Search Results (20,981)

The safety and longevity of engineering structures depend on precise and timely monitoring, especially during load testing inspections. Conventional displacement measurement methods—such as LVDT sensors, GNSS, RTS, and levels—each present benefits and limitations in terms of accuracy, applicability, and practicality. Photogrammetry has emerged as a promising alternative, offering non-contact measurement, cost-effectiveness, and adaptability in challenging environments. This study investigates the potential of photogrammetric methods for determining structural displacements during load testing in real-world conditions where such approaches remain underutilized. Two photogrammetric techniques were tested: (1) a single-image homography-based approach, and (2) a multi-image bundle block adjustment (BBA) approach using both UAV and tripod-mounted imaging platforms. Displacement results from both methods were compared against reference measurements obtained by traditional LVDT sensors and robotic total station. The study evaluates the influence of different camera systems, image acquisition techniques, and processing methods on the overall measurement accuracy. The findings suggest that the photogrammetric method, especially when optimized, can provide reliable displacement data with sub-millimeter accuracy, highlighting their potential as a viable alternative or complement to established geodetic and sensor-based approaches in structural testing. Full article

Membrane fouling remains a major challenge in full-scale membrane bioreactor (MBR) systems, reducing operational efficiency and increasing maintenance needs. This study introduces a predictive and analytic framework for membrane fouling by integrating artificial intelligence (AI)-driven feature engineering and explainable AI (XAI) using real-world data from an MBR treating food processing wastewater. The framework refines the target parameter to specific flux (flux/transmembrane pressure (TMP)), incorporates chemical oxygen demand (COD) removal efficiency to reflect biological performance, and applies a moving average function to capture temporal fouling dynamics. Among tested models, CatBoost achieved the highest predictive accuracy (R² = 0.8374), outperforming traditional statistical and other machine learning models. XAI analysis identified the food-to-microorganism (F/M) ratio and mixed liquor suspended solids (MLSSs) as the most influential variables affecting fouling. This robust and interpretable approach enables proactive fouling prediction and supports informed decision making in practical MBR operations, even with limited data. The methodology establishes a foundation for future integration with real-time monitoring and adaptive control, contributing to more sustainable and efficient membrane-based wastewater treatment operations. However, this study is based on data from a single full-scale MBR treating food processing wastewater and lacks severe fouling or cleaning events, so further validation with diverse datasets is needed to confirm broader applicability. Full article

Background: Antimicrobial resistance (AMR) is increasingly acknowledged as a critical global challenge, posing serious risks to human and animal health and potentially disrupting poultry production systems. Commensal bacteria such as Staphylococcus spp., Enterococcus spp., and Escherichia coli may serve as important reservoirs and vectors of resistance genes. Objectives: This study aimed to assess the AMR profiles of bacterial strains isolated from industrial chicken farms in the Dél-Alföld region of Hungary, providing region-specific insights into resistance dynamics. Methods: A total of 145 isolates, including Staphylococcus spp., Enterococcus spp., and E. coli isolates, were subjected to minimum inhibitory concentration (MIC) testing against 15 antimicrobial agents, following Clinical and Laboratory Standards Institute (CLSI) guidelines. Advanced multivariate statistics, machine learning algorithms, and network-based approaches were employed to analyze resistance patterns and co-resistance associations. Results Multidrug resistance (MDR) was identified in 43.9% of Staphylococcus spp. isolates, 28.8% of Enterococcus spp. isolates, and 75.6% of E. coli isolates. High levels of resistance to florfenicol, enrofloxacin, and potentiated sulfonamides were observed, whereas susceptibility to critical antimicrobials such as imipenem and vancomycin remained largely preserved. Discussion: Our findings underscore the necessity of implementing region-specific AMR monitoring programs and strengthening multidisciplinary collaboration within the “One Health” framework with proper animal hygiene and biosecurity measures to limit the spread of antimicrobial resistance and protect both animal and human health. Full article

