Search Results (4,027)

Marine algal polysaccharides have been widely investigated as antiviral candidates, yet nearly all anti-dengue studies have focused on sulfated species. Whether algal polysaccharides lacking prominent sulfation can inhibit dengue virus (DENV) remains unexplored. Here, we profiled the stage-specific antiviral activity of a heteropolysaccharide (GLHP) from Gracilaria lemaneiformis, whose Fourier-transform infrared (FT-IR) spectrum lacks characteristic sulfate ester absorption bands, against DENV serotype 2 (DENV-2) in Huh7 and BHK-21 cells. GLHP exhibited low cytotoxicity (CC₅₀ exceeding 1000 μg/mL in Huh7 cells and approximately 950 μg/mL in BHK-21 cells). Time-of-addition analysis revealed that co-inoculation GLHP treatment (Co-inoc.) produced the strongest and most consistent inhibition of intracellular viral RNA, whereas pre-inoculation GLHP treatment (Pre-inoc.) was ineffective, indicating that the antiviral activity is predominantly associated with the virus–cell contact and entry stage. GLHP additionally reduced extracellular progeny virus output under post-inoculation GLHP treatment (Post-inoc.) conditions, and this reduction exceeded the corresponding change in intracellular viral RNA levels, suggesting an additional effect that may involve either a late replication step or secondary entry blockade of progeny virions. Attenuation of virus-induced cytopathic effects under Co-inoc. conditions further supported the antiviral activity. To our knowledge, these findings identify GLHP as the first non-sulfated marine polysaccharide shown to exhibit stage-defined antiviral activity against DENV-2 and support further investigation of its antiviral potential and structural determinants. Full article

Concrete wall cavities are common hidden defects in construction engineering that seriously reduce structural safety, durability, and construction quality, especially in old buildings and projects without complete design documents. Traditional detection methods have obvious limitations: the manual tapping method relies heavily on subjective experience and lacks quantitative standards, while advanced non-destructive testing methods such as ultrasonic testing and infrared thermography are expensive, complex to operate, and difficult to apply on a large scale. At present, the quantitative correlation between acoustic signal characteristics and cavity defects has not been fully studied. To address these problems, this study combines literature analysis, controlled experiments, and acoustic signal processing to propose a quantitative identification method for concrete wall cavities based on autocorrelation analysis of sound signals. Tapping signals from normal and cavity walls are collected and processed using band-pass filtering and amplitude normalization. The autocorrelation function (ACF) is then used to extract characteristic parameters. The results show that the proposed method exhibits significantly improved accuracy and efficiency compared with traditional manual detection. Obvious differences in autocorrelation characteristics can be observed between normal and cavity walls. The method realizes the transformation from subjective auditory judgment to objective quantitative identification, with low cost, strong anti-interference ability, and high sensitivity to small defects. It provides a reliable technical tool for the rapid and quantitative non-destructive testing of concrete wall cavities in engineering practice. Full article

Cloud detection in high-resolution Gaofen-1 (GF-1) imagery is challenging due to the absence of short-wave infrared (SWIR) bands, which prevents the use of physically interpretable indices such as the Normalized Difference Snow Index (NDSI) and often leads to severe cloud–snow confusion. To address this limitation, we propose a unified framework, termed the Cross-Modality Spectral Expansion and Dual-Prior Network (CMSE-DPNet), that integrates cross-modality spectral expansion with physical–semantic dual priors. First, an improved CycleGAN reconstructs 13-band pseudo-Sentinel-2 spectra from four-band GF-1 imagery, enabling the computation of snow-sensitive physical indices. Second, a Snow-Aware Feature Attention Guidance Module (SAFAGM) introduces pixel-level physical priors derived from NDSI, while a Label-Guided Channel Attention Module (LG-CAM) injects scene-level semantic priors inferred from geographic metadata using a large language model. These complementary priors guide the network to better distinguish clouds from spectrally similar backgrounds. Experiments on the GF-1 dataset show that the proposed method achieves an F1-score of 94.41% and an Intersection over Union (IoU) of 89.40%, outperforming several state-of-the-art cloud detection methods. The results indicate that cross-modality spectral expansion combined with physical–semantic prior guidance effectively improves cloud detection performance in complex cloud–snow coexistence scenarios. Full article

