Search Results (4,106)

Hydrogen is one of the future green energy sources that could resolve issues related to fossil fuels. The widespread use of hydrogen can be enabled by composite-overwrapped pressure vessels for storage. It offers advantages due to its low weight and improved mechanical performance. However, the safe storage of hydrogen requires continuous monitoring. Combining ultrasonic guided waves with interpretable machine learning provides a powerful tool for structural health monitoring. In this study, we developed a feature extraction approach based on a similarity method that enables interpretability in the proposed machine learning model for damage detection and localization in pressure vessels. Furthermore, a systematic optimization was performed to explore and tune the model’s parameters. This resulting model provides accurate damage localization and is capable of detecting and localizing damage on hydrogen pressure vessels with an average localization error of 2 cm and a classification accuracy of 96.5% when using quantized classification. In contrast, binarized classification yields a higher accuracy of 99.5%, but with a larger localization error of 6 cm. Full article

Carbon fiber reinforced polymer (CFRP) composites are prone to developing localized material loss defects during long-term service, which can severely degrade their mechanical properties and structural reliability. To address this issue, this study proposes a multi-sensor synchronous monitoring method combining embedded fiber Bragg grating (FBG) sensors and surface-mounted electrical resistance strain gauges. First, finite element simulations based on the three-dimensional Hashin damage criterion were performed to simulate the damage initiation and propagation processes in CFRP laminates, revealing the complete damage evolution mechanism from initial defect formation to progressive failure. The simulations were also used to determine the optimal sensor placement strategy. Subsequently, tensile test specimens with prefabricated defects were prepared in accordance with ASTM D3039, and multi-sensor monitoring techniques were employed to capture multi-parameter, dynamic data throughout the damage evolution process. The experimental results indicate that embedded FBG sensors and surface-mounted strain gauges can effectively monitor localized material loss defects within composite laminate structures. Strain gauge measurements showed uniform strain distribution at all measuring points in intact specimens (with deviations less than 5%). In contrast, in defective specimens, strain values at measurement points near the notch edge were significantly higher than those in regions farther from the notch, indicating that the prefabricated defect disrupted fiber continuity and induced stress redistribution. The combined use of surface-mounted strain gauges and embedded FBG sensors was demonstrated to accurately and reliably track the damage evolution behavior of defective CFRP laminates. Full article

This paper presents a detailed analysis of the severe structural anomalies that led to the urgent rehabilitation of the Eduardo Caldeira Bridge in Machico, Madeira. Situated in a challenging coastal environment with complex volcanic geology, the bridge exhibited a critical failure of its bearing devices, which were assigned the highest defect severity rating (Grade 5). A multidisciplinary diagnostic methodology, combining visual inspection data, non-destructive testing, and geotechnical analysis, was employed to identify the root causes of this degradation. The investigation concluded that the bearing failure was not due to widespread material deterioration but was directly linked to significant lateral structural displacements, exacerbated by localized geotechnical instabilities. This paper details the data-driven rehabilitation strategy that was subsequently implemented, including the complete replacement of the bearings and substructure stabilization measures. The study provides a valuable case study of a complex, mechanics-driven failure mode and demonstrates that for such critical infrastructure, a proactive management model integrating advanced technologies like Structural Health Monitoring (SHM) and Building Information Modelling (BIM) is essential for ensuring long-term safety and resilience. Full article

Energy harvesting (EH) is a rapidly evolving domain that is primarily focused on capturing and converting ambient energy sources into more convenient and usable forms. These sources, which range from traditional renewable sources such as solar or wind power to thermal gradients and vibrations, present an alternative to typical power generation. The temptation to use energy harvesting systems is in their potential to power low-power devices, such as environment monitoring devices, without relying on conventional power grids or standard battery implementations. This improves the sustainability and self-sufficiency of IoT devices and reduces the environmental impact of conventional power systems. Applications of EH include wearable health monitors, wireless sensor networks, and remote structural sensors, where frequent battery replacement is impractical. However, these systems also face challenges such as intermittent energy availability, limited storage capacity, and low power density, which require innovative design approaches and efficient energy management. The paper provides a general overview of the subsystems present in the energy harvesting systems and a comprehensive overview of the energy transducer technologies used in energy harvesting systems. Full article

