Search Results (9,748)

The electromechanical composite transmission technology for tracked vehicles demonstrates excellent performance in energy efficiency, mobility, and ride comfort. However, due to frequent operation under harsh conditions, the components of the electric drive system, such as drive motors, are prone to failures. This paper proposes three fault-tolerant control methods for three typical fault scenarios of the electromechanical composite transmission system (ECTS) to ensure the normal operation of tracked vehicles. Firstly, an ECTS and the electromechanical coupling dynamics model of the tracked vehicle are established. Moreover, a double-layer anti-windup PID control for motors and an instantaneous optimal control strategy for the engine are proposed in the fault-free case. Secondly, an anti-windup PID control law for motors and an engine control strategy considering the state of charge (SOC) and driving demands are developed in the case of single-side drive motor failure. Thirdly, a B4 clutch control strategy during starting and a steering brake control strategy are proposed in the case of electric drive system failure. Finally, in the straight-driving condition of the tracked vehicle, the throttle opening is set as 0.6, and the motor failure is triggered at 15 s during the acceleration process. Numerical simulations verify the fault-tolerant control strategies’ feasibility, using the tracked vehicle’s maximum speed and acceleration at 30 s as indicators for dynamic performance evaluation. The simulation results show that under single-motor fault, its straight-line driving power drops by 33.37%; with electric drive failure, the drop reaches 43.86%. The vehicle can still maintain normal straight-line driving and steering under fault conditions. Full article

We present a numerical investigation comparing two entanglement generation protocols in finite XX spin chains with varying spin magnitudes ($s=1/2,1,3/2$). Protocol 1 (P1) relies on staggered couplings to steer correlations toward the ends of the chain. At the same time, Protocol 2 (P2) adopts a dual-port architecture that uses optimized boundary fields to mediate virtual excitations between terminal spins. Our results show that P2 consistently outperforms P1 in all spin values, generating higher-fidelity entanglement in shorter timescales when evaluated under the same system parameters. Furthermore, P2 exhibits superior robustness under realistic imperfections, including diagonal and off-diagonal disorder, as well as dephasing noise. To further assess the resilience of both protocols in experimentally relevant settings, we employ the pseudomode formalism to characterize the impact of non-Markovian noise on the entanglement dynamics. Our analysis reveals that the dual-port mechanism (P2) remains effective even when memory effects are present, as it reduces the excitation of bulk modes that would otherwise enhance environment-induced backflow. Together, the scalability, efficiency, and noise resilience of the dual-port approach position it as a promising framework for entanglement distribution in solid-state quantum information platforms. Full article

Sulfion oxidation reaction (SOR) has great potential in replacing oxygen evolution reaction (OER) and boosting highly efficient hydrogen evolution. The development of highly active and stable SOR electrocatalysts is crucial for assisting hydrogen production with low energy consumption. In this work, multiphase NiCoFe-based layered double hydroxide (namely NiCoFe-LDH) has been synthesized via a facile seed-assisted heterogeneous nucleation method. Benefiting from its unique microsized hydrangea-like structure and synergistic active phases, the catalyst delivers substantial catalytic interfaces and reactive centers for SOR. Consequently, NiCoFe-LDH electrode achieves a remarkably low potential of 0.381 V at 10 mA cm⁻² in 1 M KOH + 0.1 M Na₂S, representing a significant reduction of 0.98 V compared to conventional OER. Notably, under harsh industrial conditions (6 M KOH + 0.1 M Na₂S, 80 °C), the electrolysis system based on NiCoFe-LDH||NF pair exhibits a cell potential of only 0.71 V at 100 mA cm⁻², which shows a greater decreasing amplitude of 1.05 V compared with that of traditional OER/HER systems. Meanwhile, the NiCoFe-LDH||NF couple could maintain operational stability for 100 h without obvious potential fluctuation, as well as possessing a lower energy consumption of 1.42 kWh m⁻³ H₂. Multiphase eletrocatalysis for SOR could indeed produce hydrogen with low-energy consumption. Full article

