Search Results (292)

Buckwheat is a highly nutritious coarse grain crop, yet its industrial processing has long faced two major challenges: the low whole-kernel rate of domestic dehullers and the poor local adaptability of imported equipment. To address these problems, a novel grinding disc-type dehulling machine was developed, featuring upper and lower discs with alternating deep–shallow composite textures to reduce kernel breakage and improve whole kernel rate. A 0–10 mm adjustable gap mechanism was incorporated to suit different buckwheat varieties and particle sizes, enhancing dehulling efficiency. Buckwheat grains were classified into four size ranges: 4.0–4.5 mm, 4.5–5.0 mm, 5.0–5.3 mm, and 5.3–5.7 mm. For all sizes, the optimal rotational speed was 12 r/min, with corresponding optimal gaps of 2.53 mm, 2.80 mm, 3.20 mm, and 3.40 mm, respectively. The whole-kernel rates under these conditions were 32.9%, 37.5%, 45.6%, and 55.1%, respectively, all above 30%, showing substantial improvement. For the 4.5–5.0 mm fraction, orthogonal tests revealed that a small gap (2.859 mm) achieved a dehulling rate of 89.9% and a whole-kernel rate of 38.03%, making it suitable for mass production. A larger gap (3.288 mm) combined with secondary dehulling increased the cumulative whole kernel rate to 50.26%, which is advantageous for producing high value-added products. The novel grinding disc structure balanced frictional and compressive forces on kernels, while the adjustable gap design improved adaptability. Combined with size classification and parameter optimization, this approach provides precise processing schemes for various buckwheat varieties and offers both theoretical and practical value for industrial application. Full article

This study presents an integrated workflow for the characterization of fault-controlled fractured–vuggy reservoirs, demonstrated through a comprehensive analysis of the TP12CX fault zone in the Tahe Oilfield. The methodology establishes a four-element structural model—comprising the damage zone, fault core, vuggy zone, and cavern system—coupled with a multi-attribute geophysical classification scheme integrating texture contrast, deep learning, energy envelope, and residual impedance attributes. This framework achieves a validation accuracy of 91.2%. A novel structural element decomposition–integration approach is proposed, combining deterministic structural reconstruction with facies-constrained petrophysical modeling to quantify reservoir properties. The resulting models identify key heterogeneities, including caverns (Φ = 17.8%, K = 587 mD), vugs (Φ = 3.5%, K = 25 mD), and fractures (K = 1400 mD), with model reliability verified through production history matching. Field application of an optimized nitrogen foam flooding strategy, guided by this workflow, resulted in an incremental oil recovery of 3292 tons. The proposed methodology offers transferable value by addressing critical challenges in karst reservoir characterization, including seismic resolution limits, complex heterogeneity, and late-stage development optimization in fault-controlled carbonate reservoirs. It provides a robust and practical framework for enhanced oil recovery in structurally complex carbonate reservoirs, particularly those in mature fields with a high water cut. Full article

This study addresses the challenges of redundant crop identification features and low computational efficiency in complex agricultural environments, particularly in arid regions. Focusing on the Hexi region of Gansu Province, we utilized the Google Earth Engine (GEE) to integrate Sentinel-2 optical imagery (10 bands) and Sentinel-1 radar data (VV/VH polarization), constructing a 96-feature set that comprises spectral, vegetation index, red-edge, and texture variables. The recursive feature elimination random forest (RF-RFE) algorithm was employed for feature selection and model optimization. Key findings include: (1) Variables driven by spatiotemporal differentiation were effectively selected, with red-edge bands (B5–B7) during the grain-filling stage in August accounting for 56.7% of the top 30 features, which were closely correlated with canopy chlorophyll content (p < 0.01). (2) A breakthrough in lightweight modeling was achieved, reducing the number of features by 69%, enhancing computational efficiency by 62.5% (from 8 h to 3 h), and decreasing memory usage by 66.7% (from 12 GB to 4 GB), while maintaining classification accuracy (PA: 97.69%, UA: 97.20%, Kappa: 0.89). (3) Multi-source data fusion improved accuracy by 11.54% compared to optical-only schemes, demonstrating the compensatory role of radar in arid, cloudy regions. This study offers an interpretable and transferable lightweight framework for precision crop monitoring in arid zones. Full article

