Search Results (4,000)

To be acceptable for precision agriculture applications, satellite imagery must be converted to surface reflectance. To be economical, the analytics must be delivered completely by automation and free of error to preserve farmer trust. CMAC (closed-form method for atmospheric correction) software was tested for this application along with established applications, Sen2Cor and FORCE—all three software packages seek to retrieve Sentinel-2 surface reflectance. Forty-three Sentinel-2 images were selected of farmland near Burley, Idaho, corrected by this software and evaluated as reflectance time series extracted from three irrigated corn fields. NDVI of irrigated corn presented an ideal test of precision and accuracy for surface reflectance retrieval. If accurate and precise, a plotted time series will smoothly display logistic growth during crop establishment followed by a plateau, then gradual senesce before harvest: divergences from this pattern indicate errors. CMAC followed the expected smooth pattern for this dataset while, in both FORCE and Sen2Cor, divergence occurred both above and below the CMAC time series for NDVI and from individual spectral band reflectance. These divergences were systematic and directly related to the degree of atmospheric effect—overcorrecting when clear, under-correcting when hazy. Only CMAC provided surface reflectance with the accuracy required for precision agriculture: applicable for Sentinel-2 as Tier 1 data and when haze or cloud- affected and unreliable, as Tier 2 infill from daily smallsat data. Additional analyses of the CMAC-corrected dataset were performed that were also applicable to Tier 2 daily-cadence smallsat data. Further analysis of this dataset indicated that, applied as NDVI, the application of broadband NIR, though sensitive to atmospheric water vapor, exhibited minimal errors compared to NDVI from narrowband NIR. These CMAC-corrected data provided an application to index crop start dates and were capable of distinguishing the uncorrectable data of cloud, cloud shadow, or extreme haze for removal under complete automation. Full article

In recent decades, forest disturbances have increased in both frequency and intensity, driven by global warming and urbanization. Remote sensing, together with forest disturbance algorithms, offers broad opportunities for forest disturbance monitoring due to its high temporal and spatial resolution. However, operational methods capable of predicting and classifying disturbances while providing official area estimates suitable for national statistics remain scarce. The Three Indices Three Dimensions (3I3D) algorithm has proven effective in identifying forest changes and providing area estimates in Mediterranean ecosystems using Sentinel-2 imagery. Yet, while suitable for change detection, it does not distinguish among disturbance types. Here, we propose a two-step framework for forest disturbance detection and classification, tested in inland Spain for 2018. First, a binary forest change map is produced through an enhanced version of the 3I3D approach. This step incorporates Receiver Operating Characteristic (ROC) analysis to calibrate the algorithm through data-driven threshold selection, allowing adaptation to specific regional conditions. Second, detected changes are classified into four disturbance types: wildfire, clear-cut, thinning, and non-stand replacing disturbance, using Sentinel-2 spectral bands, 3I3D-derived metrics, and geometric descriptors of disturbance patches. Three machine-learning classifiers were compared: Support Vector Machine, Random Forest, and Neural Network. The detection step reached an overall accuracy of 82%, estimating that 1.43% of Spanish forests (264,900 ha) were disturbed in 2018. In the classification step, Random Forest achieved the best performance, with an overall accuracy of 72%. Of the detected disturbed area, 69% corresponded to non-stand replacing disturbances, while the remaining area was classified as thinnings (19%), wildfires (26%), and clear-cuts (55%). By integrating freely available Sentinel-2 imagery, remote sensing algorithms, and photo-interpreted reference datasets, this study provides a scalable and operational approach capable of producing annual disturbance maps that combine both detection and classification of high- and low-intensity disturbances, supporting official national-scale estimates of forest disturbance areas. Full article

