Abstract: Background: This study investigates the biomechanical and physiological demands of the pumping maneuver in Laser-class sailing, a dynamic technique requiring coordinated upper and lower body oscillations to generate propulsion in marginal wind conditions. The proposed framework utilizes a mixed-methods approach combining musculoskeletal simulation, kinematic analysis, ergonomic assessment, and subjective evaluation. Methods: Thirty-six experienced Laser sailors completed a questionnaire quantifying perceived discomfort using the Borg CR-10 scale across three temporal phases: during pumping, immediately post-sailing, and the following day. The pumping motion was replicated on land by an experienced sailor and analyzed using marker-based motion capture and Delmia® musculoskeletal simulation software. REBA ergonomic assessment was performed to evaluate postural risk. Results: Musculoskeletal simulation revealed maximal normalized activation (100.0%) in seven deep trunk stabilizers and left latissimus dorsi. Pronounced lateral asymmetry was observed, with right-sided trunk dominance. Lower extremity activation was moderate on the right and minimal on the left. Kinematic analysis identified substantial lumbar excursions (45.3° flexion-extension, 38.7° lateral flexion, 42.1° axial rotation). REBA assessment yielded a score of 11 (Very High Risk). Questionnaire data revealed a paradoxical relationship between objective activation and subjective fatigue: maximal trunk activation corresponded to lower perceived fatigue, while moderate lower limb activation corresponded to higher perceived fatigue. Musculoskeletal discomfort prevalence was 72.2%, concentrated in the lower back, shoulders, and knees. Conclusions: Findings highlight the deep trunk stabilizers, latissimus dorsi, and lower extremities as primary contributors to pumping execution, while emphasizing pronounced lateral asymmetry and high ergonomic risk. The activation-fatigue paradox suggests differential physiological mechanisms between trunk stabilizers and lower limb muscles. These insights can guide training interventions, injury prevention strategies, and ergonomic modifications to optimize performance and reduce injury risk in competitive sailing.

Abstract: The paper presents an example of a wearable system for caring for the elderly. It focuses on the relationships between the individual components of this system from a macro ergonomics point of view. The protection of older people has been identified as a standard of well-being that, in highly developed countries, is evolving from a passive social security model to an active, holistic paradigm. This new paradigm is aimed not only at prolonging life, but also at ensuring its highest quality, dignity and full social integration. This new standard goes far beyond pensions and basic healthcare, becoming a measure of a country's humanitarian maturity and social advancement. The design of such telecare systems should be user-oriented in accordance with the principles of universal design. Defining the relationship between humans and work elements is crucial. Its purpose is to ensure hygiene, safety and comfort at work, while maintaining high production efficiency.

Abstract: Modern high-speed train compartments contain intricate internal configurations. In the event of a fire emergency, the propagation velocity of flames through the passenger cabin is determined by multiple factors, including compartment design, ignition source characteristics, and airflow conditions. This study employed numerical simulation approaches to investigate the effects of fire source power, fire source location, and longitudinal ventilation velocity on the rate of flame progression. The simulation outcomes reveal that, under forward ventilation conditions, the magnitude of fire power has a minimal influence on flame propagation speed. However, stronger fire sources lead to earlier initiation of flame spread along the carriage. Central positioning of the ignition source results in bidirectional flame movement toward both ends of the carriage, with faster propagation rates than those of fires originating at the extremities. Longitudinal airflow patterns significantly. When the airflow speed within the tunnel remains below 3 meters per second, the impact of longitudinal ventilation on fire propagation speed in the train is minimal under forward ventilation conditions. Conversely, in reverse-ventilation scenarios, the rate of flame advancement shows a positive correlation with increasing ventilation speed. Nevertheless, once tunnel ventilation velocities exceed 3 m/s, combustion propagation within high-speed rail carriages becomes impossible due to intact windows, which create oxygen-deficient conditions that prevent the development of fire.

