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Search Results (281)

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Keywords = accident scenario analysis

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22 pages, 667 KB  
Review
Analysis of Physiological Parameters and Driver Posture for Prevention of Road Accidents: A Review
by Alparslan Babur, Ali Moukadem, Alain Dieterlen and Katrin Skerl
Sensors 2025, 25(19), 6238; https://doi.org/10.3390/s25196238 (registering DOI) - 8 Oct 2025
Abstract
This review provides an overview of existing accident prevention methods by monitoring the persons’ physiological state, observing movements, and physiological parameters. Firstly, different physiological parameters monitoring systems are introduced. Secondly, various systems dealing with position recognition on pressure sensing mats are presented. We [...] Read more.
This review provides an overview of existing accident prevention methods by monitoring the persons’ physiological state, observing movements, and physiological parameters. Firstly, different physiological parameters monitoring systems are introduced. Secondly, various systems dealing with position recognition on pressure sensing mats are presented. We conduct an in-depth literature search and quantitative analysis of papers published in this area and focus independently of the application (drivers, office and wheelchair users, etc.). Quantitative information about the number of subjects, investigated scenarios, sensor types, machine learning usage, and laboratory vs. real-world works is extracted. In posture recognition, most works recognize at least forward, backward, left and right movements on a seat. The remaining works use the pressure sensing mat for bedridden people. In physiological parameters measurement, most works detect the heart rate and often also add respiration rate recognition. Machine learning algorithms are used in most cases and are taking on an ever-greater importance for classification and regression problems. Although all solutions use different techniques, returning satisfactory results, none of them try to detect small movements, which can pose challenges in determining the optimal sensor topology and sampling frequency required to detect fine movements. For physiological measurements, there are lots of challenges to overcome in noisy environments, notably the detection of heart rate, blood pressure, and respiratory rate at very low signal-to-noise levels. Full article
(This article belongs to the Section Biomedical Sensors)
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31 pages, 917 KB  
Article
Safety of LNG-Fuelled Cruise Ships in Comparative Risk Assessment
by Elvis Čapalija, Peter Vidmar and Marko Perkovič
J. Mar. Sci. Eng. 2025, 13(10), 1896; https://doi.org/10.3390/jmse13101896 - 2 Oct 2025
Viewed by 166
Abstract
Although liquefied natural gas (LNG) is already widely used as a marine fuel, its use on large cruise ships is a relatively new development. By the end of 2024, twenty-four LNG-fuelled cruise ships were in operation, each carrying several thousand passengers and making [...] Read more.
Although liquefied natural gas (LNG) is already widely used as a marine fuel, its use on large cruise ships is a relatively new development. By the end of 2024, twenty-four LNG-fuelled cruise ships were in operation, each carrying several thousand passengers and making frequent port calls. These operational characteristics increase the potential risks compared to conventional cargo ships and require a rigorous safety assessment. In this study, the safety of LNG-fuelled cruise ships is assessed using the Formal Safety Assessment (FSA) framework prescribed by the International Maritime Organization (IMO). The assessment includes a hazard identification (HAZID), a risk analysis, an evaluation of risk control options, a cost–benefit analysis and recommendations for decision-making. Given the limited operational data on LNG-fuelled cruise ships, event trees are developed on the basis of LNG tanker incidents, adjusted to reflect passenger-related risks and cruise-specific operating conditions. A statistical overview of marine casualties involving cruise ships and LNG carriers of more than 20,000 GT over the last 35 years provides a further basis for the analysis. To ensure compliance, the study also analyses class requirements and regulatory frameworks, including risk assessments for ship design, bunker operations and emergency preparedness. These assessments, which are carried out at component, ship and process level, remain essential for safety validation and regulatory approval. The results provide a comprehensive framework for assessing LNG safety in the cruise sector by combining existing safety data, regulatory standards and probabilistic risk modelling. Recent work also confirms that event tree modelling identifies critical accident escalation pathways, particularly in scenarios involving passenger evacuation and port operations, which are under-researched in current practice. The results contribute to the wider debate on alternative fuels and support evidence-based decision-making by ship operators, regulators and industry stakeholders. Full article
(This article belongs to the Special Issue Maritime Security and Risk Assessments—2nd Edition)
22 pages, 5267 KB  
Article
On Ballooning and Burst Behavior of Nuclear Fuel Clad Considering Heating Rate Effect: Development of a Damage Model, a Burst Correlation and Experimental Validation
by Ather Syed and Mahendra Kumar Samal
Solids 2025, 6(4), 56; https://doi.org/10.3390/solids6040056 - 28 Sep 2025
Viewed by 260
Abstract
Nuclear fuel cladding serves as the primary barrier to the release of radioactive fission products and is subjected to high-temperature and high-pressure environments during both normal reactor operation and accident scenarios such as loss of coolant accidents (LOCAs). Predicting the burst behavior of [...] Read more.
