Search Results (269)

Rare and safety-critical traffic situations remain challenging for autonomous driving (AD) because they are underrepresented in common training data and may include objects outside standard detector classes. This paper presents a real-time RGB-LiDAR fusion framework for detecting and reacting to rare traffic situations in CARLA (Car Learning to Act), a reproducible simulator for AD research. The approach combines YOLOv8n-based RGB perception, bird’s-eye-view (BEV) LiDAR clustering, decision-level fusion, an interpretable rule-based safety agent with hysteresis, Time-to-Collision (TTC)-aware escalation, and an automatic emergency braking (AEB) override above the CARLA autopilot. Fused observations are classified as semantic–geometric detections, semantic-only detections, or geometric-only obstacle candidates, where unmatched LiDAR clusters are treated conservatively as candidate-level physical evidence rather than confirmed rare objects. The framework was evaluated on three CARLA maps and 3CSim-inspired corner-case scenarios comprising 19,253 frames, with additional weather/lighting stress tests and a public nuScenes mini cross-platform check. On a manually annotated subset of 4800 CARLA frames, corresponding to approximately $24.9\%$ of the recorded CARLA log, the full framework achieved $96.2\%$ precision, $97.3\%$ recall, and a $96.7\%$ F1-score for safety-relevant threat detection. The control experiments show that the fusion-based safety agent reduced unnecessary braking to $1.7\%$ compared with $8.6\%$ for the LiDAR-only baseline and achieved event-level success on the annotated critical intervals. The proposed CPU-only implementation maintained real-time performance, with an average processing time of $34.7\mathrm{ms}$. Full article

Background/Objectives: Hygiene theatre describes a diverse array of cleaning and sanitation protocols (such as the use of disinfectant sprays, or plexiglass dividers) that may provide a false sense of safety/security without actually or meaningfully reducing the risk of transmission of pathogens. Initially viewed as a humorous, but harmless, contrivance, the carbon footprint implications and climate impacts of unnecessary and unhelpful performative clinical activities is increasingly being scrutinized. This study examined primary health care providers’ perspectives on hygiene theatre and how to mitigate or reduce both its prevalence and its impact. Methods: Semi-structured interviews with 17 family physicians, nurses, nurse practitioners, and pharmacists were conducted. Results: The findings suggest that pervasive and persistent hygiene theatrics may reflect primary care providers’ inability to critically self-reflect on routinized clinical practices due to a lack of time, the inaccessibility of clinical evidence, and a lack of workplace supports. Conclusions: Addressing hygiene theatre may benefit from direction, guidance or regulation from external groups such as employers, unions, or licensing bodies. The further education of patients (who may have come to expect these theatrics) may also be necessary to better manage their expectations. Full article

Wide-area visual monitoring of construction sites is constrained by the reliable detection of safety-critical targets that appear small, low-resolution, and weakly textured under elevated or distant camera views. To address this problem, this study proposes Scale-Gated Edge Detection Transformer (SGE-DETR), a safety-oriented end-to-end detector for large open construction scenes. The framework incorporates scale-aware residual edge modulation to preserve weak contours and local structures, density-guided context-adaptive fusion to balance multi-level features according to contextual and edge-density responses, and spatial gated reparameterized feature refinement to suppress redundant background textures. Experiments were conducted on SODA and STWD using COCO-style scale-sensitive metrics and efficiency indicators. On SODA, SGE-DETR achieved AP50, APS, APM, and APL values of 0.8748, 0.2157, 0.4577, and 0.6013, respectively, with 32.5 GFLOPs, 14.5 M parameters, and 83.4 FPS. On STWD, it obtained the highest AP50, APS, APM, and APL among the compared methods, reaching 0.7936, 0.8132, 0.8594, and 0.9253, respectively. Ablation results further showed that the full model improved mAP50 and mAP50–95 over RT-DETR-r18 by 4.15 and 2.93 percentage points while reducing computational complexity. These results indicate that SGE-DETR improves safety-oriented small-object detection and multi-scale robustness while retaining a relatively low parameter count. Full article

