Search Results (6,130)

To address the inefficiencies of two-stage sweet potato harvesting in southern China, an integrated machine for synchronous haulm shredding and tuber excavation was developed. The equipment features a front-mounted, reverse-rotating crushing knife roller and a rear-mounted, adjustable grate-type digging shovel. The performance of the prototype was systematically evaluated through two-stage field trials in clay loam soil. First, an orthogonal test was employed to assess the haulm shredding quality. The results indicated that the knife roller speed significantly increased the qualified rate of crushed stems and leaves, whereas the forward speed exerted a negative effect. Additionally, the blade-to-ridge clearance primarily dictated the ridge-top stubble length. Second, a quadratic orthogonal rotational composite design was utilized to optimize the integrated harvesting parameters. The analysis demonstrated that shovel inclination significantly enhanced the tuber exposure rate, while both clearance and inclination exhibited quadratic nonlinear effects on the tuber damage rate. Multi-objective optimization established the optimal operational parameters as a blade-to-ridge clearance of 66.6 mm and a shovel inclination of 34.0°. Field validations under these settings achieved a tuber exposure rate of 83.7% and a damage rate of 4.3%, confirming the high reliability of the predictive models. The integrated equipment effectively shortens the harvesting cycle and demonstrates robust adaptability to clayey moist soils, thereby advancing the mechanization of sweet potato production. Full article

The synergistic regulatory mechanism of current planting patterns on cotton agronomic traits, boll source–sink development and fiber quality remains unclear. Therefore, this study used 11 upland cotton parents and 36 F1 progenies as materials. Three planting patterns, namely, one film–three rows, one film–four rows and one film–six rows, were established from 2024 to 2025. The dynamic changes in the boll morphology and dry-fresh-weight accumulation of each boll component were monitored at eight stages from 10 to 60 days after anthesis, and agronomic traits and fiber quality were measured simultaneously. The Logistic growth model was adopted to fit cotton boll growth parameters to analyze the regulatory effects of planting patterns on the developmental process and dry-matter accumulation of upland cotton. The results showed that boll morphological stability gradually decreased with the increase in planting density. The maximum cumulative boll volume (Wm) under the one-film–three-rows pattern ranged from 29.19 to 30.97 cm³, higher than those under one film–four rows (28.35–30.77 cm³) and one film–six rows (28.74–30.86 cm³). Vegetative growth indicators, including plant height, height of the first fruiting node and number of fruiting branches, declined with increasing density. The one-film–six-rows treatment exhibited a significantly higher proportion of empty fruiting branches and a lower boll-setting rate. The one-film–three-rows pattern presented prominent single-boll sink capacity, yet suffered insufficient population photosynthetic supply, leading to assimilate allocation biased toward vegetative organs. Under high-density competition stress, the one-film–six-rows pattern possessed poor source–sink stability and inhibited dry-matter accumulation in reproductive organs. The one-film–four-rows pattern achieved the highest dry-matter translocation efficiency from boll shell to cottonseed and fibers, with the maximum cumulative fiber dry weight (Wm) of 2.40–2.62 g, and obtained optimal fresh-weight accumulation and allocation of fibers. Synthesizing all indicators, the one-film–four-rows pattern is more suitable for cotton growth in southern Xinjiang, which can balance population boll-setting potential and boll dry-matter allocation efficiency. These results are only derived from two-year field-located experiments in southern Xinjiang. Multi-site trials are required in further research to clarify the applicable scope of this cultivation pattern in other cotton-producing regions. Full article

To further reveal the degradation mechanism of epoxy coatings under intense ultraviolet radiation in high-altitude environments and clarify its influence on their interfacial adhesion performance, an epoxy coating-cement mortar system was investigated. Ultraviolet (UV) aging tests were conducted, together with attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), surface free energy (SFE) measurements, atomic force microscopy (AFM)-based nano-adhesion force measurements, scanning electron microscopy and energy-dispersive X-ray spectroscopy (SEM/EDS), uniaxial tensile tests, and pull-off adhesion strength tests to investigate the evolution of the coating’s characteristic molecular structure, surface polarity, nano-adhesion, coating toughness, macroscopic adhesion performance, and interfacial failure modes at different aging stages. The results showed that the peaks associated with hydroxyl and carbonyl groups in the epoxy coating intensified with increasing UV aging duration. The surface free energy of the coating increased, and its polar component reached 3.9 times the initial value. After 28 d of UV aging, the nano-adhesion force of the coating decreased by 30.1%, its toughness decreased from 2.82 ± 0.052 to 1.21 ± 0.027 MJ·m⁻³, and the adhesion strength between the epoxy coating and the cementitious substrate decreased by 15.7%. In addition, as aging progressed, the failure path gradually shifted from fracture near the substrate surface toward regions near the coating–cementitious substrate interface and within the coating. Correlation analysis further showed that the decreases in coating toughness and nano-adhesion performance were closely associated with the deterioration of macroscopic adhesion strength. Therefore, greater attention should be paid to the optimization of these two properties in practical applications. Full article