Nuclear fuel cladding serves as the primary barrier to the release of radioactive fission products and is subjected to high-temperature and high-pressure environments during both normal reactor operation and accident scenarios such as loss of coolant accidents (LOCAs). Predicting the burst behavior of cladding is essential for ensuring structural integrity, especially under varying heating rates—an aspect inadequately addressed in existing empirical models. In this study, a finite element-based damage model is developed to simulate the ballooning and burst behavior of Zircaloy-4 cladding. The model incorporates creep deformation, stress triaxiality, and time-dependent damage accumulation. Material behavior is characterized using experimentally determined creep constants and the model is calibrated against burst test data from the literature. A new heating-rate-dependent burst correlation is proposed based on model outputs. The results indicate that increasing the heating rate raises the burst temperature due to reduced exposure time in the temperature regime where creep damage accumulates significantly. The model accurately reproduces burst behavior across a wide range of internal pressures (1–10 MPa) and heating rates (5–100 °C/s). The newly developed correlation improves predictive capability in accident analysis tools and can be directly implemented into safety analysis codes for Indian pressurized heavy water reactors (PHWRs), contributing to enhanced reactor safety evaluations. Full article
(This article belongs to the Topic Multi-scale Modeling and Optimisation of Materials)
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15 pages, 3348 KB  
Article
Performance of Electric Bus Batteries in Rollover Scenarios According to ECE R66 and R100 Standards
by Alexsandro Sordi, Bruno Gabriel Menino, Gabriel Isoton Pistorello, Vagner do Nascimento and Giovani Dambros Telli
World Electr. Veh. J. 2025, 16(9), 528; https://doi.org/10.3390/wevj16090528 - 18 Sep 2025
Viewed by 329
Abstract
With the growing adoption of electric buses in urban transportation systems, ensuring the safety and structural integrity of their battery systems under accident scenarios has become increasingly important. Among potential accidents, rollover events pose a particular risk, as they can lead to the [...] Read more.
With the growing adoption of electric buses in urban transportation systems, ensuring the safety and structural integrity of their battery systems under accident scenarios has become increasingly important. Among potential accidents, rollover events pose a particular risk, as they can lead to the penetration or deformation of the battery pack and, consequently, trigger thermal runaway. In this context, this study evaluates the structural performance of rechargeable energy storage systems (REESS) in electric buses under rollover conditions, following the guidelines of United Nations Economic Commission for Europe (UNECE) Regulations No. 100 and No. 66. The analysis focuses on the structural safety of uniformly distributing the battery pack beneath the vehicle floor during rollover scenarios. The methodology adopted includes detailed finite element modeling to accurately represent the vehicle structure and battery modules, as well as virtual instrumentation using accelerometers. Simulations were conducted to evaluate structural deformations, battery retention integrity, and acceleration levels within the REESS compartments under rollover impact conditions. The results demonstrated compliance with both regulations and highlighted the importance of properly positioning and securing the battery module to the vehicle floor. The findings contribute to the improvement of design and validation criteria for electric buses, reinforcing the need to align technological innovation with international safety standards. Finally, this research supports the development of safer and more reliable vehicles, promoting sustainable mobility solutions for urban transportation systems. Full article
(This article belongs to the Section Storage Systems)
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18 pages, 813 KB  
Article
Heart Rate Estimation Using FMCW Radar: A Two-Stage Method Evaluated for In-Vehicle Applications
by Jonas Brandstetter, Eva-Maria Knoch and Frank Gauterin
Biomimetics 2025, 10(9), 630; https://doi.org/10.3390/biomimetics10090630 - 17 Sep 2025
Viewed by 505
Abstract
Assessing the driver’s state in real time is a critical challenge in modern vehicle safety systems, as human factors account for the vast majority of traffic accidents. Heart rate (HR) is a key physiological indicator of the driver’s condition, yet contactless measurements in [...] Read more.