Maintenance scheduling of hydropower units is essential for ensuring the operational security and stability of large-scale cascaded hydropower systems and for improving the efficiency of water energy utilization. This study takes the Cascaded Hydropower System of the Lower Jinsha River (CHSJS) as a representative case, identifying four key challenges facing maintenance planning: multi-dimensional influencing factor coupling, spatial and temporal conflicts with generation dispatch, coordination with transmission line maintenance, and compound uncertainties of inflow and load. To address these issues, four strategic recommendations are proposed: (1) identifying and quantifying the impacts of multi-factor influences on maintenance planning; (2) developing integrated models for the co-optimization of power generation dispatch and maintenance scheduling; (3) formulating coordinated maintenance strategies for hydropower units and associated transmission infrastructure; and (4) constructing joint models to manage the coupled uncertainties of inflow and load. The strategy proposed in this study was applied to the CHSJS, obtaining the weight of the impact factor. The coordinated unit maintenance arrangements of transmission line maintenance periods increased from 56% to 97%. This study highlights the critical need for synergistic optimization of generation dispatch and maintenance scheduling in large-scale cascaded hydropower systems and provides a methodological foundation for future research and practical applications. Full article

The city of Al Ain is a fast-developing area. With building typology varying from low-rise to mid-rise, sustainable design in buildings is needed. As the majority of the city’s population is Emirati Citizens, the percentage of expats is increasing. The expats tend to live in mid-rise buildings. One of the central midrise areas is AL Ain Square. This study aims to investigate how an optimized mashrabiya pattern can impact the energy and the Predicted Mean Vote (PMV) in a 3-bedroom apartment, fully oriented to the south, of an expat family. The methodology is as follows: case study selection, Weather analysis, Modeling/Validation of the base case scenario, Optimization of the mashrabiya pattern, Simulation of various scenarios, and Results. Analyzing the selected case study is the initial step of the methodology. This analysis begins with the district, building typology, and the chosen apartment. The weather analysis is relevant for using the mashrabiya (screen device) and the need to improve energy consumption and thermal comfort. The modeling of the base case shall be performed in Rhino Grasshopper. The validation is based on a one-year electricity bill provided by the owner. The optimization of mashrabiya patterns is an innovative process, where various designs are compared and then optimized to select the most efficient pattern. The solutions to the selected scenarios will then yield the results of the optimal scenario. This study is relevant to industry, academia, and local authorities as an innovative approach to retrofitting buildings. Additionally, the research presents a creative vision that suggests optimized mashrabiya patterns can significantly enhance energy savings, with the hexagonal grid configuration demonstrating the highest efficiency. This finding highlights the potential for geometry-driven shading optimization tailored to specific climatic and building conditions. Contrasting earlier mashrabiya studies that assess one static pattern, we couple a geometry-agnostic evolutionary solver with a utility-calibrated EnergyPlus model to test thousands of square, hexagonal, and triangular permutations. This workflow uncovers a previously undocumented non-linear depth perforation interaction. It validates a hexagonal screen that reduces annual cooling energy by 12.3%, establishing a replicable, grid-specific retrofit method for hot-arid apartments. Full article

Accurate feature matching between optical and synthetic aperture radar (SAR) images remains a significant challenge in remote sensing due to substantial modality discrepancies in texture, intensity, and geometric structure. In this study, we proposed an attention-context-aware deep learning framework (ACAMatch) for robust and efficient optical–SAR image registration. The proposed method integrates a structure-enhanced feature extractor, RS2FNet, which combines dual-stage Res2Net modules with a bi-level routing attention mechanism to capture multi-scale local textures and global structural semantics. A context-aware matching module refines correspondences through self- and cross-attention, coupled with a confidence-driven early-exit pruning strategy to reduce computational cost while maintaining accuracy. Additionally, a match-aware multi-task loss function jointly enforces spatial consistency, affine invariance, and structural coherence for end-to-end optimization. Experiments on public datasets (SEN1-2 and WHU-OPT-SAR) and a self-collected Gaofen (GF) dataset demonstrated that ACAMatch significantly outperformed existing state-of-the-art methods in terms of the number of correct matches, matching accuracy, and inference speed, especially under challenging conditions such as resolution differences and severe structural distortions. These results indicate the effectiveness and generalizability of the proposed approach for multimodal image registration, making ACAMatch a promising solution for remote sensing applications such as change detection and multi-sensor data fusion. Full article

