Search Results (4,143)

Spaceborne imaging spectroscopy has created new opportunities for monitoring soil properties at regional scales. Its use for predicting soil heavy metal concentrations in mountainous environments, however, remains insufficiently tested, especially when EMIT hyperspectral data are used. In this study, EMIT Level-2A surface reflectance data were integrated with DEM-derived terrain variables to estimate soil arsenic (As), copper (Cu), and zinc (Zn) concentrations in Renhuai, Guizhou Province, Southwest China. Only soil samples falling within valid EMIT coverage were used for element-specific modeling, resulting in 139 samples for As, 136 for Cu, and 130 for Zn. To reduce redundancy among predictors, EMIT spectral variables and terrain factors were screened before model construction. Random forest and XGBoost models were then tested using repeated spatial cross-validation. The best-performing model for As combined EMIT predictors with elevation and achieved a validation R² of 0.460. Model performance was considerably weaker for Cu, with a validation R² of 0.188. For Zn, the model failed to outperform the mean-based benchmark, producing a negative validation R² of −0.028. The spatial prediction maps and residual patterns suggested that the EMIT-based prediction showed moderate potential for As, limited predictive value for Cu, and exploratory rather than reliable mapping capability for Zn under the current sample and predictor conditions. Full article

The widespread adoption of voice-based authentication systems has been accompanied by an escalating threat from deep learning-based synthetic speech generation techniques. This study presents a comparative and experimental investigation of cepstral feature extraction methods for deepfake speech detection. Specifically, Mel-Frequency Cepstral Coefficients (MFCC), Linear-Frequency Cepstral Coefficients (LFCC), and Constant-Q Cepstral Coefficients (CQCC) are systematically evaluated with respect to their frequency scaling characteristics, spectral resolution properties, and capacity to capture artifacts specific to synthetic speech production. Experiments were conducted on 5571 audio samples drawn from the ASVspoof 2021 Logical Access evaluation partition, with all methods assessed under identical classification conditions using a linear Support Vector Machine. Results indicate that CQCC attains the highest numerical performance, achieving 83.59% accuracy, 89.15% ROC-AUC, and 15.83% Equal Error Rate (EER); however, the performance difference between MFCC and CQCC does not reach statistical significance (p = 0.202). Five-fold cross-validation corroborates this finding (CQCC: 87.89% ± 0.81%). McNemar’s test confirms that the performance difference between LFCC and CQCC is statistically significant (p = 0.036). A fine-grained attack-wise analysis across 13 spoofing systems reveals that no single feature representation consistently outperforms the others across all attack types; CQCC achieves the highest accuracy on 6 out of 13 systems, while MFCC remains competitive on several attack categories. The overall findings indicate that deepfake detection performance is highly sensitive not only to the classifier architecture but also to the choice of frequency scale, cepstral transformation design, and data conditions. Empirical motivation is provided that multi-feature strategies integrating complementary frequency representations may offer more robust and generalizable detection solutions. Full article

This study compares the efficacy of ozonated sunflower oil (OSO) and ozonated olive oil (OOO) in terms of antioxidant properties, modulation of high-density lipoprotein (HDL) functionality, and protective effects against carboxymethyllysine (CML)-mediated stress in zebrafish embryos and adults. The spectral and electronic nose (e-nose) analyses revealed that OSO and OOO possessed markedly distinct physicochemical characteristics and volatile and olfactory constituents compared with non-ozonated sunflower (SO) and olive oil (OO). The fluorescence spectrum analysis of HDL treated with OOO and OSO exhibited a red shift (2.6~3.3 nm) in the wavelength maximum fluorescence (WMF), accompanied by pronounced quenching of tryptophan fluorescence. Additionally, a significant increase in HDL-associated paraoxonase (PON) and ferric ion reduction (FRA) activity was observed in the OSO- and OOO-treated HDL. However, compared to OOO, significantly higher PON and FRA activities were observed in HDL treated with OSO. Also, compared to OOO, OSO effectively reverses CML-induced oxidative stress, altered heart rate, and reduced embryo survival. Similarly, in adult zebrafish, CML-compromised survival, swimming impairment, and disturbed antioxidant parameters were prevented by treatment with OOO and OSO. Nonetheless, OSO showed significantly higher efficacy than OOO. Consistently, OSO substantially reduced the CML-elevated blood glucose, total cholesterol (TC), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C) levels with a marked increase in high-density lipoprotein cholesterol (HDL-C) levels. Notably, no significant effect of OOO was observed on the reduction in and augmentation of LDL-C and HDL-C, respectively. Both OOO and OSO significantly protect against CML-triggered liver and kidney damage. However, compared with OOO, OSO significantly reduced neutrophil infiltration, interleukin-6 (IL-6) production, liver steatosis, ROS generation, and cellular senescence in the kidneys. The study concludes that OSO exerts significantly higher beneficial effects than OOO on HDL functionality and antioxidant defense, thereby attenuating CML-induced inflammatory and oxidative damage. Full article

