Search Results (9,461)

Rapid, standardised discrimination of soil taxonomic units remains challenging when relying solely on conventional field descriptions and laboratory analyses, particularly at high sampling densities. This study evaluated whether proximal spectroscopy and hyperspectral imaging can support the classification of Brazilian Soil Classification System (SiBCS) suborders and pedogenetic horizons when surface and subsurface spectra are treated separately. Six intact soil monoliths (0.12 × 1.60 m) were collected in Paraná State, southern Brazil, representing one Organossolo (Ooy), three Latossolos (LVd, LVd1, and LVd2) and two Argissolos (PVAd and PVd). For each monolith, 800 spectra were acquired per sensor with a non-imaging VIS–NIR–SWIR spectroradiometer (350–2500 nm), and 800 spectra per sensor per monolith were extracted from the SWIR hyperspectral images (1200–2450 nm). Principal component analysis (PCA) was used to summarise spectral variability, and supervised classification was performed via k-nearest neighbours, random forest, decision tree and gradient boosting for suborders (10-fold cross-validation), and a neural network was used for within-profile horizon classification. PCA indicated that most of the spectral variance was captured by a dominant axis, with clearer separation among suborders in the SWIR space than in the full VIS–NIR–SWIR range. With respect to suborder classification, subsurface spectra outperformed surface spectra, and SWIR outperformed VIS–NIR–SWIR: the best accuracies were 0.96 for subsurface SWIR (gradient boosting; AUC = 0.99; MCC = 0.95) and 0.89 for surface SWIR (k-nearest neighbours; AUC = 0.98; MCC = 0.87). Within-profile horizon classification via VIS–NIR–SWIR achieved accuracies of 0.84–0.97 with the Neural Network, with most misclassifications occurring between adjacent horizons. Overall, subsurface SWIR information provided the most reliable basis for taxonomic discrimination, whereas horizon classification was feasible but reflected gradual spectral transitions along the profile. Full article

Postharvest dehydration is a major determinant of cucumber freshness and marketability, yet early reductions in internal water status are difficult to detect using conventional quality assessment methods. This study presents a non-destructive, physiology-informed deep learning approach that links cucumber surface color and texture patterns to internal hydration level for automated freshness classification. A time-resolved dataset comprising 4160 RGB images of cucumber fruits was paired with gravimetrically determined relative water content (RWC), used as an objective indicator of internal hydration status. Based on RWC, fruits were classified into four freshness categories: Very Fresh (≥98%), Moderately Fresh (95–98%), Low Freshness (90–95%), and Spoiled (<90%). A custom convolutional neural network (CNN) was trained using standardized RGB images and evaluated on an independent test set. The model achieved an overall classification accuracy of 91.35% and a Cohen’s Kappa coefficient of 0.875, indicating strong agreement between predicted and actual freshness classes. Classification performance was highest for the extreme freshness states, with F1-scores exceeding 0.94 for Very Fresh and Spoiled fruits, while intermediate classes showed greater overlap, reflecting the gradual nature of postharvest water loss. Model interpretability analyses revealed that the CNN consistently focused on physiologically meaningful surface color and texture features associated with dehydration. Overall, these findings highlight the potential of physiology-informed deep learning to advance non-destructive freshness assessment in cucumbers, offering a realistic pathway toward hydration-based sorting, improved shelf-life management, and intelligent quality monitoring in modern postharvest supply chains. Full article

The volcano-shaped dependence of the catalytic activity of the magnesia-supported ZnO nanoparticles on their diameter in CO oxidation was considered in the framework of Wolkenstein’s electron theory of catalysis on semiconductors. By analyzing the diffuse reflectance UV-Vis spectra of the ZnO nanoparticles in catalysts, we demonstrate that a narrow range of particle diameters (4.0–4.6 nm) leads to changes in the Fermi level due to quantum confinement of free electrons. As the diameter of the ZnO nanoparticles decreases, the Fermi level rises, resulting in an accelerated acceptor stage and a decelerated donor stage involving free electrons interacting with atomic oxygen and carbon dioxide on the catalyst surface, respectively. This opposing change in the rates of the donor and acceptor stages during the CO oxidation reaction, influenced by the diameter of the ZnO nanoparticles, gives rise to a volcano-shaped size dependence of the reaction rate. Furthermore, an optimal catalyst particle diameter is identified, at which the reaction rate reaches its maximum. Full article

