Search Results (81)

Hand rehabilitation exoskeletons play a critical role in restoring motor function in patients with stroke or hand injuries. However, most existing designs rely on fixed-axis assumptions, neglecting the rolling–sliding coupling of finger joints that causes instantaneous center of rotation (ICOR) drift, leading to kinematic misalignment and localized pressure concentrations. This study proposes the Instant Radius Method (IRM) based on medical imaging to continuously model ICOR trajectories of the MCP, PIP, and DIP joints, followed by the construction of an equivalent ICOR through curve fitting. Crossing-type biomimetic kinematic pairs were designed according to the equivalent ICOR and integrated into a three-loop ten-linkage exoskeleton capable of dual DOFs per finger (flexion–extension and abduction–adduction, 10 DOFs in total). Kinematic validation was performed using IMU sensors (Delsys) to capture joint angles, and interface pressure distribution at MCP and PIP was measured using thin-film pressure sensors. Experimental results demonstrated that with biomimetic kinematic pairs, the exoskeleton’s fingertip trajectories matched physiological trajectories more closely, with significantly reduced RMSE. Pressure measurements showed a reduction of approximately 15–25% in mean pressure and 20–30% in peak pressure at MCP and PIP, with more uniform distributions. The integrated framework of IRM-based modeling–equivalent ICOR–biomimetic kinematic pairs–multi-DOF exoskeleton design effectively enhanced kinematic alignment and human–machine compatibility. This work highlights the importance and feasibility of ICOR alignment in rehabilitation robotics and provides a promising pathway toward personalized rehabilitation and clinical translation. Full article

Electric vehicle (EV) drivetrains often suffer from degraded control precision due to resolver zero-position deviation. This issue becomes particularly critical under diverse automotive-grade operating conditions, posing challenges for achieving reliable and efficient drivetrain performance. To tackle this problem, we propose a dual-mode PID dynamic compensation control methodology. This approach establishes a divide-and-conquer framework that differentiates between weak-magnetic and non-weak-magnetic regions. It integrates current loop feedback with a fuzzy self-tuning mechanism, enabling real-time dynamic compensation of the resolver’s initial angle. To ensure system stability under extreme automotive conditions (−40 °C to 125 °C, ±0.5 g vibration, and electromagnetic interference), a triple-redundancy architecture is implemented. This architecture combines hardware filtering, software verification, and fault diagnosis. Our contribution lies in presenting a reliable solution for intelligent EV drivetrain calibration. The proposed method effectively mitigates resolver zero-position deviation, not only enhancing drivetrain performance under challenging automotive environments but also ensuring compliance with ISO 26262 ASIL-C safety standards. This research has been validated through its implementation in a 3.5-ton commercial logistics vehicle by a leading automotive manufacturer, demonstrating its practical viability and potential for widespread adoption in the EV industry. Full article

Micro- and nanopatterning is crucial for advanced photonic, electronic, and sensing devices. Yet achieving large-area periodic nanostructures with a 75 nm half-pitch on low-cost laboratory systems remains difficult, because conventional near-ultraviolet laser interference lithography (LIL) suffers from Gaussian-beam non-uniformity and a narrow exposure latitude. Here, we report a cost-effective deep-ultraviolet (DUV) dual-beam LIL system based on a 266 nm laser and diffractive flat-top beam shaping, enabling large-area patterning of periodical nanostructures. At this wavelength, a moderate half-angle can be chosen to preserve a large beam-overlap region while still delivering 150 nm period (75 nm half-pitch) structures. By independently tuning the incident angle and beam uniformity, we pattern one-dimensional (1D) gratings and two-dimensional (2D) arrays over a Ø 1.0 cm field with critical-dimension variation < 5 nm (1σ), smooth edges, and near-vertical sidewalls. As a proof of concept, we transfer a 2D pattern into Si to create non-metal-coated nanodot arrays that serve as surface-enhanced Raman spectroscopy (SERS) substrates. The arrays deliver an average enhancement factor of ~1.12 × 10⁴ with 11% intensity relative standard deviation (RSD) over 65 sampling points, a performance near the upper limit of all-dielectric SERS substrates. The proposed method overcomes the uneven hotspot distribution and complex fabrication procedures in conventional SERS substrates, enabling reliable and large-area chemical sensing. Compared to electron-beam lithography, the flat-top DUV-LIL approach offers orders-of-magnitude higher throughput at a fraction of the cost, while its centimeter-scale uniformity can be scaled to full wafers with larger beam-shaping optics. These attributes position the method as a versatile and economical route to large-area photonic metasurfaces and sensing devices. Full article