Background: The long-term impact of SARS-CoV-2 infection on pulmonary function remains insufficiently characterised, particularly among individuals who have experienced mild or asymptomatic disease. This study aimed to assess spirometric changes over a three-year period and evaluate potential associations with demographic and clinical variables. Methods: We retrospectively analysed spirometry data from 103 healthcare workers (HCWs) who underwent pulmonary function tests at three time points: before the pandemic (Time 0), one year post-pandemic (Time 1), and two years post-pandemic (Time 2). Linear regression models were employed to evaluate the impact of various factors, including age, BMI, gender, smoking status, history of SARS-CoV-2 infection, vaccination status prior to infection, and the number of infections, on changes in FVC and FEV₁. Results: A statistically significant decrease in both FVC and FEV₁ were observed at Time 1 and Time 2 compared to baseline (p < 0.05). Smoking habits were significantly associated with a greater decline in both FVC and FEV₁. Multiple infections were associated with larger reductions in FVC at Time 1. No significant associations were found with age, gender, BMI, or vaccination status. Even in the absence of severe symptoms of the disease, healthcare workers exhibited a measurable decline in pulmonary function over time. Smoking and reinfection emerged as relevant factors associated with reduced lung capacity. Conclusions: These findings emphasise the need for ongoing respiratory monitoring in occupational settings and the importance of targeted preventive measures. Full article

This study investigates the dynamic relationship between bearing gearbox condition indicators (BGCIs) and the lubrication oil temperature within the framework of health and usage monitoring system (HUMS) applications. Using the dynamic autoregressive distributed lag (DARDL) simulation model, we quantified both the short- and long-term responses of condition indicators to shocks in oil temperature, offering a robust framework for a counterfactual analysis. To complement the time-domain perspective, we applied a wavelet coherence analysis (WCA) to explore time–frequency co-movements and phase relationships between the condition indicators under varying operational regimes. The DARDL results revealed that the ball energy, cage energy, and inner and outer race indicators significantly increased in response to the oil temperature in the long run. The WCA results further confirmed the positive association between oil temperature and the condition indicators under examination, aligning with the DARDL estimations. The DARDL model revealed that the ball energy and the inner race energy have statistically significant long-term effects on the oil temperature, with p-values < 0.01. The adjusted R² of 0.785 and the root mean square error (MSE) of 0.008 confirm the model’s robustness. The wavelet coherence analysis showed strong time–frequency correlations, especially in the 8–16 scale range, while the frequency-domain causality (FDC) tests confirmed a bidirectional influence between the oil temperature and several condition indicators. The FDC analysis showed that the oil temperature significantly affected the BGCIs, with evidence of feedback effects, suggesting a mutual dependency. These findings contribute to the advancement of predictive maintenance frameworks in HUMSs by providing practical insights for enhancing system reliability and optimizing maintenance schedules. The integration of dynamic econometric approaches demonstrates a robust methodology for monitoring critical mechanical components and encourages further research in broader aerospace and industrial contexts. Full article

This study presents an AI-enhanced pedestrian simulation model for subway stations, combining the Social Force Model (SFM) with LiDAR trajectory data from Samseong Station in Seoul. To reflect time-dependent behavioral differences, RMSProp-based optimization is performed separately for the morning peak, leisure hours, and evening peak, yielding time-specific parameter sets. Compared to baseline models with static parameters, the proposed method reduces prediction errors (MSE) by 50.1% to 84.7%. The model integrates adaptive learning rates, mini-batch training, and L2 regularization, enabling robust convergence and generalization across varied pedestrian densities. Its accuracy and modular design support real-world applications such as pre-construction design testing, post-opening monitoring, and capacity planning. The framework also contributes to Sustainable Urban Mobility Plans (SUMPs) by enabling predictive, data-driven evaluation of pedestrian flow dynamics in complex station environments. Full article