Tropical forests harbor a significant portion of global biodiversity but are increasingly degraded by human activity. Assessing restoration efforts requires the systematic monitoring of tropical ecosystem status and recovery. Satellite-borne synthetic aperture radar (SAR) supports monitoring changes in vegetation structure and is of particular utility in tropical regions where clouds obscure optical satellite observations. To characterize tropical forest recovery in the Lowland Chocó Biodiversity Hotspot of Ecuador, we apply over a decade of dual-polarized (HH + HV) L-band SAR datasets from the Japanese Space Agency’s (JAXA) PALSAR and PALSAR-2 sensors. We assess the complementarity of the dual-polarized imagery with less frequently available fully-polarimetric imagery, particularly in the context of their respective temporal and informational trade-offs. We examine the radar image texture associated with the dual-pol radar vegetation index (DpRVI) to assess the associated determination of forest and nonforest areas in a topographically complex region, and we examine the equivalent performance of texture measures derived from the Freeman–Durden polarimetric radar decomposition classification scheme applied to the fully polarimetric data. The results demonstrate that employing a dual-polarimetric decomposition classification scheme and subsequently deriving the associated gray-level co-occurrence matrix mean from the DpRVI substantially improved the classification accuracy (from 88.2% to 97.2%). Through this workflow, we develop a new metric, the Radar Forest Regeneration Index (RFRI), and apply it to describe a chronosequence of a tropical forest recovering from naturally regenerating pasture and cacao plots. Our findings from the Lowland Chocó region are particularly relevant to the upcoming NASA-ISRO NISAR mission, which will enable the comprehensive characterization of vegetation structural parameters and significantly enhance the monitoring of biodiversity conservation efforts in tropical forest ecosystems. Full article

In industrial applications, rolling is commonly performed with lubrication to prevent undesirable modification of the sheet. Although it is well established that lubrication influences the microstructure and texture of deformed sheets through its effect on shear deformation, the underlying mechanisms remain insufficiently understood. In this study, we investigated how lubrication affects slip system activity during asymmetrical rolling, using viscoplastic modeling of BCC ferritic steel. Two conditions—lubricated and non-lubricated samples—were examined under asymmetrical rolling. Slip system activity was inferred from the rotation axes between pairs of orientations separated by low-angle grain boundaries, based on the assumption that such boundaries represent the simplest form of orientation change. A Viscoplastic Self-Consistent (VPSC) model employing an affine linearization scheme was used. This proved sufficient for evaluating slip system activity in BCC polycrystalline metals undergoing early-stage plastic deformation involving either plane strain or combined plane strain and shear. The results demonstrated that lubrication had a limiting effect by reducing the penetration of shear deformation through the thickness of the sample. Understanding this effect could enable the optimization of lubrication strategies—not only to minimize defects such as bending, but also to achieve microstructural characteristics favorable for industrial applications. Full article

In the process of heavy-duty cutting, the reciprocating motion of the sliding guide pair surface is prone to local wear, which seriously affects the overall machining accuracy and service life of the machine tool. This study proposes a biomimetic micro-texture design scheme combining elliptical grooves and shell-shaped grooves on the surface of carp as biomimetic prototypes to enhance the oil film bearing capacity, drag reduction, and wear resistance of guide rail pairs. Based on Fluent fluid simulation research, it has been shown that this texture has a better dynamic pressure lubrication effect. We used response surface methodology to optimize the texture design parameters and further verify the accuracy of the optimal parameters with the NSGA-II genetic algorithm. The results show that under lubricated conditions, the load-bearing pressure of the combined micro-textured guide rail pair increased by 53.79%, the friction coefficient decreased by 39.04%, and the temperature decreased by 15.83%. This texture can still significantly improve drag reduction and wear resistance in a low-oil state. Full article

