Search Results (13,067)

In order to improve the wear resistance of titanium alloy and apply it to the high-speed train brake disc, TiNbZrV_x (x = 0, 0.2, 0.4, 0.6, 0.8) refractory medium-entropy alloy coatings were prepared on Ti-6Al-4V (TC4) substrate. The effect of V content on the microstructure, mechanical properties, and friction and wear properties of the coatings was studied. TiNbZrV_x coatings achieved good metallurgical bonding with the substrate, forming BCC and B2 phases and AlZr₃ intermetallic compound (IMC). From TiNbZr coating to TiNbZrV_0.8 coating, V promotes element segregation and new phase formation, which decreased the average grain size from 85.055 μm to 56.515 μm, increased the average hardness from 265.5 HV to 343.4 HV, and reduced the room temperature (RT) wear rate by 97.8%. However, the ductility of the coatings decreased from 15.7% to 5.8% because the grain boundary precipitates changed the dislocation arrangement, and the tensile fracture mode changed from ductile fracture to brittle fracture. Abrasive wear was the main wear mode at RT, and adhesive wear and oxidation wear were the main wear modes at elevated temperature. The COF at elevated temperature was lower than that at RT, because a large number of friction pair components were transferred to the coating surface at high temperature and were repeatedly rolled to form a dense film, which played a certain lubricating role. Full article

To meet the requirements of non-mechanical beam scanning and acquisition in space laser communication, this study proposes a two-dimensional scanning and acquisition method based on a silicon-based optical phased array (OPA). The OPA utilizes thermo-optic phase modulation to achieve horizontal beam pointing, while vertical beam pointing is controlled by wavelength tuning. By combining the OPA with a rectangular spiral scanning strategy, non-mechanical scanning is realized and beam acquisition experiments are carried out. Experimental results demonstrate that for an 8° step signal, the horizontal and vertical rise times are 156.8 μs and 214.76 ms, respectively. A full scan of 440 points covering a ±4° field of view is completed in 8.119 s. Acquisition experiments were conducted assuming a Gaussian-distributed uncertainty region (standard deviation σ = 1°). Out of 106 independent trials, a success rate of 97.17% was achieved with an average acquisition time of 0.41 s. This work experimentally applies a rectangular spiral scanning strategy to an OPA-based acquisition system, addressing a capability that has been largely missing in previous studies. These results verify that the OPA technology has good scanning efficiency and acquisition robustness in space laser communication applications. Full article

Waterlogging stress severely limits crop photosynthesis and energy supplies, resulting in significant yield reductions. However, the critical duration of waterlogging stress during the maize jointing stage remains unclear, and the physiological and molecular mechanisms underlying its effects on photosynthetic efficiency and energy synthesis in maize require further investigation. In this study, we systematically analyzed the responses of physiological traits, transcriptomic profiles, and the yield formation in maize (Zea mays L.) to varying waterlogging durations imposed during the jointing stage, including 0 days (CK), 2 days (F2), 4 days (F4), 6 days (F6), 8 days (F8), and 10 days (F10). Our results indicate that the (1) grain weight (GW) showed no significant difference between F2 and CK. However, the GW in F4, F6, F8, and F10 decreased significantly by 17.49%, 26.45%, 60.24%, and 100.00%, respectively, compared to the CK. (2) Compared with the CK, the malondialdehyde content progressively increased from F4 to F10, while antioxidant enzyme activity gradually decreased. The chlorophyll content declined by 29.93% to 57.38%, and net photosynthetic efficiency decreased by 13.82% to 38.93%. Although the leaf sucrose content in from F4 to F10 gradually decreased, the leaf starch content remained stable in F4 and F6. In contrast, the starch content in F8 and F10 leaves was significantly reduced by 37.55% and 47.60%, respectively, compared with CK. (3) A transcriptomic analysis revealed that during from F2 to F4, genes encoding photosystem I subunit protein, such as PSAD, and the cytochrome b6f complex proteingene PETC were downregulated. At F6, these key genes encoding photosynthetic proteins were upregulated. However, at F8 and F10, their expression was significantly downregulated. Concurrently, genes related to ATP synthesis (e.g., ATPD) as well as starch and sucrose metabolism (e.g., SPP2, SS1) were also downregulated. In summary, when waterlogging stress persists for no longer than 6 days, plants can maintain their starch content to supply energy for growth, thereby ensuring basic developmental needs. When waterlogging persists for more than 6 days, energy synthesis is impaired, and the nutrient transport to the grains is significantly inhibited, ultimately resulting in a substantial reduction in yield. Therefore, 6 days of waterlogging can be considered the critical threshold for significant yield loss in maize during the jointing stage. Full article