Background/Objectives: Accurate land cover mapping from multispectral Sentinel-2 imagery is fundamental for environmental monitoring, efficient natural resource management, and spatial planning. While deep learning has become the dominant approach for semantic segmentation, the combined impact of spectral input selection and network architecture on cross-regional robustness remains insufficiently explored. This study systematically investigates multispectral land cover segmentation in Serbia and evaluates its transferability to Western Balkan regions using a structured experimental framework. Methods: A comprehensive band-combination ablation analysis (3–10 spectral bands and index-only inputs) was first conducted using Attention U-Net, followed by a comparative evaluation of representative convolutional and transformer-based architectures, including ResNet-UNet-50, ConvNeXt-UNet, DeepLabV3+ (ResNet-50), and DINOv2-S/14. Model performance is evaluated on an internal Serbian test split (Test SR), an external Serbian dataset (Ext SR), and a cross-regional Balkan dataset (Ext WB). Results: The results demonstrate that compact multispectral configurations (6–9 bands) provide the most stable performance, achieving mIoU values of approximately 0.72–0.74 under in-domain evaluation and remaining robust under external testing. The inclusion of near-infrared and shortwave infrared bands proved critical for effective land cover discrimination, whereas increasing spectral dimensionality beyond this range did not yield systematic improvements in external robustness. Notably, the magnitude of performance degradation under pronounced geographic domain shift exceeds the performance differences observed between architectures under in-domain conditions, indicating that distribution shift exerts a stronger influence on segmentation accuracy than model choice alone. Class-wise analysis revealed agricultural areas as the most domain-sensitive category, while Shapley-based explainability analysis provides additional insight into class-specific spectral dependencies and their role in generalization behavior. Conclusions: Although transformer-based models demonstrated competitive robustness, attention-enhanced convolutional architectures achieved comparable stability across evaluation scenarios. Overall, the findings emphasize the importance of balanced spectral design, class-aware robustness analysis, and explicit out-of-domain evaluation for developing transferable land cover segmentation models in remote sensing applications. Full article

Infrared radiation spectra produced by vibration–rotation transitions in multicomponent gases within the vacuum plume of attitude and orbital control engines constitute crucial radiation sources for optical target identification and space maneuver recognition, and rapid prediction of these signatures is essential for real-time forecasting. This study introduces an axisymmetric vacuum plume flow field model based on a simplified point-source approach that accommodates multicomponent combustion gases. Using the Maxwellian velocity distribution and a velocity–position angle algorithm, normalized number density, velocity, and temperature distributions are derived. A plume–freestream interaction model founded on noncentral fully elastic collision theory is incorporated, and overall plume properties are obtained via density-weighted averaging. Neglecting non-equilibrium radiation effects, the high-temperature gas absorption coefficient is calculated using a statistical narrowband model and radiative transfer is solved via the line-of-sight method. The model is validated against direct simulation Monte Carlo results for single-gas and MBB bipropellant plumes and confirmed using infrared spectral data in the 2.0–4.5 μm band. The proposed framework achieves 10²–10³-fold higher computational efficiency than conventional DSMC approaches. Freestream effects on plume diffusion and momentum exchange diminish with increasing altitude, as does the freestream velocity’s enhancement of radiation intensity, whereas greater plume expansion at higher altitudes increases overall radiation intensity. Full article

The increasing prevalence of drug-resistant microorganisms has prompted research into novel antimicrobial compounds, with 2-thiophene carboxylic acid thiourea derivatives showing promise for future therapeutic applications. However, the poor water solubility of these compounds limits their practical use. This study investigates the formation and characterization of inclusion complexes between 2-hydroxypropyl-β-cyclodextrin (HPβCD) and 2-thiophene carboxylic acid-halogenated (chlorine-, bromine-, and iodine-) thiourea derivatives, seeking to improve their physicochemical properties. The dynamic light scattering (DLS) measurements and UV-Vis spectroscopy provided information related to thiourea–HPβCD aggregates and stoichiometry. Solid-state inclusion compounds and physical mixtures were prepared in two different molar ratios (thioureas:HPβCD = 1:1 and 1:2), and the morphology of the resulting powders was observed by scanning electron microscopy (SEM). Thermogravimetry (TG) and differential scanning calorimetry (DSC) (TG-DSC) coupled analysis were used to analyze thermal profiles in the temperature range of 25 °C to 600 °C, while the spectral data obtained by Fourier transform infrared spectroscopy (FTIR) provided the characteristic vibrational bands of the pure guest molecules and data corresponding to the structural and chemical changes in the host–guest systems. The structural and thermal analyses revealed significant interactions between the host and thioureas molecules, with evidence of possible interactions involving two cyclodextrin molecules. The results demonstrate the presence of intermediate stoichiometry in the inclusion compounds, with possible enhancement of the therapeutic potential of these thiourea derivatives. Full article

