Search Results (1,443)

Service Function Chain (SFC) is a framework that dynamically orchestrates Virtual Network Functions (VNFs) and is essential to enhancing resource scheduling efficiency. However, traditional scheduling methods face several limitations, such as low matching efficiency, suboptimal resource utilization, and limited global coordination capabilities. To this end, we propose a multi-objective scheduling algorithm for SFCs based on matching games (SFC-GS). First, a multi-objective cooperative optimization model is established that aims to reduce scheduling time, increase request acceptance rate, lower latency, and minimize resource consumption. Second, a matching model is developed through the construction of preference lists for service nodes and VNFs, followed by multi-round iterative matching. In each round, only the resource status of the current and neighboring nodes is evaluated, thereby reducing computational complexity and improving response speed. Finally, a hierarchical batch processing strategy is introduced, in which service requests are scheduled in priority-based batches, and subsequent allocations are dynamically adjusted based on feedback from previous batches. This establishes a low-overhead iterative optimization mechanism to achieve global resource optimization. Experimental results demonstrate that, compared to baseline methods, SFC-GS improves request acceptance rate and resource utilization by approximately 8%, reduces latency and resource consumption by around 10%, and offers clear advantages in scheduling time. Full article

Understanding internal resonance phenomena in transformer windings is essential for evaluating insulation performance and preventing equipment failure under transient conditions. This study presents a measurement-based modeling approach to assess internal voltage distributions in a high-voltage transformer winding of a dry-type distribution transformer. Frequency-domain admittance and voltage transfer functions were experimentally obtained and approximated using vector fitting. The resulting models were employed to simulate time-domain responses through a two-step procedure that integrates electromagnetic transient simulations of the terminal circuit with frequency-derived internal voltage models. The validation was performed using a sinusoidal excitation at 51 kHz, corresponding to the first-mode resonance frequency. Simulated internal voltages and input currents showed close agreement with experimental measurements, confirming the model’s accuracy. The study identified two critical resonance frequencies at 51 kHz and 59 kHz, at which voltage amplification can become severe. At 51 kHz, the maximum overvoltage reached nearly seven times the applied voltage at the winding midpoint, indicating a substantial risk of dielectric failure. These findings highlight the importance of accurately characterizing internal resonances in transformer windings, especially during insulation coordination studies. The proposed methodology offers an effective tool for analyzing internal overvoltages and contributes to the development of more robust transformer design and protection strategies. Full article

The rapid advancement of artificial intelligence in automotive applications, particularly in Advanced Driver-Assistance Systems (ADAS) and smart battery management on electric vehicles, increases the demand for efficient near-sensor processing. While the problem of linear algebra in machine learning is well-addressed by existing accelerators, the computation of nonlinear activation functions is usually delegated to the host CPU, resulting in energy inefficiency and high computational costs. This paper introduces TOXOS, a RISC-V-compliant coprocessor designed to address this challenge. TOXOS implements the COordinateRotation DIgital Computer (CORDIC) algorithm to efficiently compute nonlinear functions. Taking advantage of RISC-V modularity and extendability, TOXOS seamlessly integrates with existing computing architectures. The coprocessor’s configurability enables fine-tuning of the area-performance tradeoff by adjusting the internal parallelism, the CORDIC iteration count, and the overall latency. Our implementation on a 65nm technology demonstrates a significant improvement over CPU-based solutions, showcasing a considerable speedup compared to the glibc implementation of nonlinear functions. To validate TOXOS’s real-world impact, we integrated TOXOS in an actual RISC-V microcontroller targeting the on-vehicle execution of machine learning models. This work addresses a critical gap in transcendental function computation for AI, enabling real-time decision-making for autonomous driving systems, maintaining the power efficiency crucial for electric vehicles. Full article

