Search Results (15,278)

Dam monitoring tracks environmental variables (water level, temperature) and structural responses (deformation, seepage, and stress) to assess safety and performance. Structural health monitoring (SHM) refers to the systematic observation and analysis of the structural condition over time, and it is essential in maintaining the safety, functionality, and long-term performance of dams. This review examines monitoring data applications, covering structural health assessment methods, historical motivations, and key challenges. It discusses monitoring components, data acquisition processes, and sensor roles, stressing the need to integrate environmental, operational, and structural data for decision making. Key objectives include risk management, operational efficiency, safety evaluation, environmental impact assessment, and maintenance planning. Methodologies such as numerical modeling, statistical analysis, and machine learning are critically analyzed, highlighting their strengths and limitations and the demand for advanced predictive techniques. This paper also explores future trends in dam monitoring, offering insights for engineers and researchers to enhance infrastructure resilience. By synthesizing current practices and emerging innovations, this review aims to guide improvements in dam safety protocols, ensuring reliable and sustainable dam operations. The findings provide a foundation for the advancement of monitoring technologies and optimization of dam management strategies worldwide. Full article

Ecological security evaluation serves as the cornerstone for ecological management decision-making and spatial optimization. This study focuses on the Dongping Lake Basin. Based on the Pressure–State–Response (PSR) model framework, it integrates ecological risk, ecosystem health, and ecosystem service indicators. Utilizing methods including Local Indicators of Spatial Association (LISA), Transition Matrix, and GeoDetector, it analyzes the spatio-temporal evolution characteristics and driving mechanisms of watershed ecological security from 2000 to 2020. The findings reveal that the Watershed Ecological Security Index (WESI) exhibited a trend of “fluctuating upward followed by periodic decline”. In 2000, the status was “relatively unsafe”. It peaked in 2015 (index 0.332, moderately safe) and experienced a slight decline by 2020. Spatially, a significantly clustered pattern of “higher in the north and lower in the south, higher in the east and lower in the west” was observed. In 2020, “High-High” clusters of ecological security aligned closely with Shandong Province’s ecological conservation red line, concentrating in core protected areas such as the foothills of the Taihang Mountains and Dongping Lake Wetland. Level transitions were characterized by “predominant continuous improvement in low levels alongside localized reverse fluctuations in middle and high levels,” with the “relatively unsafe” and “moderately safe” levels experiencing the largest transfer areas. Geographical detector analysis indicates that the Human Interference Index (HI), Ecosystem Service Value (ESV), and Annual Afforestation Area (AAA) were key drivers of watershed ecological security change, influenced by dynamic interactive effects among multiple factors. This study advances watershed-scale ecological security assessment methodologies. The revealed spatio-temporal patterns and driving mechanisms provide valuable insights for protecting the ecological barrier in the lower Yellow River and informing ecological security strategies within the Dongping Lake Watershed. Full article

Obstructive sleep apnea syndrome (OSAS) is a prevalent disorder with significant systemic and oral health consequences. This narrative review synthesizes the current knowledge on the interplay between dental health and sleep apnea, highlighting the expanding role of dentists in the screening, early detection, and management of OSAS. Validated questionnaires, anatomical assessments, and anthropometric measurements have enhanced dentists’ capacity for early screening. However, knowledge and training gaps remain, particularly in low- and middle-income countries. Dentists are uniquely positioned to identify anatomical and oral risk factors, facilitate referrals for diagnosis, and provide therapeutic interventions such as oral appliance therapy. Interdisciplinary collaboration between dental and medical professionals is essential to improve early detection, treatment outcomes, and patient quality of life. Enhancing education, standardizing protocols, and integrating dentists into multidisciplinary care pathways are critical steps for advancing the management of sleep apnea. Full article

Japanese encephalitis virus (JEV) is a mosquito-borne zoonotic flavivirus that circulates primarily within animal populations and occasionally spills over to humans, causing severe neurological disease. While humans are terminal hosts, veterinary species such as pigs and birds play essential roles in viral amplification and maintenance, making JEV fundamentally a veterinary infectious disease with zoonotic potential. This review summarizes the current understanding of JEV transmission dynamics from a veterinary and ecological perspective, emphasizing the roles of amplifying hosts and animal surveillance in controlling viral circulation. Recent genotype shifts and viral evolution have raised concerns regarding vaccine effectiveness and regional emergence. National surveillance systems and animal-based monitoring strategies are examined for their predictive value in detecting outbreaks early. Veterinary and human vaccination strategies are also reviewed, highlighting the importance of integrated One Health approaches. Advances in modeling and climate-responsive surveillance further underscore the dynamic and evolving landscape of JEV transmission. By managing the infection in animal reservoirs, veterinary interventions form the foundation of sustainable zoonotic disease control. Full article