Under the background of accelerating global transitions towards low-carbon development, digital intelligence transformation (DIT) has become a critical force that helps companies overcome green technological constraints and translate external green pressures into substantive green innovation. Taking the establishment of China’s NAIIDTZs as a quasi-natural experiment, this study investigates the impact of DIT on corporate green innovation (CGI) from an internal decision-making perspective. Based on a panel dataset of 19,440 samples from Chinese A-share listed companies during 2012–2023, our findings show that DIT significantly enhances both the quantity and quality of CGI. Mechanism analyses indicate that DIT promotes CGI’s quantity through increased R&D human capital input, while improving CGI’s quality through managerial myopia reduction. Heterogeneity analyses further reveal that the positive effects of DIT on CGI are particularly pronounced in firms operating under fierce market competition, in high industrial technological intensity, and in eastern regions. Furthermore, we find that CGI exerts a lagged effect on carbon emission reduction performance, while the effect of CGI’s quality is stronger than that of CGI’s quantity. These findings extend the dynamic capacity theory to digitalization and provide practical and policy implications for promoting CGI through digital intelligence development. Full article

To solve the problems of high resistance and blockage in stubble-breaking operations, it is necessary to reveal the interaction mechanism between disc coulters and crop root–soil composites. This study developed a discrete element method–multi-body dynamics (DEM-MBD) coupling model of the stubble-breaking operation and verified the accuracy of the model through soil bin tests (error < 20%) and field experiments (error < 32%). The model was used to investigate the effects of different design parameters (coulter type and disc radius) and operating parameters (tillage speed and depth) on the stubble-breaking operation. The results showed that due to the significant strengthening effect of roots on soil, the resistance of disc coulter stubble-breaking operation was high; the number of roots in contact with the blade edge and the amount of root deformation significantly affected the resistance of the disc coulter; irreversible deformation of roots and soil could easily lead to the holes and root hairpin effects in the seeding furrow; compared to plain disc coulters, the difference in the time of deformation and fracture of the roots made the resistance of the notched coulter lower. The wavy disc coulter with a longer edge curve made its resistance higher; the disc coulter with a greater radius, higher tillage speed, and deeper tillage depth significantly increased the tillage resistance. However, the disc coulter with a greater radius or a higher tillage speed was beneficial for improving stubble-breaking performance. This study revealed the interaction mechanism between disc coulters and maize root-soil composites, providing a theoretical basis for the optimization design of no-till stubble-breaking devices. Full article

The objective of the current research was to evaluate the influence of dietary tributyrin (TB) inclusion on the growth performance and physiological well-being of Hexagrammos otakii. Juvenile fish (initial weight 24.01 ± 1.35 g) were fed one of six isonitrogenous and isolipidic diets containing graded levels of TB (0–1.5 g/kg) for 90 days. Supplementation significantly enhanced the weight gain and specific growth rates. The optimal level of 0.9 g/kg TB improved muscle nutritional quality, muscle texture, and intestinal morphology. This dosage also significantly increased hepatic and intestinal antioxidant capacity, elevating superoxide dismutase (SOD), catalase (CAT), and total antioxidant capacity (T-AOC), while reducing malondialdehyde (MDA) content. Furthermore, dietary TB at 0.9 g/kg reduced plasma and hepatic triglycerides and total cholesterol, and elevated key plasma immune parameters, including complement components (C3, C4) and phosphatase activities (ACP, AKP). Intestinal health was further enhanced, as evidenced by increased digestive enzyme activities (amylase, lipase, chymotrypsin) and improved microbial diversity, marked by a proliferation of beneficial bacteria. In conclusion, dietary TB supplementation at 0.9 g/kg optimally promotes growth, improves muscle and intestinal health, and enhances antioxidant and immune functions in H. otakii. Full article

