Search Results (1,588)

Moderate-resolution leaf area index (LAI) retrieval over heterogeneous landscapes is affected not only by unresolved subpixel composition in coarse-resolution predictors, but also by structural bias in supervisory labels aggregated from higher-resolution products. To address this issue, we developed a reference-guided two-stage workflow to improve LAI retrieval from the Visible Infrared Imaging Radiometer Suite (VIIRS). In the first stage, aggregated Sentinel-2 LAI was calibrated against Ground-Based Observations for Validation (GBOV) LP3 reference LAI using subpixel plant functional type (PFT) fractions and forest-sensitive hinge terms to generate corrected 500 m labels. In the second stage, a random-forest model was trained using VIIRS spectral reflectance, viewing geometry, vegetation indices, texture, and subpixel compositional variables. Model development was based on 2020–2021 data from 11 U.S. GBOV sites. Performance was evaluated by same-site temporal transfer to 2019 and 2022 and by strict leave-one-site-out (LOSO) validation. Label calibration improved agreement with GBOV from a coefficient of determination (R²) of 0.752 and a root mean square error (RMSE) of 1.110 to an R² of 0.908 and an RMSE of 0.676. Under LOSO validation, the final model achieved an R² of 0.901 with an RMSE of 0.703. On the 2019/2022 overlap subset shared by the final VIIRS retrieval, the official VNP product, and the GBOV reference, the final model achieved an R² of 0.905 and an RMSE of 0.609, compared with 0.755 and 0.978 for the official VNP product. These results show that reference-guided label correction, combined with explicit subpixel compositional information, can substantially improve VIIRS LAI retrieval over mixed pixels within the evaluated study domain. Full article

Long-term monitoring of surface-water dynamics in hyper-arid reservoir systems requires consistent remote-sensing methods that can distinguish open water from bright desert surfaces, shallow water, wet sand, and mixed shoreline pixels. This study evaluates Landsat-derived spectral water indices for delineating surface water in Lake Nasser and the adjacent Tushka Lakes, generates a multi-decadal record of surface-water extent using Google Earth Engine, and places the resulting surface-water patterns in the context of available hydrogeological observations. Landsat TM and OLI surface reflectance imagery was used to compare seven commonly applied water indices (NDWI, EWI, NDX, WRI, AWEI_nsh, TCW, and NWI) based on mapped water area, relative area differences, and classification accuracy metrics derived from 1000 stratified reference samples. Among the tested indices, NDWI provided stable water–land separation (overall accuracy ≈ 93.6%; κ ≈ 0.898) and was selected for long-term mapping. The NDWI-based workflow was implemented in Google Earth Engine to generate quarterly composites of surface-water extent for the period 1987–2026. The resulting time series reveals stable, persistent surface water in the central and southern sectors of Lake Nasser, in contrast to pronounced seasonal and interannual variability in the shallow, intermittently connected Tushka basins. Total mapped water area increased from 2631 km² in 1987 to 8923 km² in early 2026, with Lake Nasser ranging from 2411 to 6060.7 km² and the Tushka Lakes expanding from no mapped water before 1998 to more than 3300 km² during 2025. To assess possible surface–subsurface interaction, daily lake-stage records (1965–2014) and monthly groundwater levels from 44 observation wells were used to estimate potential seepage losses from Lake Nasser to the Nubian Sandstone Aquifer System using Darcy’s law. Annual seepage estimates ranged from 15.58 × 10⁶ to 36.68 × 10⁶ m³/year, suggesting spatial variability in potential lake–aquifer seepage along the western lake margin. The combined remote-sensing and hydrogeologic results provide complementary, non-causal evidence for interpreting where surface-water persistence and estimated seepage may co-occur. Because spatial correlation analysis, calibrated ground-water modeling, full water-budget analysis, and independent field validation were not performed, the inferred seepage–surface-water relation should be regarded as a cautious hypothesis rather than proof of causality. Full article