Abstract:

Triboelectric nanogenerator (TENG) have gradually been applied in various practical scenarios, mainly focusing on core areas such as wearable motion monitoring devices, medical security systems, and natural resource exploration technology. However, it has the problem of low output energy and has not yet formed effective integration with mature commercially available products, which has hindered the industrialization process. This situation still significantly limits its global promotion and application. In this study, TENG was used as the sensing module for intelligent automotive airbags. We conducted tests on the voltage and current output characteristics of the system under different impact forces and frequency conditions. During the testing process, the electrical energy generated under different operating conditions is transmitted to the control system through Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) circuits. The system will quickly determine whether to trigger the airbag deployment based on the received electrical signals, and activate the ignition device when necessary to achieve rapid inflation and deployment of the airbag. Compared with traditional triggering mechanisms, the airbag system based on this designed sensor has higher sensitivity and reliability. The sensor can stably capture collision signals, and experiments have shown that as the collision speed increases, the slope of its open circuit voltage gradually approaches infinity. Applying TENG to automotive airbags not only effectively improves the triggering efficiency and accuracy of airbags, but also provides more reliable safety protection for drivers and passengers. The finite element simulation of vehicle airbags provides specific data support for safety performance evaluation. With the continuous advancement of TENG technology and further expansion of its application scenarios, we believe that such innovative safety technologies will play a more critical role in the future automotive industry.

Abstract: Maritime safety is a crucial aspect in busy and complex shipping lanes, particularly in strait areas that are prone to accidents due to high vessel traffic and dynamic envi-ronmental conditions. This study aims to calculate a maritime safety index by consid-ering various factors, including vessel characteristics, ship encounter conditions, oper-ational time parameters, and oceanographic conditions such as currents and waves. The data used consist of questionnaires, AIS data, and oceanographic information, collected over a one-month period at three-hour intervals. The case study focuses on the Bali Strait and the Lombok Strait, with spatial segmentation into grid cells to sup-port spatial analysis. The safety index is calculated using two models: Model I com-bines vessel and encounter characteristics with temporal parameters, while Model II incorporates oceanographic factors into the assessment. Following the index calcula-tion, multivariate analysis conducted to identify the key factors that significantly in-fluence maritime safety levels. The results show that navigation risks in both straits are mainly influenced by vessel traffic, sailing hours, days of the week, and environmental conditions. In the Bali Strait, the highest risks occur near Ketapang and Gilimanuk Ports, while in the Lombok Strait, Padangbai, Lembar, and the ALKI II route show ele-vated risks. Multivariate analysis reveals that longer vessels, higher speeds, and dy-namic sea conditions dominate in Lombok, whereas older vessels and closer spacing are more critical in Bali.

Abstract:

Risk management is a critical process for achieving construction project objectives and supporting more sustainable project delivery. However, most existing research focuses on isolated aspects of risk, lacking an integrated approach that examines how risks evolve across the entire project life cycle. This study addresses this gap by identifying and assessing key risks affecting construction projects in the United Arab Emirates (UAE), with attention to how improved risk understanding can contribute to more resilient and sustainable project outcomes. Through a literature review, fifteen critical risks involving various stakeholders were identified. A questionnaire survey was conducted to evaluate the probability and impact of these risks on project cost. The study analyzes how these risks manifest across the project life cycle and affect different stakeholders. Using a coordinate system, it visualizes risk behavior across phases, offering a dynamic view of risk exposure. Findings show that the construction phase was the riskiest, followed by the handover, design, and feasibility phases. Additionally, delayed payments by owners emerged as the most significant risk, followed by poor contractor management. The study proposes a modified probability–impact matrix to account for multi-phase risks. These findings provide valuable insights for construction firms, helping improve stakeholder risk allocation, inform contract negotiations, and enhance project delivery in the UAE context while contributing to more efficient, responsible, and sustainable project management practices.

Abstract:

This paper uses fuzzy logic to make intelligent agents that can show stupidity and context awareness in a simulation of operational environment. Focusing on dynamic interactions, the fuzzy inference system will help agents to change their behaviour according to factors that are increasing namely proximity, over-speed and environmental factors. The way the author explains is going to help develop responsive and resilient decision making automated components. These components will be able to optimize complex multi-stakeholder systems and will certainly be useful in modern supply chain systems. The study noted that AI can be used to increase logistics and inventory management flexibility and reactivity. Moreover, it was noted for both technical and practical.

Abstract: Climate change will pose greater challenges to occupational safety and health, and advanced approaches for risk and reliability modeling that can include sustainability perspectives are needed. This scoping review aims to map and synthesize the current methodologies on modeling OSH risks in the context of climate change and sustainability. We systematically searched the Scopus, Web of Science, PubMed, ERIC, and other databases following the PRISMA-ScR guidelines for studies published from 2010 to 2025. Seventeen studies met the inclusion criteria and were categorized into four thematic groups: the reliability engineering and probabilistic models, climate-related occupational risk models, hybrid and AI-based models, and the sustainability-oriented reviews. Results The study indicates a methodological shift from deterministic toward probabilistic and systems-based toward data-driven approaches, while heat stress is identified as the key climate-related hazard. Sustainability considerations are increasingly embedded within the risk frameworks, linking safety outcomes with productivity, labor capacity, and resilience. Still, the integration is uneven, and key performance metrics are underreported. The advanced computational methods, such as Bayesian networks and machine learning techniques, along with hybrid models, show promise but need further validation in a wide range of occupational settings. CONCLUSION This review stresses the need for more integrative, transparent, and harmonized modeling practices, providing support to climate-resilient and sustainable OSH management systems.

