Search Results (4,871)

Fabry–Perot resonator (FPR) sensors are widely implemented in optical and microwave waveguides because their interference fringe spectra enable highly sensitive, stable, and calibration-free measurements. In contrast, despite the extensive use of beams and plates as waveguides in vibration- and ultrasound-based structural health monitoring (SHM), an explicit FPR framework for these mechanical waveguides has not been established. This paper demonstrates that flexural beams can be rigorously treated as FPRs despite their inherently dispersive nature. Through analytical derivation, wave-propagation analysis, and fringe-based group-velocity extraction, we show that flexural-beam resonances arise from multi-reflection interference analogous to Fabry–Perot interference. A closed-form relationship between the frequency-dependent group velocity and the FPR free spectral range (FSR) is established, enabling inverse determination of mechanical or environmental perturbance from the FPR fringe spectrum. By extending FPR-based fringe analysis to dispersive mechanical waveguides, this work introduces a theoretical framework for implementing dispersive mechanical waveguide-based FPR sensors. Full article

Accurately and promptly acquiring the load history characteristics of impact events on composite aircraft structures is crucial for identifying impact-induced damage and developing high-fidelity digital twin models. To address this need, we propose a method for reconstructing the impact load history on composite structures, leveraging Generalized Cross-Validation (GCV) and a Fiber Bragg Grating (FBG) pattern. An equivalent expansion technique based on discretized time-domain sparse strain sampling is developed to mitigate the local distortion of impact response signals, a common issue arising from the low sampling rates of quasi-distributed FBG. By incorporating Tikhonov regularization, the ill-posed nature of the impact frequency response matrix is effectively managed. Furthermore, an adaptive optimization method based on the GCV criterion is introduced to overcome the limitations of manually selecting regularization parameters and the associated constraints on noise suppression. The results show that the proposed GCV-based reconstruction method achieves an average peak relative error of 11.4% and an average root mean square error of 0.36 N for the reconstructed impact load, demonstrating that the proposed method synergistically enhances both the reconstruction of the overall impact load waveform profile and the precise characterization of transient details, even with low-rate sampling. This provides robust technical support for health monitoring and condition-based maintenance of composite structures. Full article

Background: Ultrasonography (US) has emerged as a non-invasive method for anatomical and functional evaluation of upper airway structures in adult obstructive sleep apnea (OSA). However, its role in severity stratification, dynamic assessment, elastographic characterization, and therapeutic monitoring remain to be investigated. Background/Objectives: The goal herein is thus to systematically review and synthesize available evidence on US assessment in adults with OSA, including structural parameters, dynamic measurements, correlation with the apnea–hypopnea index (AHI), integration with artificial intelligence, and evaluation of myofunctional therapy outcomes. Methods: A PRISMA-compliant systematic review of 19 studies (2007–2025) was conducted, evaluating US in adult patients with polysomnography-diagnosed OSA. Observational, pilot, case–control, and exploratory studies were included. Risk of bias was assessed using the National Institutes of Health Quality Assessment Tool for observational studies. Due to methodological heterogeneity, a structured qualitative meta-analytic synthesis was performed. Results: The tongue base was the most frequently studied structure. Increased tongue thickness, area, and stiffness were consistently associated with higher AHI. Elastography revealed increased intrinsic rigidity in patients with OSA. Dynamic US correlated with drug-induced sleep endoscopy findings and hyoid displacement. Machine learning integration improved severity prediction. A single study evaluated anatomical changes following myofunctional therapy, representing a nascent research area. US may become a complementary, non-invasive tool for anatomical and functional assessment of upper airway structures in adult OSA. Conclusions: Further standardization of acquisition protocols and well-designed longitudinal studies are needed to clarify the clinical role of US in phenotyping and therapeutic monitoring. Full article