Assessing the driver’s state in real time is a critical challenge in modern vehicle safety systems, as human factors account for the vast majority of traffic accidents. Heart rate (HR) is a key physiological indicator of the driver’s condition, yet contactless measurements in dynamic in-vehicle environments remain difficult due to motion artifacts, vibrations, and varying operational conditions. This paper presents a novel two-stage method for HR estimation using a commercial 60 GHz frequency-modulated continuous wave (FMCW) radar sensor, specifically designed and validated for in-vehicle applications. In the first stage, coarse HR estimation is performed using the discrete wavelet transform (DWT) and autoregressive (AR) spectral analysis. The second stage refines the estimate using an inverse application of the relevance vector machine (RVM) approach, leveraging a narrowed frequency window derived from Stage 1. Final HR estimates are stabilized through sequential Kalman filtering (SKF) across time segments. The system was implemented using an Infineon BGT60TR13C radar module installed in the sun visor of a passenger vehicle. Extensive data collection was conducted during real-world driving across diverse traffic scenarios. The results demonstrate robust HR estimations with an accuracy comparable to that of commercial wearable devices, validated against a Polar H10 chest strap. This method offers several advantages over prior work, including short measurement windows (5 s), operation under varying lighting and clothing conditions, and validation in realistic driving environments. In this sense, the method contributes to the field of biomimetics by transferring the biological principles of continuous vital sign perception to technical sensorics in the automotive domain. Future work will explore the fusion of sensors with visual methods and potential extension to heart rate variability (HRV) estimations to enhance driver monitoring systems (DMSs) further. Full article
(This article belongs to the Section Bioinspired Sensorics, Information Processing and Control)
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16 pages, 1620 KB  
Article
Assessment of Radiological Plume Dispersion in LBLOCA-Type Accidents at Nuclear Power Plants
by Juliana de Sá Sanchez Machado, Diego José Silva Nuzza de Souza, Maria Lurdes Dinis and Andressa dos Santos Nicolau
Atmosphere 2025, 16(9), 1089; https://doi.org/10.3390/atmos16091089 - 16 Sep 2025
Viewed by 517
Abstract
This study analyzed the radiation dose rate in air, water and soil following a simulated Large Break LOCA (LBLOCA) accident in a Pressurized Water Reactor (PWR) nuclear power plant with a point-source release of radionuclides into the atmosphere. AERMOD and RESRAD-BIOTA 1.8 codes [...] Read more.