Background: Cystic fibrosis (CF) is a multisystemic disorder caused by CFTR gene mutations, leading to impaired protein function and affecting pulmonary, gastrointestinal, hepatobiliary, skeletal, and nutritional health. The advent of CFTR modulators—especially the triple therapy elexacaftor/tezacaftor/ivacaftor (ETI)—has revolutionized clinical management, offering genotype-specific benefits beyond pulmonary outcomes. Pediatric gastroenterologists must now recognize and address emerging gastrointestinal and nutritional challenges introduced by modulator therapy. Methods: A narrative review was conducted to assess the impact of CFTR modulators on gastrointestinal function, nutritional status, bone health, and hepatobiliary involvement in pediatric patients. A structured literature search was performed using PubMed, EMBASE, and Scopus databases. Filters included articles in English or Spanish. Following full-text review based on relevance and quality, 68 articles were selected for inclusion in this review. Results: CFTR modulators have demonstrated potential improvements in gastrointestinal function, nutrient absorption, weight gain, and bone mineral density. In pediatric populations, ETI therapy has been associated with early increases in lean mass, enhanced vitamin levels, and promising trends in bone microarchitecture. However, variable outcomes regarding liver function and bone mineral density highlight the need for careful monitoring. Conclusions: While CFTR modulators present novel opportunities for systemic improvement in CF, their long-term impact on digestive and skeletal health in children remains under investigation. Pediatric gastroenterologists play a pivotal role in monitoring nutritional and hepatobiliary outcomes, optimizing treatment plans, and guiding personalized care strategies in the era of CFTR modulation. Full article

Introduction: Diabetes mellitus negatively influences oral health, significantly contributing to the worsening of odonto-periodontal pathology. Accurate diagnosis and monitoring of lesions are essential to prevent complications. CBCT (cone beam computed tomography) provides a detailed image of bone structures, being a valuable tool in this context. Materials and methods: Thisprospective observational comparative study included 120 patients with chronic periodontitis, divided into two equal groups: 60 with type 2 diabetes mellitus and 60 without systemic diseases. All were examined clinically and radiologically using high-resolution CBCT. Results: Diabetic patients presented a significantly higher mean bone loss (4.7 mm) compared to the non-diabetic patients (3.0 mm). Periodontal pockets >5 mm were more frequent in the diabetic group (75% vs. 50%). CBCT revealed vertical defects and extensive periapical lesions especially in patients with diabetes. Three-dimensional imaging allowed for detailed assessment and clear differentiation between the severity of cases. Conclusions: CBCT is essential in the early identification and evaluation of periodontal disease in diabetic patients. The use of this method significantly contributes to the correlation of clinical and imaging data, optimizing the diagnosis and treatment plan. Full article

The EU Artificial Intelligence Act (Regulation (EU) 2024/1689) designates AI systems used in life and health insurance underwriting as high-risk systems, imposing rigorous requirements for bias testing, technical documentation, and post-deployment monitoring. Leveraging 12.4 million quote–bind–claim observations from four pan-European insurers (2019 Q1–2024 Q4), we evaluate how compliance affects premium schedules, loss ratios, and solvency positions. We estimate gradient-boosted decision tree (Extreme Gradient Boosting (XGBoost)) models alongside benchmark GLMs for mortality, morbidity, and lapse risk, using Shapley Additive Explanations (SHAP) values for explainability. Protected attributes (gender, ethnicity proxy, disability, and postcode deprivation) are excluded from training but retained for audit. We measure bias via statistical parity difference, disparate impact ratio, and equalized odds gap against the 10 percent tolerance in regulatory guidance, and then apply counterfactual mitigation strategies—re-weighing, reject option classification, and adversarial debiasing. We simulate impacts on expected loss ratios, the Solvency II Standard Formula Solvency Capital Requirement (SCR), and internal model economic capital. To translate fairness breaches into compliance risk, we compute expected penalties under the Act’s two-tier fine structure and supervisory detection probabilities inferred from GDPR enforcement. Under stress scenarios—full retraining, feature excision, and proxy disclosure—preliminary results show that bottom-income quintile premiums exceed fair benchmarks by 5.8 percent (life) and 7.2 percent (health). Mitigation closes 65–82 percent of these gaps but raises capital requirements by up to 4.1 percent of own funds; expected fines exceed rectification costs once detection probability surpasses 9 percent. We conclude that proactive adversarial debiasing offers insurers a capital-efficient compliance pathway and outline implications for enterprise risk management and future monitoring. Full article