Traditional Foreign Object Debris (FOD) detection methods face challenges such as difficulties in large-size data acquisition and the ineffective application of detection algorithms with high accuracy. In this paper, image data augmentation was performed using generative adversarial networks and diffusion models, generating images of monitoring areas under different environmental conditions and FOD images of varied types. Additionally, a three-stage image blending method considering size transformation, a seamless process, and style transfer was proposed. The image quality of different blending methods was quantitatively evaluated using metrics such as structural similarity index and peak signal-to-noise ratio, as well as Depthanything. Finally, object detection models with a similarity distance strategy (SimD), including Faster R-CNN, YOLOv8, and YOLOv11, were tested on the dataset. The experimental results demonstrated that realistic FOD data were effectively generated. The Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) of the synthesized image by the proposed three-stage image blending method outperformed the other methods, reaching 0.99 and 45 dB. YOLOv11 with SimD trained on the augmented dataset achieved the mAP of 86.95%. Based on the results, it could be concluded that both data augmentation and SimD significantly improved the accuracy of FOD detection. Full article

Ergonomic risk classification during manual lifting tasks is crucial for the prevention of workplace injuries. This study addresses the challenge of classifying lifting task risk levels (low, medium, and high risk, labeled as 0, 1, and 2) using multi-modal time-series data comprising raw facial landmarks and bio-signals (electrocardiography [ECG] and electrodermal activity [EDA]). Classifying such data presents inherent challenges due to multi-source information, temporal dynamics, and class imbalance. To overcome these challenges, this paper proposes a Multi-Adaptive Functional Neural Network (Multi-AdaFNN), a novel method that integrates functional data analysis with deep learning techniques. The proposed model introduces a novel adaptive basis layer composed of micro-networks tailored to each individual time-series feature, enabling end-to-end learning of discriminative temporal patterns directly from raw data. The Multi-AdaFNN approach was evaluated across five distinct dataset configurations: (1) facial landmarks only, (2) bio-signals only, (3) full fusion of all available features, (4) a reduced-dimensionality set of 12 selected facial landmark trajectories, and (5) the same reduced set combined with bio-signals. Performance was rigorously assessed using 100 independent stratified splits (70% training and 30% testing) and optimized via a weighted cross-entropy loss function to manage class imbalance effectively. The results demonstrated that the integrated approach, fusing facial landmarks and bio-signals, achieved the highest classification accuracy and robustness. Furthermore, the adaptive basis functions revealed specific phases within lifting tasks critical for risk prediction. These findings underscore the efficacy and transparency of the Multi-AdaFNN framework for multi-modal ergonomic risk assessment, highlighting its potential for real-time monitoring and proactive injury prevention in industrial environments. Full article

Biodegradable magnesium alloys have emerged as promising alternatives to permanent metallic implants due to their unique combination of mechanical compatibility with bone and complete resorption, addressing the persistent issues of stress shielding and secondary removal surgeries. This review critically examines the historical development of magnesium-based biomaterials, highlighting advances in alloy design, manufacturing processes, and surface engineering that now enable tailored degradation and improved clinical performance. Drawing on recent clinical and preclinical studies, we summarize improvements in corrosion resistance, mechanical properties, and biocompatibility that have supported the clinical translation of magnesium alloys across a variety of orthopedic and emerging medical applications. However, challenges remain, including unpredictable in vivo degradation kinetics, limited long-term safety data, lack of standardized testing protocols, and ongoing regulatory uncertainties. We conclude that while magnesium-based biomaterials have advanced from experimental concepts to clinically validated solutions, further progress in personalized degradation control, real-time monitoring, and harmonized regulatory frameworks is needed to fully realize their transformative clinical potential. Full article

