Search Results (185)

This research provides a new systematic solution to the essential issue of self-noise interference in underwater acoustic sensing signals induced by unmanned surface vehicles (USVs) operating at sea. The self-noise pertains to the near-field interference noise generated by the growing diversity and volume of acoustic equipment utilized by USVs. The generating mechanism of self-noise is clarified, and a self-noise propagation model is developed to examine its three-dimensional coupling properties within spatiotemporal fluctuation environments in the time-frequency-space domain. On this premise, the YOLOv11 object identification framework is innovatively applied to the delay-Doppler (DD) feature maps of self-noise, thereby overcoming the constraints of traditional time-frequency spectral approaches in recognizing noise with delay spread and overlapping characteristics. A comprehensive comparison with traditional models like YOLOv8 and SSD reveals that the suggested delay-Doppler YOLO (DD-YOLO) algorithm attains an average accuracy of 87.0% in noise source identification. An enhanced denoising method, termed optimized time-frequency regularized overlapping group shrinkage (OTFROGS), is introduced, using structural sparsity alongside non-convex regularization techniques. Comparative experiments with traditional denoising methods, such as the normalized least mean square (NLMS) algorithm, wavelet threshold denoising (WTD), and the original time-frequency regularized overlapping group shrinkage (TFROGS), reveal that OTFROGS outperforms them in mitigating USV self-noise. This study offers a dependable technological approach for optimizing the performance of USV acoustic systems and proposes a theoretical framework and methodology applicable to different underwater acoustic sensing contexts. Full article

The quality of transport services is not only a measure of operational efficiency but also an important factor of strategic logistics management in the pursuit of sustainable development. This study identifies five key transport service quality indicators (timeliness, routing, economy, safety, efficiency) and uses data from a logistics monitoring system to assess them with Harrington’s desirability function. Each indicator’s performance is converted into a partial desirability score and these scores are combined into a single overall desirability score (D), with weights determined from the data. Notably, a threshold around D = 0.63 emerged as the benchmark for acceptable service quality. This numeric threshold provides managers with a clear KPI target—if the service quality index falls below 0.63, it signals the need for corrective action, whereas consistently achieving values near 0.8 reflects very good performance aligned with strategic sustainability goals. Based on the proposed approach, an algorithm and software tool were developed to automate the assessment process. The obtained results show how improvements in service reliability, safety and efficiency can be aligned with broader sustainability goals in automotive transportation. The proposed approach offers managerial decision makers a robust tool to guide policy and investment, ensuring that enhancements in transport service performance also advance environmental and social sustainability. In doing so, the framework advances SDG 9 by turning logistics telemetry into an actionable management index that strengthens resilient transport infrastructure and fosters practical innovation at the enterprise level. Full article

Traffic-induced vibrations from road and rail systems pose a significant threat to the structural integrity and operational safety of buildings, especially masonry structures located near planned infrastructure such as tunnels. This study investigates the dynamic impact of such vibrations on a representative early 20th-century masonry building situated within the influence zone of a design railway tunnel. A comprehensive analysis combining geological, structural, and vibration propagation data was conducted. A detailed 3D finite element model was developed in Diana FEA v10.7, incorporating building material properties, subsoil conditions, and anticipated train-induced excitations. Various vibration isolation strategies were evaluated, including the use of block supports and vibro-isolation mats. The model was calibrated using pre-construction measurements, and simulations were carried out in the linear-elastic range to prevent resident-related claims. Results showed that dynamic stresses in masonry walls and wooden floor beams remain well below critical thresholds, even in areas with stress concentration. Among the tested configurations, vibration mitigation systems significantly reduced the transmitted forces. This research highlights the effectiveness of integrated numerical modelling and vibration control solutions in protecting structures from traffic-induced vibrations and supports informed engineering decisions in tunnel design and urban development planning. Full article

