Search Results (8,556)

Daily nighttime light (NTL) observations made by remote sensing satellites can monitor human activity at high temporal resolution, but are often constrained by residual physical disturbances. Even in standard products, such as NASA’s Black Marble VNP46A2, factors related to sensor viewing geometry, lunar illumination, atmospheric conditions, and seasonality can introduce noise into daily radiance retrievals. This study develops a locally adaptive framework to diagnose and correct residual disturbances in daily NTL data. By estimating location-specific regression models, we quantify the residual sensitivity of VNP46A2 radiance to multiple disturbance factors and selectively remove statistically significant components. The results show that the proposed approach effectively removes statistically significant residual disturbances from daily NTL data in the VNP46A2 product. An application for COVID-19 containment periods in China demonstrates the effectiveness of the proposed approach, where corrected daily NTL data exhibit enhanced temporal stability and improved interpretability. Further analysis based on event study approaches demonstrates that corrected daily NTL data enable the identification of short-run policy effects that are difficult to detect with lower-frequency indicators. Overall, this study enhances the suitability of daily NTL data for high-frequency socioeconomic applications and extends existing preprocessing approaches for daily NTL observations. Full article

Condition-based maintenance strategies using AI-driven health monitoring have emerged as valuable tools for industrial reliability, yet their implementation remains challenging in industries with limited operational data. Class I induction motors (≤15 kW), which power critical equipment in industries such as grain handling facilities, represent a significant portion of industrial assets but lack established healthy vibration baselines for effective monitoring. A fundamental challenge exists in deploying AI-based health monitoring systems when no historical performance data is available, creating a ’cold-start’ problem that prevents industries from adopting predictive maintenance strategies without costly pilot programs or prolonged data collection periods. This study developed a data-driven health monitoring framework for Class I induction motors that eliminates the dependency on long-term historical trends. Through extensive experimental testing of 98 configurations on new motors, a correlation between vibration amplitude at rotational frequency and motor power rating was established, enabling the creation of a synthetic signal generation algorithm. A robust Health Index (HI) model with integrated diagnostic capabilities was developed using the JPCCED-HI framework, trained on both experimental and synthetically generated healthy vibration data to detect degradation and diagnose common failure modes. The regression analysis revealed a statistically significant relationship between motor power rating and healthy vibration signatures, enabling synthetic generation of baseline data for any Class I motor within the rated range. When implemented at an operational grain silo facility, the HI model successfully detected faulty behavior and accurately diagnosed probable failure modes in equipment with no prior monitoring history, demonstrating that maintenance decisions could be made based on condition data rather than reactive responses to failures. This framework enables immediate deployment of AI-based condition monitoring in industries lacking historical data, eliminating a major barrier to adopting predictive maintenance strategies. The synthetic data generation approach provides a cost-effective solution to the data scarcity problem identified as a critical challenge in industrial AI applications, while the successful industrial implementation validates the feasibility of this approach for small-to-medium industrial facilities. Full article

High-resolution TIR missions require sustained and well-characterized radiometric accuracy to support applications such as land surface temperature retrieval, drought monitoring, and surface energy budget analysis. To address this need, we develop an operational and automated ground-based vicarious radiometric calibration framework for TIR sensors and demonstrate its performance using the Wide-swath Thermal Infrared Imager (WTI) onboard Gaofen-5 01A (GF-5A). Three arid Gobi calibration sites were selected by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products, Shuttle Radar Topography Mission (SRTM)-derived topography, and WTI-based radiometric uniformity metrics to ensure low cloud cover, flat terrain, and high spatial homogeneity. Automated ground stations deployed at Golmud, Dachaidan, and Dunhuang have continuously recorded 1 min contact surface temperature since October 2023. Field-measured emissivity spectra, Integrated Global Radiosonde Archive (IGRA) radiosonde profiles, and MODTRAN (MODerate resolution atmospheric TRANsmission) v5.2 simulations were combined to compute top-of-atmosphere (TOA) radiances, which were subsequently collocated with WTI imagery. After data screening and gain-stratified regression, linear calibration coefficients were derived for each TIR band. Based on 189 scenes from February–July 2024, all four bands exhibit strong linearity (R-squared greater than 0.979). Validation using 45 independent scenes yields a mean brightness–temperature root-mean-square error (RMSE) of 0.67 K. A full radiometric-chain uncertainty budget—including contact temperature, emissivity, atmospheric profiles, and radiative transfer modeling—results in a combined standard uncertainty of 1.41 K. The proposed framework provides a low-maintenance, traceable, and high-frequency solution for the long-term on-orbit radiometric calibration of GF-5A WTI and establishes a reproducible pathway for future TIR missions requiring sustained calibration stability. Full article