The experimental tests needed for the estimation of the fatigue limit generally require extensive time and many specimens. A valid but not standardized alternative is the thermographic analysis of the self-heating phenomenon. The present work is aimed at using Infrared thermography to determine the fatigue limit in two kinds of Ti-6Al-4V samples obtained by hot rolling: (1) with the standard dog-bone shape (unnotched specimen) and (2) with two opposed semicircular notches at the sides (notched specimen). Uniaxial tensile experiments are performed on unnotched samples, and the surface temperature variation during loading is monitored. The stress corresponding to the end of the thermoelastic stage gives a rough indication of the fatigue limit. Then, fatigue tests at different sinusoidal loads are performed, and the thermographic signal is monitored and processed. The results obtained using lock-in thermography in dissipative mode, e.g., analyzing the second harmonic, showed a sudden change in slope when the applied stress exceeded a certain limit. This slope change is related to the fatigue limit. In addition, the ratio between the fatigue limits obtained for notched and unnotched specimens, e.g., the fatigue strength reduction factor, is consistent with literature values based on the selected geometry. Full article

Urban parks are an integral component of cities; however, they are susceptible to heavy metal contamination from anthropogenic sources. Here, we investigated the moss Pleurozium schreberi and tree leaves as bioindicators for monitoring heavy metal contamination in urban parks. We determined heavy metal concentrations in P. schreberi, leaf tissues of selected tree species, and soil samples collected from various locations within a designated urban parks. The order of heavy metal accumulation was Zn > Pb > Cr > Cu > Ni > Cd > Hg in soil and Zn > Cu > Pb > Cr > Ni > Cd > Hg in P. schreberi. The order was Zn > Cu > Cr > Ni > Pb > Cd > Hg in linden and sycamore leaves, while birch leaves displayed a similar order but with slightly more Ni than Cr. The heavy metal concentration in the tested soils correlated positively with finer textures (clay and silt) and negatively with sand. The highest metal accumulation index (MAI) was noted in birch and P. schreberi, corresponding to the highest total heavy metal accumulation. The bioconcentration factor (BAF) was also higher in P. schreberi, indicating a greater ability to accumulate heavy metals than tree leaves, except silver birch for Zn in one of the parks. Silver birch displayed the highest phytoremediation capacity among the analysed tree species, highlighting its potential as a suitable bioindicator in heavy metal-laden urban parks. Our findings revealed significant variation in heavy metal accumulation, highlighting the potential of these bioindicators to map contamination patterns. Full article

Lake Chinchaycocha, Peru’s second-largest high-altitude lake and a Ramsar-designated wetland of international importance, is increasingly threatened by anthropogenic pollution and hydroclimatic shifts. This study integrates Sentinel-2 multispectral imagery with in situ water quality data from Peru’s National Water Observatory to assess spatiotemporal dynamics in 31 physicochemical parameters between 2018 and 2024. We evaluated 40 empirical algorithms developed globally for Sentinel-2 and tested their transferability to this ultraoligotrophic Andean system. The results revealed limited predictive accuracy, underscoring the need for localized calibration. Subsequently, we developed and validated site-specific models for ammoniacal nitrogen, electrical conductivity, major ions, and trace metals, achieving high predictive performance during the rainy season (R² up to 0.95). Notably, the study identifies consistent seasonal correlations—such as between total copper and ammoniacal nitrogen—and strong spectral responses in Band 1, linked to runoff dynamics. These findings highlight the potential of combining public monitoring data with remote sensing to enable scalable, cost-effective assessment of water quality in optically complex, high-Andean lakes. The study provides a replicable framework for integrating national datasets into operational monitoring and environmental policy. Full article

The development of multifunctional conductive hydrogels with rapid self-healing capabilities and powerful sensing functions is crucial for advancing wearable electronics. This study designed and prepared a polyvinyl alcohol (PVA)–borax hydrogel incorporating carbon nanotubes (CNTs) and biomass carbon nanospheres (CNPs) as dual-carbon fillers. This hydrogel exhibits excellent conductivity, mechanical flexibility, and self-recovery properties. Serving as a highly sensitive piezoresistive sensor, it efficiently converts mechanical stimuli into reliable electrical signals. Sensing tests demonstrate that the CNT/CNP/PVA–borax hydrogel sensor possesses an extremely fast response time (88 ms) and rapid recovery time (88 ms), enabling the detection of subtle and rapid human motions. Furthermore, the hydrogel sensor also exhibits outstanding cyclic stability, maintaining stable signal output throughout continuous loading–unloading cycles exceeding 3200 repetitions. The hydrogel sensor’s characteristics, including rapid self-healing, fast-sensing response/recovery, and high fatigue resistance, make the CNT/CNP/PVA–borax conductive hydrogel an ideal choice for multifunctional wearable sensors. It successfully monitored various human motions. This study provides a promising strategy for high-performance self-healing sensing devices, suitable for next-generation wearable health monitoring and human–machine interaction systems. Full article

