Causes Of Accidents Research Articles

Influence of socio-economic factors on obstetric morbidity in Eastern India: A study based on NFHS 5

Abstract Background Obstetric morbidity encompasses health issues related to pregnancy or childbirth, excluding accidental causes. The whole process of childbearing is not just a biological phenomenon, rather it is governed by personal, social, cultural, and institutional factors. The entire eastern region of India including the four provinces of Bihar, Jharkhand, Odisha, and West Bengal is susceptible to high maternal mortality and morbidity. It presents a strong background to further envisage the factors responsible for such situation. Thus, the study aims to explore regional variations and socio-economic factors associated with obstetric morbidity among women in eastern India. Methods This study uses data from the 2019-2021 National Family and Health Survey (NFHS 5). The explanatory variable, obstetric morbidity, includes issues during pregnancy and delivery- vision difficulties, bleeding, swelling, convulsions, fever, breech presentation, and prolonged labor. Predictor variables include age, age at marriage and first birth, wealth index, residence, toilet facility, caste, working status, media exposure, religion, birth order, and education level. Bivariate and binary logistic regression analyses have been used as a methodological tool. Results In the eastern region, 53.76% women experience obstetric issues. Women belonging to 15-24 years with higher educational attainment increases the prevalence of obstetric morbidity. The probable reason is, with education, awareness about reproductive morbidity increases and in turn the reporting for morbidity increases. Therefore, the findings showed age, educational attainment, wealth quintile, media exposure, residence, work status, birth order and toilet facilities are highly associated with obstetric morbidity among women. Conclusions Implementing of targeted interventions, and awareness campaigns are crucial to reducing obstetric complications and maternal mortality in the eastern region of India Key messages • Socioeconomic correlates impact obstetric morbidity in eastern India in manifolds especially education, age, and wealth quintile of women in eastern India. • Obstetric morbidity is a growing concern in eastern India, thus implementing targeted interventions and awareness campaigns are much needed.

Improving Workplace Safety and Health Through a Rapid Ergonomic Risk Assessment Methodology Enhanced by an Artificial Intelligence System

The comfort of a worker while performing any activity is extremely important. If that activity extends beyond a person’s capacity to withstand physical and psychological stress, the worker may suffer from both physical and mental ailments. Over time, if the stress persists, these conditions can become chronic diseases and can even be the cause of workplace accidents. In this research, a methodology was developed for the rapid assessment of ergonomic risks and for calculating the level of ergonomic comfort in the workplace. This methodology uses artificial intelligence through a specific algorithm and takes into account a number of factors that, when combined, can have a significant impact on workers. To achieve a more accurate simulation of a work situation or to evaluate an ongoing work situation, and to significantly correlate these parameters, we used logarithmic calculation formulas. To streamline the process, we developed software that performs these calculations, conducts a rapid assessment of ergonomic risks, estimates a comfort level, and proposes possible measures to mitigate the risks and effects on workers. To assist in diagnosing the work situation, we used a neural network with five neurons in the input layer, one hidden layer, and two neurons in the output layer. As a result, most work situations, in any industrial field, can be quickly analyzed and evaluated using this methodology. The use of this new analysis and diagnosis tool, implemented through this new research technology, is beneficial for employers and workers. Moreover, through further developments of this methodology, achieved by increasing the number of relevant input parameters for ergonomics and integrating advanced artificial intelligence systems, we aim to provide high precision in assessing ergonomic risk and calculating the level of ergonomic comfort.

Development of a Comprehensive Safety Evaluation Mechanism for the Highway Bus Industry

A suitable mechanism for evaluating the safety of the highway bus industry is crucial for ensuring public transportation safety in a country. In this study, we have combined indices from the Compliance Safety Accountability (CSA) program of the Federal Motor Carrier Safety Administration (FMCSA) in the US, the safety evaluation of renting buses in Japan, and some existing safety evaluation methods in Taiwan to develop a new safety evaluation mechanism for the highway bus industry. This paper introduces a two-stage decision making approach that includes the use of fuzzy analytic hierarchy process (Fuzzy AHP) and machine learning techniques such as decision trees, support vector machines, and random forests to develop this mechanism. The data used in this research were collected from the internet and directly from highway bus transportation companies. We calculated the safety performance of each company and assigned them different safety ranks. Compared with the causes of accidents in Taiwan, the results of this study showed that work hours, driver fitness, and administrative penalties are the three most important sub-attributes that affect the safety rank of the company.