In the present study, a novel air-floating tripod bucket foundation (AFTBF) with taut mooring is proposed. The mechanical and motion response characteristics of this foundation were investigated through model tests. Furthermore, a parametric study was performed on the factors influencing the RAOs of mooring tension, air cushion pressure, as well as motion in the surge, heave, and pitch directions. The conclusion of this research is as follows: mooring tension, air cushion pressure, and pitch angle exhibit wave-frequency responses in small periods and low-frequency responses in large periods. Surge response is characterized by dual-peak features, while heave response predominantly demonstrates wave-frequency characteristics. As draft increases, the air cushion pressure inside the buckets exhibits a decreasing trend. Changes in water depth have more pronounced impacts on mooring tension and motion responses than on air cushion pressure. The impacts of changes in mooring distance and water depth on mechanical and motion responses are significantly more pronounced than those induced by changes in draft. These findings provide a critical foundation for the optimal design of this foundation in water depths of 30–50 m. Full article

With the rapid growth of nuclear energy, effective shielding of radioactive nuclear by-products is critical for safety and environmental protection. Gadolinium (Gd) is ideal for neutron shielding due to its exceptionally high thermal neutron capture cross-section. Despite significant progress in developing various Gd-based shielding materials, poor interfacial compatibility between Gd₂O₃ and polymer matrices remains a significant limitation. In this study, we addressed this challenge by successfully modifying Gd₂O₃ nanoparticles (Gd₂O₃@SIT-M) through the construction of a dual-layer molecular coating using electrostatic interactions. Initially, Gd₂O₃ was functionalized with the silane coupling agent 3-(trihydroxysilyl) propyl-1-propane-sulfonic acid (SIT), followed by subsequent assembly of polyether amine M2070 onto this modified surface. The combined presence of hydrophilic sulfonic acid groups from SIT and amine-ether groups from M2070 endowed Gd₂O₃@SIT-M nanoparticles with excellent hydrophilicity, significantly reducing their aqueous contact angle to 14.34°. Consequently, this modification strategy notably enhanced the dispersion stability of Gd₂O₃ nanoparticles in aqueous solutions and polymer matrices. The developed approach thus provides an effective pathway for fabricating advanced polymer-based neutron shielding materials with improved dispersibility, stability, and overall performance. Full article

A critical challenge in engine research lies in minimizing harmful emissions while optimizing the efficiency of internal combustion engines. Dual-fuel engines, operating with methanol and diesel, offer a promising alternative, but their combustion modeling remains complex due to the intricate thermochemical interactions involved. This study proposes a predictive framework that combines validated CFD simulations with deep learning techniques to estimate key combustion and emission parameters in a methanol–diesel dual-fuel engine. A three-dimensional CFD model was developed to simulate turbulent combustion, methanol injection, and pollutant formation, using the RNG k-ε turbulence model. A temporal dataset consisting of 1370 samples was generated, covering the compression, combustion, and early expansion phases—critical regions influencing both emissions and in-cylinder pressure dynamics. The optimal configuration identified involved a 63° spray injection angle and a 25% methanol proportion. A Gated Recurrent Unit (GRU) neural network, consisting of 256 neurons, a Tanh activation function, and a dropout rate of 0.2, was trained on this dataset. The model accurately predicted in-cylinder pressure, temperature, NOx emissions, and impact-related parameters, achieving a Pearson correlation coefficient of ρ = 0.997. This approach highlights the potential of combining CFD and deep learning for rapid and reliable prediction of engine behavior. It contributes to the development of more efficient, cleaner, and robust design strategies for future dual-fuel combustion systems. Full article

