Search Results (131)

During the sintering process of NdFeB bulks, temperature changes and significant temperature differences between the bulk interior and the surface region will produce high residual stress. Temperature field and stress field prediction during the sintering process is one of the key techniques for analyzing residual stress. Therefore, the sintering process simulation and residual stress prediction of NdFeB bulks under different sintering temperatures were conducted based on the modified Drucker–Prager cap (DPC) model in ABAQUS (ABAQUS 2024). The calculated field cloud charts were analyzed against the microstructure of the bulks observed by scanning electron microscope (SEM). The finite element analysis (FEA) results of the sintering process and the residual stress show good agreement with SEM morphologies, which validates the accuracy and predictability of the model. The results indicate that cracks predominantly formed in edge regions. As the sintering temperature increased, longitudinal compressive stress at the edge of the cross-section transitioned into tensile stress. These results indicate that the developed simulation framework effectively identifies crack-prone areas, enabling data-driven optimization to reduce experimental trial-and-error costs in engineering applications. Full article

Airspace sectorization is an effective approach to balance increasing air traffic demand and limited airspace resources. It directly impacts the efficiency and safety of airspace operations. Traditional airspace sectorization methods are often based on fixed spatial scales, failing to fully consider the complexity and interrelationships of airspace partitioning across different spatial scales. This makes it challenging to balance large-scale airspace management with local dynamic demands. To address this issue, a multi-scale airspace sectorization framework is proposed, which integrates a multi-resolution grid system and a hierarchical deep reinforcement learning algorithm. First, an airspace grid model is constructed using Quaternary Triangular Mesh (QTM), along with an efficient workload calculation model based on grid encoding. Then, a sector optimization model is developed using hierarchical deep Q-network (HDQN), where the top-level and bottom-level policies coordinate to perform global airspace control area partitioning and local sectorization. The use of multi-resolution grids enhances the interaction efficiency between the reinforcement learning model and the environment. Prior knowledge is also incorporated to enhance training efficiency and effectiveness. Experimental results demonstrate that the proposed framework outperforms traditional models in both computational efficiency and workload balancing performance. Full article

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), poses a significant threat to wheat production in China. Previous epidemic studies have demonstrated the potential of high genetic diversity in the southwest regions of China. Among this epidemic region, the eastern Xizang (Tibet) region holds particular significance, as both wheat and barley crops are susceptible to Pst. However, limited information exists regarding the level of population genetic diversity, reproduction model, and migration patterns of the rust in eastern Xizang. The present study seeks to address this gap by analyzing 146 Pst isolates collected from the Basu, Zuogong, and Mangkang regions, genotyping by the 20K target Gene Array (Genobait). Our results showed relatively low genotypic diversity in the Basu region, while the highest genetic diversity was observed in the Mangkang area. Structural analysis revealed the abundance of admixed groups in Mangkang, which exhibited this population occurred due to sexual recombination between two different ancestor groups. Gene flow was observed between Zuogong and Basu populations, but it almost did not occur between Mangkang and Zuogong/Basu populations. This region is the world’s highest-altitude epidemic area, thus facilitating the evolution of the rust and possessing the potential to transmit newly evolved Pst races to lower wheat-growing regions. Implementing disease management strategies in this area is of potential importance to prevent the transmission of Pst races to other parts of Xizang, even neighboring regions possibly. This study facilitates our understanding of epidemiological and population genetic knowledge and the evolution of Pst in Xizang. Full article

Severe skin damage poses a significant clinical challenge, as limited availability of skin donors, postoperative skin defects, and scarring often impair skin function. Traditional two-dimensional (2D) nanofibers exhibit small pore sizes that hinder cellular infiltration, unable to simulate the three-dimensional (3D) structure of the skin. To address these issues, we developed 3D porous nanofiber scaffolds composed of polycaprolactone–polylactic acid–mussel adhesive protein (PLGA-PCL-MAP) using low-temperature electrospinning combined with nano-spray technology. Meanwhile, this 3D scaffold features high porosity, enhanced water absorption, and improved air permeability. The incorporation of mussel adhesive protein (MAP) further increased the scaffold’s adhesive properties and biocompatibility. In vitro experiments demonstrated that the 3D nanofiber scaffolds significantly promoted the adhesion, proliferation, and migration of epidermal keratinocytes (HaCaTs) and human fibroblasts (HFBs), while providing ample space for inward cellular growth. Successful co-culture of HaCaT and HFBs within the scaffold revealed key functional outcomes: HaCaTs expressed keratinocyte differentiation markers CK10 and CK14, while HFBs actively secreted extracellular matrix components critical for wound healing, including collagen I, collagen III, and fibronectin. This skin substitute with a composite structure of epidermis and dermis based on three-dimensional nanofiber scaffolds can be used as an ideal skin replacement and is expected to be applied in wound repair in the future. Full article