Fog-dependent Tillandsia landbeckii in the hyper-arid Atacama Desert lacks the red-edge reflectance pattern that supports vegetation monitoring, motivating shortwave infrared (SWIR) approaches. We evaluated a newly developed UAV-borne multispectral SWIR camera system for estimating plant water status and additional plant functional traits (fresh and dry biomass, and N uptake) from four spectral bands (1100, 1200, 1510, and 1650 nm) across 20 destructively sampled plots. Of five traits tested, only canopy water content (CWC) retained statistically robust spectral associations after multiple-testing correction, with most significant predictors concentrated in the 1200–1510 nm wavelength region. A physically interpretable predictor, the mean spectral slope between 1200 and 1510 nm, yielded conditional cross-validated $R_{\mathrm{cv}}^2=0.51$ (${\mathrm{RMSE}}_{\mathrm{cv}}\approx 170$ g m⁻²), though fully selection-corrected estimates were substantially lower ($R_{\mathrm{cv}}^2=0.10$–$0.20$), reflecting feature-selection instability at the given sample size. The absence of robust biomass- and nitrogen-related signals is physically interpretable given the species’ atypical surface optics. While expanded sampling and independent validation remain necessary to establish transferable performance estimates, these results demonstrate that SWIR-based water-status retrieval is feasible for this spectrally challenging species, opening a pathway toward functional monitoring of fog-dependent desert ecosystems. Full article

Walnut leaf necrosis causes leaf desiccation and premature abscission, substantially reducing photosynthetic efficiency, impairing fruit development, and ultimately leading to yield loss and quality deterioration. In severe cases, it accelerates branch senescence or even whole-tree mortality, resulting in considerable economic damage to the walnut industry. Rapid and accurate monitoring of this disease is therefore essential for sustainable production. This study aimed to characterize the different stages of walnut leaf necrosis using spectral analysis and develop classification models for stage-specific identification. Leaf samples representing healthy leaves and the early, middle, and late stages of necrosis were analyzed for spectral responses. Sensitive bands were identified using the variable importance in projection (VIP), successive projections algorithm (SPA), and the combined VIP-SPA method, and corresponding vegetation indices were constructed. The selected features were incorporated into classification models based on random forest (RF), extreme gradient boosting (XGBoost), and convolutional neural networks (CNNs). Results revealed that the red-edge (640–700 nm) and near-infrared (720–1000 nm) regions were identified as key diagnostic spectral ranges. Among the vegetation indices evaluated, the Simple Ratio Index (SRI) calculated from reflectance at 705.7 nm and 707.1 nm, the Normalized Difference Index (NDI) using the same band pair, and the Difference Index (DI) derived from 417.1 nm and 638.7 nm emerged as the most sensitive indicators of disease severity. Classification accuracies for different necrosis stages reached 0.9583, 0.9583, and 0.9333, respectively. These findings demonstrate that the identified spectral bands and vegetation indices provide robust tools for monitoring the progression of walnut leaf necrosis. Full article

This study uses Gonjo County, Chamdo City, Tibet, as the study area and addresses the challenges of lithological complexity and low efficiency of conventional geological surveys in the Qinghai–Tibet Plateau. This study applies the first systematic application of Chinese GF-5 AHSI data to conduct detailed lithological classification in a plateau environment. Three types of datasets were constructed, including the full-band (FB) dataset, shortwave infrared diagnostic bands (SWIR), and feature-selected bands (FS). Four classification models—Support Vector Machine (SVM), Long Short-Term Memory network (LSTM), Multi-Scale Convolutional Neural Network (MSCNN), and Spectral-Spatial Unified Network (SSUN)—were comparatively evaluated to systematically assess the performance of spectral feature selection and deep learning methods for hyperspectral lithological classification. The experimental results explicitly demonstrate the superiority of spectral-spatial feature extraction. Specifically, compared to the baseline Support Vector Machine (SVM) model, which achieved an overall accuracy of 74.67% and a kappa coefficient of 0.6952, the proposed SSUN model demonstrated an advantage, reaching an overall accuracy of 90.94% and a kappa coefficient of 0.8917. By jointly extracting spectral sequence features and spatial contextual information, SSUN effectively suppresses noise and enhances the spatial continuity of lithological boundaries. The results demonstrate the high practical applicability and spectral fidelity of GF-5 AHSI data for lithological identification in plateau stratigraphic environments. The shortwave infrared region is confirmed to be a critical spectral domain for lithological discrimination, and spectral-spatial deep learning models can maintain high classification accuracy after feature dimensionality reduction, achieving a balance between classification efficiency and accuracy. This study provides reliable methodological support for remote sensing lithological mapping and mineral resource exploration in complex plateau geological environments. Full article