This paper presents Rain-Cloud Condensation Optimizer (RCCO), a nature-inspired metaheuristic that maps cloud microphysics to population-based search. Candidate solutions (“droplets”) evolve under a dual-attractor dynamic toward both a global leader and a rank-weighted cloud core, with time-decaying coefficients that progressively shift emphasis from exploration to exploitation. Diversity is preserved via domain-aware coalescence and opposition-based mirroring sampled within the coordinate-wise band defined by two parents. Rare heavy-tailed “turbulence gusts” (Cauchy perturbations) enable long jumps, while a wrap-and-reflect scheme enforces feasibility near the bounds. A sine-map initializer improves early coverage with negligible overhead. RCCO exposes a small hyperparameter set, and its per-iteration time and memory scale linearly with population size and problem dimension. RCOO has been compared with 21 state-of-the-art optimizers, over the CEC 2022 benchmark suite, where it achieves competitive to superior accuracy and stability, and achieves the top results over eight functions, including in high-dimensional regimes. We further demonstrate constrained, real-world effectiveness on five structural engineering problems—cantilever stepped beam, pressure vessel, planetary gear train, ten-bar planar truss, and three-bar truss. These results suggest that a hydrology-inspired search framework, coupled with simple state-dependent schedules, yields a robust, low-tuning optimizer for black-box, nonconvex problems. Full article

Aiming at the speed chattering problem caused by high-frequency square wave injection in permanent magnet synchronous motors (PMSMs) during low-speed operation (200–500 r/min), this study intends to improve the rotor position estimation accuracy of sensorless control systems as well as the system’s ability to resist harmonic interference and sudden load changes. The goal is to enhance the control performance of traditional control schemes in this scenario and meet the requirement of stable low-speed operation of the motor. First, the study analyzes the harmonic error propagation mechanism of high-frequency square wave injection and finds that the traditional PI phase-locked loop (PI-PLL) is susceptible to high-order harmonic interference during demodulation, which in turn leads to position estimation errors and periodic speed fluctuations. Therefore, the extended state observer phase-locked loop (ESO-PLL) is adopted to replace the traditional PI-PLL. A third-order extended state observer (ESO) is used to uniformly regard the system’s unmodeled dynamics, external load disturbances, and harmonic interference as “total disturbances”, realizing real-time estimation and compensation of disturbances, and quickly suppressing the impacts of harmonic errors and sudden load changes. Meanwhile, a dynamic pole placement strategy for the speed loop is designed to adaptively adjust the controller’s damping ratio and bandwidth parameters according to the motor’s operating states (loaded/unloaded, steady-state/transient): large poles are used in the start-up phase to accelerate response, small poles are switched in the steady-state phase to reduce errors, and a smooth attenuation function is used in the transition phase to achieve stable parameter transition, balancing the system’s dynamic response and steady-state accuracy. In addition, high-frequency square wave voltage signals are injected into the dq axes of the rotating coordinate system, and effective rotor position information is extracted by combining signal demodulation with ESO-PLL to realize decoupling of high-frequency response currents. Verification through MATLAB/Simulink simulation experiments shows that the improved strategy exhibits significant advantages in the low-speed range of 200–300 r/min: in the scenario where the speed transitions from 200 r/min to 300 r/min with sudden load changes, the position estimation curve of ESO-PLL basically overlaps with the actual curve, while the PI-PLL shows obvious deviations; in the start-up and speed switching phases, dynamic pole placement enables the motor to respond quickly without overshoot and no obvious speed fluctuations, whereas the traditional fixed-pole PI control has problems of response lag or overshoot. In conclusion, the “ESO-PLL + dynamic pole placement” cooperative control strategy proposed in this study effectively solves the problems of harmonic interference and load disturbance caused by high-frequency square wave injection in the low-speed range and significantly improves the accuracy and robustness of PMSM sensorless control. This strategy requires no additional hardware cost and achieves performance improvement only through algorithm optimization. It can be directly applied to PMSM control systems that require stable low-speed operation, providing a reliable solution for the promotion of sensorless control technology in low-speed precision fields. Full article

