Search Results (2,763)

Sensors are a key component of virtual reality (VR) technology, as they enable motion tracking, interaction with the environment, and realistic representation of user behaviour in virtual space. VR technology is gaining increasing importance in engineering, offering new ways to support research, analysis, and training. This article examines its applications in four key areas: surveying and geomatics, mining, environmental protection, and occupational safety. The study is based on a review of the scientific literature indexed in the Scopus database, with the aim of highlighting both the potential of VR and directions for its future development. The findings indicate that VR provides effective tools for analyzing, interpreting, and visualizing complex geospatial data. It enables realistic simulations of mining processes, supports the monitoring of environmental impacts, and facilitates environmental education by creating engaging, immersive experiences. In occupational safety, VR allows hazard scenarios and accident events to be reproduced in a safe yet highly realistic environment, significantly enhancing the effectiveness of training. This is made possible through the integration of sensors with virtual reality, further enhancing immersion in the environment. Despite these advantages, several barriers have been identified. They include technological challenges, insufficient numbers of trained specialists, health and ergonomics concerns, resistance to organizational change, ethical considerations, and limited funding. It is clear that the future of VR in engineering will be shaped by continuous technological progress combined with growing attention to behavioural aspects of training and user interaction. These trends are expected to drive the creation of increasingly advanced and effective tools. The article thus provides a foundation for further exploration of VR as an integral part of engineering practice. Full article

Maritime accidents are low-probability, high-consequence events, making mechanism analysis crucial for risk mitigation. Existing studies often focus on single scenarios or factors and frequently mix pre-incident observational data with subjective unsafe behavior labels, limiting causal-chain construction for proactive risk prediction. To address these issues, this study proposes a Bow-Tie-based causal-chain Bayesian network, establishing a hierarchical inference chain of “observed parameters–unsafe causes–accident types” to capture causal interactions among multiple factor categories and enable inference from pre-incident data to potential unsafe causes and accident types. Applied to the Bohai Sea region, sensitivity analysis quantified the effects of risk factors under varying conditions on collision, sinking, and grounding probabilities. The results show that the method can infer accident types and unsafe causes using only pre-incident data, achieving over 70% accuracy and closely matching accident investigation findings. Moreover, it reveals layer-by-layer mechanisms of key contributing factors and provides targeted management interventions, supporting quantitative decision-making for maritime regulators and shipping companies, with significant practical applicability. Full article

Construction accidents continue to threaten worker safety despite advances in management systems. Existing research catalogs accident attributes but rarely explains how triggers like human error, equipment failure, or procedural lapses interact with project types and tasks. This limits recognition of high-risk scenarios and hampers targeted prevention. To address this, a two-step framework combining Multiple Correspondence Analysis (MCA) and Association Rule Mining (ARM) is proposed. Using the Korean Construction Safety Management Integrated Information (CSI) database, MCA reduces dimensionality and clusters similar accident cases, while ARM extracts context-specific rules linking accident types, causes, and activities. The analysis reveals the following key patterns: (i) worker negligence during setup or formwork often leads to tool-related cuts; (ii) poor judgment or inadequate waste removal during excavation heightens hit or stuck incidents; and (iii) negligence frequently triggers hit and fall accidents during transportation, dismantling, and finishing. By mapping causes to operational risk factors, the framework supports actionable guidance for daily risk assessments. Safety professionals can align planned tasks with identified risks, enabling proactive interventions such as focused training, stricter supervision, and engineering controls. Thus, the MCA–ARM method establishes a data-driven foundation for improving safety decision-making and reducing construction accidents. Full article

The increasing prevalence of lithium-ion batteries in energy storage and electric transportation has led to a rise in overcharge-induced thermal runaway (TR) incidents. Particularly, the TR of Lithium Iron Phosphate (LFP) batteries demonstrates distinct evolutionary stages and multimodal hazard signals. This study investigated the TR process of LFP batteries under various charging rates through five sets of gradient C-rate experiments, collecting multimodal data (temperature, voltage, gas, sound, and deformation). Drawing on the collected data, this study proposes a three-stage evolution model that systematically identifies key characteristic signals and tracks their progression pattern through each stage of TR. Subsequently, fusion-based models (for both single- and multi-rate scenarios) and a time-series-based LSTM model were developed to evaluate their classification accuracy and feature importance in the classification of TR stages. Results indicate that the fusion-based models offer greater generalization, while the LSTM model excels at modeling time-dependent dynamics. These models demonstrate complementary strengths, providing a comprehensive toolkit for risk assessment. Furthermore, for the severe TR stage, this study proposes an innovative three-dimensional dynamic emergency decision matrix comprising a toxicity index (TI), flammability index (FI), and visibility (V) to provide quantitative guidance for rescue operations in the post-accident phase. Ultimately, this study establishes a comprehensive, closed-loop framework for LFP battery safety, extending from multimodal signal acquisition and intelligent early warning to quantified emergency response. This framework provides both a robust theoretical basis and practical tools for managing TR risk throughout the entire battery lifecycle. Full article

