Search Results (17,557)

This paper studies joint data collection and wireless power transfer in a UAV-assisted IoT network. A rotary-wing UAV follows a fly–hover–communicate cycle. At each hover, it simultaneously receives uplink data in full-duplex mode while delivering radio-frequency energy to nearby devices. Using a realistic propulsion-power model and a nonlinear energy-harvesting model, we formulate trajectory and hover control as a multi-objective optimization problem that maximizes the aggregate data rate and total harvested energy while minimizing the UAV’s energy consumption over the mission. To enable flexible trade-offs among these objectives under time-varying conditions, we propose a dynamic, state-adaptive weighting mechanism that generates environment-conditioned weights online, which is integrated into an enhanced deep deterministic policy gradient (DDPG) framework. The resulting dynamic-weight MODDPG (DW-MODDPG) policy adaptively adjusts the UAV’s trajectory and hover strategy in response to real-time variations in data demand and energy status. Simulation results demonstrate that DW-MODDPG achieves superior overall performance and a more favorable balance among the three objectives. Compared with the fixed-weight baseline, our algorithm increases total harvested energy by up to 13.8% and the sum data rate by up to 5.4% while maintaining comparable or even lower UAV energy consumption. Full article

The sexual generation of Schlechtendalia chinensis (Bell) is pivotal for gallnut yield yet cannot feed due to mouthpart degeneration. Could respiratory metabolic rate (RMR) modulation compensate for nutritional deficits? We quantified the RMR across key developmental stages of sexual morphs (including parental sexuparae and progeny fundatrices) using an LI-6400XT portable photosynthesis system equipped with a customized insect respiration chamber (6400-89). All morphotypes exhibited significantly lower nocturnal RMRs compared to their diurnal rates (p < 0.05), while RMRs did not differ significantly between morning (9:00–12:00) and afternoon (14:00–17:00) (p > 0.05). Significant RMR variation occurred among morphotypes: females and sexuparae displayed the lowest rates, fundatrices were intermediate, and males exhibited remarkably elevated rates (2–3 times higher than those of females or sexuparae). Both sexes showed a characteristic RMR trajectory: elevated at birth and declining during early postnatal development, followed by a gradual resurgence that culminated in peak values on postnatal day 8, coinciding with mating. This physiological zenith was immediately succeeded by marked respiratory metabolic downregulation following copulation, with RMRs decreasing substantially during the post-copulatory phase. Our findings demonstrate significant intergenerational and intersexual RMR differentiation. This research addresses critical knowledge gaps in the respiratory metabolism of S. chinensis, is the first to elucidate a nutrient adaptation strategy through respiratory metabolic regulation under non-trophic conditions, and provides actionable insights for optimizing gallnut production in controlled cultivation systems. Full article

This study presents the design and evaluation of AquaSignalNet, a deep learning-based system for recognizing underwater visual commands to enable the autonomous navigation of a Remotely Operated Vehicle (ROV). The system is built on a ResNet50 V2 architecture and trained with a custom dataset, UVSRD, comprising 33,800 labeled images across 12 gesture classes, including directional commands, speed values, and vertical motion instructions. The model was deployed on a Raspberry Pi 4 integrated with a TIVA C microcontroller for real-time motor control, a PID-based depth control loop, and an MPU9250 sensor for orientation tracking. Experiments were conducted in a controlled pool environment using printed signal cards to define two autonomous trajectories. In the first trajectory, the system achieved 90% success, correctly interpreting a mixed sequence of turns, ascents, and speed changes. In the second, more complex trajectory, involving a rectangular inspection loop and multi-layer navigation, the system achieved 85% success, with failures mainly due to misclassification resulting from lighting variability near the water surface. Unlike conventional approaches that rely on QR codes or artificial markers, AquaSignalNet employs markerless visual cues, offering a flexible alternative for underwater inspection, exploration, and logistical operations. The results demonstrate the system’s viability for real-time gesture-based control. Full article

