Search Results (842)

The mining industry in Ghana, despite its economic significance, continues to grapple with occupational health and safety (OHS) issues, which include but are not limited to high accident rates and a largely reactive approach to safety. In this study, current practices in OHS management are assessed and a model is developed that reflects the interdependencies and associations between workplace hazards, accidents, health effects and preventative actions in a probabilistic fashion. A quantitative analytical design was employed and a sample of 298 workers, safety officers and supervisors from mines were surveyed. The data were analysed using Principal Component Analysis (PCA) to derive latent OHS factors, K-modes clustering and Hierarchical Clustering to classify worker safety profiles, and a Bayesian Network model was employed to investigate probabilistic dependencies. Three major OHS dimensions emerged from PCA: perceived adequacy of safety measures, formal training exposure, and safety resources. Three distinct worker profiles were identified, suggesting that the provision of physical safety equipment does not necessarily reflect perceived operational safety. Moreover, the Bayesian Network model indicated a conditional dependency between workers’ reported health issues and formal accident reporting, and that high hazard environments more than double the probability of an accident occurring (from 0.216 to 0.453). The results indicate that the industry is now operating in an incident-based manner. In order to mitigate the likelihood of accidents, management needs to move towards anticipatory safety management systems, which involve proactive health monitoring, equipment maintenance, and implementation of safety policies in practice. Full article

Roadways serving island longwall panels are highly susceptible to severe asymmetric deformation under extreme eccentric loading from multiple adjacent goafs. To address the difficulties in characterizing the surrounding rock load imbalance and the high false-negative rates of conventional algorithms under extremely imbalanced monitoring data, an intelligent assessment method integrating spatially asymmetric physical features with cost-sensitive learning was developed. Implicit equation analysis and numerical simulation clarified the mechanical mechanism by which principal stress axis deflection induces butterfly-shaped asymmetric rotational failure, enabling the construction of dimensionless integrated asymmetry and structural transfer asymmetry coefficients. Reconstruction of the cost-sensitive objective function increased the recall rate for hazardous samples from 37.5% (baseline model) to 92.2%, while maintaining a precision of 96.7%. Following the field implementation of a three-tier differentiated roadway control scheme, the integrated asymmetry coefficients at critically eccentrically loaded stations (i.e., Station 12 and Station 07) remained below 0.2 during the monitoring period, enabling real-time intelligent perception and proactive stability control of roadways subjected to complex eccentric loading. Ultimately, this study confirms the viability of integrating physics-informed features with machine learning, demonstrating significant potential for advancing the transition toward intelligent and proactive safety management in complex underground construction. Full article

Background: The emergence of oral anticancer medications (OAMs) has increased demand for specialized clinical pharmacy services (CPSs). Concurrently, Canadian cross-sectional data highlights undergraduate education gaps, with fewer than 14% of community pharmacists reporting adequate training on OAM therapies. We designed, implemented, and evaluated a pharmacist-led Oral Anticancer Medication Clinical Co-op Program (OAMCCP) to bridge this experiential education gap while cost-effectively expanding institutional capacity. Methods: Launched in Fall 2022, the OAMCCP integrated one PharmD student per 16-week term into a multidisciplinary oncology specialty pharmacy. Trainees executed core ambulatory oncology clinical pharmacy key performance indicators (AOcpKPIs), including Best Possible Medication Histories (BPMHs), drug–drug interaction (DDI) screenings, and proactive adherence and toxicity follow-up. Student competency was verified by a licensed oncology pharmacist to maintain patient safety. Impact was quantified via self-reported task logging over 15 weeks, standardized 10-point patient satisfaction surveys, payroll expenditure comparisons, and student testimonials. Results: Trainees completed 673 clinical tasks (~45 tasks/week), contributing 16.2 h of direct clinical support weekly—effectively adding +0.43 full-time equivalent (FTE) to service capacity. Mean patient satisfaction was 9.5/10 (n = 29), with students successfully managing inquiries in 96.5% of encounters. Financially, a 1.0 FTE student (44,700 CAD/year cost) captured clinical capacity valued at 69,300 CAD/year (0.43 FTE pharmacist equivalence). Conclusions: The OAMCCP resolves experiential training gaps while presenting a safe, scalable, and financially viable human resource framework that expands oncology pharmacy services. Full article

