Search Results (2,414)

The constitutive analysis model and hot processing map of the ZM6 alloy across various deformation conditions were investigated during hot compression experiments. True stress-strain curves within 300–450 °C and 0.0001–0.1 s⁻¹ were obtained from compression tests on a Gleeble-1500 platform. The results showed that higher strain rates (e.g., 0.1 s⁻¹) induced pronounced work hardening, whereas high temperatures (300–400 °C) combined with low strain rates (10⁻⁴ s⁻¹) promoted conditions conducive to dynamic recrystallization (DRX), leading to a softening tendency of steady-state flow stress. Additionally, a modified strain-compensated constitutive model was built for flow stress prediction. Material constants were plotted as fifth-order polynomial functions of strain (0.025–0.80) for precise stress predictions. The derived activation energy (Q = 182.38 kJ/mol) falls within the typical range for Mg-RE alloys. Leave-one-temperature-out cross-validation showed average AARE values of 7.2–9.8%, demonstrating the model’s interpolation capability and its sensitivity to extrapolation. Cross-validation within the training dataset showed reasonable consistency between experimental and predicted stresses (R > 0.997, AARE < 4.35%). Using the dynamic materials model, hot processing maps identified safe deformation zones and instability zones of the ZM6 alloy. Flow instability was observed at strain rates >0.01 s⁻¹, particularly at low temperatures (300–350 °C). Optimal processing windows appeared in high-energy dissipation (η > 30%) regions, e.g., 400–450 °C/10⁻⁴–10⁻³ s⁻¹. Optical microscopy confirmed that at high temperatures (≥400 °C) and low strain rates (≤0.001 s⁻¹), a uniform, fine-grained, fully recrystallized structure can be obtained, whereas low temperatures (350 °C) and high strain rates (0.1 s⁻¹) produce coarse elongated grains with limited DRX, consistent with the instability regime predicted by the processing maps. Under intermediate conditions (e.g., 400 °C, 0.01 s⁻¹), a bimodal grain distribution indicates incomplete recrystallization. Although EBSD analysis was not performed in this study, the optical microstructures directly validate the predicted safe and unstable windows. Together, all these findings provide preliminary model-based guidance for optimizing hot working parameters to balance microstructural stability and processing efficiency. Full article

The early and accurate detection of lung cancer (LC) is one of the primary challenges in the clinical diagnostics process, which plays a vital role in the treatment of the disease. Although various deep learning (DL) techniques have been presented, the existing DL methods are mainly focused on single-modal images, either computed tomography (CT) or histopathological images, which are associated with poor generalization, diversity, and applicability. To mitigate the existing issues, the present work aims to develop a modality-independent ensemble DL framework that is independently evaluated on CT and histopathological image datasets for LC classification. In this work, the proposed framework was developed using the Beta Normalization Aggregation (BNA) technique, where the performance of three state-of-the-art pre-trained convolutional neural network (CNN) architectures was compared on two distinct imaging modalities images. Based on the comparative analysis of the performance metrics, Xception, DenseNet121, and MobileNetV2, are chosen to develop the Ensemble model. Predictions generated by the selected CNN models are aggregated using the proposed BNA strategy to improve classification robustness, which improves the confidence of the prediction results and discriminative capabilities. The experiments using public data sets have confirmed the excellent performance of the model. On the CT dataset, the proposed BNA Ensemble achieved a testing accuracy of 97.45%, with a precision of 97.88%, recall of 97.45%, F1-score of 97.45%, and an AUC of 0.9986. On the histopathological dataset, the framework achieved an accuracy of 99.80%, with precision, recall, and F1-score all reaching 99.80%, and an AUC of 1.0000. These results demonstrate the effectiveness, robustness, and generalizability of the proposed BNA framework. The analysis of the results using t-SNE plots, confusion matrices, ROC curves, and confidence distributions provided additional insights into feature separability, classification performance, and prediction confidence of the proposed framework. Full article