Stunting is a chronic growth disorder that not only affects height but may also impair craniofacial development. Facial proportions, especially in the vertical dimension, may provide additional anthropometric insight into growth status among children. Objectives: To assess and compare the vertical and horizontal facial proportions of stunted and non-stunted children, and to explore the potential of facial dimensions as supportive indicators for early-stunting detection in community-based settings. Methods: This cross-sectional analytical study involved 266 children aged 7–72 months (mean age 42.63 ± 13.82 months) from several community health centers in Bandung, Indonesia. Children were categorized as stunted or non-stunted based on WHO height-for-age Z-scores. Facial dimensions were measured directly by calibrated pediatric dentistry residents using manual instruments. The vertical dimensions included Nasion–Subnasale (N–SN) and Subnasale–Menton (SN–M), while horizontal dimensions included zygomatic width and intergonion width. Data were analyzed using the Mann–Whitney U test and Spearman correlation. Results: Significant differences were found in vertical facial dimensions between stunted and non-stunted children: median N–SN (32.4 mm vs. 33.6 mm; p = 0.003) and SN–M (42.5 mm vs. 45.1 mm; p < 0.001). No significant differences were observed in horizontal dimensions. All facial parameters showed a positive correlation with age (p < 0.001). No significant differences were found based on sex. Conclusions: Stunted children exhibited shorter vertical facial dimensions compared to their non-stunted peers, while horizontal dimensions remained stable across groups. Vertical facial proportions may serve as supportive indicators in the screening and monitoring of childhood stunting. This method has potential for integration into community-based growth monitoring using simple or digital anthropometric tools. Full article

The widespread adoption of heartbeat monitoring sensors has increased the demand for secure and trustworthy multimodal cardiac monitoring systems capable of accurate heartbeat pattern recognition. While existing systems offer convenience, they often suffer from critical limitations, such as variability in the number of available modalities and missing or noisy data during multimodal fusion, which may compromise both performance and data security. To address these challenges, we propose MultiHeart, which is a robust and secure multimodal interactive cardiac monitoring system designed to provide reliable heartbeat pattern recognition through the integration of diverse and trustworthy cardiac signals. MultiHeart features a novel multi-task learning architecture that includes a reconstruction module to handle missing or noisy modalities and a classification module dedicated to heartbeat pattern recognition. At its core, the system employs a multimodal autoencoder for feature extraction with shared latent representations used by lightweight decoders in the reconstruction module and by a classifier in the classification module. This design enables resilient multimodal fusion while supporting both data reconstruction and heartbeat pattern classification tasks. We implement MultiHeart and conduct comprehensive experiments to evaluate its performance. The system achieves 99.80% accuracy in heartbeat recognition, surpassing single-modal methods by 10% and outperforming existing multimodal approaches by 4%. Even under conditions of partial data input, MultiHeart maintains 94.64% accuracy, demonstrating strong robustness, high reliability, and its effectiveness as a secure solution for next-generation health-monitoring applications. Full article

Background/Objectives: Oral diseases are highly prevalent in Hungary and driven in part by unhealthy beverage consumption, smoking, and other behaviors. No prior study has examined the impact of beverage consumption patterns on oral health in a representative Hungarian population. This study investigated the association between beverage intake, lifestyle factors, and oral health outcomes among Hungarian adults. Methods: Data were drawn from the 2019 Hungarian European Health Interview Survey, a nationally representative cross-sectional study. Oral health outcomes and key exposures, including beverage consumption, smoking, alcohol use, and sociodemographic variables, were self-reported. Associations were assessed using multiple logistic regression models. Results: Among 5425 adults, higher dairy intake was linked to less gum bleeding (odds ratio = 0.76; 95% confidence intervals [0.59–0.96]) and lower odds of teeth missing (0.63 [0.47–0.86]). Weekly juice intake reduced gum bleeding (0.62 [0.51–0.76]) and missing teeth (0.83 [0.71–0.96]). Daily soda was associated with more gum bleeding (1.94 [1.53–2.47]), caries (1.57 [1.27–1.94]), and poor self-perceived oral health (1.32 [1.10–1.59]). Alcohol (1–4 times/week) increased gum bleeding (1.38 [1.07–1.77]) and tooth mobility (1.47 [1.02–2.11]). Smoking raised odds for caries (1.42 [1.21–1.66]) and missing teeth (1.81 [1.55–2.10]). Conclusions: Increasing dairy and fresh juice intake while reducing sugar-sweetened and acidic beverages, alongside tobacco and alcohol control and routine oral health screening, are effective strategies for improving population oral health across all sociodemographic groups. Full article