Vibration and noise generated by rail transit systems pose significant constraints on their environmental sustainability. Although extensive research has been conducted by scholars on vibration and noise in rail transit, quantitative studies specifically investigating the influence of train speed on the vibration and noise of elevated rail transit are scarce. Therefore, this study selected a typical elevated section of Wuhan Metro Line 21 and systematically performed field tests to measure the vibration and noise induced by trains passing at speeds of 20, 40, 60 and 80 km·h⁻¹. Based on the test results, the vibration characteristics of the rails, track slab, and bridge structure, as well as the radiation characteristics of wheel–rail noise and bridge structure-borne noise under different speeds, were investigated. The study further explored the impact of train speed variation on the vibration and noise of the elevated rail transit system. The results indicate that the vibration acceleration levels of both the outer and inner rails increase significantly with train speed. Each time the speed doubles, the vibration level rises by approximately 11.5 dB for the outer rail and 10.0 dB for the inner rail. The vibration of the track slab and bridge structure is notably lower than that of the rails. Each time the speed doubles, the vibration acceleration level at various measurement points increases by an average of about 8.5–9.0 dB. Wheel–rail noise is primarily concentrated in the frequency bands around 630 Hz and 3150 Hz. Each time the speed doubles, the trackside noise level increases by an average of approximately 7.2–7.6 dB(A). Noise measured under the bridge shows a distinct peak around 100 Hz, which aligns with the vibration frequency of the bottom slab. Due to the shielding effect of shrubs, noise in the 63–100 Hz frequency band is attenuated at measurement points above ground level. Each time the speed doubles, bridge structure-borne noise increases by about 4.5–5.0 dB(A), representing a lower growth rate compared to wheel–rail noise. The findings of this research are expected to contribute to vibration and noise reduction strategies and support the sustainable development of rail transit systems. Full article

Quercetin, one of the most abundant flavonoids in nature, has attracted the attention of many researchers due to its chemical and biological properties. A series of metal–quercetin complexes (Cu²⁺, Co²⁺, Zn²⁺, Sn²⁺, Al³⁺, Cd²⁺ and Mg²⁺) were synthesized and systematically characterized by Fourier transform infrared spectroscopy (FTIR), UV-visible spectroscopy (UV–Vis) and nuclear magnetic resonance (NMR). These analyses confirmed that the complexes predominantly form through coordination with the 4-carbonyl group and adjacent phenolic hydroxyls. This induces measurable shifts in the ν(C=O), ν(O–H), and π→π* transition bands relative to free quercetin. The antioxidant capacity of the complexes was evaluated using 2,2-Diphenyl-1-Picrylhydrazyl (DPPH^•) radical scavenging method, 2,2′-Azinobis(3-Ethylbenzothiazoline-6-Sulfonic Acid) (ABTS^•)⁺ radical activity, and Ferric Reducing Antioxidant Power (FRAP) assay. Several complexes exhibited higher radical scavenging efficiency than quercetin, with inhibition percentages exceeding 80% in the DPPH^• and ABTS^•+ assays. Others showed reduced activity due to the masking of redox-active hydroxyl groups during metal coordination. FRAP results corroborated these trends, indicating metal-dependent modulation of reducing power. Antimicrobial evaluation revealed that selected complexes were more active than free quercetin, particularly against Staphylococcus aureus and Candida spp., with minimum inhibitory concentrations (MICs) ranging from 75–250 μg mL⁻¹. Overall, metal complexation significantly alters the electronic structure and biological behavior of quercetin, highlighting the potential of metal–flavonoid complexes as multifunctional antioxidants and antimicrobials. Full article

Incipient fault signatures in rolling bearings are often compromised by intense background noise and stochastic impulses. Conventional resonance demodulation frequently relies on rigid frequency partitioning, which tends to disrupt the physical continuity of resonance bands and results in the incomplete capture of essential diagnostic information. Furthermore, the robustness of prevailing optimal demodulation frequency band (ODFB) selection indicators remains limited under heavy noise interference. This study develops the WLERgram framework, which utilizes regularized Fourier series to capture the global morphology of the vibration spectrum. By anchoring filter boundaries at natural energy troughs, the method mitigates spectral truncation based on inherent signal characteristics. The framework integrates an Adaptive Morphological Consensus (AMC) strategy, employing multi-scale operators to extract rotation-correlated components and enhance resistance to incoherent interference. By incorporating a Weighted Logarithmic Energy Ratio (WLER) metric, the method utilizes a nonlinear operator to implement differential mapping between coherent fault harmonics and stochastic noise, enabling autonomous optimization of the demodulation band. Validations using synthetic simulations and experimental benchmarks (CWRU and UORED) suggest that WLERgram offers reliable feature extraction performance and diagnostic robustness under harsh noise environments. Full article

