Search Results (183)

The complex environment of electrical work sites presents hazards that are diverse in form, easily concealed, and difficult to distinguish from their surroundings. Due to poor model generalization, most traditional visual recognition methods are prone to errors and cannot meet the current safety management needs in electrical work. This paper presents a novel framework for hazard identification that integrates chain-of-thought reasoning and self-verification mechanisms within a visual-language large model (VLLM) to enhance accuracy. First, typical hazard scenario data for crane operation and escalator work areas were collected. The Janus-Pro VLLM model was selected as the base model for hazard identification. Then, designing a chain-of-thought enhanced the model’s capacity to identify critical information, including the status of crane stabilizers and the zones where personnel are located. Simultaneously, a self-verification module was designed. It leveraged the multimodal comprehension capabilities of the VLLM to self-check the identification results, outputting confidence scores and justifications to mitigate model hallucination. The experimental results show that integrating the self-verification method significantly improves hazard identification accuracy, with average increases of 2.55% in crane operations and 4.35% in escalator scenarios. Compared with YOLOv8s and D-FINE, the proposed framework achieves higher accuracy, reaching up to 96.3% in crane personnel intrusion detection, and a recall of 95.6%. It outperforms small models by 8.1–13.8% in key metrics without relying on massive labeled data, providing crucial technical support for power operation hazard identification. Full article

Industrial heritage sites hold significant historical and architectural value and their attractive urban locations make them frequent targets for adaptive reuse. Yet decades of industrial activity have left hazardous residues embedded in building fabric, posing risks to public health. Current conservation practice rarely incorporates systematic identification and mapping of such contamination, creating a critical gap that can undermine both safety and the authenticity and integrity of historical material layers. This article proposes an interdisciplinary methodological framework for identifying, analysing, and managing contamination in post-industrial heritage. The model extends the Conservation Management Plan (CMP) by integrating chemical and toxicological analyses, GIS-based diagnostics, and ontological data modelling (CIDOC CRM). It supports value-based decision-making by enabling the safe recognition and preservation of historical layers that may contain toxic residues. The framework is being tested at the former Gdańsk Shipyard through integrated historical research, conservation surveys, and laboratory analyses to assess its applicability and scalability. The proposed approach is intended as a transferable tool for managing polluted heritage environments, aligned with SDGs 11 and 12. Full article

Real-time monitoring of hazardous gas emissions in open environments remains a critical challenge. Conventional spectrometers and filter wheel systems acquire spectral and spatial information sequentially, which limits their ability to capture multiple gas species and dynamic dispersion patterns rapidly. A High-Resolution Snapshot Multispectral Imaging System (HRSMIS) is proposed to integrate high spatial fidelity with multispectral capability for near real-time plume visualization, gas species identification, and concentration retrieval. Operating across the 7–14 $\mathsf{\mu }$m spectral range, the system employs a dual-path optical configuration in which a high-resolution imaging path and a multispectral snapshot path share a common telescope, allowing for the simultaneous acquisition of fine two-dimensional spatial morphology and comprehensive spectral fingerprint information. Within the multispectral path, two $5\times 5$ microlens arrays (MLAs) combined with a corresponding narrowband filter array generate 25 distinct spectral channels, allowing concurrent detection of up to 25 gas species in a single snapshot. The high-resolution imaging path provides detailed spatial information, facilitating spatio-spectral super-resolution fusion for multispectral data without complex image registration. The HRSMIS demonstrates modulation transfer function (MTF) values of at least 0.40 in the high-resolution channel and 0.29 in the multispectral channel. Monte Carlo tolerance analysis confirms imaging stability, enabling the real-time visualization of gas plumes and the accurate quantification of dispersion dynamics and temporal concentration variations. Full article

Phase consistency is a critical prerequisite for achieving high-precision time-series InSAR deformation retrieval. However, conventional SBAS-InSAR methods provide only limited consideration of phase consistency during the inversion process. Within the SBAS-InSAR workflow, two principal categories of error sources are primarily responsible for phase inconsistency, manifested as non-zero closure phase (NCP): (1) fading biases introduced during multilooking and filtering prior to phase unwrapping; and (2) unwrapping errors caused by large deformation gradients, low coherence, or inappropriate selection of unwrapping algorithms. To address these issues, this study introduces an improved SBAS-InSAR processing workflow, termed NCP-SBAS, designed to improve the accuracy of deformation field estimation and thereby enhance its applicability to geological hazard monitoring. The key idea of the method is to enforce phase consistency as a constraint, jointly accounting for the spatiotemporal characteristics of fading biases and the valid deformation signals, thereby enabling effective correction of NCP. To evaluate the effectiveness of NCP-SBAS, this study conducted a detailed analysis of deformation differences in Hualong County, Qinghai Province, before and after NCP correction, highlighting the significant advantages of the proposed approach. The results indicate that the influence of fading biases on deformation estimates depends on both the magnitude and direction of deformation, while unwrapping errors primarily lead to an underestimation of deformation. In addition, the study provides an in-depth discussion of how fading biases and unwrapping errors affect landslide monitoring and identification. Full article

