Search

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

• Syncretic K-shell is developed to identify the node importance of urban rail transit network. • The invulnerability of urban rail transit network is evaluated. • Cumulative core value is proposed considering both global and local centrality of nodes. • Syncretic core value is the node importance identification criterion of the syncretic K-shell. • Relative size of maximal connected subgraph and relative network efficiency are applied to evaluate the invulnerability performance. In this paper, the Syncretic K-shell algorithm is developed and applied to identify the node importance and evaluate the invulnerability in an urban rail transit network, which is based on the idea that the nodes removed later are more important than those removed former, the nodes with the same core value also have different importance. It has a very important strategic significance for optimizing network structure, improving operational efficiency, and providing targeted protection and designated accident prevention measures for stations with different importance. Syncretic Core Value, as the node importance recognition criterion of Syncretic K-shell algorithm, considers the influence of node degree, cumulative core value and syncretic weight of adjacent nodes on node importance. Firstly, we use space L to establish the indirect and non-weighted urban rail transit network. Then, the node importance recognition steps of Syncretic K-shell algorithm are introduced. Then, a numerical study of the proposed Syncretic K-shell algorithm is conducted, and the Degree Centrality, Betweenness Centrality, Closeness Centrality, Mixed Degree Decomposition and Classical K-shell are used as comparison approaches. The node anti-destruction under node failure (including intentional attack and random attack) is evaluated, and finally, an example study is conducted with the background of Chengdu Metro Network. The results show that the important nodes identified by Syncretic K-shell have a greater impact on anti-destruction performance. The research found that Chengdu Metro Network is more vulnerable to attack and easy to be destroyed under intentional attack. Finally, two coping strategies are provided. According to the research results, we can conclude that compared with the previous node importance recognition methods, Syncretic K-shell algorithm has five advantages: (i) it can distinguish the importance between nodes with the same core value; (ii) considering the global and local centrality of nodes, the Syncretic Core Value of nodes can better reflect the importance of nodes; (iii) it can recognize the importance of nodes in networks with the same degree value or without obvious aggregation characteristics; (iv) the time complexity of Syncretic K-shell algorithm can be used for the recognition of node importance in large-scale networks; (v) it can better distinguish and amplify the differences between nodes, and quickly and effectively rank the importance of nodes. [ABSTRACT FROM AUTHOR]

• We introduce eight years' worth of New Zealand forestry incident data. • We analyse forestry incidents from the perspective of worker-failure and fatigue. • 70% of incidents in this data are attributed, at least in part, to worker-failures. • 78% of worker-failure based causes show indications of fatigue. Reports of incidents in dangerous work environments can be analysed to identify common hazards, in turn aiding in the prevention of future accidents. Whilst studies exist that do this, most focus on causes that involve physical risks. In this paper we propose an alternative approach, and illustrate causes of forestry incidents from the perspective of worker-failure and fatigue. This paper outlines the analysis of eight years' worth of New Zealand forestry incident data, with a focus on the cause of, and time that, incidents occur. This has resulted in two main findings. First, 70% of incidents can be attributed, at least in part, to worker-failures. Second, 78% of worker-failure based causes show indications of fatigue. This indicates that a significant number of forestry incidents are caused by worker-fatigue. Finally, this dataset showed inconsistencies in data quality, similar to those that exist in other datasets. This did not affect our analysis. However, these types of errors have the potential to affect the data quality in the national reporting system. The results from this study will be used in a larger project on detecting fatigue in forestry workers for injury and incident prevention. It is also our hope that other researchers may find these results of interest for further fatigue prevention research in hazardous industries. [ABSTRACT FROM AUTHOR]

Accident prevention on the manufacturing shop floor is essential. This paper aims to develop a proximity warning system capable of reducing accidents on the manufacturing shop floor, by notifying employers of any possible danger within the workplace. The system consists of an ultrasonic sensor, Arduino microcontroller, alarm system and an easy to understand user interface that can detect and alert workers within the danger zone. The system was developed using the Unified Modelling Language, python programming language and the Arduino software. Preliminary test conducted on volunteer participants revealed that the system is easy to use and provides real-time easy-to-understand audio and visual feedback to workers. The system is useful for reducing accidents on shop floors. [ABSTRACT FROM AUTHOR]