Carbon tetrachloride (CT) is a toxic volatile chlorinated hydrocarbon, posing a serious hazard to ecosystem and human health. This study discussed the bioremediation possibility of groundwater contaminated by CT. Enhanced reductive dechlorination bioremediation (ERD) was used to promote the reductive dechlorination process of CT by adding yeast extract as a supplementary electron donor. The microcosm samples of the Control and Experi group were setup in the experiment, and the CT degradation efficiency and microbial community structure changes over 150 days were monitored. The results showed that the Experi group achieved complete degradation of CT within 40 days, while the control group had no significant change. By analyzing the physical and chemical indexes such as VFAs, sulfate ions, oxidation–reduction potential, pH value and so on, the key changes in the degradation process of CT were revealed. Microbial community analysis showed that specific microorganisms such as Acinetobacter johnsonii, Aeromonas media and Enterobacter Mori played a significant role in the degradation of CT. They may produce hydrogen through fermentation to provide electron donors for the reductive dechlorination of CT. In addition, the genes of reductive dehalogenase synthase related to CT degradation were also identified, which provided molecular evidence for understanding the biodegradation mechanism of CT. The results deliver a scientific basis for optimizing the bioremediation strategy of CT-contaminated groundwater. Full article

Fractal theory provides a powerful tool for quantifying complex geometric patterns such as concrete cracks. The box-counting method is widely employed for fractal dimension (FD) calculation due to its intuitive principles and compatibility with image data. However, two critical limitations persist in existing studies: (1) the selection of scale parameters (including minimum measurement scale and cutoff scale) lacks systematization and exhibits significant arbitrariness; (2) insufficient attention to the sensitivity of counting origins compromises the stability and comparability of FDs, severely limiting reliable engineering application. To address these limitations, this study first employs classical fractal images and crack samples to systematically analyze the impact of four minimum measurement scales (2, $\sqrt{2}$, 3, $\sqrt{3}$) and three cutoff scale coefficients (cutoff-to-minimum image side ratios: 1, 1/2, 1/3) on computational accuracy. Subsequently, the farthest point sampling (FPS) method is adopted to select counting origins, comparing two optimization strategies—Count-FD-Mean (mean of fits from multiple origins) and Count-Min-FD (fit using minimal box counts across scales). Finally, the optimized approach is validated through static loading tests on concrete beams. Key findings demonstrate that: the optimal scale combination (minimum scale: $\sqrt{2}$; cutoff coefficient: 1) yields a mere 0.5% average error from theoretical FDs; the Count-Min-FD strategy delivers the highest stability and closest alignment with theoretical values; FDs of beam cracks increase continuously with loading, exhibiting an exponential correlation with midspan deflection that effectively captures crack evolution; uncalibrated scale parameters and counting strategies may induce >40% errors in inferred mechanical parameters; results stabilize with 40–45 counting origins across three tested fractal patterns. This work advances standardization in fractal analysis, enhances reliability in concrete crack assessment, and provides critical support for the practical application of fractal theory in structural health monitoring and damage evaluation. Full article

Weeds are among the primary factors that adversely affect crop yields. Chlorophyll fluorescence, as a sensitive indicator of photosynthetic activity in green plants, provides direct insight into photosynthetic efficiency and the functional status of the photosynthetic apparatus. This makes it a valuable tool for assessing plant health and stress responses. Active chlorophyll fluorescence technology uses an external light source to excite plant leaves, enabling the rapid acquisition of fluorescence signals for real-time monitoring of vegetation in the field. This technology shows great potential for weed detection, as it allows for accurate discrimination between crops and weeds. Furthermore, since weed-induced stress affects the photosynthetic process of plants, resulting in changes in fluorescence characteristics, chlorophyll fluorescence can also be used to detect herbicide resistance in weeds. This paper reviews the progress in using active chlorophyll fluorescence sensor technology for weed detection. It specifically outlines the principles and structure of active fluorescence sensors and their applications at different stages of field operations, including rapid classification of soil and weeds during the seedling stage, identification of in-row weeds during cultivation, and assessment of herbicide efficacy after application. By monitoring changes in fluorescence parameters, herbicide-resistant weeds can be detected early, providing a scientific basis for precision herbicide application. Full article

The integration of robotics into Structural Health Monitoring (SHM) is rapidly reshaping how infrastructure is assessed and maintained. This review critically examines the current landscape of robotic technologies applied in SHM, with a specific focus on their implementation within the Saudi Arabian construction sector. It explores recent advancements in robotic platforms, such as unmanned aerial vehicles (UAVs), wall-climbing robots, and AI-driven inspection systems, and assesses their roles in damage detection, vibration monitoring, and real-time diagnostics. In addition to outlining technological capabilities, this paper identifies major adoption challenges related to system readiness, regulatory gaps, workforce limitations, and environmental constraints. Drawing on comparative experiences in the healthcare, energy, and legal domains, this review extracts cross-sectoral insights that offer practical guidance for accelerating robotic integration in SHM. This paper concludes by outlining research gaps and actionable recommendations to support scholars, policymakers, and industry professionals in advancing robotics-based monitoring in complex infrastructure environments. Full article