Abstract:

Coal and gas outbursts constitute a major hazard for mining safety, which is critical for the sustainable development of China’s energy industry. Rapid, accurate and reliable prediction is pivotal for preventing and controlling outburst incidents. Nevertheless, the mechanisms driving coal and gas outbursts involve highly complex influencing factors. By examining the attributes of these factors and their association to outburst intensity, four major geological and environmental indicators were identified. This study developed a machine learning-based prediction model for outburst risk. Five algorithms were evaluated: K-Nearest Neighbors (KNN), Back Propagation Neural Network (BPNN), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost). Model optimization was performed via Bayesian hyperparameter (BO) tuning. Model performance was assessed by the Receiver Operating Characteristic (ROC) curve; the optimized XGBoost model demonstrated strong predictive performance. To enhance model transparency and interpretability, the SHapley Additive exPlanations (SHAP) method was implemented. The SHAP analysis identified geological structure was the most important predictive feature, providing a practical decision-support tool for mine executives to prevent and control outburst incidents.

Abstract: Existing experimental results are sometimes difficult to guide the design of a water mist fire extinguishing system ascribing to many factors that affect the fire extinguishing per-formance of water mist. This paper sums up the factors and combs the logical relation-ships between them based on fire extinguishing mechanism of water mist and existing literature. Direct influence factors on fire extinguishing performance are analyzed em-phatically by the model of movement, heat transfer and mass transfer of water mist in the flame zone. The results show that the velocity and diameter of water mist entering the flame zone can be determined according to the temperature difference and the height of flame without considering the action of the flame plume. The water mist will enter the flame zone from the top and the periphery of the flame when the plume effect cannot be ignored. And the maximum heat absorption power of the water mist entering through the two ways should be obtained when determining the velocity and diameter of the water mist. This research can serve as a theoretical basis for the design of a water mist fire ex-tinguishing system.

Abstract: In this paper, we presented BlockShare, a blockchain-basedsystem developed to facilitate privacy-preserving data sharing across de-centralized networks. The proposed system enables users to retain controlover their sensitive data while enabling secure, verifiable sharing with au-thorized parties. We implemented an authenticated data structure (ADS)to support decentralized verification and utilized zero-knowledge proofmechanisms to validate conditions without exposing the underlying data.Experimental analysis demonstrated that BlockShare performs efficientlyin constructing data structures, generating proofs, and verifying themwith minimal computational overhead. The platform successfully reducedprivacy risks and enhanced trust in cross-organization data exchanges.

Abstract: The present study evaluates occupational health and safety (OHS) risks at the 400/220/110/20 kV Arad Power Substation, a critical infrastructure node in Romania’s energy network, within the context of industrial development and the need to prevent energy crises. As the demand for electricity grows alongside industrial expansion, substations face increasing operational pressures, making risk management essential for ensuring workforce safety and system reliability. The assessment integrates hazard identification, risk analysis, and mitigation strategies specific to high-voltage environments, including electrical, mechanical, ergonomic, and environmental hazards. Particular attention is given to high-voltage exposure, fire hazards, equipment malfunction, and emergency response readiness. Using a combination of qualitative and quantitative approaches, the study identifies high-risk operations and proposes targeted interventions, such as improved protective equipment, training programs, maintenance protocols, and real-time monitoring systems. The findings underscore that proactive OHS measures not only safeguard personnel but also enhance operational continuity, thereby contributing to regional energy security and supporting industrial growth. By aligning health and safety management with strategic energy planning, the study demonstrates how systematic risk assessment at high-voltage substations can mitigate industrial disruptions and prevent cascading energy crises. The results provide a framework for policymakers, engineers, and OHS professionals seeking to balance workforce protection with energy infrastructure resilience.

