Search Results (557)

Internal waves play a critical role in material transport, vertical mixing, and energy dissipation within stratified aquatic systems. Their dynamics are strongly modulated by thermal stratification and surface meteorological forcing. This study examines the influence of interannual meteorological variability from 1980 to 2010 on internal wave behavior using a series of numerical simulations in Lake Biwa in Japan. In each simulation, air temperature, wind speed, or precipitation was perturbed by ±2 standard deviations relative to the climatological mean. Power spectral analysis of simulated velocity fields was conducted for the surface, thermocline, and bottom layers, focusing on super-inertial (6–16 h), near-inertial (~16–30 h), and sub-inertial (>30 h) frequency bands. The results show that higher air temperatures intensify stratification and enhance near-inertial internal waves, particularly within the thermocline, whereas cooler conditions favor sub-inertial wave dominance. Increased wind speeds amplify internal wave energy across all layers, with the strongest effect occurring in the high-frequency band due to intensified wind stress and vertical shear, while weaker winds suppress wave activity. Precipitation variability primarily affects surface stratification, exerting more localized and weaker impacts. These findings highlight the non-linear, depth-dependent responses of internal waves to atmospheric drivers and improve understanding of the coupling between climate variability and internal wave energetics. The insights gained provide a basis for more accurate predictions and sustainable management of stratified aquatic ecosystems under future climate scenarios. Full article

Aiming at the problems of high-dimensional nonlinear constraints, multi-objective conflicts, and low solution efficiency in microgrid optimal scheduling, this paper proposes a multi-objective Harris Hawk–Grey Wolf hybrid intelligent algorithm (IMOHHOGWO). The problem of balancing the global exploration and local exploitation of the algorithm is solved by introducing an adaptive energy factor and a nonlinear convergence factor; in terms of the algorithm’s exploration scope, the stochastic raid strategy of Harris Hawk optimization (HHO) is used to generate diversified solutions to expand the search scope, and constraints such as the energy storage SOC and DG outflow are finely tuned through the α/β/δ wolf bootstrapping of the Grey Wolf Optimizer (GWO). It is combined with a simulated annealing perturbation strategy to enhance the adaptability of complex constraints and local search accuracy, at the same time considering various constraints such as power generation, energy storage, power sales, and power purchase. We establish the microgrid multi-objective operation cost and carbon emission cost objective function, and through the simulation examples, we verify and determine that the IMOHHOGWO hybrid intelligent algorithm is better than the other three algorithms in terms of both convergence speed and convergence accuracy. According to the results of the multi-objective test function analysis, its performance is superior to the other four algorithms. The IMOHHOGWO hybrid intelligent algorithm reduces the grid operation cost and carbon emissions in the microgrid optimal scheduling model and is more suitable for the microgrid multi-objective model, which provides a feasible reference for future integrated microgrid optimal scheduling. Full article

To address the issue of output anomalies that frequently occur in position sensors of permanent magnet synchronous motors within electromechanical actuation systems operating in harsh environments and can lead to degradation in system performance or operational interruptions, this paper proposes an integrated method for fault diagnosis and fault-tolerant control based on the Cubature Kalman Filter (CKF). This approach effectively combines state reconstruction, fault diagnosis, and fault-tolerant control functions. It employs a CKF observer that utilizes innovation and residual sequences to achieve high-precision reconstruction of rotor position and speed, with convergence assured through Lyapunov stability analysis. Furthermore, a diagnostic mechanism that employs dual-parameter thresholds for position residuals and abnormal duration is introduced, facilitating accurate identification of various fault modes, including signal disconnection, stalling, drift, intermittent disconnection, and their coupled complex faults, while autonomously triggering fault-tolerant strategies. Simulation results indicate that the proposed method maintains excellent accuracy in state reconstruction and fault tolerance under disturbances such as parameter perturbations, sudden load changes, and noise interference, significantly enhancing the system’s operational reliability and robustness in challenging conditions. Full article