Fusarium oxysporum-induced root rot severely threatens global soybean production, yet limited understanding of resistance mechanisms constrains breeding progress. This study conducted comparative transcriptomic analysis between highly resistant (Xiaoheiqi) and susceptible (L83-4752) soybean accessions following pathogen inoculation across four time points (8–17 days post-infection). RNA-seq analysis identified 1496 differentially expressed genes following pathogen challenge. KEGG pathway enrichment analysis revealed significant enrichment in MAPK signaling pathway (12 genes) and plant–pathogen interaction pathway (13 genes). Eight genes co-occurred in both pathways, with GmCML (Glyma.10G178400) exhibiting the most dramatic differential expression among these candidates. This gene encodes a 151-amino acid calmodulin-like protein showing 185-fold higher expression in resistant plants at 17 days post-inoculation, confirmed by qRT-PCR validation. Functional validation through transgenic hairy root overexpression demonstrated that GmCML significantly enhanced disease resistance by coordinately activating antioxidant defense systems. Overexpression of GmCML in transgenic soybean enhanced resistance to F. oxysporum by modulating the activity of antioxidant enzymes (superoxide dismutase, SOD; peroxidase, POD; catalase, CAT) and the accumulation of osmoregulatory substances (proline and soluble sugars). Population genetic analysis of 295 diverse soybean accessions revealed three GmCML haplotypes based on promoter region polymorphisms. Two favorable variants (Hap2 and Hap3) conferred significantly lower disease indices and exhibited evidence of positive selection during domestication, indicating evolutionary importance in disease resistance. This research provides the first comprehensive characterization of GmCML’s role in soybean–Fusarium interactions, establishing this calmodulin-like protein as a regulatory hub linking calcium signaling to coordinated defense responses. The identified natural variants and functional mechanisms offer validated targets for both marker-assisted breeding and genetic engineering approaches to enhance soybean disease resistance. Full article

Fixed-time coordination is critical for networked unmanned aerial vehicle (UAV) systems to accomplish time-sensitive missions such as rapid target encirclement, cooperative search, and emergency response. However, dynamic topology variations, caused by mission reassignment, obstacle avoidance, or communication disruptions, along with model uncertainties and external disturbances, present significant challenges to robust and timely coordination. To address these issues, this paper investigates the fixed-time bipartite containment tracking control problem of uncertain multi-UAV systems under switching communication topologies. A neuroadaptive robust containment tracking controller is developed to guarantee that all follower UAVs converge within a fixed time to the region spanned by multiple dynamic leaders, regardless of initial conditions. To handle unknown nonlinear dynamics, a neuroadaptive estimator is constructed using online parameter adaptation. A topology-dependent multiple Lyapunov function framework is employed to rigorously establish fixed-time convergence under switching topologies. Moreover, an explicit upper bound on the convergence time is analytically derived as a function of system parameters and dwell time constraints. Comparative analysis demonstrates that the proposed method reduces conservativeness in convergence time estimation and enhances robustness against frequent topology changes. Simulation results are provided to validate the effectiveness and advantages of the proposed control scheme. Full article

Collecting crop growth data in field environments is crucial for breeding research. The team’s current autonomous soybean phenotyping system requires manual control to start and stop data collection. To address the aforementioned issues, this study innovatively proposes an elliptical calibration rotating geofencing technique. Preprocess coordinates using Z-scores and mean fitting perform global error calibration via weighted least squares, calculate the inclination angle between the row direction and the relative standard direction by fitting a straight line to the same row of data, and establish a rotation model based on geometric feature alignment. Results show that the system achieves an average response time of 0.115 s for geofence entry, with perfect accuracy and Recall rates of 1, meeting the requirements for starting and stopping geographic fencing in soybean ridge trial plots. This technology provides the critical theoretical foundation for enabling a dynamic, on-demand automatic start–stop functionality in smart data collection devices for soybean field trial zones within precision agriculture. Full article

We developed a self-consistent double-null description of an evaporating white-hole spacetime by embedding the outgoing Vaidya solution in a coordinate system that remains regular across the future horizon. Starting from the radiation-coordinate form, we specialize in retarded time so that a monotonically decreasing mass function $M\left(u\right)$ encodes outgoing positive-energy flux. Expressing the metric in null coordinates $(u,v)$, Einstein’s equations for a single-directional null-dust stress–energy tensor, $T_{uu}=\rho \left(u\right)$, then reduce to one first-order PDE for the areal radius: ${\mathsf{\partial }}_{v}r=B\left(u\right)\left(1-2M\left(u\right)/r\right)$. Its integral, $r+2M\left(u\right)ln|r-2M(u\left)\right|=v-C\left(u\right)$, defines an implicit foliation of outgoing null cones. The metric coefficient follows algebraically as $f(u,v)=1-2M\left(u\right)/r$. Residual gauge freedom in $B\left(u\right)$ and $C\left(u\right)$ is fixed so that u matches the Bondi retarded time at null infinity, while v remains analytic at the apparent horizon, generalizing the Kruskal prescription to dynamical mass loss. In the limit $M\left(u\right)\to M$, the construction reduces to the familiar Eddington–Finkelstein and Kruskal forms. Our solution, therefore, provides a compact analytic framework for studying white-hole evaporation, Hawking-like energy fluxes, and back-reaction in spherically symmetric settings without encountering coordinate singularities. Full article

