Search Results (8,976)

The production of “winter wines” in south-eastern Brazil, enabled by the double pruning technique, requires precise assessment of grape technological maturity to ensure high-quality outputs. However, conventional monitoring approaches are destructive, labor-intensive, and limited in their ability to capture vineyard spatial variability. This study aimed to develop and validate a non-destructive predictive framework for Soluble Solids (°Brix) and Titratable Acidity (TA) by integrating spatial remote sensing data with temporal agrometeorological information. Multispectral imagery was acquired via an unmanned aerial vehicle in a vineyard cultivated with Sauvignon Blanc and Syrah, from which vegetation indices were derived and combined with Growing Degree-Days to train machine learning models, including Random Forest, Multilayer Perceptron, and XGBoost. The incorporation of agrometeorological data significantly improved predictive performance compared to models based solely on vegetation indices. Among the tested algorithms, XGBoost achieved the highest accuracy, with coefficients of determination of 0.89 for °Brix and 0.77 for TA, achieved by XGBoost on an independent hold-out test set. Model interpretability analysis indicated that Growing Degree-Days and cultivar were the primary drivers of maturation dynamics, while vegetation indices refined predictions by accounting for spatial variability in plant vigor. Overall, the proposed approach represents a promising proof-of-concept framework for non-destructive maturity monitoring in precision viticulture, supporting improved monitoring of grape maturation. However, multi-season validation across diverse vineyard conditions is required to confirm its generalizability and support its application as a routine decision-support tool. Full article

Pomegranate (Punica granatum L.) is widely valued for its rich nutritional profile and distinctive sensory characteristics. As one of the oldest cultivated fruits, it has an extensive history of cultivation in China and possesses abundant germplasm resources. Nevertheless, systematic evaluation of these resources remains inadequate, limiting progress in germplasm innovation and utilization. In this study, we analyzed 16 fruit quality traits across 142 pomegranate accessions. Most traits showed wide phenotypic variation, with coefficients of variation (CVs) ranging from 2.10% to 108.83%. Notably, titratable acidity (TA) and anthocyanin content showed high variability (coefficient of variation, CV > 78%), while fruit shape index and total soluble solids (TSS) showed relatively low variability (CV < 10%). Cluster analysis delineated three distinct phenotypic groups. The first group comprised accessions characterized by large fruit size, thick peel, high acidity, and soft seeds. The second group exhibited high seed hardness, low acidity, and elevated sugar-acid and TSS–acid ratios. The third group displayed reduced levels of bioactive compounds such as tannins, phenols, and anthocyanins, combined with high seed hardness. Correlation analysis followed by principal component analysis (PCA) extracted six principal components, and based on comprehensive scoring, SXXA23, AHHB13, SD41, SXXA27, AHHB40, HS1, AHHB8, HY22, HN4, and AH27 were identified as priority accessions for further evaluation within this repository panel. Full article

Fused deposition modeling (FDM) is a material-extrusion additive manufacturing technology that is widely used in rapid prototyping, complex product modeling, and functional part fabrication. However, process-parameter drift and environmental disturbances may induce underfilling, overfilling, warping, delamination, and other defects, thereby reducing part quality or interrupting the manufacturing process. Since FDM is characterized by point-wise extrusion and layer-by-layer deposition, layer-surface images naturally contain both spatial morphology and temporal evolution information. Existing image-based diagnostic methods often treat layer images as independent samples, and the selection of the image-stream length is still insufficiently supported by experimental evidence. Moreover, spatial compression in spatiotemporal neural networks may remove local defect information that is important for distinguishing similar process-parameter drifts. This study provides a deployment-oriented analysis of FDM image-stream diagnosis by systematically examining how layer-window length, spatial feature preservation, and strict data partitioning influence process-parameter drift recognition. To address these issues, this paper studies ConvLSTM-based FDM image-stream process-parameter drift diagnosis. Continuous region-of-interest image streams are constructed for one nominal condition and six process-parameter drift conditions. In this paper, the time step T denotes the number of consecutive layer-surface images, or, equivalently, the number of consecutive printed layers, contained in one diagnostic image stream. A ConvLSTM-Flatten baseline is first developed to preserve complete spatial feature maps and to evaluate the effect of different time-step lengths. Then, a ConvLSTM model with adaptive spatial pooling and temporal attention (ASP-TA) is constructed to analyze the influence of spatial pooling granularity and temporal feature fusion. The experiments show that the ConvLSTM-Flatten model achieves the highest average test accuracy of 0.7288 at $T=9$, whereas $T=3$ is identified as a practical optimal time step when test accuracy, image-frame computation, diagnosis latency, and convergence behavior are considered together. The paired trial-wise accuracy difference between $T=9$ and $T=3$ is small and not statistically significant over ten repeated trials. Thus, the diagnostic window corresponding to $T=3$ covers three consecutive deposited layers; after the initial window is available, stride-one stream construction allows the diagnosis to be updated with each newly acquired layer image. ASP-TA with a pooling size of eight consistently outperforms ASP-TA with a pooling size of four, but both are lower than the Flatten baseline, indicating that preserving sufficient spatial information is essential for distinguishing FDM process-parameter drift states. The results reveal the non-monotonic influence of time-step length and clarify the tradeoff between spatial feature preservation and model compactness in FDM image-stream process-parameter drift diagnosis. Full article