Background/Objectives: The optimal management of periodontal treatment in patients receiving direct oral anticoagulants (DOACs) remains poorly established, and bleeding concerns may lead clinicians to delay or limit treatment. We evaluated the clinical efficacy and safety of nonsurgical periodontal treatment in patients receiving DOAC therapy. Methods: This retrospective study included 67 patients receiving continuous DOAC therapy (edoxaban, apixaban, dabigatran, or rivaroxaban) who underwent nonsurgical periodontal treatment without drug discontinuation. Pocket probing depth, bleeding on probing, the periodontal inflamed surface area (PISA), the periodontal epithelial surface area (PESA), and serum inflammatory cytokine levels were evaluated. A ligature-induced-periodontitis mouse model was also used to assess the effect of apixaban on alveolar bone loss. Results: Nonsurgical periodontal treatment significantly reduced the PISA (42.9%) and PESA (10.8%) in the overall DOAC cohort, with no postoperative bleeding complications. Significant reductions in periodontal parameters were seen in the edoxaban (PISA: 44.2%, PESA: 10.9% reduction), apixaban (PISA: 45.6%, PESA: 11.7% reduction), and dabigatran (PISA: 61.3%, PESA: 16.1% reduction) groups, with no significant changes in the rivaroxaban group. Baseline PISA was positively correlated with baseline serum levels of interleukin-4 (r = 0.33), interleukin-1 beta (r = 0.29), interleukin-6 (r = 0.25), and interleukin-10 (r = 0.30). In the mouse model, apixaban administration did not aggravate ligature-induced alveolar bone loss. Conclusions: Basic periodontal treatment appears to be safe and effective for patients prescribed DOACs and may reduce local periodontal inflammation, resulting in systemic inflammatory burden. These findings support proactive periodontal intervention in patients receiving DOAC therapy, regardless of the specific anticoagulant used. Full article

Background and Objectives: Antibody–drug conjugates (ADCs) have become a major therapeutic platform in breast cancer and other solid tumors. Trastuzumab deruxtecan (T-DXd), trastuzumab emtansine (T-DM1), and sacituzumab govitecan (SG) differ substantially in antibody target, linker, payload, drug-to-antibody ratio, and bystander effect, resulting in heterogeneous pulmonary and cardiac toxicity profiles. This narrative review critically compares interstitial lung disease (ILD)/pneumonitis and cardiotoxicity associated with these three agents, aiming to prevent inappropriate extrapolation of toxicity algorithms and to provide a practical, agent-specific framework for multidisciplinary care. Materials and Methods: A targeted narrative search of PubMed/MEDLINE, Google Scholar, ClinicalTrials.gov, regulatory product information, and oncology/cardio-oncology guidance was performed and updated on 24 August 2026. Priority was given to regulatory documents, pivotal trials, pooled safety analyses, real-world cohorts, systematic reviews, and multidisciplinary recommendations. Pharmacovigilance data and case reports were included only to characterize rare events. Results: T-DXd is associated with a clinically important ILD/pneumonitis risk (approximately 12–15% in pooled analyses), predominantly grade 1–2 but occasionally fatal, requiring proactive surveillance, immediate interruption for suspected disease, and grade-directed corticosteroid therapy. T-DM1 shows a low but established pneumonitis incidence of approximately 1%, with permanent discontinuation recommended upon diagnosis. SG-related pneumonitis is rare and incompletely defined, without a T-DXd-like surveillance mandate. Both T-DM1 and T-DXd retain trastuzumab-derived cardiac monitoring requirements; symptomatic heart failure remains uncommon, although protocol-defined LVEF declines appear more frequent with T-DXd. SG lacks an established cardiomyopathy signal. Conclusions: Cardiopulmonary toxicity of ADCs is agent-specific rather than a class effect. Monitoring intensity, diagnostic thresholds, and management pathways must be tailored to the individual drug, regimen, indication, dose, patient comorbidity, and prior therapy. Close collaboration among oncology, radiology, pulmonology, and cardio-oncology is essential to preserve both treatment efficacy and patient safety. Full article

The deployment of intelligent automated vehicles (AVs) introduces in-cabin hazards that have received limited systematic attention: entrapment, crushing, and pinch-point injuries produced by powered mechanisms such as folding electric seats. Children, elderly occupants, and disabled persons are disproportionately exposed, yet no composite, human-centered metric exists to evaluate the protective performance of these mechanisms holistically. This paper introduces the Human-Centered Smart Safety Sustainability Index (HS³I), a five-dimensional framework integrating Human–Machine Interaction Quality, Proactive and Predictive Safety Performance, Perceived Psychological Safety, Operational Reliability and Risk Mitigation, and Social Equity and Accessibility in Safety. A Vulnerable Human Factor (VHF) parameter enables adaptive threshold scaling. A four-sensor fusion architecture embedded in a five-layer decision stack was evaluated through MATLABR2026a simulation of a child-occupant entrapment scenario, augmented by Monte Carlo sensitivity examination (27,000 runs) and biomechanical benchmarking. The final HS³I score is 0.898 (95% confidence interval [0.873, 0.923] from 27,000 Monte Carlo simulation runs). Full article

