Accident Reports Research Articles

The Effects of Patient Safety Culture Perception and Safety Control on the Patient Safety Management Activities of Psychiatric Ward Nurses

Purpose: To provide basic data to find ways to increase the patient safety management activities of psychiatric ward nurses and establish measures for patient safety.Methods: The research was conducted with psychiatric nurses who had worked for > 6 months in 9 psychiatric hospitals across 3 regions. Data collection spanned June 16 to July 7, 2022, the research tools being patient safety culture perception, safety control, and patient safety nursing activities.Results: A hierarchical regression analysis showed the factors influencing patient safety management activities. In Model 1, factors of the position of the subject and number of accident reports in the past year appeared significant (F=6.63, <i>p</i><.001); the explanatory power was 9.8 %. In Model 2, patient safety culture perception appeared significant (F=15.26, <i>p</i><.001); explanatory power increased to 45.1 %. In Model 3, safety control appeared significant (F=14.63, <i>p</i><.001); explanatory power increased to 46.6 %. Among the perceptions of patient safety culture, communication, and procedures, incident-reporting frequency and hospital environment were identified as significant.Conclusion: The factors for psychiatric nurses' patient safety management activities are patient safety culture perception and safety control. Patient safety culture perception is a particularly important factor, and a strategy is needed to increase it.

A task-level emergency experience reuse method for freeway accidents onsite disposal with policy distilled reinforcement learning

A large number of freeway accident disposals are well-recorded by accident reports and surveillance videos, but it is not easy to get the emergency experience reused from past recorded accidents. To reuse emergency experience for better emergency decision-making, this paper proposed a knowledge-based experience transfer method to transfer task-level freeway accident disposal experience via multi-agent reinforcement learning algorithm with policy distillation. First, the Markov decision process is used to simulate the emergency decision-making process of multi-type freeway accident scene at the task level. Then, an adaptive knowledge transfer method named policy distilled multi-agent deep deterministic policy gradient (PD-MADDPG) algorithm is proposed to reuse experience from past freeway accident records to current accidents for fast decision-making and optimal onsite disposal. The performance of the proposed algorithm is evaluated on instantiated cases of freeway accidents that occurred on the freeway in Shaanxi Province, China. Aside from achieving better emergency decisions performance than various typical decision-making methods, the result shows decision maker with transferred knowledge owns 65.22%, 11.37%, 9.23%, 7.76% and 1.71% higher average reward than those without in the five studied cases, respectively. Indicating that the emergency experience transferred from past accidents contributes to fast emergency decision-making and optimal accident onsite disposal.

PILOT DECISIONS ARE THE RESULT OF MANAGEMENT DETERMINING FLIGHT SAFETY

Aviation safety is the ultimate goal of aviation in both civil and military in several accident reports and data from the aviation safety directorate of the Indonesian Army Aviation Center (Puspenerbad) human factors rank first cause of accidents as much as 74%. The purpose of this study is to determine the magnitude of the influence Flight Experience and Flight Proficiency against pilots decision making which has implications for aviation safety. The method in this study used quantitative descriptive analysis, with 99 respondents. Data collection is carried out by distributing questionnaires Google Forms and analyzed with the app SmartPLS 4.0. The conclusions obtained in this study put Flight Experience and Flight Proficiency does not affect flight safety if not mediated by the pilot decision making and flight safety is affected by pilots decision making.

Comprehensive Evaluation of Marine Ship Fires Risk Based on Fuzzy Broad Learning System

Ship fires exhibit the main characteristics of a high possibility of occurrence, large load, fast spreading, high difficulty in extinguishing, and serious losses. Therefore, once a fire occurs, it will cause huge loss in terms of economic and personnel safety. Firstly, a ship fire risk evaluation indicator system was constructed based on the causes and severity of the fires. Secondly, a comprehensive evaluation method for the fuzzy broad learning system (FBLS) was proposed. The fuzzy system was used to implement feature mapping on the input data, and the extracted fuzzy features were further input into the BLS enhancement layer. A fuzzy broad learning neural network structure was constructed by combining fuzzy features, feature nodes, and enhancement nodes. The method was applied to the field of risk assessment for the first time, and is a reference for subsequent studies. Finally, the risk levels of ship fires were classified and compared with evaluation methods such as fuzzy support vector machine (FSVM) and Fuzzy BP neural network (FBPNN) to demonstrate effectiveness and accuracy. The proposed FBLS method was used to predict actual cases, and the results showed consistency with the level determined by the accident investigation report published by the Maritime Bureau Administration.