Laser local forming is an effective method for reshaping optical glass, yet the deformation of the material during the cooling phase remains poorly understood. This study investigates the dynamic evolution of the molten pool, specifically focusing on the transition from an initial convex shape to a final “M-shaped” profile. A combined approach using thermal-fluid simulation and high-speed imaging experiments was employed to track the surface changes throughout the heating and cooling cycles. The results show that while the surface bulges outward during laser irradiation, the material redistributes after the laser is switched off due to non-uniform cooling and volumetric shrinkage. The specific roles of viscosity and surface tension in driving this reverse flow were identified. Furthermore, the study established a quantitative model linking laser parameters to the final surface dimensions, providing a reliable tool for predicting and controlling the precision of glass forming. Full article

This paper addresses the optimization challenges in urban logistics with the aim of enhancing the sustainability of last-mile delivery. By focusing on the collaborative delivery between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), we propose a novel approach to reducing energy consumption and operational inefficiencies. A bilevel mixed-integer linear programming (Bilevel-MILP) model is developed, integrating road network topology with dynamic energy constraints. Departing from traditional single-delivery modes, the paper establishes a multi-task continuous delivery framework. By incorporating a dynamic charging point selection strategy and path–energy coupling constraints, the model effectively mitigates energy limitations and the issue of repeated returns for UAV charging in complex urban road networks, thereby promoting more efficient resource utilization. At the algorithmic level, a Collaborative Delivery Path Optimization (CDPO) framework is proposed, which embeds an Improved Sparrow Search Algorithm (ISSA) with directional initialization and a Hybrid Genetic Algorithm (HGA) with specialized crossover strategies. This enables the synergistic optimization of UAV delivery sequences and UGV charging decisions. The simulation results demonstrate that, in scenarios with a task density of 20 per 100 km², the proposed CDPO algorithm reduces the total delivery time by 33.9% and shortens the UAV flight distance by 24.3%, compared to conventional fixed charging strategies (FCSs). These improvements directly contribute to lowering energy consumption and potential emissions. The road network discretization approach and dynamic candidate charging point generation confirm the method’s adaptability in high-density urban environments, offering a spatiotemporal collaborative optimization paradigm that supports the development of sustainable and intelligent urban logistics systems. The obtained results provide practical insights for the design and deployment of efficient UAV–UGV collaborative logistics systems in urban environments, particularly under high-task-density and energy-constrained conditions. Full article

Fine particulate matter (PM2.5)-induced ovarian damage has attracted widespread attention, but differences in cytotoxicity and underlying mechanisms of water-soluble (WS-PM2.5) and water-insoluble (WIS-PM2.5) fractions are unclear. To investigate potential effects of PM2.5 from livestock farming environments on animal ovaries, PM2.5 samples were collected from large-scale cattle barns. There were significant differences between fractions regarding elemental composition, proportion of water-soluble ions, polycyclic aromatic hydrocarbon content, and endotoxin concentrations. Based on transcriptome sequencing results, in a cowshed PM2.5 exposure model (rats), differentially expressed ovarian mRNAs were significantly enriched in signaling pathways such as cytokine interaction and the Hippo pathway, with the expression of thioredoxin-interacting protein (Txnip) significantly increased. In vitro (primary rat ovarian granulosa cells), short-term exposure to WS-PM2.5 (12 h) significantly induced inflammatory factor release, acute oxidative stress, mitochondrial dysfunction, and intracellular Ca²⁺ overload, with characteristics of rapid acute injury. However, extended (24 h) WIS-PM2.5 exposure had greater disruptive effects on estrogen homeostasis, intracellular enzyme release (LDH), and mitochondrial structure (subacute characteristics). Furthermore, downregulating Txnip expression via inhibitors effectively mitigated cowshed PM2.5-induced ovarian granulosa cell toxicity, oxidative stress, and mitochondrial and hormonal dysfunction. In summary, solubility of cowshed PM2.5 components affected cytotoxic characteristics, and Txnip was a key factor linking oxidative stress to granulosa cell damage. The study provided a mechanistic basis and potential targets for preventing and controlling PM2.5-induced ovarian damage in livestock environments. Full article