To achieve proactive preventive maintenance of green and low-carbon infrastructure, this study systematically investigated the long-term durability and resistance degradation of prefabricated recycled aggregate concrete (RAC) bridge components. A 80-month multi-field coupled damage experiment under sustained flexural loading, natural atmospheric exposure, and chloride drying-wetting cycles was conducted, and an in-situ physical exposure and multi-source data-driven Support Vector Regression (SVR) dynamic surrogate model was established. Results indicate a prominent time-dependent ebb-and-flow mechanism of degradation drivers: environmental and stress boundaries dominate the early stage, whereas the material replacement rate ($R_r$) surges to become the absolute dominant driving variable (36.8%) in the ultra-long term ($t=80 \mathrm{months}$), proving the cumulative dominance of recycled aggregates. Concurrently, the residual capacity exhibits a distinct two-stage decay characterized by a 10% critical reinforcement mass loss threshold, beyond which the degradation rate of RAC100 accelerates to 1.63 times that of conventional concrete. Driven by the multi-stage injection of in-situ experimental inspection data (surface crack profiling, 2D spatial chloride profiles, and rebar mass loss), the SVR network successfully achieves a collapse-like convergence of the remaining useful life (RUL) confidence interval, precisely locking the RUL of the RAC100 component at 34.5 years within a 1.4-year error margin. This framework provides critical algorithmic support for the life-cycle safety paradigm shift in low-carbon structures. Full article

Proactive lane-change risk assessment requires estimating whether an intended maneuver will lead to unsafe interactions before the maneuver is completed. This is challenging in naturalistic driving data because actual crashes are extremely rare, and binary collision labels provide little discriminative information for learning risk. We propose IntentDiff, an intent-conditioned diffusion framework for proactive lane-change risk assessment. The framework uses predicted future trajectories as the basis for risk estimation. A vectorized scene context learning module combines a VectorNet backbone with a Vector Quantized Variational Autoencoder (VQ-VAE) to map agent–map interactions into discrete intent codes. These codes organize complex traffic situations into interpretable intent prototypes and provide semantic guidance for trajectory generation. Conditioned on the learned intent code, a diffusion model generates kinematically consistent multimodal trajectories of the target vehicle. On the forecast trajectories, Monte Carlo rear-end risk is evaluated against the four bounding vehicles and fused into a Lane-Change Risk Index (LCRI). On the highD dataset, the framework attains an average displacement error of 0.42 m over a 5-s horizon. The forecast-based LCRI agrees closely with the index computed from realized future trajectories, indicating that most high-risk lane changes can be identified before the maneuver is completed. Grouping LCRI by intent code further reveals systematic variation in risk across lane-change maneuvers, suggesting that the learned codebook captures risk-relevant interaction patterns in addition to maneuver semantics. Full article

Purpose: Excessive drill advancement after cortical breakthrough is a potential safety concern in orthopaedic procedures. We developed a data-driven approach to estimate the drill-tip position relative to the far cortex prior to breakthrough using time-series thrust force and spindle torque signals. Methods: Drilling experiments were performed on 268 porcine cortical bone specimens at a constant feed rate of 0.5 mm/s. A long short-term memory network was trained to estimate the drill-tip position from filtered force and torque signals. The reference position was derived from breakthrough timing confirmed by high-speed imaging and the programmed feed rate. Performance was evaluated using mean absolute error within the −2 to +2 mm peri-breakthrough interval. Two post hoc analyses examined whether model performance exceeded an elapsed-time baseline and whether pre-breakthrough force patterns were more consistent when expressed relative to breakthrough position than to drilling onset time. Results: The combined-input LSTM achieved an MAE of 0.20 mm, compared with 0.23 mm for force alone and 0.24 mm for torque alone. Among the representative architectures evaluated, LSTM showed the lowest regression error. A signal-blind time-only baseline yielded an MAE of 0.54 mm. The association between cortical thickness and force-decline onset was weaker when expressed in spatial coordinates relative to breakthrough than when expressed as time from drilling onset (R² = 23% vs. 74%). These findings suggest that force and torque signals contained information associated with proximity to breakthrough beyond that provided by average drilling duration alone. Conclusion: Converting sensor-derived resistance patterns into spatially anchored positional information may support proactive strategies such as controlled deceleration before penetration. The proposed approach represents a step toward exemplifying the emerging concept of surgeon-assisting Physical AI. Full article