Methods for Human Reliability Analysis in Dentistry.

Human error (HE) is one of the main causes of accidents in different organizations and industries. Dentistry is a medical branch with a high risk of error since it involves complex manual tasks that must be performed with a high degree of accuracy. To understand the various aspects of HE in dentistry, which is crucial for developing strategies to mitigate its impact on patients' safety, it is necessary to perform a human reliability analysis (HRA). However, there is scarce data on the use of HRA in dentistry. In this paper, we give a brief description of the main phases of HRA with an emphasis on HRA methods that could be used in dentistry. Since HRA methods have been designed for diverse industrial applications, we discuss their possible application in dentistry. Among the discussed methods, the Systematic Human Error Reduction and Prediction Approach (SHERPA) and the Human Error Assessment and Reduction Technique were identified as the best candidates for performing HRA in dentistry. This is of great importance since understanding and addressing HEs is crucial for improving patient safety and the overall quality of dental care.

A fuzzy bayesian quantitative risk assessment for language and communication induced accidents in maritime operations

Language and communication issues pose a formidable risk to maritime safety. In maritime transportation, the effectiveness of speech acts and closed-loop communication is vulnerable to a wide array of factors. However, literature quantifying the impact of these factors on language and communication-induced accident risk is scarce. To that end, this study conducts a holistic quantitative risk assessment for the language and communication issues induced accidents in maritime transportation. The methodology consists of employing expert judgments, past literature, and accident reports to identify causation factors and a Fuzzy Bayesian approach to risk analysis. Bidirectionally propagated inferential analysis was conducted to determine the impact of factors under consideration in various scenarios. Lack of language proficiency and violations of the roles and regulations of communication were identified as prominent accident causation factors. Simultaneous evidence-setting analysis was conducted to determine the domino effect of the causation factors. A sensitivity analysis was performed to reveal the most critical factors causing language and communication issues induced accident risk. This study unravels the nuanced dynamics of language-related risk in maritime safety. The findings of this study can be used to propose guidelines and policies for effective communication and hence safer maritime operations.

A multi-aware graph convolutional network for driver drowsiness detection

Driver drowsiness is a leading cause of traffic accidents. Utilizing deep neural networks, facial feature-based methods have achieved promising results in drowsiness detection. However, these methods suffer from two limitations. Firstly, they only focus on features from one or two facial regions, thus overlooking the relationships between features across different facial regions. Secondly, these methods struggle to account for individual driver variability, a common phenomenon where drivers may display dissimilar signs of drowsiness. These limitations lead to inaccurate drowsiness detection. To address these issues, in this paper we propose a multi-aware graph convolutional network (MAGCN). At the heart of MAGCN are two feature extractors: the class- and attention-aware extractor (CAAE), and the composite temporal-aware extractor (CTAE). The CAAE explores interdependencies within global and local facial features, while the CTAE leverages temporal information to capture dynamic changes in features. Moreover, a task-oriented graph convolutional network is designed to refine the drowsiness feature space for precise detection. Experiment results show that the proposed MAGCN exhibits competitive detection performance, when compared with state-of-the-art drowsiness detection approaches on two public datasets. In summary, the proposed model not only learns and analyzes correlations between features from various facial regions, but also tackles individual driver variability.