This study proposes an Improved Whale Migration Algorithm (IWMA) to overcome the shortcomings of the original Whale Migration Algorithm, which suffers from premature convergence and insufficient local exploitation in high-dimensional multimodal optimization. IWMA introduces three enhancements: circle chaotic initialization to improve population diversity, a three-layer cooperative search framework to achieve a stronger balance between exploration and exploitation, and a dynamic adaptive mechanism with t-distribution re-exploration to reinforce both global escaping and local refinement. On the CEC2017 benchmark suite, IWMA demonstrates clear superiority over seven representative algorithms, delivering the best results on 27 out of 29 functions by best, 25 by mean, and 23 by standard deviation in 30 dimensions, and on 25, 18, and 18 functions, respectively, in 50 dimensions. Compared with other migration-based optimizers, its average rank improves by more than 30 percent, while runtime analysis shows only a small additional overhead of 7 to 12 percent. These outcomes, supported by convergence curves, boxplots, radar charts, and Wilcoxon tests, confirm the effectiveness of the proposed improvements. In six multi-UAV path planning scenarios, IWMA reduces the average cost by 14.5 percent compared with WMA and achieves up to 32.1 percent reduction in the most complex case. Overall, its average cost decreases by 27.4 percent across seven competitors, with a 23.6 percent improvement in the best solutions. These results demonstrate that the proposed modifications are effective, enabling IWMA to transfer its performance gains from benchmark tests to practical multi-UAV cooperative mission planning, where it consistently produces safer and smoother trajectories under complex constraints. Full article

The control of a biped robot is a challenging task due to the hard-to-stabilize dynamics. Generating a suitable walking reference trajectory is a key aspect of this problem. This article proposes a novel method of generating walking patterns for biped robots. The method integrates the double support phase and the single support phase into one step, and uses this step as the unit for trajectory generation through quadratic optimization with terminal constraints based on the Linear Inverted Pendulum Model, enabling us to shorten the optimization horizon while still generating natural walking trajectories. Moreover, by restricting the position and acceleration of the center of mass (COM) in the vertical direction, an excessive constraint is formed on the Zero Moment Point (ZMP) to offset the nonlinear effects of the COM’s vertical motion on the ZMP. This allows the COM of the robot to change in the vertical direction while maintaining the linearity of the optimization problem. Finally, the performance of the proposed method is validated by simulations and experiments of walking on flat ground and stairs using a position-controlled biped robot, Neubot. Full article

This study focuses on the design and implementation of an Adaptive High-Order sliding mode control for by-wire ground vehicle systems. The controller integrates advanced technologies such as Active Front Steering (AFS) and Rear Torque Vectoring (RTV), aimed at enhancing vehicle dynamics. However, lateral velocity remains one of the most challenging variables to measure, even in modern vehicles. To address this limitation, a High-Order Sliding Mode (HOSM)-based observer with adaptive gains is proposed. The HOSM observer provides critical information for the operation of the dynamic controller, ensuring the tracking of desired references. Compared with traditional observers, the proposed adaptive HOSM observer achieves finite-time convergence of state estimation errors and exhibits enhanced robustness against external disturbances, as confirmed through simulation results. The adaptive gains dynamically adjust the system parameters, enhancing its precision and flexibility under changing environmental conditions. This dynamic approach ensures efficient and reliable performance, enabling the system to respond effectively to complex scenarios. The stability of the dynamic HOSM controller with adaptive gain is analyzed through a Lyapunov-based approach, providing solid theoretical guarantees. Its performance is evaluated using detailed simulations conducted in CarSim 2017 software. The simulation results demonstrate that the proposed controller is highly effective in ensuring accurate trajectory tracking. Full article

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

Columnar-structured thermal barrier coatings deposited via the suspension plasma spray process have attracted significant attention due to their long thermal cycling life and high cost-effectiveness. In this work, the effects of suspension properties, including solvent type, viscosity, and particle size, on the formation of different coating microstructures were investigated via a comparative study. Two different kinds of solvents (water and ethanol) and particles of different sizes (D50 = 0.45 μm and 1.2 μm) were used to prepare suspensions for coating deposition, respectively. When using suspensions containing small-sized particles as feedstock, coatings deposited from the ethanol-based suspension showed columnar microstructures with inter-column crevices, while the water-based suspension resulted in cracked–columnar microstructures, showing a mixture of columns and cracks. When the large-sized particles were used to prepare the suspension, both the ethanol-based suspension and the water-based suspension resulted in homogeneous coating microstructures. The formation mechanism of different microstructures was investigated by modelling the diverted plasma jet and the in-flight particle movement during the impingement period. Particles smaller than 2 μm were strongly affected by the diverted plasma gas, showing obvious oblique impinging trajectories, while particles larger than 3 μm kept their original trajectories and impinged on the substrate orthogonally. The formation mechanism of different microstructures was elaborated by analyzing the impinging trajectories of particles transitioning from different suspensions. Full article