This study analyzed the radiation dose rate in air, water and soil following a simulated Large Break LOCA (LBLOCA) accident in a Pressurized Water Reactor (PWR) nuclear power plant with a point-source release of radionuclides into the atmosphere. AERMOD and RESRAD-BIOTA 1.8 codes were used, with meteorological data processed by AERMET and terrain elevation data generated using AERMAP. AERMOD performed dispersion calculations using Gaussian and bi-Gaussian models. The simulations identified atmospheric stability classes C and F, which, combined with other external factors, directly influenced the dose rates and the distances reached by the radioactive plume. The dose rate analysis, based on calculated concentrations in the air, water and soil, indicated that, in this scenario, the potential release of radioactive material does not pose a threat to the population. The adopted methodology proved effective in mapping the behavior of the radioactive plume across the three media, providing accurate and reliable results for use in safety assessments and emergency response planning. Full article
(This article belongs to the Section Air Quality)
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21 pages, 1624 KB  
Article
Data Analysis of Two-Vehicle Accidents Based on Machine Learning
by Dongguang Gao, Jiawei Chen, Tianyu Luo, Zijun Liu, Libo Cao, Zhongxiang Chen and Jun Wu
Appl. Sci. 2025, 15(17), 9819; https://doi.org/10.3390/app15179819 - 8 Sep 2025
Viewed by 699
Abstract
Road traffic accidents are the eighth leading cause of human deaths. In order to study two-vehicle accidents, this paper extracted data from 493 two-vehicle accidents from the CIDAS database from 2011 to 2022, used machine learning methods to analyze the accident data, and [...] Read more.
Road traffic accidents are the eighth leading cause of human deaths. In order to study two-vehicle accidents, this paper extracted data from 493 two-vehicle accidents from the CIDAS database from 2011 to 2022, used machine learning methods to analyze the accident data, and obtained the significance of two-vehicle accident parameters. Finally, five typical scenarios of two-vehicle accidents were obtained based on this. The results of the significance analysis show that vehicle parameters have a greater impact on occupant injury in the host vehicle; clustering results show that lighting, the number of lanes, the other vehicle’s type, and the speed of the host vehicle have a large impact on occupant injury (for example, the injury rate for the high-speed, nighttime Scenario II was 52.9%, compared to just 10.9% for the lower-speed Scenario IV). Factor analysis results show that precipitation has a large impact on occupant injury, as the frequency of injuries in rainy conditions was 13.4% higher, and the frequency of serious injuries was 7.9% higher, than in accidents without rain. This paper innovatively uses factor analysis to reduce the dimensionality of categorical variables, which provides research ideas for related research. At the same time, the clustering results obtained in this paper also provide references for the establishment of corresponding test scenarios for autonomous driving and the establishment of standards. Full article
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19 pages, 2115 KB  
Article
Situational Awareness for Oil Storage Tank Accidents Based on Complex Networks and Evidence Theory
by Yunlong Xia, Junmei Shi, Cheng Xun, Bo Kong, Changlin Chen, Yi Zhu and Dengyou Xia
Fire 2025, 8(9), 353; https://doi.org/10.3390/fire8090353 - 5 Sep 2025
Viewed by 642
Abstract
To address the difficulty frontline commanders face in accurately perceiving fireground risks during the early stages of oil storage tank fires, in this study, we propose a method that integrates complex network theory with a multi-source information fusion approach based on cloud models [...] Read more.
To address the difficulty frontline commanders face in accurately perceiving fireground risks during the early stages of oil storage tank fires, in this study, we propose a method that integrates complex network theory with a multi-source information fusion approach based on cloud models and Dempster-Shafer (D-S) evidence theory for situational analysis and dynamic perception. Initially, the internal evolution of accident scenarios within individual tanks is modeled as a single-layer network, while scenario propagation between tanks is represented through inter-layer connections, forming a multi-layer complex network for the storage area. The importance of each node is evaluated to assess the risk level of scenario nodes, enabling preliminary situational awareness, with limited reconnaissance information. Subsequently, the cloud model’s capability to handle fuzziness is combined with D-S theory’s strength in fusing multi-source data. Multi-source heterogeneous information is integrated to obtain the confidence levels of key nodes across low, medium, and high-risk categories. Based on these results, high-risk scenarios in oil storage tank emergency response are dynamically adjusted, enabling the updating and prediction of accident evolution. Finally, the proposed method is validated using the 2015 Gulei PX plant explosion case study. The results demonstrate that the approach effectively identifies high-risk scenarios, enhances dynamic situational perception, and is generally consistent with actual accident progression, thereby improving emergency response capability. Full article
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42 pages, 5040 KB  
Systematic Review
A Systematic Review of Machine Learning Analytic Methods for Aviation Accident Research
by Aziida Nanyonga, Ugur Turhan and Graham Wild
Sci 2025, 7(3), 124; https://doi.org/10.3390/sci7030124 - 4 Sep 2025
Cited by 1 | Viewed by 980
Abstract
The aviation industry prioritizes safety and has embraced innovative approaches for both reactive and proactive safety measures. Machine learning (ML) has emerged as a useful tool for aviation safety. This systematic literature review explores ML applications for safety within the aviation industry over [...] Read more.