Structural damage identification provides a theoretical foundation for the operational safety and preventive maintenance of in-service bridges. However, practical bridge health monitoring faces challenges in poor signal quality, difficulties in feature extraction, and insufficient damage classification accuracy. This study presents a bridge damage identification framework integrating time-varying filtering-based empirical mode decomposition (TVFEMD) with pre-trained convolutional neural networks (CNNs). The proposed method enhances the key frequency-domain features of signals and suppresses the interference of non-stationary noise on model training through adaptive denoising and time–frequency reconstruction. TVFEMD was demonstrated in numerical simulation experiments to have a better performance than the traditional EMD in terms of frequency separation and modal purity. Furthermore, the performances of three pre-trained CNN models were compared in damage classification tasks. The results indicate that ResNet-50 has the best optimal performance compared with the other networks, particularly exhibiting better adaptability and recognition accuracy when processing TVFEMD-denoised signals. In addition, the principal component analysis visualization results demonstrate that TVFEMD significantly improves the clustering and separability of feature data, providing clearer class boundaries and reducing feature overlap. Full article

Serious games that integrate educational content with engaging gameplay mechanics hold promise for reducing cognitive load and increasing student motivation in STEM and health science education. This preliminary study presents the development and evaluation of the Medimon NASA Demo, a game-based learning prototype designed to teach undergraduate students about the musculoskeletal and visual systems—two critical domains in space medicine. Participants (n = 23) engaged with the game over a two-week self-regulated learning period. The game employed mnemonic-based characters, visual storytelling, and turn-based battle mechanics to reinforce medical concepts. Quantitative results demonstrated significant learning gains, with posttest scores increasing by an average of 23% and a normalized change of c = 0.4. Engagement levels were high across multiple dimensions of situational interest, and 74% of participants preferred the game over traditional formats. Qualitative analysis of open-ended responses revealed themes related to intrinsic appeal, perceived learning efficacy, interaction design, and cognitive resource management. While the game had minimal impact on short-term STEM career interest, its educational potential was clearly supported. These findings suggest that mnemonic-driven serious games like Medimon can effectively enhance engagement and learning in health science education, especially when aligned with real-world contexts such as space medicine. Full article

Recent advances in computational biology have provided powerful tools for analyzing, modeling, and optimizing probiotic microorganisms, thereby supporting their development as promising agents for improving human health. The essential role of the microbiota in regulating physiological processes and preventing disease has driven interest in the rational design of next-generation probiotics. This review highlights progress in in silico approaches for enhancing the functionality of probiotic strains. Particular attention is given to genome-scale metabolic models, advanced simulation algorithms, and AI-driven tools that provide deeper insight into microbial metabolism and enable precise probiotic optimization. The integration of these methods with multi-omics data has greatly improved our ability to predict strain behavior and design probiotics with specific health benefits. Special focus is placed on modeling probiotic–prebiotic interactions and host–microbiome dynamics, which are essential for the development of functional food products. Despite these achievements, key challenges remain, including limited model accuracy, difficulties in simulating complex host–microbe systems, and the absence of unified standards for validating in silico-optimized strains. Addressing these gaps requires the development of integrative modeling platforms and clear regulatory frameworks. This review provides a critical overview of current advances, identifies existing barriers, and outlines future directions for the application of computational strategies in probiotic research. Full article

Mastitis is a significant challenge in the buffalo industry, affecting both milk production and animal health and resulting in economic losses. This study presents the first fully automated AI-driven thermal imaging system integrated with robotic milking, specifically developed for the real-time, non-invasive monitoring of udder health in Italian Mediterranean buffalo. Unlike traditional approaches, the system leverages the synchronized acquisition of thermal images during milking and compensates for environmental variables through a calibrated weather station. A transformer-based neural network (SegFormer) segments the udder area, enabling the extraction of maximum udder skin surface temperature (USST), which is significantly correlated with somatic cell count (SCC). Initial trials demonstrate the feasibility of this approach in operational farm environments, paving the way for scalable, precision diagnostics of subclinical mastitis. This work represents a critical step toward intelligent, automated systems for early detection and intervention, improving animal welfare and reducing antibiotic use. Full article