Gallium nitride (GaN) enhancement-mode high-electron-mobility transistors ( E-HEMTs) deliver superior performance compared to traditional silicon (Si) and silicon carbide (SiC) counterparts. Their faster switching speeds, lower on-state resistances, and higher operating frequencies enable more efficient and compact power converters. However, their integration into high-power applications is limited by critical reliability concerns, particularly regarding their short-circuit (SC) withstand capability and overvoltage (OV) resilience. GaN devices typically exhibit SC withstand times of only a few hundred nanoseconds, needing ultrafast protection circuits, which conventional desaturation (DESAT) methods cannot adequately provide. Furthermore, their high switching transients increase the risk of false activation events. The lack of avalanche capability and the dynamic nature of GaN breakdown voltage exacerbate issues related to OV stress during fault conditions. Although SC-related behaviour in GaN devices has been previously studied, a focused and comprehensive review of protection strategies tailored to GaN technology remains lacking. This paper fills that gap by providing an in-depth analysis of SC and OV failure phenomena, coupled with a critical evaluation of current and next-generation protection schemes suitable for GaN-based high-power converters. Full article

The growing global demand for energy necessitates the efficient utilization of unconventional petroleum resources, particularly heavy oil reserves. However, extracting, transporting, and processing these resources remain challenging due to their low mobility, low API gravity, and significant concentrations of resins, asphaltenes, heteroatoms, and metals. In recent years, various in situ upgrading techniques have been explored to enhance heavy oil quality, with catalytic aquathermolysis emerging as a promising approach. The effectiveness of this process largely depends on the development of cost-effective, environmentally friendly catalysts. This study investigates the upgrading performance of water-soluble ammonium alum, (NH₄)Al(SO₄)₂·12H₂O, for an extra-heavy oil sample from the Zarafshan Depression, located along the Tajikistan–Uzbekistan border. Comprehensive analyses demonstrate that the catalyst facilitates the breakdown of heavy oil components, particularly resins and asphaltenes, into lighter fractions. As a result, oil viscosity was significantly reduced by 94%, while sulfur content decreased from 896 ppm to 312 ppm. Furthermore, thermogravimetric (TG-DTG) analysis, coupled with Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), and X-ray diffraction (XRD), revealed that the thermal decomposition of ammonium alum produces catalytically active Al₂O₃ nanoparticles. These findings suggest that ammonium alum is a highly effective water-soluble pre-catalyst for hydrothermal upgrading, offering a viable and sustainable solution for the development of extra-heavy oil fields. Full article

While electric unmanned aerial vehicles (UAVs) offer advantages in noise reduction, safety, and operational efficiency, their endurance is limited by current battery technology. Extending flight autonomy without compromising performance is a critical challenge in UAV system development. Previous studies introduced hybrid micro-turbogenerator architectures, but limitations in control stability and output power constrained their practical implementation. This study aimed to finalize the design and experimental validation of an optimized hybrid power system featuring a micro-turboprop engine mechanically coupled to an upgraded electric generator. A fuzzy logic-based control algorithm was implemented on a single-board computer to enable autonomous voltage regulation. The test bench architecture was reinforced and instrumented to allow stable multi-stage testing across increasing power levels. Results demonstrated stable voltage control at 48 VDC and electrical power outputs up to 3 kW, with an estimated maximum of 3.5 kW at full throttle. Efficiency was calculated at approximately 67%, and analysis of the generator’s KV constant revealed that using a lower KV variant (KV80) could reduce required rotational speed (RPM) and improve performance. These findings underscore the value of adaptive hybridization in UAVs and suggest that tuning generator electromechanical parameters can significantly enhance overall energy efficiency and platform autonomy. Full article

This study explores the impact of graphene integration on lithium niobate (LiNbO₃, LN) ridge waveguides and directional couplers, focusing on coupling efficiency, polarization-dependent light absorption, and temperature sensitivity. Experimental and simulation results reveal that graphene loading significantly alters the effective mode refractive index and enhances waveguide coupling, enabling precise control over light transmission and power distribution. The temperature-dependent behavior of graphene–LN structures demonstrates strong thermal sensitivity, with notable changes in output power ratios between cross and through ports under varying temperatures. These findings highlight the potential of graphene–LN hybrid devices for compact, high-performance photonic circuits and temperature sensing applications. This study provides valuable insights into the design of advanced integrated photonic systems, paving the way for innovations in optical communication, sensing, and quantum technologies. 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

