Search Results (340)

Obesity is a growing global health concern with widespread impacts on physical, psychological, and social well-being. Clinically, it is a major driver of type 2 diabetes (T2D), cardiovascular disease (CVD), non-alcoholic fatty liver disease (NAFLD), and cancer, reducing life expectancy by 5–20 years and imposing a staggering economic burden of USD 2 trillion annually (2.8% of global GDP). Despite its significant health and socioeconomic impact, earlier obesity medications, such as fenfluramine, sibutramine, and orlistat, fell short of expectations due to limited effectiveness, serious side effects including valvular heart disease and gastrointestinal issues, and high rates of treatment discontinuation. The advent of glucagon-like peptide-1 (GLP-1) receptor agonists (e.g., semaglutide, tirzepatide) has revolutionized obesity management. These agents demonstrate unprecedented efficacy, achieving 15–25% mean weight loss in clinical trials, alongside reducing major adverse cardiovascular events by 20% and T2D incidence by 72%. Emerging therapies, including oral GLP-1 agonists and triple-receptor agonists (e.g., retatrutide), promise enhanced tolerability and muscle preservation, potentially bridging the efficacy gap with bariatric surgery. However, challenges persist. High costs, supply shortages, and unequal access pose significant barriers to the widespread implementation of obesity treatment, particularly in low-resource settings. Gastrointestinal side effects and long-term safety concerns require close monitoring, while weight regain after medication discontinuation emphasizes the need for ongoing adherence and lifestyle support. This review highlights the transformative potential of incretin-based therapies while advocating for policy reforms to address cost barriers, equitable access, and preventive strategies. Future research must prioritize long-term cardiovascular outcome trials and mitigate emerging risks, such as sarcopenia and joint degeneration. A multidisciplinary approach combining pharmacotherapy, behavioral interventions, and systemic policy changes is critical to curbing the obesity epidemic and its downstream consequences. Full article

This paper reviews the variability of passenger service time (PST) at the platform–train interface (PTI), a critical performance indicator in metro systems shaped by the infrastructure design, affecting passenger behavior and accessibility. Despite its operational importance, PST remains underexplored in relation to crowd management strategies. This review synthesizes findings from empirical and experimental research to clarify the main factors influencing PST and their implications for platform-level interventions. Key contributors to PST variability include door width, gap dimensions, crowd density, and user characteristics such as mobility impairments. Design elements—such as platform edge doors, yellow safety lines, and vertical handrails—affect flow efficiency and spatial dynamics during boarding and alighting. Advanced tracking and simulation tools (e.g., PeTrack and YOLO-based systems) are identified as essential for evaluating pedestrian behavior and supporting Level of Service (LOS) analysis. To complement traditional LOS metrics, the paper introduces Level of Interaction (LOI) and a multidimensional LOS framework that captures spatial conflicts and user interaction zones. Control strategies such as platform signage, seating arrangements, and visual cues are also reviewed, with experimental evidence showing that targeted design interventions can reduce PST by up to 35%. The review highlights a persistent gap between academic knowledge and practical implementation. It calls for greater integration of empirical evidence into policy, infrastructure standards, and operational contracts. Ultimately, it advocates for human-centered, data-informed approaches to PTI planning that enhance efficiency, inclusivity, and resilience in high-demand transit environments. Full article

As the world’s deepest hydraulic tunnels, the Jinping ultra-deep tunnels provide world-class conditions for research on deep rock mechanics under extreme conditions. This study analyzed the time-dependent behavior of different tunneling sections in the Jinping tunnels using the Nishihara creep model implemented in Abaqus. Validated numerical simulations of representative cross-sections at 1400 m and 2400 m depths in the diversion tunnel reveal that long-term creep deformations (over a 20-year period) substantially exceed instantaneous excavation-induced displacements. The stress concentrations and strain magnitudes exhibit significant depth dependence. The maximum principal stress at a 2400 m depth reaches 1.71 times that at 1400 m, while the vertical strain increases 1.46-fold. Based on this, the long-term mechanical behavior of the surrounding rock during the expansion of the Jinping auxiliary tunnel was further calculated and predicted. It was found that the stress concentration at the top and bottom of the left sidewall increases from 135 MPa to 203 MPa after expansion, identifying these as critical areas requiring focused monitoring and early warnings. The total deformation of the rock mass increases by approximately 5 mm after expansion, with the cumulative deformation reaching 14 mm. Post-expansion deformation converges within 180 days, with creep deformation of 2.5 mm–3.5 mm observed in both sidewalls, accounts for 51.0% of the total deformation during expansion. The surrounding rock reaches overall stability three years after the completion of expansion. These findings establish quantitative relationships between the excavation depth, time-dependent deformation, and stress redistribution and support the stability design, risk management, and infrastructure for ultra-deep tunnels in a stress state at a 2400 m depth. These insights are critical to ensuring the long-term stability of ultra-deep tunnels and operational safety assessments. Full article