Simultaneous hydrogen fuel and value-added chemical production from renewable resources is a key strategy in sustainable catalysis. This work presents a novel strategy employing metal–organic frameworks (MOFs) as precursors for synthesizing advanced titanium dioxide (TiO₂) photocatalysts with enhanced structural and optical properties. Two photocatalysts, M-BDC and M-2,5PDC, were synthesized via controlled calcination of MIL-125(Ti) using terephthalic and 2,5-pyridinedicarboxylic acids, respectively. Characterization confirmed the formation of mixed anatase/rutile TiO₂ phases with mesoporous structures. Notably, nitrogen incorporation in M-2,5PDC reduced the optical band gap to 2.94 eV compared with 3.08 eV for M-BDC, enhancing visible-light absorption. Photocatalytic experiments conducted at near-neutral pH (6.0) demonstrated effective simultaneous glycerol oxidation and hydrogen evolution without the use of alkaline additives. M-BDC achieved 30% glycerol conversion with 78.85% selectivity toward dihydroxyacetone and 21.15% toward glyceraldehyde, while M-2,5PDC exhibited selectivities of 71.55% and 28.45%, respectively. Glycerol underwent partial oxidation without complete mineralization, generating high-value products in parallel with hydrogen production. Both catalysts displayed excellent reuse stability across three consecutive cycles, with M-BDC showing enhanced dihydroxyacetone selectivity (78.85% to 84.42% between cycles). This MOF-derived TiO₂ platform integrates controlled synthesis, near-neutral pH operation, high selectivity, and catalytic stability, thereby establishing a viable strategy for the simultaneous production of clean fuel and value-added chemicals from renewable resources. Full article

Accurate and timely cotton-field mapping is essential for irrigation management, water resource allocation, and regional yield assessment in arid irrigated agroecosystems. However, existing deep-learning-based crop mapping approaches generally lack interpretability and often exhibit performance variability across phenological stages, thereby limiting their reliability for operational deployment. To address these limitations, we developed an interpretable semantic segmentation framework for cotton mapping in the Wei-Ku Oasis, Xinjiang, China, under multi-source remote sensing conditions. The proposed model integrates Sentinel-2 surface reflectance, Sentinel-1 VV/VH backscatter, DEM, vegetation indices, and GLCM texture features. By incorporating a receptive-field enhancement mechanism together with an embedded feature-attribution module, the framework enables importance estimation of multi-source predictors within the network architecture, thereby providing intrinsic model interpretability. Under a unified training and evaluation protocol, the proposed model achieved an mIoU of 85.62% and an F1-score of 92.96% on the test set, outperforming U-Net, DeepLabV3+, and SegFormer baselines. Monthly classification results indicated that August provided the most discriminative acquisition window (mIoU = 85.54%, F1 = 92.83%), while June–July also maintained high recognition accuracy. Feature attribution results indicate that the importance of different predictors varies across phenological stages: Sentinel-2 red-edge bands remained highly influential throughout the growing season, NDVI/EVI exhibited increased contributions during June–August, SAR VH showed relatively higher importance during peak canopy development, and DEM maintained stable information contribution across all stages. Cross-year and cross-region experiments further demonstrated the model’s generalization capability, achieving an mIoU of 82.81% in same-region cross-year evaluation and 74.56% under cross-region transfer. Overall, the proposed segmentation framework improves classification accuracy while explicitly modeling and quantifying feature importance, providing a methodological reference for cotton-field mapping and acquisition timing selection in arid irrigated regions. Full article

Human transglutaminases (hTGs) are Ca²⁺-dependent enzymes that catalyze protein crosslinking, deamidation and other post-translational modifications, thus acting as key stabilizers of tissue architecture and modulators of protein function across diverse physiological contexts. This family comprises eight catalytically active members, TG1-7, the blood coagulation factor FXIII, and the inactive structural protein Band 4.2 of the erythrocyte membrane. Recent structural and biochemical advances have refined our understanding of the molecular principles governing transglutaminase function. Thus, current evidence reveals how domain organization and catalytic architecture integrate calcium binding, nucleotide-dependent regulation in TG2 and proteolytic activation in selected isoforms to control enzymatic activity. In this review, we provide an updated and comprehensive overview of the active hTGs, combining structural, biochemical and functional data to explain how closely related enzymes achieve isoform-specific regulation and distinct biological roles. We further examine how disruption of these mechanisms contributes to human pathology, highlighting representative examples in autoimmunity, inherited disorders and complex diseases. By integrating recent biochemical and structural findings with disease-associated evidence, we aim to offer a coherent framework for understanding how TG regulation underlies their diverse biological functions and clinical relevance. Full article