As a significant energy source enabling the global energy transition, efficient shale gas development is critical for diversifying supplies and reducing carbon emissions. Zipper fracturing widely enhances the stimulated reservoir volume (SRV) by generating complex fracture networks of shale reservoirs. However, recent trends of reduced well spacing and increased injection intensity have significantly intensified interwell interference, particularly fracture-driven interactions (FDIs), leading to early production decline and well integrity issues. This study develops a fully coupled hydro–mechanical–damage (HMD) numerical model incorporating an explicit discrete fracture network (DFN), opening and closure of fractures, and an aperture–permeability relationship to capture the nonlinear mechanical behavior of natural fractures and their role in FDIs. After model validation, sensitivity analyses are conducted. Results show that when the horizontal differential stress exceeds 12 MPa, fractures tend to propagate as single dominant planes due to stress concentration, increasing the risks of FDIs and reducing effective SRV. Increasing well spacing from 60 m to 110 m delays or eliminates FDIs while significantly improving reservoir stimulation. Fracture approach angle governs the interaction mechanisms between hydraulic and natural fractures, influencing the deflection and branching behavior of primary fractures. Injection rate exerts a dual influence on fracture extension and FDI risk, requiring an optimized balance between stimulation efficiency and interference control. This work enriches the multi-physics coupling theory of FDIs during fracturing processes, for better understanding the fracturing design and optimization in shale gas production. Full article

To address the insufficient adaptability of imported peanut harvesting equipment’s soil-engaging components to the specific soil conditions in Xinjiang, this study conducted Discrete Element Method (DEM)-based calibration of soil mechanical parameters using field soil samples with 1–20% moisture content from typical peanut cultivation areas in southern Xinjiang. Through the EDEM simulation platform, a comprehensive approach integrating the Hertz–Mindlin with the JKR adhesion model and Hertz–Mindlin with the Bonding model was employed to systematically calibrate nine key parameters: coefficient of restitution, static friction coefficient, rolling friction coefficient, JKR surface energy, normal/tangential stiffness per unit area, critical normal/tangential force, and soil bonding disk radius. Adopting static angle of repose (SAOR) and unconfined compressive force (UCF) as dual-response indicators, a hybrid experimental design strategy combining Central Composite Design (CCD), Plackett–Burman (PB) screening, and Box–Behnken Design (BBD) optimization was implemented. Regression models for SAOR and UCS were established, yielding six sets of soil parameters optimized for different moisture conditions through parameter optimization. Field validation demonstrated the following: ≤3.27% error in SAOR, ≤1.46% error in UCF, and ≤5.05% error in drawbar resistance validation for field digging shanks. Experimental results confirm that the model demonstrates strong prediction accuracy for soils in typical peanut harvesting regions of southern Xinjiang, thereby providing key parameter references for the future self-developed, highly adaptive soil-engaging components with drag reduction optimization in peanut harvesters for the Xinjiang region. Full article

Deep learning-based object detection has achieved remarkable maturity after years of intensive research. However, as multi-platform data acquisition becomes increasingly prevalent, spanning satellite, UAV, and ground-based platforms, a critical challenge emerges involving significant vertical perspective variations in captured images. The current object detection literature largely neglects this perspective dimension, particularly the robustness evaluation of single models across diverse viewing angles. To bridge this gap, we first conduct a systematic review categorizing existing approaches into standard and rotated object detection paradigms. Second, we build the Multi-Vertical-Perspective Object Detection (MVPOD) dataset; this dataset is the first comprehensive benchmark integrating spaceborne (nadir), airborne (oblique) and ground-level (horizontal) imagery with dual annotation schemes. Third, rigorous cross-perspective evaluation protocols reveal that vertical viewpoint discrepancies cause measurable performance degradation. Finally, representative methods are benchmarked on the MVPOD dataset, establishing baselines for future research. Full article