This study evaluates the effectiveness of laser cleaning techniques for the non-contact removal of unwanted deposits from the surface of contemporary urban mural paintings. Two sets of mock-up samples, painted with popular graffiti spray paints on lime-based plaster, and artificially contaminated, were subjected to various cleaning procedures using Nd:YAG lasers operated in Q-switched (QS), long Q-switched (LQS) or short free-running mode (SFR). A multi-analytical approach—including X-ray fluorescence spectroscopy (XRF), Fourier-transform infrared spectroscopy (FTIR), colorimetry, and hyperspectral imaging (HSI)—was used to identify pigments and binders, and to evaluate cleaning efficiency and pigment preservation. XRF and FTIR were useful in understanding the composition of the sprays, while colorimetric ΔE values quantified cleaning efficiency and potential damage, and hyperspectral reflectance and LSU (linear spectral unmixing) abundance maps provided spatial distribution insights into contaminant removal and pigment preservation. The results demonstrate that laser cleaning effectiveness and selectivity are strongly dependent on the operational regime and fluence. In particular, long Q-switched laser irradiation at moderate fluence levels achieved effective contaminant removal with minimal chromatic and chemical alteration of the original paint layers. These findings support the development of tailored, sustainable, and non-contact laser cleaning protocols for the conservation of contemporary urban murals and contribute to the establishment of objective, multi-parameter criteria for evaluating cleaning outcomes in street art conservation. Full article

GNSS reflectometry (GNSS-R) altimetry has been widely used for retrieving surface elevation over oceans, cryosphere, and land. Recently, UAV-borne GNSS-R systems have gained attention due to their flexibility for low-altitude and localized observations. However, lightweight UAV platforms impose strict payload and real-time processing constraints. At low altitudes, the small geometric delay between direct and reflected signals often leads to waveform overlap, degrading conventional altimetry algorithms. In this study, a lightweight UAV-borne GNSS-R receiver and a signal-separation-based altimetry method are proposed. Direct and reflected signals are separated using waveform characteristics without relying on external height information, mitigating the impact of waveform overlap. Simulations and experiments using a SPIRENT 9000 GNSS simulator demonstrate stable height retrieval under dynamic low-altitude conditions while maintaining real-time capability, confirming the feasibility of lightweight UAV GNSS-R altimetry for rapid elevation monitoring. Full article

In desert regions, frequent aeolian dust events lead to rapid dust accumulation on greenhouse films, critically compromising light transmittance and inhibiting crop growth. To address this challenge, this study integrated Computational Fluid Dynamics–Discrete Phase Model (CFD-DPM) simulations with field experiments to conduct a comprehensive investigation spanning from microscopic deposition mechanisms to macroscopic physiological responses. Particle characterization revealed a distinct aerodynamic sorting effect, wherein fine particles (<65 μm) preferentially adhered to film surfaces driven by airflow, contrasting sharply with the gravitational settling of coarse ground particles. Numerical simulations further confirmed that as wind speeds increased from 2 to 7 m/s, dust deposition rates exhibited a significant exponential reduction, with accumulation predominantly concentrated in the windward and wake zones. The dust layer covering the film induced a substantial reduction in the indoor daily light integral (DLI), which leads to influence tomato growth that stunted plant height and suppressed the net photosynthetic rate. Physiologically, antioxidant enzyme activities exhibited an initial surge followed by a decline, reflecting photosynthetic constraints and oxidative stress. Consequently, a high-frequency cleaning interval of 7–14 days is recommended to significantly enhance photosynthetic capacity and stress resilience. Full article

Land surface bidirectional reflectance distribution functions (BRDF) are critical for quantitative remote sensing but are significantly constrained by scale effects, limiting the interoperability of multi-resolution data and the accuracy of quantitative inversion, thereby rendering the investigation of BRDF multi-scale effects increasingly urgent. This study utilized UAV (Unmanned Aerial Vehicle)-based multi-angular observations and the RPV model to retrieve the BRDF of typical land covers, employing the Window Averaging Method to simulate multi-scale responses and systematically investigate the relationship between BRDF characteristics and spatial scale. The results indicate the following key findings: (1) The RPV (Rahman–Pinty–Verstraete) model demonstrated high robustness and inversion accuracy, yielding RMSE (Root Mean Square Error) below 0.06 and RRMSE (Relative RMSE) below 25% across all land covers, with the 840 nm band exhibiting superior performance. (2) Significant spatial scale effects were observed, where BRDF characteristics varied distinctively with scale but eventually stabilized at specific thresholds; specifically, the stabilization scales were identified as 1.3 m for bare soil, 1.5 m for tea plantations, 1 m for rice, and 2 m for forests. (3) The scale evolution of BRDF features exhibited a parallel trend with spatial heterogeneity, a correlation that enables the quantitative identification of optimal observation scales for different land cover types. Full article