The South China Sea (SCS) is a critical fishery region facing sustainability challenges due to overexploitation, geopolitical tensions, and inadequate monitoring. Traditional monitoring methods, such as AIS and VMS, have limitations due to data gaps and vessel deactivation. We developed an improved remote sensing algorithm using VIIRS nighttime light observations (2013–2022) to detect and classify lit fishing boats in the SCS. The study introduces a Two-Dimensional Constant False Alarm Rate (2D-CFAR) algorithm integrated with morphological analysis, which enhances boats’ detection accuracy. The classification of fishing boat types was based on light power thresholds derived from spatial entropy analysis, where distinct clustering patterns indicated three operational categories: small interfering lights (<1.2–3.7 kW), small-to-medium-sized lit fishing boats (1.2–3.7 to 28.6–43.2 kW), and large lit fishing boats (>28.6–43.2 kW). Our findings reveal a 4.4-fold dominance of small-to-medium-sized lit fishing boats over large lit fishing boats. China’s summer fishing moratorium effectively reduces large lit fishing boats activity by 85%, yet small-to-medium-sized lit fishing boats, primarily from neighboring countries like Vietnam, persist, exploiting this period illegally. Spatially, small-to-medium-sized lit fishing boats concentrate in the central SCS, southeast Vietnam, and Nansha Islands, while large lit fishing boats target upwelling zones near Hainan and Guangdong. Moreover, a new fishing hotspot emerged in eastern SCS, reflecting intensified resource and geopolitical competition. Light intensity analysis reveals rapid growth in contested areas (10% annually, p < 0.01), underscoring ecological risks. These findings highlight the limitations of unilateral policies and the urgent need for regional cooperation to curb illegal, unreported, and unregulated (IUU) fishing. Our algorithm offers a robust tool for monitoring fishing dynamics, providing quantitative insights into vessel distribution, policy impacts, and resource-driven patterns. This supports evidence-based fisheries management and biodiversity conservation in the SCS, adaptable to other marine regions facing similar challenges. Full article

Understanding the impoundment effects of cascade reservoirs on fish reproduction is essential for the conservation and management of river ecosystems. Using the lower Jinsha River as an eco-hydraulic reference, this study conducted laboratory experiments to investigate how hydrodynamic–microtopography interactions influence the near-bed transport of drifting fish eggs and how sediment deposition affects the hatching success of adhesive demersal eggs. A predictive formula for near-bed egg drift was established, and a novel threshold for near-bed drift was proposed. In a separate set of experiments, sediment deposition was found to significantly reduce the hatching success of adhesive demersal eggs—specifically Schizothorax prenanti and Procypris rabaudi—primarily by decreasing dissolved oxygen levels (p < 0.05). These findings provide a scientific basis for improving reservoir operation strategies and mitigating the ecological impacts of sedimentation on fish reproduction in impounded rivers. Full article

CO₂-enhanced oil recovery (CO₂-EOR) has gained prominence as an effective oil displacement method with low carbon emissions, yet its microscopic mechanisms remain incompletely understood. This study introduces a novel high-pressure microfluidic visualization system capable of operating at 0.1–10 MPa without confining pressure and featuring stratified porous media with a 63 μm minimum throat size to provide unprecedented insights into CO₂ and CO₂-foam EOR processes at the microscale. Through quantitative image analysis and advanced machine learning modeling, we reveal that increasing the CO₂ injection pressure nonlinearly reduces residual oil saturation, achieving near-complete miscibility at 6 MPa with only 2% residual oil—a finding that challenges conventional thresholds for miscibility in heterogeneous systems. Our work uniquely demonstrates that CO₂-foam flooding not only mobilizes capillary-trapped oil films but also dynamically alters interfacial tension and the pore-scale fluid distribution, a phenomenon previously underexplored. Support Vector Regression (R² = 0.71) further uncovers a nonlinear relationship between the surfactant concentration and residual oil saturation, offering a data-driven framework for parameter optimization. These results advance our fundamental understanding by bridging microscale dynamics with field-applicable insights, while the integration of machine learning with microfluidics represents a methodological leap for EOR research. Full article