Precipitation in high mountain areas is of critical importance as these regions are major sources of freshwater, supporting river basins, ecosystems, and downstream communities. Changes in precipitation regimes in these regions can have cascading impacts on water availability, agriculture, hydropower, and biodiversity. The present study aims to give new information about precipitation variability in high mountain regions of Bulgaria (Musala, Botev Peak, and Cherni Vrah) and to assess the role of large-scale atmospheric circulation patterns for the occurrence of extreme precipitation months. The study period is 1937–2024, and the classification of extreme precipitation months is based on the 10th and 90th percentiles of precipitation distribution. The temporal distribution of extreme precipitation months was analyzed by comparison of two periods (1937–1980 and 1981–2024). The impact of atmospheric circulation was evaluated by correlation between the number of extreme precipitation months and indices for the North Atlantic Oscillation (NAO) and Western Mediterranean Oscillation (WeMO). Results show a statistically significant decrease in winter and spring precipitation at Musala and Cherni Vrah, and a persistent drying tendency at Cherni Vrah across all seasons. The frequency of extremely wet months in winter and autumn has sharply declined since 1981, whereas extremely dry months have become more common, particularly during the cold season. Precipitation erosivity also exhibits station-specific responses, with Musala and Cherni Vrah showing reduced monthly concentration, while Botev Peak retains pronounced warm-season erosive rainfall. Circulation analysis indicates that positive NAOI phases favor dry extremes, while positive WeMOI phases enhance wet extremes. These findings reveal a shift toward drier and more seasonally uneven conditions in Bulgaria’s alpine zone, increasing hydrological risks related to drought, water scarcity, and soil erosion. The identified shifts in precipitation seasonality and intensity offer essential guidance for forecasting hydrological risks and mitigating soil erosion in vulnerable mountain ecosystems. The study underscores the need for adaptive water-resource strategies and enhanced monitoring in high-mountain areas. Full article

Freezing and thawing affect the structural integrity and quality of meat, yet these processes remain difficult to monitor due to spatial temperature gradients and non-uniform phase transitions. This study investigates the ability of ultrasound to detect dynamic freezing and thawing events in pork tissues with different fat contents. Specimens of water, lean meat, marbled meat, layered lean–fat structures, and lard were subjected to controlled freeze–thaw cycles while ultrasonic signals and internal temperatures were continuously monitored. Consistent amplitude drops in the megahertz range at entering the freezing phase formed characteristic signal patterns that differed sharply between lean and fatty tissues. Lean meat, dominated by water content, exhibited rapid signal loss at the onset of ice crystallization and a clear recovery of amplitude once fully frozen. Fat-rich tissues demonstrated prolonged attenuation and near disappearance of high-frequency signals, with incomplete recovery even at deep-frozen states. A jump of ultrasound velocity from 1.4–1.6 km/s in a warm state to 2.6–3.7 km/s in a frozen state indicated complete freezing. Hysteresis between temperature readings and actual phase transition moments was found. Distinct ultrasonic freeze–thaw signatures reflecting tissue composition suggest a novel approach for monitoring the true onset and completion of freezing and thawing in meat. Full article

Evapotranspiration (ET) plays a crucial role in the terrestrial water cycle, especially in arid and semi-arid agricultural regions where precise water management is essential. However, the limited spatial resolution and temporal frequency of existing ET products hinder their application in fine-scale agricultural monitoring. In this study, we first improved the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model by replacing the relative humidity-based soil moisture constraint with the land surface water index (LSWI), aiming to enhance model performance in water-limited environments. Second, we developed a Crop Unmixing and Weight Fusion Model for ET (CUWFM) to generate daily ET products at a 30 m spatial resolution by integrating high-resolution but infrequent PT-JPL-ET data with coarse-resolution but frequent PML-V2-ET data. The CUWFM employs a hybrid approach combining sub-pixel crop fraction decomposition with similarity-weighted regression, allowing for more accurate ET estimation over heterogeneous agricultural landscapes. The proposed methods were evaluated in the Changji region of Xinjiang, China, using field-measured ET data from two-flux-tower sites. The results show that the improved PT-JPL model increased ET estimation accuracy compared with the original version, with higher R² and Nash–Sutcliffe efficiency (NSE), and lower root mean square error (RMSE). The CUWFM outperformed benchmark spatiotemporal fusion methods, including STARFM, ESTARFM, and Fit-FC, in both pixel- and field-scale assessments, achieving the highest overall performance scores based on the All-round Performance Assessment (APA) framework. This study demonstrates the potential of integrating vegetation indices and crop-specific spatial decomposition into ET modeling, providing a feasible pathway for producing high spatiotemporal resolution ET datasets to support precision agriculture in arid and semi-arid regions. Full article