Petrophysical parameters of rocks and ores, influenced by composition, porosity, temperature, and pressure, are generally distributed uniformly or continuously in space—relatively homogeneous within individual geological units and varying smoothly across stratigraphic transition zones and contact boundaries. Based on this geological characteristic, this paper establishes a three-dimensional (3-D) piecewise linear continuous spectral parameter model to compute forward responses of apparent spectral parameters under low-frequency current excitation. The calculation follows a two-step workflow: finite-element forward simulation of multi-frequency apparent complex resistivity, followed by recursive inversion to obtain apparent spectral parameters. The subsurface medium is discretized with hexahedral meshes, with four Cole–Cole parameters (zero-frequency resistivity, chargeability, time constant, and frequency exponent) assigned to each mesh node. Linear interpolation is adopted for complex resistivity and potential within each element, ensuring piecewise linear continuity of both physical properties and simulated fields. To improve accuracy, the total complex potential is decomposed into a primary field from the source current and a secondary field from complex conductivity variations, and the corresponding boundary value problem and variational form are derived. On this basis, we implement the finite-element algorithm for 3-D piecewise linear continuous media and the recursive inversion algorithm for spectral parameters, and develop an interactive 3-D SIP forward modeling program. Comparison with analytical solutions for a continuous layered model shows good agreement, with relative errors below 1.5% for the real part and 3.8% for the imaginary part of apparent complex resistivity. Two numerical cases—a cubic anomaly in homogeneous half-space and a sandbox model—further verify the performance of the proposed method. Full article

Translating Synthetic aperture radar (SAR) images into optical images is intrinsically ill-posed because microwave backscatter and optical reflectance describe different physical properties of the observed scene. Although frequency-domain modeling has been introduced into diffusion-based translation, existing methods mainly rely on independent weighting of individual Fourier coefficients and provide limited modeling of interactions among neighboring frequencies and feature channels. To address this limitation, we propose dualCDM, a conditional diffusion model that jointly exploits spatial- and frequency-domain representations. In the diffusion backbone, a spatial-frequency hybrid residual block (SFHRB) combines a spatial convolution branch with complex-valued convolution in the Fourier domain. The complex convolution aggregates neighboring Fourier coefficients across all input feature channels, enabling local cross-frequency and cross-channel modeling, while its response is modulated by the diffusion timestep. In the SAR conditional encoder, an adaptive frequency-domain feature recalibration block (AFFRB) predicts input-dependent real-valued gains from magnitude and trigonometric phase representations of intermediate GRD features. These gains adaptively recalibrate the complex frequency responses without introducing an additional phase shift, while the residual connection preserves the original conditional information. A dual-domain objective further constrains both the predicted diffusion noise and the one-step optical reconstruction in the spatial and frequency domains. We also construct the S1S2 dataset using 16-bit Sentinel-2 reflectance data, retaining the original 0–10,000 value range and including the near-infrared band. Experiments on SEN1-2 and S1S2 show that dualCDM improves radiometric accuracy, spectral consistency, and structural preservation over six representative methods. Paired statistical tests further confirm significant improvements over the strongest competing method across all six evaluation metrics on both datasets. Full article

Decametric-resolution leaf area index (LAI) is an essential parameter for fine-scale crop growth monitoring and ecosystem modeling. Prior-guided approaches using existing hectometric-resolution LAI products have demonstrated potential in large-scale decametric-resolution LAI estimation. However, within such approaches, the impacts of algorithm selection and band combination on retrieval accuracy remain insufficiently quantified, and the lack of model interpretability limits methodological transferability. To address these challenges, a multi-source data integration (MSDI) framework is developed to systematically assess the sensitivity of prior-guided LAI estimation to retrieval algorithms and spectral bands using Sentinel-2 imagery. In addition, Shapley Additive Explanations (SHAP) is employed to quantify the contributions of individual bands and interpret model behavior. The MSDI LAI was evaluated using ground LAI measurements and compared with Simplified Level 2 Product Prototype Processor (SL2P)-derived LAI and MODIS LAI products. The results indicated that Support Vector Regression (SVR) achieved the best performance in LAI estimation among six machine learning algorithms, likely due to its robustness in modeling nonlinear relationships across different training samples. Band optimization further reduced estimation uncertainty by >24% and increased R² by >44% for SVR-derived LAI estimates. Moreover, MSDI outperformed SL2P, especially at 20 m resolution, with Bias, RMSE, and R² values of 0.26, 0.76, and 0.71, respectively. Meanwhile, MSDI LAI exhibited a similar spatial distribution to MODIS LAI while providing substantially enhanced spatial detail and accuracy. SHAP analysis revealed that red-edge (RE) and shortwave-infrared (SWIR) bands contributed the most to LAI prediction, consistent with their sensitivity to vegetation canopy biophysical properties. Overall, this study highlights the importance of retrieval strategy optimization and model interpretability for improving prior-guided decametric-resolution LAI estimation and offers practical guidance for generating consistent LAI estimations across various scales. Full article