Remote sensing approaches can be cost-effective for estimating forest structural attributes. This study aims to use airborne LiDAR data to assess the robustness of multispectral satellite imagery and topographic attributes derived from DEMs to predict the density of three vegetation layers in a wet eucalypt forest in Tasmania, Australia. We compared the predictive capacity of medium-resolution Landsat-8 Operational Land Imager (OLI) surface reflectance and three pixel sizes from high-resolution WorldView-3 satellite imagery. These datasets were combined with topographic attributes extracted from resampled LiDAR-derived DEMs and a geology layer and validated with vegetation density layers extracted from high-density LiDAR. Using spectral bands, indices, texture features, a geology layer, and topographic attributes as predictor variables, we evaluated the predictive power of 13 data schemes at three different pixel sizes (1.6 m, 7.5 m, and 30 m). The schemes of the 30 m Landsat-8 (OLI) dataset provided better model accuracy than the WorldView-3 dataset across all three pixel sizes (R² values from 0.15 to 0.65) and all three vegetation layers. The model accuracies increased with an increase in the number of predictor variables. For predicting the density of the overstorey vegetation, spectral indices (R² = 0.48) and texture features (R² = 0.47) were useful, and when both were combined, they produced higher model accuracy (R² = 0.56) than either dataset alone. Model prediction improved further when all five data sources were included (R² = 0.65). The best models for mid-storey (R² = 0.46) and understorey (R² = 0.44) vegetation had lower predictive capacity than for the overstorey. The models validated using an independent dataset confirmed the robustness. The spectral indices and texture features derived from the Landsat data products integrated with the low-density LiDAR data can provide valuable information on the forest structure of larger geographical areas for sustainable management and monitoring of the forest landscape. Full article

Accurate identification of dust emission sources is crucial for simulating dust aerosols in atmospheric chemical models. Therefore, a dynamically updated dust source function (DSF) was developed within the dust emission scheme of the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) to simulate an East Asian dust storm event from 13 to 16 March 2021. Utilizing satellite-derived input of vegetation cover, snow cover, soil texture, and land use, the DSF was updated to better identify dust source areas over bare soils and sparsely vegetated regions in western China and central-western Mongolia. With the updated DSF, simulated dust emissions increase significantly over western China and Mongolia. The dust aerosol simulations demonstrate substantial improvements in near-surface PM₁₀ concentrations, a better agreement with remotely sensed dust aerosol optical depth (DOD), and a more accurate representation of the vertical distribution of dust extinction coefficients compared to observations. This study highlights the importance of integrating real-time data to accurately characterize dust emission sources, thereby improving atmospheric environment simulations. Full article

This study provides a comprehensive assessment of the HYDRUS-1D model for predicting root-zone soil moisture (RZSM) and evapotranspiration (ET). It evaluates different soil hydrodynamic parameter (SHP) schemes—soil type-based, soil texture-based, and inverse solution—under varying cropping systems (Zea mays–Glycine max rotation and continuous Zea mays) and moisture conditions (irrigated and rainfed), aiming to understand water transport across different cultivation patterns. Using field measurements from 2002, the SHPs were optimized for each scheme and applied to predict RZSM and ET from 2003 to 2007. The inverse solution scheme produced nearly unbiased RZSM predictions with a root mean square error (RMSE) of 0.011 m³m⁻³, compared to RMSEs of 0.036 m³m⁻³ and 0.042 m³m⁻³ for the soil type-based and soil texture-based schemes, respectively. For ET predictions, comparable accuracy was achieved, with RMSEs of 66.4 Wm⁻², 69.5 Wm⁻², and 68.2 Wm⁻² across the three schemes. RZSM prediction accuracy declined over time in the continuous Zea mays field for all schemes, while systematic errors predominated in the Zea mays–Glycine max rotation field. ET accuracy trends mirrored RZSM in irrigated systems but diverged in rainfed croplands due to the decoupling of ET and RZSM under arid conditions. Full article