Understanding turbidity in coastal systems is essential to ensure the sustainable management of these ecosystems, which are increasingly under pressure from natural factors and human activities. Thus, this study aims to develop a local Sentinel-2-based turbidity model for the Aveiro lagoon (Portugal) by combining Sentinel-2 records with in situ measurements. A field campaign synchronized with a Sentinel-2 overpass was conducted across the lagoon channels on 28 May 2025, to capture spatial variability by measuring near-surface turbidity and Secchi depth, for correspondence with the spectral records of satellite. Remote Sensing Reflectance (Rrs) and turbidity were derived using various algorithms integrated within the ACOLITE software (v20250114.0). Additionally, new turbidity models were developed and empirically adjusted based on the Rrs data, with their performance quantified through the coefficient of determination (R²) and Root Mean Square Error (RMSE). The results showed that the existing algorithms are not directly suitable for the Aveiro lagoon, as they underestimate the highest turbidity values. The ratio between 665 and 560 nm bands (RG_ratio) proved to be the most suitable spectral index, performing best in estimating turbidity (R² = 0.822 and RMSE = 1.77 NTU). This study highlights the importance of locally calibrated models over standard ACOLITE algorithms for turbidity retrieval in shallow coastal lagoons, while emphasizing that the proposed model was calibrated for the tidal, wind, and river discharge conditions sampled during the campaign and has not yet been independently validated. Full article

We propose a family of spectral hypergraph algorithms for early detection of connectivity collapse in pharmaceutical supply chain networks. The Fiedler eigenvalue ${\lambda }_2$ of the normalised hypergraph Laplacian serves as the order parameter. Five geometry-aware early warning indicators (TSI, HSST, HOMFA, HOTV, ORC) monitor network topology rather than scalar residuals, with provable detection guarantees under geometric ergodicity. A Greedy Dejamming algorithm restores connectivity via rank-2 Laplacian updates, achieving a $(1-1/e)$-approximation within a procurement budget constraint. Monte Carlo validation on a calibrated pharmaceutical distribution hypergraph demonstrates substantially higher detection sensitivity and shorter lead times than classical statistical process control. Hyperedge representation yields detection gains exceeding $90\%$ for simultaneous multi-party failures that pairwise graph projections miss entirely. A COVID-19 lockdown episode provides a held-out directional consistency check. Full article

Infrared Fourier transform spectrometers using interferometric spectroscopy are widely used in space remote sensing owing to their high spectral resolution and sensitivity. We investigated the distorted spectral characteristics introduced by nonlinear errors of different orders through simulation for infrared detectors with strong nonlinear effects. A high-order nonlinear correction scheme was proposed based on two iterative correction methods for in-band and out-of-band spectra. Further, the effects of second-order, third-order, in-band, and out-of-band correction methods were compared using prelaunch radiometric calibration experimental data from the DQ-2 satellite infrared hyperspectral atmospheric composition sounder. The results showed that the third-order in-band correction scheme performed the best, while various other correction schemes also effectively reduced nonlinear errors. The maximum average deviation was 0.18–0.25 K for the long-wave band and 0.11–0.19 K for the mid-wave band in the temperature range of 230–300 K. According to the correction evaluation and methods comparison, the proposed method is appropriate for nonlinearity detectors to improve radiometric calibration accuracy. Full article