Non-industrial private forestlands (NIPF) have often been subjected to logging practices that remove the highest quality trees of the highest value species, leaving behind less-desirable stems and species; a practice termed high-grading or selective harvesting. Timber stand improvement (TSI) can be used to correct high-grading practices by removing poorly-formed or low-value tree species in order to promote the growth of higher value trees and species. The felled trees may be removed for biomass fuel or left in place. At study sites in southwestern Virginia, we monitored tree growth across experimental TSI with biomass removal, TSI cut-and-leave felled stems, and control plots in mixed-pine hardwood forests from 2012–2025, measuring diameter at breast height (DBH) for multiple species. Scarlet Oak (Quercus coccinea) and Yellow Poplar (Liriodendron tulipifera) had the largest growth increments during the study period, while Black Gum (Nyssa sylvatica) and Hickory species (Carya spp.) showed consistently low growth. Larger trees tended to grow at faster rates, consistent with allometric expectations. The two TSI treatments had similar growth increments and were 60%–100% higher than control plots over the tree blocks of treatments in this study. Mortality at the longest-term measured block was more than twice as high as TSI plots. These results suggest that TSI can reduce competition for light and nutrients promoting diameter growth, whereas untreated plots may experience resource limitations that suppress growth and increase mortality. The study provides a baseline for understanding forest dynamics and highlights the importance of management interventions in maintaining productivity and structural diversity in selectively-logged forests. Full article

Rice–fish co-culture is widely promoted for mitigating nitrous oxide (N₂O) emissions from paddy soils, yet how the duration of co-culture reshapes the underlying nitrogen-cycling microbial community under low-nitrogen input remains poorly understood. This study aimed to (i) characterize how co-culture duration alters the rhizosphere microbial functional potential for N₂O production and consumption, and (ii) identify the water and soil variables linking fish activity to that response. The experiment was conducted during the 2024 rice growing season in the Qingtian rice–fish system (Zhejiang Province, China), a traditional agricultural heritage system managed without chemical fertilizer or supplementary feed. Three treatments (i.e., rice monoculture, first-year co-culture, and long-established (~10-year) co-culture) were compared using six independently bunded replicate plots each. Rhizosphere soils were collected at the tillering, heading and maturity stages for shotgun metagenomic profiling of nitrogen-cycling functional genes, with concurrent measurement of N₂O flux and water and soil physicochemical properties. Fluxes were uniformly low and did not differ among treatments (p > 0.05), defining a substrate-limited baseline. Against this baseline, first-year co-culture induced a coordinated shift toward complete denitrification (nosZ increased by 25–33% across all stages; nosZ/(nirK + nirS) rose to 0.99 at heading), associated with a transient water organic carbon pulse and dissolved-oxygen availability. The long-established system resembled monoculture, indicating a non-monotonic, duration-dependent response. Full article

Reservoir conformance control is a necessary production measure in the oil field, which significantly impacts the efficiency of enhanced oil recovery (EOR). Polymers, hydrophobic associating polymers (HAPs), polymer microgels (MGs), and preformed particle gel (PPG) are typical polymer/gel dispersion fluids that are widely used as conformance control agents. Currently, there is still no combined conformance control method to realize the equilibrium production of the reservoir. This paper first evaluates the reservoir adaptability of polymers, HAPs, and MGs by the three-parallel core displacement experiments. Then, the displacement equilibrium factor (DEF) was established by comprehensively considering the profile improvement, oil increment, and oil recovery to optimize the fluid switching time. Based on the above oil displacement experiments, a scatter plot of the DEF with respect to the ultimate recovery of each layer can be plotted, which has an inflection point when the DEF is 45%. When the DEF is lower than 45%, the difference in the oil displacement effect of each layer is enhanced. Therefore, the best time to switch the injection fluid is when the DEF is reduced to 45%. Finally, based on the above results, a graph guiding the combined conformance control method under different reservoir variation coefficients and reservoir median permeability was established, and an equilibrium production method for heterogeneous reservoirs was developed. The five-parallel core flooding experiments with the DEF < 45% as the switching guidance can increase the oil recovery by 17.79% based on association polymer flooding, which is 9.68% higher than that of the conventional conformance control method. This paper can provide theoretical and experimental support for the optimal design of conformance control in oilfields. Full article