Abstract: The widespread use of textual data sanitization techniques,such as identifier removal and synthetic data generation, has raised ques-tions about their effectiveness in preserving individual privacy. This studyintroduced a comprehensive evaluation framework designed to measureprivacy leakage in sanitized datasets at a semantic level. The frameworkoperated in two stages: linking auxiliary information to sanitized recordsusing sparse retrieval and evaluating semantic similarity between orig-inal and matched records using a language model. Experiments wereconducted on two real-world datasets, MedQA and WildChat, to assessthe privacy-utility trade-off across various sanitization methods. Resultsshowed that traditional PII removal methods retained significant privateinformation, with over 90% of original claims still inferable. Syntheticdata generation demonstrated improved privacy performance, especiallywhen enhanced with differential privacy, though often at the cost ofdownstream task utility. The evaluation also revealed that text coher-ence and the nature of auxiliary knowledge significantly influenced re-identification risks. These findings emphasized the limitations of currentsurface-level sanitization practices and highlighted the need for robust,context-aware privacy mechanisms that balance utility and protection insensitive textual data releases.

Abstract: This paper presents an outline of the problems facing the Polish energy sector. It high-lights the significant role of wind energy in the National Power System, while limiting the possibility of installing new wind farms. It is suggested that repowering and ex-tending the operational life of wind turbines will be an important solution to this problem. The possibility of using data from existing turbines to inform operational strategies was analyzed. Historical data was obtained for selected wind turbines and statistically analyzed. The main goal of the study was to develop regression models for wind conditions and electricity production. The best fit between the actual distribu-tions of the analyzed variables and selected theoretical distributions was determined. It was demonstrated that in the analyzed case, the Log-Normal distribution provided a better fit than the Weibull distribution, preferred by the energy industry.

Abstract: Unexploded ordnance (UXO) poses a significant hazard in military training areas. This paper assesses the effectiveness of aerial drone-mounted magnetometry for detecting buried UXO located outside the designated impact zones of the National Training Center (CENAD) of San Gregorio (Zaragoza, Spain), considered the largest maneuver area in Europe. To this end, a high-resolution aeromagnetic survey was conducted using a GEM GSMP-35U proton magnetometer mounted on a hexacopter drone. Data were collected at flight altitudes of 7 m and 2 m above ground level along a grid with 1-m line spacing. For its validation, eleven UXOs were deliberately buried at known coordinates to evaluate the system’s sensitivity and spatial resolution under operational conditions. The results demonstrate the capability of aerial drone-based magnetometry to detect small magnetic anomalies (with amplitudes between 2 and 18 nT) associated with buried UXO in complex environments characterised by high ferromagnetic noise, achieving signal-to-noise ratios greater than 5 (SNR > 5) at 2-m altitude and a geolocation accuracy of approximately 0.5 m. These findings support the use of unmanned aerial magnetometry as a viable tool for identifying hazardous remnants in military training ranges and operational scenarios, enabling coverage of 0.53 ha in less than one hour of effective flight time.

Abstract: In complex energy storage operating scenarios, batteries seldom undergo complete charge–discharge cycles required for periodic capacity calibration. Methods based on accelerated aging experiments can indicate possible aging paths; however, due to uncertainties like changing operating conditions, environmental variations, and manufacturing inconsistencies, the degradation information obtained from such experiments may not be applicable to the entire lifecycle. To address this, we develop a stage-wise state-of-health (SOH) prediction approach that combines offline training with online updating. During the offline training phase, multiple single-cell experiments were conducted under various combinations of depth of discharge (DOD) and C-_rate. Multi-dimensional health features (HFs) were extracted and an accelerated aging probability p_AA was defined. Based on the correlation statistics between HF, k_HF, SOH, and p_AA, all cells in the dataset were divided into general early, middle, and late aging stages. For each stage, cells were further classified by their longevity (long, medium, short), and multiple models were trained offline for each category. The results show that models trained on cells following similar aging paths achieve significantly better performance than a model trained on all data combined. Meanwhile, HF optimization was performed via a three-step process: an initial screening based on expert knowledge, a second screening using Spearman correlation coefficients, and an automatic feature importance ranking using a random forest regression (RFR) model. The proposed method offers the following innovations: (1) The stagewise multi-model strategy significantly improves SOH prediction accuracy across the entire lifecycle, maintaining the mean absolute percentage error (MAPE) within 1%. (2) The improved model provides uncertainty quantification, issuing a warning signal at least 50 cycles before the onset of accelerated aging, thereby enabling early detection of accelerating degradation. (3) Analysis of feature importance from the model outputs allows indirect identification of the primary aging mechanisms at different stages. (4) The model is robust against missing or low-quality HFs—if certain features cannot be obtained or are of poor quality, the prediction process does not fail.

