Search Results (1,033)

To enhance the electromagnetic performance of flat-wire permanent magnet synchronous motors, three different groove structures were designed for the rotor, and a multi-objective optimization algorithm combining a genetic algorithm (GA) with the TOPSIS method was proposed. Firstly, an 8-pole 48-slot flat-wire motor model was established, and the cogging torque was analytically calculated to compare the motor’s performance under different groove schemes. Secondly, global multi-objective optimization of the rotor groove dimensions was performed using a combined simulation approach involving Maxwell, Workbench, and Optislang, and the optimal rotor groove size structure was selected using the TOPSIS method. Finally, a comparative analysis of the motor’s performance under both rated-load and no-load conditions was conducted for the pre- and post-optimization designs, followed by verification of the mechanical strength of the optimized rotor structure. The research results demonstrate that the combined optimization approach utilizing the genetic algorithm and the TOPSIS method significantly enhances the torque characteristics of the motor. The computational results indicate that the average torque is increased to 165.32 N·m, with the torque ripple reduced from 28.37% to 13.32% and the cogging torque decreased from 896.88 mN·m to 187.9 mN·m. Moreover, the total distortion rates of the air-gap magnetic flux density and the no-load back EMF are significantly suppressed, confirming the rationality of the proposed motor design. Full article

The hydrostatic guideway has been widely used in ultra-precision machine tools. The flow stability of the hydrostatic guideway has a significant impact on its bearing characteristics, and the flow controller is critical to safeguard the flow stability of the hydrostatic guideway. Currently, most engineering applications use fixed, fluid-resistance flow controllers, which have a simple structure, low cost, and high reliability. However, when facing complex working conditions, the fixed, fluid-resistance flow controller cannot maintain the flow stability of the hydrostatic guide. In this study, a membrane-type flow controller with variable fluid resistance is designed, and a theoretical model of the flow controller’s bearing characteristics is established, which is verified by fluid–solid coupling simulation and flow rate experiments. Analyzing the influence of the design parameters of the membrane-type flow controller on the performance according to the theoretical model, the design guidelines of the membrane-type flow controller are established, the key structure of the flow controller is clarified, and the design range of the key structure dimensions is given. The results show that the gasket thickness of the membrane-type flow controller has the greatest impact on the performance of the hydrostatic guideways, which should be ensured to have a machining error of less than 0.005 mm. This study is a guide for the design and manufacture of flow controllers, as well as for engineering applications. Full article

Porcine Rotavirus (PoRV), a predominant causative agent of neonatal diarrhea in piglets, shares substantial genetic homology with human rotavirus and represents a considerable threat to both public health and the global swine industry in the absence of specific antiviral interventions. The VP6 protein, an internal capsid component, is characterized by exceptional sequence conservation and robust immunogenicity, rendering it an ideal candidate for viral genotyping and vaccine development. In the present study, the recombinant plasmid pET28a(+)-VP6 was engineered to facilitate the high-yield expression and purification of the VP6 antigen. BALB/c mice were immunized to generate monoclonal antibodies (mAbs) through hybridoma technology, and the antigenic specificity of the resulting mAbs was stringently validated. Subsequently, a panel of truncated protein constructs was designed to precisely map linear B-cell epitopes, followed by comparative conservation analysis across diverse PoRV strains. Functional validation demonstrated that all three mAbs exhibited high-affinity binding to VP6, with a peak detection titer of 1:3,000,000 and exclusive specificity toward PoRVA. These antibodies effectively recognized representative genotypes such as G3 and X1, while exhibiting no cross-reactivity with unrelated viral pathogens; however, their reactivity against other PoRV serogroups (e.g., types B and C) remains to be further elucidated. Epitope mapping identified two novel linear B-cell epitopes, ¹²⁸YIKNWNLQNR¹³⁷ and ¹³⁸RQRTGFVFHK¹⁴⁷, both displaying strong sequence conservation among circulating PoRV strains. Collectively, these findings provide a rigorous experimental framework for the functional dissection of VP6 and reinforce its potential as a valuable diagnostic and immunoprophylactic target in PoRV control strategies. Full article

