Search Results (791)

Strain gauges play a crucial role in numerous fields such as bridge and building structural health monitoring. However, traditional strain gauges generate spurious signals due to the temperature effect, which in turn affects their measurement accuracy. Herein, we propose a resistance strain gauge based on a double-layer composite film, which is characterized by an adjustable resistance temperature coefficient (TCR), an ultra-near-zero temperature effect, and good TCR repeatability. It is precisely through the combination of materials with positive and negative TCR, leveraging their opposing temperature resistance characteristics, that a low temperature effect has been achieved. Compared with the single-layer alloy-based strain gauge, the developed strain gauge based on double-layer composite film has greatly reduced sensitivity to temperature interference, and its TCR can be reduced to a ultra-near-zero value, approximately 0.8 ppm/°C, while the stability of TCR is excellent. In addition, the gauge factor of the strain gauge is 1.83, and it maintains excellent linearity. This work fully highlights the potential application value of the developed strain gauge in stress monitoring of bridges and building structures. Full article

Sustainable food production for a growing population requires farming practices that reduce chemical inputs while maintaining soil as a living, renewable foundation for productivity. This review synthesizes current advances in understanding how endophytic fungi (EFs) interact with the soil microbiome and contribute to the physicochemical and biological dimensions of soil health in arable ecosystems. We examine evidence showing that EFs enhance plant nutrition through phosphate solubilization, siderophore-mediated micronutrient acquisition, and improved nitrogen use efficiency while also modulating plant hormones and stress-responsive pathways. EFs further increase crop resilience to drought, salinity, and heat; suppress pathogens; and influence key soil properties including aggregation, organic matter turnover, and microbial network stability. Recent integration of multi-omics, metabolomics, and community-level analyses has shifted the field from descriptive surveys toward mechanistic insight, revealing how EFs regulate nutrient cycling and remodel rhizosphere communities toward disease-suppressive and nutrient-efficient states. A central contribution of this review is the linkage of EF-mediated plant functions with soil microbiome dynamics and soil structural processes framed within a translational pipeline encompassing strain selection, formulation, delivery, and field scale monitoring. We also highlight current challenges, including context-dependent performance, competition with native microbiota, and formulation and deployment constraints that limit consistent outcomes under field conditions. By bridging microbial ecology with agronomy, this review positions EFs as biocontrol agents, biofertilizers, and ecosystem engineers with strong potential for resilient, low-input, and climate-adaptive cropping systems. Full article

Background: Nutritional status assessment is the cornerstone of the Nutrition Care Process, guiding diagnosis, intervention, and monitoring. The classical ABCD model (Anthropometry, Biochemical, Clinical, Dietary) has been widely applied; however, it presents limitations in addressing current nutritional and epidemiological challenges. Objective: This narrative review aims to synthesize and update the scientific evidence on the expanded nutritional assessment model, known as ABCDEFG, which incorporates the Ecological–microbiota (E), Functional (F), and Genomic–nutrigenomic (G) approaches. Methods: A narrative review of the literature was conducted through PubMed, Scopus, and Web of Science, covering publications from 2013 to 2025. Articles were selected based on relevance to at least one of the seven assessment domains. Findings were synthesized descriptively and critically, highlighting applications, strengths, and limitations. Results: The ABCDEFG framework offers a multidimensional perspective of nutritional assessment. While anthropometric, biochemical, clinical, and dietary methods remain essential, the inclusion of ecological dimensions (gut microbiota, environmental influences), functional measures (e.g., muscle strength, physical performance), and genomics enables a more sensitive and personalized evaluation. This integrative approach supports better clinical decision-making and research innovation in nutrition and health sciences. Conclusions: The seven-method model broadens the scope of nutritional assessment, bridging traditional and emerging tools. Its application enhances the capacity to identify nutritional risks, design targeted interventions, and advance precision nutrition. Full article