The Ossa-Morena Zone (SW Iberian Massif) hosts the largest set of Cambro–Ordovician alkaline magmatic plutons related to the Palaeozoic rifting of the northern Gondwana margin so far described. An organized framework for their classification at different scales is proposed through data-driven ranks based on their distinctive petrological features relative to other rift-related magmatic rocks found throughout western Europe. The classification method aims to enhance geological mapping at different scales, regional- and continental-scale correlations, and, as such, facilitate the petrogenetic interpretation of this magmatism. The hierarchical scheme, from highest to lowest rank, is as follows: rank-1 (supersuite) assembles rocks that have distinctive characteristics from other magmatic units emplaced in the same magmatic event; rank-2 (suite) categorizes the units based on their major textural features, indicating if the body is plutonic, sub-volcanic, or a strongly deformed magmatic-derived unit; rank-3 (subsuite) clusters according to their spatial arrangement (magmatic centres) or association to larger structures (e.g., shear zones or alignments); rank-4, the fundamental mapping unit, characterizes the lithotype (alkaline granite, alkaline gabbro, syenite, albitite, etc.) by considering higher ranks (alkalinity and textural aspects); rank-5 characterizes the geometry of individual plutons (with several intrusions) or swarms; rank-6 (smallest mappable unit) corresponds to each intrusion or individual body from a swarm. Although this classification scheme is currently presented solely for the Ossa-Morena Zone, the scheme can be easily extended to incorporate other co-magmatic alkaline bodies, such as those in the NW Iberian allochthonous units or other peri-Gondwanan zones or massifs, in order to facilitate regional correlations of the rift-related magmatism. Full article

In Brazil, several silicic rocks can be used as powder-based K sources, which can reduce production costs in agriculture. The optimized supply of K not only increases yield but also contributes to soil fertility preservation and long-term sustainability by curtailing nutrient losses and reducing the risk of nutrient imbalances. Therefore, this study aimed to investigate the effects of K application timing, source, and doses on nodulation, productive components, and productivity of soybeans in a not-tillage system in the Savannah. The experiment was carried out in the field, for two years, in an Oxisol, with a clayey texture. The experimental design was in random blocks, in a 2 × 4 × 3 factorial scheme, as follows: two application timings (early and sowing), four K₂O rates (0, 40, 80, and 120 kg ha⁻¹), and three sources (KCl, Potasil, and Ekosil), with four replicates. Potassium fertilizer was broadcasted on the soil without incorporation into the soil. Due to the interactions between doses and K₂O sources, there was a linear adjustment for KCl sources, the higher the dose, the lower the nodule mass. Also, for nodule mass, the interaction between dose and application time was significant for the early application of the Ecosil and Potasil sources for 80 kg ha⁻¹. The highest estimated soybean grain productivity was 3262 kg ha⁻¹ with 78 kg ha⁻¹ of K₂O, being the most suitable for growing soybeans under a no-tillage system. Full article

The precise selection of agricultural land is essential for guaranteeing global food security and sustainable development. Additionally, agricultural land suitability (AgLS) analysis is crucial for tackling issues including resource scarcity, environmental degradation, and rising food demands. This research examines the synergies and trade-offs among the sustainable development goals (SDGs) using a hybrid geographic information system (GIS)–fuzzy analytic hierarchy process (FAHP)–geostatistical framework for AgLS analysis in Attur Taluk, India. The area was chosen for its varied agro-climatic conditions, riverine habitats, and agricultural importance. Accordingly, data from ten topographical, climatic, and soil physiochemical variables, such as slope, temperature, and soil texture, were obtained and analyzed to carry out the study. The geostatistical analysis demonstrated the spatial variability of soil parameters, providing essential insights into key factors in the study area. Based on the receiver operating characteristic curve analysis, the results showed that the FAHP method (AUC = 0.71) outperformed the equal-weighting scheme (AUC = 0.602). Moreover, suitability mapping designated 17.31% of the study area as highly suitable (S1), 41.32% as moderately suitable (S2), and 7.82% as permanently unsuitable (N2). The research identified reinforcing and conflicting correlations with SDGs, emphasizing the need for policies to address trade-offs. The findings showed 40% alignment to climate action (SDG 13) via improved resilience, 33% to clean water (SDG 6) by identifying low-salinity zones, and 50% to zero hunger (SDG 2) through sustainable food systems. Conflicts arose with SDG 13 (20%) due to reliance on rain-fed agriculture, SDG 15 (11%) from soil degradation, and SDG 2 (13%) due to inefficiencies in low-productivity zones. A sustainable action plan (SAP) can tackle these issues by promoting drought-resistant crops, nutrient management, and participatory land-use planning. This study can provide a replicable framework for integrating agriculture with global sustainability objectives worldwide. Full article

