Search Results (2,170)

South Africa’s public hospital system faces mounting pressure from ageing infrastructure, rising patient demand, and constrained maintenance budgets. While significant investment has been directed toward the construction of new healthcare facilities, the diffusion and adoption of advanced technologies within operations and maintenance (O&M) remain uneven and underdeveloped. This misalignment limits the long-term performance, safety, and sustainability of hospital assets. This study investigates technological diffusion within the O&M environment of a newly commissioned 500-bed regional hospital in Durban, KwaZulu-Natal. A qualitative single-case study approach was adopted, drawing on semi-structured interviews with 14 stakeholders across project delivery and facility management functions. Data were analysed thematically to identify systemic patterns and operational constraints. Findings reveal a persistent reliance on manual, reactive maintenance practices, with minimal integration of digital tools, including building management systems, predictive maintenance technologies, and real-time monitoring platforms. Key barriers include unclear institutional roles, inadequate handover processes, limited technical capacity, and the absence of strategic leadership to drive innovation. A critical disconnect was also identified between managerial expectations and operational realities. The study argues that technological adoption in hospital O&M is not merely a technical challenge but an institutional one. It recommends targeted capacity development, structured transition frameworks, and stronger governance mechanisms to enable sustainable digital integration. Full article

Bionic sensors are emerging as powerful analytical platforms driving the development of next-generation detection technologies, particularly for small molecule sensing in complex environmental and biological systems. However, accurate and selective detection of small molecules remains fundamentally challenging due to their low molecular weight, limited structural specificity, and strong interference from complex matrices. This review provides a comprehensive overview of recent advances in bionic sensor technologies, focusing on how the integration of synthetic biology, nanomaterials, and artificial intelligence (AI) addresses these limitations. Key biorecognition elements, including enzymes, antibodies, aptamers, and molecularly imprinted polymers, are examined for their suitability in small molecule sensing applications. Advances in nanomaterials such as graphene, carbon nanotubes, quantum dots, and MXenes are discussed in relation to signal transduction enhancement, sensitivity improvement, and device miniaturization. In parallel, the roles of AI and machine learning in signal denoising, adaptive calibration, and molecular fingerprinting for complex datasets are highlighted. Applications in wearable and implantable biosensors, environmental monitoring, and food safety are analyzed, emphasizing real-time detection of metabolites, pollutants, and toxins. Key challenges associated with AI-driven systems, including scalability, cost, data reliability, and ethical concerns, are also discussed. Emerging trends such as hybrid sensing platforms, self-powered biosensors, and secure data integration frameworks are presented as future directions. This review aims to provide a problem-driven perspective on how next-generation bionic sensors can overcome current limitations and enable robust small molecule detection in real-world applications. Full article

Background: Digital health technologies have expanded access to home fetal heart rate (FHR) monitoring devices, enabling fetal surveillance outside clinical settings. However, evidence on women’s awareness, acceptance, and experiences with these devices remains limited. Objective: To assess awareness, adoption, user experience, perceived reassurance, and attitudes toward home FHR monitoring among pregnant and postpartum women. Methods: A cross-sectional online survey was conducted using a structured questionnaire distributed via Google Forms. Eligible participants were women aged ≥18 years who were currently pregnant or had been pregnant within the previous two years. The survey evaluated awareness and use of home FHR monitoring devices, usage patterns, sources of recommendation and instruction, emotional responses, perceived reassurance, mobile application integration, and overall attitudes. Descriptive statistics and exploratory subgroup analyses were performed. Results: A total of 225 women completed the survey; 166 (73.8%) reported using a home FHR monitoring device during pregnancy. Most users reported positive emotional experiences, with calmness as the most common response. Home monitoring was generally perceived as reassuring, and many participants felt calmer on days of device use. Gynecologists were the primary source of device recommendations and usage instructions. Participants highlighted the importance of professional guidance, clear instructions, and mobile application support. Primiparous women had significantly higher adoption rates than multiparous women (p < 0.001). Conclusions: Home FHR monitoring was widely accepted and commonly perceived as reassuring. These devices may support patient-centered prenatal care when accompanied by appropriate professional guidance. Further prospective studies are needed to assess their clinical utility, safety, and integration into prenatal care pathways. Full article