Fault diagnosis is a core technology ensuring the safe and efficient operation of industrial systems. A paradigm shift has been observed wherein traditional signal analysis has been replaced by intelligent, algorithm-driven approaches. In recent years, large language models, digital twins, and knowledge graphs have been introduced. A new stage of intelligent integration has been reached that is characterized by data-driven methods, knowledge guidance, and physical–virtual fusion. In the present paper, the evolutionary context of fault diagnosis technologies was systematically reviewed, with a focus on the theoretical methods and application practices of traditional machine learning, digital twins, knowledge graphs, and large language models. First, the research background, core objectives, and development history of fault diagnosis were described. Second, the principles, industrial applications, and limitations of supervised and unsupervised learning were analyzed. Third, innovative uses were examined involving physical–virtual mapping in digital twins, knowledge modeling in knowledge graphs, and feature learning in large language models. Subsequently, a multi-dimensional comparison framework was constructed to analyze the performance indicators, applicable scenarios, and collaborative potential of different technologies. Finally, the key challenges faced in the current fault diagnosis field were summarized. These included data quality, model generalization, and knowledge reuse. Future directions driven by the fusion of large language models, digital twins, and knowledge graphs were also outlined. A comprehensive technical map was established for fault diagnosis researchers, as well as an up-to-date reference. Theoretical innovation and engineering deployment of intelligent fault diagnosis are intended to be supported. Full article

The cryptochromes (CRYs) perceive blue light to regulate various developmental and metabolic events. However, the role of CRYs in flavonoid biosynthesis and flower pigmentation in safflower (Carthamus tinctorius L.) remains unknown. In this study, we determined flower color diversity among 485 safflower genotypes using the integrated CIELAB color space parameters and cluster analysis. On this basis, distinct colors were categorized into four groups, namely white (WW), yellow (YY), orange–red (OR), and yellow–red (YR). A genome-wide association study (GWAS) via 933,444 high-quality SNPs showed CtCRY2 as a flower color variation gene. Subsequently, genomic analysis identified three genes of the CRY family, including CtCRY1.1, CtCRY1.2, and CtCRY2. In silico analysis, such as gene structure, phylogeny and cis-acting elements, suggested CtCRY1.1 as a key candidate in pigment biosynthesis and was, therefore, selected for functional validation. Overexpression of CtCRY1.1 in Arabidopsis accumulated a high flavonoid content, particularly upregulating the expression of CHS, FLS, and ANS, proving its role as a positive regulator of flavonoid biosynthesis in safflower. These findings provide insights into the molecular mechanisms underlying flower color regulation in safflower and highlight CtCRY1.1 as a new target to enhance pigment-related traits in plants. Full article

Within the domain of intelligent picking robotics, fruit recognition and positioning are essential. Challenging conditions such as varying light, occlusion, and limited edge-computing power compromise fruit maturity detection. To tackle these issues, this paper proposes a lightweight algorithm YOLO-ELS based on YOLOv8n. Specifically, we reconstruct the backbone by replacing the bottlenecks in the C2f structure with Edge-Information-Enhanced Modules (EIEM) to prioritize morphological cues and filter background redundancy. Furthermore, a Large Separable Kernel Attention (LSKA) mechanism is integrated into the SPPF layer to expand the effective receptive field for multi-scale targets. To mitigate occlusion-induced errors, a Spatially Enhanced Attention Module (SEAM) is incorporated into the decoupled detection head to enhance feature responses in obscured regions. Finally, the Inner-GIoU loss is adopted to refine bounding box regression and accelerate convergence. Experimental results demonstrate that compared to the YOLOv8n baseline, the proposed YOLO-ELS achieves a 14.8% reduction in GFLOPs and a 2.3% decrease in parameters, while attaining a precision, recall, and mAP@50% of 92.7%, 83.9%, and 92.0%, respectively. When compared with mainstream models such as DETR, Faster-RCNN, SSD, TOOD, YOLOv5s, and YOLO11n, the mAP@50% is improved by 7.0%, 4.7%, 11.4%, 8.6%, 3.1%, and 3.2%. Deployment tests on the NVIDIA Jetson Orin Nano Super edge platform yield an inference latency of 25.2 ms and a detection speed of 28.2 FPS, successfully meeting the real-time operational requirements of automated harvesting systems. These findings confirm that YOLO-ELS effectively balances high detection accuracy with lightweight architecture, providing a robust technical foundation for intelligent fruit picking in resource-constrained greenhouse environments. Full article