Multiphase pumps are key equipment for the efficient transport of multiphase fluids in the petroleum industry, and their transient stability under variable-speed conditions directly affects system reliability. By combining numerical simulation with experimental validation, this study systematically investigates the evolution of external characteristics, energy conversion mechanisms, and the dynamic response of the internal flow field during a 0.4 s variable-frequency speed regulation cycle at inlet gas volume fractions (IGVFs) of 10% and 20%. The numerical model was validated against experimental measurements of a four-stage multiphase pump under pure-water steady-state conditions, with deviations in head, efficiency, and power all within 5%. The results show that during acceleration, the increase in hydraulic efficiency at the lower IGVF is greater than that at the higher IGVF; once deceleration begins, IGVF has no significant effect on hydraulic efficiency. At the investigated IGVFs of 10% and 20%, a higher IGVF increases the transient sensitivity of the internal flow field to speed variation, and increasing IGVF suppresses energy conversion in the impeller. The principal novelty of this work lies in the temporal decomposition of impeller work into dynamic and static pressure components during transient speed variation, revealing that static pressure power consistently accounts for more than 50% of the total power throughout the speed regulation cycle. As rotational speed increases, dynamic pressure power rises because the circumferential velocity of the fluid increases with impeller peripheral speed, while static pressure power also increases continuously owing to the enhanced static pressure work of the blades. During deceleration, the impeller’s energy transfer capability weakens with decreasing rotational speed, and both dynamic and static pressure power decline. These findings elucidate the coupled evolution of gas–liquid two-phase flow under variable-speed conditions and provide a theoretical basis for the operational optimization and speed control of multiphase pumps. Full article

The compressive strength of 3D-printed concrete (3DPC) varies with the loading direction, and the lowest-strength direction is not fixed across printing groups. Testing in a limited number of directions may miss the governing direction and bias the acceptance decision toward false acceptance. This study used the RILEM TC 304-ADC interlaboratory database comprising 27 laboratories, 34 printing groups, and 907 directional compressive test specimens. Exact sampling-without-replacement enumeration and leave-one-laboratory-out (LOLO) cross-validation were used to evaluate the relationships among directional coverage, false acceptance, and specimen consumption. The observed governing directions of the 34 printing groups were distributed across the U, V, and W directions. At an operational threshold of 40 MPa, the observed false-acceptance rates of the single-direction V3 and two-direction VW6 strategies were 13.7% and 10.6%, respectively, compared with 4.9% for the complete-direction UVW9 baseline. The early-acceptance strategy reduced the expected specimen count from 9.00 to 4.97, a reduction of 44.8%, but the observed false-acceptance rate increased from 4.93% to 9.15%. The risk difference was 4.22 percentage points, exceeding the 2.5-percentage-point screening criterion. Under the restricted-direction strategy, partial-direction results could trigger only early rejection, and final acceptance required complete U, V, and W directional evidence; the strategy therefore produced no acceptance decisions beyond those of the UVW9 baseline. In the present evaluation set, the restricted-direction strategy yielded group-by-group decisions identical to those of the UVW9 baseline, with an expected specimen count of 6.49, a reduction of 27.9% (95% interval 16.8% to 39.9%). Adaptive direction ordering saved 0.47 specimens relative to the original fixed order, and a simple fixed order that tests the most frequently governing direction first achieved the same saving; the overall savings arose primarily from printing groups that did not meet the strength requirement. Overall, the results show that cross-directional historical information can optimize the testing order and identify nonconforming printing groups earlier, whereas final acceptance should continue to require complete directional evidence. In production settings where staged specimen preparation and testing are feasible, this procedure could reduce the cost of destructive testing and bring forward the review and process correction of anomalous printing groups. Full article