Slope stability in open-pit mining is not a static condition but evolves continuously as excavation progresses and geomechanical conditions change. In this study, an integrated approach combining ground-based radar monitoring, satellite-based InSAR time-series analysis, and numerical stability modeling was applied to evaluate slope behavior in a large-scale open-pit copper mine with complex geological and structural characteristics. Radar data revealed progressive and episodic deformation concentrated in specific slope sectors, while InSAR observations showed that deformation continued at lower rates after the main movement phase, providing a longer-term perspective of slope response. Stability analyses using limit equilibrium and finite element methods indicate that the slope operates close to a limit equilibrium condition, particularly under saturated scenarios where factors of safety approach critical levels and strain localization becomes more pronounced. The results show a clear link between observed deformation patterns and calculated stability conditions, with structural discontinuities and groundwater playing a dominant role in controlling slope behavior. Based on these findings, an integrated workflow is proposed that links monitoring data with stability assessment, enabling the identification of critical zones and supporting the evaluation of slope conditions during ongoing mining operations. This approach contributes to more reliable decision-making and supports safer and more sustainable open-pit mining practices. Full article

Triethylamine (TEA), a widely used volatile organic compound (VOC), poses severe threats to environmental safety and human health upon accidental leakage, making the development of high-performance TEA detection techniques urgently needed. Herein, we report a Sn-based metal–organic framework (Sn-MOF) constructed from 4,5-dichloroimidazole ligands synthesized via a solvothermal approach. The resulting MOF-derived SnO₂ materials were obtained by calcination at 400–600 °C, yielding SnO₂ with tunable specific surface area and surface defect-site density. Structural and surface characterizations revealed that the materials consist of primary nanoparticles in the range of 10–50 nm, forming aggregated particles of 1–2 µm. The gas sensing performance toward TEA was systematically evaluated. The SnO₂-400 °C sensor exhibited the highest response (S = 85.0) to 100 ppm TEA at 190 °C, with a low detection limit of 1 ppm, superior selectivity, good repeatability, and excellent long-term stability. The observed performance variation was attributed to the combined effects of specific surface area, abundant defect-associated surface sites, and suitable mesoporous structure. This work not only provides a high-performance TEA sensor for industrial and food safety monitoring but also offers a rational strategy for designing MOF-derived metal oxide gas sensors with tailored microstructures and surface defect chemistry. Full article

Rainfall is a critical factor inducing slope instability, and accurate prediction of the factor of safety (FOS) of slopes under rainfall conditions is of paramount importance for disaster prevention and mitigation. Conventional numerical simulation methods incur high computational costs, while individual machine learning models are often insufficient to adequately capture the nonlinear spatiotemporal evolution characteristics of multiple factors under coupled multi-physics fields. To address these limitations, this paper proposes a Transformer–LSTM prediction framework. First, a fluid–structure coupling model for rainfall-affected slopes is constructed using COMSOL, and multi-factor orthogonal experiments are performed to generate multi-dimensional time-series data. Subsequently, a Transformer–LSTM fusion deep learning model is built, in which LSTM is employed to extract the temporal dynamic characteristics of rainfall infiltration, and the self-attention mechanism of the Transformer is leveraged to enhance feature extraction and global dependency modeling of key disaster-causing factors. Experimental results demonstrate that the Transformer–LSTM model significantly outperforms traditional PSO-LSTM, PSO-SVM, and standalone Transformer or LSTM models in terms of both prediction accuracy and generalization capability. Its coefficient of determination (R²) remains above 0.94, and key evaluation metrics—including mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE)—attain the lowest values among the compared models. Furthermore, the SHAP (SHapley Additive exPlanations) interpretability framework is introduced to quantitatively elucidate the model’s predictive decision-making and to establish a physically grounded causal mapping with geotechnical mechanisms. It is confirmed that effective cohesion and slope angle exert a dominant interactive effect on the degradation of slope stability, providing data-driven support for wide-area monitoring of rainfall-induced landslides. Full article