Addressing the challenge of high-precision time-coordinated path planning for manned and unmanned aerial vehicle (UAV) clusters operating in complex dynamic environments during missions like high-level autonomous coordination, this paper proposes a reinforcement learning-based Adaptive Grey Wolf Optimizer (RL-GWO) method. We formulate a comprehensive multi-objective cost function integrating total flight distance, mission time, time synchronization error, and collision penalties. To solve this model, we design multiple improved GWO strategies and employ a Q-Learning framework for adaptive strategy selection. The RL-GWO algorithm is embedded within a dual-layer “global planning + dynamic replanning” framework. Simulation results demonstrate excellent convergence and robustness, achieving second-level time synchronization accuracy while satisfying complex constraints. In dynamic scenarios, the method rapidly generates safe evasion paths while maintaining cluster coordination, validating its practical value for heterogeneous UAV operations. Full article

Background: Early childhood is crucial for motor and cognitive development, with physical activity playing a key role. Mini-trampoline exercises may offer an effective approach to enhance these domains. Methods: This study assessed the effects of a mini-trampoline program on executive functions and motor skills in Tunisian preschoolers. Fifty-four children (age 3.87 ± 0.47 years) participated in a 12-week intervention, divided into a control group (n = 27), following standard activities, and an experimental group (n = 27), engaging in mini-trampoline exercises. Pre- and post-tests measured motor skills like postural steadiness, balance, and coordination, as well as cognitive functions, including working memory (WM) and inhibition. Results: Significant improvements were observed in the experimental group for functional mobility, postural steadiness, lower body strength, and inhibition (p < 0.001), whereas the control group showed minimal changes. ANOVA revealed no significant group × time effects, except for a trend in postural steadiness (p = 0.062), suggesting a potential benefit of the intervention. Conclusions: These findings highlight the potential of mini-trampoline exercises to enhance motor skills and specific executive functions in preschoolers, supporting their overall development. Full article

In DC microgrids, the combination of pulsed loads and renewable energy sources significantly impairs system stability, especially in highly dynamic operating environments. The resilience and reaction time of conventional proportional–integral (PI) controllers are often inadequate when managing the nonlinear dynamics of hybrid energy storage systems. This research suggests a frequency-decomposed fractional-order control strategy for stabilizing DC microgrids with solar, batteries, and supercapacitors. The control architecture divides system disturbances into low- and high-frequency components, assigning high-frequency compensation to the ultracapacitor (UC) and low-frequency regulation to the battery, while a fractional-order controller (FOC) enhances dynamic responsiveness and stability margins. The proposed approach is implemented and assessed in MATLAB/Simulink (version R2023a) using comparison simulations against a conventional PI-based control scheme under scenarios like pulsed load disturbances and fluctuations in renewable generation. Grey Wolf Optimizer (GWO), a metaheuristic optimization procedure, has been used to tune the parameters of the FOPI controller. The obtained results using the same conditions were compared using an optimal fractional-order PI controller (FOPI) and a conventional PI controller. The microgrid with the best FOPI controller was found to perform better than the one with the PI controller. Consequently, the objective function is reduced by 80% with the proposed optimal FOPI controller. The findings demonstrate that the proposed method significantly enhances DC bus voltage management, reduces overshoot and settling time, and lessens battery stress by effectively coordinating power sharing with the supercapacitor. Also, the robustness of the proposed controller against parameters variations has been proven. Full article