A risk assessment model of spontaneous combustion for sulfide ores using Bayesian network combined with grounded theory

Sulfide ores, collectively referred to as a class of minerals with extensive applications, are increasingly susceptible to spontaneous combustion incidents as the mining environment evolves. The spontaneous combustion of sulfide ores is a complex nonlinear process, and the mechanisms of which remain not fully understood. Consequently, it is challenging to delineate the accident trajectory through traditional accident causation models and subsequently construct a probabilistic model for spontaneous combustion risk. To address this issue, this paper proposed a risk assessment model based on grounded theory and Bayesian network. The advantage of this model is that it employs grounded theory to categorize and organize factors influencing the occurrence of accidents, thereby forming a clear accident trajectory and providing a framework and basis for constructing a probabilistic assessment model. Furthermore, the model presented in this paper introduces the D number theory and the Noisy-OR model to handle uncertainties in the risk assessment process and to establish more rational conditional probability tables. The findings suggest that environmental temperature, heat dissipation conditions, safety awareness, and safety skills are the most influential factors in spontaneous combustion incidents when considering a particular point in time. However, when considering the entire process of sulfide ore pile spontaneous combustion, the temperature of the ore pile is the most critical accident factor to monitor. Therefore, the key to suppressing the spontaneous combustion of sulfide ores lies in reducing environmental temperatures, enhancing heat dissipation, and elevating the safety awareness of relevant personnel.

Real time distracted driver detection using Xception architecture and Raspberry Pi

Researchers are concentrating on developing technologies to identify and caution drivers against driving while distracted because it is a major cause of traffic accidents. According to the National Highway Traffic Safety Administrator's report, distracted driving is to blame for roughly one in every five car accidents.Our goal is to create an accurate and dependable method for identifying distracted drivers and alerting them to their lack of focus. We take inspiration from the success of convolutional neural networks in computer vision to do this. Our strategy entails putting in place a CNN-based system that can recognize when a driver is distracted as well as pinpoint the precise cause of their preoccupation. Real-time detection, however, necessitates three apparently mutually exclusive requirements for an optimal network: a small number of parameters, high accuracy, and fast speed.

Unsafe behavior identification on construction sites by combining computer vision and knowledge graph–based reasoning

PurposeWorkers' unsafe behavior is the main cause of construction safety accidents, thereby highlighting the critical importance of behavior-based management. To compensate for the limitations of computer vision in tackling knowledge-intensive issues, semantic-based methods have gained increasing attention in the field of construction safety management. Knowledge graph provides an efficient and visualized method for the identification of various unsafe behaviors.Design/methodology/approachThis study proposes an unsafe behavior identification framework by integrating computer vision and knowledge graph–based reasoning. An enhanced ontology model anchors our framework, with image features from YOLOv5, COCO Panoptic Segmentation and DeepSORT integrated into the graph database, culminating in a structured knowledge graph. An inference module is also developed, enabling automated the extraction of unsafe behavior knowledge through rule-based reasoning.FindingsA case application is implemented to demonstrate the feasibility and effectiveness of the proposed method. Results show that the method can identify various unsafe behaviors from images of construction sites and provide mitigation recommendations for safety managers by automated reasoning, thus supporting on-site safety management and safety education.Originality/valueExisting studies focus on spatial relationships, often neglecting the diversified spatiotemporal information in images. Besides, previous research in construction safety only partially automated knowledge graph construction and reasoning processes. In contrast, this study constructs an enhanced knowledge graph integrating static and dynamic data, coupled with an inference module for fully automated knowledge-based unsafe behavior identification. It can help managers grasp the workers’ behavior dynamics and timely implement measures to correct violations.

Rupture energy coupling of alkali washing tower cylinder

The energy generated by the container during the explosion induces destructive effects on the surrounding objects and itself, such as deformation, compression, rupture, etc. The explosive energy is one of the richest pieces of evidence in the analysis of the accident cause, and therefore a comprehensive analysis of the accident explosive energy is essential for determining accident properties. Considering the rupture accident of an alkali washing tower in an aromatic hydrocarbon plant as an example, the destructive energy and the rupture internal pressure are estimated to ascertain the explosive properties of the accident by analyzing the field damage characteristics. This study aims to conduct a comprehensive analysis and calculation of the energy associated with an alkali washing tower rupture accident. The objective is to determine the cause of the accident, which can be attributed to the combined effect of wall thickness reduction and fluctuations of production process parameters. The findings of this analysis will serve as a scientific basis for the examination of similar accidents and the formulation of safety protection measures.