Accurate detection of structural damage is essential for ensuring the safety and reliability of bridges. However, traditional vibration-based approaches often struggle to capture rich feature representations and adequately model spatial dependencies among sensors. This study proposes a novel bridge damage detection framework, LGSTA-GNN, which integrates local–global spatiotemporal learning with graph neural networks. The framework first extracts multi-scale temporal–frequency features using a multi-scale feature extraction module. A local graph feature extraction module then models intrinsic spatial relationships through graph convolutions, while a global graph attention module adaptively captures inter-sensor dependencies by emphasizing structurally informative nodes. A benchmark dataset generated from a scaled bridge model under progressive damage states is used to evaluate the proposed method. Extensive experiments demonstrate that LGSTA-GNN outperforms multiple graph neural network variants and conventional deep learning techniques, achieving superior accuracy, precision, recall, and F1-score. The confusion matrix and t-SNE visualization further verify its enhanced discriminative capability and robustness. Ablation studies confirm the contribution of each module, highlighting the effectiveness of global attention in identifying subtle structural deterioration. Overall, LGSTA-GNN provides an effective and interpretable solution for intelligent bridge damage detection, with strong potential for practical structural health monitoring and real-time safety assessment. Full article

Structural Health Monitoring (SHM) of large-scale civil infrastructure is essential to ensure safety, minimise maintenance costs, and support informed decision-making. Unsupervised anomaly detection has emerged as a powerful tool for identifying deviations in structural behaviour without requiring labelled damage data. The study initially reproduces and implements a state-of-the-art methodology that combines local density estimation through the Cumulative Distance Participation Factor (CDPF) with Semi-parametric Extreme Value Theory (SEVT) for thresholding, which serves as an essential baseline reference for establishing normal structural behaviour and for benchmarking the performance of the proposed anomaly detection framework. Using modal frequencies extracted via Stochastic Subspace Identification from the Z24 bridge dataset, the baseline method effectively identifies structural anomalies caused by progressive damage scenarios. However, its performance is constrained when dealing with subtle or non-linear deviations. To address this limitation, we introduce an innovative ensemble anomaly detection framework that integrates two complementary unsupervised methods: Principal Component Analysis (PCA) and Autoencoder (AE) are dimensionality reduction methods used for anomaly detection. PCA captures linear patterns using variance, while AE learns non-linear representations through data reconstruction. By leveraging the strengths of these techniques, the ensemble achieves improved sensitivity, reliability, and interpretability in anomaly detection. A comprehensive comparison with the baseline approach demonstrates that the proposed ensemble not only captures anomalies more reliably but also provides improved stability to environmental and operational variability. These findings highlight the potential of ensemble-based unsupervised methods for advancing SHM practices. Full article

To address the limitations of existing cable damage identification methods in terms of environmental robustness and measurement dependency, this study proposes a novel damage identification approach based on the second-order difference characteristics of main beam deflection. Through theoretical derivation, the intrinsic relationship between cable damage and local deflection field disturbances in the main beam was revealed, leading to the innovative definition of a second-order difference of deflection (DISOD) index for damage localization. By analyzing the second-order deflection differences at the anchorage points of a three-cable group (a central cable and its two adjacent cables), the damage status of the central cable can be directly determined. The research comprehensively employed finite element numerical simulations and scaled model experiments to systematically validate the method’s effectiveness in identifying single-cable and double-cable (both adjacent and non-adjacent) damage scenarios under various noise conditions. This method enables damage localization without direct cable force measurement, demonstrates anti-noise interference capability, achieves rapid and accurate identification, and provides a technically promising solution for the health monitoring of long-span cable-stayed bridges. Full article

Bridge health monitoring is crucial for ensuring the safety and durability of infrastructure. In traditional methods, physics-based models have high interpretability but are difficult to handle complex nonlinear problems, while purely data-driven machine learning methods are limited by data scarcity and physical inconsistency. Physics-informed machine learning, as an emerging “gray box” paradigm, effectively integrates the advantages of both by embedding physical laws (such as control equations) into machine learning models in the form of constraints, priors, or residuals. This article systematically elaborates on the core fusion mechanism of physics-informed machine learning (PIML) in bridge engineering, innovative applications throughout the entire lifecycle of design, construction, operation, and maintenance, as well as its unique data augmentation strategy. Research has shown that PIML can significantly improve the accuracy and robustness of damage identification, load inversion, and performance prediction, and is the core engine for constructing dynamic and predictive digital twin systems. Despite facing challenges in complex physical modeling, loss function balancing, and engineering interpretability, PIML represents a fundamental shift in bridge health monitoring towards intelligent and predictive maintenance by combining advanced strategies such as active learning and meta learning with IoT technology. Full article