The integration of renewable energy systems and electrified transportation requires advanced energy storage solutions capable of providing both high energy density and fast dynamic response. Hybrid energy storage systems offer a promising approach by combining complementary battery chemistries, exploiting their respective strengths while mitigating individual limitations. This study presents the design, modeling, and optimization of a hybrid energy storage system composed of two high-energy lithium nickel manganese cobalt batteries and one high-power lithium titanate oxide battery, interconnected through a triple dual-active multi-port converter. A nonlinear model predictive control strategy was employed to optimally distribute battery currents while respecting constraints such as state of charge limits, current bounds, and converter efficiency. Equivalent circuit models were used for real-time state of charge estimation, and converter losses were explicitly included in the optimization. The main contributions of this work are threefold: (i) verification of the model predictive control strategy in diverse applications, including residential renewable energy systems with photovoltaic generation and electric vehicles following the World Harmonized Light-duty Vehicle Test Procedure driving cycle; (ii) explicit inclusion of the power converter model in the system dynamics, enabling realistic coordination between batteries and power electronics; and (iii) incorporation of converter efficiency into the cost function, allowing for simultaneous optimization of energy losses, battery stress, and operational constraints. Simulation results demonstrate that the proposed model predictive control strategy effectively balances power demand, extends system lifetime by prioritizing lithium titanate oxide battery during transient peaks, and preserves lithium nickel manganese cobalt cell health through smoother operation. Overall, the results confirm that the proposed hybrid energy storage system architecture and control strategy enables flexible, reliable, and efficient operation across diverse real-world scenarios, providing a pathway toward more sustainable and durable energy storage solutions. Full article

To address the problems of low efficiency and poor real-time performance in underwater acoustic modeling, as well as the requirement of maximizing search coverage for underwater target search path planning, this paper proposed an efficient path planning method based on Sound Speed Profile (SSP) clustering. Firstly, the SSPs were dimensionally reduced via Empirical Orthogonal Function (EOF) decomposition, and the sea area was divided into 10 acoustic sub-areas using K-means clustering after fusing geographic coordinates and terrain information, thereby constructing a block-wise sound field model. Secondly, with the active sonar equation as the core, sonar parameters such as the noise level and target strength were solved, respectively, to generate a spatial distribution matrix of search distances. Finally, an Improved Ant Colony Optimization (IACO) algorithm was modified by dynamically setting the pheromone evaporation rate and improving the heuristic information for search path optimization. Numerical experiments showed that clustering significantly improves the efficiency of sound field modeling, reducing the time consumption of the transmission loss calculation from 24.74 h to 10.84 min. The IACO increased the average search coverage from 47.96% to 86.01%, with an improvement of 79.34%. The performance of IACO is superior to those of the compared algorithms, providing support for efficient underwater target search. Full article

Background: Driving is a coordinated psychomotor activity that involves reaction time, attention, and decision-making. Psychoactive substances such as 2-phenylethylamine (PEA) derivatives and opioids may affect these functions and contribute to traffic safety. This systematic review revealed the effects of the selected PEA derivatives and opioids on psychomotor performance among drivers and potential road safety outcomes. Methods: The review followed PRISMA 2020 standards. Using the PICO method, we conducted a systematic search in Embase, PubMed, and Web of Science (2000–2025). Included studies involved adult participants and quantified the effect of PEA derivatives or opioids on driving-related psychomotor function. Thirty-one articles, such as randomized controlled trials, crossover studies, observational studies, and simulator-based studies, were examined. Risk of bias was evaluated using the RoB2 tool. Results: Evidence indicates therapeutic amphetamine and methylphenidate doses can enhance psychomotor function and safety in patients with ADHD. Recreational or high-dose use of methamphetamine and MDMA is associated with impaired coordination, variable speed, and increased impulsivity. Opioid effects are tolerance- and dose-dependent. Small therapeutic doses of fentanyl in chronically treated patients do not notably impair driving. On the other hand, methadone and tramadol commonly cause somnolence, retardation of reaction, and increased accident risk. Conclusions: The impact of opioids and PEA derivatives on psychomotor function is multifactorial, depending on dose, time, route of administration, and patient status. These substances can either improve or impair driving safety. The findings confirm the need for individual-specific pharmacotherapy treatment. They also highlight the importance of further studies to formulate evidence-based clinical and legislative guidelines. Full article