We propose a U-Net regression network model for sliced data to reconstruct a three-dimensional irregular steady-state sound field filling inhomogeneous anisotropic media. Through an innovative sliced data processing strategy, the 3D reconstruction problem is decomposed into a combination of 2D problems, thereby significantly reducing the computational cost. The designed multi-channel U-Net fully utilizes the strengths of both the encoder and decoder, exhibiting strong feature extraction and spatial detail recovery capabilities. Numerical experiments show that the model can not only effectively reconstruct the complex sound field structure containing non-convex regions, but it can also synchronously restore the spatial distribution of the media and their parameter matrix, successfully achieving the dual reconstruction of the shape and physical parameters of the steady-state sound field. Full article

Background and Objectives: Robotic exoskeletons show potential in PD gait rehabilitation. But the optimal assistive force and its equivalence to clinical gold standard assessments are unclear. This study aims to explore the clinical equivalence of the lower limb exoskeleton in evaluating PD patients’ gait disorders and find the best assistive force for clinical use. Methods: In this prospective controlled trial, 60 PD patients (Hoehn and Yahr stages 2–4) and 60 age-matched controls underwent quantitative gait analysis using a portable exoskeleton (Relink-ANK-1BM) at four assistive force levels (0 N, 40 N, 80 N, 120 N). Data from 57 patients and 57 controls were analyzed with GraphPad Prism 10. Different statistical tests were used based on data distribution. Results: ROC analysis showed that exoskeleton-measured velocity had the strongest power to distinguish PD patients from controls (AUC = 0.9198, p < 0.001). Other parameters also had high reliability and validity. There was a strong positive correlation between UPDRS-III lower extremity sub-score changes and gait velocity changes in PD patients (r = 0.8564, p < 0.001). The 80 N assistive force led to the best gait rehabilitation, with a 58% increase in gait velocity compared to unassisted walking (p < 0.001). Conclusions: 80 N is the optimal assistive threshold for PD gait rehabilitation. The wearable lower limb exoskeleton can be an objective alternative biomarker to UPDRS-III, enabling personalized home-based rehabilitation. Full article

A solid solution of rare-earth atoms in the iron matrix is a prerequisite for the microalloying effect in steels. However, to date, there has been considerable controversy regarding whether rare-earth atoms can form solid solutions within the iron matrix. Here, the effect of mixing entropy (S_mix) on the solid solubility of the rare-earth element lanthanum in Fe alloys was quantitatively analyzed using the non-aqueous solution electrolysis method. The results indicate that the solid solubility of lanthanum in Fe alloys increases with an increase in mixing entropy. Meanwhile, the thermodynamic essence of the formation of the solid solution was analyzed via the combination of first-principles calculation, thermodynamic analysis, and microstructure analysis. Full article

Tight oil reservoirs offer significant development potential. Due to the pronounced capillary forces in their nanopores, phase behavior differs markedly from that in conventional reservoirs, challenging traditional equations of state and numerical simulation methods. This paper presents a multiscale compositional numerical simulation method that incorporates capillary forces, leveraging the parallel advantages of the multiscale finite volume method. The approach decouples the compositional model using a sequential format to derive pressure and transport equations, then solves the pressure equation iteratively in a multiscale format to enhance computational efficiency. Results show that the proposed method significantly improves simulation speed while maintaining accuracy. By considering capillary forces in phase equilibrium calculations, this model effectively characterizes phase behavior in tight oil reservoir development, making it highly relevant for Pressure Volume Temperature (PVT) simulation, development simulation, and forecasting development strategies. Full article

Traditional sweet potato disease and pest detection methods have the limitations of low efficiency, poor accuracy and manual dependence, while deep learning-based target detection can achieve an efficient and accurate detection. This paper proposed an efficient sweet potato leaf disease and pest detection method SPLDPvB, as well as a low-complexity version SPLDPvT, to achieve accurate identification of sweet potato leaf spots and pests, such as hawk moth and wheat moth. First, a residual module containing three depthwise separable convolutional layers and a skip connection was proposed to effectively retain key feature information. Then, an efficient feature extraction module integrating the residual module and the attention mechanism was designed to significantly improve the feature extraction capability. Finally, in the model architecture, only the structure of the backbone network and the decoupling head combination was retained, and the traditional backbone network was replaced by an efficient feature extraction module, which greatly reduced the model complexity. The experimental results showed that the mAP0.5 and mAP0.5:0.95 of the proposed SPLDPvB model were 88.7% and 74.6%, respectively, and the number of parameters and the amount of calculation were 1.1 M and 7.7 G, respectively. Compared with YOLOv11S, mAP0.5 and mAP0.5:0.95 increased by 2.3% and 2.8%, respectively, and the number of parameters and the amount of calculation were reduced by 88.2% and 63.8%, respectively. The proposed model achieves higher detection accuracy with significantly reduced complexity, demonstrating excellent performance in detecting sweet potato leaf pests and diseases. This method realizes the automatic detection of sweet potato leaf pests and diseases and provides technical guidance for the accurate identification and spraying of pests and diseases. Full article