The aviation industry prioritizes safety and has embraced innovative approaches for both reactive and proactive safety measures. Machine learning (ML) has emerged as a useful tool for aviation safety. This systematic literature review explores ML applications for safety within the aviation industry over the past 25 years. Through a comprehensive search on Scopus and backward reference searches via Google Scholar, 87 of the most relevant papers were identified. The investigation focused on the application context, ML techniques employed, data sources, and the implications of contextual nuances for safety analysis outcomes. ML techniques have been effective for post-accident analysis, predictive, and real-time incident detection across diverse aviation scenarios. Supervised, unsupervised, and semi-supervised learning methods, including neural networks, decision trees, support vector machines, and deep learning models, have all been applied for analyzing accidents, identifying patterns, and forecasting potential incidents. Notably, data sources such as the Aviation Safety Reporting System (ASRS) and the National Transportation Safety Board (NTSB) datasets were the most used. Transparency, fairness, and bias mitigation emerge as critical factors that shape the credibility and acceptance of ML-based safety research in aviation. The review revealed seven recommended future research directions: (1) interpretable AI; (2) real-time prediction; (3) hybrid models; (4) handling of unbalanced datasets; (5) privacy and data security; (6) human–machine interface for safety professionals; (7) regulatory implications. These directions provide a blueprint for further ML-based aviation safety research. This review underscores the role of ML applications in shaping aviation safety practices, thereby enhancing safety for all stakeholders. It serves as a constructive and cautionary guide for researchers, practitioners, and decision-makers, emphasizing the value of ML when used appropriately to transform aviation safety to be more data-driven and proactive. Full article
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31 pages, 1511 KB  
Article
Economic Evaluation During Physicochemical Characterization Process: A Cost–Benefit Analysis
by Despina A. Gkika, Nick Vordos, Athanasios C. Mitropoulos and George Z. Kyzas
ChemEngineering 2025, 9(5), 95; https://doi.org/10.3390/chemengineering9050095 - 2 Sep 2025
Viewed by 675
Abstract
As academic institutions expand, the proliferation of laboratories dealing with hazardous chemicals has risen. While the physicochemical characterization equipment employed in these academic chemical laboratories is widely recognized, its usage presents a notable risk to researchers at various levels. This paper presents a [...] Read more.
As academic institutions expand, the proliferation of laboratories dealing with hazardous chemicals has risen. While the physicochemical characterization equipment employed in these academic chemical laboratories is widely recognized, its usage presents a notable risk to researchers at various levels. This paper presents a simplified approach for evaluating the effects of the implementation of prevention investments in regard to working with nanomaterials on a lab scale. The evaluation is based on modeling the benefits (avoided accident costs) and costs (safety training), as opposed to an alternative (not investing in safety training). Each scenario analyzed in the economic evaluation reflects a different level of risk. The novelty of this study lies in its objective to provide an economic assessment of the benefits and returns from safety investments—specifically training—in a chemical laboratory, using a framework that integrates qualitative insights to explore and define the context alongside quantitative data derived from a cost–benefit analysis. The Net Present Value (NPV) was evaluated. The results of the cost–benefit analysis demonstrated that the benefits exceed the cost of the investment. The findings from the sensitivity analysis highlight the significant influence of insurance benefits on safety investments in the specific case study. In this case study, the deterministic analysis yielded a Net Present Value (NPV) of €280,414.67, which aligns closely with the probabilistic results. The probabilistic NPV indicates 90% confidence that the investment will yield a positive