This study presents a computational, simulation-based investigation of the dielectric response of human hand tissues, skin, fat, muscle, and bone to radiofrequency (RF) electromagnetic fields emitted by mobile devices. The widespread adoption of handheld devices and the deployment of fifth-generation (5G) networks, including millimetre-wave (mmWave) bands, have intensified concerns regarding localized human exposure to RF radiation, particularly in the hand, which serves as the primary interface during device operation. Using validated dielectric property datasets, numerical simulations were performed across the frequency range of 0.5–40 GHz, employing the Finite-Difference Time-Domain (FDTD) method to solve Maxwell’s equations, with analytical evaluations conducted in Maple-18. A heterogeneous multilayer hand phantom was developed, and simulations were conducted under controlled exposure conditions, including a transmitted power of 1 W, antenna gain of 2 dBi, and incident power density of 5 W/m², consistent with ICNIRP and NCC safety guidelines. Tissue responses were assessed over a temperature range of 10–40 °C to account for thermal variability. The results demonstrate strong frequency- and temperature-dependent behaviour of dielectric properties, intrinsic impedance, reflection coefficient, attenuation, and specific absorption rate (SAR). At lower frequencies (<1 GHz), RF energy penetrated more deeply with distributed absorption and relatively low SAR values, whereas higher frequencies (3–40 GHz) produced highly localized absorption in superficial tissues, particularly skin and muscle. Increasing temperature led to significant increases in permittivity, conductivity, and SAR, with up to a twofold enhancement observed between 10 °C and 40 °C. These findings confirm that 5G and mmWave exposures result in predominantly surface-confined energy deposition in hand tissues. The study provides a robust computational framework for evaluating hand device electromagnetic interactions and offers quantitative insights relevant to antenna design, exposure compliance assessment, and the development of evidence-based safety guidelines. Full article

Background: Tumor-derived small extracellular vesicles (sEV), which we call TEX, carry a cargo of molecules that resembles the producer tumor cells. Circulating freely in body fluids, TEX potentially serve as a liquid tumor biopsy. TEX horizontally transfer their cargo to various recipient cells, imparting to them pro-tumor activity. Mechanisms of TEX-driven reprogramming might involve nucleic acids, especially double-stranded (ds)DNA. Methods: TEX isolated from supernatants of human tumor cells were identified as sEV, based on their size, endocytic origin and morphology. TEX treated with DNase/RNase cocktail were examined by transmission and cryo-electron microscopy and tested for biologic activity. DNA was extracted from enzyme-treated TEX, quantified by Qubit and analyzed for fragment sizes. The presence of genomic DNA in TEX was confirmed by PCR, and sequencing of the TP53 gene fragment for a mutational signature was performed. Results: Enzymatic and microscopic studies of TEX showed that nucleic acids are present in the biocorona on the outer surface. Their removal interfered with the biocorona integrity. A short TEX exposure to DNase/RNase altered their morphology without impairing vesicle functions; longer treatments induced TEX re-organization into smaller membrane-bound vesicles. The TEX lumen contained long fragments of protected genomic DNA with a mutational signature reflecting that of the tumor. Conclusions: Nucleic acids present on the TEX surface support the vesicular integrity. The TEX lumen contains membrane-protected large (ds)DNA fragments with the mutational signature of the parent tumor. The presence of surface and luminal nucleic acids in TEX, and especially their mutational signature, suggests that TEX may serve as highly promising cancer-specific biomarkers. Full article

Clinical discharge summaries contain rich patient information but remain difficult to convert into structured representations for downstream analysis. Recent advances in large language models (LLMs) have introduced new approaches for clinical text extraction, yet their relative strengths compared with supervised methods remain unclear. This study presents a controlled evaluation of three dominant strategies for structured clinical information extraction from electronic health records: prompting-based extraction using LLMs, retrieval-augmented generation for terminology canonicalization, and supervised fine-tuning of domain-specific transformer models. Using discharge summaries from the MIMIC-IV dataset, we compare zero-shot, few-shot, and verification-based prompting across closed-source and open-source LLMs, evaluate retrieval-augmented canonicalization as a post-processing mechanism, and benchmark these methods against a fine-tuned BioClinicalBERT model. Performance is assessed using a multi-level evaluation framework that combines exact matching, fuzzy lexical matching, and semantic assessment via an LLM-based judge. The results reveal clear tradeoffs across approaches: prompting achieves strong semantic correctness with minimal supervision, retrieval augmentation improves terminology consistency without expanding extraction coverage, and supervised fine-tuning yields the highest overall accuracy when labeled data are available. Across all methods, we observe a consistent $40-50\%$ gap between exact-match and semantic correctness, highlighting the limitations of string-based metrics for clinical Natural Language Processing (NLP). These findings provide practical guidance for selecting extraction strategies under varying resource constraints and emphasize the importance of evaluation methodologies that reflect clinical equivalence rather than surface-form similarity. Full article