In Hungary, the consumption of any alcoholic beverage before driving is illegal. A person is considered drunk if they have a blood alcohol concentration of 0.5 g per liter or more. The situation regarding drug use is also disappointing. This research analyses these effects on transport and their “outcome” by evaluating analyses based on police data, driver training data, and experimental data. The research aims to further raise awareness of the public health importance of this problem through a case–control study. Descriptive and correlational, statistical calculations were performed with a significance value of p < 0.05. Between 2019 and 2023, there were 10–13.000 drunk driving offenses and 1.000–1.300 drunk-driving accidents on the roads each year, most of which occurred in the capital and caused minor injuries. The results will be used to discover synergies to improve road safety. Full article

(1) Background: Austria’s use of fully mechanized harvesting systems has been continuously increasing. Technical developments, such as traction aid winches, have made it possible to drive on increasingly steep terrain. However, this has led to challenges and potential hazards for the operators, resulting in higher stand damage rates and risks of workplace accidents. Since these systems and working environments involve a highly complex interplay of various parameters, the purpose of this protocol is to propose a new set of methodologies that can be used to obtain a holistic interpretation of the psychophysiological interrelationship between the working conditions and stress of harvester and forwarder drivers. (2) Methods: We developed a research protocol to analyse the (a) environmental and (b) machine-related parameters; (c) psychological and psychophysiological responses of the operators; and (d) technical outcome parameters. Within this longitudinal exploratory field study, experienced drivers were monitored for over an hour at the beginning and the end of their workday while operating in varying steep terrains with and without a traction aid winch. The analysis is based on macroscopic (collected using cameras), microscopic (eye-tracking glasses and AI-driven emotion recognition), quantitative (standardized questionnaires), and qualitative (interviews) data. This multimodal research protocol aims to improve the health and safety of forest workers, increase their productivity, and reduce damage to remaining trees. Full article

Distracted driving continues to be a major contributor to road accidents, highlighting the growing research interest in advanced driver monitoring systems for enhanced safety. This paper seeks to improve the overall performance and effectiveness of such systems by highlighting the importance of recognizing the driver’s activity. This paper introduces a novel methodology for assessing driver attention by using multi-perspective information using videos that capture the full driver body, hands, and face and focusing on three driver tasks: distracted actions, gaze direction, and hands-on-wheel monitoring. The experimental evaluation was conducted in two phases: first, assessing driver distracted activities, gaze direction, and hands-on-wheel using a CNN-based model and videos from three cameras that were placed inside the vehicle, and second, evaluating the multi-task fusion algorithm, considering the aggregated danger score, which was introduced in this paper, as a representation of the driver’s attentiveness based on the multi-task data fusion algorithm. The proposed methodology was built and evaluated using a DMD dataset; additionally, model robustness was tested on the AUC_V2 and SAMDD driver distraction datasets. The proposed algorithm effectively combines multi-task information from different perspectives and evaluates the attention level of the driver. Full article

Leadership is context-dependent in its influence on various employee attitudes and behaviors, particularly in high-risk environments. Despite this, few studies have explored the role of leadership in shaping safety-related outcomes within high-risk public sector settings. This study posits that leadership’s impact may differ in high-risk contexts such as firefighting, where safety is of utmost importance. Using survey data collected from firefighters in Gyeonggi-do, the largest province in South Korea, this study examines the relationship between transformational leadership, perceived accident likelihood, and three safety-related attitudes: safety motivation, safety compliance, and safety participation. With sample sizes for the three dependent variables ranging from 1502 to 1504, the ordinary least squares (OLS) regression results indicate that transformational leadership is positively associated with all three safety attitudes. However, perceived accident likelihood shows a positive relationship with only one of the safety-related attitudes: safety motivation. More importantly, perceived accident likelihood moderates the relationship between transformational leadership and safety attitudes; as perceived accident likelihood increases, the positive impact of transformational leadership on these attitudes diminishes. These findings underscore the contextual nature of leadership effectiveness in high-risk settings and highlight the importance of contextual factors in understanding leadership styles. Full article