Pentagonal NiAs₂ is a low-symmetry two-dimensional material relevant to nanoelectronic and thermoelectric applications, but its low lattice thermal conductivity (κ) may limit heat dissipation in device-related scenarios. In this work, the strain-dependent lattice thermal transport of monolayer penta-NiAs₂ is investigated using first-principles calculations combined with the phonon Boltzmann transport equation. The lattice thermal conductivity increases monotonically with tensile strain. Mode-resolved analysis shows that this enhancement mainly originates from the selective reinforcement of the out-of-plane acoustic ZA branch, rather than from a uniform increase in all phonon branches. Tensile strain weakens low-frequency anharmonicity, suppresses phonon scattering, and prolongs the ZA phonon lifetime. Meanwhile, the modified ZA dispersion increases its group velocity, further enhancing its contribution to heat transport. The reduced group velocities of the TA, LA, and most optical branches further limit their contributions to thermal conductivity. The results reveal a ZA-phonon-mediated mechanism for strain-enhanced thermal transport in penta-NiAs₂ and provide guidance for tuning phonon transport in pentagonal two-dimensional materials. Full article

The Tieshilong Pb-Zn polymetallic district is located along the northern margin of the Dongpo ore field in the middle section of the Nanling metallogenic belt, southern Hunan, China. It represents a typical granite-related, fault-controlled hydrothermal vein-type lead–zinc polymetallic deposit in southern Hunan. In recent years, large-scale tin mineralization has been newly discovered during exploration in the deeper and peripheral areas of the district. However, the timing and genetic nature of this tin mineralization remain undetermined, which limits understanding of the characteristics of the deposit’s metallogenic system and its deep exploration potential. In this study, we present in situ cassiterite U–Pb geochronology and trace element data from deep Pb-Zn-Sn orebodies in the Tieshilong mining district. LA-ICP-MS U-Pb analyses of cassiterite yield a Tera–Wasserburg lower-intercept age of 159.2 ± 6.2 Ma (MSWD = 1.4), indicating that Sn mineralization occurred during the Late Jurassic. This age overlaps, within uncertainty, with the main ca. 160~150 Ma W–Sn metallogenic event recognized throughout the Nanling belt. Trace element data reveal that Tieshilong cassiterite is enriched in Fe (1100–5800 ppm) and W (120–11,660 ppm), and depleted in Nb (0.1–87 ppm) and Ta (0–7.1 ppm). The Zr/Hf ratios range from 23 to 52, with a mean value of approximately ~36, which is close to the chondritic values. These geochemical signatures, together with the occurrence of cassiterite intergrown with hydrothermal quartz and its replacement by later sulfides, support precipitation from a granite-related magmatic–hydrothermal system. Based on the findings and the literature, the Tieshilong deposit is therefore interpreted as a Pb–Zn-dominant expression of a Late Jurassic granite-related polymetallic system, in which deeper Sn ± W mineralization was overprinted by later Pb–Zn–Cu sulfide mineralization along fault-controlled fluid pathways. The recognition of cassiterite-bearing, medium- to high-temperature assemblages at depth suggests that down-dip extensions, fault intersections, and strike-inflection zones of the ore-controlling structures represent priority targets for future exploration. Full article