Long-distance sewerage network systems have serious vulnerabilities, specifically pipeline blockage, leakage, sedimentation, mixed connection, and other problems. A vulnerability evaluation system for a sewage network was established in this study with the comprehensive consideration of three aspects: basic attributes of the sewage network, operation and maintenance (O&M) drivers, and structural level. First, we obtained vulnerability indicators for the sewage pipeline network system through data collection and the preliminary selection and screening of indicators. The extent of the importance of each criterion level to the vulnerability was clarified through principal component analysis (PCA), with the basic attribute indicators being the per capita GDP (X3) and the urbanization rate (X5), the O&M-driven indicators being the daily per capita wastewater treatment volume (X7) and the industrial wastewater discharge volume (X8), and the structural-level indicators being the pipe diameter (X13) and the flow capacity (X15). Qingshanhu District, Jiangxi province, was taken as an example for diagnosing and evaluating vulnerability. Using the ranking size of PCA indicators as the evaluation level of the importance for the analytic hierarchy process (AHP) indicators, a hierarchical structure model was established. The evaluation value was obtained by weighting the hierarchical structure model results with the scores of each indicator. The comprehensive evaluation values of basic attributes, operation and maintenance drivers, and structural level were 58.38, 68.67, and 73.17, which corresponded to vulnerability levels of III, II, and II, respectively. Full article

Advancements in Industry 4.0 and smart manufacturing have increased the demand for precise and intricate part surface geometries, making the analysis of surface morphology essential for ensuring assembly precision and product quality. This study presents an innovative fitness landscape-based methodology for extreme point analysis of part surface morphology, effectively addressing the limitations of existing techniques in accurately identifying and analyzing extremum points. The proposed approach integrates adaptive Fitness-Distance Correlation (FDC) with a roughness index to dynamically determine the number and spatial distribution of initial points within the pattern search algorithm, based on variations in surface roughness. By partitioning the feasible domain into subregions according to FDC values, the algorithm significantly reduces optimization complexity. Regions with high ruggedness are further subdivided, facilitating the parallel implementation of the pattern search algorithm within each subregion. This adaptive strategy ensures that areas with intricate surface features are allocated a greater number of initial points, thereby enhancing the probability of locating both regional and global extremum points. To validate the effectiveness and robustness of the proposed method, extensive testing was conducted using five diverse test functions treated as black-box functions. The results demonstrate the method’s capability to accurately locate extremum points across varying surface profiles. Additionally, the proposed method was applied to flatness error evaluation. The results indicate that, compared to using only the raw measurement data, the flatness error increases by approximately 3% when extremum points are taken into account. Full article

To address the limitations of the current discrete element model of rice plants in terms of accurately reflecting structural differences and threshing characteristics, this study proposes a whole-rice-plant modeling method based on segmented hollow stalks and establishes a whole-rice-plant model that accurately represents the bending and threshing characteristics of the actual rice plant. Initially, based on the characteristics of the rice plant, the rice stalk was segmented into three sections of hollow stalks with distinct structures—namely, the primary stalk, the secondary stalk, and the tertiary stalk—ensuring that the model closely resembles actual rice plants. Secondly, the mechanical and contact parameters for each structure of the rice plant were measured and calibrated through mechanical and contact tests. Subsequently, utilizing the Hertz–Mindlin contact model, a multi-dimensional element particle arrangement method was employed to establish a discrete element model of the entire rice plant. The bending characteristics of the stalk and the threshing characteristics of the rice were calibrated using three-point bending tests and impact threshing tests. The results indicated calibration errors in the bending resistance force of 4.46%, 3.95%, and 2.51% for the three-section stalk model, and the calibration error for the rice model’s threshing rate was 1.86%, which can accurately simulate the bending characteristics of the stalk and the threshing characteristics of the rice plant. Finally, the contact characteristics of the model were validated through a stack angle verification test, which revealed that the relative error of the stacking angle did not exceed 7.52%, confirming the accuracy of the contact characteristics of the rice plant model. The findings of this study provide foundational models and a theoretical basis for the simulation of and analytical applications related to rice threshing and cleaning. Full article