Strong seismic waves induced by drill-and-blast tunnel excavation threaten the structural integrity of adjacent existing tunnels; however, prevailing safety evaluation methods mostly simplify tunnel linings as homogeneous continua, failing to accurately characterize the meso-scale uncoordinated dynamic response between rebar and concrete under blast impact. To fill this research gap, a 1:1 full-scale separated three-dimensional finite element model of reinforced concrete composite linings was established using the LS-DYNA explicit dynamic numerical algorithm, which was verified by previous 1:25 scaled physical model tests. This study systematically quantifies the spatiotemporal evolution of lining dynamic responses under two core parameters—tunnel clear distance (10 m to 60 m) and single-delay detonating charge quantity (10.8 kg to 28.8 kg)—to validate the response differences between materials. It is abstracted that the structural failure is dominated by axial tensile stress, with the embedded rebar being significantly more sensitive to internal stress surges (reaching 3.5 times the peak stress of concrete), while the concrete is more sensitive to particle vibration velocity amplification, a mismatch that is particularly acute within a 30 m clear distance. This study highlights the severe interfacial stress gradient between rebar and concrete, providing an indirect but critical indicator for the potential risk of interface debonding under adjacent blasting, and offers a quantitative theoretical basis for extending safety assessments from macro-surface vibration control to refined meso-scale internal stress monitoring. Full article

In the process of deep hard rock mining, dynamic disasters, such as rockbursts and large-scale collapses, pose a serious threat to the production safety and sustainable development of mines. Microseismic monitoring has been widely used in mines as an efficient disaster monitoring tool. However, microseismic monitoring signals exhibit obvious nonlinear and disordered attributes due to the complex rock behavior, mine structure, and excavation disturbance. This poses great challenges for precise monitoring and forewarning of disasters in deep hard rock mines. This study introduced fractal theory and methods to characterize the spatiotemporal energy information of microseismic monitoring signals. Theoretical analysis, numerical simulation, in situ testing, and field monitoring were integrated to establish a comprehensive spatiotemporal energetic fractal characterization model of microseismic monitoring sources. A scale conversion method for the spatial and energy parameters of microseismic events was developed, and the fractal evolution of microseismic monitoring events induced by deep mining activities was systematically investigated. On this basis, a fractal-based grading forewarning system for deep mines was established, providing theoretical and methodological support for accurate disaster prediction in deep hard rock mines. Full article

Typhoons and extreme rainfall significantly exacerbate engineering risks during deep excavation construction. Based on an asymmetric deep excavation project in Hainan under the influence of Super Typhoon Yagi, this study analyzes the evolution of Soil Mixing Wall (SMW) pile deformation and prestressed anchor cable axial forces through field monitoring. PLAXIS 3D 2023.2.0.1059 finite element software is employed to investigate the deformation response of the supporting structure under the coupled effects of excavation and extreme rainfall, revealing the optimal design for embedment depth under such adverse conditions. The results indicate that the presence of existing buildings leads to asymmetric deformation and pronounced corner effects. The synergistic action between the capping beam and the waler transforms the pile displacement profile from a cantilever mode to a bow-shaped distribution. Parametric analysis determines the optimal embedment depth to be 10.6 m and the critical safety embedment depth to be 7.6 m. Under a 400 mm/d typhoon rainfall condition, the maximum horizontal displacement of the supporting structure increases by 1.6–2.0 mm compared to non-rainfall conditions. With a 3.5 m water head, increasing the embedment depth from 6.1 m to 10.6 m reduces the maximum horizontal displacements on the east, south, and west sides by 98%, 42%, and 10%, respectively. This study provides a theoretical basis and practical reference for embedment depth optimization in typhoon-prone regions. Full article