As a novel prefabricated structural element, the pre-tensioned, prestressed concrete T-beam with polygonal tendons layout demonstrates advantages including reduced prestress loss, streamlined construction procedures, and stable manufacturing quality, showing promising applications in medium-span bridge engineering. This paper conducted a full-scale experiment and numerical simulation research on a 30 m pre-tensioned, prestressed concrete T-beam with polygonal tendons practically used in engineering. The full-scale experiment applied symmetrical four-point bending to create a pure bending region and used embedded strain gauges, surface sensors, and optical 3D motion capture systems to monitor the beam’s internal strain, surface strain distribution, and three-dimensional displacement patterns during loading. The experiment observed that the test beam underwent elastic, crack development, and failure phases. The design’s service-load bending moment induced a deflection of 18.67 mm (below the 47.13 mm limit). Visible cracking initiated under a bending moment of 7916.85 kN·m, which exceeded the theoretical cracking moment of 5928.81 kN·m calculated from the design parameters. Upon yielding of the bottom steel reinforcement, the maximum of the crack width reached 1.00 mm, the deflection in mid-span measured 148.61 mm, and the residual deflection after unloading was 10.68 mm. These results confirmed that the beam satisfied design code requirements for serviceability stiffness and crack control, exhibiting favorable elastic recovery characteristics. Numerical simulations using ABAQUS further verified the structural performance of the T-beam. The finite element model accurately captured the beam’s mechanical response and verified its satisfactory ductility, highlighting the applicability of this beam type in bridge engineering. Full article

Artificial reservoirs in arid regions provide unique ecological environments for studying the spatial and functional dynamics of plankton communities under the combined stressors of climate change and anthropogenic activities. This study conducted a systematic investigation of the phytoplankton community structure and its environmental drivers in 17 artificial reservoirs in the Ili region of Xinjiang in August and October 2024. The Ili region is located in the temperate continental arid zone of northwestern China. A total of 209 phytoplankton species were identified, with Bacillariophyta, Chlorophyta, and Cyanobacteria comprising over 92% of the community, indicating an oligarchic dominance pattern. The decoupling between numerical dominance (diatoms) and biomass dominance (cyanobacteria) revealed functional differentiation and ecological complementarity among major taxa. Through multivariate analyses, including Mantel tests, principal component analysis (PCA), and redundancy analysis (RDA), we found that phytoplankton community structures at different ecological levels responded distinctly to environmental gradients. Oxidation-reduction potential (ORP), dissolved oxygen (DO), and mineralization parameters (EC, TDS) were key drivers of morphological operational taxonomic unit (MOTU). In contrast, dominant species (SP) were more responsive to salinity and pH. A seasonal analysis demonstrated significant shifts in correlation structures between summer and autumn, reflecting the regulatory influence of the climate on redox conditions and nutrient solubility. Machine learning using the random forest model effectively identified core taxa (e.g., MOTU1 and SP1) with strong discriminatory power, confirming their potential as bioindicators for water quality assessments and the early warning of ecological shifts. These core taxa exhibited wide spatial distribution and stable dominance, while localized dominant species showed high sensitivity to site-specific environmental conditions. Our findings underscore the need to integrate taxonomic resolution with functional and spatial analyses to reveal ecological response mechanisms in arid-zone reservoirs. This study provides a scientific foundation for environmental monitoring, water resource management, and resilience assessments in climate-sensitive freshwater ecosystems. Full article