Search Results (832)

This study investigates the hydrodynamics of the net system of the bottom-mounted aquaculture farms located in the Bohai Sea, addressing the growing demand for high-quality aquatic products and the limitations of coastal aquaculture. Based on the validation part, the established lumped-mass method integrated with the finite element method ABAQUS/AQUA was employed to evaluate the structural responses of the net system with three arrangement schemes under diverse environmental loads. The hydrodynamic loads on net twines are modeled with Morison formulae. With the motivation of investigating the trade-offs between volume expansions, load distributions, and structural reliabilities, Scheme 1 refers to the baseline design enclosing the basic aquaculture volume, while Scheme 2 targets to increase the aquaculture volume and utilization rate and Scheme 3 seeks to optimize the load distributions instead. The results demonstrate that Scheme 1 provides the optimal balance of structural safety and functional efficiency. Specifically, under survival conditions, Scheme 1 reduces peak bottom tension rope loads by 14% compared to Scheme 2 and limits maximum netting displacement to 4.0 m. It is 21.3% lower than Scheme 3, of which the displacement is 5.08 m. It has been confirmed that Scheme 1 effectively minimizes collision risks, whereas the other schemes exhibit severe collisions. Scheme 1 trades off maximum volume expansion for optimal load management, minimal deformation, and the highest overall structural reliability, making it the recommended design. These findings offer valuable insights for the design and optimization of net systems in offshore aquaculture structures serviced in comparable offshore regions. Full article

The prediction of stock prices is challenging due to their volatility, irregular patterns, and complex time-series structure. Reliably forecasting stock market data plays a crucial role in minimizing financial risk and optimizing investment strategies. However, traditional models often struggle to capture temporal dependencies and extract relevant features from noisy inputs, which limits their predictive performance. To improve this, we developed an enhanced recursive feature elimination (RFE) method that blends the importance of impurity-based features from random forest and gradient boosting models with Kendall tau correlation analysis, and we applied SHapley Additive exPlanations (SHAP) analysis to externally validate the reliability of the selected features. This approach leads to more consistent and reliable feature selection for short-term stock prediction over 1-, 3-, and 7-day intervals. The proposed deep learning (DL) architecture integrates a temporal convolutional network (TCN) for long-term pattern recognition, a gated recurrent unit (GRU) for sequence capture, and multi-head attention (MHA) for focusing on critical information, thereby achieving superior predictive performance. We evaluate the proposed approach using daily stock price data from three leading companies—HDFC Bank, Tata Consultancy Services (TCS), and Tesla—and two major stock indices: Nifty 50 and S&P 500. The performance of our model is compared against five benchmark models: temporal convolutional network (TCN), long short-term memory (LSTM), GRU, Bidirectional GRU, and a hybrid TCN–GRU model. Our method consistently shows lower error rates and higher predictive accuracy across all datasets, as measured by four commonly used performance metrics. Full article

Background: Autologous tooth-derived grafts have recently gained attention as an innovative alternative to conventional biomaterials for alveolar ridge preservation (ARP) and augmentation (ARA). Their structural similarity to bone and osteoinductive potential support clinical use. Methods: This systematic review was conducted according to PRISMA 2020 guidelines and registered in PROSPERO (CRD420251108128). A comprehensive search was performed in PubMed, Scopus, and Web of Science (2010–2025). Randomized controlled trials (RCTs), split-mouth, and prospective clinical studies evaluating autologous dentin-derived grafts were included. Two reviewers independently extracted data and assessed risk of bias using Cochrane RoB 2.0 (for RCTs) and ROBINS-I (for non-randomized studies). Results: Nine studies involving 321 patients were included. Autologous dentin grafts effectively preserved ridge dimensions, with horizontal and vertical bone loss significantly reduced compared to controls. Histomorphometric analyses reported 42–56% new bone formation within 4–6 months, with minimal residual graft particles and favorable vascularization. Implant survival ranged from 96–100%, with stable marginal bone levels and no major complications. Conclusions: Autologous tooth-derived biomaterials represent a safe, biologically active, and cost-effective option for alveolar bone regeneration, showing comparable or superior results to xenografts and autologous bone. Further standardized, long-term RCTs are warranted to confirm their role in clinical practice. Full article