Promoting domestic fire safety is crucial for preventing and effectively managing risky situations. This study evaluated the effectiveness of virtual environments (VEs) in fire drills to improve citizens’ knowledge and safe behavior in domestic settings. Conducted at the Citizen School for Risk Prevention (CSRP) in Zaragoza (Spain), the experiment involved 20 participants facing a simulated kitchen fire using a combination of physical and virtual extinguishing equipment. A theoretical session accompanied the drills to reinforce learning. Participants were divided into two groups: one completed the drill before and after the theoretical session, while the other completed it only afterward. Performance was assessed based on the ability to extinguish, control, or lose control of the fire. Surveys administered before, immediately after, and three months after training measured knowledge retention and behavioral changes. The results indicate a significant improvement in fire safety awareness and lasting adoption of safe practices. Participants also emerged as safety advocates. This study highlights the potential of combining theoretical instruction with immersive practical training and identifies strategies for replicating this approach in other prevention schools. Full article

Slope stability analysis is conventionally performed using the strength reduction method with the proportional reduction in shear strength parameters. However, during actual slope failure processes, the attenuation characteristics of rock mass cohesion ($c$) and internal friction angle ($\phi$) are often inconsistent, and their reduction paths exhibit clear nonlinearity. Relying solely on proportional reduction paths to calculate safety factors may therefore lack scientific rigor and fail to reflect true slope behavior. To address this limitation, this study proposes a novel approach that considers the non-proportional reduction of $c$ and $\phi$, without dependence on predefined reduction paths. The method begins with an analysis of slope stability states based on energy dissipation theory. A Bayesian Gaussian Mixture Model (BGMM) is employed for intelligent interpretation of the dissipated energy data, and, combined with energy mutation theory, is used to identify instability states under various reduction parameter combinations. To compute the safety factor, the concept of a “reference slope” is introduced. This reference slope represents the state at which the slope reaches limit equilibrium under strength reduction. The safety factor is then defined as the ratio of the shear strength of the target analyzed slope to that of the reference slope, providing a physically meaningful and interpretable safety index. Compared with traditional proportional reduction methods, the proposed approach offers more accurate estimation of safety factors, demonstrates superior sensitivity in identifying critical slopes, and significantly improves the reliability and precision of slope stability assessments. These advantages contribute to enhanced safety management and risk control in slope engineering practice. Full article

Termites pose significant threats to the structural integrity of embankments due to their nesting and tunneling behavior, which leads to internal voids, water leakage, or even dam failure. This review systematically classifies and evaluates current termite detection techniques in the context of embankment maintenance, focusing on physical sensing technologies and biological characteristic-based methods. Physical sensing methods enable non-invasive localization of subsurface anomalies, including ground-penetrating radar, acoustic detection, and electrical resistivity imaging. Biological characteristic-based methods, such as electronic noses, sniffer dogs, visual inspection, intelligent monitoring, and UAV-based image analysis, are capable of detecting volatile compounds and surface activity signs associated with termites. The review summarizes key principles, application scenarios, advantages, and limitations of each technique. It also highlights integrated multi-sensor frameworks and artificial intelligence algorithms as emerging solutions to enhance detection accuracy, adaptability, and automation. The findings suggest that future termite detection in embankments will rely on interdisciplinary integration and intelligent monitoring systems to support early warning, rapid response, and long-term structural resilience. This work provides a scientific foundation and practical reference for advancing termite management and embankment safety strategies. Full article