Detecting and identifying faults in industrial chemical plants is essential for safe and stable operation, and modern monitoring systems increasingly rely on deep learning to classify faults from multivariate sensor data. A practical obstacle to adoption is trust: most deep-learning diagnosers reach their decisions through a classification layer that operators cannot inspect, making it hard to see how the model maps process signals to a particular fault. This study targets fault diagnosis on the Tennessee Eastman (TE) process, a standard benchmark of simulated chemical-plant sensor data, and asks whether this final decision stage can be made directly inspectable without sacrificing accuracy. We propose CNN–Transformer–KAN (CTKAN), a hybrid model that learns local temporal patterns with a one-dimensional convolutional encoder, captures global inter-time-step dependencies with a Transformer encoder, and classifies faults with a Kolmogorov–Arnold Network (KAN) head whose learnable B-spline activations can be plotted and examined individually, in place of a conventional multi-layer perceptron (MLP). On the TE benchmark, CTKAN attains a Macro-F1 of 91.38 ± 0.26% over ten independent runs, comparable to a CNN + Transformer + MLP ablation (91.21 ± 0.32%) and a capacity-matched MLP-head variant (91.43 ± 0.37%) within seed-to-seed variability. The main finding is therefore not a higher score: at matched capacity the KAN and MLP heads are statistically indistinguishable in accuracy, so the KAN head’s value is to add a directly inspectable view of the classification stage at no measurable accuracy cost, helping process engineers sanity-check how the diagnoser separates faults in safety-critical settings. Full article

Background/Objectives: The Observational Clinical Human Reliability Assessment (OCHRA) system evaluates surgical performance by identifying intraoperative errors, yet evidence on error patterns and procedural safety in laparoscopic cholecystectomy (LC) remains limited. This study aimed to assess LC safety using established parameters and to describe intraoperative errors through the OCHRA system in patients with gallstone disease in Veracruz, Mexico. Methods: An observational, retrospective, analytical study was conducted between January 2022 and March 2025. Surgical videos from 11 surgical teams were reviewed. Intraoperative errors were classified using the OCHRA system across the three key steps of LC, while procedural safety was assessed through achievement of the Critical View of Safety (CVS) using the Doublet Photographic Score (DPS). Comparisons were performed according to the Parkland Grading Scale. Statistical analysis was conducted using SPSS version 26. Results: A total of 106 patients were included (67% women; mean age 45 ± 13 years; BMI 25.1 ± 3.2 kg/m²). Total LC was performed in 95% of cases and subtotal LC in 5%. Parkland grade 3 was the most frequent (32.1%). Overall, 3180 operative steps were evaluated, and 705 errors (22.1%) were identified. Procedural errors predominated across all phases (97–99%), mainly due to step repetition or additional steps, whereas execution errors were uncommon (1–3%). A satisfactory CVS was achieved in 54.7% of cases. No bile duct injuries were observed. Conclusions: The OCHRA system enabled detailed the identification of intraoperative error patterns and their relationship with surgical difficulty. Higher anatomical severity was associated with increased procedural errors and lower rates of adequate CVS achievement. These findings support structured video-based performance assessment as a complementary tool to established safety principles, with the potential to guide targeted training and improve surgical consistency in laparoscopic cholecystectomy. Full article

Accurate multi-horizon Significant Wave Height (SWH) forecasting is vital for offshore safety and efficiency. Beyond scheduling maintenance windows, reliable lead-time predictions provide critical early warnings to protect personnel and high-value assets from hazardous high-wave conditions. However, the non-stationary and multi-scale nature of sea states poses challenges for consistent long-term accuracy. To address this challenge, we propose a robust three-stage framework for decomposition, feature selection, and multi-horizon forecasting. Specifically, Optimal Variational Mode Decomposition (OVMD) is adopted to construct multiscale and multi-view representations of nonlinear SWH sequences, while a Triangulated Maximally Filtered Graph (TMFG) constructs a sparse dependency network to select informative and non-redundant predictors from decomposed components and environmental variables. A hybrid prediction model then combines a Temporal Convolutional Network (TCN) for local multi-scale patterns with a Bidirectional Gated Recurrent Unit (BiGRU) for long-range dependencies. Experiments on real-world buoy observations show that the proposed approach improves accuracy and robustness over commonly used statistical and deep-learning baselines across short-, medium-, and long-term horizons. Ablation studies confirm that integrating modal decomposition with sparse feature selection enhances model robustness, offering reliable decision support for offshore window planning and high-wave condition monitoring. Full article