With the global advancement of sustainable development concepts, the logistics industry is confronting significant environmental challenges, making green innovation a critical driver for industrial transformation and upgrading. However, during the green innovation process in logistics service supply chains, the differing roles of logistics service integrators and logistics service providers, combined with high costs and uncertain returns, hinder coordination efficiency. Therefore, it is imperative to enhance the coordination of supply chain contracts. Nevertheless, existing literature provides limited insights into the coordination capacities and impacts of different contracts on green innovation in logistics service supply chains. This study develops a Stackelberg game model where the logistics service integrator acts as the leader and logistics service providers serve as followers, examining the effects of cost-sharing contracts, revenue-sharing contracts, and hybrid cost-sharing and revenue-sharing contracts on supply chain coordination. Numerical simulations are employed to validate the findings. The results indicate that hybrid contracts provide the strongest incentives for green innovation among supply chain participants, whereas cost-sharing contracts offer relatively weaker incentives for integrators’ green design innovation. In addition, revenue-sharing contracts and hybrid contracts were effective in reducing the wholesale price of green logistics services, although all three contract types resulted in higher market prices. Finally, all three contract types achieve Pareto improvements in the supply chain, with hybrid contracts maximizing the total profit of the supply chain. This study not only elucidates the incentive mechanisms and relative advantages of different contracts in supply chain collaboration, but also offers critical theoretical and practical insights for designing contracts to foster green innovation in the logistics sector. Full article

Seed quality testing is crucial for ensuring food security and stability. To accurately detect the germination status of corn seeds during the paper medium germination test, this study proposes a corn seed germination status detection model based on YOLO v8n (CSGD-YOLO). Initially, to alleviate the complexity encountered in conventional models, a lightweight spatial pyramid pooling fast (L-SPPF) structure is engineered to enhance the representation of features. Simultaneously, a detection module dubbed Ghost_Detection, leveraging the GhostConv architecture, is devised to boost detection efficiency while simultaneously reducing parameter counts and computational overhead. Additionally, during the downsampling process of the backbone network, a downsampling module based on receptive field attention convolution (RFAConv) is designed to boost the model’s focus on areas of interest. This study further proposes a new module named C2f-UIB-iAFF based on the faster implementation of cross-stage partial bottleneck with two convolutions (C2f), universal inverted bottleneck (UIB), and iterative attention feature fusion (iAFF) to replace the original C2f in YOLOv8, streamlining model complexity and augmenting the feature fusion prowess of the residual structure. Experiments conducted on the collected corn seed germination dataset show that CSGD-YOLO requires only 1.91 M parameters and 5.21 G floating-point operations (FLOPs). The detection precision(P), recall(R), mAP_0.5, and mAP_0.50:0.95 achieved are 89.44%, 88.82%, 92.99%, and 80.38%. Compared with the YOLO v8n, CSGD-YOLO improves performance in terms of accuracy, model size, parameter number, and floating-point operation counts by 1.39, 1.43, 1.77, and 2.95 percentage points, respectively. Therefore, CSGD-YOLO outperforms existing mainstream target detection models in detection performance and model complexity, making it suitable for detecting corn seed germination status and providing a reference for rapid germination rate detection. Full article

Hydraulic mechanical continuously variable transmission (HMCVT) is widely used in powerful tractors due to its excellent performance. This paper aims to find universal methods for analyzing and optimizing the transmission efficiency of HMCVT. The energy efficiency improvement of HMCVT is important for the economy of powerful tractors. Firstly, by correctly analyzing the transmission efficiency of HMCVT, the transmission efficiency during the operation of HMCVT can be accurately calculated. Secondly, an improved NSGA-II genetic algorithm was adopted to achieve dynamic optimization of shifting points through transmission parameter combination optimization, ensuring smooth shifting while improving overall transmission efficiency. According to the transmission efficiency simulation platform, the accuracy of the transmission efficiency calculation was verified. Adopting an improved NSGA-II genetic algorithm to continuously optimize the design of HMCVT configurations achieves dynamic optimization of HMCVT parameters without being limited by shifting speed. The specific HMCVT structure proposed in this study can meet the requirements of a three-speed continuously variable transmission at speeds of 0–50 km/h. Meanwhile, the improved NSGA-II genetic algorithm can effectively provide support for the design of various HMCVT powertrain systems. Full article