Amid global sustainability transitions, campus energy systems confront growing pressure to balance operational efficiency, resilience to extreme weather events, and sustainable development education. This study proposes an artificial intelligence-driven framework for smart campus microgrids that synergistically advances environmental sustainability and disaster resilience, while deepening students’ understanding of sustainable development. The framework integrates an enhanced multi-scale gated temporal attention network (MS-GTAN+) to realize end-to-end meteorological hazard-state recognition for adaptive dispatch mode selection. Compared with Transformer and Informer baselines, MS-GTAN+ reduces prediction RMSE by approximately 48.5% for wind speed and 46.0% for precipitation while maintaining a single-sample inference time of only 1.82 ms. For daily operations, a multi-intelligence co-optimization algorithm dynamically balances economic efficiency with carbon reduction objectives. During disaster scenarios, an improved PageRank algorithm incorporating functional necessity and temporal sensitivity enables precise identification of critical loads and adaptive power redistribution, achieving an average critical-load assurance rate of approximately 75%, nearly doubling the performance of the traditional topology-based method. Furthermore, the framework bridges the divide between theoretical knowledge and educational practice via an educational digital twin platform. Simulation results demonstrate that the framework substantially improves carbon footprint reduction, resilience to power disruptions, and student sustainability competency development. By unifying technical innovation with pedagogical advancement, this study offers a holistic model for educational institutions seeking to advance sustainability transitions while preparing the next generation of sustainability leaders. Full article

Gas leak detection in industrial environments poses critical safety challenges that require algorithms capable of balancing rapid source identification with comprehensive spatial coverage. Conventional approaches using fixed sensor networks provide limited coverage, while manual inspection methods expose personnel to hazardous conditions. This paper presents a novel Gradient-Guided Depth-First Search (GG-DFS) algorithm designed for autonomous mobile robots, which integrates gradient-following behavior with systematic exploration guarantees. The algorithm utilizes local concentration gradient estimation to direct movement toward leak sources while implementing depth-first search with backtracking to ensure complete environmental coverage. We assess the performance of GG-DFS through extensive simulations comprising 160 independent runs with varying leak configurations (1–4 sources) and starting positions. Experimental results show that GG-DFS achieves rapid initial source detection $(9.3\pm 7.3\mathrm{steps};\mathrm{mean}\pm \mathrm{SD})$, maintains 100% coverage completeness with 100% detection reliability, and achieves 50% exploration efficiency. In multi-source conditions, GG-DFS requires 70% fewer detection steps in four-leak scenarios compared to single-leak environments due to gradient amplification effects. Comparative evaluation demonstrates a substantial improvement in detection speed and efficiency over standard DFS, with GG-DFS achieving a composite performance score of 0.98, compared to 0.65 for standard DFS, 0.64 for the lawnmower pattern, and 0.53 for gradient ascent. These findings establish GG-DFS as a robust and reliable framework for safety-critical autonomous environmental monitoring applications. Full article

Endocrine disruptors, including bisphenol A, S, AF, and F, have been demonstrated to exhibit endocrine-disrupting activity. This phenomenon has been associated with a variety of health problems, including (but not limited to) neurological and reproductive disorders. Given the potential hazards, it is essential to have effective tools to assess their toxicity. The nematode Caenorhabditis elegans has become a widely used model organism for studying bisphenols because of its genetic simplicity and the conservation of its fundamental biological processes. This review article summarizes current knowledge of bisphenol toxicity and the use of the model organism C. elegans as a high-throughput system for investigating the toxicological profiles of BPA and its emerging alternatives. Furthermore, we highlight the specific methodologies for assessing the toxic effects of bisphenols in C. elegans. While highlighting its advantages, we critically discuss its limitations, including the absence of specific metabolic organs, which constrain direct extrapolation to mammalian systems. Based on available evidence, we conclude that C. elegans serves as an essential bridge between in vitro assays and mammalian models, offering a powerful platform for the early hazard identification and mechanistic screening of bisphenol analogues. Full article