Surface deformation induced by underground coal mining is characterized by strong nonlinearity and spatial heterogeneity, which complicates early warning and ecological assessment. While InSAR-driven data assimilation models and remote sensing-based ecological indices are widely used for long-term monitoring, three fundamental limitations remain unresolved: (1) severe spatial imbalance in deformation samples biases data-driven models toward mean-reverting predictions, (2) recursive multi-step forecasting accumulates errors, leading to instability in long-horizon extrapolation, and (3) in ecological monitoring, vegetation resilience further induces a multi-year observation lag, resulting in a “pseudo-stable” bias in optical indicators. To address these issues, this study proposes an unified framework integrating multi-step deformation prediction and ecological time-lag analysis. Taking the Datong Coalfield as the study area, we utilized 231 Sentinel-1A images from March 2017 to December 2024 for SBAS-InSAR deformation inversion. A spatial stratified sampling strategy is used to extract 5894 representative points. A 24-step backward and 15-step forward windows were reconstructed to systematically compare six predictive models. Simultaneously, the Remote Sensing Ecological Index (RSEI) derived from Landsat data is used for cross-lagged analysis. The results demonstrate that: (1) The maximum deformation rate reached −276.75 mm/year, with cumulative subsidence exceeding −2000 mm. (2) At 3-step short-term forecasting, all models proved robust, with LSTM performing best (RMSE = 5.78 mm). At 15-step extreme extrapolation, however, traditional recursive models diverged significantly (Kalman, RMSE = 45.70 mm), whereas N-BEATS maintained stability and effectively mitigated temporal error cascades with an RMSE of 17.98 mm. (3) The core collapse zone exhibited concurrent ecological degradation (Lag 0), while the marginal basin presented a hidden degradation period of one to two years. It provides reliable scientific support for precise tracking and proactive safety management in complex mining areas. Full article

Background: The rapid integration of Artificial Intelligence (AI), specifically Clinical Decision Support Systems (CDSS), into healthcare offers substantial efficiency but introduces critical ethical and legal challenges, particularly the perpetuation of systemic bias against older adults (“Digital Ageism”). While technological advances may improve care, they can violate fundamental bioethical principles when models are trained on unrepresentative data. Aim: This article argues that traditional clinical risk-management models are structurally insufficient to address opaque algorithmic bias and presents a conceptual, multidimensional governance framework designed to prevent the codification of human ageism into AI infrastructure. Methods: Drawing on systemic failures observed during the COVID-19 pandemic, the normative model integrates legal and governance standards aligned with the EU AI Act, Explainable AI (XAI) tools, and a three-phase implementation protocol. Results: To illustrate potential application without overburdening medical staff, the article introduces a theoretical Targeted Escalation Protocol and an Autonomous High-Load Safety Mode. The latter applies deterministic hardcoded constraints to contain age-dominant outputs during acute surges while preserving attending-clinician authority. The framework is explored through an Intensive Care Unit (ICU) thought experiment. Conclusions: The framework provides a structured roadmap for policymakers, ethicists, and healthcare administrators to move from reactive defensive medicine toward proactive ethical safety, safeguarding the dignity of the aging population while aiming to mitigate institutional legal exposure. Full article

High-risk industries (HRI) face persistent safety challenges due to complex environments and multi-factor risks. Traditional manual monitoring is inefficient and reactive. While computer vision (CV) and deep learning (DL) have enabled automated risk perception, existing research lacks systematic review of the transition toward risk cognition. This systematic review, following PRISMA 2020 guidelines, searched Web of Science, Scopus, and IEEE Xplore for studies published from January 2021 to December 2025. After two-stage screening, 108 eligible studies were included. These studies span construction, mining, oil and gas, petrochemical, rail, energy, and maritime industries, with perception-layer applications predominating while cognition and early warning layer studies remain limited. We propose a “perception–cognition–early warning” framework to map the evolution from data to information, knowledge, and actionable decisions. Our findings reveal that visual perception technologies (e.g., Personal Protective Equipment (PPE) detection, object tracking) have matured, but significant bottlenecks persist in multimodal information fusion, knowledge reasoning, and decision-making. Achieving a true cognitive leap requires multimodal semantic alignment, scene graph construction, and causal inference. The proposed framework enables practitioners to design cognitive safety vision systems with scene understanding and interpretable decision-making capabilities. Key implementation strategies include addressing challenges in few-shot learning, cross-domain generalization, and human–machine collaboration. By integrating AI-driven perception, cognitive reasoning, and proactive intervention, this review supports the United Nations Sustainable Development Goals. Relevant goals include Goal 3 (health and well-being), Goal 8 (decent work), and Goal 9 (industry and innovation). Full article