The pitot tube is the core sensor for aircraft to obtain external atmospheric data, and its failure has a very important impact on flight safety. However, as its structure and principle are relatively simple, all manufacturers have not adopted available monitoring methods for its health status due to the perspective of cost and complexity reduction. The pitot tube fault warning method is conducted in this paper with a fully connected neural network (FCNN) method based on the data collected by the pitot tube itself. By constructing and selecting parameters and extracting fault features from flight record data, a pitot tube fault warning model based on an FCNN is constructed. The effectiveness of the proposed method is verified through pitot tube fault warning experiments based on actual flight record data, which can provide technical reference for pitot tube fault warning during aircraft route operation in the future. Full article

Background: Gastric cancer survivors frequently encounter a “care gap” after discharge because of complex postgastrectomy syndromes. We evaluated “WECARE,” a bidirectional digital health platform designed to provide real-time symptom monitoring and multidisciplinary support. The primary goal of this study was to assess the efficacy of the platform by measuring the change in the Korean Quality of Life Questionnaire for Gastric Cancer Survivors (KOQUSS-40) total score over a six-month recovery period. Methods: This nationwide, multicenter pilot randomized controlled trial was conducted by the Korean Quality of Life in Stomach Cancer Patients Study Group (KOQUSS) across nine tertiary centers in Korea. A total of 88 patients who underwent curative gastrectomy were enrolled. Following an initial optimization phase involving 22 patients, the remaining 66 patients were randomized at a 1:1 ratio to the WECARE group or the control group. The WECARE group used a platform integrating the KOQUSS-40 algorithm for structured symptom reporting, real-time feedback on nutrition and exercise, and educational content on meal planning, symptom coping, and recovery. Assessments were performed at baseline and at 1, 3, and 6 months after discharge. Results: The WECARE group showed high feasibility and acceptability, with an adherence rate of 86.7% and an 82% satisfaction rate. At 6 months, the KOQUSS-40 total score (primary endpoint) did not differ significantly between the WECARE and control groups (85.3 ± 1.6 vs. 83.8 ± 1.6, p = 0.603). However, the WECARE group showed a numerically favorable recovery trajectory from the acute postoperative phase. Subgroup analysis revealed a positive trend in reflux symptom management in the WECARE group (p = 0.0856). In addition, more than 77% of users reported that the platform improved their self-management capabilities. Conclusions: The WECARE platform is a feasible and acceptable digital intervention for gastric cancer survivors. Although the primary endpoint was not significantly different, the favorable recovery trajectory, high adherence, and patient engagement support further evaluation in larger studies with longer follow-up and broader healthcare settings. Full article

Masonry arch bridges are critical assets in aging transportation networks, yet their Structural Health Monitoring (SHM) remains challenging. Smart bricks—piezoresistive sensing units compatible with masonry structures and capable of acting simultaneously as load-bearing components and strain sensors—offer a promising solution for embedding self-sensing capability directly within the masonry. While previous work by the authors has investigated their use in masonry walls, their application to arched structures remains unexplored. This gap is particularly significant given that arches, characterized by a predominantly compressive stress state, represent a natural context for smart-brick implementation. This study presents a numerical investigation assessing the potential of smart bricks for strain-based SHM of masonry arch bridges. A Finite Element (FE) model, derived from a validated experimental benchmark representative of typical Italian railway arch bridges, was used to virtually embed smart bricks at selected cross-sections along the arch. Damage progression was simulated through cyclic loading–unloading stages, enabling direct correlation between strain evolution and structural deterioration. Results demonstrate that smart bricks accurately capture damage-driven strain redistributions, closely mirroring both the sequence of damage formation and the associated collapse mechanism. These findings support the use of smart bricks for early detection of localized structural changes in masonry arches, providing a foundation for future experimental validation and real-world deployment of minimally invasive SHM systems. Full article