Background/Objectives: Unanticipated difficult airway remains a critical patient safety concern in perioperative care. Despite routine preoperative assessment, difficult intubation may still occur in patients without obvious high-risk findings. This study aimed to evaluate perioperative factors associated with unanticipated difficult intubation and to examine the relationship between preoperative assessment and intraoperative intubation difficulty in routine clinical practice. Methods: This retrospective case–control study included adult patients undergoing general anesthesia with tracheal intubation between 2015 and 2020 at a tertiary care hospital. Unanticipated difficult intubation was defined as requiring ≥3 intubation attempts without documented preoperative suspicion of difficult airway. Patients with anticipated difficult airway or preoperative mechanical ventilation were excluded. A total of 95 cases and 429 controls were analyzed. Associations were explored using multivariable logistic regression. Results: Among 524 patients, cases more frequently had ASA physical status III and airway/neck/oral deformity. Notably, intubation difficulty became evident only at laryngoscopy, characterized by poorer visualization, increased intubation attempts (median 4 vs. 1), and frequent escalation to video laryngoscopy. Severe laryngoscopic views (Cormack–Lehane grade III–IV: 74.8% vs. 3.0%) were markedly overrepresented among cases. In multivariable analysis, ASA III and airway deformity remained independently associated with unanticipated difficult intubation. The model demonstrated modest discrimination (AUC 0.685). Conclusions: Unanticipated difficult intubation was uncommon but clinically important and frequently became apparent only during airway management. Although several associated factors were identified, routine bedside airway assessment alone may not reliably predict all cases of intraoperative difficult intubation. These findings highlight the limitations of routine bedside airway assessment in identifying all patients who subsequently experience difficult intubation and support the need for improved strategies to identify patients at risk. Full article

Ground deformation monitoring is critical for safety and environmental management in modern mining. Active mining sites are highly exposed to terrain instabilities and subsidence, risking infrastructure integrity, disrupting operations, and posing hazards to communities. In this context, Differential Synthetic Aperture Radar Interferometry (DInSAR) techniques provide an effective and non-invasive tool capable of detecting millimetric surface displacements. This study implements the Small Baseline Subset (SBAS) technique through an open-source workflow based on the Python package hyp3_sbas, enabling semi-automated and reproducible interferometric processing by combining HyP3 with MintPy. The workflow is applied to the Björkdal gold mine (Sweden), a pilot site of the Horizon Europe XTRACT project focused on enhancing resilience in critical raw material supply chains. Integrating Sentinel-1 viewing geometries resolves the true vertical deformation field, yielding an overall mean velocity of −3.99 mm/year across the mining complex, with significant displacement rates concentrated below the 25th percentile (Q1) at −11.07 mm/year. Sector-specific analysis reveals localised subsidence accelerating over underground footprints and tailings storage facilities (mean velocities of −6.56 and −3.98 mm/year; Q1 thresholds near −13.00 mm/year), contrasting with the geomechanical stability observed at the open-pit area (mean: −0.45 mm/year). The proposed open-source framework shows strong potential for operational satellite-based monitoring, supporting predictive maintenance and early-warning strategies for risk management in mining environments while simplifying and standardising the interferometric processing workflow. Full article

Under the Active Vision hypothesis, eye movements are not passive responses to visual stimuli but are actively guided by task demands and internal goals. In driving, scanpaths may therefore reflect an ongoing process of information sampling for risk assessment. However, current computational models often isolate scanpath prediction from risk assessment, overlooking their intrinsic cognitive coupling. In this study, we investigate whether driver scanpaths and traffic risk perception can be jointly modeled within a unified framework. We propose a computational approach based on the introduced Adversarial Inverse Reinforcement Learning (AIRL), where gaze behavior is interpreted as a policy that maximizes a latent safety-related reward. By employing a generator to simulate human-like sequences of fixations and saccades, and a discriminator to approximate the internal reward signal, our framework ensures that generated scanpaths synergistically inform downstream risk perception. To facilitate this research, we constructed the BDDA dataset, aggregating over 13,000 spatio-temporal gaze points with explicit risk annotations to study this joint mechanism. Experimental results indicate that simultaneously modeling the “where” (scanpath dynamics) and the “why” (risk perception) significantly outperforms the compared baseline methods on the proposed BDDA dataset. These findings provide computational evidence for a functional coupling between visual attention and risk perception, supporting the view that eye movements serve as an active mechanism for acquiring task-relevant information in safety-critical environments. Full article

Healthcare quality systems have evolved from narrow inspection and compliance mechanisms into broader, multi-level architectures that combine standards, measurement, organizational learning, patient safety, equity and patient-reported outcomes. Yet the field remains fragmented, with substantial variation in how quality is defined, measured and operationalized across countries and healthcare settings. This narrative review synthesizes major international quality systems and frameworks used in healthcare delivery, examines principal methods for evaluating and improving quality, and critically discusses organizational and policy conditions associated with successful implementation. A purposive review of the seminal conceptual literature and authoritative documents from major international organizations was undertaken to identify cross-cutting themes relevant to hospitals, ambulatory care and health systems. The review shows that influential approaches—including the World Health Organization’s quality and patient safety frameworks, Joint Commission International accreditation, NCQA/HEDIS, the EFQM model, ISO-based management systems, AHRQ quality indicators and OECD performance initiatives such as PaRIS—should be viewed as complementary rather than competing models. Their effectiveness depends less on formal adoption alone than on leadership commitment, workforce engagement, data infrastructure, patient involvement and alignment with financing and regulation. Evidence is strongest for gains in standardization, safety processes, teamwork and selected efficiency outcomes; direct causal effects on patient outcomes remain less consistent, particularly when quality systems become compliance-driven or are insufficiently adapted to local context. Future healthcare quality systems should integrate equity, digital interoperability, AI-enabled learning capabilities, patient-reported measures and continuous improvement while reducing measurement burden and indicator proliferation. Full article