As internal combustion engines (ICEs) remain dominant in maritime transport, reducing their greenhouse gas (GHG) emissions is critical to meeting IMO’s decarbonization targets. Ammonia (NH₃) has gained attention as a carbon-free fuel due to its zero CO₂ emissions and high hydrogen density. However, its low flame speed and high ignition temperature pose combustion challenges. This study investigates the combustion and emission performance of NH₃-diesel dual-fuel engines, applying Response Surface Methodology (RSM) for multi-objective optimization of key operating parameters: ammonia fraction (AF: 0–30%), engine speed (1200–1600 rpm), and altitude (0–2000 m). Experimental results reveal that increasing AF led to a reduction in Brake Thermal Efficiency (BTE) from 39.2% to 37.4%, while significantly decreasing NO_x emissions by 82%, Total hydrocarbon emissions (THC) by 61%, and CO₂ emissions by 36%. However, the ignition delay increased from 8.2 to 10.8 crank angle degrees (CAD) and unburned NH₃ exceeded 6500 ppm, indicating higher incomplete combustion risks at high AF. Analysis of variance (ANOVA) confirmed AF as the most influential factor, contributing up to 82.3% of the variability in unburned NH₃ and 53.6% in NO_x. The optimal operating point, identified via desirability analysis, was 20% AF at 1200 rpm and sea level altitude, achieving a BTE of 37.4%, NO_x of 457 ppm, and unburned NH₃ of 6386 ppm with a desirability index of 0.614. These findings suggest that controlled NH₃ addition, combined with proper speed tuning, can significantly reduce emissions while maintaining engine efficiency in dual-fuel configurations. Full article

The evaluation of the interfacial shear strength between sand and steel materials plays a fundamental role in the design of geotechnical foundations and structures. However, testing equipment cannot consider the dual effects of particle size and steel roughness on a uniform stress state. In this study, a novel torsion shear apparatus was designed that can measure arbitrary displacement within the interface. On this basis, the influence of the sand particle size and contact surface roughness on interface shear behavior was studied, and the sand–steel interface mechanical responses, including stress state, sample deformation, and friction properties, were evaluated. The results of the torsional interface shear test (TIST) were compared with those of the conventional direct interface shear test (DIST). The results indicate that the shear strength of rough interfaces exceeds that of smooth interfaces but remains below the shear strength observed in pure soil shear tests. Moreover, a critical value of relative roughness exists, beyond which the peak shear stress or friction angle does not significantly increase. Despite variations in the sand grain sizes used in the tests, the corresponding friction angles were approximately equal. In pure soil shear tests, the friction angle was positively correlated with grain size, indicating that grain size directly affects the friction angle in pure soil shear. Additionally, the normalized interface friction angles obtained from the torsional interface shear tests showed good agreement with those derived from interface direct shear tests. Full article

Aiming at the problem of poor autonomy and weak time performance of reentry trajectory planning for Reusable Launch Vehicle (RLV), an online reentry trajectory planning and guidance method based on Twin Delayed Deep Deterministic Policy Gradient (TD3) is proposed. In view of the advantage that the drag acceleration can be quickly measured by the airborne inertial navigation equipment, the reference profile adopts the design of the drag acceleration–velocity profile in the reentry corridor. In order to prevent the problem of trajectory angle jump caused by the unsmooth turning point of the section, the section form adopts the form of four multiple functions to ensure the smooth connection of the turning point. Secondly, considering the advantages of the TD3 dual Critic network structure and delay update mechanism to suppress strategy overestimation, the TD3 algorithm framework is used to train multiple strategy networks offline and output profile parameters. Finally, considering the reentry uncertainty and the guidance error caused by the limitation of the bank angle reversal amplitude during lateral guidance, the networks are invoked online many times to solve the profile parameters in real time and update the profile periodically to ensure the rapidity and autonomy of the guidance command generation. The TD3 strategy networks are trained offline and invoked online many times so that the cumulative error in the previous guidance period can be eliminated when the algorithm is called again each time, and the online rapid generation and update of the reentry trajectory is realized, which effectively improves the accuracy and computational efficiency of the landing point. Full article