Nitrifying bacteria, including ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB), are players in the nitrogen cycle but pose serious health risks when colonizing drinking water distribution networks (DWDNs). While the global impact of these bacteria is increasingly recognized, a significant research gap remains concerning their effects in tropical regions, particularly in developing countries. This study aims to bridge that gap by systematically reviewing the existing literature on nitrifying bacteria in DWDNs, their behavior in biofilms, and associated public health risks, particularly in systems reliant on surface water sources in tropical climates. Using the PRISMA guidelines for systematic reviews, 51 relevant studies were selected based on content validity and relevance to the research objective. The findings highlight the critical role of nitrifying bacteria in the formation of nitrogenous disinfection by-products (N-DBPs) and highlight specific challenges faced by developing countries, including insufficient monitoring and low public awareness regarding safe water storage practices. Additionally, this review identifies key surrogate indicators, such as ammonia, nitrite, and nitrate concentrations, that influence the formation of DBPs. Although health risks from nitrifying bacteria are reported in comparable studies, there is a lack of epidemiological data from tropical regions. This underscores the urgent need for localized research, systematic monitoring, and targeted interventions to mitigate the risks associated with nitrifying bacteria in DWDNs. Addressing these challenges is essential for enhancing water safety and supporting sustainable water management in tropical developing countries. Full article

The integration of connected autonomous vehicles (CAVs), advanced driver assistance systems (ADAS), and conventional vehicles necessitates the development of robust methodologies to enhance traffic efficiency and ensure safety across heterogeneous traffic streams. A comprehensive understanding of vehicle interactions and operating speed variability is essential to support informed decision-making in traffic management and infrastructure design. This study presents operating speed models aimed at estimating the 85th percentile speed (V₈₅) on straight road segments, utilizing floating car data (FCD) for both calibration and validation purposes. The dataset encompasses approximately 2000 km of the Italian road network, characterized by diverse geometric features. Speed observations were analyzed under three traffic conditions: general traffic, free-flow, and free-flow with dry pavement. Results indicate that free-flow conditions improve the model’s explanatory power, while dry pavement conditions introduce greater speed variability. Initial models based exclusively on geometric parameters exhibited limited predictive accuracy. However, the inclusion of posted speed limits significantly enhanced model performance. The most influential predictors identified were the V₈₅ on the preceding curve and the length of the straight segment. These findings provide empirical evidence to inform road safety evaluations and geometric design practices, offering insights into driver behavior in mixed-traffic environments. The proposed model supports the development of data-driven strategies for the seamless integration of automated and non-automated vehicles. Full article

Background: Fever and pain are among the most frequent symptoms in pediatric care, requiring timely and appropriate management. While evidence-based guidelines are available, adherence in real-world practice remains variable. This study aimed to explore the attitudes and prescribing behaviors of Italian Primary Care Pediatricians (PCPs) in the management of fever and pain, and to assess their alignment with current clinical recommendations. Materials and Methods: An anonymous, cross-sectional survey consisting of 30 multiple-choice questions was administered to 900 PCPs between 1 July and 30 October 2024. The questionnaire assessed therapeutic preferences, dosing strategies, and perceived knowledge gaps. Invitations were distributed via pediatric scientific societies and regional professional networks. Results: A total of 244 PCPs completed the survey (response rate 27.1%). The majority were aged over 55 years (72.1%), worked in urban settings (71.3%), and had more than 20 years of clinical experience (74.6%). Most respondents (77%) reported managing pediatric fever or pain on a daily basis. Paracetamol was the preferred first-line treatment for fever (95.9%), primarily due to its perceived safety (82.4%). Ibuprofen was favored by 51.6% of those who selected it for its greater effectiveness. The alternating use of paracetamol and ibuprofen for fever was never adopted by 49.6%, while 31.6% employed this strategy, believing it to be more effective. For pain, 67.6% used paracetamol and 26.2% used ibuprofen as first-line treatments; 15.2% reported alternating the two drugs. Correct dosage practices were followed by 63.9% for both medications, although 40.2% did not differentiate dosages between fever and pain management. Conclusions: While general trends showed alignment with current guidelines, notable inconsistencies were observed in drug selection, dosage, and the use of alternating therapies. These findings highlight a pressing need to improve the dissemination and implementation of pediatric fever and pain management guidelines among PCPs in order to reduce unsafe practices, avoid therapeutic errors, and prevent unnecessary strain on emergency care services. Full article