NPV ranging from €283,053 to €337,356. The cost–benefit analysis results demonstrate that the benefits outweigh the costs, showing that with an 87% training success rate, this investment would generate benefits of approximately €6328 by preventing accidents in this study. To the best of the researchers’ knowledge, this is the first study to evaluate the influence of safety investment through an economic evaluation of laboratory accidents with small-angle X-ray scattering during the physicochemical characterization process of engineered nanomaterials. The proposed approach and framework are relevant not only to academic settings but also to industry. Full article
(This article belongs to the Special Issue New Advances in Chemical Engineering)
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23 pages, 4182 KB  
Article
A Long Sequence Time-Series Forecasting Method for Early Warning of Long Landing Risks with QAR Flight Data
by Zeyuan Zhou, Xiaolei Chong, Zhenglei Chen, Jicheng Zhou, Jichao Zhang and Pengshuo Guo
Aerospace 2025, 12(8), 744; https://doi.org/10.3390/aerospace12080744 - 21 Aug 2025
Viewed by 615
Abstract
Long landings can reduce runway utilization and increase the probability of runway incursions and excursions. Previous studies on long landings often lacked support from actual operational data and primarily relied on event-triggering logic established by airlines for parameter exceedance detection and retrospective analysis. [...] Read more.
Long landings can reduce runway utilization and increase the probability of runway incursions and excursions. Previous studies on long landings often lacked support from actual operational data and primarily relied on event-triggering logic established by airlines for parameter exceedance detection and retrospective analysis. In response, a comprehensive risk prediction framework for aircraft long landings, supported by Quick Access Recorder (QAR) data, was constructed. The framework includes a data analysis pipeline, a sequence prediction model, and performance evaluation metrics for accident warning efficiency. Specifically, approximately 3 million rows of real QAR data were collected, and reasonable landing intervals were extracted based on pilots’ correct landing sightlines, attention allocation, and actual visual scenarios at departure heights. Gradient Boosting Decision Trees (GBDT) were employed to develop a method for extracting landing interval feature data, based on monitored parameters and ranges of landing distance. Additionally, the GBDT-Informer long-sequence time series prediction model was developed to forecast landing distance, accompanied by the construction of effective metrics for evaluating prediction performance. The results indicate that the GBDT-Informer model effectively models the temporal dimensions of landing intervals, accurately predicting ground speed (GS), radio altitude (RALT), and landing distance sequences. Compared to other prediction models, the GBDT-Informer model consistently achieved the smallest RMSE, MAE, and MAPE values, demonstrating high prediction accuracy. This predictive framework allows for the analysis of the coupling relationships among multiple parameters in flight data and their interrelations with exceedance anomalies. The findings can be applied in actual flight landings to promptly assess whether landing distances exceed limits, providing quick references for flight crews during landing or go-around decisions, thereby enhancing operational safety margins during the landing phase. Full article
(This article belongs to the Section Air Traffic and Transportation)
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27 pages, 3824 KB  
Article
Sustainable Data Construction and CLS-DW Stacking for Traffic Flow Prediction in High-Altitude Plateau Regions
by Wu Bo, Xu Gong, Fei Chen, Haisheng Ren, Junhao Chen, Delu Li and Fengying Gou
Sustainability 2025, 17(16), 7427; https://doi.org/10.3390/su17167427 - 17 Aug 2025
Viewed by 606
Abstract
This study proposes a novel vehicle speed prediction model for plateau transportation—CLS-DW Stacking (Constrained Least Squares Dynamic Weighting Model Stacking)—which holds significant implications for the sustainable development of transportation systems in high-altitude regions. Research on sharp-curved roads on mountainous plateaus remains scarce. Compared [...] Read more.