Quartz is widely employed in optoelectronic and sensing applications owing to its excellent mechanical and chemical properties. However, its intrinsic transparency up to 5 μm limits its direct use as a photodetection substrate across the near- and mid-infrared spectral regions. Laser surface texturing for the fabrication of the so-called black quartz represents a promising strategy to overcome this limitation. In this work, different femtosecond (fs) laser texturing strategies were investigated on a 1 mm thick α-quartz wafer, namely uniform milling, grid-patterned grooves, and localized arrays of ablated craters. The fs-laser-treated quartz samples showed a transmittance reduction of up to 60% within the quartz transparency window in the infrared range, with crater matrices providing the most effective blackening performance. The enhanced absorption was attributed to light-trapping effects induced by the tapered crater geometry, which promotes multiple internal reflections and increased optical confinement within the substrate. The proposed strategy demonstrates a reliable, maskless, and chemical-free surface functionalization strategy for the fabrication of quartz-based substrates for broadband infrared photodetection in sensing applications. Full article

Diabetes mellitus represents a major global health burden, necessitating the development of safer and more effective therapeutic alternatives. Medicinal plants have gained increasing attention due to their bioactive compounds with potential hypoglycemic and antioxidant effects. The present study aimed to investigate the physicochemical characteristics, phytochemical composition, antioxidant capacity, and toxicological profile of hydroalcoholic tinctures obtained from Agrimonia pilosa Ledeb., Calendula arvensis L., and Polygonum hydropiper L. The tinctures were prepared by simple percolation using 70% ethanol and evaluated according to pharmacopoeial standards, including organoleptic properties, relative density, refractive index, alcohol content, and purity parameters. Phytochemical analysis was performed using thin-layer chromatography and spectrophotometric methods, highlighting the presence of flavonoids and polyphenolcarboxylic acids, with several bands showing chromatographic and spectral similarities to chlorogenic and caffeic acid standards. Antioxidant activity was assessed through total polyphenol and flavonoid content, with Polygonum hydropiper exhibiting the highest values. The hypoglycemic effect was evaluated using the oral glucose tolerance test in normoglycemic mice, demonstrating significant reductions in blood glucose levels, particularly for Agrimonia pilosa at higher doses. Acute toxicity studies indicated a low toxicity profile, with no mortality observed even at high doses (up to 9 g/kg body weight), corresponding to GHS category 5. However, subacute toxicity assessment revealed species-dependent effects, ranging from minimal hepatic changes for Calendula arvensis to moderate hepatotoxicity for Polygonum hydropiper and more pronounced hepatic, renal, and pancreatic alterations for Agrimonia pilosa. These findings suggest that the investigated tinctures possess significant hypoglycemic and antioxidant potential, with generally favorable safety profiles following acute administration. Nevertheless, prolonged use may induce organ-specific toxicity, highlighting the need for further pharmacological and clinical investigations to establish their therapeutic applicability and safety in diabetes management. Full article

In this work, the structural, optical, vibrational, and stability properties of a series of Al_xAs_x nanostructures are systematically investigated using density functional theory (DFT) and time-dependent density functional theory (TD-DFT). Starting from the fundamental cubic-like Al₄As₄ building block, progressively larger nanostructures were constructed through directional elongation and structural rearrangements, allowing for the exploration of one-dimensional chains, two-dimensional planar structures, and several exotic geometries. The calculated UV–visible absorption spectra reveal that structural dimensionality and topology strongly influence the electronic transitions of the nanostructures, with elongated and distorted configurations exhibiting broader absorption features and richer spectral distribution. Vibrational analysis shows that increasing structural complexity and reducing symmetry lead to a higher density of IR-active modes and more complex infrared spectra. The stability of the nanostructures is evaluated through binding energy calculations, which indicate a clear size-dependent stabilization trend, with the Al₂₄As₂₄-L1 configuration exhibiting the highest stability among the examined systems. In addition, the calculated HOMO-LUMO gaps reveal the semiconducting character of the clusters and demonstrate their sensitivity to geometric topology. The present results establish clear structure–property relationships between dimensional growth and the optical response of AlAs nanoparticles and provide theoretical reference data for future experimental investigations of III-V semiconductor nanostructures. Full article