The remaining useful life (RUL) of complex mechanical systems is the primary aspect of prognostics and health management, which is critical for ensuring reliability and safety. Recent developments have shifted towards a data-driven approach, emphasizing empirical insights over expert opinions. The similarity-based data-driven approach operates on the premise that systems with similar historical behaviors will likely exhibit similar future behaviors, making it suitable for RUL estimation. Conventionally, most similarity-based approaches utilize all historical data to identify reference systems for RUL estimations. However, not all historical events within a system hold equal significance for RUL. Certain events have a substantial impact on the remaining lifespan of a system. These significant and impactful events are called degradation events (DEs) in this study. Based on the hypothesis that systems undergoing similar DEs may share the same RUL, this study presents an innovative framework for RUL estimation that leverages only the DEs of a test system to identify reference systems that have experienced similar DEs. Furthermore, the model incorporates novel strategies for adjusting the RUL of the reference system based on the initial wear and degradation rates, thereby improving estimation accuracy. The effectiveness of the proposed model, in comparison with similar state-of-the-art models, is demonstrated through experiments on widely recognized jet engine datasets provided by NASA and bearing degradation data from the XJTU-SY. Full article

With the increasing complexity of power grid field operations, frequent operational violations have emerged as a major concern in the domain of power grid field operation safety. To support dispatchers in accurately identifying and addressing violation risks, this paper introduces a profiling approach for power grid field operation violations based on knowledge graph techniques. The method enables deep modeling and structured representation of violation behaviors. In the structured data processing phase, statistical analysis is conducted based on predefined rules, and mutual information is employed to quantify the contribution of various operational factors to violations. At the municipal bureau level, statistical modeling of violation characteristics is performed to support regional risk assessment. For unstructured textual data, a multi-source embedding-based named entity recognition (NER) model is developed, incorporating domain-specific power lexicon information to enhance the extraction of key entities. High-weight domain terms related to violations are further identified using the TF-IDF algorithm to characterize typical violation behaviors. Based on the extracted entities and relationships, a knowledge graph of field operation violations is constructed, providing a computable and inferable semantic representation of operational scenarios. Finally, visualization techniques are applied to present the structural patterns and distributional features of violations, offering graph-based support for violation risk analysis and dispatch decision-making. Experimental results demonstrate that the proposed method effectively identifies critical features of violation behaviors and provides a structured foundation for intelligent decision support in power grid operation management. Full article

The Hajj pilgrimage involves high crowd density within limited time and space, making traditional crowd control methods insufficient for real-time alerts or predictive safety measures. This research proposes a machine learning-based system to enhance crowd management by detecting abnormal behavior and forecasting future conditions. The study utilizes the Hajjv2 dataset, which consists of annotated video frames capturing various crowd behaviors across multiple Hajj locations. After data preprocessing and feature extraction, including crowd density, speed, direction, and object area, two models are employed: the Isolation Forest algorithm for anomaly detection and a Long Short-Term Memory (LSTM) neural network for forecasting crowd behavior. The system integrates the results of both models to issue real-time alerts based on predefined thresholds. Evaluation results indicate that the Isolation Forest model achieved an average accuracy of 91% across all test sets, effectively identifying abnormal movement patterns. The LSTM model produced reliable predictions of average crowd speed with a low Mean Squared Error (MSE) of 0.000439. Together, these models form a robust alert mechanism that enables early identification of risks. In summary, this study presents an intelligent, scalable solution for enhancing crowd safety during the Hajj. It illustrates the practical value of machine learning in enabling proactive and informed crowd management strategies. Full article