To clarify the effects of biochar application on leaf photosynthesis, dry matter accumulation, and productivity in a maize–soybean intercropping system, a two-year field experiment was conducted in the Yellow River irrigation area of Inner Mongolia from 2024 to 2025. A split-plot design was adopted with two biochar application rates (0 and 5 t ha⁻¹) and three cropping patterns, including maize monoculture, soybean monoculture, and maize–soybean 2:4 intercropping. Leaf SPAD values, photosynthetic characteristics (Pn, Tr, Gs, and Ci), yield components, and land equivalent ratio (LER) were determined. Compared with maize monoculture, intercropping significantly increased maize SPAD values at the V12 and VT stages by 12.80% and 13.39% in 2024 and by 15.41% and 20.58% in 2025, respectively, and enhanced maize Pn, Tr, and Gs at the V12 and R1 stages. Soybean showed greater sensitivity to intercropping, with reduced SPAD values, Pn, Tr, and Gs during the branching, flowering, and pod-setting stages, whereas biochar application partially alleviated these inhibitory effects. Intercropping increased maize kernel number per ear and thousand-kernel weight but reduced soybean effective plant density, grain number per plant, and grain yield. Biochar application improved the grain yield of both intercropped maize and soybean. Under biochar application, the LER values reached 1.04 in 2024 and 1.21 in 2025, indicating a clear advantage in land-use efficiency. Overall, biochar application and maize–soybean intercropping were associated with improved photosynthetic performance, higher land-use efficiency, and increased system productivity. Full article

Sowing density affects the tillering and the number of spikes, which are important wheat yield components. Meanwhile, biostimulants stimulate plant growth and development, which usually improves the yield and grain quality. In our experiment, we investigated the impact of different grain sowing densities (200, 250, 300, 350, 400 and 450 grains m⁻²) and the timing of application of Methylobacterium symbioticum Pascual et al. 2021 bacteria (control, tillering, stem elongation) on winter wheat (“RGT Kilimanjaro” variety) grain yield size and quality. The three-year experiment (2022/2023–2024/2025) was conducted in a split-plot design. The content of macroelements in the soil (Haplic Cambisol) was high, and the contents of micronutrients were medium or low. It was shown that varied weather conditions modified plant responses in individual years. In general, along with the increase in canopy density, the physiological parameters of plants (Fv/Fm, Fv/Fo, PI, RC/ABS), gas exchange parameters (Gs, E, Ci, PN) and SPAD index values. The highest grain yield was obtained in 2023, and the yield in 2025 was significantly lower by 0.39 t ha⁻¹. On average, in the conducted experiment, the best results were obtained with a sowing density of 350 grains m⁻² and 400 grains m⁻². The yields obtained at these densities were 8.21 t ha⁻¹ and 8.34 t ha⁻¹, respectively. However, the highest protein content (14.6%) was identified at a sowing density of 300 grains m⁻². The application of M. symbioticum bacteria, especially in the stem elongation stage, had a positive effect on the yield as well as on the grain protein and gluten content. In contrast, antioxidant capacity was generally higher in the control treatment, while total phenols and flavonoids were most favorably affected by biostimulant application at the tillering stage. PCA and Pearson correlation analysis revealed an inverse relationship between physiological performance and antioxidant-related traits, indicating that climatic variability played an important role in modulating bioactive compound accumulation. Overall, moderate sowing densities combined with M. symbioticum application at stem elongation improved wheat productivity and grain quality, while antioxidant-related traits were mainly influenced by environmental conditions. Full article

Biochar, a carbon-rich by-product of wood pyrolysis, improves soil structure, water retention, and plant growth. A two-year field experiment (2024–2025) was conducted in Poggibonsi (Tuscany, Italy) on tomato cv. “Canestrino” under contrasting climatic conditions. A single biochar application (15 t ha⁻¹) was evaluated for its effects on soil properties, water dynamics, plant water status, and ecophysiological and tissue-level responses. From the results, it emerged that biochar improved soil quality by increasing organic matter (+7.7%) and the C/N ratio (+10.6%), while reducing bulk density (1.42 to 1.25 Mg m⁻³). Soil water content was higher in amended plots, particularly in 2024 (32.84% vs. 24.87%), with a smaller increase in 2025 (24.66% vs. 24.08%). Improved soil water availability enhanced plant water status, as shown by less negative leaf water potential under stress conditions. Microscopic analyses confirmed better xylem integrity in treated plants, with reduced formation of tyloses and improved hydraulic functionality during drought. Agronomic responses reflected climatic variability: yield increased in biochar in 2024, whereas in 2025 drought stress reduced productivity in both treatments, with no significant differences. Overall, biochar improved soil moisture retention, plant water status, and ecophysiological performance, with effects dependent on seasonal rainfall patterns and environmental stress intensity. Full article