Tailless unmanned aerial vehicles (UAVs) achieve high-agility maneuvers with flight control systems. The attainable moment set (AMS) provides critical theoretical foundations and constraints for their optimization. A computational method is proposed herein to address controllability limitations caused by nonlinear aerodynamic effectiveness. This method incorporates dual constraints on control surface angles and angular rates for the nonlinear AMS, aiming to meet the demands of attitude tracking dynamics in flight control systems. First, a quantitative model is established to correlate dual deflection constraints with aerodynamic moment amplitude and bandwidth limitations. Next, we construct a computational framework for the incremental attainable moment set (IAMS) based on differential inclusion theory. For monotonic nonlinear aerodynamic effectiveness, the vertices of the IAMS are updated using local interpolation, yielding the incremental nonlinear attainable moment set (INAMS). When non-monotonic nonlinearity occurs, stationary points are calculated to adjust the control effectiveness matrix and admissible control set, thereby reducing computational errors induced by non-monotonic characteristics. Furthermore, the effective actions set, derived from a time-varying incremental nonlinear attainable moment set, quantifies the residual moment envelope of tailless UAVs during maneuvers. Comparative simulations indicate that the proposed method achieves correct computation under nonlinear aerodynamic conditions while reliably determining safe flight boundaries during control failure. Full article

The array of regular square pyramid microstructures with zero-spacing features is an ideal structural topology for building superhydrophobic functional surfaces due to its excellent anti-wetting performance and low surface adhesion properties. In the framework of existing studies, this microstructured array is usually considered to exist only in two typical wetting states, the stable Cassie state and the Wenzel state. In this study, a third type of wetting state, the incomplete Wenzel state, was discovered for the first time using experimental characterization, and the evolution mechanism of this new wetting state was revealed based on critical contact angle theory and numerical simulation. It is revealed that the faces and edges of the square pyramid microstructures exhibit different tilting angles, and this unique geometrical design endows them with dual critical contact angles. When the intrinsic contact angle of the microstructure is between the critical contact angles for the edges and faces, the wetting behavior of the droplet contact line in the directions parallel to the edges and faces will generate spontaneous and non-spontaneous competition effects, which lead to the formation of the incomplete Wenzel state. The dual-critical-angle theoretical model constructed in this study provides a new perspective for improving the theoretical system of wetting dynamics on pyramid arrays. Full article

In factory-controlled recirculating aquaculture systems, precise regulation of water velocity is crucial for optimizing shrimp feeding behavior and improving aquaculture efficiency. However, quantitative analysis of the impact of water velocity on shrimp behavior remains challenging. This study developed an innovative multi-objective behavioral analysis framework integrating detection, tracking, and behavioral interpretation. Specifically, the YOLOv8 model was employed for precise shrimp detection, ByteTrack with a dual-threshold matching strategy ensured continuous individual trajectory tracking in complex water environments, and Kalman filtering corrected coordinate offsets caused by water refraction. Under typical recirculating aquaculture system conditions, three water circulation rates (2.0, 5.0, and 10.0 cycles/day) were established to simulate varying flow velocities. High-frequency imaging (30 fps) was used to simultaneously record and analyze the movement trajectories of Litopenaeus vannamei during feeding and non-feeding periods, from which two-dimensional behavioral parameters—velocity and turning angle—were extracted. Key experimental results indicated that water circulation rates significantly affected shrimp movement velocity but had no significant effect on turning angle. Importantly, under only the moderate circulation rate (5.0 cycles/day), the average movement velocity during feeding was significantly lower than during non-feeding periods (p < 0.05). This finding reveals that moderate water velocity constitutes a critical hydrodynamic window for eliciting specific feeding behavior in shrimp. These results provide core parameters for an intelligent Litopenaeus vannamei feeding intensity assessment model based on spatiotemporal graph convolutional networks and offer theoretically valuable and practically applicable guidance for optimizing hydrodynamics and formulating precision feeding strategies in recirculating aquaculture systems. Full article