Structural health monitoring (SHM) increasingly contributes to the safety and durability of key infrastructure, especially bridges. This research introduces a rotation-based approach for damage detection and quantification using a damage candidate grid search technique (DCGS) on simply supported girder bridges under quasi-static or slowly moving loading conditions. Applying the principle of virtual work, the healthy and candidate-damaged rotation responses are analytically obtained and compared with the rotation observed directly at the moving load location. Damage is defined in terms of three key parameters: the start and the end of the damage, $L_1$ and $L_2$, respectively, and the damage severity $\beta$. The DCGS method is validated using finite element model simulations of 12 damage scenarios subjected to different noise levels. A statistical analysis and confidence interval characterize the accuracy and consistency of the top ten estimations produced by the DCGS method. A damage length ratio (DLR), defined from the span of the beam, $L$, and the damage location, $L_1$ and $L_2$, improves the robustness of the methodology against measurement noise by reducing possible false positive estimations. Additionally, the experimental results on two beam structures further validate the method. Absolute relative errors (AREs) of about 6% and absolute errors (AEs) of around 0.16 between the estimated and real damage parameters characterize the performance of the technique, considering damage location and damage severity, respectively. The results show that the DCGS methodology can effectively locate damage and estimate its severity in the presence of noise. The developed framework provides a sensitive and practical SHM tool that is suitable for early damage detection in railway and road bridges. Full article

Within the field of the structural monitoring of bridges, numerous technologies and methodologies have been developed. Among these, methods based on synthetic aperture radar (SAR) which utilise satellite data from missions such as Sentinel-1 (European Space Agency-ESA) and COSMO-SkyMed (Agenzia Spaziale Italiana—ASI) to capture displacements, temperature-related changes, and other geophysical measurements have gained increasing attention. However, SAR has yet to establish its value and potential fully; its broader adoption hinges on consistently demonstrating its robustness through recurrent applications, well-defined use cases, and effective strategies to address its inherent limitations. This study presents a systematic literature review (SLR) conducted in accordance with key stages of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework. An initial corpus of 1218 peer-reviewed articles was screened, and a final set of 25 studies was selected for in-depth analysis based on citation impact, keyword recurrence, and thematic relevance from the last five years. The review critically examines SAR-based techniques—including Differential Interferometric SAR (DInSAR), multi-temporal InSAR (MT-InSAR), and Persistent Scatterer Interferometry (PSI), as well as approaches to integrating SAR data with ground-based measurements and complementary digital models. Emphasis is placed on real-world case studies and persistent technical challenges, such as atmospheric artefacts, Line-of-Sight (LOS) geometry constraints, phase noise, ambiguities in displacement interpretation, and the translation of radar-derived deformations into actionable structural insights. The findings underscore SAR’s significant contribution to the structural health monitoring (SHM) of bridges, consistently delivering millimetre-level displacement accuracy and enabling engineering-relevant interpretations. While standalone SAR-based techniques offer wide-area monitoring capabilities, their full potential is realised only when integrated with complementary procedures such as thermal modelling, multi-sensor validation, and structural knowledge. Finally, this document highlights the persistent technical constraints of InSAR in bridge monitoring—including measurement ambiguities, SAR image acquisition limitations, and a lack of standardised, automated workflows—that continue to impede operational adoption but also point toward opportunities for methodological improvement. Full article

Background: West Nile Virus (WNV) is a mosquito-borne flavivirus responsible for seasonal outbreaks in temperate and tropical regions, including Europe, the Mediterranean, and the Middle East. Its transmission via mosquitoes, particularly Culex species, poses persistent challenges to public health. Despite ongoing efforts, comprehensive prevention and treatment strategies remain limited. Methods: A comprehensive search of peer-reviewed literature, clinical trials, and government surveillance data from Italy and Iran was conducted using PubMed, Scopus, Web of Science, and supplementary web-based resources. Inclusion criteria focused on molecular studies of WNV, vaccine and antiviral drug development, and regional outbreak reports. Results: WNV transmission is influenced by climatic conditions, as well as vector distribution and ecological patterns. While human vaccines are currently under development, only veterinary vaccines yielded promising but still limited evidence of effectiveness. Notably, therapeutic measures are currently limited to supportive care, whereas investigational antiviral drugs are in early-stage trials. Interestingly, Italy demonstrates robust surveillance with regular reporting of outbreaks, whereas data from Iran indicate that despite a widespread serological footprint, especially in southern and southwestern provinces, the reported clinical impact on humans and animals appears comparatively less severe. Conclusions: Bridging gaps in vaccine availability, therapeutic innovation, and disease monitoring is essential for effective WNV management to prepare for potential severe future outbreaks in Europe and the Middle East. On the other hand, regional differences between Italy and Iran reveal the need not only for tailored public health interventions and enhanced surveillance, but also for sustained investment in research. In our view, collaborative frameworks across Mediterranean and Middle Eastern countries in a “One Health” approach may improve preparedness and response to future WNV outbreaks. Full article