We present an innovative, cost-effective framework integrating laboratory Hyperspectral Imaging (HSI) of the Bechar 010 Lunar meteorite with ground-based lunar HSI and supervised Machine Learning (ML) to generate high-fidelity mineralogical maps. A 3 mm thin section of Bechar 010 was imaged under a microscope with a 30 mm focal length lens at 150 mm working distance, using 6x binning to increase the signal-to-noise ratio, producing a data cube (X × Y × $\lambda$ = $791\times 1024\times 224$, $0.24$ mm × $0.2$ mm resolution) across 400 nm to 1000 nm (224 bands, $2.7$ nm spectral sampling, $5.5$ nm full width at half maximum spectral resolution) using a Specim FX10 camera. Ground-based lunar HSI was captured with a Celestron 8SE telescope (3 km/pixel), yielded a data cube ($371\times 1024\times 224$). Solar calibration was performed using a Spectralon reference (99% reflectance < 2% error) ensured accurate reflectance spectra. A Support Vector Machine (SVM) with a radial basis function kernel, trained on expert-labeled spectra, achieved 93.7% classification accuracy (5-fold cross-validation) for olivine (92% precision, 90% recall) and pyroxene (88% precision, 86% recall) in Bechar 010. LIME analysis identified key wavelengths (e.g., 485 nm, 22.4% for M3; 715 nm, 20.6% for M6) across 10 pre-selected regions (M1 to M10), indicating olivine-rich (Highland-like) and pyroxene-rich (Mare-like) compositions. SAM analysis revealed angles from $0.26$ rad to $0.66$ rad, linking M3 and M9 to Highlands and M6 and M10 to Mares. K-means clustering of Lunar data identified 10 mineralogical clusters (88% accuracy), validated against Chandrayaan-1 Moon mineralogy Mapper (${\mathrm{M}}^3$) data (140 m/pixel, 10 nm spectral resolution). A novel push-broom HSI approach with a telescope achieves 0.8 arcsec resolution for lunar spectroscopy, inspiring full-sky multi-object spectral mapping. Full article

The FOCUS mission is an integrative project developed at the Universidad Nacional de San Martín (UNSAM), Argentina, featuring a constellation of small satellites equipped with X-band Synthetic Aperture Radar (SAR) sensors. Designed with autonomous orbit control, the mission enables Interferometric SAR (InSAR) applications for critical infrastructure monitoring, providing scalable and cost-effective global observation capabilities. This paper presents the modeling, design, and numerical evaluation of the Attitude and Orbit Determination and Control System (AODCS) for the FOCUS mission. The analysis incorporates realistic constraints, including actuator saturation, sensor noise, underactuation effects, and hardware limitations—specifically regarding magnetorquer magnetic moments, reaction wheel capacities, and propulsion unit impulse bounds. Utilizing the NASA 42 attitude and orbit simulator, numerical simulations were conducted to assess stability, pointing accuracy, and agile maneuver tracking through specialized guidance laws. The results confirm that the proposed AODCS architecture achieves stable, responsive performance and supports continuous orbit maintenance, ensuring adequate target acquisition per orbit. Additionally, the selection of star trackers allows achieving a secondary objective through the detection of Resident Space Objects. Full article

Donor–acceptor (D-A)-type conjugated porous polymers (CPPs) have emerged as highly competitive photocatalysts for aerobic oxidation reactions. Herein, we rationally design and synthesize a series of D-A structured photocatalysts by employing dibenzothiophene-S, S-dioxide (BTDO) as the acceptor unit, and 4,8-bis(thiophen-2-yl) benzo [1,2-b:4,5-b’] dithiophene (DBD) and pyrene (Py) as the donor units. The effects of acceptor content on the optoelectronic and photocatalytic properties are systematically investigated. With the gradual increase in BTDO proportion and the decrease in pyrene content, the photocatalysts exhibit gradually narrowed band gaps, significantly promoted charge separation efficiency, and broadened visible light absorption range. Among the five as-prepared photocatalysts, DBD-T displays superior catalytic performance toward blue light-driven aerobic oxidation. Under mild conditions, benzyl sulfide and benzyl amine are selectively converted into benzyl sulfoxide and benzyl imine with a high conversion efficiency up to 96%. Moreover, DBD-T shows good universality toward a wide range of substrates, together with excellent recyclability and long-term stability. This work demonstrates that enhancing the electron-withdrawing capability of the acceptor unit represents a feasible and effective strategy to boost the photocatalytic performance of D-A-type conjugated polymers. Full article

To overcome the intrinsic trade-off among miniaturization, ultrawideband (UWB) performance, and structural simplicity in conventional frequency-selective rasorber (FSR) design, this paper proposes a miniaturized UWB absorption–transmission–absorption (A-T-A) FSR based on an inter-cell current-interaction mechanism. The structure comprises a dielectric matching layer (DML), a lossy frequency-selective surface (FSS), a lossless FSS layer, and air/dielectric spacers. Both FSS layers are fabricated on Rogers 4350B substrates without any metallized via or multiple lossy/lossless FSS stacking. The proposed FSR achieves a miniaturized structure with dimensions of 0.085 λ_L × 0.085 λ_L × 0.118 λ_L (where λ_L corresponds to the wavelength at the lowest absorption frequency). A fractional operational bandwidth around 144% is obtained, covering 2.88–12.87 GHz and 14.98–17.61 GHz with absorptivity over 80%, together with a low-loss transmission band of 13.57–14.56 GHz exhibiting a minimum insertion loss of 0.41 dB. As the incident angle increases up to 40°, the FSR retains more than 134% bandwidth for both TE and TM polarizations. A prototype was fabricated and measured, and the results agree well with the simulations. Full article