Background: Mental health issues among university students are a growing global concern, including in Malaysia. This exploratory study aims to examine mental health support systems in Malaysian universities using a dyadic perspective, capturing both students’ and counsellors’ experiences. This addresses a key gap in the literature, which has largely relied on single-perspective accounts. Methods: A qualitative exploratory design was employed using purposive sampling. Semi-structured interviews were conducted with 15 students receiving mental health support and 10 university counsellors. The data were analysed using hybrid deductive–inductive thematic analysis. Results: Three themes emerged from the findings. The first theme showed that students sought support once distress affected their daily functioning, valuing trust, safety, and emotional guidance, while counsellors described a parallel process of assessing student needs and facilitating longer term recovery. The second theme identified barriers shared by both groups, namely limited awareness of services and stigma, alongside additional constraints reported only by counsellors, including staffing shortages, unclear referral pathways, and limited institutional support. The third theme centred on strategies for improvement, including early assessment, awareness campaigns, digital accessibility, resilience building, and stronger collaboration among students, counsellors, and university stakeholders. Conclusions: This study contributes to the literature by integrating three complementary theoretical perspectives to explain university students’ mental health support experiences, support networks, and help-seeking behaviour. The findings also provide practical guidance for university administrators, counsellors, and policymakers seeking to develop more accessible, proactive, and student-centred mental health support systems. As the study is based on qualitative, cross-sectional data, the findings should be interpreted as indicative rather than causal. Nonetheless, they suggest that strengthening such initiatives may contribute to more supportive and responsive mental health provision within higher education institutions. Full article

Aviation ground safety and protective devices are critical for flight safety; however, their unintentional retention on aircraft after maintenance remains a persistent risk. Existing deep learning-based approaches for aviation safety have predominantly followed a reactive paradigm, detecting FOD on runways or inspecting the aircraft for inadvertently retained tools post-maintenance. In contrast, this paper advocates a proactive philosophy: using a neural network to recognize and inventory all ground safety and protective devices immediately after maintenance closure, thereby preventing retention incidents at their source. However, realizing this proactive verification is technically challenging—object detection for these devices often suffers from loss of fine-grained detail due to downsampling and inherently sparse semantic information of the targets. To this end, we propose an Information Retention and Feature Screening Synergistic Network (RS-Net) grounded in information bottleneck theory. The network comprises a main branch that enhances discriminative features through attention-guided screening, and an auxiliary branch, used only during training, that preserves fine-grained spatial details via information-retentive convolutions. A Dual-State Region Refinement Module (DRM) provides configurable support for both branches, decoupling the conflicting objectives of background compression and detail preservation. Experiments on a self-constructed dataset collected from real airline maintenance operations demonstrate that RS-Net substantially outperforms the strong YOLOv9 baseline, achieving gains of 4.531% in F1-score, 2.533% in mAP_0.5, and 1.429% in mAP_0.5:0.95. Cross-dataset experiments further validate its strong generalization capability. Full article

Safety hazards in coal mines are characterized by significant spatiotemporal heterogeneity, dynamic evolution, and multi-actor coupling. Traditional safety management models, which center on periodic inspections and accident rectification, struggle to achieve proactive risk identification and full-process control. To address this issue, this paper proposes a multi-scale, collaborative “four-in-one” safety management framework oriented toward the hazard lifecycle, based on the spatiotemporal evolution patterns of safety hazards. This framework integrates systems safety theory with the safety philosophy of socio-technical systems, viewing safety hazards as an evolutionary process shaped by the combined effects of spatial exposure, human behavior, organizational management, and dynamic states. It establishes a comprehensive safety governance system comprising precise risk identification, active personnel participation, closed-loop accountability governance, and intelligent dynamic feedback. By integrating the “Area–Point–Number” risk classification method; the “Two-way Risk Purchasing” incentive mechanism; the “Six-level, Six-step, and Three-chain” closed-loop management model; and the Hazard Alert System, the framework achieves the coordinated optimization of risk identification, hazard management, and information feedback. Application validation based on safety hazard data from a coal mine between 2017 and 2020 demonstrates that this method enhances the ability to identify potential risks and effectively reduces major hazard types, such as management deficiencies, unsafe behaviors, and unsafe conditions. The research findings indicate that this framework overcomes the limitations of traditional safety management—such as a single-entity approach, static inspections, and passive responses—and facilitates a shift in coal mine safety governance from hazard control to risk prevention and from manual, experience-based management to intelligent, collaborative decision-making, thereby providing a new theoretical approach for enhancing the safety resilience of complex coal mine production systems. Full article