Low-visibility environments induced by sea fog severely constrain the navigational efficiency and safety in narrow waterways, where traditional radar and Automatic Identification Systems (AIS) frequently encounter challenges such as perception blind spots and information lag. To address this critical issue, this study proposes a UAV-based perception and decision-making methodology for main navigational routes in fog, integrating physical priors with unmanned aerial vehicle (UAV) vision. Firstly, a joint physical dehazing and fog-domain adaptive detection network is constructed. This network addresses the overcomes the interference of non-uniform fog through feature-level enhancement, generating a spatio-temporally continuous visibility field and ship probability grids under a bird’s-eye view (BEV). Subsequently, a quantified “Sailability Score” model is established, providing a scientific basis for the dynamic diversion, speed limitation, and safe distance maintenance of main navigational routes. Simulation-based verifications using real-world fog navigation scenarios in the Qiongzhou Strait, coupled with a joint analysis of Vessel Traffic Service (VTS) and AIS data, suggest that at the critical visibility threshold (≤500 m), the proposed method improves the recall rate of long-distance small target detection by approximately 16.2% and reduces the visibility estimation error by 19.3%. Furthermore, the consistency between the proposed Sailability Score and the actual VTS navigation restriction windows reaches 82.1%, exhibiting a conservative preference for safety (i.e., risk preference ratio $\gamma >1$). Additionally, by introducing a temporal anti-jitter mechanism (parameterized by a smoothing window $\Delta t$), the proposed method extends the navigable time window of the main routes by approximately 12.4% while ensuring navigational safety. The simulation results indicate the framework’s potential perception capabilities and engineering applicability, providing reliable technical support for smart shipping and intelligent VTS systems. Full article

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous combustion-derived contaminants that represent a significant cross-cutting threat to human, animal, and environmental health. Viewed through an explicit One Health lens, this review shows how the shared combustion sources, evolutionarily conserved toxicological mechanisms, and food-web linkages connecting environmental contamination to wildlife and human exposure justify an integrated, cross-domain approach to PAH risk assessment and management. PAHs are generated predominantly through incomplete combustion of organic materials and are globally distributed through atmospheric transport, aquatic runoff, and food-web transfer, persisting in soils and sediments for decades. The present review synthesizes current knowledge on PAHs through an explicit One Health lens, examining shared sources, environmental fate, and convergent health effects across species and health domains, while also highlighting the need to move beyond the classical US EPA priority PAHs to include high-molecular-weight PAHs (>302 Da), alkylated homologues, and transformation products such as oxy- and nitro-PAHs. Common pathways such as dietary intake of grilled and smoked foods, inhalation of contaminated air, and occupational exposure create parallel toxicological burdens in both human and wildlife populations, particularly through genotoxic mechanisms mediated by aryl hydrocarbon receptor (AhR) activation and CYP1A1/CYP1B1-catalyzed bioactivation to reactive diol epoxides. The resulting DNA adduct formation links environmental PAH exposure to carcinogenicity, reproductive toxicity, immunosuppression, and developmental impairment across vertebrate species with remarkable mechanistic consistency. Wildlife, especially fish, marine mammals, and seabirds, serve as critical sentinels for environmental PAH contamination, while simultaneously facing direct health impacts on immune function, reproduction, and population viability. Vulnerable human populations, including children, subsistence communities, occupational workers, and residents near combustion-intensive industries, bear disproportionate burdens reflecting underlying environmental justice concerns. Integrated intervention strategies encompassing source control, dietary exposure reduction, site remediation, and coordinated biomonitoring are urgently needed. By incorporating emerging PAH classes with distinct persistence, trophic behavior, and toxicological potency, the One Health paradigm provides a more comprehensive conceptual framework for modern environmental surveillance, food safety, and integrated risk assessment, recognizing that the health of terrestrial and aquatic ecosystems is inseparable from that of the animals and humans within them. Full article