Cardiovascular diseases are the primary causes of mortality worldwide, often characterized by subtle onset and acute progression. Traditional ECG electrodes may cause skin irritation, limiting routine monitoring and early risk assessment. Relying on the advantages of non-contact monitoring, millimeter-wave radar-based cardiac monitoring combined with deep learning has become a popular research direction recently. To overcome the poor generalization of methods trained from single-source datasets, this study designed seven experimental scenarios covering wakefulness and sleep. A novel deep learning network consisting of encoder and decoder structures named PMG-SATNet was proposed. The encoder comprises a parallel multi-scale feature extraction module and a global temporal relationship modeling module to capture fine-grained local patterns and long-range dependencies. The decoder employs a temporal convolutional network augmented with a spectral attention mechanism to emphasize clinically relevant ECG frequency bands and suppress respiration and body motion interference. After being validated on the self-built dataset, PMG-SATNet outperformed baseline models in terms of Pearson correlation coefficient and root mean square error, with an improvement of 3.3% and 3.8%, and 16.4% and 23.8%, respectively. The validation results imply that PMG-SATNet is capable of recovering ECG signals from millimeter-wave radar-derived chest vibrations with high fidelity and can potentially be implemented in real-life cardiac health monitoring. Full article

Notable advances in modal analysis in the last 50 years have paved the way for more widespread use of modal parameters, including those from in situ measurements, in Structural Health Monitoring and finite element model updating. Current state-of-the-art techniques in output-only modal analysis include Stochastic Subspace Identification techniques, such as Canonical Variate Analysis (SSI), and the Natural Excitation Technique with the Eigensystem Realization Algorithm (NExT-ERA). The former have been shown to struggle on very large systems and the latter suffers from the usual fitting problems arising in noisy environments. In this work, an output-only version of the frequency domain technique known as the Loewner Framework (LF) is pioneeringly applied to an aeronautical system. The implementation pairs the LF with NExT (NExT-LF) to exploit the fitting process efficiency of the former and robustness to noise of the latter. The thus-defined NExT-LF is then applied to the well-known experimental benchmark of the eXperimental BeaRDS 2 high-aspect-ratio wing main spar. The results are compared to the known experimental values and those obtained from SSI and NExT-ERA. Full article

Flexible resistive sensors are promising for wearable and ergonomic applications because they can be easily fabricated on textiles or flexible substrates and enable real-time monitoring of human movement and posture, especially in health monitoring systems. This review presents an overview of recent developments in an interdisciplinary way and summarises advances in materials, fabrication methods, and ergonomic applications. A structured literature search was conducted across major databases, including only studies focused on resistive sensing. The selected works were analysed in terms of conductive materials, fabrication techniques (e.g., direct ink writing (DIW) and textile-based methods), and their integration into wearable systems. Flexible resistive sensors are widely used for monitoring joint motion, posture, and physiological signals in healthcare and industrial environments. However, several challenges remain, including limitations in sensitivity, signal stability, material durability, and the need for reliable calibration in real-world conditions. This review highlights current progress and existing limitations and outlines future research directions toward more robust and user-friendly wearable sensing solutions for ergonomic applications. Full article