Bolted joints are critical load-transfer components in bridges, wind turbines, aerospace systems, mechanical equipment, and offshore platforms, where preload loss can degrade stiffness, accelerate fatigue, and compromise safety. For structural health monitoring, early monitoring of preload reduction before marked loosening is essential, yet existing ultrasonic guided wave indicators remain affected by frequency dependence, non-monotonic responses, amplitude drift, and environmental disturbances. This study proposes an early-warning-oriented preload identification method that combines broadband excitation, multi-frequency narrowband reconstruction, spectral centroid extraction, optimized weighted fusion, and fixed SC-domain linear calibration from one reference loading group. Using a 20–250 kHz Chirp response, 14 narrowband signals from 50 to 180 kHz were reconstructed for an M20 single-bolt specimen tested over 50–90 N·m. The fused spectral centroid index exhibited a stable, monotonic, and approximately linear relationship with preload. When fixed weights and calibration coefficients were transferred to held-out repeated-loading groups, all Pearson correlation coefficients exceeded 0.99. Feature-level robustness tests showed that the arithmetic mean of the spectral centroid reduced temperature-induced Range% by 98.42–99.08% and RSD by 98.89–99.31% relative to energy-based features. This work provides an interpretable multi-frequency spectral descriptor and a calibration transfer framework for repeatable early warning of preload loss in a controlled single-bolt configuration. Full article

Urban freshwater ecosystems affected by eutrophication and recurrent algal blooms require monitoring approaches capable of representing optical complexity and spatial heterogeneity. This study evaluated an integrated workflow combining proximal in situ hyperspectral sensing, radiometric calibration, spectral filtering, predictor-band selection, data transformation, and machine-learning regression to estimate chlorophyll-a (chl-a) in a tropical eutrophic urban reservoir. Monthly field campaigns were conducted from September 2022 to February 2023, with simultaneous chl-a measurements and hyperspectral image acquisition. After preprocessing, noise removal, and exclusion of anomalous spectra, 82 matched hyperspectral–chl-a observations were retained for model development. Predictor bands were selected using Pearson correlation and F-test analysis, identifying five relevant wavelengths: 530, 535, 682, 687, and 732 nm. Multiple Linear Regression, Random Forest Regressor, Support Vector Regressor, and XGBoost Regressor were tested under different data transformations. The Support Vector Regressor with logarithmic transformation achieved the best performance, with R² = 0.86 and RMSE = 6.89 µg L⁻¹. The selected wavelengths correspond to spectral regions associated with green reflectance, red chl-a absorption, and red-edge/NIR responses in productive waters. The results indicate that proximal hyperspectral sensing combined with machine learning can support chl-a estimation in optically complex urban reservoirs and provide complementary information for eutrophication monitoring and bloom-management strategies. Full article

The radiometric stability of satellite thermal infrared (TIR) channels is an indispensable prerequisite for the accurate retrieval of land surface temperature (LST) and the generation of reliable climate data records. This study evaluates the on-orbit radiometric calibration stability of the Fengyun-3D (FY-3D)/MEdium Resolution Spectral Imager-II (MERSI-II) TIR channels (channels 24 and 25) over four years (2021–2024) via a rigorous cross-calibration framework against Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS). By imposing stringent spectral, spatial, temporal, and angular constraints to ensure the high fidelity of collocated pixel pairs, the cross-calibration results demonstrate that FY-3D/MERSI-II exhibits exceptional radiometric stability. Absolute brightness temperature biases are typically less than 0.1 K, with root mean square errors (RMSEs) limited to 1.20 K over a range of diurnal and seasonal conditions, demonstrating no noticeable systematic degradation. Furthermore, the downstream impact of this calibration on LST retrieval was quantified using the adapted National Oceanic and Atmospheric Administration Joint Polar Satellite System Enterprise algorithm. Validated against independent ground-based longwave radiation measurements collected from the Heihe Watershed Allied Telemetry Experimental Research network (HiWATER) and the Surface Radiation Budget Network (SURFRAD), the retrieved LST yielded overall biases of 0 K and −0.37 K, respectively, with RMSEs below 2.5 K. Cross-calibration demonstrates a limited and context-dependent impact on daytime LST, while the nighttime LST accuracy can be marginally improved using seasonal calibration coefficients derived from combined day/night matchups. Mechanistically, the integration of a soil directional emissivity model into the retrieval algorithm effectively mitigates viewing-zenith-angle (VZA)-induced uncertainties, systematically reducing biases by 0.12–0.20 K and RMSEs by 0.04–0.06 K. These findings confirm that the on-orbit radiometric calibration of FY-3D/MERSI-II meets scientific quality requirements and provide practical guidance for optimizing LST retrieval. Full article