Background: Elite athletes are frequently subjected to high-intensity training regimens, which can result in cumulative physical stress, overtraining, and potential health risks. Monitoring autonomic responses to such load is essential for optimizing performance and preventing maladaptation. Objective: The present study aimed to assess changes in autonomic regulation immediately and two hours after training in athletes, using an integrated framework (combining time- and frequency-domain HRV indices with nonlinear and recurrence quantification analysis). It was investigated how repeated assessments over a 4-month period can reveal cumulative effects and identify athletes at risk. Special attention was paid to identifying signs of excessive fatigue, autonomic imbalance, and cardiovascular stress. Methods: Holter ECGs of 12 athletes (mean age 21 ± 2.22 years; males, athletes participating in competitions) over a 4-month period were recorded before, immediately after, and two hours after high-intensity training, with HRV calculated from 5-min segments. Metrics included HRV and recurrent quantitative analysis. Statistical comparisons were made between the pre-, post-, and recovery phases to quantify autonomic changes (repeated-measures ANOVA for comparisons across the three states, paired t-tests for direct two-state contrasts, post hoc analyses with Holm–Bonferroni corrections, and effect size estimates η²). Results: Immediately after training, significant decreases in SDNN (↓ 35%), RMSSD (↓ 40%), and pNN50 (↓ 55%), accompanied by increases in LF/HF (↑ 32%), were observed. DFA α1 and Recurrence Rate increased, indicating reduced complexity and more structured patterns of RR intervals. After two hours of recovery, partial normalization was observed; however, RMSSD (−18% vs. baseline) and HF (−21% vs. baseline) remained suppressed, suggesting incomplete recovery of parasympathetic activity. Indications of overtraining and cardiac risk were found in three athletes. Conclusion: High-intensity training in elite athletes induces pronounced acute autonomic changes and incomplete short-term recovery, potentially increasing fatigue and cardiovascular workload. Longitudinal repeated testing highlights differences between well-adapted, fatigued, and at-risk athletes. These findings highlight the need for individualized recovery strategies and ongoing monitoring to optimize adaptation and minimize the risk of overtraining and health complications. Full article

Controlled demolition is a critical engineering practice that enables the safe and efficient dismantling of structures while minimizing risks to the surrounding environment. This study presents, for the first time, a detailed, structured framework for understanding the fundamental principles of controlled demolition by outlining key procedures, methodologies, and directions for future research. Through original, carefully designed charts and full-scale numerical simulations, including two 23-story building scenarios with different delay and blasting sequences, this paper provides real-life insights into the effects of floor-to-floor versus axis-by-axis delays on structural collapse behavior, debris spread, and toppling control. Beyond traditional techniques, this study explores how emerging technologies, such as real-time structural monitoring via object tracking, LiDAR scanning, and Unmanned Aerial Vehicle (UAV)-based inspections, can be further advanced through the integration of artificial intelligence (AI). The potential Deep learning (DL) and Machine learning (ML)-based applications of tools like Convolutional Neural Network (CNN)-based digital twins, YOLO object detection, and XGBoost classifiers are highlighted as promising avenues for future research. These technologies could support real-time decision-making, automation, and risk assessment in demolition scenarios. Furthermore, vision-language models such as SAM and Grounding DINO are discussed as enabling technologies for real-time risk assessment, anomaly detection, and adaptive control. By sharing insights from full-scale observations and proposing a forward-looking analytical framework, this work lays a foundation for intelligent and resilient demolition practices. Full article

Healthcare waste (HCW) represents a growing yet frequently underestimated threat to public health, due to its complex toxicological profile. Exposure to HCW has been associated with a broad spectrum of adverse effects, including infections of bacterial, viral, or fungal origin, as well as systemic consequences such as endocrine disruption, metabolic disturbances, and mutagenic, carcinogenic, or teratogenic outcomes. These risks are particularly elevated among healthcare professionals and waste management personnel, who are directly exposed to hazardous materials. This narrative review aims to consolidate current knowledge on the toxic potential of HCW, emphasizing the variability of risks according to waste category and point of origin. A critical reevaluation of the toxicity–health risk–waste management triad is needed to strengthen preventive and protective strategies in both clinical and waste-handling settings, and the review is therefore structured around targeted questions along this axis. Priority should be given to waste prevention, minimization, and segregation at source, as downstream treatment processes may introduce additional hazards. Each category of hazardous HCW exhibits specific mechanisms of toxicity, underlining the importance of targeted and informed management approaches. Future directions should include enhanced training for waste handlers, the development of unified regulatory frameworks, and improved international data collection and reporting systems. Strengthening these components is essential for reducing occupational and environmental health risks and ensuring safer conditions across healthcare systems. Full article