In recent years, research on remote sensing scene classification (RSSC) has mainly focused on high-resolution imagery, which provides limited spectral information, whereas hyperspectral imaging (HSI) offers richer cues about material properties and compositional structure. Despite its potential, hyperspectral scene classification (HSI-SC) remains challenging because pixel- or patch-based representations fail to preserve spatial structures and regional boundaries. In addition, labeled hyperspectral samples are often scarce, making it difficult to learn stable class-discriminative representations from high-dimensional spectral observations. To address these issues, this paper proposes a dual-branch fusion framework. Superpixels are used to aggregate high-dimensional spectral signals into compact, boundary-aware tokens. The spectral branch is initialized with pretrained model weights and further adapted via a lightweight adaptation strategy for efficient transfer under limited supervision. In parallel, a pseudo-RGB spatial branch complements structural and textural information. Spectral and spatial features are fused additively to generate a more discriminative scene representation. Experimental results demonstrate that the proposed method outperforms compared hyperspectral scene classification approaches. Full article

UAV-assisted communication is widely regarded as a key component of next-generation Space-Air-Ground Integrated Networks (SAGINs), where integrated sensing and communication (ISAC) further drives the demand for accurate and reliable channel modeling. Terahertz (THz) communications are particularly attractive for UAV platforms, offering ultra-high data rates and physically secure transmission. However, the physical heterogeneity between reflection and scattering mechanisms in THz UAV channels poses significant modeling challenges, as conventional unified approaches tend to introduce energy distribution distortion and non-stationary prediction errors. To address this, we propose a 3D non-stationary geometry-based stochastic model (GBSM) based on an ellipse-sphere hierarchical geometric framework, where reflection paths are confined to ground-plane ellipses and scattering paths are distributed over spatial spheres. The model accounts for atmospheric molecular absorption, multipath fading, and non-stationarity induced by random 3D UAV trajectories. A cluster birth-death mechanism is introduced to capture the time-varying evolution of scattering clusters. Key statistical properties, including the temporal auto-correlation function (T-ACF), spatial cross-correlation function (S-CCF), and Doppler power spectral density (DPSD), are derived and analyzed. Simulation results agree well with theoretical derivations, validating the proposed model and providing practical guidance for THz UAV-ISAC system design. Full article

Al₂O₃:Cr³⁺ coatings were synthesized on tungsten substrates by plasma electrolytic oxidation in a phosphate-aluminate electrolyte containing dispersed Cr₂O₃ nanoparticles, and their structural, photoluminescent, and temperature-sensing properties were investigated. The coatings exhibited a typical porous PEO morphology with a uniform thickness of approximately 31 μm, and EDS analysis confirmed the incorporation of Cr species from the electrolyte, with Cr content increasing with the concentration of Cr₂O₃ particles. XRD analysis showed that the coatings were composed predominantly of α-Al₂O₃, with minor contributions from metastable γ-Al₂O₃, confirming that our previously established process for forming the thermodynamically stable α-Al₂O₃ phase directly on a non-aluminum substrate remains robust upon the introduction of dopant nanoparticles. The Al₂O₃:Cr³⁺ coatings displayed characteristic ruby-like photoluminescence, including broad excitation bands associated with the ⁴A₂⟶⁴T₁ and ⁴A₂⟶⁴T₂ transitions and sharp R-line emission arising from the spin-forbidden ²E⟶⁴A₂ transition. The strongest emission was obtained for coatings prepared with 0.05 g/L Cr₂O₃, while higher concentrations resulted in concentration quenching. Temperature-dependent photoluminescence revealed two complementary thermometric mechanisms: R-line spectral shifting and thermally induced redistribution between the ²E and ⁴T₂ emissions. The deconvolution-based intensity-ratio approach provided a stronger temperature response than simple spectral partitioning, demonstrating the potential of PEO-derived Al₂O₃:Cr³⁺ coatings on tungsten as robust luminescent temperature-sensing layers. Full article