Background and Objectives: Asymmetries in body composition and movement patterns are common in professional basketball due to the sport’s repetitive and unilateral demands. While both structural and functional asymmetries have been independently studied, little is known about their interaction under real training conditions. The aim of this study was to compare structural asymmetries, obtained from bioelectrical impedance analysis, with functional asymmetries, measured through inertial devices in professional basketball players. Methods: Twenty-five male professional basketball players from two Spanish teams were monitored over a two-month period. Structural asymmetries were assessed via the TANITA MC-780MA multi-frequency analyzer, while functional asymmetries were quantified using WIMU Pro™ inertial units during 43 training sessions. Descriptive, correlational, and cluster analyses were performed, followed by linear mixed-effects models adjusted for individual random effects, with statistical significance set at p < 0.05. Results: Descriptive results revealed low overall fat mass and no relevant group-level asymmetries in muscle mass or functional variables, although fat mass asymmetry showed greater variability across players. Correlation analyses indicated weak and non-significant relationships between structural and functional asymmetries. Cluster analysis grouped muscle mass and functional asymmetries together, while fat mass asymmetry formed a distinct cluster. Linear mixed-effects models confirmed significant differences for muscle mass asymmetry and demonstrated high inter-individual variability. Conclusions: Structural and functional asymmetries behave independently, with muscle mass asymmetry showing greater variability and functional relevance. These findings highlight the need for individualized monitoring approaches integrating morphological and functional assessments to optimize performance and reduce injury risk in elite basketball players. Full article

Background/Objectives: Despite clonal expansion during a primary immune response, or after subsequent antigen encounters, the frequency of memory B cells (B_mem) specific for an antigen remains low, making their detection difficult. However, unlike serum antibodies, which have a short half-life in vivo and thus require continuous replenishment to maintain stable titers, circulating B_mem are long-lived; they preserve immunological preparedness through their ability to rapidly engage in recall responses and differentiate into antibody-secreting cells (ASCs) upon antigen encounter. To this end, development of assays suited for the reliable detection of rare antigen-specific B_mem is critical and can provide insights into an individual’s antigen exposure history and immune status beyond that offered by traditional serum antibody measurements alone. Methods: ImmunoSpot^® has emerged as a suitable technique for the detection of individual antigen-specific B cells through visualizing their antibody-derived secretory footprints. Here, we report the theoretical and practical foundations for detecting rare antigen-specific B_mem in human peripheral blood mononuclear cells (PBMC). Leveraging the unique availability of verifiably naïve vs. antigen-experienced human samples, we used SARS-CoV-2 Spike (S-) and Nucleocapsid (NCAP) antigens to interrogate the presence of B_mem with these respective specificities. Results: While 100% diagnostic accuracy was achieved for both antigens, detection of NCAP-specific B_mem required reducing the lower detection limit of the standard assay. Specifically, this was achieved by testing a total of 2 million PBMC across multiple replicate assay wells and assessing the cumulative number of secretory footprints detected. Conclusion: The protocols described here should facilitate the reliable detection of ASCs present at varying precursor frequencies and serve as guidance for routine immune monitoring of rare B_mem with specificity for any antigen. Full article

Acoustic-based anomaly detection in elevators is crucial for predictive maintenance and operational safety, yet it faces significant challenges in real-world settings, including pervasive multi-source acoustic interference within confined spaces and severe class imbalance in collected data, which critically degrades the detection performance for minority yet critical acoustic events. To address these issues, this study proposes a novel hierarchical self-distillation framework. The method embeds auxiliary classifiers into the intermediate layers of a backbone network, creating a deep teacher–shallow student knowledge transfer paradigm optimized jointly via Kullback–Leibler divergence and feature alignment losses. A self-attentive temporal pooling layer is introduced to adaptively weigh discriminative time-frequency features, thereby mitigating temporal overlap interference, while a focal loss function is employed specifically in the teacher model to recalibrate the learning focus towards hard-to-classify minority samples. Extensive evaluations on the public UrbanSound8K dataset and a proprietary industrial elevator audio dataset demonstrate that the proposed model achieves superior performance, exceeding 90% in both accuracy and F1-score. Notably, it yields substantial improvements in recognizing rare events, validating its robustness for elevator acoustic monitoring. Full article

This paper proposes a regional inertia estimation method in power systems using ambient data measured by phasor measurement units (PMUs). The proposed method employs band-pass filtering to suppress the low-frequency influence of mechanical power and to attenuate high-frequency noise and discrepancies between rotor speed and electrical frequency. By utilizing a simple first-order AutoRegressive Moving Average with eXogenous input (ARMAX) model, this process allows the inertia constant to be directly identified. This method requires no prior model order selection, rotor speed estimation, or computation of the rate of change of frequency (RoCoF). The proposed method was validated through simulation on three benchmark systems: the Kundur two-area system, the IEEE Australian simplified 14-generator system, and the IEEE 39-bus system. The method achieved area-level inertia estimates within approximately ±5% error across all test cases, exhibiting consistent performance despite variations in disturbance models and system configurations. The estimation also maintained stable performance with short data windows of a few minutes, demonstrating its suitability for near real-time monitoring applications. Full article