The impact of arbuscular mycorrhiza fungi (AMF) on root–shoot scaling strategies under zinc and phosphorus deficiency remains poorly understood in maize. The aims of this study were (i) To quantify the effects of zinc/phosphorus deficiency on AMF colonization, (ii) to quantify biomass accumulation in different plant parts in the presence of AMF, and (iii) to characterize how AMF alter root–shoot allometric scaling under zinc/phosphorus deficiency. We conducted a pot experiment arranged in RCBD split plot with 6 replications. SUWAN 5819 maize seeds were grown for 22 days under five Hoagland’s solution-based nutrient regimes (+Zn+P, −Zn−P, +Zn−P, −Zn+P, and deionized water), with and without AMF. AMF colonization was highest (49.6%) under −Zn+P contrary to hypothesis 1 which predicted highest colonization under dual deficiency, while the deionized water treatment had the lowest colonization (30.1%). Phosphorus was the dominant factor affecting biomass accumulation with a 2–4-fold reduction in organ dry weights for phosphorus-deficient treatments compared to phosphorus-sufficient treatments. AMF colonization significantly reduced dry weights in +Zn+P by 8.6%, 19.0%, and 47.5% in the leaf, stem, and roots, respectively, consistent with mycorrhiza-induced growth depression (MGD). Nutrient deficiency resulted in root biomass accumulation, consistent with the optimal partitioning theory. AMF increased shoot mass fraction from 50% to 63% in +Zn+P, and from 41% to 52.5% in −Zn−P, suggesting AMF role in modulating biomass accumulation. Root–shoot scaling slopes derived from LMM revealed that zinc deficiency caused negative scaling trajectory, and AMF was associated with positive root–shoot scaling trajectory in the −Zn+P treatment, though the scaling relationship was not confirmed by SMA analysis. These findings highlight nutrient specific AMF-mediated growth dynamics in early vegetative stage. Full article

Regarding the path planning problem for the local leveling operation of the land leveler, this paper proposes a path planning method based on the improved dung beetle optimizer (IDBO) algorithm. Firstly, a comprehensive evaluation objective function was established for the local operation path planning of the land leveler, which included the path length, under-excavation amount, under-filling amount, as well as the total amount of excavated and filled soil. Then, IDBO algorithm was constructed, an initialization population strategy based on Fuch chaotic mapping and reverse learning strategy was designed, as well as an improved ball-rolling behavior that integrates the search strategy of the Aquila high soar with the vertical stoop from the Aquila optimizer algorithm. Test functions were used to verify the superiority of the IDBO algorithm compared to the dung beetle optimizer (DBO) algorithm, the particle swarm optimization (PSO) algorithm and the gray wolf optimizer (GWO) algorithm. Finally, taking the paddy fields in a real environment as the object, a hardware platform for data acquisition was constructed, and data collection, analysis, terrain modeling, and path planning experiments were carried out with paddy fields in the natural environment as the measured objects. The experimental results show that, for the primary optimization objective of load variation cost, as well as path length cost, compared with the other three algorithms (PSO, GWO, DBO), the IDBO algorithm achieved improvements of 7.0%, 12.3%, and 6.6% on Plot 1, 1.6%, 9.2%, and 1.6% on Plot 2, 4.0%, 6.1%, and 1.5% on Plot 3, and 3.3%, 24.1%, and 3.4% on Plot 4. Full article