The suppression of residual reflectivity in optical elements has become a hot research topic as it addresses the degradation of optical system imaging quality caused by stray light. Antireflective coatings on the outer surface of window glasses require low reflectivity, high hardness, and resistance to mechanical wear. This study investigates the role of reactive gas stoichiometry in tailoring the structure and performance of ${\mathrm{SiO}}_x$${\mathrm{N}}_y$ antireflection (AR) coatings deposited on GG7i glass via capacitively coupled radio-frequency magnetron sputtering. First, the influence of three ${\mathrm{N}}_2$/${\mathrm{O}}_2$ flow ratios on the optical and mechanical properties of ${\mathrm{SiO}}_x$${\mathrm{N}}_y$ films discussed under identical process parameters. Results show that the refractive index, hardness, and surface roughness of the ${\mathrm{SiO}}_x$${\mathrm{N}}_y$ films increase with increasing ${\mathrm{N}}_2$/${\mathrm{O}}_2$ ratio and that the stress of the ${\mathrm{SiO}}_x$${\mathrm{N}}_y$ films increases according to the Stoney formula. The wear resistance of the ${\mathrm{SiO}}_x$${\mathrm{N}}_y$ films combined with an antifingerprint (AF) coating is tested using steel wool. Experimental results show that the water contact angle of the AF decreases with increasing surface roughness of the film. Finally, on the basis of a comprehensive evaluation of optical and mechanical properties, the antireflection coating on the outer surface of the window glass was prepared by optimizing the process parameters. At $0^{°}$ incidence, the average reflectivity from 420 to 680 nm is <1%, the maximum value is <1.2%, the surface hardness is 17.2 GPa, and the water contact angle is ${100}^{°}$ after the steel wool wear test, showing its suitability for durable antifingerprint applications. This work provides a strategic pathway for designing high-performance optical coatings with tailored mechanical robustness. Full article

Soil texture exerts fundamental control over nutrient retention in arid ecosystems; however, its mechanistic coupling with nutrient stoichiometry in wind-eroded desert steppes remains poorly resolved. We investigated soil particle-size distribution and nutrient characteristics across contrasting vegetation types in a desert steppe on the northern slope of the Yinshan Mountains. The interactions between soil texture and nutrient distribution were quantified through field sampling and laboratory analyses. The Caragana grassland was dominated by fine-textured soils, with a silt-to-sand ratio of 21.58% and a fractal dimension ranging from 2.1 to 3.95, indicating a complex soil structure with strong nutrient-retention capacity. In contrast, the Leymus grassland and desert sites were characterized by higher sand content, with a median particle size of 1.67 mm and sorting coefficients ranging from 0.06 to 4.2, reflecting a simpler structure and comparatively lower nutrient levels. Overall, soils in the region were nutrient-deficient, with widespread phosphorus and potassium limitations, whereas nitrogen was relatively more abundant. Total nitrogen (<0.75 mg kg⁻¹), total phosphorus (0.2–0.4 mg kg⁻¹), total potassium and available nutrients were predominantly classified as ‘deficient’ to ‘extremely deficient’, exhibiting a clear surface accumulation pattern. The Poaceae meadow surface layer showed the highest total nitrogen and phosphorus contents. The sorting coefficient and fractal dimension were identified as key particle-size parameters regulating soil nutrient stoichiometric ratios. The silt-to-sand ratio exerted negative path effects (−0.11 to −0.18) on SOC/TN and AK/AN, whereas fractal dimension showed positive path effects (0.17–0.23) on AK/AN. These findings provide a scientific basis for ecological restoration and soil management in the region. Full article