Chimeric antigen receptor (CAR) T-cell therapy has revolutionized the treatment of relapsed or refractory (r/r) diffuse large B-cell lymphoma (DLBCL); however, a significant proportion of patients fail to achieve a durable response, underscoring the need for reliable predictive biomarkers. We characterize T-lymphocyte subpopulations in apheresis samples from 23 r/r large B-cell lymphoma (LBCL) patients who received axicabtagene ciloleucel (axi-cel) to identify pre-treatment cell biomarkers associated with CAR T-cell kinetics and clinical outcomes. Immunophenotyping of T-cells within fresh apheresis samples and monitoring of circulating CAR T-cells were performed by multiparametric flow cytometry. The median peak CAR T-cell count was 45.2 CAR T-cells/mL. Strong CAR-T expanders (≥45.2 CAR T-cells/mL) exhibited higher values of both CD4⁺ (p = 0.011) and CD8⁺ (p = 0.023) central memory T-cells (T_CM; CCR7⁺CD45RA⁻), as well as lower proportions of CD8⁺CD38⁺ T-cells in apheresis samples. In apheresis, a cut-off value of >4.3% of CD8⁺ T_CM predicted strong CAR-T expansion (AUC: 0.80; p = 0.023) and superior progression-free survival (p = 0.04) compared with patients who had CD8⁺ T_CM below the cut-off. Our data suggest that high frequencies of CD8⁺ T_CM cells in apheresis samples may represent a promising pre-treatment biomarker associated with strong CAR-T expansion and superior clinical outcome in r/r LBCL patients following axi-cel. Full article

Monitoring mechanical vibration is crucial for ensuring the structural integrity and optimal performance of unmanned aerial vehicles (UAVs). This study introduces a portable and low-cost system that enables integrated acquisition and analysis of UAV vibration data in a single step, using a Raspberry Pi 4B, data acquisition (DAQ) through a MCC128 DAQ HAT card, and six accelerometers positioned at strategic structural points. Ground-based engine tests at 2700 RPM allowed vibration data to be recorded under conditions similar to those of real operation. Data was processed with a Kalman filter, a Hann window function application, and frequency analysis via Fast Fourier Transform (FFT). The first and second wing bending natural frequencies were identified at 12.3 Hz and 17.5 Hz, respectively, as well as a significant component around 23 Hz, which is a subharmonic of the propulsion system excitation frequency near 45 Hz. The results indicate that the highest vibration amplitudes are concentrated at the wingtips and near the engine. The proposed system offers an accessible and flexible alternative to commercial equipment, integrating acquisition, processing, and real-time visualization. Moreover, its implementation facilitates the early detection of structural anomalies and improves the reliability and safety of UAVs. Full article

We examine geographic spillovers in digital credit markets by studying how natural disasters affect borrowing behavior in adjacent, physically undamaged regions. Using granular loan-level data from Indonesia’s largest FinTech lender (2021–2023) and leveraging quasi-random variation in disaster timing and location, we estimate fixed-effects specifications that incorporate spatially lagged disaster exposure (an SLX-type spatial approach) to quantify spillovers. Disasters generate economically significant spillovers in neighboring provinces: a 1% increase in disaster frequency raises local borrowing by 0.036%, approximately 20% of the direct effect. Spillovers vary sharply with geographic connectivity—land-connected provinces experience effects about 6.6 times larger than sea-connected provinces. These results highlight that digital lending platforms can transmit geographically proximate risks beyond directly affected areas through channels that differ from traditional banking networks. The systematic nature of these spillovers suggests that disaster-response strategies may be more effective when they consider adjacent regions. That platform risk management can be strengthened by integrating spatial disaster exposure and connectivity into credit monitoring and decision rules. Full article

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

The construction industry relies on the Internet of Things (IoT) and wearable technologies to enhance workplace safety. This research investigates the use of IoT and wearable technology among Saudi Arabian construction sector employees, analyzing their implementation difficulties and the factors contributing to successful implementation. A structured questionnaire was distributed to 567 construction professionals across different roles and projects. Frequency analysis was used to study adoption patterns, chi-square tests to study demographic factors, and principal component analysis for exploratory factor analysis to discover hidden adoption factors. The findings show that smart safety vests and helmets receive the highest level of recognition. On the other hand, advanced monitoring systems, including fatigue and environmental sensors, are not used enough. Group differences in device adoption were investigated in terms of years of experience, academic qualification, job role, and project budget. The findings from factor analysis show that three main factors determine adoption rates, which include (1) safety and operational effectiveness, (2) worker acceptance and support structures, and (3) technical and adoption barriers. A data-driven system is created to help policymakers and industry leaders accelerate construction safety digitalization efforts. Full article