Search Results (18,490)

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

Coxiella burnetii, the etiological agent of Q fever, is a globally distributed zoonotic pathogen affecting both animals and humans. Despite its known endemicity in various Mediterranean regions, data on human seroprevalence in Sardinia are still lacking. This study aimed to assess seroprevalence in patients and to analyze the annual positivity rate related to the serum samples collected in Sardinia over a ten-year period (2015–2024). For this purpose, a total of 1792 patients were involved in the survey, and 4310 serum samples were analyzed using indirect immunofluorescence assay (IFI) to detect IgM and IgG antibodies against C. burnetii. The global seroprevalence rates relating to all the patients over a ten-year period were determined along with the annual positivity rate and trends from all sera. An overall seroprevalence of 27.0% and an average of annual positivity rate of 16.0% were determined, with the IFI detecting IgG antibodies in 15.2% of positive samples and IgM antibodies in 0.9%, suggesting significant prior exposure of the population evaluated. Annual positivity rates ranged from 24.8% in 2016 to 8.0% in 2020. These results confirmed the endemic circulation of C. burnetii in Sardinia and the ongoing risk of human exposure. A GIS-based map was built to evidence the spatial distribution of Q fever in Sardinia. Interestingly, areas with higher seroprevalence appear to coincide with the distribution of sheep and goat farms, indicating a link between livestock and human exposure. These findings confirm the circulation of C. burnetii in Sardinia and underscore the importance of epidemiological monitoring, public health interventions, and educational efforts in populations at increased risk. 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

This article focuses on activities addressed in the European project hydrogen lightweight & innovative tank for zero-emission aircraft, H2ELIOS. The authors propose a preliminary approach oriented to the design of a structural health monitoring SHM system conceived for a cryo-tank liquid hydrogen storage for medium range vehicles. The system was ideated to be installed on board and operating during service, to provide early detection and localization of potential damage, critical both in terms of safety and maintenance. The use of optical fibers for strain measurement is justified, on one hand, by the capability of pure silica fiber to prevent hydrogen darkening effects and, on the other hand, by the absence of metal components, which eliminates the risk of embrittlement. In detail, distributed and fiber Bragg grating FBG sensors designed for this specific application have demonstrated reliable monitoring capabilities, even after exposure to hydrogen and at cryogenic temperatures. Furthermore, another key contribution of this preliminary activity is the analysis of thermoplastic material faults by correlating damage characteristics with static and dynamic response. This is due to the fact that the investigated physics strongly depend on the nature of occurring damage. Achievements lie in the demonstrated ability to assess the health status of the reference composite structure, establishing the first steps for a future qualification of the proprietary system, made of commercial and original hardware and software. 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

Background: Thallium is a metal that is ubiquitous in our natural environment. Despite its potential for high toxicity, thallium is understudied and not regulated in food. The California Department of Public Health was alerted to a household cluster of elevated urine thallium levels noted among a mother (peak 5.6 µg/g creatinine; adult reference: ≤0.4 µg/g creatinine) and her three young children (peak 10.5 µg/g creatinine; child reference: ≤0.8 µg/g creatinine). Objectives: This case report identifies questions raised after a public health investigation linked a household’s thallium exposure to a commercially available food product. We provide an overview of the public health investigation. We then explore concerns, such as gaps in toxicological data and limited surveillance of thallium in the food supply, which make management of individual and population exposure risks challenging. Methods: We highlight findings from a cross-agency investigation, including a household exposure survey, sampling of possible environmental and dietary exposures (ICP-MS analysis measured thallium in kale chips at 1.98 mg/kg and 2.15 mg/kg), and monitoring of symptoms and urine thallium levels after the source was removed. We use regulatory and research findings to describe the challenges and opportunities in characterizing the scale of thallium in our food supply and effects of dietary exposures on health. Discussion: Thallium can bioaccumulate in our food system, particularly in brassica vegetables like kale. Thallium concentration in foods can also be affected by manufacturing processes, such as dehydration. We have limited surveillance data nationally regarding this metal in our food supply. Dietary reviews internationally show increased thallium intake in toddlers. Limited information is available about low-dose or chronic exposures, particularly among children, although emerging evidence shows that there might be risks associated at lower levels than previously thought. Improved toxicological studies are needed to guide reference doses and food safety standards. Promising action towards enhanced monitoring of thallium is being pursued by food safety agencies internationally, and research is underway to deepen our understanding of thallium toxicity. 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 a comprehensive evaluation and selection framework for extremely low-frequency electromagnetic field (ELF-EMF) measurement instruments. Recognizing the diversity of application environments and technical constraints, the framework addresses the challenges of selecting appropriate tools for specific scenarios. It integrates a structured, quantitative approach through a weighted scoring matrix that evaluates instrumentation across six criteria: monitoring duration, sensitivity, environmental adaptability, biological/regulatory relevance, usability, and cost. Complementing this is a logic-based flowchart that visually guides decision-making based on user-defined operational needs. The framework is applied to a realistic occupational case study, demonstrating its effectiveness in producing evidence-based, scenario-sensitive instrument recommendations. This method provides stakeholders with a transparent and adaptable tool for ELF-EMF device selection. 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