Seed dormancy is an important adaptive mechanism in plants. However, some seeds, such as those of Xanthoceras sorbifolium (a valuable economic and medicinal species), exhibit strong dormancy, resulting in a very low natural germination rate. This work investigated the mechanism by which low temperatures (LT: −20 °C storage for 60 days) release seed dormancy and promote germination. This was achieved by examining seed germination conditions, applying scanning electron microscopy (SEM), and measuring physiological indicators of seeds and the hormone levels. Targeted metabolomic analysis of sugar and fatty acid metabolism was also performed. The results were as follows: (1) A high germination rate of 47.3% was observed under LT condition, compared to 32.7% at room temperature (RT: 25 °C). Of four germination methods, direct GMS (germination in moist sand) was the most effective. (2) The two stages of VI (after storage) and VII (on the 7th day of germination) were key stages to breaking the seed dormancy and triggering germination. At both stages, the high integrity of the seed shells and kernels—particularly, the kernels—was observed using SEM. The activities of SOD, CAT, and POD, as well as the levels of IAA and IPA, and the ratios of IAA/ABA and (IAA + GA3 + ZR + IPA)/ABA (tHor/ABA) were found to be higher, especially in stage VII, where tHor/ABA increased by 62.7%, while ABA decreased by 23.2% in the LT treatment compared to the RT treatment. An optimal germination condition (GMS) created a suitable microenvironment, and this could have maintained highly active antioxidant enzymes and kept H₂O₂ (one of reactive oxygen species, or ROS) within signal transduction levels, and cross-talk to hormones. These changes (enzymes, hormones, ROS, and microenvironment) ensured the seeds reaching an optimal state for dormancy release in the VI stage, and facilitated the seed germination in the VII stage. (3) LT treatment promoted the degradation of starch and fats in the seeds. Significant accumulations of eight soluble sugars, such as glucose, D-fructose, and trehalose, as well as three fatty acids, such as Cis-11,14,17-eicosatrienoic acid (C20-3n3) and γ-linolenic acid (C18-3n6), were observed, while two soluble sugars and one fatty acid decreased under LT conditions. In conclusion, low temperature, as an external signal, together with ROS and GA-ABA-IAA co-regulated the seed dormancy release and germination. The moist-sand microenvironment was also a key factor in awakening the embryos of X. sorbifolium seeds. Full article

Thiol-modified attapulgite (TM) combined with manganese sulfate (MnSO₄) can reduce cadmium (Cd) availability in alkaline soils, but the influence of MnSO₄ application timing on Cd transfer to wheat grain remains unclear. In this study, a soil incubation experiment and two wheat pot experiments were conducted to evaluate soil Cd immobilization, organ-specific Cd distribution, and the effects of applying MnSO₄ before sowing, at the jointing stage, or at the grain-filling stage. Low- and high-dose MnSO₄ treatments were applied alone or with TM, and the combined treatments were defined as TM+LS and TM+HS. Compared with the untreated control, TM+LS and TM+HS decreased dissolved Cd from 1.80 μg L⁻¹ to 0.35 and 0.20 μg L⁻¹, respectively, and reduced grain Cd from 0.19 mg kg⁻¹ to 0.08 and 0.06 mg kg⁻¹. These reductions were associated with decreased DTPA-extractable Cd, enhanced Cd adsorption, lower Cd desorption, and restricted Cd transfer from roots to shoots and from glumes to grains. Jointing-stage MnSO₄ application produced the lowest grain Cd concentration and glume-to-grain transport coefficient. These findings indicate that optimizing MnSO₄ application timing can improve TM-assisted Cd immobilization and wheat grain safety in alkaline Cd-contaminated soil. Full article

Multi-view clustering aims to discover consistent cluster structures from heterogeneous features without supervision. Anchor-based methods improve scalability by representing samples through a compact set of anchors, but fixed anchors may be misaligned with the evolving cluster geometry. This mismatch is the main problem addressed here: the shared sample–anchor graph and the global anchor geometry need to be refined together across heterogeneous views, rather than in two disconnected stages. This paper proposes Learnable Anchor Graph Network (LAG-Net), a deep unfolding framework that jointly learns a shared anchor graph, view-specific anchor indicators, and global anchor alignment within a unified model. The global anchor alignment provides geometric guidance for shared anchor graph learning and promotes anchor consistency across heterogeneous views. By unfolding the derived optimization procedure into a trainable network, LAG-Net enables the anchor representations and sample–anchor relationships to be progressively refined. Experiments on six benchmark datasets demonstrate the effectiveness and scalability of the proposed method compared with representative multi-view clustering approaches. Full article