Background/Objectives: Advances in pediatric oncology have improved survival; however, critically ill children with cancer remain at high risk for adverse outcomes and frequently require admission to the pediatric intensive care unit (PICU). Despite the rising burden of pediatric cancer in Saudi Arabia, data on PICU utilization and outcomes remain limited. This study aimed to describe the clinical characteristics, critical care interventions, and outcomes of pediatric oncology patients admitted to a tertiary PICU and to identify predictors of mortality. Methods: This is a retrospective cohort study was conducted including pediatric oncology patients (<14 years) admitted to the PICU at King Abdullah Specialized Children’s Hospital, Riyadh, between 2015 and 2021. Demographic, oncologic, and clinical variables; admission indications; PRISM-IV scores; and PICU interventions were collected. Predictors of mortality were evaluated using Cox proportional hazards modeling. Results: A total of 126 pediatric oncology patients were admitted to the PICU during the study period. The median age was 6 years (IQR 3–11), and 59% were female. Hematologic malignancies accounted for 63% of admissions. Sepsis (41%) and respiratory failure (21%) were the leading indications for PICU admission. Comorbidities were present in 33% of patients, and 70% had received prior therapeutic interventions, most commonly chemotherapy. Organ dysfunction occurred in 39% of patients, including 32% with multiorgan failure. Mechanical ventilation was required in 35% of patients, vasopressor support in 30%, and dialysis in a smaller proportion. The overall mortality rate was 19%, with more than half of deaths occurring during the PICU stay. Non-survivors had higher rates of comorbidities and invasive organ support, and higher PRISM scores. Mechanical ventilation (HR 3.02; 95% CI 1.16–7.60) and prior therapeutic interventions (HR 3.19; 95% CI 1.24–8.19) were independent predictors of mortality. Conclusions: Pediatric oncology patients admitted to the PICU experience substantial morbidity and mortality, underscoring the need for early risk identification and optimized supportive care. Full article

Wastewater management plays a critical role in advancing the circular economy, as wastewater is increasingly considered a recoverable resource rather than a waste product. This paper reviews physical, chemical, biological, and combined treatment methodologies, highlighting a lack of a holistic framework in current research which includes both the operational phases of wastewater treatment and proper risk analysis tools. To address this gap, an innovative methodological framework for wastewater recovery and risk management within an integrated logistic–production process is proposed. The framework is structured in five steps: description of the logistic–production process, hazard identification, risk assessment through the Failure Modes, Effects, and Criticality Analysis (FMECA), prioritization of interventions using the Action Priority (AP) method, and definition of corrective actions. The application of the proposed methodology can optimize the usage of available resources across various sectors while minimizing waste products, thus supporting environmental sustainability. Furthermore, political, economic and social implications of adopting the proposed approach in the field of energy transition are discussed. Full article

Hazardous gas detection requires portable, low-power sensors with high sensitivity, where micro-heater design is critical for semiconductor metal oxide (SMO) sensors. This study presents a standardized evaluation framework for quantitatively comparing patterned micro-heaters under equal-power conditions, ensuring objective comparison across geometries. Two key metrics—power efficiency and temperature uniformity—were defined, normalized, and integrated into a single optimal score through weighted summation. The framework was validated through coupled electro-thermal simulations and experiments on six geometries, including spiral and meander patterns. Results demonstrated that the framework enables accurate identification of designs combining low power consumption with high temperature uniformity. Notably, the meander-based design showed superior efficiency and uniformity, demonstrating its suitability for practical applications. This framework thus offers a rational tool for micro-heater design, supporting the development of reliable, energy-efficient devices for portable and Internet of Things (IoT) applications. Full article

Landslide risk assessment along high-altitude transportation corridors is critical for infrastructure resilience. This study presents an integrated framework combining Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) deformation data and machine learning (ML) to systematically identify and assess landslide susceptibility along the entire 245.5 km West Kunlun Highway. We first compiled a landslide inventory through visual interpretation and SBAS-InSAR analysis. Subsequently, fourteen causative factors were selected to construct and compare six ML models: random forest (RF), K-nearest neighbours (KNN), artificial neural network (ANN), gradient boosting decision trees (GBDT), support vector machine (SVM), and logistical regression (LR). Research findings indicate that along the Hotan–Kangziva Highway in the Western Kunlun Mountains, there exist 21 potential risk points for small-scale landslides, 12 for medium-scale landslides, and 5 for large-scale landslides, with hazard identification accuracy reaching 80%. The random forest model demonstrated outstanding performance, classifying areas with 5.10%, 4.55% and 4.96% probability as extremely high, high and medium susceptibility, respectively. This work provides a robust methodology and a high-accuracy assessment tool for landslide risk management in the data-scarce Western Kunlun Mountains. Full article