Eco-enzymatic stoichiometry provides a basis for understanding soil ecosystem functions, with implications for land management and ecological protection. Long-term climatic factors and human interferences have caused significant land-use transformations in the Qinghai–Tibet Plateau region, affecting various ecological functions, such as soil nutrient cycling and chemical element balance. It is currently unclear how large-scale land-use conversion affects soil ecological stoichiometry. In this study, 763 soil samples were collected across three land-use types: farmland, grassland, and forest land. In addition, changes in soil physicochemical properties and enzyme activity and stoichiometry were determined. The soil available phosphorus (SAP) and total phosphorus (TP) concentrations were the highest in farmland soil. Bulk density, pH, SAP, TP, and NO₃⁻-N were lower in forest soil, whereas NH₄⁺-N, available nitrogen, soil organic carbon (SOC), available potassium, and the soil nutrient ratio increased. Land-use conversion promoted soil β-1,4-glucosidase, N-acetyl-β-glucosaminidase, and alkaline phosphatase activities, mostly in forest soil. The eco-enzymatic C:N ratio was higher in farmland soils but grassland soils had a higher enzymatic C:P and N:P. Soil microorganisms were limited by P nutrients in all land-use patterns. C limitation was the highest in farmland soil. The redundancy analysis indicated that the ecological stoichiometry in farmland was influenced by TN, whereas grass and forest soils were influenced by SOC. Overall, the conversion of cropland or grassland to complex land-use types can effectively enhance soil nutrients, enzyme activities, and ecosystem functions, providing valuable insights for ecological restoration and sustainable land management in alpine regions. Full article

Underground pumped storage hydropower stations (UPSH) are of great significance for energy structure adjustment, and coal mine underground reservoirs are an integral part of UPSH. This study investigates the fracture mechanics behavior of coal in mine water immersion environments with varying loading rates and layer direction. Three types of samples were analyzed: Crack-arrester, Crack-splitter, and Crack-divider types. The immersion duration extended up to 120 days. The results indicate that, after immersion in mine water for 120 days, the fracture toughness (K_IC), fracture modulus (E_S), and absorbed energy (U_T) of coal decreased by 60.87%, 53.38%, and 63.21%, respectively, compared to the unsaturated coal samples. An immersion period of 30 days significantly weakens the mechanical properties of coal fractures. The K_IC, E_S, and U_T of coal demonstrate a positive correlation with loading rate, primarily influenced by the duration of coal damage. At the same loading rate, the order of fracture toughness among the three coal types is as follows: Crack-divider > Crack-arrester > Crack-splitter. This hierarchy is determined by the properties of the coal matrix and bedding planes, as well as the mechanical structures composed of them. This study holds significant implications for the safe construction and operational design of underground water reservoirs in coal mines. Full article

Previous research has demonstrated that the Train Wave Signature (TWS) method enables rapid calculation of pressure waves in straight tunnels. However, its application to subway tunnels with complex structural features remains insufficiently explored. This study proposes a generalized mathematical method integrating TWS with graph theory for the simulation of pressure wave generation, propagation, and reflection in complex tunnel systems. A computational program is implemented using this method for efficient simulation. The proposed method achieves high-accuracy prediction of pressure waves in tunnels with complex geometries compared with field measurements conducted in a high-speed subway tunnel with two shafts. We discuss the impact of iteration time intervals on the results and clarify the minimum time interval required for the calculation. Moreover, the sin-type definition of TWSs enhances the precision of pressure gradient prediction, and omitting low-amplitude pressure and reflected waves from the train can improve computational efficiency without compromising accuracy. This study advances the application of TWSs in tunnels with complex structures and provides a practical solution for aerodynamic analysis in high-speed subway tunnels, balancing accuracy with computational efficiency. Full article