Against the background of observed climate change, which increases the risk of glacier-system degradation and the formation of hidden crevasses, the development of lightweight, wideband, and highly directional antenna systems has become a key factor in ensuring the safety of logistics operations and enhancing the spatial resolution and interpretability of ground-penetrating radar monitoring of near-surface snow–ice layers. The effectiveness of such systems is largely determined by the characteristics of the antenna unit, including the operating frequency band, gain, radiation pattern, weight, and resilience under extreme climatic conditions. The aim of this review is to provide a systematic analysis of modern Vivaldi antenna designs and Vivaldi-based antenna arrays, as well as to assess their prospects for application in X-band ground-penetrating radar systems for probing Antarctic snow-ice media. The paper considers the main types of ground-penetrating radar (GPR) antennas, their advantages and limitations, substantiates the priority of detecting hazardous near-surface inhomogeneities, and analyzes the capabilities of the X-band for the high-resolution identification of these inhomogeneities. Particular attention is paid to modern modifications of Vivaldi antennas, including antipodal, balanced, director-loaded, metamaterial-based, and array configurations. The analysis shows that Vivaldi antennas represent a promising basis for lightweight, wideband, and directional GPR systems; however, they require further improvement in terms of gain enhancement, sidelobe and back-lobe suppression, radiation-pattern stabilization, and adaptation to Antarctic operating conditions. Future research should focus on the development of adaptive and phased Vivaldi arrays, the use of metamaterials, Electromagnetic Band-Gap/Frequency-Selective Surfaces (EBG/FSS) structures, and director elements, the creation of lightweight, frost-resistant substrate materials, the advancement of multi-polarization multiple-input multiple-output (MIMO) systems, and the integration of antenna arrays with synthetic aperture radar (SAR) processing adapted to a multilayer snow–ice medium. Full article

Background: Artificial intelligence (AI) is increasingly being proposed for postharvest food-safety control of animal-source foods, but its practical value depends on whether models can support real decisions rather than only report high accuracy. Methods: This narrative review used a structured literature mapping of peer-reviewed work, mainly from 2020 to 2025, identified through database searches and citation tracking using combined terms for artificial intelligence, machine learning, animal-source foods, postharvest food safety, slaughterhouse inspection, cold-chain monitoring, traceability, authenticity, HACCP, validation, and regulatory applicability. Results: The most implementation-proximate applications are computer vision prescreening in slaughterhouses and processing plants, sensor- and IoT-based cold-chain surveillance, freshness and adulteration screening, and digital traceability systems. Across these areas, stronger evidence is associated with clearly defined control points, transparent reference methods, external or temporal validation, auditable data flows, and documented human oversight. The main weaknesses are single-site datasets, retrospective designs, incomplete reporting of reference methods, limited workflow testing, and insufficient attention to false alerts, fallback procedures, and governance. Conclusions: AI should be viewed as targeted decision support, not as a replacement for established food-safety control. Future studies should prioritize prospective, multi-site, workflow-embedded validation and show how alerts lead to documented corrective or verification actions. Full article

Accurate prediction of pile-cap strain is crucial for the safety of wind-turbine foundations, yet conventional methods struggle to screen key features from high-dimensional monitoring data and to model the nonlinear coupling between endogenous and exogenous variables, hindering both accuracy and interpretability. To address these limitations, this paper proposes a pile-cap-strain prediction method integrating XGBoost-SHAP feature selection with the TimeXer deep-learning model. XGBoost-SHAP first identifies critical predictors from high-dimensional pile-stress data; the TimeXer model then exploits its endogenous–exogenous fusion mechanism for strain prediction. The results show that XGBoost-SHAP effectively selected 10 key features, of which the upper-middle and middle windward-side stresses (Z1-4A, Z1-5A) contributed over 40% of the explanatory power. This stage performs dimensionality reduction and sensor-importance interpretation, halving the input dimensionality while maintaining accuracy comparable to the full 19-channel input. TimeXer achieved a coefficient of determination (R²) of 0.993 in single-step prediction, comparable to the best-performing baselines, and maintained stable performance over a 120 min multi-step horizon. In a zero-shot cross-site transfer test, TimeXer attained the highest eight-step average R² (0.914) among all models, indicating strong cross-site generalization. Attention-mechanism visualization further suggested consistency between the model’s prediction logic and structural mechanics principles. The proposed framework provides a technical solution combining high accuracy with strong interpretability for wind-turbine foundation health monitoring. Full article