This study proposes a novel vehicle speed prediction model for plateau transportation—CLS-DW Stacking (Constrained Least Squares Dynamic Weighting Model Stacking)—which holds significant implications for the sustainable development of transportation systems in high-altitude regions. Research on sharp-curved roads on mountainous plateaus remains scarce. Compared with plain areas, data acquisition in such regions is constrained by government confidentiality policies, while complex environmental and topographical conditions lead to substantial variations in road alignment and elevation. To address these challenges, this study presents a sustainable data acquisition and construction method: unmanned aerial vehicle (UAV) video data are processed through road image segmentation, trajectory tracking, and three-dimensional modeling to generate multi-source heterogeneous datasets for both single-curve and continuous-curve scenarios. Building upon these datasets, the proposed framework integrates constrained least squares with multiple deep learning methods to achieve accurate traffic flow prediction. Bi-LSTM (Bidirectional Long Short-Term Memory), Informer, and GRU (Gated Recurrent Unit) are employed as base learners, and the loss function is redefined with non-negativity and normalization constraints on the weights. This ensures optimal weight coefficients for each base learner, with the final prediction obtained via weighted summation. The experimental results show that, compared with single deep learning models such as Informer, the proposed model reduces the mean squared error (MSE) by 1.9% on the single curve dataset and by 7.7% on the continuous curve dataset. Furthermore, by combining vehicle speed predictions across different altitude gradients with decision tree-based interpretable analysis, this research provides scientific support for developing altitude-specific and precision-oriented speed limit policies. The outcomes contribute to accident risk reduction, traffic congestion mitigation, and carbon emission reduction, thereby improving road resource utilization efficiency. This work not only fills the research gap in traffic prediction for sharp-curved plateau roads but also supports the construction of green transportation systems and the broader objectives of sustainable development in high-altitude regions. Full article
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15 pages, 2450 KB  
Article
Health Risk Assessment of Toluene and Formaldehyde Based on a Short-Term Exposure Scenario: A Comparison of the Reference Concentration, Reference Dose, and Minimal Risk Level
by Ji-Eun Moon, Si-Hyun Park, Young-Hyun Kim, Hyeok Jang, Ji-Yun Jung, Sung-Won Yoon and Cheol-Min Lee
Toxics 2025, 13(8), 683; https://doi.org/10.3390/toxics13080683 - 16 Aug 2025
Viewed by 641
Abstract
Conventional health risk assessments do not adequately reflect short-term exposure characteristics following chemical accidents. We aimed to evaluate the efficacy of existing assessment methods and propose a more suitable risk assessment approach for short-term exposure to hazardous chemicals. We analyzed foundational studies used [...] Read more.
Conventional health risk assessments do not adequately reflect short-term exposure characteristics following chemical accidents. We aimed to evaluate the efficacy of existing assessment methods and propose a more suitable risk assessment approach for short-term exposure to hazardous chemicals. We analyzed foundational studies used to derive reference concentration (RfC), reference dose (RfD), and minimal risk level (MRL) values and applied these health guidance values (HGVs) to a hypothetical chemical accident scenario. An analysis of the studies underlying each HGV revealed that, except for the RfC for formaldehyde and the RfD for toluene, all values were derived under research conditions comparable to their respective exposure durations. Given the differing toxicity mechanisms between acute and chronic exposures, MRLs that were aligned with the corresponding exposure durations supported more appropriate risk management decisions. The health risk assessment results showed that RfC/RfD-based hazard quotients (HQs) were consistently higher than MRL-based HQs across all age groups and both substances, indicating that RfC/RfD values tend to yield more conservative risk estimates. For formaldehyde, the use of RfC instead of MRL resulted in an additional 208 tiles (2.08 km2) being classified as areas of potential concern (HQ > 1) relative to the MRL-based evaluation. These findings highlighted that the selection of HGVs can significantly influence the spatial extent of areas of potential concern, potentially altering health risk determinations for large population groups. This study provides a scientific basis for improving exposure and risk assessment frameworks under short-term exposure conditions. It also serves as valuable foundational data for developing effective and rational risk management strategies during actual chemical accidents. To the best of our knowledge, this is the first study to apply MRLs to a short-term chemical accident scenario and directly compare them with traditional reference values. Full article
(This article belongs to the Section Exposome Analysis and Risk Assessment)
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35 pages, 2525 KB  
Article
Structured Risk Identification for Sustainable Safety in Mixed Autonomous Traffic: A Layered Data-Driven Approach
by Hyorim Han, Soongbong Lee, Jeongho Jeong and Jongwoo Lee
Sustainability 2025, 17(16), 7284; https://doi.org/10.3390/su17167284 - 12 Aug 2025
Viewed by 787
Abstract
With the accelerated commercialization of autonomous vehicles, new accident types and complex risk factors have emerged beyond the scope of existing traffic safety management systems. This study aims to contribute to sustainable safety by establishing a quantitative basis for early recognition and response [...] Read more.