Smart tourism destinations, shaped by the integration of tourism and information technology, have become a central theme in international academic research. This study employs bibliometric methods using CiteSpace to conduct co-authorship, co-citation, keyword co-occurrence, and burst analyses, with the aim of mapping the knowledge structure and research evolution of the field. Drawing on 232 articles from the Web of Science Core Collection (2013–2025), the results reveal a shift from technology-centered approaches toward themes of visitor experience, collaborative governance, and sustainable development. The Universitat d’Alacant (Spain) and The Hong Kong Polytechnic University (China) have emerged as leading research hubs, with Ivars-Baidal and colleagues as major contributors. Foundational studies by Buhalis and Gretzel continue to shape the domain. Keyword trends highlight increasing attention to technological efficiency and sustainable ethics. Overall, the study traces the developmental trajectory of smart tourism destinations, proposes a systematic knowledge framework, and identifies future directions for theoretical integration and methodological innovation. The findings provide both conceptual insights for academic research and strategic guidance for destination governance and policy. Full article

In this paper, a UAV target tracking method with 6G integrated sensing and communication (ISAC) is proposed to address the surveillance requirements for unmanned aerial vehicle (UAV) targets in the context of the rapid development of low-altitude economy. Firstly, a target tracking system model for UAVs is established based on the ISAC base station transceiver architecture. Then, an unscented Kalman filter (UKF) target tracking framework is designed to tackle the occlusion effect during UAV navigation. Specifically, the measurement position information of the UAV is obtained through a spatial rotation-based parameter estimation method. Subsequently, occlusion is detected by analyzing the Line-of-Sight (LoS) visibility between the UAV and the base station. On this basis, the problem of short-term and long-term trajectory loss under occlusion is solved by integrating cubic interpolation with a constant velocity (CV) model, which enables real-time UAV trajectory tracking. Finally, simulation results demonstrate that: (1) under no occlusion, the average estimation errors of the X/Y/Z axes are 0.82 m, 0.79 m, and 0.68 m, respectively; (2) under short-term occlusion, the average errors of the X/Y/Z axes are 1.25 m, 2.18 m, and 1.05 m, with a convergence time of 1 s after LoS recovery; (3) under long-term occlusion, the average errors of the X/Y/Z axes are 2.87 m, 3.79 m, and 1.85 m, with a convergence time of 5 s after LoS recovery; (4) the velocity estimation error can quickly converge to within 0.2 m/s after re-acquiring observations. The proposed method exhibits small trajectory and velocity estimation errors in different occlusion scenarios, effectively meeting the requirements for UAV target tracking. Full article

In this study, a vision-based remote and synchronized control scheme is proposed for the double six-DOF robotic manipulators. Using an Intel RealSense D435 depth camera and MediaPipe skeletal image feature technique, the operator’s 3D hand pose is captured and mapped to the robot’s workspace via coordinate transformation. Inverse kinematics is then applied to compute the necessary joint angles for synchronized motion control. Implemented on double robotic manipulators with the MoveIt framework, the system successfully achieves a collaborative teleoperation control task to transfer an object from a robotic manipulator to another one. Further, moving average filtering techniques are used to enhance trajectory smoothness and stability. The framework demonstrates the feasibility and effectiveness of non-contact, vision-guided multi-robot control for applications in teleoperation, smart manufacturing, and education. Full article

The development of FGFR1-driven stem cell leukemia and lymphoma syndrome (SCLL) in mouse models is accompanied by an increase in highly heterogenous myeloid derived suppressor cells (MDSCs), which promote immune evasion. To dissect this heterogeneity, we used a combination of CyTOF and scRNA-Seq to define the phenotypes and genotypes of these MDSCs. CyTOF demonstrated increased levels of circulating macrophages in the peripheral blood of leukemic mice, and flow cytometry demonstrated that these macrophages were derived from Ly6C^Hi M-MDSC as well as the Ly6C^Int and Ly6C^Low monocytic populations. Consistently, scRNA-Seq analysis demonstrated the accumulation of non-classical monocytes (ncMono) during leukemia progression, which also express macrophage markers. These leukemia-induced macrophages show continuous transcriptional reprogramming during leukemia progression, with the upregulation of cellular stress response genes Hspa1a and Hspa1b and inflammation-related gene Nfkbia. Trajectory analysis revealed a transition from classical monocytes (cMono) to ncMono, and potential genes orchestrating this transition process have been identified. Furthermore, T-cell suppression assays demonstrated the immune suppressive abilities of leukemia-induced circulatory macrophages. Targeting these macrophages with the GW2580 CSF1R inhibitor leads to restored immune surveillance and improved survival. Overall, we demonstrate that circulating macrophages are responsible, at least in part, for the immune suppression in SCLL leukemia models, and targeting macrophages in this system improves the survival of leukemic mice. Full article