How Do We Analyze the Accident Causation of Shield Construction of Water Conveyance Tunnels? A Method Based on the N-K Model and Complex Network

In the construction of water conveyance tunnels with the shield method, accidents have occurred from time to time, such as collapses and explosions, and it is of practical significance to explore the cause mechanism of the accident. However, previous research has not considered the effects of dependence between risks on the risk spread. In response, we propose a method based on the Natural Killing Model (the N-K Model) and complex network theory to analyze the cause of shield construction accidents in water conveyance tunnels. By deeply exploring the transmission mechanism and action intensity between system risks, this method can scientifically clarify the accident cause mechanism and provide support for engineering construction safety management. The method constructs a risk index system. Secondly, we introduce the N-K model to reveal the risk coupling mechanism. Then, based on complex network theory, we construct the incident causation model and revise the node’s centrality with the coupling value. Finally, the network topology parameters are calculated to quantitatively describe the causal characteristics of accidents, revealing the risk evolution process and critical causes. The research results indicate that the key causes of accidents are failure to construct according to regulations, inadequate emergency measures, poor ability of judgment and decision-making, and insufficient understanding of abnormal situations. The front end of critical links is subject to human or management risks and should be carefully controlled during construction.

Research on safety risk factors of metro shield tunnel construction in China based on social network analysis

PurposeDuring metro construction using the shield method, the construction process's complexity, the construction environment's variability, and other factors can easily lead to tunnel construction accidents. This paper aims to explore the interconnections between risk factors and related accident types, as well as the risk chain formed between risk factors, and to analyze the key risk factors and vulnerabilities in shield tunnel construction through empirical data.Design/methodology/approachBased on the social network analysis theory, the connection of various risk factors in subway shield tunnel construction is explored, and the mechanism of multiple risk factors is studied. Through literature analysis, articles on safety risk factors in metro shield tunnel construction are organized and studied, and the identified safety risk factors can comprehensively reflect the significant risks that need to be concerned in metro shield tunnel construction.FindingsThe results show that a small world characterizes the SNA network of safety risk factors for metro shield tunnel construction: The frequency of association between the five risk factors “unsafe behavior,” “site management,” “safety supervision and inspection,” “safety education system” and “safety protection” is higher than that of other factors. Only a few risks, such as “site management,” “safety supervision and inspection,” and “rapid response capability,” directly lead to accidents. In addition, risk factors such as the “safety education system” and “safety protection” will indirectly cause unsafe behaviors of construction personnel.Research limitations/implicationsDuring construction, the probability of occurrence of risk factors may vary with the construction phase and area and is not considered in this paper. In addition, although this paper identifies, determines and analyzes the risk factors affecting the safety of metro shield tunnel construction, including the importance of each risk factor and the connection between them, more detailed information before and after the accident could not be obtained based on the accident investigation report alone. Therefore, future research can collect the same accident case from more sources to obtain more information.Practical implicationsThe theory of accident causation has been improved at the theoretical level. The identified safety risk factors can comprehensively reflect the significant risks that need to be paid attention to in metro shield tunnel construction. From a practical point of view, the results of the study provide a basis for the rational control of the risk factors in the construction of subway shield tunnels, which can help guide practitioners to do a good job of risk prevention before the construction of metro shield tunnels and reduce the probability of related accidents.Originality/valueThis study expands the application of social network analysis in the field of subway tunnel construction risk, quantitatively analyzes the key risk factors and vulnerabilities in shield method tunnel construction and proposes policy recommendations for future metro tunnel construction safety management.

Analysis of causes of traffic accidents for taxi drivers based on MIMIC and machine learning

Through questionnaire survey, information on individual attributes, physical fatigue perception, work pressure, risky driving behavior and traffic accident experience of 2391 taxi drivers was collected. The multi-indicator multi-cause (MIMIC) model was used for path analysis to explore the inducing effect of physical fatigue perception and risky driving behavior on traffic accidents, and to verify the influence of gender, age and work pressure on physical fatigue perception and distracted driving behavior. Four machine learning algorithms, logistic regression, naive Bayes, support vector machine and random forest, were selected to predict taxi accidents. The results show that the increase of physical fatigue perception, negligent driving behavior, aggressive driving behavior and distracted driving behavior can lead to an increase in accident rate, and gender, age and work pressure will affect the frequency of physical fatigue perception and distracted driving behavior. The accident prediction model based on machine learning has excellent effect, among which the random forest has the best prediction effect. The prediction accuracy of using single feature variables "risky driving behavior", "distracted driving behavior" and "physical fatigue perception" is acceptable. When the personal attribute indicators of "work pressure", "age" and "gender" are introduced, the prediction accuracy is further improved.