Sensory neurons at barrier tissues were once seen as passive detectors of environmental stimuli. However, in the last five years, increasing evidence has challenged this view, redefining these cells as active immune sentinels that directly affect tissue immunity in the skin, lungs, and gastrointestinal tract. Nociceptors and pruriceptors express various immune-sensing receptors, including Toll-like receptors, cytokine receptors, and alarmin sensors, which allow them to directly detect pathogens, allergens, and tissue damage. When activated, sensory neurons quickly release neuropeptides such as calcitonin gene-related peptide (CGRP), substance P, vasoactive intestinal peptide (VIP), and PACAP (pituitary adenylate cyclase-activating polypeptide), which guide immune cell recruitment, activation, and resolution. Reciprocally, immune-derived mediators, including IL-33, IL-31, thymic stromal lymphopoietin (TSLP), IL-4/IL-13, and TNF-α, modulate neuronal excitability and plasticity, forming bidirectional neuroimmune circuits that control inflammation, host defense, pain, and itch. Landmark studies published in 2024–2025, including neuronal control of gut Treg function and the identification of sensory nerve immune niches, have further refined this framework and revealed tissue-specific circuit specialization. This review synthesizes recent insights from molecular, cellular, and systems levels into the sensory neuroimmune axis, emphasizes its protective versus pathogenic roles, and critically evaluates emerging therapeutic strategies and safety concerns, positioning sensory neuroimmunology as a unifying framework for tissue barrier homeostasis and disease. Full article

Population-based metaheuristic algorithms are widely used for multi-objective city evacuation planning, yet their opaque internal dynamics limit practitioner trust in safety-critical contexts. This study introduces, to the best of our knowledge, the first unified coupling of fractional calculus and fractal analysis with the EvoMapX process-level explainability framework in the context of evacuation optimization. In contrast with classical integer-order EvoMapX paired with exponential moving averages of operator credit, the proposed formulation embeds long-range memory directly into the explainability pipeline through Caputo and Grünwald–Letnikov derivatives. The Operator Attribution Matrix (OAM), Population Evolution Graph (PEG), and Convergence Driver Score (CDS) are extended with fractional-order formulations employing Caputo and Grünwald-Letnikov fractional derivatives with adaptive memory parameters, alongside Mittag–Leffler urgency escalation dynamics. A Fractional-Order PSO variant (FO-EPSO) with segment-specific fractional velocity updates and a fractal fitness landscape analysis module for adaptive parameter tuning are introduced. The framework incorporates nine evacuation-specific operators, a spatial OAM for zone-level attribution, and a multi-stakeholder explanation pipeline. Experiments across 520 disaster scenarios demonstrate that explainability and optimization performance are not mutually exclusive: the EvoMapX-integrated NSGA-II achieved a mean hypervolume of 0.731 versus 0.728 for the standard variant, with less than 5% computational overhead. The OAM revealed disaster-type-specific operator patterns invisible to conventional analysis. Real-world validations on Beijing Chaoyang District and Kigali, Rwanda, confirmed these findings. From an operational standpoint, the most consequential outcome of this work concerns its impact on human decision-makers: a controlled study with 45 emergency-management professionals showed that incorporating EvoMapX explanations cut the time required to commit to an evacuation plan by 24.9%, raised reported decision confidence by 20.3%, and lifted self-assessed algorithm understanding from 18.1% to 78.9% (all p < 0.001). Equally important for real-time disaster response, this entire layer of process-level transparency is delivered with a runtime penalty of under 5% relative to the non-explainable baselines, which we view as a key practical advantage for field deployment. This work establishes fractional-order process-level transparency as a feasible and beneficial paradigm for interpretable optimization in safety-critical domains. Full article

Train door actuation systems are critical safety components in railway vehicles, where early fault detection is essential for safe operation and reduced service disruptions. Conventional monitoring approaches often rely on additional sensors such as infrared detectors or vision systems, which increase system complexity and cost. To overcome these limitations, this study proposes a wavelet-based health monitoring structure for detecting electrical and mechanical faults using motor current signal analysis. A dynamic model of the train door actuation mechanism, including a DC motor, gearbox, and lead screw, was developed in MATLAB/Simulink to simulate conditions such as armature electrical faults, brush wear, increased friction, and lead screw misalignment. Motor current signals were analyzed using the Discrete Wavelet Transform with a Daubechies (db10) mother wavelet to extract diagnostic features based on the L1-norms of wavelet coefficients at levels W8 and W9 along with the motor starting current peak. Experimental validation using a LabVIEW-based test platform demonstrated fault detection accuracy above 96% with a response time below 0.3 s, confirming the effectiveness of the proposed approach for predictive maintenance of railway door systems. Full article