This study presents an approach based on machine learning (ML) techniques to analyze the relationship between emergency room (ER) admissions for cardiorespiratory diseases (CRDs) and environmental factors. The aim of this study is the development and verification of an interpretable machine learning framework applied to environmental and health data to assess the relationship between environmental factors and daily emergency room admissions for cardiorespiratory diseases. The model’s predictive accuracy was evaluated by comparing simulated values with observed historical data, thereby identifying the most influential environmental variables and critical exposure thresholds. This approach supports public health surveillance and healthcare resource management optimization. The health and environmental data, collected through meteorological sensors and air quality monitoring stations, cover eleven years (2013–2023), including meteorological conditions and atmospheric pollutants. Four ML models were compared, with XGBoost showing the best predictive performance (R² = 0.901; MAE = 0.047). A 10-fold cross-validation was applied to improve reliability. Global model interpretability was assessed using SHAP, which highlighted that high levels of carbon monoxide and relative humidity, low atmospheric pressure, and mild temperatures are associated with an increase in CRD cases. The local analysis was further refined using LIME, whose application—followed by experimental verification—allowed for the identification of the critical thresholds beyond which a significant increase in the risk of hospital admission (above the 95th percentile) was observed: CO > 0.84 mg/m³, P_atm ≤ 1006.81 hPa, Tavg ≤ 17.19 °C, and RH > 70.33%. The findings emphasize the potential of interpretable ML models as tools for both epidemiological analysis and prevention support, offering a valuable framework for integrating environmental surveillance with healthcare planning. Full article

Urban street canyons in high-density areas exacerbate PM_2.5 accumulation, posing significant public health risks. Through integrated empirical and computational methods—including empirical PM_2.5 and microclimate measurements, multivariate regression analysis, and high-resolution ENVI-met5.1 simulations—this study quantifies the threshold effects of pedestrian-oriented morphological indicators on PM_2.5 exposure in east–west-oriented residential streets. Key findings include the following: (1) the height-to-width ratio (H/W) negatively correlates with exposure, where H/W = 2.0 reduces the peak concentrations by 37–41% relative to H/W = 0.5 through enhanced vertical advection; (2) the Build-To-Line ratio (BTR) exhibits a positive correlation with exposure, with BTR = 63.2% mitigating exposure by 12–15% compared to BTR = 76.8% by reducing aerodynamic stagnation; (3) pollution exposure can be mitigated by enhancing airflow ventilation within street canyons through architectural facade design. These evidence-based morphological thresholds (H/W ≥ 1.5, BTR ≤ 70%) provide actionable strategies for reducing health risks in polluted urban corridors, supporting China to meet its national air quality improvement targets. Full article

Critical illness profoundly disrupts the gut microbiota leading to a state of dysbiosis characterized by reduced microbial diversity and overrepresentation of pathogenic taxa such as Enterobacteriaceae and Proteobacteria. This dysbiotic shift compromises gut barrier integrity and modulates immune responses, contributing to systemic inflammation and increasing susceptibility to nosocomial infections and multi-organ dysfunction. Nutritional strategies in the ICU significantly influence the composition and function of the gut microbiota. Enteral nutrition supports the maintenance of microbial diversity and gut mucosal health, whereas parenteral nutrition is associated with mucosal atrophy and further microbial imbalance. Emerging interventions, including the administration of probiotics, prebiotics, synbiotics, and fermented products like kefir, show promise in restoring microbial equilibrium and improving patient outcomes. This review presents current evidence on the alterations of the gut microbiota in critically ill patients, explores the systemic consequences of dysbiosis, and evaluates the impact of nutritional and microbiota-targeted therapies in improving patient outcomes. Full article

This study analyses the effectiveness of a contextualized teaching and learning sequence (TLS) based on forensic entomology (FE) to disprove the idea of spontaneous generation (SG) among students enrolled in the Higher Vocational Education and Training (VET) Cycle in Pathological Anatomy and Cytodiagnosis. Through an inquiry- and project-based learning approach, students replicate a version of Francesco Redi’s historical experiments, enabling them to engage with core scientific concepts such as the metamorphic cycle of insects and the role of entomology in forensic science. The research adopts a semiquantitative and exploratory design. It investigates: (1) whether students’ prior knowledge about FE and related biological processes is sufficient to refute SG; (2) to what extent this knowledge is influenced by their previous academic background and gender; and (3) whether a contextualized TLS can significantly enhance their conceptual understanding. The results reveal that most students begin with limited initial knowledge of FE and multiple misconceptions related to SG, irrespective of their previous study. Gender differences were observed at baseline, with women showing lower prior knowledge, but these differences disappeared after the intervention. The post-intervention data demonstrate a significant improvement in student’s ability to reject SG and explain biological processes coherently. The study highlights the importance of integrating entomology into health-related VET programs, both as a means to promote scientific literacy and correct misconceptions and as a pedagogical tool to foster critical thinking. It also highlights the potential and historically grounded methodologies to equalize learning outcomes and strengthen the scientific preparation of future healthcare professionals. Full article