Abstract: Workplace safety and health remain a major global challenge, with work-related accidents and diseases still causing millions of deaths each year despite decades of regulatory, technical and organizational advances. In parallel, the digitalization of Industry 4.0/5.0 is generating unprecedented volumes of safety-relevant data and new opportunities to move from reactive analysis to proactive, data-driven prevention. This review maps how artificial intelligence (AI), with a specific focus on natural language processing (NLP) and large language models (LLMs), is being applied to occupational risk prevention across sectors. A structured search of the Web of Science Core Collection (2013– October 2025), combined OSH-related terms with AI, NLP and LLM terms. After screening and full-text assessment, 126 studies were discussed. Early work relied on text mining and traditional machine learning to classify accident types and causes, extract risk factors and support incident analysis from free-text narratives. More recent contributions use deep learning to predict injury severity, potential serious injuries and fatalities (PSIF) and field risk control program (FRCP) levels, and to fuse textual data with process, environmental and sensor information in multi-source risk models. The latest wave of studies deploys LLMs, retrieval-augmented generation and vision–language architectures to generate task-specific safety guidance, support accident investigation, map occupations and job tasks, and monitor personal protective equipment (PPE) compliance. Together, these developments show that AI-, NLP- and LLM-based systems can exploit unstructured OSH information to provide more granular, timely and predictive safety insights. However, the field is still constrained by data quality and bias, limited external validation, opacity, hallucinations and emerging regulatory and ethical requirements. In conclusion, AI and LLMs should be positioned as human-in-the-loop decision-support tools and outlines a research agenda centered on high-quality OSH datasets, hybrid models integrating domain knowledge and AI, and rigorous evaluation of fairness, robustness, explainability and governance.

Abstract: Early detection of bubble generation from tube arrays in systems such as fast reactor steam generators, Pressurized Water Reactor (PWR) cores, and Liquefied Natural Gas (LNG) regasification units is critical for safety. While various methods have been proposed, they face challenges such as high spatial resolution requirements, rapid response times, and varying strengths and weaknesses, suggesting the need for a combined approach. This study integrates the ultrasonic testing (UT) with Machine Learning (ML) to identify the presence, location, and direction of bubbles within a complex tube array that cause signal attenuation. A Convolutional Neural Network (CNN) successfully achieved 100% identification accuracy. Furthermore, a novel method was developed that uses an autoencoder as a feature extractor, combined with a One-Class Support Vector Machine (SVM) and k-means. This approach achieved high accuracy and a correct decision basis. It also demonstrated strong generalization, successfully detecting anomalies without requiring labels for anomalous data, enabling robust bubble identification.

Abstract: The acceptance or rejection of a measurement is determined based on its associated measurement uncertainty. This decision-making process inherently carries the risk of errors, including the possible rejection of compliant measurements or the acceptance of non-conforming ones. This study introduces a mathematical model for the spatial evaluation of global risk to both producers and consumers, grounded in Bayes' theorem and with the application of a decision rule incorporating a guard band. The proposed model is well-suited for risk assessment within the framework of multivariate linear regression. The model's applicability was demonstrated through an example involving the flatness of the workbench table surface of the CMM. The least-risk direction on the workbench was identified, and risks were calculated under varying selections of the reference planes and differing measurement uncertainties anticipated in future measurement processes. Model evaluation was conducted using performance metrics derived from confusion matrices. The spaces of the most used metrics over the domain limited by the dimensions of the CMM workbench were constructed. Using the tested metrics, the optimal widths of the guard band were determined, which ensures the smallest values of the global producer's and consumer's risk.

Abstract: Corporate bankruptcy prediction has become increasingly critical amid economic uncertainty. This study proposes a novel two-stage machine learning approach to enhance bankruptcy prediction accuracy, applied to Tokyo Stock Exchange-listed companies. First, models were trained using 173 financial indicators. Second, a wrapper-based feature selection process was employed to reduce dimensionality and eliminate noise, thereby identifying an optimal seven-feature set. Two ensemble learning methods, Random Forest and LightGBM, were used. Random Forest correctly predicted 566 bankruptcies using the reduced feature set (88 more than when using all features) compared with 451 by LightGBM (31 more than when using all features). The study also addresses challenges posed by imbalanced data by employing resampling techniques (SMOTE, SMOTE-ENN, and KMeans). Additionally, the need for industry-specific modeling is recognized by constructing models for the six industry sectors. These findings highlight the importance of feature selection and ensemble learning for improving model generalizability and uncovering industry-specific patterns. This study contributes to the field of bankruptcy prediction by providing a robust framework for accurate and interpretable predictions for both academic research and practical applications. Future work will focus on further enhancing prediction accuracy to identify more potential bankruptcies.