Microbial contamination in aquatic environments poses severe threats to aquaculture sustainability, ecological balance and public health. Traditional culture-based detection methods, while standardized, are time-consuming and labor-intensive, often failing to meet the urgent need for rapid on-site monitoring required to prevent disease outbreaks and manage water quality effectively. By integrating latest research advances (2020–2025), this study reviews advances in rapid detection technologies for aquatic microorganisms, including the evolution of nucleic acid amplification strategies, with a focused comparison of the analytical sensitivity and field deployability of quantitative polymerase chain reaction (qPCR) and mainstream isothermal amplification techniques (loop-mediated isothermal amplification, LAMP; recombinase polymerase amplification, RPA). Furthermore, this study reports on the emergence of Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)-associated protein (Cas) systems as next-generation diagnostic tools, highlighting their integration with microfluidic Lab-on-a-Chip (LOC) platforms to achieve attomolar sensitivity. We also consider the application of portable nanopore sequencing for real-time pathogen identification and the growing role of Artificial Intelligence (AI) in analyzing complex diagnostic datasets. Advanced molecular methods have achieved significant reductions in time consumption—from days to less than one hour—while challenges regarding sample preparation and environmental matrix inhibition remain. The future of aquatic monitoring lies in integrated, automated systems that combine the specificity of CRISPR-Cas diagnostics with the connectivity of IoT-enabled biosensors. Comparative analysis indicates that isothermal amplification methods (LAMP, RPA) coupled with CRISPR-Cas systems offer the optimal balance of sensitivity, speed, and field deployability for point-of-care aquaculture diagnostics, while qPCR/dPCR remain indispensable for quantitative regulatory applications. We propose a structured technology selection framework to guide researchers and practitioners in choosing appropriate detection modalities based on specific sensitivity, cost, throughput, and deployment requirements. Full article

Early diagnosis of ovarian cancer remains one of the most important unmet needs in gynecologic oncology because survival is strongly stage-dependent and most patients still present with disseminated disease. Conventional non-invasive tools, particularly CA-125, transvaginal ultrasound, and composite triage algorithms, remain clinically useful but are limited by suboptimal sensitivity for stage I disease and by reduced specificity in premenopausal women and in benign inflammatory or endometriosis-associated conditions. Circulating tumor DNA (ctDNA) has therefore emerged as a candidate biomarker capable of extending liquid biopsy beyond conventional serology. In ovarian cancer, however, ctDNA implementation is constrained by low tumor shedding in early-stage disease, marked biologic heterogeneity across histotypes, clonal hematopoiesis-related background noise, and major pre-analytical and analytical sources of variability. This narrative review, informed by structured searches of PubMed, Scopus, and Web of Science, examines the evolving evidence for ctDNA mutations, methylation-based assays, multi-omic platforms, and machine-learning models across three distinct clinical contexts: population screening, preoperative triage of adnexal masses, and post-treatment assessment of molecular residual disease. We also discuss positive predictive value, false-positive harms, health-economic implications, standardization initiatives, and ongoing prospective studies. Overall, current evidence suggests that the most plausible near-term role for liquid biopsy in ovarian cancer is not as a universal stand-alone screening test, but as an integrated component of risk stratification and disease-monitoring frameworks that combine molecular signals with clinicopathologic and imaging data. Full article

This study proposes a comprehensive methodology for the experimental characterization and non-linear dynamic modelling of Polycrystalline Diamond (PCD) bearings, establishing a high-fidelity digital twin approach for the condition monitoring of rotating machinery. The research addresses complex rotor–stator interactions through the development of a multibody numerical framework. A structural 1D Finite Element (FE) model of the stator assembly was first calibrated via experimental modal analysis, achieving a high correlation with the first four bending modes and a maximum frequency discrepancy of only 1.4%. This validated structure was integrated into a non-linear multibody environment to simulate transient rub-impact events at rotational speeds up to 5500 rpm across varying clearance configurations. The model successfully captures the transition from stable periodic orbital motion to the stochastic and chaotic regimes observed in high-clearance setups. Frequency-domain validation further confirms the model’s accuracy in identifying supersynchronous harmonics and energy distribution patterns. Quantitative analysis shows that high-clearance configurations generate impact forces exceeding 6000 N, providing critical data for structural health assessment. These results demonstrate that the proposed digital twin serves as a robust physical foundation for diagnostic systems, enabling the identification of contact-induced vibrational signatures that are essential for training prognostic algorithms. This approach facilitates the autonomous monitoring of critical rotating machinery in demanding industrial and subsea applications, supporting the transition toward active balancing and model-based vibration control strategies. Full article