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality in the elderly worldwide. Over the past two decades, there has been a wealth of evidence of a close relationship between autonomic nervous system activity and cardiovascular mortality, including sudden cardiac death. Heart rate variability (HRV), derived from photoplethysmographic (PPG) signals, is increasingly recognized as a promising non-invasive digital marker for evaluating autonomic nervous system function and stratifying CVD risk. The application of machine learning algorithms to PPG-derived HRV analysis offers a promising approach for improving CVD risk stratification and facilitating the development of personalized medicine strategies. Background/Objectives: To evaluate the potential of heart rate variability indicators in predicting the risk of developing CVD in individuals aged 65 years and older. Methods: The study involved individuals aged 65 years and older, divided into two groups: those with a risk of developing CVD (n = 54) and those without risk (n = 46). The first stage included a questionnaire as well as anthropometric and hemodynamic measurements. At the second stage, a PPG was performed using the Eldar computer photoplethysmograph and Eldar-Vario software, followed by an analysis of time-domain and spectral HRV parameters. Statistical data analysis was conducted using the SPSS Statistics 22.0 software package, focusing on the evaluation of associations between HRV indicators and the presence of CVD. Interpretable machine learning models were developed using logistic regression and a random forest algorithm within a nested cross-validation framework. In addition to the discriminatory characteristics, Brier score, LogLoss, calibration analysis, error matrices, permutation importance, and SHAP interpretation were analyzed in the study. Results: In patients with cardiovascular diseases, a statistically significant decrease in heart rate variability was revealed: SDNN by 2 times (26 [Q1–Q3: 15, 35] ms), pNN50 by 3.5 times (4 [3, 5]%), TINN by 5 times (31 [20, 51] ms), and HRV by 2.5 times (6 [4, 8.7]). In addition, a decrease was seen in the spectral components of VLF by one-fold (2450 [Q1–Q3: 2450, 4500] ms²), LF by four-fold (750 [750, 1500] ms²) and HF by five-fold (450 [450, 750] ms²) (p < 0.05). At the same time, there was a significant increase in the VLF/HF and LF/HF ratios, which indicates a predominance of sympathetic activity. According to the results of the correlation analysis, statistically significant associations of HRV indicators with age, physical activity level, body mass index and systolic blood pressure were revealed. The results of machine learning also revealed the association of HRV with arterial hypertension, physical activity and BMI. The best final results were demonstrated by a random forest model with a combined set of clinical and HRV signs of HF and RMSSD (ROC-AUC was 0.9988). The signs of heart rate variability obtained by photoplethysmography demonstrated additional prognostic value in relation to clinical signs. PPG-derived HRV features demonstrated additional discriminatory value for cardiovascular risk stratification. Conclusions: The obtained data demonstrate a close association between the risk of developing cardiovascular disease and autonomic nervous system dysfunction. The decrease in heart rate variability is most pronounced in elderly individuals with existing cardiovascular disease and can be considered a potential tool for developing diagnostic, prognostic, and risk stratification strategies. The use of machine learning demonstrated that heart rate variability features obtained using photoplethysmography improve diagnostic prognostication and classification of cardiovascular diseases compared to models based solely on clinical data. Full article

