobility to prevent falls and related injuries. While the literature highlights the vulnerability of elderly individuals and those with OSA to falls, no established link between DS, gait, balance, and orofacial trauma necessitating medical and dental intervention was found. Thus, further research is warranted to address this gap and promote better understanding and management of these conditions.

Geo-sustainability Challenges and Remediation Strategies: A GIS-based Assessment of Sripur Coalmine Areas, West Bengal, India

This study examines the health, risk, and safety aspects of coal mining in the Sripur area, focusing on the Bhanora West Block, Asansol, West Bengal, India as one of the 14 operational mines of Eastern Coalfields Limited in Asansol. The study addresses the increasing risks of accidents, fires, and subsidence in active, ageing, and closed mines and storage yards. The study’s incorporation of field surveys, satellite imagery, and environmental data ensures that the findings are based on a well-rounded and multi-faceted dataset. The satellite imagery data (GIS and Remote Sensing) are perceived along with available literature and environmental impact compliance reports from time to time, with particular interest in the Bhanora West coal mine, both open cast and underground. The aspect, Hill shade, LULC and stream order maps of the Damodar are generated using Q-GIS, 3.28.7. The environmental clearance data, accident data, subsidence area, and landslide data are analysed. A vast area in Sripur is observed to be under subsidence, with cracks appearing even near residential areas. The flow of the Nunia nallah is nearby, and old closed mines are not adequately backfilled, so the chance of seepage cannot be neglected though regularly attended. Pneumoconiosis (CWP), a lung disease (black lung), chronic obstructive pulmonary disease (COPD), and silicosis are common in Sripur due to dust and substandard water. Effective and riskless mining extractions using proper technologies can avoid mining hazards, such as subsidence, the influx of water in underground mines, health hazards, air/water pollution, mine fire, and intact physical landscape and observing criteria of sustainable Development Goal SDG-1 SDG-3, SDG-6, SDG 12, SDG-13, and SDG 16.

Advanced Human Reliability Analysis Approach for Ship Convoy Operations via a Model of IDAC and DBN: A Case from Ice-Covered Waters

The melting of Arctic ice has facilitated the successful navigation of merchant ships through the Arctic route, often requiring icebreakers for assistance. To reduce the risk of accidents between merchant vessels and icebreakers stemming from human errors during operations, this paper introduces an enhanced human reliability assessment approach. This method utilizes the Dynamic Bayesian Network (DBN) model, integrated with the information, decision, and action in crew context (IDAC) framework. First, a qualitative analysis of crew maneuvering behavior in scenarios involving a collision with the preceding vessel during icebreaker assistance is conducted using the IDAC model. Second, the D–S evidence theory and cloud models are integrated to process multi-source subjective data. Finally, the human error probability of crew members is quantified using the DBN. The research results indicate that during convoy operations, the maximum probability that the officer on watch (OOW) chooses an incorrect deceleration strategy is 8.259×10−2 and the collision probability is 4.129×10−3. Furthermore, this study also found that the factors of Team Effectiveness and Knowledge/Abilities during convoy operations have the greatest impact on collision occurrence. This research provides important guidance and recommendations for the safe navigation of merchant ships in the Arctic waters. By reducing human errors and adopting appropriate preventive measures, the risk of collisions between merchant ships and icebreakers can be significantly decreased.

Proposal of a Cost-Effective and Adaptive Customized Driver Inattention Detection Model Using Time Series Analysis and Computer Vision

Advanced Driver Assistance Systems, such as Forward Collision Warning and Lane Departure Warning, play a crucial role in accident prevention by alerting drivers to potential hazards. With the advent of fully autonomous driving technology that requires no driver input, there is now a greater emphasis on monitoring the state of vehicle occupants. This is particularly important because, in emergency situations where control must suddenly be transferred to an unprepared occupant, the risk of accidents increases significantly. To mitigate this risk, new monitoring technologies are being developed to analyze driver behavior and detect states of inattention or drowsiness. In response to the emerging demands of driver monitoring technology, we have developed the Customized Driver Inattention Detection Model (CDIDM). This model employs video analysis and statistical techniques to accurately and rapidly classify information on drivers’ gazes. The CDIDM framework defines the components of inattentive or drowsy driving based on the Driver Monitoring System (DMS) safety standards set by the European New Car Assessment Programme (EuroNCAP). By defining six driving behavior-related scenarios, we have improved the accuracy of driver inattention assessment. The CDIDM estimates the driver’s gaze while simultaneously analyzing data in real-time. To minimize computational resource usage, this model incorporates a series of preprocessing steps that facilitate efficient time series data analysis, utilizing techniques such as DTW Barycenter Averaging (DBA) and K-means clustering. This results in a robust driver attention monitoring model based on time series classification.