This study presents a wireless, non-invasive sensing system for monitoring the dielectric permittivity of materials, with a particular focus on applications in cultural heritage conservation. The system integrates a passive split-ring resonator tag, electromagnetically coupled to a compact antipodal Vivaldi antenna, operating in the reactive near-field region. Both numerical simulations and experimental measurements demonstrate that shifts in the antenna’s reflection coefficient resonance frequency correlate with variations in the dielectric permittivity of the material under test. A calibration curve was established using reference materials—including low-density polyvinylchloride, polytetrafluoroethylene, polymethyl methacrylate, and polycarbonate—and validated through precise permittivity measurements. The system was subsequently applied to wood samples (fir, poplar, beech, and oak) at different humidity levels, revealing a sigmoidal relationship between moisture content and permittivity. The behavior was also confirmed using a portable and low-cost setup, consisting of a point-like coaxial sensor that could be easily moved and positioned as needed, enabling localized measurements on specific areas of interest of the sample, together with a miniaturized Vector Network Analyzer. These results underscore the potential of this portable, contactless, and scalable sensing platform for real-world monitoring of cultural heritage materials, enabling minimally invasive assessment of their structural and historical integrity. Moreover, by enabling the estimation of moisture content through dielectric permittivity, the system provides an effective method for early detection of water-induced deterioration in wood-based heritage items. This capability is particularly valuable for preventive conservation, as excessive moisture—often indicated by permittivity values above critical thresholds—can trigger biological or structural degradation. Full article

Heavy rainfall events, characterized by extreme downpours that exceed 100 mm per day, pose an intensifying hazard to the densely settled valleys of the western Himalaya; however, their coupling with expanding urban land cover remains under-quantified. This study mapped the spatiotemporal exposure of built-up areas to heavy-day rainfall (HDR) across Jammu, Kashmir, and Ladakh and the adjoining areas by integrating daily Climate Hazards Group InfraRed Precipitation with Stations product (CHIRPS) precipitation (0.05°) with Global Human Settlement Layer (GHSL) built-up fractions within the Google Earth Engine (GEE). Given the limited sub-hourly observations, a daily threshold of ≥100 mm was adopted as a proxy for HDR, with sensitivity evaluated at alternative thresholds. The results showed that HDR is strongly clustered along the Kashmir Valley and the Pir Panjal flank, as demonstrated by the mean annual count of threshold-exceeding pixels increasing from 12 yr⁻¹ (2000–2010) to 18 yr⁻¹ (2011–2020), with two pixel-scale hotspots recurring southwest of Srinagar and near Baramulla regions. The cumulative high-intensity areas covered 31,555.26 km², whereas 37,897.04 km² of adjacent terrain registered no HDR events. Within this hazard belt, the exposed built-up area increased from 45 km² in 2000 to 72 km² in 2020, totaling 828 km². The years with the most expansive rainfall footprints, 344 km² (2010), 520 km² (2012), and 650 km² (2014), coincided with heavy Western Disturbances (WDs) and locally vigorous convection, producing the largest exposure increments. We also performed a forecast using a univariate long short-term memory (LSTM), outperforming Autoregressive Integrated Moving Average (ARIMA) and linear baselines on a 2017–2020 holdout (Root Mean Square Error, RMSE 0.82 km²; measure of errors, MAE 0.65 km²; R² 0.89), projecting the annual built-up area intersecting HDR to increase from ~320 km² (2021) to ~420 km² (2030); 95% prediction intervals widened from ±6 to ±11 km² and remained above the historical median (~70 km²). In the absence of a long-term increase in total annual precipitation, the projected rise most likely reflects continued urban encroachment into recurrent high-intensity zones. The resulting spatial masks and exposure trajectories provide operational evidence to guide zoning, drainage design, and early warning protocols in the region. Full article

This study assessed the baseline indoor environmental quality (IEQ) of secondary school classrooms in Tanzania by measuring temperature, relative humidity, noise, lighting, and indoor air quality. Objective measurements were conducted using calibrated sensors in 14 classrooms across five schools, with data collected during occupied school hours and additional noise measurements during unoccupied periods. All classrooms are naturally ventilated through operable windows and doors. The findings reveal a pattern of cumulative IEQ deficiencies: classroom temperatures frequently exceeded the recommended 20–24 °C range, reaching means as high as 30.4 °C, while relative humidity varied widely, with levels occasionally surpassing 65%. Noise levels consistently exceeded the World Health Organization (WHO)’s recommended 35 dBA threshold, with significant differences observed between occupied and unoccupied periods (p = 0.02). Light distribution was uneven, with significantly higher lux levels near windows than at classroom centers (p < 0.001), and artificial lighting was generally insufficient due to infrastructure limitations. Although CO₂ concentrations remained below the 1000 ppm threshold, indicating adequate ventilation, particulate matter levels were often elevated, with PM2.5 reaching up to 58.80 µg/m³ and PM10 up to 96.90 µg/m³, exceeding health-based guidelines. Together, these findings suggest that students are exposed to multiple environmental stressors that may impair health, comfort, and academic performance. This study provides a critical baseline for future research and context-specific interventions aimed at improving learning environments in Tanzanian schools and similar settings in East Africa. Full article