Crop mapping using remote sensing is a reliable and efficient approach to obtaining timely and accurate crop information. Previous studies predominantly focused on large-scale regions characterized by simple cropping structures. However, in complex agricultural regions, such as China’s Huang-Huai-Hai region, the high crop diversity and fragmented cropland in localized areas present significant challenges for accurate crop mapping. To address these challenges, this study introduces a landscape-clustering zoning strategy utilizing multi-temporal Sentinel-1 and Sentinel-2 imagery. First, crop heterogeneity zones (CHZs) are delineated using landscape metrics that capture crop diversity and cropland fragmentation. Subsequently, four types of features (spectral, phenological, textural and radar features) are combined in various configurations to create different classification schemes. These schemes are then optimized for each CHZ using a random forest classifier. The results demonstrate that the landscape-clustering zoning strategy achieves an overall accuracy of 93.52% and a kappa coefficient of 92.67%, outperforming the no-zoning method by 2.9% and 3.82%, respectively. Furthermore, the crop mapping results from this strategy closely align with agricultural statistics at the county level, with an R² value of 0.9006. In comparison with other traditional zoning strategies, such as topographic zoning and administrative unit zoning, the proposed strategy proves to be superior. These findings suggest that the landscape-clustering zoning strategy offers a robust reference method for crop mapping in complex agricultural landscapes. Full article

Silicate agrominerals (SA) may be sustainable soil amendments that can minimize dependence on conventional fertilizers (CF). We evaluated the residual effects of SA application as a source of Si and as a soil remineralizer, using soils with contrasting chemical-physical features cultivated with soybean. The experiment was conducted under greenhouse conditions and treatments were arranged in a 5 × 2 + 2 factorial scheme: five rates of SA, two soils in addition to CF. The soil was incubated before cultivation, followed by the sequential sowing of corn and soybean. At the R4 phenological stage, when the pods were fully developed, soybean plants were harvested for anatomical leaf tissue analysis and P, Ca, Mg, and Si accumulation. After harvest, the soil was analyzed. Application of SA rates reduced potential acidity (H + Al) and exchangeable acidity (Al³⁺) and increased soil pH, sum of bases (SB), cation-exchange capacity (CEC), and base saturation (BS), in addition to promoting the nutrient’s availability and Si. Stomatal density was higher on the adaxial face of plants cultivated in the medium-textured soil. Silicate agrominerals can be used as a soil acidity corrector and remineralizer, improving the root environment and increasing the availability of nutrients and silicon. Full article

Background/Objectives: Extracting spatial features (texture analysis) from dose distributions (dosiomics) for outcome prediction is a rapidly evolving field in radiotherapy. To account for fraction size differences, the biological effective dose (BED) is often calculated. We evaluated the impact and added value of the BED in the dosiomics prediction modelling of grade ≥ 2 late rectal bleeding (LRB) probability within 5 years after treatment in three parts. Methods: For N = 656 prostate cancer patients previously treated in a randomized trial with conventional (CF) or hypofractionated (HF) radiotherapy, 42 dosiomic features were extracted from the dose distributions of the delineated rectum in physical doses and from dose distributions converted to the BED. Part 1: To assess whether an HF BED dosiomics model is generalizable to CF and vice versa, multivariate logistic regression BED models were constructed for HF and CF separately and tested on the other fractionation scheme. Part 2: The BED models were fitted to combined HF and CF data together to test whether this resulted in better models. Part 3: Separate physical HF and CF models were constructed and compared to the BED models. Results: Part 1: Dosiomics related to large-zone and long-run high-dose levels were predictive for both HF and CF. Deviation from the mean gray level was only predictive for HF. The BED HF model calibrations with CF data and vice versa were generally poor. AUCs ranged from 0.55 to 0.65. Part 2: Compared to the separate models, the models fitted to the combined HF and CF data showed better discriminative ability in CF but not in HF. Part 3: The apparent performances of models for the BED and physical dose were similar. Conclusions: Using the BED in the predictive dosiomic modelling of late rectal bleeding after prostate cancer radiotherapy to account for differences in fraction doses was of limited value. Full article