Collaboration Tripartite Elements Accident Reporting and Potential Hazards Management Coal Mining, Indonesia: Systematic Review

Objective: Occupational accidents and diseases in Indonesia are still a scourge because they not only cause minor and serious accidents, but often result in fatalities. The same occupational accidents and diseases’ would seem to be happening in the coal mining business sector. According to the Badan Penyelenggara Jaminan Sosial Indonesia, 234,370 cases of work accidents and work-related illnesses occurred in 2021, of which the mining sector contributed 6,565 cases. Collaboration between companies, workers and local governments in the management of work accident reporting needs special attention so that similar incidents do not occur in the future.Material and Methods: This literature review supports the researchers hypothesis or belief in disclosing that there is an increasingly open space for collaboration between companies, workers and local governments in accident reporting management to reduce accidents. The literature review search term used is work accident reporting sourced from Google Scholar, National Library of Indonesia, PubMed, and other sources in the 2017-2022 period.Results:This literature review found a number of facts that companies have an obligation as stipulated in the Regulation of the Minister of Manpower of the Republic of Indonesia No. 03 of 1998 concerning Accident Reporting and Investigation Procedures to report every work accident and work-related illness that occurs both periodically and specifically to the competent authority. Likewise, workers have the responsibility to report any accidents and potential hazards found in the workplace to the occupational safety and health officer. The Regional Government is no exception, which is a regulator and at the same time supervises companies in carrying out their business activities.Conclusion: To reduce workplace accidents requires diligent and constructive cooperation between the business managers, the workers, and the government regulators to support a zero-accident work environment.

An object-oriented Bayesian network model for the quantitative risk assessment of navigational accidents in ice-covered Arctic waters

Merchant ship operations in the ice-covered Arctic waters may encounter traditional navigational accident risks (i.e., grounding, collision, etc.) and risks from sea ice, such as ship besetting in ice. However, describing, modeling, and quantifying the multiple risks in ice navigation are challenges from maritime risk assessment perspective. This paper proposes an object-oriented Bayesian network (OOBN) model for the quantitative risk assessment of multiple navigational accidents in ice-covered Arctic waters. The OOBN model makes use of the accident database from Lloyd's intelligence and maritime accident investigation reports. The proposed model decomposes navigational accidents into five levels based on accident causation theory: environment, unsafe condition, unsafe act, probability of navigational accident, and consequence of the navigational accident. Consequently, collision, grounding, ship besetting in ice, and ship–ice collision accidents are selected as the cases to interpret the quantitative risk assessment for navigational risk factors identification, risk analysis, and evaluation. The results demonstrate that (1) the risk is the highest in grounding accidents, followed by besetting in ice, collision, and ship–ice collision in ice-covered Arctic waters; (2) unsafe speed and unsafe condition are the critical mutual factors of these four accident scenarios; (3) and the critical risk influencing factors for the specific navigational accidents are identified to propose corresponding risk control options. The proposed OOBN model can be used for quantitative risk assessment of navigational accidents in ice-covered Arctic waters.

Comprehensive analysis of occupational accidents contributing factors in the electrical industry

This research investigates the linkages between the contributing factors of occupational accidents in the electrical industry, i.e., the business unit factor, the time of accidents, the level of injuries, the gender, and the day when occurred. The research also analyzes the linkage between the contributing factors of accidents to the occurrence of occupational accidents. The research method employed crosstab analysis as a quantitative technique through reports of occupational accidents, especially in the Indonesia Electrical Industry. Moreover, dummy variable regression is utilized to analyze the impact of contributing factors on the likelihood of work accidents. The results show that there is no significant statistical relationship between the contributing factors of occupational accidents, namely the business unit to the level of injury, the business unit to the day, the time of the accident to the level of injury, the time of the accident to the day, and the level of injury to the day. This research also finds that the significant variable in occupational accidents is the type of generation unit. As for the variable type of injury and the days, both are not proven to correlate statistically. This study can help the electrical industry improve occupational health and safety (OHS) performance through analysis to optimize the company's resources. The Management of the electrical company can allocate the right policy to increase OHS operational performance. This research integrates the identifying significant factors of occupational accidents and analyses their linkage, which can help guide future research in this area.