Background/Objectives: To develop and evaluate a deep learning-based quality control system for Lumbar Spinal Digital Radiographs (LSDR), designed to automate and improve their evaluation and reduce reliance on manual reviews. Methods: This retrospective study utilized a deep learning workflow comprising image segmentation, feature extraction, and a classification model. The dataset, including anteroposterior (AP) and lateral (LAT) X-ray images, was expanded through data augmentation techniques. Four U-Net-based models were assessed: standard U-Net, Swin-UNet, Attention U-Net, and Attention U-Net with the weight map, with the latter selected for its superior performance. Extracted features, such as brightness, contrast, and anatomical positioning, were used in an XGBoost classifier, which was evaluated using mean intersection over union (mIoU), accuracy, sensitivity, specificity, and AUC. Results: The Attention U-Net with weighted attention outperformed the other models, achieving high mIoU scores in both AP and LAT views. The XGBoost classifier achieved the best performance in classifying images as “qualified” or “unqualified,” with an AUC of approximately 0.9, high accuracy, and balanced sensitivity and specificity. This approach effectively addressed class imbalances and improved model accuracy compared to traditional machine learning models such as MLP and SVM. Conclusions: The developed automated quality control system demonstrated potential for enhancing image quality, enhancing diagnostic reliability, and optimizing clinical workflow efficiency. Full article

Accurate field-scale surface soil moisture (SSM) measures are important for agricultural water management. Conventional satellite SSM products remain too coarse for within-field applications. Here, we developed sequence-aware deep learning models for growing-season SSM estimation by fusing data from Sentinel-1 C-band SAR, harmonized Landsat-8/Sentinel-2 (HLS), and local ancillary datasets. We assembled a multi-source dataset on Sentinel-1 overpass time for 2016–2024 (May–September), yielding 1469 samples and 65 features per sample, including SAR and optical features, meteorological data, soil texture and bulk density, topography, crop labels, irrigation-likelihood flag, and irregular-time-step encoding. We compared long short-term memory (LSTM) and temporal convolutional neural network (TCN) architectures together with attention-augmented variants, including feature attention (FA), temporal attention (TA), and the combined feature–temporal attention (FTA). Models were trained and tested on seven years of data and were validated based on a temporal generalization using combined data of a wet year (2016) and a dry year (2023). The best model, FTA-TCN, achieved R² = 0.851, RMSE = 0.024 m³.m⁻³, and MAE = 0.020 m³.m⁻³ on the withheld validation years, outperforming the base LSTM (R² = 0.422; RMSE = 0.053 m³.m⁻³; MAE = 0.043 m³.m⁻³) and the base TCN (R² = 0.746; RMSE = 0.034 m³.m⁻³; MAE = 0.022 m³.m⁻³). Shapley additive explanations (SHAP) analysis indicated that antecedent precipitation and short-term rainfall accumulations were dominant forcings, while soil texture, elevation, incidence angle, and vegetation indices modulated SSM variability. Satellite-derived features accounted for ~28.5% of aggregated SHAP importance. Overall, the results show that dual-attention temporal convolution can capture field-scale SSM dynamics across wet and dry seasons when satellite signals are coupled with local soil-meteorological-management context. Full article

The electrification of high-temperature industrial processes requires refractory materials that combine thermal stability with tailored electrical functionality. In this study, Ta/Nb-Al₂O₃ composites were prepared by spark plasma sintering (SPS) to investigate densification, metal-phase deformation, electrical conductivity and percolation behavior. Coarse, fine and superfine alumina powders were combined with tantalum or niobium and sintered at 1300–1600 °C for 5 min with 50 MPa uniaxial pressure. The results show that the alumina particle size and morphology strongly influence the formation of conductive metal networks. Coarse alumina promotes deformation and elongation of the metallic phase, thereby improving metal-phase connectivity and lowering the operational percolation threshold. Fine and superfine alumina enhance densification but can delay percolation by embedding metal particles in a dense ceramic matrix. Combining these fractions, both effects can be balanced, enabling improved densification while maintaining effective conductive pathways. An operational percolation threshold of 7.5 vol.-% was obtained for Ta/coarse alumina, indicating highly effective metal-phase connectivity after SPS. Microstructural analysis supports the interpretation that matrix-controlled metal-particle deformation and spatial distribution govern the electrical response. Tailored alumina matrix design can reduce the refractory metal content required for conductive ceramic–metal composites. Full article