Optimizing the interaction between planting density and nitrogen (N) application rate is critical for simultaneously improving grain yield and nitrogen use efficiency (NUE) in winter wheat (Triticum aestivum L.). However, the underlying regulatory mechanism remains poorly understood in the fluvo-aquic soil region of the southern Huang–Huai–Hai Plain. This study aimed to elucidate the physiological mechanism by which planting density and nitrogen application interactively regulate source–sink coordination to achieve synergistic high grain yield and high NUE, and to screen the optimal local cultivation combination for winter wheat in southeastern Henan. A two-year consecutive field experiment was conducted from 2018 to 2020 in Shangshui, Henan, using a split-plot design. Three planting densities (D1: 225 × 10⁴ plants ha⁻¹; D2: 375 × 10⁴ plants ha⁻¹; D3: 525 × 10⁴ plants ha⁻¹) and five N rates (N0: 0; N1: 180; N2: 240; N3: 300; N4: 360 kg N ha⁻¹) were established. Results demonstrated that planting density, N rate, and their interaction significantly regulated grain yield, NUE, and dry matter and N allocation, with consistent trends across both years. Increasing density enhanced total biomass and N accumulation, but dry matter and N partitioning to grains declined when density exceeded 375 × 10⁴ plants ha⁻¹. Grain yield exhibited a quadratic response to N rate; the optimal N rate for maximum yield decreased from 296.33 kg ha⁻¹ at low density (D1) to 237.50–245.38 kg ha⁻¹ at medium and high densities. The combination of 240 kg N ha⁻¹ and 375 × 10⁴ plants ha⁻¹ (D2N2) produced the highest average grain yield (8875.35 kg ha⁻¹), with simultaneous improvements in spike number and kernels per spike as well as superior dry matter and N partitioning to grains. This combination also maintained high nitrogen recovery efficiency (NRE) and nitrogen agronomic efficiency (NAE). Correlation analysis revealed that grain yield and NUE were significantly positively correlated with dry matter accumulation, N accumulation, and their partitioning proportions to grains. Overall, D2N2 achieved simultaneous high yield and high NUE by coordinately optimizing dry matter and N partitioning to grains. We therefore recommend reducing N fertilizer to approximately 240 kg ha⁻¹ combined with a moderate planting density of 375 × 10⁴ plants ha⁻¹ as the preferred strategy for sustainable and intensive winter wheat production in the fluvo-aquic soil region of southeastern Henan and adjacent areas. Full article

This study aimed to determine the optimal biochar application rate for sustaining cotton productivity in moderately saline soils under dry sowing with wet emergence (DSWE) conditions in Shaya County, Xinjiang. A two-year field experiment, arranged in a randomized complete block design with two replicates, evaluated six biochar application rates (S1–S6) against a non-amended control (CK). The biochar, derived from fruit-wood via limited-oxygen pyrolysis at 500 °C (pH 9.82, porosity 64.5%), was applied as a single pre-sowing amendment. Soil water–salt dynamics, crop emergence, and growth parameters were continuously monitored. The results indicated that biochar application consistently reduced soil salinity; specifically, seedling-stage salinity decreased by 30.1–42.2% in the first year compared with the CK. Cotton emergence and yield improved significantly across both seasons. However, the optimal application rate for maximizing yield varied between years. While a high rate (S5: 25 t·hm⁻²) produced the highest first-year yield (6243.8 kg·hm⁻²), a moderate rate (S3: 15 t·hm⁻²) demonstrated greater yield stability and achieved the maximum yield (5975.2 kg·hm⁻²) in the second year. This interannual shift is likely attributable to biochar aging and structural pore saturation in the high-dose plots. Combined with high regional evaporation, these factors exacerbated secondary salinization and reduced the residual benefits of the amendment over time. In contrast, the moderate dose maintained a more effective balance between continuous water–salt regulation and nutrient availability. Under the experimental conditions, a single pre-sowing application of 15 t·hm⁻² biochar, combined with a 375 m³·hm⁻² drip irrigation volume, is recommended as an effective strategy to ameliorate salinity and support long-term yield stability. Full article