Agrivoltaic systems can improve renewable energy generation, water management, and agricultural productivity. This study proposes an artificial intelligence-assisted energy and water management framework integrating photovoltaic (PV) generation, battery energy storage systems, groundwater-fed irrigation, water storage, bidirectional grid interaction, and electric vehicle charging. The framework consists of two stages: day-ahead PV forecasting using a Bayesian optimization-based long short-term memory model, followed by mixed-integer linear programming for daily operating cost minimization under electrical, hydraulic, battery, soil-moisture, water-storage, and grid constraints. Agrivoltaic microclimate-related influences on evapotranspiration and precipitation transmission are represented in the soil moisture dynamics through literature-based coefficients. Six forecasting model families are evaluated in 21 configurations over 316 daily forecast origins. The selected model achieves a mean absolute error of 0.268 MW, equal to 4.37% of plant capacity, a weighted mean absolute percentage error of 14.5%, and R² = 0.915, reducing persistence baseline error by 42.0%. Applied to a five-decare tomato-based system in Antalya, Türkiye, under eight operating scenarios, the framework achieves a minimum daily operating cost of EUR −31.321. It also maintains soil-water and storage tank levels within prescribed limits and provides up to 300 kW continuous grid support under emergency conditions while satisfying local agricultural and electrical demands. Full article

Growing air traffic and dynamic missions require conflict detection and resolution (CD&R) during the execution phase, when ad hoc airspace must be inserted into an already conflict-free baseline plan without global re-optimization. This paper proposes a grid-based online optimization framework built on Geographical Coordinate Subdivision grid with One dimension integer coding on 2ⁿ-tree (GeoSOT) discretization that transforms four-dimensional spatiotemporal conflict judgment into efficient grid-code matching and interval comparison. Incremental conflict detection restricts pairwise checks to candidate ad hoc-related pairs, reducing detection scale by over 99% relative to full screening. A lexicographic two-stage resolution policy prioritizes ad hoc adjustments—incorporating horizontal, altitude, temporal, and grid-shrinkage operations—and activates limited baseline coordination only when necessary. The The Incremental Ad-hoc Operation—Tiered Priority Time-Sliced Search (IAO-TPTS) algorithm implements this policy under a hard time budget through Phase A (ad-hoc-restricted Dimension-wise Conflict-Driven Assignment, DCDA-Lite) for fast ad hoc-only feasibilization and Phase B (Hybrid Adaptive Large Neighborhood Search, Hybrid-ALNS) for tiered refinement, with dual validation to prevent secondary conflicts in neighboring airspace. Experiments including visualization, ablation, algorithm comparison, and scalability analysis on Small, Medium, and Large scenarios show 100% feasibility within 180 s, median solve times as low as 0.069 s, competitive objective values versus mixed-integer linear programming (MILP) and Adaptive Large Neighborhood Search (ALNS), and sub-linear scalability from 20 to 300 baseline airspaces. The novelty is this integrated execution-phase framework (incremental detection, lexicographic baseline-protective scheduling, and time-budgeted IAO-TPTS with dual validation), rather than a new grid-coding scheme or a standalone MILP. Full article

Drive systems for robot joints must provide a high gear ratio within limited axial space. When two magnetic gears are axially stacked to form a two-stage transmission, the axial lengths of the individual stages accumulate. This paper therefore proposes a magnetically geared machine (MGM) comprising a radial-flux magnetic gear and an axial-flux magnetic gear. The permanent-magnet synchronous motor and the radial-flux magnetic gear occupy the inner space of the axial-flux magnetic gear, while shared rotors connect the three electromagnetic components. For this topology, the magnetic field modulation and torque relationships are derived, and the main design parameters are determined through two-stage optimization and three-dimensional transient finite-element analysis. The results show that the air gaps of both magnetic gears contain the required working harmonics and that the steady-state torques of the three rotors follow the two-stage transmission relationship. The optimized design achieves an overall gear ratio of 84.64 and a maximum transferable torque of $1098.97$ N m. At this operating point, the volumetric torque density based on the overall cylindrical envelope volume is $328.00$ N m L⁻¹. Full article

Continuous monocropping of tomato causes soil degradation, secondary salinization, and yield decline. Mushroom–vegetable rotation coupled with spent mushroom substrate (SMS) incorporation offers a recycling strategy to mitigate these obstacles. Here, a five-stage progressive field experiment (2023–2025, Yantai, Shandong, China) was conducted to screen the optimal local configuration of Stropharia rugosoannulata–tomato rotation and reveal its soil improvement mechanisms. The combination of variety Nieyang and an apple woodchip-based substrate achieved the highest mushroom yield, with the substrate formula (F = 11.64, p = 0.0006) dominating productivity. Deep incorporation of SMS into the 0–20 cm plow layer (M treatment) avoided the seedling stress and mortality caused by surface mulching and increased the marketable yield of the large-fruited tomato R35 by 28.0% (79,560 kg·ha⁻¹, p < 0.05) without altering fruit soluble solids (p = 0.611). Two consecutive rotation years increased soil organic carbon by 17.6% (from 3.29 to 3.87 g·kg⁻¹) and total nitrogen by 243% (from 0.79 to 2.71 g·kg⁻¹) and raised soil desalination efficiency from 33.5% to 56.2%, while soil EC and pH remained within the optimal range for tomato growth. Cross-regional verification showed universal regulation of soil pH and EC but background-dependent nutrient accumulation. The system improves soil fertility through mycelium-mediated biological desalination, progressive SMS-derived carbon pool accumulation, and complementary acid–base homeostasis, generating a net annual economic benefit of approximately 255,000 CNY·ha⁻¹ (≈38,060 USD·ha⁻¹). This recyclable rotation pattern is suitable for popularization in Jiaodong facility-grown tomato production. Full article