• Analyzed the cause of secondary explosion accident in Hushan Gold mine in China. • Clarified the direct and indirect causes of the accident. • Calculated and verified the direct cause of the secondary explosion accident. • Put forward the suggestions for preventing secondary explosion accidents in mines. Explosion accidents often occur during mining, especially sudden secondary explosions, which pose a serious threat to the life and property safety of employees. Based on the development process and consequences of typical secondary explosion accidents in Hushan Gold Mine, Qixia City, Shandong Province, China, this paper uses accident analysis model and cone measurement calculation method to determine the direct cause of secondary explosion accidents. The main conclusions are as follows: The HFACS-CM model is used to analyze the causes of secondary explosion from five aspects: external environment, organizational influence, unsafe leadership, the prerequisite conditions of unsafe behavior and unsafe behavior. It is found that the CO ignition explosion in the mining process is the direct cause of secondary explosion. Then, combined with the experimental data of the cone measuring instrument, the field CO amount was calculated, and the M CO ≥ 101.4 m3 was obtained, indicating that the CO production did reach the condition of explosion range, and the analysis results of the model were verified. Through HFACS-CM model analysis and calculation verification, the key causes of secondary explosion accidents in such mines are found and lessons are learned, which provides an effective reference for the effective prevention of deflagration accidents in the field of mine safety. [ABSTRACT FROM AUTHOR]

The acquisition and maintenance of non-technical skills by the pilots are fundamental factors for the prevention of aviation accidents. The aviation authorities are promoting that air crew training be carried out through simulator sessions using scenarios specifically designed to develop and assess the global performance of pilots in such skills. When designing custom flight training scenarios, choosing the correct events and conditions from the myriad of possible combinations with respect to their potential utility in training specific competencies is a costly task that depends entirely on highly specialized expert knowledge. In this paper, we present EBTOnto, an OWL DL ontology that allows to formalize this knowledge and other useful data from real cases, laying the foundations for a semantic knowledge base of scenarios for airline pilots training. Previous advances in this matter and possible applications of this system are reviewed. EBTOnto is built on top of a source validated by experts, the Evidence-Based Training Implementation Guide by the International Air Transport Association, and then checked using an automatic reasoner and a database of 37,568 aviation safety incidents, extracted from the widely regarded Aviation Safety Reporting System by the U.S. National Aeronautics and Space Administration. The results suggest that it is possible to classify real aviation scenarios in terms of non-technical competencies and filter useful incident reports for design and enrichment of these training scenarios. EBTOnto opens up new possibilities for interoperability between incident databases and training organizations, and smoothes the path to represent, share and generate custom simulation training scenarios for pilots based on real data. [ABSTRACT FROM AUTHOR]

Buried pipeline is the most common mode of natural gas transportation. The analysis of methane diffusion characteristics in leakage accident is of great significance for accident detection and secondary explosion accident prevention. The existing numerical simulation and experimental studies focused on the diffusion of methane in the soil from the leakage of buried natural gas pipeline, and the coupling process of methane passing through the soil and entering the atmosphere was rarely mentioned. However, the surface atmosphere is the main area of human activities, which increases the possibility of fire and other risk factors. In this paper, the coupling process of methane leakage passing through soil and entering the atmosphere was studied by numerical simulation, and the effects of various factors on the methane concentration in the first danger zone (FDZ) and the second danger zone (SDZ) were analyzed. Based on the least square method and multiple regression theory, combined with MATLAB mathematical calculation software, a methane concentration prediction model on the high-risk side of the FDZ was established. The results showed that after methane entered the atmosphere, the diffusion velocity increased significantly. The downstream building surface of the FDZ was the high-risk side of methane distribution, and the upstream building surface of the SDZ was the high-risk side. The average calculation error of the methane concentration prediction equation on the high-risk side of the FDZ was 9.97%, which can effectively evaluate the methane concentration under any leakage working condition. When the buildings floors were different, the high building surface between high building and low building was the high-risk side of methane distribution. This study provides guidance for the design of buried natural gas pipelines in residential buildings, and supplies basis for improving detector installation and gas leakage maintenance specifications. • The soil-atmosphere coupling process of methane diffusion in the cavity of buildings is studied. • The influence of underground and aboveground factors on methane diffusion is considered. • The prediction equation of methane concentration in the atmosphere is established. • Provide suggestions for leakage monitoring of buried natural gas pipeline. • Provide guidance for the laying of buried natural gas pipelines in residential buildings. [ABSTRACT FROM AUTHOR]