Near-field acoustic holography (NAH) provides an effective way to achieve wide-band, high-resolution visualization measurement of the sound insulation performance of building components. However, based on Green’s function, the microphone array’s inherent amplitude and phase mismatch errors will exponentially amplify the sound field inversion process, significantly reducing the measurement accuracy. To systematically evaluate this problem, this study combines numerical simulation with actual measurements in a soundproof room that complies with the ISO 10140 standard, quantitatively analyzes the influence of array system errors on NAH reconstructed sound insulation and acoustic images, and proposes an error correction strategy based on channel transfer function normalization. The research results show that when the array amplitude and phase mismatch mean values are controlled within 5% and 5°, respectively, the deviation of the weighted sound insulation measured by NAH can be controlled within 1 dB, and the error in the key frequency band of building sound insulation (200–1.6k Hz) does not exceed 1.5 dB; when the mismatch mean value increases to 10% and 10°, the deviation of the weighted sound insulation can reach 2 dB, and the error in the high-frequency band (≥1.6k Hz) significantly increases to more than 2.0 dB. The sound image shows noticeable spatial distortion in the frequency band above 250 Hz. After applying the proposed correction method, the NAH measurement results of the domestic microphone array are highly consistent with the weighted sound insulation measured by the standard method, and the measurement difference in the key frequency band is less than 1.0 dB, which significantly improves the reliability and applicability of low-cost equipment in engineering applications. In addition, the study reveals the inherent mechanism of differential amplification of system errors in the propagating wave and evanescent wave channels. It provides quantitative thresholds and operational guidance for instrument selection, array calibration, and error compensation of NAH technology in building sound insulation detection. Full article

Ground temperature (GT) variation significantly affects the energy performance of ground source heat pump (GSHP) systems. Both long-term thermal accumulation and short-term dynamic responses contribute to the degradation of the coefficient of performance (COP), especially under cooling-dominated conditions. This study develops a mechanism-based TRNSYS simulation that integrates building loads, subsurface heat transfer, and dynamic heat pump operation. A 20-year case study in Shanghai reveals long-term performance degradation driven by thermal boundary shifts. Results show that GT increases by over 12 °C during the simulation period, accompanied by a progressive increase in ΔT by approximately 0.20 K and a consistent decline in COP. A near-linear inverse relationship is observed, with COP decreasing by approximately 0.038 for every 1 °C increase in GT. In addition, ΔT is identified as a key intermediary linking subsurface thermal disturbance to efficiency loss. A multi-scale response framework is established to capture both annual degradation and daily operational shifts along the Load–GT–ΔT–COP pathway. This study provides a quantitative explanation of the thermal degradation process and offers theoretical guidance for performance forecasting, operational threshold design, and thermal regulation in GSHP systems. Full article

Cross-line operation and express–local train coordination are both crucial for enhancing the efficiency of multi-level urban rail transit systems. Most studies address suburban railway operations in isolation, overlooking coordination and inducing supply–demand mismatches that weaken system efficiency. This study addresses the joint optimization of cross-line operation and express–local scheduling by proposing a novel train timetable model. The model determines train service plans and departure times to minimize total system cost, including train operating and passenger travel costs. A space–time network represents integrated train–passenger interactions, and an extended adaptive large neighborhood search (E-ALNS) algorithm is developed to solve the model efficiently. Numerical experiments verify the effectiveness of the proposed approach. The E-ALNS achieves near-optimal solutions with less than 4% deviation from Gurobi. Comparative analysis shows that the proposed hybrid operation mode reduces total passenger travel cost by 6% and improves the cost efficiency ratio by 13% compared to independent operations. Sensitivity analyses further confirm the model’s robustness to variations in transfer walking time, passenger penalties, and waiting thresholds. This study provides a practical and scalable framework for optimizing train timetables in complex cross-line transit systems, offering insights for enhancing system coordination and passenger service quality. Full article