Investigating Tire Ground Contact Mechanics Under Partial Hydroplaning Conditions: A Multivariate Analysis Approach

Tire hydroplaning is one of the main causes of traffic accidents on slippery roads. In order to study the ground contact mechanical characteristics of tires under partial hydroplaning conditions, 205/55the R16 passenger car tire was taken as the research object, a tire hydroplaning CEL simulation model was established, and the test plan was created using the orthogonal experimental design method. Through range analysis, partial correlation analysis and multivariate linear regression analysis, a tire side slip UA model suitable for different working conditions on slippery roads was established. Comparative analysis shows that the model can accurately predict the tire ground contact mechanical characteristics on slippery roads, and provide a theoretical reference for studying the tire multi-condition hydroplaning performance.

Analysis of the Meteorological Conditions and Atmospheric Numerical Simulation of an Aircraft Icing Accident

With the rapid development of the general aviation industry in China, the influence of high-impact aeronautical weather events, such as aircraft icing, on flight safety has become more and more prominent. On 1 March 2021, an aircraft conducting weather modification operations crashed over Ji’an City, due to severe icing. Using multi-source meteorological observations and atmospheric numerical simulations, we analyzed the meteorological causes of this icing accident. The results indicate that a cold front formed in northwestern China and then moved southward, which is the main weather system in the icing area. Based on the icing index, we conducted an analysis of the temperature, relative humidity, cloud liquid water path, effective particle radius, and vertical flow field, it was found that aircraft icing occurred behind the ground front, where warm-moist airflows rose along the front to result in a rapid increase of water vapor in 600–500 hPa. The increase of water vapor, in conjunction with low temperature, led to the formation of a cold stratiform cloud system. In this cloud system, there were a large number of large cloud droplets. In addition, the frontal inversion increased the atmospheric stability, allowing cloud droplets to accumulate in the low-temperature region and forming meteorological conditions conducive to icing. The Weather Research and Forecasting model was employed to provide a detailed description of the formation process of the atmospheric conditions conducive to icing, such as the uplifting motion along the front and supercooled water. Based on a real case, we investigated the formation process of icing-inducing meteorological conditions under the influence of a front in detail in this study and verified the capability of a numerical model to simulate the meteorological environment of frontal icing, in order to provide a valuable reference for meteorological early warnings and forecasts for general aviation.

Development of an Indicator System for Evaluating Risks in Trekking Tourism

In today’s fast-paced and and high-stress society, trekking tourism is a leisure activity for stress relief and closer nature engagement. Given the adventurous nature inherent in trekking tourism, risk assessment is deemed both crucial and imperative. This study aims to develop a risk assessment framework for trekking tourism based on the Delphi method and Analytic Hierarchy Process (AHP). Initially, we conducted a literature review and expert interviews grounded in the Systems Theory of Accident Causation and the Human Factors Theory to establish the risk assessment paradigm specific to trekking tourism. Subsequently, we ascertained specific weights for each evaluation criterion and delineated explicit data sources for each operational indicator. The findings indicate that the formulated risk assessment system for trekking tourism is both scientifically grounded and practical, furnishing stakeholders in the tourism industry, organizational teams, and management authorities with an efficacious tool for evaluating the safety conditions of trekking tourism activities.