This study assessed the applicability of visible–near-infrared (vis-NIR) spectroscopy to predicting the water retention characteristics of forest soils in Japan, which vary widely owing to the presence of volcanic ash. Soil samples were collected from 34 sites, and the volumetric water content was measured at eight levels of matric suction. Spectral data were processed by using the second derivative of the absorbance, and regression models were developed by using explainable boosting machine (EBM), which is an interpretable machine learning method. Although the prediction accuracy was limited owing to the small sample size and soil heterogeneity, EBM performed better under saturated conditions (R² = 0.30), which suggests that vis-NIR spectroscopy can capture water-related features, especially under wet conditions. Importance analysis consistently selected wavelengths that were associated with organic matter and hydrated clay minerals. The important wavelengths clearly shifted from free-water bands in wet soils to mineral-related absorption bands in dry soils. These findings highlight the potential of coupling vis-NIR spectroscopy with interpretable models like EBM for estimating the hydraulic properties of forest soils. Improved accuracy is expected with larger datasets and stratified models by soil type, which can facilitate more efficient soil monitoring in forests. Full article

Ferrocene (Fc) is a redox-active organometallic scaffold whose unique electronic properties, stability, and modularity have enabled a broad range of catalytic and sensing applications. This review critically examines recent advances in Fc-based systems for hydrogen evolution and carbon dioxide (CO₂) conversion, encompassing electrochemical, photochemical, and thermochemical strategies. Fc serves diverse functions: it operates as a reversible redox mediator, an electron reservoir, a ligand framework, and a structural modulator. Each role contributes differently to enhancing catalytic performance, improving selectivity, or increasing operational stability. We highlight how Fc integration facilitates proton-coupled electron transfer in hydrogen evolution, supports selective CO₂ reduction in molecular and hybrid catalysts, and promotes efficient CO₂ fixation and capture within functionalised frameworks. Emerging applications in electrosynthetic organic transformations are also discussed. Together, these findings position Fc as a foundational motif for designing future electrocatalytic and carbon management platforms. Full article

Chestnut (Castanea sativa Mill.) is an edible nut recognized for its nutritional attributes, particularly its elevated levels of carbohydrates (starch) and proteins. Chestnuts are popular for their health-promoting properties and hold significant environmental and economic importance in Europe. During this study, after the characterization of the fruit, attention was directed toward the valorization of chestnut shells, a predominant by-product of industrial chestnut processing that is typically discarded. Valuable bioactive compounds were extracted from the shells using Pressurized Liquid Extraction (PLE), a green, efficient, scalable method. Response surface methodology (RSM) was utilized to determine optimal extraction conditions, identified as 40% v/v ethanol as the solvent at a temperature of 160 °C for 25 min under a constant pressure of 1700 psi. High total polyphenol content (113.68 ± 7.84 mg GAE/g dry weight) and notable antioxidant activity—determined by FRAP (1320.28 ± 34.33 μmol AAE/g dw) and DPPH (708.65 ± 24.8 μmol AAE/g dw) assays—were recorded in the optimized extracts. Ultrahigh-performance liquid chromatography coupled with a hybrid trap ion mobility-quadrupole time-of-flight mass spectrometer (UHPLC-TIMS-QTOF-MS) was applied to further characterize the compound profile, enabling the identification of phenolic and antioxidant compounds. These findings highlight the possibility of using chestnut shell residues as a long-term resource to make valuable products for the food, medicine, cosmetics, and animal feed industries. This study contributes to the advancement of waste valorization strategies and circular bioeconomy approaches. Full article

With the development of digital power systems, the establishment of digital twin models for transformers is of great significance in enhancing power system stability. Consequently, greater demands are placed on the real-time performance and accuracy of thermal-fluid-coupled transient multi-physics field calculations for transformers. However, traditional numerical methods, such as finite element or computational fluid dynamics techniques, often require days or even weeks to simulate full-scale high-fidelity transformer models containing millions of grid nodes. The high computational cost and long runtime make them impractical for real-time simulations in digital twin applications. To address this, this paper employs the dynamic mode decomposition (DMD) method in conjunction with Koopman operator theory to perform data-driven reduced-order modeling of the transformer’s thermal–fluid-coupled multi-physics field. A fast computational approach based on extended dynamic mode decomposition (EDMD) is proposed to enhance the modal decomposition capability of nonlinear systems and improve prediction accuracy. The results show that this method greatly improves computational efficiency while preserving accuracy in high-fidelity models with millions of grids. The errors in the thermal and flow field calculations remain below 3.06% and 3.01%, respectively. Furthermore, the computation time is reduced from hours to minutes, with the thermal field achieving a 97-fold speed-up and the flow field an 83-fold speed-up, yielding an average speed-up factor of 90. This enables fast computation of the transformer’s thermal–fluid-coupled field and provides effective support for the application of digital twin technology in multi-physics field simulations of power equipment. Full article