To effectively mitigate risks in highway construction and thereby ensure the sustainable development of the transportation sector, this study identifies 27 risk factors across five dimensions—human–machine–environment–process–management—through a combination of literature review, construction accident case analyses, and expert interviews. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, combined with the Maximum Mean Deviation Entropy (MMDE) approach for threshold determination, quantifies centrality and causality of these factors. An Interpretive Structural Modeling (ISM) is employed to construct a multi-level hierarchical framework. The research reveals that highway construction safety risks follow a seven-tier structure: “risk characterization-process assurance-source governance-driven”. Safety education and regulatory systems serve as fundamental drivers, while hazard identification and mitigation, extreme weather response protocols, and equipment compliance form critical safeguard mechanisms. Building on this framework, the study proposes a risk control pathway of “source governance–process interruption–terminal response”, offering practical recommendations for safety management and providing new perspectives for engineering risk assessment and method optimization. Full article

This paper presents a novel integration of System-Theoretic Process Analysis (STPA) and System Dynamics (SD) for hazard and resilience analysis in safety-critical infrastructure systems. The methodology is applied iteratively to assess the safety and continuity of a hospital’s oxygen supply system, a key element of critical health infrastructure, addressing both technical and managerial factors. STPA identifies unsafe interactions between system components, which are systematically translated into a system dynamics simulation model. This dynamic perspective allows for the exploration of how hazards evolve over time and how control strategies influence overall system resilience. Unlike previous conceptual approaches, this study applies the integrated framework to a real-world incident of oxygen supply failure. The model structure is derived from STPA artifacts and validated using expert input and incident data. Simulation experiments uncovered emergent risk patterns, such as alarm delays, staff stress, and insufficient training, that are not evident through STPA alone. These insights support targeted interventions, including enhanced drill frequency and resource allocation, to strengthen infrastructure resilience. By embedding dynamic simulation within the STPA framework, this research moves beyond static hazard identification to enable scenario-based testing and conditional estimation of system response to support risk-informed decision-making. The resulting methodology is traceable, repeatable, and adaptable, offering a practical and generalizable tool for systemic risk analysis in critical infrastructures. Full article

Water and mud inrush hazards pose significant threats to the safety, environmental stability, and resource efficiency of tunnel construction, representing a critical barrier to the development of sustainable transportation infrastructure. Misjudgment—especially missed detections of severe hazards—can lead to extensive geological disturbance, excessive energy consumption, and severe socio-environmental impacts. However, pre-trained large-scale models still face two major challenges when applied to tunnel hazard classification: limited labeled samples and the high cost associated with misclassifying severe hazards. This study proposes a sustainability-oriented intelligent classification framework that integrates a large-scale pre-trained model with multi-strategy data augmentation to accurately identify hazard levels during tunnel excavation. First, a Synthetic Minority Over-Sampling Technique (SMOTE)-based multi-strategy augmentation method is introduced to expand the training set, mitigate class imbalance, and enhance the model’s ability to recognize rare but critical hazard categories. Second, a deep feature extraction architecture built on the robustly optimized BERT pretraining approach (RoBERTa) is designed to strengthen semantic representation under small-sample conditions. Moreover, a hierarchical weighting mechanism is incorporated into the weighted cross-entropy loss to emphasize the identification of severe hazard levels, thereby ensuring zero missed detections. Experimental results demonstrate that the proposed method achieves an accuracy of 99.26%, representing a 27.96% improvement over the traditional SVM baseline. Importantly, the recall for severe hazards (Levels III and IV) reaches 100%, ensuring zero misjudgment of major hazards. By effectively reducing safety risks, minimizing environmental disruptions, and promoting resilient tunnel construction, this method provides strong support for sustainable and low-impact underground engineering practices. Full article

In the Yangquan coal mining region, China, muffled blasting sounds commonly occur in mine surrounding rocks resulting from instantaneous energy release following the elastic deformation of overlying brittle rock layers; they are related to fracture development. Although these events rarely cause immediate hazards, their acoustic signatures contain critical information about cumulative rock damage. Currently, conventional monitoring of muffled blasting sounds and surrounding rock stability relies on microseismic systems and on-site sampling techniques. However, these methods exhibit low identification efficiency for muffled blasting events, poor real-time performance, and strong subjectivity arising from manual signal interpretation and empirical threshold setting. This article proposes retentive depthwise gated network (RDGNet). By combining retentive network sequence modeling, depthwise separable convolution, and a gated fusion mechanism, RDGNet enables multimodal feature extraction and the fusion of acoustic emission sequences and audio Mel spectrograms, supporting real-time muffled blasting sound recognition and lithology classification. Results confirm model robustness under noisy and multisource mixed-signal conditions (overall accuracy: 92.12%, area under the curve: 0.985, and Macro F1: 0.931). This work provides an efficient approach for intelligent monitoring of coal mine rock stability and can be extended to safety assessments in underground engineering, advancing the mining industry toward preventive management. Full article