High-fidelity transient neutronics/thermal-hydraulics coupling analyses of control rod ejection accident in a prismatic gas-cooled reactor core

This study develops a probabilistic dynamic risk assessment framework for grounding and collision/contact accidents in narrow waterways by integrating the Human Factors Analysis and Classification System for Passenger Vessels (HFACS-PV) with Bayesian Networks (BN). Marine accident reports from the Dover Strait (2004–2020) were systematically analysed to identify human, organisational, technical, and environmental risk factors, which were subsequently structured into a Bayesian Network to model their interdependencies and dynamic influence on accident occurrence. Conditional probability tables were derived from accident data and supplemented through structured expert elicitation. The resulting model enables real-time inference and predictive risk estimation under evolving operational conditions. Model performance was evaluated using detailed grounding and collision case studies, demonstrating its capability to replicate accident evolution and quantify the contribution of key causal factors. The results indicate that unsafe acts, particularly decision-based and perceptual errors, combined with deficiencies in voyage planning, supervision, and situational awareness, dominate accident causation in the Dover Strait. The proposed framework provides a quantitative decision-support tool for vessel traffic services and maritime operators, supporting proactive risk mitigation and safety optimisation in high-density and constrained navigational environments. • Analysis of Dover Strait grounding and collision accidents using HFACS to identify human and organisational causes. • A novel dynamic risk model enables real-time assessment of collision, contact, and grounding risks for operators. • The model supports risk control measures and optimises best practices to improve safety in narrow waterways.

Accident analysis of CO₂ pipeline failures: Patterns, detection, and consequences

Abstract— This project presents the design and implementation of an IoT-enabled smart vehicle black box system aimed at enhancing road safety, accident analysis, and real-time monitoring. The system integrates sensors, a microcontroller, and wireless communication modules to continuously capture critical parameters such as vehicle speed, location, acceleration, and impact force. In the event of an accident, the system automatically records and transmits essential data to a cloud platform, enabling quick access for emergency response and post-incident investigation. Additionally, features such as GPS tracking, driver behaviour monitoring, and remote data access improve vehicle security and accountability. The proposed solution offers a cost-effective, reliable, and scalable approach to modern vehicular data logging, contributing to smarter transportation systems and improved safety outcomes. Keywords— IoT, Smart Vehicle System, Black Box, Accident Detection, GPS Tracking, Data Logging, Real-Time Monitoring, Vehicle Safety, Cloud Storage, Sensor Integration, Emergency Alert System, Telematics

ABSTRACT This study applies text mining to South Korean news data to identify weak signals related to elderly drivers and analyze their policy implications. A total of 2264 news articles published between January 2024 and March 2025 were collected, and keyword-based weak signals were identified. Word network analysis interpreted relationships between weak signals and co-occurring terms. The results revealed five signal categories—acceleration/brake/activation, free, black box, sidewalk, and system—corresponding to driving errors, incentive-based policies, accident documentation, pedestrian safety, and administrative structures. The findings highlight the need for technical measures to prevent pedal misapplication, mobility-related support for those ceasing to drive, recording devices for accident analysis, protection infrastructure against sidewalk intrusion, and conditional licensing and integrated management systems for elderly drivers. Recognizing elderly driver-related accidents as a structural risk, this study provides insights for traffic safety and criminal justice policy using a weak signal detection approach grounded in public discourse.

Corrigendum to “Initial accident scenario analysis in support of a preliminary DEMO tritium plant design” [Fusion Engineering and Design 189 (2023) 113482

Quantitative risk evaluation for construction methods using accident rate analysis based on working days by occupation

Accident analysis of lead-bismuth cooled fast reactor and SCO2 Brayton cycle system based on Modelica language

Sailing in Dangerous Waters: Advanced Vessel Operation Features Extraction for Global Maritime Accident Analysis

Road safety is a serious concern in urban environments and smart cities, involving the well-being of all road users, with a special emphasis on those considered vulnerable such as pedestrians, bicyclists, and motorcyclists among others. Being a difficult challenge, it becomes evident that the traditional methods of road safety are not sufficient to address these challenges and needs a more Intelligent Transportation Systems (ITS). This study aims to analyze traffic accident data, identify factors behind severe injuries, and develop predictive solutions using Machine Learning (ML). For this study, data were collected from the Public Safety Data Portal in Toronto and the District Emergency Office in Rawalpindi, Pakistan (RTA). The data were preprocessed by applying different data preprocessing methods using Python. A number of popular ML and Deep Learning (DL) models have been trained and tested on the preprocessed datasets for traffic accident analysis. Results showed that the XGBoost and Random Forest exhibited excellent performance with an accuracy of 74% on KSI dataset without under-sample and hyperparameter tuning methods. Random Forest achieved high accuracy 99% after applying the Grid Search method of hyperparameter methods and undersample technique Moreover, the current study has utilized the Association Rule Mining technique to determined the underlying hidden factors from both dataset that lead to collisions and fatal or major injuries. The extracted rules revealed that certain factors, such as over speeding, aggressive driving, pedestrian collisions, disobeying traffic rules, lost control, driver’s inattentiveness and the absence of traffic controls at major roads and intersections are associated with a higher risk of fatal or major injuries. Furthermore, the study provided a comprehensive comparison of factors contributing to severe collisions in Pakistan and Canada. It found that there is a need for targeted enforcement in both countries including stricter licensing and education initiatives for young drivers about responsible driving behaviors. The proposed framework can be deployed in a real environment for improving road safety in urban environments and Smart cites.