Black-Spot Identification from Ramol Cross Road to Kathwada Cross Road

Road accidents are a vital issue that is occurring all over the world. Accident severity is increasing with the exponential increase in vehicle population. Accident leads to disability, pain, suffering, grievous injuries, hospitalization expenditure, damage to health and property, social suffering, general degradation of the environment, and, in extreme cases, death. This study aims to identify black spots in the selected stretch involving four major intersections from Ramol crossroad to Kathwada crossroad of Ahmedabad. Data collection consists of collecting past accident data from traffic police station headquarters. The collected data are categorized into fatal accidents, grievous significant accidents, and minor accidents. The data is analyzed using the accident data collected, the Weighted Severity Index (WSI) method, and the PTV Visum software. WSI is estimated for the accident data for the year 2019-2023. By accident analysis, seven black spots were identified in this study. Inspecting the road stretch noted several issues and probable causes of accidents were identified. Poor road conditions, including cracks and potholes, affect road safety. Improper road markings and signs are one of the causes of accidents. The audited road stretch includes recommendations to prevent the number and severity of accidents.

Основні принципи обстеження та призначення заходів з покращення безпеки дорожнього руху в місцях / на ділянках концентрації дорожньо-транспортних пригод на автомобільних дорогах

Introduction. The increase in accidents on highways leads to the emergence of places and areas of concentration of traffic accidents, which, in turn, require a detailed examination to identify possible causes of accidents, determine ways to eliminate the problem and ensure the safety of all road users. Problem statement. Traffic accidents in places / areas of their concentration are characterized by great severity of consequences, especially involving vulnerable road users, which requires a detailed analysis of each such place / area and the implementation of measures that will improve road safety. Purpose. Ensuring the safety of highways in places / areas of concentration of traffic accidents, reducing the severity of their consequences and ensuring comfortable conditions for road users.

Dynamic modeling of human error in industrial maintenance through structural analysis and system dynamics.

Human error constitutes a significant cause of accidents across diverse industries, leading to adverse consequences and heightened disruptions in maintenance operations. Organizations can enhance their decision-making process by quantifying human errors and identifying the underlying influencing factors, thereby mitigating their repercussions. Consequently, it becomes crucial to examine the value of human error probability (HEP) during these activities. The objective of this paper is to determine and simulate HEP in maintenance tasks at a cement factory, utilizing performance shaping factors (PSFs). The research employs the cross-impact matrix multiplication applied to classification (MICMAC) analysis method to evaluate the dependencies, impacts, and relationships among the factors influencing human error. This approach classifies and assesses the dependencies and impacts of different factors on HEP, occupational accidents, and related costs. The study also underscores that PSFs can dynamically change under the influence of other variables, emphasizing the necessity to forecast the behavior of human error over time. Therefore, this paper utilizes the MICMAC method to analyze the interdependencies, relationships, and impact levels among different variables. These relationships are then utilized to optimize the implementation of the system dynamics (SD) method. An SD model is employed to forecast the system's behavior, and multiple scenarios are presented. By considering the HEP value, managers can adjust organizational conditions and personnel to ensure acceptability. The paper also presents various scenarios related to HEP to assist managers in making informed decisions.

A study of the relation between traffic accidents and election posters

Inattention in traffic is a risk factor for the occurrence of accidents. Elements surrounding traffic could potentially be a safety risk for road users. This study investigates the relation between election posters and traffic accidents using a seasonal autoregressive-moving-average model with exogeneous variables (seasonal ARMAX) correcting for variations in meteorological conditions and general traffic volume indicated through public holidays. Danish traffic accident micro data are used to construct a time series for daily number of accidents in city zones adjusted for probable known accident causes, namely drunk driving, narcotics, drivers deemed unfit to drive, and accidents related to low visibility. Model parameters are determined from Maximum Likelihood estimation on the period from 1 January 1997 to 15 December 2009 with an indicator for general election periods. The presence of general election posters is associated with an increased risk of traffic accidents (p = 0.003), when correcting for meteorological variables and public school holidays. While a causal relation between traffic accidents and the presence of election posters cannot be concluded from the analysis, we find distraction from election posters to be the most likely explanation for the associated increase in traffic accidents. On average, the model indicates that 1.18 additional daily accidents occur for general elections in Denmark. In total, that amounts to 127 additional accidents for the investigated period of time. This study shows that such sudden changes in traffic surroundings are significantly related to increased safety risks which could lead to a better discussion of relevant traffic legislation.