Seasonal hydrology reshapes large-river food webs by altering habitat connectivity and basal resource availability. Trophic baselines from before the 2021 Yangtze ten-year fishing ban are now valuable because monitoring has shifted from fish abundance alone toward food-web function and ecological recovery. We analysed carbon (δ¹³C) and nitrogen (δ¹⁵N) stable isotopes of fish and the baseline bivalve Corbicula fluminea collected in March (dry season) and August (wet season) 2016 from the Jingjiang section of the lower Yangtze River. In the dry season, 100 individuals of 27 species were analysed; species mean δ¹³C ranged from −30.52‰ (Micropercops swinhonis) to −21.19‰ (Aristichthys nobilis) and δ¹⁵N from 6.30‰ (Hypophthalmichthys molitrix) to 14.90‰ (Lophiogobius ocellicauda). In the wet season, 187 individuals of 47 species were analysed; species mean δ¹³C ranged from −32.07‰ (Pseudobrama simoni) to −20.84‰ (Salanx ariakensis) and δ¹⁵N from 6.27‰ (Misgurnus anguillicaudatus) to 14.87‰ (Saurogobio gymnocheilus). Among 24 shared species, δ¹³C differed significantly between seasons (paired t = 4.30, p < 0.001), but δ¹⁵N did not (t = 1.52, p = 0.143). Mean trophic level fell from 3.07 to 2.74 (t = 3.85, p < 0.001). This decline remained significant in a trophic-enrichment-factor sensitivity analysis using 2.5–4.0‰. Community-wide carbon range (CR), nitrogen range (NR), total convex-hull area (TA), mean nearest-neighbour distance (NND), and the standard deviation of nearest-neighbour distance (SDNND) showed larger wet-season CR (9.08 vs. 7.51), slightly larger NR, TA and NND, and lower SDNND. Seasonal hydrology thus mainly altered basal carbon pathways and relative trophic positions rather than reorganising feeding guilds. The dataset provides a pre-ban isotopic baseline for assessing whether post-ban recovery in the lower Yangtze includes restoration of trophic structure and energy-flow pathways. Full article

The corrosion resistance of metallic materials is closely related to their service environment. In ammonia-based desulfurization post-treatment systems, 316L stainless steel, 2507 duplex stainless steel, and TA2 commercially pure titanium are widely used as candidate materials for key components such as desulfurization heat exchangers. In this study, the pitting corrosion behavior of 316L, 2507, and TA2 was investigated in simulated ammonia desulfurization post-treatment solutions with different pH. The results show that increasing solution acidity leads to a decrease in the capacitive arc radius and polarization resistance, while the donor concentration and pitting susceptibility of the three materials increase. Under the same pH condition, TA2 exhibits the highest stability and corrosion resistance, followed by 2507, whereas 316L shows the poorest corrosion resistance. The composition of the TA2 passivation film (TiO₂) does not change as the pH of the simulated solution is modified. With increasing solution acidity, the relative XPS peak-area fraction of TiO₂ in TA2 increases, indicating that TiO₂ remains the dominant component of the passive film. In contrast, the relative contents of Cr- and Mo-containing oxides/hydroxides in 316L and 2507 decrease, and MoO₃ is replaced by MoO₂ under acidic conditions. These changes suggest weakened passive-film stability and reduced protection of the substrate. Full article

Nanotechnology has become an important platform in the fields of gene therapy and genome editing, providing delivery strategies that address persistent therapeutic challenges by improving the precision, efficiency, and safety of genetic modifications. This review highlights the central role of nanomaterials in overcoming persistent barriers to genetic interventions, including inefficient delivery, instability of genetic cargo, and off-target effects. Specifically, we emphasize the combined use of nanomaterials with clustered regularly interspaced short palindromic repeats and CRISPR-associated proteins (CRISPR-Cas) systems, which can improve editing specificity and therapeutic efficacy. Beyond the classical CRISPR/Cas9 platform, this review also discusses next-generation modalities such as base editors, Cas13, prime editing, and the recently described Tandem Interspaced Guide RNA and TIGR-associated protein (TIGR-Tas) system, while considering their therapeutic potential and distinct delivery challenges. By using nanomaterials, the stability and intracellular delivery of genome-editing systems are improved, enabling more effective treatments for genetic disorders and acquired diseases such as cancer and infectious diseases. In addition, nanocarriers provide controlled release, protection from degradation, and better biocompatibility, thereby improving the safety and reliability of gene-editing therapies. Despite these advances, important translational challenges remain, including immunotoxicity, large-scale manufacturing, and regulatory integration. Overall, the continued convergence of nanotechnology and genome engineering may support the development of personalized medicine strategies that adapt genetic engineering tools for patient-specific applications. Full article