With the accelerated commercialization of autonomous vehicles, new accident types and complex risk factors have emerged beyond the scope of existing traffic safety management systems. This study aims to contribute to sustainable safety by establishing a quantitative basis for early recognition and response to high-risk situations in urban traffic environments where autonomous and conventional vehicles coexist. To this end, high-risk factors were identified through a combination of literature meta-analysis, accident history and image analysis, autonomous driving video review, and expert seminars. For analytical structuring, the six-layer scenario framework from the PEGASUS project was redefined. Using the analytic hierarchy process (AHP), 28 high-risk factors were identified. A risk prediction model framework was then developed, incorporating observational indicators derived from expert rankings. These indicators were structured as input variables for both road segments and autonomous vehicles, enabling spatial risk assessment through agent-based strategies. This space–object integration-based prediction model supports the early detection of high-risk situations, the designation of high-enforcement zones, and the development of preventive safety systems, infrastructure improvements, and policy measures. Ultimately, the findings offer a pathway toward achieving sustainable safety in mixed traffic environments during the initial deployment phase of autonomous vehicles. Full article
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32 pages, 10173 KB  
Article
Field-Calibrated Nonlinear Finite Element Diagnosis of Localized Stern Damage from Tugboat Collision: A Measurement-Driven Forensic Approach
by Myung-Su Yi and Joo-Shin Park
J. Mar. Sci. Eng. 2025, 13(8), 1523; https://doi.org/10.3390/jmse13081523 - 8 Aug 2025
Viewed by 486
Abstract
This study conducts a high-resolution forensic evaluation of stern structural damage resulting from a tugboat collision during berthing, integrating real-world measurement data with calibrated nonlinear finite element analysis. Based on field-acquired deformation geometry and residual dent profiles at Frame 76, five distinct collision [...] Read more.
This study conducts a high-resolution forensic evaluation of stern structural damage resulting from a tugboat collision during berthing, integrating real-world measurement data with calibrated nonlinear finite element analysis. Based on field-acquired deformation geometry and residual dent profiles at Frame 76, five distinct collision scenarios varying in impact orientation, contact area, and load path were simulated using shell-based nonlinear plastic analysis. Particular attention is given to comparing the plastic equivalent strain (PEEQ), von-Mises stress fields, and residual deformation contours at Point A—the critical zone identified from damage surveys. Among the five cases, Case-2, defined by a vertically eccentric external impact, demonstrated the highest plastic strain intensity (PEEQ > 2.0%), the sharpest post-yield drops in stiffness, and the closest match to the residual dent profile observed in the actual structure. The integrated correlation between field damage and some of the results (strain, stress, and deformed shape) enabled clear identification of the most probable accident mechanism with engineering accuracy. This study proposes a validated, measurement-calibrated nonlinear finite element analysis framework to diagnose stern damage from tugboat collisions, enhancing repair decision-making and structural safety assessment. Such a calibrated forensic strategy enhances the reliability of structural safety predictions in marine collision incidents and supports eco-friendly rescue engineering by minimizing unnecessary structural renewal through precise damage localization. The proposed approach establishes a new benchmark for scenario-driven collision assessment, particularly relevant to sustainable, automation-compatible, and damage-tolerant ship design practices. Full article
(This article belongs to the Special Issue Advanced Studies in Marine Mechanical and Naval Engineering)
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