To improve the adaptability of 4WID electric vehicles under various operating conditions, this study introduces a model predictive control approach utilizing a neural network for adaptive weight parameter prediction, which integrates four-wheel steering and longitudinal driving force control. To address the difficulty in adjusting the MPC weight parameters, the neural network undergoes offline training, and the Snake Optimization method is used to iteratively optimize the controller parameters under diverse driving conditions. To further enhance vehicle stability, the real-time stability state of the vehicle is assessed using the $\beta -\dot{\beta }$ phase plane method. The influence of vehicle speed and road adhesion on the instability boundary of the phase plane is comprehensively considered to design a stability controller based on different instability degree zones. This includes an integral sliding mode controller that accounts for both vehicle tracking capability and stability, as well as a PID controller, which calculates the additional yaw moment based on the degree of instability. Finally, an optimal distribution control algorithm coordinates the longitudinal driving torque and direct yaw moment while also considering the vehicle’s understeering characteristics in determining the torque distribution for each wheel. The simulation results show that under various operating conditions, the proposed control strategy achieves smaller tracking errors and more concentrated phase trajectories compared to traditional controllers, thereby improving path tracking precision, vehicle stability, and adaptability to varying conditions. Full article

The increasing adoption of electric delivery fleets introduces significant challenges related to uneven energy utilization and suboptimal scheduling efficiency. Vehicle-to-Vehicle (V2V) energy sharing presents a promising solution, but its effectiveness critically depends on precise matching and co-optimization within dynamic urban traffic environments. This paper proposes a hierarchical optimization framework to minimize total fleet operational costs, incorporating a comprehensive analysis that includes battery degradation. The core innovation of the framework lies in coupling high-level path planning with low-level real-time speed control. First, a high-fidelity energy consumption surrogate model is constructed through model predictive control simulations, incorporating vehicle dynamics and signal phase and timing information. Second, the spatiotemporal longest common subsequence algorithm is employed to match the spatio-temporal trajectories of energy-provider and energy-consumer vehicles. A battery aging model is integrated to quantify the long-term costs associated with different operational strategies. Finally, a multi-objective particle swarm optimization algorithm, integrated with MPC, co-optimizes the rendezvous paths and speed profiles. In a case study based on a logistics network, simulation results demonstrate that, compared to the conventional station-based charging mode, the proposed V2V framework reduces total fleet operational costs by a net 12.5% and total energy consumption by 17.4% while increasing the energy utilization efficiency of EV-Ps by 21.4%. This net saving is achieved even though the V2V strategy incurs a marginal increase in battery aging costs, which is overwhelmingly offset by substantial savings in logistical efficiency. This study provides an efficient and economical solution for the dynamic energy management of electric fleets under realistic traffic conditions, contributing to a more sustainable and resilient urban logistics ecosystem. Full article

The continuous growth in global demand for mineral resources and the increasing difficulty of mineral exploration have created bottlenecks for traditional mineral prediction methods in handling complex geological information and large amounts of data. This review aims to explore the latest research progress in machine learning technology in the field of large-scale mineral prediction from 2016 to 2025. By systematically searching the Web of Science core database, we have screened and analyzed 255 high-quality scientific studies. These studies cover key areas such as mineral information extraction, target area selection, mineral regularity modeling, and resource potential evaluation. The applied machine learning technologies include Random Forests, Support Vector Machines, Convolutional Neural Networks, Recurrent Neural Networks, etc., and have been widely used in the exploration and prediction of various mineral deposits such as porphyry copper, sandstone uranium, and tin. The findings indicate a substantial shift within the discipline towards the utilization of deep learning methodologies and the integration of multi-source geological data. There is a notable rise in the deployment of cutting-edge techniques, including automatic feature extraction, transfer learning, and few-shot learning. This review endeavors to synthesize the prevailing state and prospective developmental trajectory of machine learning within the domain of large-scale mineral prediction. It seeks to delineate the field’s progression, spotlight pivotal research dilemmas, and pinpoint innovative breakthroughs. Full article