Constructing a heterojunction is considered one of the most effective strategies for enhancing photocatalytic activity. Herein, we employ Ta₃N₅ and tubular graphitic carbon nitride (TCN) to construct a Ta₃N₅/TCN van der Waals heterojunction via electrostatic self-assembly for enhanced photocatalytic H₂ production. SEM and TEM results show that Ta₃N₅ particles (~300 nm in size) are successfully anchored onto the surface of TCN. The light absorption capability of the Ta₃N₅/TCN heterojunction is between those of Ta₃N₅ and TCN. The strong interaction between Ta₃N₅ and TCN with different energy structures (Fermi levels) by van der Waals force renders the formation of an interfacial electric field to drive the separation and transfer of photogenerated charge carriers in the Ta₃N₅/TCN heterojunction, as evidenced by the photoluminescence (PL) and photoelectrochemical (PEC) characterization results. Consequently, the optimal Ta₃N₅/TCN heterojunction exhibits a remarkable H₂ production rate of 12.73 mmol g⁻¹ h⁻¹ under visible light irradiation, which is 3.3 and 16.8 times those of TCN and Ta₃N₅, respectively. Meanwhile, the cyclic experiment demonstrates excellent stability of the Ta₃N₅/TCN heterojunction upon photocatalytic reaction. Notably, the photocatalytic performance of 15-TaN/TCN outperforms the most previously reported CN-based and Ta₃N₅-based heterojunctions for H₂ production. This work provides a new avenue for the rational design of CN-based van der Waals heterojunction photocatalysts with enhanced photocatalytic activity. Full article

Accurate prediction of drone motion within structured urban air corridors is essential for ensuring safe and efficient operations in Urban Air Mobility (UAM) systems. Although real-world Remote Identification (Remote ID) regulations require drones to broadcast critical flight information such as velocity, access to large-scale, high-quality broadcast data remains limited. To address this, this study leverages a Digital Twin (DT) framework to augment Remote ID spatio-temporal broadcasts, emulating the sensing environment of dense urban airspace. Using Remote ID data, we propose BiDGCNLLM, a hybrid prediction framework that integrates a Bidirectional Graph Convolutional Network (BiGCN) with Dynamic Edge Weighting and a reprogrammed Large Language Model (LLM, Qwen2.5–0.5B) to capture spatial dependencies and temporal patterns in drone speed trajectories. The model forecasts near-future speed variations in surrounding drones, supporting proactive conflict avoidance in constrained air corridors. Results from the AirSUMO co-simulation platform and a DT replica of the Cranfield University campus show that BiDGCNLLM outperforms state-of-the-art time series models in short-term velocity prediction. Compared to Transformer-LSTM, BiDGCNLLM marginally improves the $R^2$ by 11.59%. This study introduces the integration of LLMs into dynamic graph-based drone prediction. It shows the potential of Remote ID broadcasts to enable scalable, real-time airspace safety solutions in UAM. Full article

To overcome the limitations in the perception performance of individual robots and homogeneous robot teams, this paper presents a distributed multi-robot collaborative SLAM method based on air–ground cross-domain cooperation. By integrating environmental perception data from UAV and UGV teams across air and ground domains, this method enables more efficient, robust, and globally consistent autonomous positioning and mapping. First, to address the challenge of significant differences in the field of view between UAVs and UGVs, which complicates achieving a unified environmental understanding, this paper proposes an iterative registration method based on semantic and geometric features assistance. This method calculates the correspondence probability of the air–ground loop closure keyframes using these features and iteratively computes the rotation angle and translation vector to determine the coordinate transformation matrix. The resulting matrix provides strong initialization for back-end optimization, which helps to significantly reduce global pose estimation errors. Next, to overcome the convergence difficulties and high computational complexity of large-scale distributed back-end nonlinear pose graph optimization, this paper introduces a multi-level partitioning majorization–minimization DPGO method incorporating loss kernel optimization. This method constructs a multi-level, balanced pose subgraph based on the coupling degree of robot nodes. Then, it uses the minimization substitution function of non-trivial loss kernel optimization to gradually converge the distributed pose graph optimization problem to a first-order critical point, thereby significantly improving global pose estimation accuracy. Finally, experimental results on benchmark SLAM datasets and the GRACO dataset demonstrate that the proposed method effectively integrates environmental feature information from air–ground cross-domain UAV and UGV teams, achieving high-precision global pose estimation and map construction. Full article