This study presents a comparative analysis of two geometric representations of the flow domain for RANS simulations of a high-temperature gas-cooled reactor (HTGR) core segment: a realistic model with explicitly resolved fuel pebbles and a porous-medium representation based on volume-averaged properties. The computations were performed with the LOGOS software package using the k–ω SST turbulence model. The realistic model reproduces local flow and heat-transfer features of helium, including recirculation, local acceleration in interstitial gaps, and temperature hot spots, with peak values of 2.69 m/s and 1310 K. In contrast, the porous-medium approach yields a spatially smoothed solution with markedly lower extrema: the maximum velocity does not exceed 0.5 m/s and the maximum temperature is 727 K. Using precomputed resistance coefficients, the porous model predicts an average helium velocity in the pebble bed that is 76.4% lower, while the mean temperature and pressure drop differ by about 9%. The realistic representation is preferable when local thermal-hydraulic quantities are required, e.g., for transient and accident analyses. The porous-medium approximation, owing to its computational efficiency, is well suited for preliminary calculations and integral assessments in large computational domains, provided that the model is properly parameterized.

Abstract This document presents a historical analysis of major industrial accidents in the chemical sector across the Americas, highlighting their causes, consequences, impact, and frequency. The analysis covers the Pan‐American region (North, Central, and South America) and reports results at the continental level. The regulations imposed by various organizations responsible for controlling and improving safety within the industry, such as the EPA (United States Environmental Protection Agency), CEPA (Canadian Environmental Protection Act), and SAICM (Strategic Approach to International Chemicals Management) are mentioned. The objective is to prevent as many accidents as possible in the chemical industry, as most of them are avoidable if effective strategies are implemented and adequate information and training are provided to all employees. This document will discuss and illustrate these points. Over 1300 industrial accidents were identified in the Americas between 1900 and 2025. North America accounted for the highest proportion of incidents among the three regions analyzed, representing 49.3% of all cases. South America followed with 26.7%, and Central America came in last, at 24.0%. Human error was the most frequent cause of these accidents, associated with 348 incidents. Accidents resulting from external events followed, totalling 276 cases. Instrumental or mechanical failure accounted for 191 recorded occurrences.

Traffic accidents on state roads represent a major concern, resulting in injuries, fatalities, and significant economic losses. Identifying the primary factors that contribute to these accidents is vital for developing effective prevention measures. This study examines the frequency of traffic accidents on state roads in Ankara by employing Poisson regression and regression tree models, focusing on the comparison of three regression tree algorithms. A detailed simulation study was carried out to assess the performance of the tree algorithms in terms of variable selection bias, Type I error rates, and statistical power. Among them, GUIDE demonstrated the most balanced performance, with unbiased variable selection and strong power, effectively controlling Type I errors. In contrast, the CART algorithm outperformed others in scenarios involving overdispersion. Both CART and GUIDE exhibited similar estimation errors in real-world applications, highlighting their robustness and reliability. Additionally, the study observed that the MOB algorithm tended to favor numerical variables over categorical ones when performing splits. Although it maintained adequate control over Type I errors, MOB showed relatively lower power, reflecting its limitations in data partitioning. In conclusion, the findings offer valuable insights into selecting appropriate regression tree algorithms for traffic accident analysis, providing guidance for enhancing road safety.

The sustainable development of university safety governance is an important component of the national security management system and also serves as a fundamental safeguard for protecting the life and health of students and staff on campus. The improvement of university safety risk governance relies on analyzing the identification of various safety risks and maintaining an effective crisis management process for potential sudden safety risks. The 24Model and the 4R model have respectively demonstrated strong analytical advantages in the fields of accident causation analysis and emergency crisis management; however, few studies have examined the internal relationship between them. This study attempts to integrate the 24Model and the 4R crisis management framework to propose and analyze a 2-4-4R model for university safety risk management. Through a case study, the model is applied to analyze a laboratory explosion accident at a university. The results show that the risk factors leading to campus safety accidents can be analyzed from four aspects: safety culture, safety management system, individual factors, and unsafe acts and physical conditions. University safety management should comprehensively identify these four types of factors and propose governance measures sequentially from the four stages of reduction, readiness, response, and recovery in order to improve safety management capacity. The case analysis confirms that the 2-4-4R model has applicability and practical value in the identification and governance analysis of university safety risks. It provides a systematic research perspective for the identification and management of safety risks in universities, and is of great significance for promoting the sustainable development of universities.

A forensic investigation and simulation-based analysis of a chemical plant explosion accident.

Background: Mechanical failure, age and poor quality of machines, and heavy equipment can cause mining accidents or disasters. Accidents in mining can be caused by equipment and handling of heavy loads, mechanical failure, safety management, poor leadership and work environment, unsafe actions, inexperienced workers, and lack of rules, regulations, training and safety education. Purpose: The purpose of this study was to analyse risk opportunities for enhancing heavy equipment employee protection performance. Method: This study used a qualitative method, by using Hazard Identification, Risk Assessment, and Risk Opportunities (HIRARO) methods to identify work hazards in mining areas and opportunities to control them to improve safety performance. Result: The risk management that has been carried out by the company does not use a hierarchy of controls, but goes directly to preventive measures with moderate status. Conclusion: Risk management in in East Kalimantan heavy equipment companies still needs to be improved in terms of preventing accidents and occupational health by adding risk level after initial controls, to obtain post-initial control risk levels, as well as proper additional controls and take into account the opportunities to enhance OSH performance or reduce risks.

Analysis of neutron emissions from pebble nuclear fuels.

Enhancing maritime accident causation analysis through a hybrid machine learning approach

Integrating potential risk paths into railway accident causation analysis: A complex network approach with cascading failure and multimodal data

A generative AI–driven intelligent rules framework for traffic accident detection and analysis