Multiphysics calculation of fuses of medium voltage measuring transformers

Introduction. In the medium voltage power grid of 6-35 kV, there is a problem of protecting voltage instrument transformers. This is due to the insufficiently effective level of their protection with fuses. Recently, there have been more and more reports of accidents associated with the failure of voltage instrument transformers not only in Ukraine, but also abroad. The issue of conducting an analytical study of the problem of protection of medium voltage instrument transformers by fuses is relevant. Goal. Investigation of designs and characteristics of fuses for medium voltage instrument transformers to improve the efficiency of their protection. Results. The article shows that protection of medium voltage instrument transformers with epoxy insulation is often provided by fuses, in which the nominal current of the combustion insert is significantly higher than the maximum permissible long-term current of the primary winding of the instrument transformer. A comparative analysis of the current values of the primary winding of medium voltage instrument transformers with the values of the nominal currents of the fusing inserts of fuses of various manufacturers presented on the Ukrainian market is carried out. The design features and technical characteristics of fuses for medium voltage instrument transformers have been investigated in order to increase the efficiency of their protection. The advantages and disadvantages of the designs of fuses from various manufacturers have been investigated and it has been established that the design of fuses requires further improvement in order to increase the efficiency of protection of medium voltage instrument transformers. Discussion and prospects for further development. Since in Ukraine, the need for fuses to protect medium voltage instrument transformers is provided mainly by supplies abroad, a promising direction for further development in this direction is the creation of a domestic competitive design of a fuse and its introduction into production.

The role of human factors in aviation ground operation-related accidents/incidents: A human error analysis approach

Aviation is a complex socio-technical system with interconnected and interdependent subsystems, such as flight operations, air traffic control, aircraft maintenance and ground operations. However, safety and risk research has not paid, thus far, adequate attention to all subsystems, resulting in possibly undetected or underestimated risks. This study focuses on Ground Operations (GO) as a subsystem and analyses the role of human factors in ground operations related accidents and incidents. 87 accident and incident reports (from 2000 to 2020) were analysed in three stages, using the Human Factors Dirty Dozen (HF DD) Model and the Human Factors Analysis and Classification Scheme (HFACS) as a basis for the third stage, a systematic thematic analysis. The findings indicate that lack of situational awareness and failure to follow prescribed procedures are the main causal and contributing factors in GO-related accidents and incidents. Three operational actions were identified as most critical: aircraft pushback/towing, aircraft arrival and departure, and aircraft weight and balance. An agenda for future research and recommendations for industry corrective action are proposed.

AI and IOT Based Road Accident Detection and Reporting System

Abstract: Road accidents are increasing daily as the number of automobiles rises. An annual global death toll of 1.4 million and an injury toll of 50 million are reported by the World Health Organisation (WHO). The absence of medical assistance at the scene of the accident or the lengthy response time during the rescue effort are the main causes of mortality. We can reduce delays in a rescue operation that has the potential to save many lives by using cognitive agent-based collision detection and a smart accident alarm and rescue system. To gather and send accident-related data to the cloud or server, the suggested system consists of a vibration sensor, accelerometer, alcohol sensor, IR sensor, GPS module, camera, and Raspberry Pi. When any accident happens, the module immediately sends accident-related data to the cloud and through the Virtuo 6 application.

A Machine Learning Approach for Classifying Road Accident Hotspots

Road accidents are a worldwide problem, affecting millions of people annually. One way to reduce such accidents is to predict risk areas and alert drivers. Advanced research has been carried out on identifying accident-influencing factors and potential highway risk areas to mitigate the number of road accidents. Machine learning techniques have been used to build prediction models using a supervised classification based on a labeled dataset. In this work, we experimented with many machine learning algorithms to discover the best classifier for the Brazilian federal road hotspots associated with severe or nonsevere accident risk using several features. We tested with SVM, random forest, and a multi-layer perceptron neural network. The dataset contains a ten-year road accident report by the Brazilian Federal Highway Police. The feature set includes spatial footprint, weekday and time when the accident happened, road type, route, orientation, weather conditions, and accident type. The results were promising, and the neural network model provided the best results, achieving an accuracy of 83%, a precision of 84%, a recall of 83%, and an F1-score of 82%.

Prototype Sistem Tracking Pada Aplikasi Ambulance Dan Satlantas Berbasis Protokol MQTT

Traffic accidents often result in victims who need help quickly. Communities who know about incidents are often confused about how to take victims to health facilities quickly because there are no vehicles available to transport victims, besides that at the location of the incident there is usually a crowd of residents which causes traffic to be obstructed. This research will build a prototype accident report application that can be used by the public in reporting a traffic accident incident, POLICE and Ambulance Drivers will get information about the location of the incident that occurred to provide fast handling, and the built prototype will be measured access speed using various types of networks (GPRS, 3G, 4G), this is to see if the application can be used in various internet network conditions. The results of this study are an application that can be used to improve services for health facilities and the police, the results of the access speed test show that the built prototype can run well even though it uses a GPRS network with a speed of 1,37 seconds.