High-strength bolts are critical structural components that are highly susceptible to corrosion in complex environments, posing significant threats to structural safety and reliability. Although acoustic emission (AE) technology has been widely applied in structural health monitoring, existing studies mainly focus on damage mode identification or source localization, while the identification of corrosion evolution stages based on AE signals remains insufficient. This study develops an intelligent corrosion diagnosis framework for high-strength bolts by integrating multimodal feature fusion and optimized machine learning. AE signals are first collected from the near-end and far-end of bolts using a wireless sensor network and then transformed into time–frequency representations via continuous wavelet transform (CWT). The resulting time–frequency images are fed into a modified ResNet-18 network to extract deep features, while statistical features are simultaneously extracted from the raw signals to preserve global information. These heterogeneous features are subsequently fused to form a comprehensive representation of corrosion characteristics. Furthermore, an artificial protozoa optimizer (APO) is introduced to adaptively optimize the hyperparameters of the XGBoost model. The results demonstrate that AE signals generated by hammering bolts with different corrosion levels can be successfully distinguished. The proposed method achieves high accuracy in corrosion stage classification and outperforms conventional approaches. Even when evaluated on an additional M30 bolt dataset, the proposed method maintains robust performance, demonstrating excellent generalization capability across different bolt sizes. These results demonstrate the practical potential of the proposed method for intelligent bolt corrosion diagnosis. Full article

The occurrence of Escherichia coli resistant to high-priority critically important antimicrobials (HPCIA) in the food chain is a growing concern for food safety and public health. This study aimed to evaluate whether HPCIA-resistant E. coli isolated from pork and chicken meat at retail markets in La Plata, Buenos Aires, Argentina, exhibit source-associated genomic differentiation through whole-genome sequencing. The isolates displayed a polyclonal population structure, encompassing multiple phylogenetic groups and sequence types. Virulence gene profiles were highly diverse, with chicken-derived isolates harbouring a substantially higher number of virulence genes than pork isolates. Notably, one pork isolate carried a complete set of virulence genes characteristic of diarrheagenic E. coli. Single Nucleotide Polymorphism-based phylogenetic analysis revealed several closely related subclusters, including strains recovered from both pork and chicken meat from the same retail markets, suggesting recent clonal sharing or cross-contamination at the point of sale. These findings highlight the circulation of genetically diverse HPCIA-resistant E. coli in retail meat, underscoring the potential public health risk and the importance of monitoring resistance and virulence determinants throughout the food production chain. Full article

Bovine spongiform encephalopathy (BSE) is a fatal transmissible spongiform encephalopathy caused by the misfolding of the host prion protein (PrP), representing a unique intersection between molecular pathology, neuroanatomy, and public health regulation. Although historically framed as a single feedborne epizootic, BSE is now recognized as a spectrum of strain-defined prion disorders encompassing classical and atypical forms with distinct origins, neuroanatomical trajectories, and surveillance implications. This review integrates advances in prion biology, neurodegenerative mechanisms, and anatomical pathways of neuroinvasion to reframe BSE as a heterogeneous disease entity. We synthesize evidence on PrP^C structure, trafficking, and proteolytic processing to explain how normal cellular physiology enables strain-specific conversion to pathogenic PrP^Sc and subsequent neurotoxicity. Distinct patterns of neuroinvasion and regional vulnerability are discussed for classical versus atypical (H- and L-type) BSE, highlighting differences in lymphoid involvement, brainstem targeting, and cortical or cerebellar tropism. We further examine how these biological differences translate into diagnostic sensitivity, surveillance design, and zoonotic risk assessment. By integrating molecular strain diversity with neuroanatomical connectivity, this review underscores the limitations of obex-centered surveillance for atypical BSE and emphasizes the need for proportionate yet precautionary monitoring strategies. These considerations should be interpreted in light of surveillance-dependent detection biases, which influence the apparent distribution of BSE forms. Ultimately, BSE emerges as a critical model for understanding how protein misfolding disorders bridge cellular mechanisms, animal health, and human public health policy. Full article