Agricultural production in arid regions is strongly constrained by water stress, making timely evaluation of crop water conditions increasingly important. However, conventional measurements of plant moisture content (PMC) primarily rely on destructive oven-drying methods, which are not only labor-intensive and time-consuming but also constrained by limited sample size and spatial coverage. These shortcomings make it difficult to capture the spatial heterogeneity of crop water status across large agricultural regions, thereby restricting regional-scale water diagnosis and precision irrigation decision-making. Focusing on silage maize cultivated in the arid region of Gansu Province, China, this work develops a regional PMC estimation approach by combining multi-source remote sensing data. High-resolution unmanned aerial vehicle (UAV) observations were integrated with Sentinel-2 and Sentinel-3 imagery, while radiometric and temperature corrections were applied to improve data consistency. A set of spectral, textural, and thermal features was derived from multispectral, visible, and thermal infrared datasets. Feature selection based on Pearson correlation was then carried out, followed by the construction of three models, namely Random Forest (RF), Support Vector Machine (SVM), and Partial Least Squares Regression (PLSR). Among them, the RF model performed more reliably, achieving a validation R² of 0.92 with relatively low prediction error. In addition, calibration using UAV data led to a clear improvement in satellite-based estimates, with R² increasing from 0.52–0.62 to 0.71–0.74. The generated PMC maps captured both the temporal decline during the growing season and the spatial variability across the study area. Overall, the proposed approach offers a practical option for large-scale monitoring of crop water status and can support irrigation management in water-limited environments. Full article

The stage subdivision of gravity-flow deposits is crucial for identifying deep-water reservoirs. Traditional methods relying on visual core observation or macroscopic well log correlation are often subjective and lack the resolution to decipher dynamic fluid evolution. In this study, we introduced the Wavelet Synchrosqueezed Transform (WSST) to process a multi-log reconstructed curve from the Daluhu Area in the Dongying Depression. Compared to the traditional Continuous Wavelet Transform (CWT), WSST effectively eliminates spectral smearing, reassigning energy into highly focused energy ridges within the 0.5–2.5 m scale window to provide objective mathematical boundaries for depositional stages. Four quantitative parameters—Time-Frequency Concentration (TFC), Instantaneous Bandwidth (IBW), Energy Evolution Gradient (EEG), and Ridge Center Offset (RCO)—were extracted from the WSST matrix. Results show that each parameter serves a distinct geological purpose: EEG spikes accurately locate basal erosional scour surfaces; TFC characterizes the rhythmic amalgamation of gravity flow pulses; IBW captures the high-frequency algorithmic noise unique to massive homogeneous sandstones; and RCO tracks the macroscopic energy decay trajectory of individual flow events. This method offers a highly automated and objective tool for characterizing the hydrodynamic evolution of gravity flows, provided it is properly calibrated with core data to avoid algorithmic ambiguity. Full article

Remotely Piloted Aircrafts (RPAs) equipped with multispectral sensors have emerged as promising tools for estimating foliar nitrogen content (TFN). So, this study applied a methodological approach aimed at simulating UAV multispectral data using hyperspectral leaf data obtained in a controlled environment, with the objective of evaluating its predictive potential and its transferability to field data collected by UAVs for TFN estimation. To this end, spectral bands and spectral indices (SIs) equivalent to those of UAV-mounted sensors were simulated based on hyperspectral data acquired by a benchtop sensor, and subsequently used in modeling via Partial Least Squares Regression (PLSR) and Random Forest (RF). The results showed similar performance across the levels, with R² values of 0.75 and 0.76 for PLSR and RF on the UAV data, and 0.75 and 0.74 for PLSR and RF on the simulated data, respectively. The RF model also performed well in cross-domain validation, with R² = 0.70 when calibrated with simulated data and applied to UAV data. Furthermore, the simulated data maintained high predictive power even with a reduced sample size. It is con Full article