In response to the need for a simple, non-destructive method for evaluating avocado ripeness, we measured chlorophyll-related fluorescence and chromaticity of the outer skin using simple optical equipment and evaluated their relationship with whole-fruit compression (wfc), which was used as a firmness-based ripeness index. A compact system consisting of a blue LED excitation source and a small spectrometer was used to measure fluorescence spectra, and a commercially available colorimeter was used to evaluate chromaticity. Hass avocado samples purchased from multiple retail stores in Japan and stored for different periods were examined. The combination of the fluorescence intensity ratio I₇₄₀/I₆₈₅ and the lightness parameter L* showed a moderate correlation with wfc, with R² = 0.48. The fluorescence ratio I₇₄₀/I₆₈₅ was treated not as a direct measure of chlorophyll content, but as a spectral index associated with ripening-related changes in avocado skin, including chlorophyll-related fluorescence and skin optical properties. These results suggest that the combination of simple blue LED-excited fluorescence and chromaticity measurements may be useful as a practical screening approach for roughly estimating avocado ripeness in commercially available fruit. Full article

Heavy and highly viscous oils account for a significant proportion of the world’s hydrocarbon reserves. The development of these reserves in harsh climates is associated with technological risks due to paraffin deposits and equipment corrosion. Ensuring reliable transportation requires operational monitoring of the physical and chemical properties of fluids directly at the wellhead. Traditional laboratory methods such as SARA fractionation and gas chromatography (GC) are time-consuming and can yield to distortions in the sample composition during transportation. Field optical methods, such as an infrared (IR) spectroscopy are complicated by the optical heterogeneity of crude oils due to emulsified water, supramolecular associations of resins, asphaltenes, and paraffins. In this paper, ultrasonic (US) sample preparation for high-viscosity oils is justified as a method for increasing the reliability and information content of field IR spectroscopic analysis by unmasking the diagnostic extrema of absorption bands that are initially distorted by emulsified water, baseline scattering, and radiation scattering from large resin–asphaltene–paraffin aggregates. The technique is based on cavitation-induced destruction of emulsion shells and disaggregation of the structural framework without volume thermal heating. Experimental data obtained from watered high-viscosity oil has shown that 9 min of the US exposure reduces the light scattering index $I_{\mathrm{t}\mathrm{r}\mathrm{s}}$ by 92.83%, bringing the system into a less heterogeneous state. Statistical correlation analysis confirmed that emulsions and aggregates are the main scattering centers, and their destruction correlates directly with the transparency of the medium. Stability of spectral indices $I_{{\mathrm{C}\mathrm{H}}_3/{\mathrm{C}\mathrm{H}}_2}$, $I_{\mathrm{f}\mathrm{o}\mathrm{c}}$ and $I_{\mathrm{C}=\mathrm{O}}$ indicates the absence of chemical degradation or oxidation at the US exposure intensity of 0.12 W/mL, confirming the physical nature of the effect. The proposed method makes it possible to implement automated monitoring of the properties of high-viscosity oil directly at the wellhead, minimizing logistic costs and risks of the sample degradation. The practical significance of the proposed method is to improve the reliability and information content of wellhead monitoring by reducing optical heterogeneity and making diagnostic significant IR absorption extremes more distinguishable for further interpretation. Full article

Remote sensing reflectance (R_rs) in optically complex waters is controlled by the combined effects of phytoplankton, colored dissolved organic matter (CDOM), and suspended mineral particles. In the Baltic Sea, strong CDOM absorption and variable particle loads complicate the interpretation of ocean color signals and the retrieval of biogeochemical properties. In this study, we investigate the individual and combined influence of these optically significant constituents on R_rs using a set of HydroLight radiative transfer simulations representing typical Baltic Sea conditions. A wide range of chlorophyll-a (0.25–10 mg·m⁻³), CDOM absorption (0.5–15 m⁻¹), and particulate inorganic matter (0.04–4 g·m⁻³) was considered. To quantify the influence of each component, a spectral response function was applied, defined as the change in R_rs relative to a normalized perturbation of each input parameter. This approach preserves information about the magnitude of the reflectance signal and allows direct comparison of the impact of different constituents across the visible spectrum. The spectral response analysis reveals that the relative influence of each constituent varies with wavelength and environmental conditions, highlighting the limitations of single-band or ratio-based algorithms in optically complex waters. These findings provide a quantitative framework for interpreting spectral variability of R_rs in the Baltic Sea and other optically complex water basins, support the development of more robust bio-optical algorithms for Case 2 waters. Similar spectral response analysis can be conducted in other water basins in order to quantify combined constituent-specific effects on R_rs. Full article