To overcome the limitations of complex dispersion structures, low diffraction efficiency at band edges, and continuous spectral aliasing in dispersion-interference coupled static Fourier transform spectrometers operating in the 3–5 μm mid-infrared band, a compact bandpass sampling Fourier transform spectrometer based on a horizontal roof mirror and a segmented blazed grating with differentiated blaze angles (HRMSG-FTS) and a stepwise continuous spectrum reconstruction method (SCSR) were proposed. The HRMSG-FTS integrates dispersion and compensation through dual-pass coupling of the segmented blazed grating and the horizontal roof mirror, thereby reducing system complexity and improving diffraction efficiency at the band edges. Simulation results show that the HRMSG-FTS achieves a spectral resolution of 0.625 cm⁻¹ while significantly reducing the longitudinal system size, and improves diffraction efficiency at the band edges by more than 20% compared with a single-blaze-angle grating. To mitigate continuous spectral aliasing, the SCSR method progressively decouples and accurately reconstructs the aliased spectrum. Simulations show that the reconstruction error coefficient is reduced from 0.683 to 0.031, and the effectiveness of SCSR is verified experimentally. The proposed system and method combine compactness with high accuracy and are suitable for applications such as airborne atmospheric monitoring, industrial gas detection, and portable infrared spectral analysis. Full article

The increasing demand for better fruit flavor and eating quality has driven the need for rapid and non-destructive assessment of internal attributes to support fruit grading and precision supply. Visible–near-infrared hyperspectral imaging (VNIR-HSI) provides rich spectral–spatial information for evaluating sweetness in citrus fruit, but its practical use is constrained by high spectral dimensionality, redundancy, and system cost. Here, we propose a reconstruction-assisted, attention-guided band-selection framework for non-destructive prediction of soluble solids content (SSC) in Shimen honey mandarins. The framework integrates spectral–spatial attention, probability-based differentiable band selection, and full-band reconstruction into a unified end-to-end architecture, enabling compact and informative band learning. Using 952 samples, the model selected 56 informative bands from the original 176-band hyperspectral data and achieved competitive SSC prediction on the test set (RMSE = 0.63 °Brix, R² = 0.80) while maintaining high-fidelity reconstruction of the full-band hyperspectral cube from the compact input (peak signal-to-noise ratio, PSNR = 36.47 dB; structural similarity index, SSIM = 0.89). These findings support the proposed framework as a methodological proof of concept for non-destructive citrus quality evaluation, indicating that substantial spectral compression can be achieved under the current VNIR setting while largely preserving predictive performance. The selected bands may provide candidate spectral regions for future compact citrus-quality sensing systems. Full article

Deuteration provides a controlled perturbation for probing isotope and symmetry effects in organometallic vibrational spectra. Here, density functional theory (DFT) is used to systematically examine the evolution of far-infrared (400–600 cm⁻¹) Fe–Cp vibrational modes in fully protonated, partially deuterated, and fully deuterated ferrocene. All three characteristic modes—the a₂″ torsional mode and the two e₁′ bending modes—exhibit monotonic red-shifts with increasing deuteration. The a₂″ mode shows the largest isotope sensitivity, shifting by ~28 cm⁻¹ across the DFT series, whereas the e₁′ modes shift by ~11–12 cm⁻¹ and undergo symmetry-dependent splitting of up to ~2 cm⁻¹ under partial deuteration. These results establish the a₂″ band as a sensitive probe of the degree of deuteration and the e₁′ splitting as a diagnostic of symmetry reduction. A physics-based AI surrogate model reproduces the overall red-shift trends but deviates at high deuteration, with maximum errors of ~16.6 cm⁻¹, highlighting the limits of reduced-mass scaling. Full article