Reliable estimation of biochar’s calorific value is essential for optimizing its use as a renewable energy source. Traditional bomb calorimetry provides accurate measurements but is hindered by its destructive, time-consuming nature, limiting the high-throughput screening capabilities needed for large-scale deployment. In this study, an innovative, non-destructive approach utilizing laser-induced breakdown spectroscopy (LIBS) combined with advanced multivariate analysis is presented for predicting the Higher Heating Value (HHV) of biochar derived from pine and beech biomass. The developed empirical model incorporates spectral signatures of key elements: carbon, hydrogen, nitrogen, sulfur, and oxygen. Model validation using 36 independent biochar samples revealed a statistically significant correlation between experimentally measured and LIBS-predicted HHVs (p-value = 0.045, t-statistic = 2.08). The developed model yielded a mean absolute error (MAE) of 1.33 MJ kg⁻¹ and a root mean square error (RMSE) of 1.72 MJ kg⁻¹. The findings demonstrate the feasibility of using LIBS-derived elemental data for rapid HHV estimation and provide a basis for further model refinement through the inclusion of additional biochar types and calibration datasets. The model effectively captures the complex nonlinear relationships between spectral features and energy content, addressing the heterogeneity inherent in biochar matrices. These findings highlight LIBS’s potential as a rapid, scalable, and environmentally sustainable tool for real-time biochar evaluation. Implementing this approach could significantly accelerate biomass resource assessment, optimize bioenergy production, and advance sustainable energy management strategies aligned with global environmental goals. Full article

Dynamic community detection seeks to identify changing structural groups in temporal graphs; however, current neural methodologies are susceptible to misinterpreting transient edges, noisy temporal variations, or unusual spectral disturbances as authentic structural changes. This research introduces TriMeta-BFNet, a tri-meta stacked atypical-frequency Bayesian Fourier neural network designed for hallucination-resistant community discovery. The proposed system presents a three-dimensional meta-counterbalance mechanism that includes topological consistency, Fourier-domain atypical frequency modeling, and Bayesian posterior uncertainty estimation. Initially, temporal graph signals are converted into the Fourier domain to distinguish stable low-frequency community patterns from erratic high-frequency disturbances. Secondly, unusual frequency points are detected by spectral energy deviation and integrated into a stacked neural representation module, enabling the model to differentiate significant structural alterations from extraneous oscillations. Third, Bayesian inference is employed to assess posterior uncertainty regarding community assignments, therefore mitigating overconfident predictions in the presence of ambiguous or noisy graph evolution. The three components are simultaneously optimized via a cohesive objective function that integrates community detection loss, structural consistency regularization, atypical-frequency penalty, temporal stability management, and Bayesian calibration loss. The resultant structure offers both resilient community divisions and comprehensible hallucination-risk assessments. TriMeta-BFNet theoretically conceptualizes hallucination in dynamic community detection as an imbalance of structural, spectral, and uncertainty factors, and it develops a mathematically rigorous counterbalance mechanism to mitigate erroneous community evolution. The suggested model presents a novel approach to uncertainty-aware, frequency-sensitive, and interpretable dynamic graph learning. Full article

High-speed imaging offers a non-intrusive approach for monitoring falling-film flows over horizontal tube bundles, but reflective images are difficult to quantify because grayscale variations are jointly affected by film geometry, interfacial curvature, surface slope, viewing angle, and local highlights. This study proposes an interpretable visual-proxy sensing framework for comparative state assessment of such flows. Isothermal water experiments were conducted on a five-row horizontal tube bundle over Re_Γ = 184 − 960. For each condition, grayscale frames were acquired at fps and analyzed within five fixed row-wise regions of interest. The image sequence was transformed by temporal-median background subtraction, local spatiotemporal mapping, moving-average detrending, and median-absolute-deviation normalization. The resulting normalized map M_n and dynamic renewal field G were used to extract four scalar descriptors: noise-corrected apparent renewal intensity I_R, high-frequency fraction R_HF, spectral peak frequency f_p, and burst-event rate F_B. Results show that M_n and G capture the transition from sparse column flow to more continuous sheet flow and reveal row-dependent activity organization. The descriptors provide complementary information on renewal intensity, frequency composition, dominant time scale, and intermittent events. Zero-response, noise-correction, and sensitivity tests confirm that the framework avoids structured pseudo-waves and maintains stable row-wise comparisons. The method provides a low-calibration visual sensing tool for relative falling-film state assessment. Full article