Background: Undercover police work is a psychologically high-risk profession that exposes officers to chronic stress, identity conflicts, and moral dilemmas. The aim of the present review is to evaluate the psychological consequences associated with undercover police work, focusing on specific psychopathological risk factors. Methods: A systematic search was conducted in PubMed, PsycINFO, Web of Science, and Scopus databases. Studies were conducted in the United States, the United Kingdom, New Zealand, and Canada. The present systematic review analyzed data from 380 current undercover operatives, 372 former UCOs, 578 officers without undercover experience, and 60 pre-operational agents. Results: From an initial pool of 365 records, 10 studies were identified, of which 6 met the inclusion criteria. The most frequently reported psychological risk factors included anxiety, hypervigilance, identity issues, dissociative symptoms, and substance misuse. These were assessed using validated self-report instruments (e.g., SCL-90), structured interviews, and clinical evaluations. Long-term consequences were more prominent post-deployment, particularly among former UCOs. Conclusions: Undercover work is associated with an elevated risk of mental health problems, especially after the end of operations. Future research should focus on standardizing assessment tools and identifying protective factors. The findings support the development of targeted interventions such as pre-deployment psychological screening, ongoing monitoring, and structured reintegration programs to safeguard UCOs’ well-being. Full article

Smart walking aids represent a growing trend in assistive technologies designed to support individuals with mobility impairments in their daily lives and rehabilitation. Previous research has introduced sensor-integrated systems that provide user feedback to enhance safety and functional mobility. However, a comprehensive overview of their technological and functional characteristics is lacking. To address this gap, this scoping review systematically mapped the current state of research in sensor-based walking aids, focusing on device types, sensor technologies, application contexts, target populations, and reported outcomes. In addition, integrated artificial intelligence (AI)-based approaches for functional support and health monitoring were examined. Following PRISMA-ScR guidelines, 35 peer-reviewed articles were identified from three databases: ACM Digital Library, IEEE Xplore, and Web of Science. Extracted data were thematically analyzed and synthesized across device types (e.g., walking canes, crutches, walkers, rollators) and use cases, including gait training, fall prevention, and daily support. Findings show that, while many prototypes show promising features, few have been evaluated in clinical settings or over extended periods. A lack of standardized methods for sensor location assessment, often the superficial implementation of feedback modalities, and limited integration with other assistive technologies were identified. In addition, system validation and user testing lack consensus, with few long-term studies and often incomplete demographic data. Diversity in data communication approaches and the heterogeneous use of AI algorithms were also notable. The review highlights key challenges and research opportunities to guide the future development of intelligent, user-centered mobility systems. Full article

Depression represents one of the most prevalent mental health disorders globally, significantly impacting quality of life and posing substantial healthcare challenges. Traditional diagnostic methods rely on subjective assessments and clinical interviews, often leading to misdiagnosis, delayed treatment, and suboptimal outcomes. Recent advances in biosensing technologies offer promising avenues for objective depression assessment through detection of relevant biomarkers and physiological parameters. This review examines multi-modal biosensing approaches for depression by analyzing electrochemical biosensors for neurotransmitter monitoring alongside wearable sensors tracking autonomic, neural, and behavioral parameters. We explore sensor fusion methodologies, temporal dynamics analysis, and context-aware frameworks that enhance monitoring accuracy through complementary data streams. The review discusses clinical validation across diagnostic, screening, and treatment applications, identifying performance metrics, implementation challenges, and ethical considerations. We outline technical barriers, user acceptance factors, and data privacy concerns while presenting a development roadmap for personalized, continuous monitoring solutions. This integrative approach holds significant potential to revolutionize depression care by enabling earlier detection, precise diagnosis, tailored treatment, and sensitive monitoring guided by objective biosignatures. Successful implementation requires interdisciplinary collaboration among engineers, clinicians, data scientists, and end-users to balance technical sophistication with practical usability across diverse healthcare contexts. Full article

Cyclin-dependent kinase (CDK)4/6 inhibitors have become a key component of adjuvant treatment for patients with hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2−) early breast cancer who are at high risk of recurrence. The addition of abemaciclib and ribociclib to standard endocrine therapy has demonstrated clinically meaningful improvements in invasive disease-free survival, supported by the monarchE and NATALEE trials, respectively. With expansion of patient eligibility for CDK4/6 inhibitors, multidisciplinary coordination among medical oncologists, surgeons, nurses, pharmacists, and other health care providers is critical to optimizing patient identification, monitoring, and management of adverse events. This expert guidance document provides practical recommendations for implementing adjuvant CDK4/6 inhibitor therapy in routine clinical practice, incorporating insights from multiple specialties and with patient advocacy representation. Key considerations include patient selection based on clinical trial data, treatment duration, dosing schedules, adverse event profiles, monitoring requirements, drug–drug interactions, and patient-specific factors such as tolerability, cost, and quality of life. This guidance aims to support Canadian clinicians in effectively integrating CDK4/6 inhibitors into clinical practice, ensuring optimal patient outcomes through a multidisciplinary and patient-centric approach. Full article