Background/Objectives: Human immunodeficiency virus (HIV) continues to be a global public health concern. Several antiretroviral drugs have been approved for the treatment, post-exposure, and pre-exposure prophylaxis of HIV. Darunavir (DRV) is a protease inhibitor (PI) approved for the management of HIV globally. This study aims to generate safety signals for DRV through data mining and analysis of adverse events (AEs) reported to the United Kingdom (UK) Medicines and Healthcare products Regulatory Agency (MHRA) Yellow Card Scheme. Methods: Disproportionality analysis was conducted using reporting odds ratio (ROR), proportional reporting ratio (PRR), and Bayesian confidence propagation neural network (BCPNN) approaches to identify potential safety signals. Results: The MHRA database contained n = 779 reports (n = 1791 AEs) attributed to DRV. The majority of AEs were reported for males. Positive safety signals were identified at both the system organ class (SOC, n = 5) and preferred term level (PT, n = 95). At SOC level, endocrine disorders emerged as a signal of interest n = 33 cases (ROR: 8.17, 95% CI: 5.78–11.56; PRR:7.96, 95% CI: 5.68–11.15; and IC: 2.85, IC025: 2.51). Among the results, 40 new potential safety signals are not listed on the product labelling in the UK. These include serious AEs such as cerebrovascular accident, brain injury, thrombosis, and pregnancy, puerperium, and perinatal AEs. Conclusions: This study provides additional real-world safety data for DRV in the UK and paves the way for future observational studies to investigate the identified safety signals. Full article

In recent years, the issue of drivers under the influence of medications and psychoactive substances as a cause of road accidents has gained increasing importance. This study aimed to assess the prevalence and blood concentration ranges of alcohol and psychoactive substances among drivers in northeastern Poland between 2013 and 2024. To determine the prevalence of medications and psychoactive substances in drivers’ blood, data were collected from 266 blood samples obtained from drivers (251 men and 15 women). Among these, 79 drivers died immediately, 61 drivers survived the accident, and 126 drivers were stopped for roadside checks. The presence of the studied substances was confirmed using gas chromatography combined with mass spectrometry detection (GC-MS) and liquid chromatography combined with mass spectrometry detection (LC-MS). Blood alcohol content was measured using headspace gas chromatography with a flame ionisation detector (HS-GC-FID). Psychoactive substances were detected in 152 of the 266 samples. Drivers testing positive for medications and psychoactive substances were most frequently stopped during roadside controls—67.46%. Among the total positive cases, psychoactive substances used alone or in combination included THC—46.3% (range 0.2–20 ng/mL), alcohol—26.8% (range 0.1–4.1‰), amphetamines—20.7% (range 15–2997 ng/mL), opiates—4.3% (morphine 66.0 ng/mL; methadone 174.0 ng/mL; ranges: tramadol 15.0–600.0 ng/mL; fentanyl 45.0–100.0 ng/mL), benzodiazepines—9.8% (ranges: diazepam 55.0–480.0 ng/mL; midazolam 17.0–1200.0 ng/mL; clonazepam 21.0–36.0 ng/mL), stimulants—6.10% (ranges: amphetamine 15.0–2997.0 ng/mL; cocaine 4.0–30.0 ng/mL; benzoylecgonine 38.0–602.0 ng/mL; PMMA 45.0–360.0 ng/mL; MDMA 20.0–75.0 ng/mL; mephedrone 37.5 ng/mL; alfa-PVP 120 ng/mL), psychotropic drugs—3.1% (carbamazepine 8.0–2100.0 ng/mL; zolpidem 233.0 ng/mL; citalopram 320.0 ng/mL; opipramol 220 ng/mL). The most commonly used substance among car and motorcycle drivers was THC (37.7% of car drivers and 60% of motorcyclists). Among operators of other types of vehicles, alcohol was the most frequently detected substance, present in 35% of cases. The majority of drivers (81.1%) were under the influence of a single substance. Among the drivers, 7.3% consumed alcohol in combination with at least one other substance, and 11.6% used two or more substances excluding alcohol. Among the psychoactive substances most frequently used alone or in combination with others, THC was predominant. Roadside testing, based on effects similar to alcohol intoxication, was mainly conducted on male drivers. Full article

Emergency response to construction safety accidents is the focus of this study. Despite the abundance of data and materials available for emergency response in construction safety, the unstructured nature of the knowledge and the disordered state of storage have limited the timely application of this knowledge in decision-making for emergency response. In this study, scenario-response theory, natural language processing, and deep learning technologies were employed to construct a domain knowledge graph for emergency response in the field of safety accidents. First, based on scenario-response theory and domain-specific materials, four categories of scenario domains and 14 types of scenario elements were identified. Second, according to the mapping relationships between scenario elements and emergency response knowledge, 14 entity types and 10 relationship types were determined, thereby forming the knowledge structure pattern of this field. Subsequently, 4877 entities and 5783 relationships were extracted by means of the BERT-BiLSTM-CRF model and the BERT-CNN model, with F1 values reaching approximately 0.8. Finally, the Neo4j graph database was adopted for data storage, and a domain knowledge graph was constructed. Based on this graph, services such as knowledge association, knowledge retrieval, and intelligent question-answering were implemented. These services effectively addressed key challenges in information acquisition and decision support for on-site safety management, thereby significantly enhancing response efficiency and quality while strengthening overall safety management practices within the construction industry. Full article