Safety Challenges and Analysis of Autonomous Electric Vehicle Development: Insights from On-Road Testing and Accident Reports

Autonomous electric vehicles (AEVs) hold great promise for the future of automotive engineering, but safety remains a significant challenge in their development and commercialization. Therefore, conducting a comprehensive analysis of AEV development and reported accidents is crucial. This paper reviews the levels of automation in AEVs, their disengagement frequencies, and on-road accident reports. According to the report, numerous manufacturers thoroughly tested AEVs across a distance of more than 3.9 million miles between 2014 and 2022. Disengagement frequencies vary among manufacturers, and approximately 65% of accidents during this period occurred while AEVs were operating in autonomous mode. Notably, the majority of accidents (90%) were caused by other road users, with only a small fraction (approximately 8%) directly attributed to AEVs. Enhancing AEVs' ability to detect and mitigate safety risks from external sources has the potential to significantly improve their safety. This paper provides valuable insights into AEV safety by emphasizing the importance of comprehensively understanding AEV development and reported accidents. Through the analysis of disengagement and accident reports, the study highlights the prevalence of passive accidents caused by other road users. Future research should concentrate on enabling AEVs to effectively detect and respond to safety risks originating from external sources to enhance AEV safety. Overall, this analysis contributes to the ongoing efforts in AEV development and provides guidance for strategies aimed at improving their safety features.

Knowledge graph construction based on ship collision accident reports to improve maritime traffic safety

As an important data source, marine accident investigation reports are frequently used for accident analysis. However, it is hard to extract effective information since key knowledge is normally hidden in large blocks of text. In most cases, the collection of accident-related data is done manually. In this paper, a new knowledge graph construction approach to explore ship collision accidents is proposed, aiming to show the correlation among important factors of the accidents. In this research, 241 investigation reports on ship collision accidents from 2018 to 2021 published on the official website of the China Maritime Safety Administration (CMSA) were collected and analyzed. Then, the ship collision accident ontology module is constructed. According to the ontology information in the accident reports, entities were divided into context-based metadata and content-based metadata, which were used for describing different types of data. To extract the information for the accident report with semi-structured data, an information extraction module based on ontology was proposed. In this process, natural language processing (NLP) was used to obtain text information about the ontology. On this basis, the Ship Collision Accident Knowledge Graph (SCAKG) including 910 entity nodes and 1920 relation edges was constructed and stored in the graph database Neo4j. Finally, two case retrievals were conducted using the SCAKG to show the potential utilization of the method. The results show the effectiveness of the proposed approach in terms of discovering the internal relationship of the accident and could be used to expedite the judicial process, which simplifies the process of marine accident investigation.

Study on leakage and explosion law of buried gas pipeline based on scenario construction.

Based on the Jilin Songyuan gas pipeline accident, FLACS software was used to numerically simulate its leakage and explosion process in order to study the change law of the equivalent gas cloud volume of gas leakage diffusion under various influencing factors. The simulation results were compared and analyzed with the accident investigation report in order to ensure the accuracy of the simulation results. On this premise, the three primary influencing factors-the obstacle distribution technique, the ambient wind speed magnitude, and the ambient temperature-are varied in order to study the equivalent gas cloud volume variation features of the leaking gas cloud. The findings indicate a positive association between the maximum equivalent gas cloud volume of the leaking gas cloud and the density of the obstacle distribution. There is a positive correlation between ambient wind speed and the equivalent gas cloud volume when the ambient wind speed is less than 5.0 m/s, and a negative correlation between ambient wind speed and the equivalent gas cloud volume when the ambient wind speed is greater than or equal to 5.0 m/s. A drop in Q8 is proportionately increased by around 5% for every 10 °C increase in ambient temperature when the temperature is below room temperature. There is a positive association between ambient temperature and the equivalent gas cloud volume Q8. When the temperature is higher than room temperature, the drop in Q8 is correspondingly increased by about 3% for every 10 °C increase in ambient temperature.