Background/Objectives: Heart disease affects 0.1% to 4% of pregnant women, with congenital heart defects being the leading cause in developed countries. While maternal mortality is generally low, pre-existing cardiac conditions substantially increase adverse outcome risks. This report describes the multidisciplinary management of a pregnant patient with a bicuspid aortic valve, severe aortic stenosis, and ascending aortic ectasia. Case Presentation: A 34-year-old pregnant woman, asymptomatic but at high risk (World Health Organization Class III) for hemodynamic decompensation, was closely monitored throughout gestation. At 36 weeks, intrauterine growth restriction was detected, prompting an elective cesarean delivery at 38 weeks. Postpartum, the patient developed pre-eclampsia, which was managed successfully. Imaging revealed progressive aortic dilation, leading to surgical aortic valve replacement and ascending aorta reduction plasty. Post-operatively, atrioventricular reentrant tachycardia from an unrecognized accessory pathway developed; medical therapy effectively controlled the arrhythmia after failed catheter ablation. One year later, both mother and child remained in good health. Discussion: This case illustrates the complexity of managing pregnancy in women with congenital heart disease and significant aortic pathology. The physiological changes of pregnancy can exacerbate underlying lesions, necessitating individualized risk assessment, vigilant monitoring, and timely intervention. Conclusions: A multidisciplinary approach involving cardiology, obstetrics, anesthesiology, and genetics is essential to optimize outcomes for pregnant women with significant heart disease. As advances in care allow more women with congenital heart defects to reach childbearing age, structured care pathways remain vital for ensuring safe pregnancies and long-term cardiovascular health. Full article

The aim of this comprehensive review is to synthesize current knowledge on bovine respiratory disease (BRD), enhance diagnostic strategies, and support effective prevention and management practises. BRD remains a leading cause of morbidity and mortality in cattle, driven by a complex interplay of viral and bacterial pathogens, host factors, environmental stressors, and management conditions. Its prevalence (2.1% to 20.2%) varies across geographical regions, age groups, and diagnostic methods. BRD leads to significant economic losses through direct impacts such as mortality, reduced growth rates, and lighter carcass weights, as well as indirect costs like market restrictions and long-term productivity declines. Diagnosing BRD is challenging due to its non-specific clinical signs and frequent subclinical presentations. Traditional diagnostic tools like clinical respiratory scoring (CRS) systems provide structure but suffer from low sensitivity and subjectivity. Behavioural monitoring shows promise by detecting early changes in feeding, movement, and social behaviours. Thoracic auscultation is widely used but limited in accuracy. Thoracic ultrasonography (TUS) stands out as a more sensitive method for detecting subclinical disease and correlating with growth outcomes. Combining CRS with TUS enhances early and accurate detection. Advancing diagnostic approaches is critical for improving animal health and minimizing economic losses in cattle production systems. Full article

Concrete compressive strength is a critical property for structural performance and construction scheduling. Traditional non-destructive testing (NDT) methods, such as rebound hammer and ultrasonic pulse velocity, offer limited reliability and resolution, particularly at early ages. This study presents an AI-powered structural health monitoring (SHM) framework that integrates multi-type and multi-position piezoelectric (PZT) sensor networks with machine learning for in situ prediction of concrete compressive strength. Signals were collected from various PZT types positioned on the top, middle, bottom, and surface sides of concrete cubes during curing. A series of machine learning models were trained and evaluated using both the full and selected feature sets. Results showed that combining multiple PZT types and locations significantly improved prediction accuracy, with the best models achieving up to 95% classification accuracy using only the top 200 features. Feature importance and PCA analyses confirmed the added value of sensor heterogeneity. This study demonstrates that multi-sensor AI-enhanced SHM systems can offer a practical, non-destructive solution for real-time strength estimation, enabling earlier and more reliable construction decisions in line with industry standards. Full article

Ensuring the structural safety of components or facilities is essential for the smooth operation of industrial production and transportation. As a key index to evaluate structural health, the crack depth detection method has evolved from the early single physical field detection to the contemporary multi-physical field collaborative artificial intelligence algorithm. This paper presents a systematic review of crack depth detection technology under specific engineering conditions, such as those found in roads, train tracks, and engine blades. The framework categorizes and reviews detection technology according to detection principles, including physical principles, model inversion, hybrid methods, and evaluation indicators such as detection accuracy, speed, and cost. The paper compares various detection technologies, highlighting their advantages and limitations in real-world applications. The analysis reveals key challenges, which include complex environmental interference, the detection of microcracks and deep cracks, and the balance between accuracy and cost. Addressing these challenges is imperative to improving the reliability and generalization of detection technology. This paper proposes future research directions focusing on integrating multi-physical field detection with artificial intelligence, utilizing AI’s robust capabilities to develop more advanced methods for detecting crack depth. Full article