As hydraulic buildings are constantly subjected to complex interactions with water, particularly variations in hydrostatic pressure and temperature, deformation structural behavior is inherently sensitive to environmental fluctuations. Monitoring dam deformation with high accuracy and robustness is critical for ensuring the long-term safety and operational integrity of hydraulic structures. However, traditional physics-based models often struggle to fully capture the nonlinear and time-dependent deformation responses in hydraulic structures driven by such coupled environmental influences. To address these limitations, this study presents an advanced deep learning (DL)-based deformation monitoring for hydraulic buildings using long-sequence monitoring data of hydrostatic pressure and temperature. Specifically, the Bidirectional Stacked Long Short-Term Memory (Bi-Stacked-LSTM) is proposed to capture intricate temporal dependencies and directional dynamics within long-sequence hydrostatic and thermal time series. Then, hyperparameters, including the number of LSTM layers, neuron counts in each layer, dropout rate, and time steps, are efficiently fine-tuned using the Gaussian Process-based surrogate model optimization (GP-SMO) algorithm. Multiple deformation monitoring points from hydraulic buildings and a variety of advanced machine-learning methods are utilized for analysis. Experimental results indicate that the developed GP-SMO-optimized Bi-Stacked-LSTM dam deformation monitoring model shows better comprehensive representation capability of both past and future deformation-related sequences compared with benchmark methods. By approximating the behavior of the target function, the GP-SMO algorithms allow for the optimization of critical parameters in DL models while minimizing the high computational costs typically associated with direct evaluations. This novel DL-based approach significantly improves the extraction of deformation-relevant features from long-term monitoring data, enabling more accurate modeling of temporal dynamics. As a result, the developed method offers a promising new tool for safety monitoring and intelligent management of large-scale hydraulic structures. Full article

Background/Objectives: The administration of parenteral medications is essential in managing acute arousal within the Behavioral Assessment Unit (BAU) of the emergency department (ED), where timely and effective intervention is critical. This study aims to evaluate current practices surrounding the use of parenteral medications for patients with acute agitation, focusing on adherence to protocols, medication safety, documentation accuracy, and patient outcomes. Methods: A retrospective analysis was conducted on 177 cases from December 2023 to February 2024. The study assessed the demographics, diagnoses, treatment protocols, and patient outcomes, with a particular emphasis on the use of parenteral medications such as benzodiazepines and antipsychotics. The relationship between medication administration and involuntary admission, mechanical restraint usage, and patient outcomes was also explored. Results: The majority of patients were aged between 21 and 30 years, and there was a predominance of male patients across both groups. Schizophrenia was the most common diagnosis, with a higher prevalence in the parenteral group (34%) compared to the oral-only group (24%), and personality disorders were more frequent in the parenteral group. Intramuscular (IM) medication administration was strongly associated with the use of mechanical restraint, with patients receiving IM medication being 35 times more likely to require restraint, emphasizing the link between more intensive treatment approaches and behavioral challenges. The most frequently administered medications were diazepam (40.6%) and olanzapine (36.5%), with olanzapine, droperidol, and diazepam most commonly used parenterally. Documentation of physical assessments prior to parenteral administration was present in most cases, though comprehensive evaluations such as ECGs were inconsistently performed. Conclusions: Parenteral medications, including benzodiazepines and antipsychotics, were effective in rapidly stabilizing patients, but the study emphasizes reducing dependency on mechanical restraints. Tailoring treatment to patient characteristics and employing alternative de-escalation strategies can improve safety and align with recovery-oriented care. This study highlights the need for evidence-based practices to optimize care and improve patient outcomes in ED settings. Further research is needed to explore long-term outcomes and refine non-coercive care approaches. Full article

The World Health Organization reports approximately 1.35 million fatalities annually due to road traffic accidents, with pedestrians constituting 23% of these deaths. This highlights the critical need to enhance pedestrian safety, especially given the significant role human error plays in road accidents. Autonomous vehicles present a promising solution to mitigate these fatalities by improving road safety through advanced prediction of pedestrian behavior. With the autonomous vehicle market projected to grow substantially and offer various economic benefits, including reduced driving costs and enhanced safety, understanding and predicting pedestrian actions and intentions is essential for integrating autonomous vehicles into traffic systems effectively. Despite significant advancements, replicating human social understanding in autonomous vehicles remains challenging, particularly in predicting the complex and unpredictable behavior of vulnerable road users like pedestrians. Moreover, the inherent uncertainty in pedestrian behavior adds another layer of complexity, requiring robust methods to quantify and manage this uncertainty effectively. This review provides a structured and in-depth analysis of pedestrian intention prediction techniques, with a unique focus on how uncertainty is modeled and managed. We categorize existing approaches based on prediction duration, feature type, and model architecture, and critically examine benchmark datasets and performance metrics. Furthermore, we explore the implications of uncertainty types—epistemic and aleatoric—and discuss their integration into autonomous vehicle systems. By synthesizing recent developments and highlighting the limitations of current methodologies, this paper aims to advance the understanding of Pedestrian intention Prediction and contribute to safer and more reliable autonomous vehicle deployment. Full article