Does organisational myopia mediate the effect of occupational health and safety practices on the risk of occupational accidents in Turkish healthcare institutions?

Occupational health and safety (OHS) policies in healthcare institutions must be well managed, because healthcare practice involves many physical, biological, ergonomic, chemical, and psychosocial hazards that can affect the health of healthcare workers. In addition, their work performance may be affected by the so-called organisational myopia. In this context, the aim of our study was to determine how organisational myopia affects OHS practices in healthcare institutions and whether it increases the risk of occupational accidents. The study population consisted of a convenience sample of 420 healthcare professionals working throughout Turkey who completed a questionnaire addressing these three domains: organisational myopia, OHS practices, and risk of occupational accidents. Their responses were analysed with exploratory factor analysis, reliability analysis, and Spearman's correlation to assess the adequacy of measurement tools and identify relationships between variables, followed by mediation analysis. We found that OHS practices mitigate organisational myopia and the risk of occupational accidents. We also found no significant effect of organisational myopia on the risk of occupational accidents. Our findings underscore the importance of OHS practices in healthcare institutions and that organisational myopia should be evaluated in special contexts such as working time, experience, or routinisation.

Shipping Safety Innovation: The Role of Big Data and IoT in Reducing Risk

Shipping safety is a crucial aspect in the maritime industry which faces various risks of accidents. This research aims to examine the innovative role of Big Data and the Internet of Things (IoT) in improving shipping safety through accident prediction and prevention. The research method used is qualitative using secondary data from industry reports, journal articles and related publications. The research results show that Big Data can improve the ability to predict and prevent shipping accidents by analyzing weather data, ship movements and sea conditions to produce accurate predictive models. In addition, IoT makes a specific contribution in real-time monitoring and risk mitigation through sensors and AIS systems that provide real-time data for fast and informed decision making. However, the integration of Big Data and IoT faces major challenges such as cybersecurity, implementation costs, and the need for adequate infrastructure. Nevertheless, the prospects for developing this technology are very promising with increased security protocols, reduced costs through innovation, and development of maritime telecommunications infrastructure. In conclusion, Big Data and IoT have great potential to improve shipping safety, but require attention to existing challenges to realize their full benefits.

Addressing Imbalanced EEG Data for Improved Microsleep Detection: An ADASYN, FFT and LDA-Based Approach

Microsleep, brief lapses in consciousness lasting less than 15 seconds, are often accompanied by feelings of fatigue and are detectable through a deceleration in electroencephalogram (EEG) signal frequencies. Accurate identification of microsleep is critical for assessing driver alertness and preventing accidents. This paper introduces a novel approach to detecting driver microsleep by leveraging EEG signals and advanced machine learning techniques. The methodology begins with preprocessing raw EEG data to improve quality and balance, utilizing the ADASYN algorithm to address dataset imbalances. After preprocessing, features are extracted using Fast Fourier Transform (FFT), which provides a comprehensive frequency domain analysis of the EEG signals. For classification, Linear Discriminant Analysis (LDA) is employed to effectively distinguish between microsleep events and normal wakefulness based on the extracted features. The proposed framework was rigorously validated using a well-established publicly available EEG dataset, which included recordings from 76 healthy individuals. The validation results revealed a high testing accuracy of 92.71% in detecting microsleep episodes, demonstrating the effectiveness of the proposed approach. These results underscore the potential of combining EEG signal analysis with machine learning models for practical applications in monitoring driver alertness. The framework could significantly enhance driver safety by providing an effective tool for detecting microsleep and thereby reducing the risk of accidents caused by drowsy driving. This research highlights the promising application of advanced signal processing and machine learning techniques in the field of driver alertness monitoring.