To address the stochastic, nonlinear, and strongly coupled characteristics of multivariate long-term structural response in bridge health monitoring, this study proposes the Informer-SEnet prediction model. The model integrates a Squeeze-and-Excitation (SE) channel attention mechanism into the Informer framework, enabling adaptive recalibration of channel importance to suppress redundant information and enhance key structural response features. A sliding-window strategy is used to construct the datasets, and extensive comparative experiments and ablation studies are conducted on one public bridge-monitoring dataset and two long-term monitoring datasets from real bridges. In the best case, the proposed model achieves improvements of up to 54.67% in MAE, 52.39% in RMSE, and 7.73% in R². Ablation analysis confirms that the SE module substantially strengthens channel-wise feature representation, while the sparse attention and distillation mechanisms are essential for capturing long-range dependencies and improving computational efficiency. Their combined effect yields the optimal predictive performance. Five-fold cross-validation further evaluates the model’s generalization capability. The results show that Informer-SEnet exhibits smaller fluctuations across folds compared with baseline models, demonstrating higher stability and robustness and confirming the reliability of the proposed approach. The improvement in prediction accuracy enables more precise characterization of the structural response evolution under environmental and operational loads, thereby providing a more reliable basis for anomaly detection and early damage warning, and reducing the risk of false alarms and missed detections. The findings offer an efficient and robust deep learning solution to support bridge structural safety assessment and intelligent maintenance decision-making. Full article

Background. Extensive research confirms that hormonal fluctuations during the menstrual cycle significantly influence female athletic performance, with profound implications for public health, including promoting equitable access to sports and enhancing women’s overall physical and mental well-being. Numerous scientifically validated methods are available to monitor hormonal status and menstrual cycle phases. However, our prior investigations revealed that these insights are rarely applied in practice due to the complexity and invasiveness of existing methods. This study examines the effects of hormonal fluctuations on elite female basketball players. It assesses practical, non-invasive, cost-effective, and field-applicable methods for hormonal monitoring, with a focus on cervical mucus analysis for estrogen crystallization. The goal is to optimize training, promote equity in women’s sports, and support public health strategies for female empowerment through sustained physical activity, addressing the limitations of male-centric training models. Materials and Methods. This exploratory field study employed a multifaceted approach, beginning with a comprehensive meta-analysis via literature searches on PubMed, SCOPUS, and Google Scholar to evaluate hormonal impacts on physical performance, supplemented by an expert survey of 20 sports scientists and coaches using Kendall’s concordance coefficient for reliability and an experimental phase involving 25 elite female Ukrainian basketball players assessed over three months through daily performance tests (e.g., sprints, jumps, agility drills, and shooting) integrated into six weekly training sessions, with cycle phases tracked via questionnaires, basal body temperature, and the fern leaf method for estrogen levels. Results. Performance peaked during the postmenstrual and post-ovulatory phases (e.g., a 7.5% increase in sprint time and a 5.1% improvement in running jump). It declined in the premenstrual phase (e.g., a 2.3% decrease in acceleration). The estrogen crystallization test using cervical mucus provided preliminary insights into hormonal status but was less precise than laboratory-based methods, such as LC-MS/MS, which remain impractical for routine use due to cost and complexity. The fern test and basal body temperature showed limited precision due to external factors. Conclusions. There is a critical need to develop simple, non-invasive, field-applicable devices for accurate, real-time hormonal monitoring. This will bridge the gap between research and practice, enhancing training personalization, equity in women’s fitness and sports, and public health outcomes by increasing female participation in physical activities, reducing gender-based health disparities, and fostering inclusive wellness programs. Full article