The proper selection of surface topography (ST) parameters is crucial for ensuring the effective performance of machine components, including their wear and corrosion resistance. In the literature, research on the ST of hardened stainless steels (SSs) after finish turning using cubic boron nitride (CBN) inserts, as well as comparisons with cemented carbide (CC) inserts depending on cutting parameters, is still limited. In this study, the ST of X20Cr13 martensitic hardened SS under dry finish turning with various cutting speeds and feed rates was investigated. Experiments were conducted using a CNC lathe with CBN and CC inserts. A Sensofar S Neox 3D optical profilometer was employed to characterize the ST features, including height surface roughness (SR) parameters, SR profiles, and 2D and 3D surface images. The Parameter Space Investigation method was used to design the experimental plan. For both CBN and CC inserts, the feed rate was the dominant factor influencing the overall SR, described by the Sa and Sq parameters. The extreme parameters Sp, Sv, and Sz were determined by the relationship between feed rate and cutting speed. With appropriately selected turning parameters, it is possible to obtain low Sa values (0.4–0.6 µm), which can eliminate the need for grinding operations. CBN inserts ensured a more regular shape of the ST, while CC inserts contributed to a wavy surface characteristic, associated with more intense plastic deformation. However, low Sa values may be accompanied by isolated peaks, indicating that this parameter does not always fully reflect the presence of extreme micro-irregularities. On the machined surfaces, adhesive bonds of chips and cutting tool material were observed. In addition, micro-scratches were registered for CBN inserts, and a side flow phenomenon for CC inserts. The results confirm that dry turning of hardened SSs can be effectively performed using both CC and CBN inserts. Full article

Wetlands in semi-arid regions are critical for ecological resilience but are increasingly degraded. Ecological water consumption (EWC), reflecting wetland water demand, is essential for understanding wetland sustainability. This study investigated the spatiotemporal dynamics of marsh wetland EWC in the Bashang Plateau, China, from 1986 to 2021, and identified its main driving forces. A Random Forest model was used to downscale GLASS evapotranspiration (ET) product from 0.05° to a 250 m monthly resolution, showing good agreement with flux measurements (RMSE = 21.94 mm, R² = 0.83). Marsh wetland EWC was estimated using the downscaled ET and land cover data, and Granger causality analysis was applied to explore driving mechanisms. Results indicate that the marsh wetland area declined by 74% (from 552.81 to 143.69 km²) while forestland expanded by 217%. Correspondingly, marsh wetland EWC decreased by 67.2%, from 125 to 41 million m³. Precipitation and surface water area were identified as direct drivers of marsh wetland EWC decline, whereas groundwater table, forest EWC, and cropland EWC acted as indirect drivers. While cropland water use has been widely reported as an important factor, results suggest that increased forest EWC associated with large-scale afforestation contributed considerably to groundwater table decline, thereby influencing marsh wetland EWC. Full article

Monitoring surface water quality over large river systems remains challenging due to sparse in situ sampling and the need for decision-ready indicators. This study aims to address this problem by developing and evaluating an integrated Landsat 8-based backpropagation neural network and Canadian Council of Ministers of the Environment Water Quality Index (L8-BPNN-CCME-WQI) for precise surface water quality assessment over the Saint John River (SJR), New Brunswick, Canada. The proposed approach combines atmospherically corrected Landsat 8 imagery, BPNN for estimating multiple surface water quality parameters (SWQPs), and CCME-WQI to translate SWQP fields into transparent water quality levels. The L8-BPNN-CCME-WQI models were trained using in situ measurements of turbidity, total suspended solids (TSS), total solids (TS), total dissolved solids (TDS), chemical oxygen demand (COD), biochemical oxygen demand (BOD), dissolved oxygen (DO), pH, electrical conductivity (EC), and temperature collected during our five field campaigns (from June 2015 to August 2016) and surface reflectance from five Landsat 8 scenes. The developed models achieved high performance during internal calibration and testing (R² ≥ 0.80 for all SWQPs) and demonstrated robust performance (R² ≈ 0.75–0.88) when applied to two independent surface water quality datasets from additional rivers across New Brunswick. Pixel-wise SWQP predictions were then input to the CCME-WQI formulation to derive reach-scale water quality levels, revealing that the lower Saint John River basin (below the Mactaquac Dam) is generally classified as “Fair” (CCME-WQI ≈ 67), whereas the middle basin upstream (above the Mactaquac Dam) is “Marginal” (CCME-WQI ≈ 59), reflecting stronger industrial and agricultural pressures. Overall, the L8-BPNN-CCME-WQI framework provides a scalable methodology for converting multi-parameter satellite-derived water quality information into spatially exhaustive CCME-WQI classes, supporting targeted regulation, prioritization of mitigation in critical reaches, and evaluation of management actions in large river systems. Full article