Healthcare cost acceleration and resource allocation issues have worsened across European health systems, where a small group of patients drives excessive healthcare spending. The prediction of high-cost utilization patterns is important for the sustainable management of healthcare and focused intervention measures. The aim of our study was to derive and validate machine learning algorithms for high-cost healthcare utilization prediction based on detailed administrative data and by comparing three algorithmic methods for the best risk stratification performance. The research analyzed extensive insurance beneficiary records which compile data from health group collective funds operated by non-life insurers across EU countries, across multiple service classes. The definition of high utilization was equivalent to the upper quintile of overall health expenditure using a moderate cost threshold. The research applied three machine learning algorithms, namely logistic regression using elastic net regularization, the random forest, and support vector machines. The models used a comprehensive set of predictor variables including demographics, policy profiles, and patterns of service utilization across multiple domains of healthcare. The performance of the models was evaluated using the standard train–test methodology and rigorous cross-validation procedures. All three models demonstrated outstanding discriminative ability by achieving area under the curve values at near-perfect levels. The random forest achieved the best test performance with exceptional metrics, closely followed by logistic regression with comparable exceptional performance. Service diversity proved to be the strongest predictor across all models, while dentistry services produced an extraordinarily high odds ratio with robust confidence intervals. The group of high utilizers comprised approximately one-fifth of the sample but demonstrated significantly higher utilization across all service classes. Machine learning algorithms are capable of classifying patients eligible for the high utilization of healthcare services with nearly perfect discriminative ability. The findings justify the application of predictive analytics for proactive case management, resource planning, and focused intervention measures across private group health insurance providers in EU countries. Full article

Machine predictive maintenance plays a critical role in reducing unplanned downtime, lowering maintenance costs, and improving operational reliability by enabling the early detection and classification of potential failures. Artificial intelligence (AI) enhances these capabilities through advanced algorithms that can analyze complex sensor data with high accuracy and adaptability. This study introduces an explainable AI framework for failure detection and classification using symbolic expressions (SEs) derived from a genetic programming symbolic classifier (GPSC). Due to the imbalanced nature and wide variable ranges in the original dataset, we applied scaling/normalization and oversampling techniques to generate multiple balanced dataset variations. Each variation was used to train the GPSC with five-fold cross-validation, and optimal hyperparameters were selected using a Random Hyperparameter Value Search (RHVS) method. However, as the initial Threshold-Based Voting Ensembles (TBVEs) built from SEs did not achieve a satisfactory performance for all classes, a meta-dataset was developed from the outputs of the obtained SEs. For each class, a meta-dataset was preprocessed, balanced, and used to train a Random Forest Classifier (RFC) with hyperparameter tuning via RandomizedSearchCV. For each class, a TBVE was then constructed from the saved RFC models. The resulting ensemble demonstrated a near-perfect performance for failure detection and classification in most classes (0, 1, 3, and 5), although Classes 2 and 4 achieved a lower performance, which could be attributed to an extremely low number of samples and a hard-to-detect type of failure. Overall, the proposed method presents a robust and explainable AI solution for predictive maintenance, combining symbolic learning with ensemble-based meta-modeling. Full article

Laboratory data from in-cell tests at and near open circuit potentials (OCV) and ex-situ H₂O₂ vapor exposure tests are used to develop a fluoride emission rate (FER) model for a state-of-the-art 12-µm thin, low equivalent weight, long-chain perfluorosulfonic acid (PFSA) ionomer membrane that is mechanically reinforced with expanded PTFE and chemically stabilized with 2 mol% cerium as an anti-oxidant. The anode FER at OCV linearly correlates with O₂ crossover from the cathode and the high yield of H₂O₂ at anode potentials, as observed in rotating ring disk electrode (RRDE) studies. The cathode FER may be linked to the energetic formation of reactive hydroxyl radicals (·OH) from the decomposition of H₂O₂ produced as an intermediate in the two-electron ORR pathway at high cathode potentials. Both anode and cathode FERs are significantly enhanced at low relative humidity and high temperatures. The modeled FER is strongly influenced by the gradients in water activity and cerium concentration that develops in operating fuel cells. Membrane stability maps are constructed to illustrate the relationship between the cell voltage, temperature, and relative humidity for FER thresholds that define H₂ crossover failure by chemical degradation over a specified lifetime. Full article