This study investigates the relationship between cardiorespiratory fitness and driving behavior in a highway environment using a driving simulator. A total of 46 young drivers aged 19 to 27 years participated in the experiment. Cardiorespiratory fitness was assessed through the Queen’s College Step Test and heart rate monitoring, allowing participants to be classified into high-fitness and low-fitness groups based on estimated maximum oxygen consumption. Each participant completed three simulated highway driving scenarios under varying traffic and lighting conditions. Driving performance data were continuously recorded, while additional individual and behavioral characteristics were collected through a structured questionnaire. The analysis focused on key performance indicators, including headway distance variability, average speed, and time to collision. Statistical analysis was conducted using regression models. The results indicate that higher physical fitness is associated with greater adaptability in driving behavior, reflected in increased headway variability and slightly higher driving speeds. At the same time, high-fitness drivers exhibited longer time to collision, suggesting improved anticipation and more effective management of traffic conditions. Environmental factors, particularly traffic volume and lighting conditions, remained dominant in shaping driving behavior. Overall, the findings suggest that physical fitness contributes to a more adaptive driving style on highways. By integrating physiological condition into the analysis of driver behavior, this study highlights the importance of considering health-related factors in road safety research and provides insights for developing preventive strategies targeting young drivers. Full article

The massive generation of coal-based solid wastes (CBSWs) poses severe environmental challenges globally, while widespread soil degradation threatens food security and ecosystem stability. This review critically evaluates the technical feasibility and agro-ecological benefits of valorizing CBSWs—including coal gangue, fly ash, gasification slag, and desulfurization gypsum—as soil amendments within a circular economy framework. We systematically examine the physicochemical characteristics of major CBSW types, analyze modification methods that enhance their performance and safety, and assess their multifaceted effects on soil physical structure, chemical properties, nutrient dynamics, heavy metal immobilization, and microbial communities. A dedicated section addresses environmental risks, particularly toxic element leaching, and outlines integrated control strategies from source selection to post-application monitoring. Critical knowledge gaps persist regarding long-term contaminant stability under climate change scenarios, molecular-scale immobilization mechanisms, and economic scalability. Future research must prioritize advanced low-energy modification technologies, robust long-term field studies, and harmonized international regulations. We conclude that with scientifically guided modification and stringent risk management, CBSWs can be transformed into safe, multifunctional soil conditioners, simultaneously addressing industrial waste management and contributing to global restoration of degraded soil health. Full article

Artificial intelligence (AI) is rapidly transforming healthcare through applications in clinical decision support, diagnostic imaging, population health management, and workflow optimization. Despite these advances, real-world deployment continues to expose critical challenges related to safety, bias, transparency, and integration into clinical workflows. Algorithmic bias can exacerbate health disparities, limited explainability may undermine clinician trust, and insufficient validation and post-deployment monitoring can compromise patient safety. Although the World Health Organization (WHO) has established six ethical principles for AI in health, including autonomy, well-being and safety, transparency, accountability, equity, and sustainability, translating these high-level principles into practical and enforceable governance mechanisms remains a persistent challenge. This narrative review synthesizes insights from bioethics, health policy, computer science, and clinical medicine to identify gaps in current AI governance approaches and proposes a lifecycle-aligned governance-by-design framework that operationalizes WHO ethical principles across key stages of the healthcare AI lifecycle, including data collection, model development, validation, deployment, and post-deployment monitoring. The framework integrates concrete governance mechanisms such as consent governance, fairness evaluation, external validation, explainability, clinician oversight, and continuous performance monitoring. Overall, this work advances a practical, lifecycle-integrated approach to AI governance and provides a structured foundation for developing safe, equitable, and trustworthy AI systems in healthcare. Full article