This study systematically investigated the effects of Co/Ni ratios (0.6–2.0) on the solidification behavior, as-cast microstructure, and element segregation of Co-Al-W-based superalloys, and elucidated the mechanism of thermodynamic and kinetic synergistic regulation. The results show that increasing the Co/Ni ratio has a negligible effect on the liquidus and solidus temperatures, but it significantly lowers the dissolution temperature of the γ′ phase, thereby expanding the alloy’s heat treatment window (HTW) from 215 °C to 269 °C. As the Co/Ni ratio increased from 0.6 to 2, the SDAS at the center of the alloy ingot decreased from 112.4 μm to 43.3 μm, resulting in a significant refinement of the as-cast microstructure. The dendritic segregation coefficients for positively segregating elements such as Ta, Hf, and Al, as well as negatively segregating elements such as W, all approached 1 significantly, effectively suppressing microsegregation during solidification. This study reveals the multidimensional synergistic regulation mechanism of the Co/Ni ratio on the non-equilibrium solidification behavior of highly alloyed Co-Al-W-based superalloys and quantitatively elucidates the relationship between the Co/Ni ratio, the microstructural uniformity of as-cast specimens, and the heat treatment process window. For the first time in a highly alloyed multi-component Co-Al-W system, a correlation has been established between the Co/Ni ratio, element segregation, dendrite coarsening coefficient, and heat treatment window. Full article

Using electrophysical models of MIS-capacitor gas-sensing elements, the influence of measuring circuit parameters on the metrological characteristics of hydrogen sensors was investigated. Recommendations for selecting optimal measurement circuit modes are provided, both in general and using sensor elements with a Pd-Ta₂O₅-SiO₂-nSi structure as an example. This article presents the results of an analysis and comparative study of three methods for measuring the capacitance of MISC sensors: (a) the AC bridge with a balance indicator (ACB + BI), (b) the divider method (DM), and (c) the bridge method (BM). The advantages and disadvantages of each method for practical implementation in gas analytical instruments are discussed. Furthermore, experimental data on the long-term stability of MISC sensor characteristics are provided, including the sensor response to hydrogen and the zero-point drift. Full article

Understanding indoor and outdoor airborne organic mixtures, including semi-volatile organic compounds (sVOCs), remains challenging as quantitative monitoring is often costly and difficult to scale across buildings and individuals. Here we present a low-cost, miniaturised passive sampler-based methodology for static and wearable deployment to generate time-integrated chemical fingerprints and source prioritisation. New sampler devices containing replicate 9 mm sorbent discs (Tenax^® TA and/or polydimethylsiloxane) were deployed for 28 days in indoor (kitchen, bedroom) and outdoor (roadside) environments and worn by five participants; extracts were analysed by liquid extraction and gas chromatography–mass spectrometry (GC-MS) using conservative, transparent criteria for tentative compound identification. Across the household deployments, 52 compounds met inclusion criteria and distinct room-specific and outdoor chemical signatures were observed. Wearable deployments also produced differentiable chemical profiles, with greater similarity among co-inhabitants, but still could differentiate co-habitant activities based on exposure. These results demonstrate the feasibility of using miniature passive samplers to obtain reproducible, information-rich profiles that can help discriminate environments and exposure scenarios. Full article

Titanite is a widespread accessory mineral in magmatic, metamorphic, and hydrothermal systems and can incorporate trace elements that are sensitive to ore-forming processes. Although titanite trace-element chemistry has been widely applied to individual ore systems and deposit comparisons, its potential for supervised machine-learning-based discrimination across multiple ore deposit types remains less systematically explored. In this study, we compiled a literature-based LA-ICP-MS titanite trace-element dataset comprising 1679 analyses from five major ore deposit types: porphyry, skarn, iron oxide–apatite (IOA), iron oxide copper–gold (IOCG), and orogenic Au deposits. A common feature set of 21 trace elements, including REE, Y, Zr, Hf, Nb, Ta, Th, and U, was used to evaluate six supervised machine-learning algorithms: K-nearest neighbors, support vector machine, random forest, XGBoost, TabMap, and TabPFN. Two-dimensional element and element-ratio diagrams showed substantial overlap among deposit types, whereas machine-learning models better captured deposit-type-related multielement patterns in the compiled dataset. TabPFN achieved the highest stratified 5-fold cross-validation performance, with an accuracy of 0.957 ± 0.011 and a macro-F1 score of 0.944 ± 0.012, followed by TabMap and XGBoost. SHAP and TabMap-SHAP interpretations suggest that deposit classification is mainly associated with coupled variations in REE-Y, Eu, HFSE, and Th-U systematics rather than with a single diagnostic element. These results indicate that titanite trace-element compositions may provide a useful quantitative and interpretable approach for deposit-type discrimination within compiled geochemical datasets, while broader application requires expanded standardized datasets and independent validation samples. Full article