Stress has become one of the most pervasive health challenges in modern societies, contributing to cardiovascular, cognitive, and emotional disorders that degrade overall well-being and productivity. Continuous monitoring of stress in everyday settings is thus critical for preventive healthcare. Recent advances in wearable sensing technologies, particularly photoplethysmography (PPG)-based devices, have enabled unobtrusive measurement of physiological signals linked to stress. However, the analysis of such data increasingly relies on deep learning models whose complex and non-transparent decision mechanisms limit clinical interpretability and user trust. To address this gap, this study introduces a novel LLM-assisted explainable framework that combines data-driven analysis of photoplethysmography (PPG) features with physiological reasoning. First, handcrafted cardiac variability features such as Root Mean Square of Successive Differences (RMSSD), high-frequency (HF) power, and the percentage of successive NN intervals differing by more than 50 ms (pNN50) are extracted from wearable PPG signals collected in daily conditions. After algorithmic threshold selection via ROC–Youden analysis, an LLM is used solely for physiological interpretation and literature-based justification of the resulting rules. The resulting transparent rule set achieves approximately 75% binary accuracy, rivaling CNN, LSTM, Transformer, and traditional ML baselines, while maintaining full interpretability and physiological validity. This work demonstrates that LLMs can function as scientific reasoning companions, bridging raw biosignal analytics with explainable, evidence-based models—marking a new step toward trustworthy affective computing. Full article

Background: Due to the lack of curative treatments for inflammatory bowel disease (IBD), patients need lifelong follow-up care. Telehealth offers a valuable solution to balance routine visits with necessary monitoring. Objectives: To map what is known about the benefits and barriers encountered by nurses in their use of telehealth for the follow-up care of patients with IBD. Methods: Following the methodology from the Joanna Briggs Institute, we conducted a scoping review across four electronic databases from June 2024 to September 2025. Key search terms included “inflammatory bowel disease,” “nurse experience,” and “telehealth.” A content analysis was employed to summarize the key findings. Results: We screened 1551 records, ultimately including four original research articles from four countries. Benefits identified were as follows: (1) the vital contributions of IBD telenursing in empowering patients by bridging health literacy and self-care skills; (2) optimal use of staffing time supports patient-centred care; and (3) ease of use. Barriers included the following: (1) increased workload and task imbalances; (2) the need for customized interventions; (3) technical issues and concerns regarding the security of digital systems; (4) telehealth as a supplementary option or a standard procedure; and (5) concerns related to the patient–nurse relationship. Conclusions: Nurses view telehealth as a promising approach that enhances patients’ health literacy and self-care skills and improves patient outcomes through effective monitoring. To fully realize telehealth’s potential, implementing strategies like triage protocols, algorithmic alerts, electronic health record integration, and comprehensive nurse training to enhance patient care and engagement may be beneficial. This scoping review highlights the need for more research on nurses’ experiences with telehealth in IBD due to limited publications. Full article

The rapid urbanization of coastal regions has intensified the demand for durable underground infrastructure like shield tunnels, where reinforced concrete (RC) structures are critical yet susceptible to long-term degradation in marine environments. This study develops an integrated machine vision-based framework for assessing the long-term durability of RC in marine shield tunnels by synergistically combining point cloud analysis and deep learning-based damage recognition. The methodology involves preprocessing tunnel point clouds to extract the centerline and cross-sections, enabling the quantification of geometric deformations, including segment misalignment and elliptical distortion. Concurrently, an advanced YOLOv8 model is employed to automatically identify and classify surface corrosion damages—specifically water leakage, cracks, and spalling—from images, achieving high detection accuracies (e.g., 95.6% for leakage). By fusing the geometric indicators with damage metrics, a quantitative risk scoring system is established to evaluate structural durability. Experimental results on a real-world tunnel segment demonstrate the framework’s effectiveness in correlating surface defects with underlying geometric irregularities. This integrated approach offers a data-driven solution for the continuous health monitoring and residual life prediction of RC tunnel linings in marine conditions, bridging the gap between visual inspection and structural performance assessment. Full article