The Internet of Medical Things (IoMT) is a rapidly expanding network of medical devices, sensors, and software that exchange patient health data. While IoMT supports personalized care and operational efficiency, it also introduces significant privacy risks, especially when handling sensitive health information. Data Identification and Classification (DIC) are therefore critical for distinguishing which data attributes require stronger safeguards. Effective DIC contributes to privacy preservation, regulatory compliance, and more efficient data management. This study introduces SDAIPA (SDAIA-HIPAA), a standardized hybrid IoMT data classification framework that integrates principles from HIPAA and SDAIA with a dual risk perspective—uniqueness and harm potential—to systematically classify IoMT health data. The framework’s contribution lies in aligning regulatory guidance with a structured classification process, validated by domain experts, to provide a practical reference for sensitivity-aware IoMT data management. In practice, SDAIPA can assist healthcare providers in allocating encryption resources more effectively, ensuring stronger protection for high-risk attributes such as genomic or location data while minimizing overhead for lower-risk information. Policymakers may use the standardized IoMT data list as a reference point for refining privacy regulations and compliance requirements. Likewise, AI developers can leverage the framework to guide privacy-preserving training, selecting encryption parameters that balance security with performance. Collectively, these applications demonstrate how SDAIPA can support proportionate and regulation-aligned protection of health data in smart healthcare systems. Full article

Biosurfactants are amphiphilic compounds synthesized by microorganisms, providing environmentally sustainable alternatives to synthetic surfactants owing to their biodegradability and minimal toxicity. This review examines bacterial origins of biosurfactants, with a focus on surfactin derived from Bacillus species including B. subtilis, B. amyloliquefaciens, B. licheniformis, and B. pumilus. The cyclic lipopeptide structure of surfactin, which consists of a heptapeptide attached to a β-hydroxy fatty acid chain, imparts remarkable surface-active characteristics, such as a reduced surface tension of 27 mN/m and a low critical micelle concentration of 20 µM. In medical applications, surfactin demonstrates antimicrobial, antiviral, and anticancer properties through mechanisms such as apoptosis induction and metastasis inhibition, as well as promoting wound healing by enhancing angiogenesis and decreasing fibrosis. In the realm of food processing, it functions as a natural antimicrobial agent against pathogens such as Listeria and Salmonella, improves emulsion stability in products like mayonnaise, prolongs shelf life, and influences gut microbiota composition. The safety profiles correspond with the Generally Recognized as Safe (GRAS) status for compounds derived from Bacillus; however, it is essential to optimize dosing to reduce the risks associated with hemolysis. Challenges encompass production expenses, scalability issues, and regulatory obstacles, with genetic engineering suggested as a means to achieve improved yields. Surfactin demonstrates potential as a sustainable bioactive component within the food and health industries. Full article

Urban flood damage is increasing globally, particularly in major cities. Factors contributing to flood risk include urban environmental changes, such as watershed development and precipitation variations caused by climate change. Rapid urbanization and weather anomalies further complicate flood management and damage mitigation. Additionally, detailed analyses at small spatial units (e.g., roads, buildings) remain insufficient. Hence, urban flood analysis considering such spatial variations is required. This study analyzed flood risk in Ulsan, Korea, under a severe flood scenario. Land cover changes from the 1980s to 2010s were examined in 10-year intervals, along with the frequency of heavy rainfall and high river water levels that trigger severe floods. Flood risk was structured as a matrix of likelihood and impact. The results revealed that land cover changes, influenced by development policies or regulations, had a minimal impact on urban flood risk, which is likely because effective drainage systems and stringent urban planning regulations mitigated their effects. However, the frequency and intensity of extreme precipitation events had a substantial effect. These findings were validated using a comparative analysis of an inundation damage trace map and flood range simulated by a physical model. The 10 m grid resolution and time-series likelihood-and-impact framework used in this study can inform budget allocation, resource mobilization, disaster prevention planning, and decision-making during disaster response efforts in major cities. Full article