This study examined the effect of east–west orientation of willow tree (Salix alba L.) rows on soil biological activity and weed dynamics in a temperate maize (Zea mays L.) intercropped agroforestry (AF) system in Eastern Hungary. The experiment evaluated how the year (2022, 2023), location (distance from the rows), and irrigation (IR) influenced spatial patterns of earthworm (EW) parameters and weed cover. The study aimed to assess how willow-based AF systems influence soil biological and weed community dynamics under varying IR and row spacing, in comparison with monoculture cropland (MC) systems, and to evaluate their potential role in climate change adaptation in arable farming. Both soil sampling for the EW survey and vegetation studies were conducted along perpendicular transects extending from the tree rows to measure EW abundance and biomass, as well as total weed cover. Experimental results revealed clear spatial gradients in EW distribution and weed abundance near the tree rows, driven by litter input, shading, moisture, and reduced disturbance. These effects were intensified under IR at narrower row spacings. No significant differences were observed between AF-South (shaded), AF-Center, and MC plots; however, significantly higher EW abundance and biomass were found on the AF-North (sunny) side. As for the location, significantly greater total EW abundance was found at AF-North (105.0 individual m⁻²) compared with the MC plots. AF systems enhance soil biological activity and shape weed dynamics through spatial ecological gradients influenced by tree row spacing and irrigation, supporting their role as sustainable land-use systems while emphasizing the need for site-specific management and further long-term optimization. Full article

This study addresses the challenge of modeling the complex non-linear relationship between process parameters and relative density in selective laser melting (SLM) of IN718 nickel-based superalloy under small-sample conditions. A data-driven prediction framework integrating data augmentation, physics-informed feature engineering, machine learning, and model interpretability analysis was developed and systematically validated. Fourteen sets of experimental data covering both vertical and horizontal building directions were collected by varying laser power (P), scan speed (v), and hatch spacing (h). To overcome the small-sample limitation, three augmentation strategies—radial basis function (RBF) interpolation, generative adversarial network (GAN), and K-nearest neighbors (KNN)—were systematically compared under unified physical constraints combining local perturbation and volumetric energy density (E_vol) filtering, with Pearson correlation coefficient consistency used to select the optimal strategy. Eight physically meaningful input features were constructed, including E_vol and line energy density (E_line), explicitly embedding SLM process physics into the learning framework. Support vector regression (SVR), random forest (RF), and artificial neural network (ANN) models were trained and their hyperparameters were systematically optimized via exhaustive grid search combined with leave-one-out cross-validation (LOO-CV), ensuring robust model selection under small-sample constraints. A physics-based baseline model (E_vol quadratic fitting, LOO-CV average R² = 0.2534) was established to quantify the gain of machine learning over empirical formulas. LOO-CV results show that ANN achieves the highest average R² of 0.9269, followed by SVR (0.9148) and RF (0.8393), all of which substantially outperform the physical baseline. Feature importance analysis reveals that E_vol accounts for 51.58% of the predictive power, and ablation experiments confirm that introducing physics-derived features improves the average R² by 0.0246 compared with raw process parameters alone. To further elucidate the predictive mechanism of the optimal ANN model, Partial Dependence Plot (PDP) analysis was conducted for all eight input features, visualizing their marginal effects on predicted density and confirming physical consistency with SLM mechanisms. This framework provides a reliable, interpretable, data-driven solution for intelligent SLM process optimization with limited experimental data. Full article

Water limitations in the Brazilian semi-arid region require saline water utilization. Hydroponic cultivation combined with salicylic acid (SA) elicitation represents a strategy to manage salt stress in cherry tomatoes. This study evaluated the effects of foliar SA application on the production and quality of cherry tomatoes under saline nutrient solutions. An NFT hydroponic greenhouse experiment at UFCG, Pombal, Brazil, evaluated five nutrient solution salinities (ECns: 2.1, 2.6, 3.1, 3.6, and 4.1 dS m⁻¹) and five SA concentrations (0, 0.8, 1.6, 2.4, and 3.2 mM) in a split-plot design with three replications. SA concentrations from 1.3 to 3.2 mM enhanced fruit diameter, fruit number, average weight, and yield under baseline salinity (2.1 dS m⁻¹). At 3.2 mM, SA functioned as an optimal ratio regulating nutritional quality, increasing titratable acidity and ascorbic acid under 2.1 and 2.6 dS m⁻¹, respectively. Conversely, high salinity (4.1 dS m⁻¹) established a promotion pattern on soluble solids, maturity index, and flavonoids, while reducing yield components by up to 58.3%, demonstrating explicit operational limitations of SA under severe stress. These baseline findings validate the applicability of SA within specific salinity thresholds, establishing a foundational framework for subsequent physiological profiling, fruit quality characterization at harvest, and commercial greenhouse upscale validation. Full article