Porcine rotavirus (PoRV), a primary etiological agent of viral diarrhea in piglets, frequently co-infects with other enteric pathogens, exacerbating disease severity and causing substantial economic losses. Its genetic recombination capability enables cross-species transmission potential, posing public health risks. Globally, twelve G genotypes and thirteen P genotypes have been identified, with G9, G5, G3, and G4 emerging as predominant circulating strains. The limited cross-protective immunity between genotypes compromises vaccine efficacy, necessitating genotype surveillance to guide vaccine development. While conventional molecular assays demonstrate sensitivity, they lack rapid genotyping capacity and face technical limitations. To address this, we developed a novel diagnostic platform integrating reverse transcription recombinase-aided amplification (RT-RAA) with CRISPR–Cas12a. This system employs universal primers for the simultaneous amplification of G4/G5/G9 genotypes in a single reaction, coupled with sequence-specific CRISPR recognition, achieving genotyping within 50 min at 37 °C with 10⁰ copies/μL sensitivity. Clinical validation showed a high concordance with reverse transcription quantitative polymerase chain reaction (RT-qPCR). This advancement provides an efficient tool for rapid viral genotyping, vaccine compatibility evaluation, and optimized epidemic control strategies. Full article

The effects of octenyl succinic anhydride-modified hydrophobic starch (OSA starch) on the properties of myofibrillar protein (MP) emulsions were investigated. The results show that the stability of protein emulsions was significantly enhanced with the addition of OSA starch (0.25–1.0%), with the most pronounced effect observed at a 1% concentration. Concomitantly, increasing OSA starch concentrations led to a reduction in the fat globule size. Electrostatic interactions between anionic groups in the modified starch and myofibrillar proteins were observed, which effectively decreased the zeta potential of the emulsion to a minimum of −52.3 mV. However, in the composite emulsion system, a competitive relationship between OSA starch and myofibrillar proteins was evident, as reflected by the decrease in interfacial protein content from 1.16 mg/mL in the control (CK) group to 0.78 mg/mL in the OSA starch-treated group. Despite this competition, the overall emulsion stability was improved due to the synergistic effects of the modified starch and proteins. These findings suggest that OSA-modified starch holds promise as a stabilizer for enhancing the stability of myofibrillar protein emulsions. Full article

With the increasing penetration of distributed generation and the diversification of electrical equipment, distribution networks face issues like three-phase unbalance and harmonic currents, while the voltage stability and inertia of the grid-connected system also decrease. A certain amount of energy storage is needed in a Distribution Static Synchronous Compensator (DSTATCOM) to manage power quality and actively support voltage and inertia in the network. This paper first addresses the limitations of traditional $dq0$ compensation algorithms in effectively filtering out negative-sequence twice-frequency components. An improved $dq0$ compensation algorithm is proposed to reduce errors in detecting positive-sequence fundamental current under unbalanced three-phase conditions. Second, considering the impedance ratio characteristics of the distribution network, while reactive power voltage regulation is common, active power regulation is more effective in high-resistance distribution networks. A grid-forming model-based active and reactive power coordinated voltage regulation method is proposed. This method uses synchronous control to establish a virtual three-phase voltage internal electromotive force, forming a comprehensive compensation strategy that combines power quality improvement and active voltage support, exploring the potential of energy storage DSTATCOM applications in distribution networks. Finally, simulation and experimental results demonstrate the effectiveness of the proposed control method. Full article