Learning from demonstration with multiple executions must contend with time warping, sensor noise, and alternating quasi-stationary and transition phases. We propose a label-free pipeline that couples unsupervised segmentation, duration-explicit alignment, and probabilistic encoding. A dimensionless multi-feature saliency (velocity, acceleration, curvature, direction-change rate) yields scale-robust keyframes via persistent peak–valley pairs and non-maximum suppression. A hidden semi-Markov model (HSMM) with explicit duration distributions is jointly trained across demonstrations to align trajectories on a shared semantic time base. Segment-level probabilistic motion models (GMM/GMR or ProMP, optionally combined with DMP) produce mean trajectories with calibrated covariances, directly interfacing with constrained planners. Feature weights are tuned without labels by minimizing cross-demonstration structural dispersion on the simplex via CMA-ES. Across UAV flight, autonomous driving, and robotic manipulation, the method reduces phase-boundary dispersion by 31% on UAV-Sim and by 30–36% under monotone time warps, noise, and missing data (vs. HMM); improves the sparsity–fidelity trade-off (higher time compression at comparable reconstruction error) with lower jerk; and attains nominal 2σ coverage (94–96%), indicating well-calibrated uncertainty. Ablations attribute the gains to persistence plus NMS, weight self-calibration, and duration-explicit alignment. The framework is scale-aware and computationally practical, and its uncertainty outputs feed directly into MPC/OMPL for risk-aware execution. Full article

Excavations in urban cores, in close proximity to existing structures, can significantly influence the structural loading. This study, based on a specific section of the foundation pit for the Yaoziqiu area road network project, constructs a mechanical analysis model to assess the impact of foundation pit construction on adjacent buildings. The research examines how various factors, including the distance between the building and the pit, pile length, retaining wall thickness, and the depth of the retaining wall’s embedment, affect the deformation response of the structures. The results indicate that when the building is 5 m away from the foundation pit, the maximum pile foundation settlement is 13.45 mm. When the distance is 30 m, the settlement value is 6.91 mm, with a decrease of 6.54 mm. The effect of foundation pit excavation on pile foundation settlement is significantly reduced when the pile length exceeds 15 m. When the thickness of the retaining wall increases from 0.5 m to 0.7 m, the maximum settlement of the building foundation is reduced by 1.34 mm, a decrease of 8.38%. When the thickness increases from 0.9 m to 1.1 m, the maximum settlement of the building foundation is reduced by 0.6 mm, a decrease of 4.17%. When the embedded depth of the retaining structure increases from 10 m to 15 m, the maximum settlement is reduced by only 1.05 mm. When the embedded depth is increased to 25 m, the change in the settlement value of the building foundation is within 0.2 mm. This study offers a detailed quantitative analysis of the factors influencing structural deformation, providing specific guidance for developing risk minimization strategies in planning excavations near existing structures. Full article

Digital Building Logbooks (DBLs) have been proposed to preserve lifecycle data across the design, construction, operation, and renovation phases of buildings. Yet, implementation has been hindered by the absence of standardized data models across jurisdictions and stakeholder practices. This paper argues that Large Language Models (LLMs) offer a solution that reduces reliance on rigid standardization. To test this approach, we first draw on parallels from the healthcare sector, where LLMs have extracted structured information from unstructured electronic health records. Second, we present an LLM-based workflow for processing unstructured building inspection reports. The workflow encompassed three tasks: (1) qualitative summary, (2) quantitative summary, and (3) risk level assessment. Sixteen inspection reports were processed through GPT-4o across 320 runs via a Python script. Results showed perfect consistency for categorical fields and Boolean indicators, minimal variability for ordinal severity ratings (σ ≤ 0.6), and stable risk assessments with 87.5% of reports showing low standard deviations. Each report was processed in under 10 s, representing up to a 100-fold speed improvement over manual review. These findings demonstrate the feasibility of post hoc standardization, positioning DBLs to evolve into large-scale knowledge bases that can substantially advance research on the built environment. Full article