Renewable energy scenarios are widely used for power system stochastic optimization and risk evaluation, yet massive redundant scenarios boost computational complexity, waste resources and destabilize results, necessitating scenario reduction. This paper proposes an improved two-stage clustering method to overcome the manual parameter tuning defects of conventional clustering-based reduction. Specifically, the Snow Ablation Optimizer is embedded into DBSCAN to auto-adjust core hyperparameters, realizing efficient initial scenario reduction and suppressing the interference of abnormal data. Afterwards, K-means optimized via the Calinski–Harabasz index is adopted for secondary reduction to adaptively identify optimal cluster numbers and enhance the representativeness of reserved scenarios. Basic comparative simulations validate its technical superiority: handling 5000 raw scenarios only takes around 2 min, with the Wasserstein distance decreased by 7.73% and 17.79% versus backward reduction and forward selection, while the silhouette coefficient rises by 12.73% and Davies–Bouldin index drops by 7.95% compared with classic K-means. Further stochastic unit commitment tests quantify tangible economic and low-carbon gains in day-ahead scheduling, and empirical coefficient-based scaling analysis extends these benefits to long-term grid operation and policy deployment for TSOs/DSOs. The integrated results confirm the method’s technical, economic and sustainable merits, delivering actionable quantitative support for high-renewable power system low-carbon planning and energy policy formulation. Full article

‘Nagami’ kumquat (Fortunella margarita Swingle), a citrus fruit cultivated in Corfu, Greece, contains bioactive constituents whose levels may vary during fruit maturation. This study investigated changes in the physicochemical composition and bioactive potential of Corfu ‘Nagami’ kumquat during the commercial harvesting period (December–March) and optimized ultrasound-assisted extraction conditions for standardized comparisons among harvest stages. Fruits collected during the study period were analyzed for moisture, soluble sugars, proteins, lipids, total phenolic content, and free-radical scavenging capacity. Response surface methodology was used to optimize methanol concentration, extraction temperature, and extraction time, while principal component analysis (PCA) was applied to evaluate patterns associated with fruit maturation. During maturation, moisture and protein contents decreased, whereas lipid content increased. The optimized extraction conditions were 14% methanol, 45 min, and 54 °C. PCA showed clear separation of early- and late-harvest-stage samples, with the first two principal components explaining 97.9% of the total variability. The observed differentiation was primarily associated with DPPH scavenging, moisture, proteins, lipids, soluble sugars, and phenolic content. Late-harvest-stage fruits exhibited the highest free-radical scavenging capacity and phenolic content, with harvest stage related to variations in the bioactive potential of Corfu ‘Nagami’ kumquat. Full article

Ordered-risk assessment is a safety-critical learning problem in which delayed or biased risk estimation can affect prioritization and response planning. Existing expert-knowledge and data-driven approaches face a trade-off among transparent indicator design, nonlinear representation capability, and stable optimization when reliable labeled data are scarce. To address this gap, we developed a two-stage adaptive differential evolution-optimized extreme learning machine framework (TA-DE-ELM) for six-level ordered-risk assessment and evaluated it in a controlled physics-informed synthetic simulation benchmark. The benchmark encodes kinematic, capability, sensing/interference, and resilience priors as explicit scoring rules for model evaluation, rather than as an application simulator. The method combines transparent risk-logic specification, a stage-wise exploration–refinement optimizer with stagnation-triggered restart, and a validation objective that jointly considers cross-entropy, macro-F1, ordinal error, and accuracy. Under a unified finite budget, TA-DE-ELM ranked first among all tested ELM-family baselines for accuracy, macro-F1, quadratic weighted kappa, and ordinal mean absolute error, with paired tests indicating improvements over the closest competitor ($p<0.013$). These results show that TA-DE-ELM can recover an expert-rule-induced ordered-risk mapping more effectively than the tested baselines under controlled finite-sample conditions. Further validation with higher-fidelity simulators, externally collected datasets, and richer temporal perturbation protocols remains necessary before application-specific use. Full article