Human Factors Analysis by Classifying Chemical Accidents into Operations

In the chemical industry, organizational and operational human factors significantly contribute to accidents. Chemical accidents occur in various operations of the industry due to a range of factors. Understanding the relationship between these factors and the accidents that happen is crucial in preventing similar accidents from happening repeatedly and promoting sustainability. Therefore, this study was divided into five operations: maintenance repair, process, loading unloading, storage, and shutdown startup of the chemical industry, to provide a more concrete, intuitive explanation of the interplay between causes and illustrate the routes to failure. The data were collected from 251 accident reports from various online data. The study was analyzed using the Human Factors Analysis and Classification System (HFACS) method as a conceptual framework. Each level’s frequency variables were obtained to define nominal and ordinal data. The chi-square test and Fisher’s exact test were used in the difference analysis of data in the model. The results show that the high-frequency accidents caused under the HFACS framework were organizational processes in the process (63.73%), in the storage (70.58%), and in the shutdown startup (91.66%), and skill-based errors in the maintenance repair (81.81%) and in the loading unloading (66.03%). Furthermore, resource management, technological environment, and personal readiness were significantly correlated with the operations. Human factors have differences in different operations in the chemical industry.

Risk Chain Mining of Hazard Sources in Metro Operation System Safety: A New Method to Mine and Control Risk for Safety Management

With the promotion of the major strategy of national transportation power, the super-large-scale metro operation network has become an inevitable trend, and operation safety has become increasingly prominent. Metro operation dispatch logs and accident reports were taken as the research object, the hazard sources were efficiently and accurately identified, the risk chains of hazard sources were mined, and the risk evolution mechanism was revealed. Firstly, the transportation lexicon was constructed to improve the accuracy of word segmentation, the text features were extracted based on the term frequency-inverse document frequency (TF-IDF) algorithm, and the key eigenvalues were mined to identify the key hazard sources. Secondly, pattern matching was used to extract explicit causality, and the Self-Attention BiLSTM extracted implicit causality and integrated event trigger word position identification to enhance the effectiveness of implicit causality extraction. Finally, an example was given to verify the efficiency and accuracy of the model, the experiments showed that the F value of extraction effect was increased by nearly 10%, the extraction accuracy was 87.53%, the identification of operational hazard sources was more accurate, and the visualization of risk evolution law was realized.

Where risk, where capability? Building the emergency management capability structure of coal mining enterprises based on risk matching perspective

The emergency management capability of coal mine enterprises is a crucial element of coal mine safety management. Exploring the structure of the emergency management capability of coal mine enterprises under the perspective of risk matching is conducive to clarifying the core nodes of emergency management capability construction. A qualitative analysis of Chinese underground coal mine accident reports, related laws and regulations, and relevant academic papers was conducted using grounded theory. Currently, the risks in underground coal mines in China can be divided into source-generated risks, derivative risks, and residual risks. The elements of emergency management capabilities that match them are absorption capability, detection and control capability, and response capability. At the same time, some capability elements across different risks are summarized as internal enhancement and external enhancement, respectively. The study provides a reference for coal mining enterprises to improve emergency management capability and reduce risks.

Alert System for Emergency State of Vehicle Users

Abstract: One of the main reasons for road collisions is over-speeding. The sharp rise in traffic accidents is currently one of the main causes of human deaths. The worth of human life is above all other factors, and providing immediate assistance is more important than lending a hand help. If emergency personnel had been able to get accident reports and get there in time, more lives might have been saved. When it comes to saving lives, the time it takes from the moment of the accident until the ambulance arrives at the scene is critical. We can reduce the time between an accident occurring and a medical ambulance being dispatched to the scene by lowering death rates. Today, GPS is a crucial part of a vehicle's system. The accelerometer senses a quick shift in the axles of the vehicle. It will be evaluated by Arduino. The warning message and its position are sent to the police control room or a rescue team by the Arduino using the GSM module. Therefore, once they get the information, the authorities can use the GPS module to trace the location automatically. The appropriate action will be carried out after the location has been verified.

Road Accident Severity Prediction using Machine Learning

Road accidents have become a major concern globally, causing a significant number of fatalities and injuries every year. The aim of this project is to predict road accidents severity using machine learning techniques, in order to reduce their occurrence and mitigate the associated risks. The project uses data collected from various sources such as accident reports, weather conditions, and road infrastructure to train and evaluate various supervised learning algorithms and predict the accident severity. Four algorithms were compared, including Decision Tree, Naive Bayes, Random forest . Most probably occurring road accident locations are identified and that particular region is indicated as black pot. The proposed method can be used to provide real-time risk information to road users, helping them to make informed decisions and avoid potential accidents. The project highlights the importance of using machine learning techniques in road safety analysis, providing a foundation for further research in this field