Background: Mental health is increasingly recognized as an integral component of occupational health and safety, particularly in high-risk industries such as construction. However, in Saudi Arabia, limited attention has been given to understanding mental health knowledge, beliefs, and workplace support mechanisms, especially among a diverse workforce that includes both migrant and national employees. Methods: This qualitative study employed semi-structured interviews with 30 construction sector participants occupying a range of professional roles. Thematic analysis was conducted using NVivo 15 software, guided by the COM-B model and Health Belief Model, to explore perceptions related to mental health, safety practices, and organizational interventions. Results: The findings highlight significant disparities between migrant and national workers. Migrant workers reported greater challenges related to language barriers, cultural stigma, and a lack of access to culturally appropriate mental health support. National workers described slightly better access to safety and health initiatives but still reported inadequate mental health training. Key barriers across the workforce included limited leadership engagement, stigma, resource constraints, and insufficient organizational training. Existing health and safety programmes were largely focused on physical safety, with minimal incorporation of mental health concerns. Conclusions: The study reveals a pressing need to integrate mental health into occupational safety frameworks in the Saudi construction sector. Culturally sensitive, leadership-supported mental health initiatives are essential to addressing disparities and promoting holistic workers’ well-being across both migrant and national populations. Full article

Brain gliomas are highly infiltrative and heterogenous tumors, whose early and accurate detection as well as therapeutic management are challenging. Artificial intelligence (AI) has the potential to redefine the landscape in neuro-oncology and can enhance glioma detection, imaging segmentation, and non-invasive molecular characterization better than conventional diagnostic modalities through deep learning-driven radiomics and radiogenomics. AI algorithms have been shown to predict genotypic and phenotypic glioma traits with remarkable accuracy and facilitate patient-tailored therapeutic decision-making. Such algorithms can be incorporated into surgical planning to optimize resection extent while preserving eloquent cortical structures through preoperative imaging fusion and intraoperative augmented reality-assisted navigation. Beyond resection, AI may assist in radiotherapy dose distribution optimization, thus ensuring maximal tumor control while minimizing surrounding tissue collateral damage. AI-guided molecular profiling and treatment response prediction models can facilitate individualized chemotherapy regimen tailoring, especially for glioblastomas with MGMT promoter methylation. Applications in immunotherapy are emerging, and research is focusing on AI to identify tumor microenvironment signatures predictive of immune checkpoint inhibition responsiveness. AI-integrated prognostic models incorporating radiomic, histopathologic, and clinical variables can additionally improve survival stratification and recurrence risk prediction remarkably, to refine follow-up strategies in high-risk patients. However, data heterogeneity, algorithmic transparency concerns, and regulatory challenges hamstring AI implementation in neuro-oncology despite its transformative potential. It is therefore imperative for clinical translation to develop interpretable AI frameworks, integrate multimodal datasets, and robustly validate externally. Future research should prioritize the creation of generalizable AI models, combine larger and more diverse datasets, and integrate multimodal imaging and molecular data to overcome these obstacles and revolutionize AI-assisted patient-specific glioma management. Full article

Growing evidence links processed red meat consumption to increased diabetes risk, with oxidized proteins and/or amino acids proposed as potential mediators. We investigated whether dityrosine (Dityr), a key oxidation biomarker in high-oxidative pork (HOP) and structural analog of thyroid hormone T3, mediates HOP-induced glucose dysregulation via thyroid hormone (TH) signaling disruption. C57BL/6J mice were fed control, low-oxidative pork (LOP), HOP, LOP + Dityr, or Dityr diets for 12 weeks. HOP and Dityr impaired glucose tolerance and induced hyperglycemia and hypoinsulinemia. Both induced oxidative stress and inflammation that partly contributed to pancreatic β-cell dysfunction and reduction in insulin secretion. Crucially, they downregulated pancreatic thyroid hormone receptor β1 (TRβ1) and monocarboxylate transporter 8 (MCT-8), impairing TH signaling. This reduced TH transport in pancreatic tissue and triggered β-cell apoptosis by modulating TRβ1-mediated expression of TH-responsive genes and proteins involved in pancreatic function, ultimately leading to diminished insulin secretion and elevated blood glucose levels. Dityr alone recapitulated the metabolic and molecular disruptions of HOP. We conclude that Dityr drives HOP-induced glucose metabolism disorders primarily by disrupting TH signaling, along with promoting oxidative stress and inflammation that collectively impair β-cell function. Minimizing dietary Dityr exposure via modified cooking methods or antioxidant-rich diets may mitigate diabetes risk. Full article