The adulteration of wheat flour with borax poses a serious food safety risk, yet conventional rapid non-destructive screening methods remain limited. This study developed a machine learning-based microwave non-destructive semi-quantitative detection method for identifying borax adulteration in wheat flour. Using a proprietary microwave detection system, which acquires broadband frequency-domain amplitude attenuation and phase shift responses in the 2.5–11.5 GHz band, amplitude attenuation spectra and dimensional phase offset spectra were obtained from 155 samples prepared at three adulteration levels (0%, 0.1–0.9%, 1–5%). These samples simulated real-world adulteration scenarios. To address high-dimensionality and class imbalance, a hybrid Random Forest-Whale Optimization Algorithm (RF-WOA) was employed to synergistically optimize feature selection and model hyperparameters. Through hierarchical repeated validation and macro-level metric evaluation, this approach achieved an overall classification accuracy of 94.6% and a macro F1 score of 0.95 while compressing the original 1800-dimensional feature space to approximately 200 effective features. Confusion matrix analysis indicates 100% recall for undiluted samples, with misclassifications primarily occurring between adjacent adulteration levels and no false negatives introduced for adulterated samples. These results demonstrate that microwave sensing combined with the RF-WOA provides a rapid, non-destructive, and robust preliminary screening and grading evaluation strategy for borax adulteration in wheat flour, exhibiting significant potential in food safety monitoring and regulatory inspection. Full article

Metasurfaces offer an unprecedented avenue to facilitate light-matter interactions. However, traditional design methodologies rely on computationally intensive trial-and-error processes. Moreover, existing deep learning (DL) schemes are predominantly hindered by their massive data requirements and limited exploration of freeform design spaces. To overcome these challenges, a multi-model-driven generative-evolutionary strategy (GES) is proposed, for the on-demand inverse design of bespoke Terahertz (THz) metasurface sensors. By leveraging a Conditional Diffusion Generator (CDG) and an Attention-Enhanced Residual Network (ARN), this framework enables the exploration of an expansive design space encompassing $2^{100}$ possible configurations. The GES effectively overcomes the data bottleneck by selectively generating high-potential data in stages. Full-wave simulations confirm that the inversely designed metasurfaces exhibit high-contrast resonance peaks and exceptional sensitivity across low, mid, and high THz bands. This work provides a versatile paradigm for the efficient design of high-performance functional metamaterials, significantly accelerating the advancement of application-specific THz sensing. Full article

Graph Neural Networks (GNNs) have emerged as the standard for learning representations from graph-structured data. While traditional architectures relying on message-passing mechanisms excel in homophilic settings, they essentially function as fixed low-pass filters. However, this smoothing operation limits their ability to generalize to heterophilic graphs, where connected nodes often exhibit dissimilar labels and high-frequency signals are crucial for discrimination. Furthermore, existing Mixture-of-Experts (MoE) methods for graphs often suffer from local-view routing, failing to capture global structural context during expert selection. To address these challenges, this paper proposes SS-AdaMoE, a novel Spatio-Spectral Adaptive Mixture of Experts framework designed for robust node classification across diverse graph patterns. Specifically, a Dual-Domain Expert System is constructed, integrating heterogeneous spatial aggregators with learnable spectral filters based on Bernstein polynomials. This allows the model to adaptively capture arbitrary frequency responses—including high-pass and band-pass signals—which are overlooked by standard GNNs. To resolve the locality bias, a Hierarchical Global-Prior Gating Network augmented by a Linear Graph Transformer is introduced, ensuring that expert selection is guided by both local node features and global topological awareness. Extensive experiments are conducted on five benchmark datasets spanning both homophilic and heterophilic networks. The results demonstrate that SS-AdaMoE consistently outperforms baselines, achieving accuracy improvements of up to 2.65% on Chameleon and 1.41% on Roman-empire over the strongest MoE baseline, while surpassing traditional GCN architectures by margins exceeding 28% on heterophilic datasets such as Texas. These findings validate that the synergy of learnable spectral priors and global gating effectively bridges the gap between spatial aggregation and spectral filtering. Full article