The aim of this study was to examine the potential of multispectral imaging derived from unmanned aerial vehicles (UAVs) for detecting the spread of mistletoe (Viscum album ssp. austriacum L.) in Scots pine stands and to assess the information potential of selected vegetation indices in mistletoe detection. UAV campaigns were performed in the Niepołomice Primeval Forest (Niepołomice Forest District, Regional Directorate of the Polish State Forests National Holding, Kraków, Poland). A fixed-wing UAV Trinity F90+ (Quantum Systems GmbH) equipped with a five-band multispectral MicaSense RedEdge-M camera and an RGB Sony UMC-R10C camera was employed. The number of trees infected by mistletoe, as well as the quantity and area of mistletoe biogroups, were derived based on the classification of true multispectral orthophotos using a support vector machine (SVM) classifier. The spectral information potential assessment identified NIR (B5) as the most important single spectral source of information, while the greatest information potential among vegetation indices was found in NormG, CIG, and GRVI. The mistletoe classification of the 22.5-ha compartment revealed 1735 mistletoe biogroups covering a total area of 489 m², with 58.6% of the 2917 detected tree crowns identified as infected (Kappa = 0.74). The results confirm that UAV-based multispectral data, particularly when combined with green-sensitive vegetation indices, enable effective differentiation of mistletoe from host tree crowns. The integration of the near-infrared (NIR) band further enhanced classification performance. This study evaluates UAV-based multispectral and RGB imagery for detecting common mistletoe (Viscum album ssp. austriacum) in Scots pine stands. The information potential of 22 vegetation indices was assessed to identify the most effective spectral features for mistletoe classification. Full article

Accurate paper fiber identification is essential for cultural heritage conservation. Traditional staining methods are destructive, while macroscopic AI models often lack physicochemical interpretability. This study explores the feasibility of a non-destructive analytical approach using micro-hyperspectral imaging (Micro-HSI) to overcome both limitations. Three traditional Japanese bast fibers, Kozo, Mitsumata, and Gampi, were analyzed as standard reference samples. Relative reflectance spectra were extracted from microscopic fiber regions using Micro-HSI. Dynamic normalization and Savitzky–Golay first-derivative filtering were applied to suppress scattering effects and baseline drift. Principal component analysis (PCA) and linear discriminant analysis (LDA) were applied in parallel for dimensionality reduction and supervised classification, respectively. The results indicated that unsupervised PCA exhibited substantial inter-class overlap because of the shared cellulose matrix among the fiber types. In contrast, supervised LDA amplified subtle chemical differences and achieved clear separation among the three fibers. Feature-loading analysis indicated that the classification was mainly associated with visible range reflectance characteristics, lignin π→π* absorption bands in the 400–450 nm region, and near-infrared O−H and C−H overtone vibrations near 835 nm. Leave-One-Specimen-Out Cross-Validation yielded an overall accuracy of 77.8%, with error-free classification of Kozo (F1 = 1.00) and misclassification limited to the chemically similar Gampi and Mitsumata pair. This proof-of-concept study demonstrates that combining Micro-HSI with chemometric analysis enables non-destructive fiber discrimination while retaining physicochemically interpretable spectral features. The findings also establish a microscopic spectral reference framework for future non-destructive analysis of historical paper materials. Full article

Nance fruits are produced worldwide in small cultivars and are valued for their characteristic aroma, flavor, and rich vitamins and fiber, as well as for their antioxidant characteristics. The use of herbal infusions in various communities is common, and considerable knowledge behind such usage remains empirical. In this work, we investigated the bioactive profile of nance fruit pulp water and ethanol extracts obtained at various temperatures, as well as their feasibility to inhibit selected pathogenic bacteria strains and biofilm formation. The extracts showed a significant content of vitamin C that increased from 11 to 17 mg/100 mL when temperatures rose to 75–90 °C. Antioxidant capacity by DPPH• and ABTS•⁺ also increased with extraction temperature (75–90 °C), and phenolic compounds correspondingly depicted maximum values of 8.0 and 11.2 mg GAE/100 mL at the same temperatures. The higher values of bioactive compounds and antioxidant capacity at high extraction temperatures was possibly due to the disruption of cell walls and membranes at these temperatures that allowed for the release of bioactive compounds. Fourier transform infrared spectroscopy bands indicated that the aqueous extracts of nance pulp contained a combination of hydroxyl, amide, and methylene functional groups, demonstrating the coexistence of phenolic compounds, amino acids, and lipids, which supported the presence of molecules with potential biological activity. Inhibition of microbial growth by aqueous extracts obtained at 20 °C was observed against S. aureus and P. aeruginosa, and none of the extracts prevented biofilm formation against S. aureus. Full article