The regulation of the active sites of a catalyst is important for its application. Herein, a series of Ag₁Cu_x/SBA-15 catalysts with different molar ratios of Ag to Cu were synthesized via the impregnation method, and the active sites of Ag₁Cu_x were regulated via various pretreatment conditions. These as-prepared Ag₁Cu_x/SBA-15 catalysts were characterized by many technologies, and their catalytic performance was estimated through CO catalytic oxidation. Among these catalysts, Ag₁Cu_0.025/SBA-15, with a Ag/Cu molar ratio of 1:0.025 and pretreated under the condition of 500 °C O₂/Ar for 2 h, followed by 300 °C H₂ for another 2 h, presented optimal CO degradation performance, which could realize the oxidation of 98% CO at 34 °C (T₉₈ = 34 °C). Meanwhile, Ag₁Cu_0.025/SBA-15 also displayed great reusability. Characterization results, such as X-ray diffraction (XRD), ultraviolet–visible diffuse reflectance spectra (UV-vis DRS), temperature-programmed H₂ reduction (H₂-TPR), and physical adsorption, suggested that the optimal catalytic performance of Ag₁Cu_0.025/SBA-15 was ascribed to its high interspersion of Ag nanoparticles, better low-temperature reduction ability, the interaction between Ag and Cu, and its high surface area and large pore volume. This study provides guidance for the regulation of active sites for low-temperature catalytic degradation. Full article

This research focuses on improving the characteristics of recycled concrete and utilizing solid waste resources through the combination of industrial waste pre-impregnation and the carbonation process. A novel pre-impregnation–carbonation aggregate method is proposed to increase the content of carbonatable components in the surface-bonded mortar of recycled coarse aggregate by pre-impregnating it with carbide slag slurry (CSS). This approach enhances the subsequent carbonation effect and thus the properties of recycled aggregates. The experimental results showed that the method significantly improved the water absorption, crushing value, and apparent density of the recycled aggregate. Additionally, it enhanced the compressive strength, split tensile strength, and flexural strength of the recycled concrete produced using the aggregate improved by this method. Microanalysis revealed that CO₂ reacts with calcium hydroxide and hydrated calcium silicate (C-S-H) to produce calcite-type calcium carbonate and amorphous silica gel. These reaction products fill microcracks and pores on the aggregate and densify the aggregate–paste interfacial transition zone (ITZ), thereby improving the properties of recycled concrete. This study presents a practical approach for the high-value utilization of construction waste and the production of low-carbon building materials by enhancing the quality of recycled concrete. Additionally, carbon sequestration demonstrates broad promise for engineering applications. Full article

Fractures play a critical role in the storage and migration of hydrocarbons within subsurface reservoirs, and their characteristics can be effectively studied through core sample analysis. This study proposes an automated fracture extraction method for full-diameter core Computed Tomography (CT) images based on a deep learning framework. A semantic segmentation network called SCTNet is employed to perform high-precision semantic segmentation, while a sliding window strategy is introduced to address the challenges associated with large-scale image processing during training and inference. The proposed method achieves a mean Intersection over Union (mIoU) of 72.14% and a pixel-level segmentation accuracy of 97% on the test dataset, outperforming traditional thresholding techniques and several state-of-the-art deep learning models. Besides fracture detection, the method enables quantitative characterization of fracture-related parameters, including fracture proportion, dip angle, strike, and aperture. Experimental results indicate that the proposed approach provides a reliable and efficient solution for the interpretation of large-volume CT data. Compared to manual evaluation, the method significantly accelerates the analysis process—reducing time from hours to minutes—and demonstrates strong potential to enhance intelligent workflows for geological core fracture analysis. Full article