Following the Great East Japan Earthquake in 2011, there has been an increased focus on risk assessment and the practical application of its findings to safety enhancement. In particular, dynamic probabilistic risk assessment (PRA) used in conjunction with plant dynamics analysis is being considered for accident management (AM) and operational support. Determining countermeasure priorities in AM can be challenging due to the diversity of accident scenarios. In multi-unit operations, the complexity of scenarios increases in cases of simultaneous disasters, which makes establishing response operations priorities more difficult. Dynamic PRA methods can efficiently generate and assess complex scenarios by incorporating changes in plant state. This paper introduces the continuous Markov chain Monte Carlo (CMMC) method, a dynamic PRA approach, as a tool for prioritizing countermeasures to support nuclear power plant operations. The proposed method involves three steps: (1) generating exhaustive scenarios that include events, operator actions, and system responses; (2) classifying scenarios according to countermeasure patterns; and (3) assigning priority based on risk data for each pattern. An evaluation was conducted using a simple plant model to analyze event countermeasure patterns for addressing steam generator tube rupture during single-unit operation. The generated scenario patterns included depressurization by opening a pressurizer relief valve (DP), depressurization via heat removal through the steam generator (DSG), and both operations combined (DP + DSG). The timing of the response operations varied randomly, resulting in multiple scenarios. The assessment, based on reactor pressure vessel water level and the potential for core damage, showed that the time margin to core damage depended on the countermeasure pattern. The findings indicate that the effectiveness of each countermeasure can be evaluated and that it is feasible to identify which countermeasure should be prioritized. Full article

This study aims to enhance safety within the Budapest tram network by developing methods to assess and manage tram drivers’ cognitive workload. While defensive driving reduces accident risk, it becomes less effective when drivers are mentally overloaded. There is currently no reliable method to objectively measure this workload, which this research aims to develop. Trams frequently interact with unpredictable road users, increasing the likelihood of sudden braking and related injuries. Using accident data, high-risk locations were identified, and cognitive workload was assessed via eye-tracking (blinks and fixations) in a tram simulator. Participants drove two predefined routes and responded to unexpected events as they would in real traffic. Results reveal a correlation between blink/fixation frequency and cognitive load, enabling the identification of mentally demanding locations. These insights support targeted interventions to reduce driver workload and enhance operational safety. Full article

The decarbonisation of the built environment has increased reliance on Environmental Life Cycle Assessment (E-LCA) to evaluate the impacts of construction materials. However, social aspects—particularly those affecting workers—remain underexplored. This study presents a regionalised approach to support socially and environmentally informed decision-making in the Italian construction sector. For this purpose, we have integrated worker health and safety indicators into the E-LCA of two representative building products assessed across key life cycle stages. These indicators are incorporated into the evaluation of Global Warming Potential (GWP), thus serving as a decision-support tool during the design phase. From a design perspective, the aim is to promote a broader understanding of sustainability—encompassing both environmental and social dimensions—within building projects. Methodologically, the contribution is twofold. First, it addresses the current gap in context-specific data on the critical indicator of worker health and safety in the construction sector, an essential requirement for robust and scientifically recognised S-LCA studies. To this end, the study develops a regionalised scoring system based on publicly available occupational health and safety data from the Italian National Accident Database (INAIL), disaggregated by sector and region. Second, we propose a framework to combine these social indicators with LCA-based environmental impact metrics, which remain central to building-scale E-LCA. It is clear that no single region performs best, while a critical need for multi-criteria decision-making in sustainable design is evident. Full article

A proactive traffic safety approach provides a forward-looking method for managing traffic and preventing accidents by identifying high-risk conditions before they occur. Previous studies have often focused on historical crash data or demographic factors, relying on limited single-source inputs and neglecting spatial, temporal, and environmental interactions. This study develops a multimodal spatiotemporal deep fusion framework for predicting traffic accidents in Toronto, Canada, by integrating spatial, temporal, environmental, and lighting features within a proactive modeling structure. Three fusion approaches were investigated: (1) environmental feature fusion, (2) extended fusion incorporating lighting and road surface conditions, and (3) a double-stage fusion combining all feature types. The double-stage fusion achieved the best performance, reducing RMSE from 0.50 to 0.41 and outperforming conventional models across multiple error metrics. The framework supports fine-grained hotspot analysis, improves proactive traffic safety management, and provides a transferable roadmap for applying deep fusion in real-world intelligent transportation and urban planning systems. Full article