Background: Human noroviruses are the leading cause of foodborne gastroenteritis worldwide. Accumulating evidence suggests that food matrices containing fucosylated or histo-blood group antigen (HBGA)-like glycans may facilitate viral attachment and persistence, yet the molecular mechanisms underlying these interactions remain unclear. Methods: In this study, we performed a comparative computational analysis of norovirus–glycan interactions by integrating AlphaFold3-based structure prediction, molecular docking, and molecular dynamics simulations. A total of 182 P-domain models representing all genotypes across five human norovirus genogroups (GI, GII, GIV, GVIII, and GIX) were predicted and docked with a lettuce-derived core-fucosylated hexasaccharide (H₂N₂F₂) previously identified by our group. The three complexes exhibiting the most favorable docking energies were further examined using 40 ns molecular dynamics simulations, followed by MM/GBSA binding free energy calculations and per-residue decomposition analyses. Results: Docking results indicated that the majority of modeled P proteins were able to adopt energetically favorable interaction poses with H₂N₂F₂, with predicted binding energies ranging from −3.7 to −7.2 kcal·mol⁻¹. The most favorable docking energies were observed for GII.6_S9c_KC576910 (−7.2 kcal·mol⁻¹), GII.3_MX_U22498 (−7.1 kcal·mol⁻¹), and GII.4_CARGDS11182_OR700741 (−6.8 kcal·mol⁻¹). Molecular dynamics simulations suggested stable ligand engagement within canonical HBGA-binding pockets, with recurrent residues such as Asp374, Gln393, and Arg345 contributing to electrostatic and hydrophobic interactions, consistent with previously reported HBGA-binding motifs. MM/GBSA analyses revealed comparatively favorable binding tendencies among these complexes, particularly for globally prevalent genotypes including GII.3, GII.4, and GII.6. Conclusions: This work provides a large-scale structural and energetic assessment of the potential interactions between a naturally occurring lettuce-derived fucosylated hexasaccharide and human norovirus P domains. The results support the notion that core-fucosylated food-associated glycans can serve as interaction partners for diverse norovirus genotypes and offer comparative molecular insights into glycan recognition patterns relevant to foodborne transmission. The integrative AlphaFold3–docking–dynamics framework presented here may facilitate future investigations of virus–glycan interactions within food matrices. Full article

In response to the demand for real-time monitoring of the nitrogen nutritional status of peanut populations, this paper proposes a real-time monitoring system for the nitrogen nutritional status of peanut populations based on the YOLOv11 framework and spectral attention module. Traditional nitrogen detection methods have problems such as low efficiency and difficulty in achieving population-scale monitoring, while crop phenotyping technology based on computer vision faces challenges such as small leaf targets, severe occlusion, easy confusion of nitrogen deficiency symptoms, and difficulty in deploying deep learning models on mobile terminals. This study improves the YOLOv11 model, introduces the ASF (Attentional Scale Fusion) module and the DySample dynamic upsampling mechanism, enhances the model’s perception and feature expression capabilities for multi-scale targets, and effectively improves the monitoring accuracy and robustness of the nitrogen nutritional status of peanut populations. Experimental results show that the ADS-YOLO model performs well in evaluation indicators such as accuracy, recall, and mean average precision (mAP), providing technical support for precision fertilization of peanuts. Full article