Spiking Neural Networks (SNNs), with their event-driven and energy-efficient characteristics, have shown great promise in processing data from neuromorphic sensors. However, the sparse and non-stationary nature of event-based data poses significant challenges to optimization, particularly when using conventional algorithms such as AdamW, which assume smooth gradient dynamics. To address this limitation, we propose MemristiveAdamW, a novel algorithm that integrates memristor-inspired dynamic adjustment mechanisms into the AdamW framework. This optimization algorithm introduces three biologically motivated modules: (1) a direction-aware modulation mechanism that adapts the update direction based on gradient change trends; (2) a memristive perturbation model that encodes history-sensitive adjustment inspired by the physical characteristics of memristors; and (3) a memory decay strategy that ensures stable convergence by attenuating perturbations over time. Extensive experiments are conducted on two representative event-based datasets, Prophesee NCARS and GEN1, across three SNN architectures: Spiking VGG-11, Spiking MobileNet-64, and Spiking DenseNet-121. Results demonstrate that MemristiveAdamW consistently improves convergence speed, classification accuracy, and training stability compared to standard AdamW, with the most significant gains observed in shallow or lightweight SNNs. These findings suggest that memristor-inspired optimization offers a biologically plausible and computationally effective paradigm for training SNNs on event-driven data. Full article

Accurate classification of optical communication signal quality is crucial for maintaining the reliability and performance of high-speed communication networks. While existing supervised learning approaches achieve high accuracy on laboratory-collected datasets, they often face difficulties in generalizing to real-world conditions due to the lack of variability and noise in controlled experimental data. In this study, we propose a targeted data augmentation framework designed to improve the robustness and generalization of binary optical signal quality classifiers. Using the OptiCom Signal Quality Dataset, we systematically inject controlled perturbations into the training data including label boundary flipping, Gaussian noise addition, and missing-value simulation. To further approximate real-world deployment scenarios, the test set is subjected to additional distribution shifts, including feature drift and scaling. Experiments are conducted under 5-fold cross-validation to evaluate the individual and combined impacts of augmentation strategies. Results show that the optimal augmentation setting (flip_rate = 0.10, noise_level = 0.50, missing_rate = 0.20) substantially improve robustness to unseen distributions, raising accuracy from 0.863 to 0.950, precision from 0.384 to 0.632, F1 from 0.551 to 0.771, and ROC-AUC from 0.926 to 0.999 compared to model without augmentation. Our research provides an example for balancing data augmentation intensity to optimize generalization without over-compromising accuracy on clean data. Full article

Background/Objectives: The post-COVID-19 condition frequently includes dysphonia. We aimed to assess objective and subjective voice disorders and short-term responses to multimodal therapy in patients with isolated post-COVID-19 dysphonia. Methods: This retrospective, single-center pre–post study screened 244 post-COVID-19 patients; a subset of 14 with isolated dysphonia underwent standardized assessment at baseline and at 1-month follow-up. Patient-reported outcomes (Voice Handicap Index, VHI; Voice-Related Quality of Life, V-RQOL) and endoscopic evaluation were performed using videolaryngostroboscopy (LVS) and high-speed videoendoscopy (HSV) with kymographic analysis to quantify parameters describing vocal fold oscillations. The treatment included short-term systemic corticosteroids, inhaled corticosteroids, hyaluronic-acid inhalations, and structured voice therapy. Results: At baseline, HSV revealed signs of glottal insufficiency—irregular and asymmetric vocal fold motion, reduced amplitude and pliability, a disrupted mucosal wave, and an increased open quotient. At follow-up, HSV showed increased oscillation, amplitude, and cycle regularity with reduced left–right asymmetry and phase differences; phonovibrograms displayed clearer and more structured patterns. Perturbation indices decreased across jitter and shimmer measures, and the mean fundamental frequency was lower. Improvements in instrumental measures aligned with better VHI and V-RQOL scores. Conclusions: In patients with persistent dysphonia after acute SARS-CoV-2 infection, comprehensive ENT evaluation with instrumental laryngeal assessment is warranted. Short-term multimodal management was associated with improvements in both HSV-derived measures and patient-reported outcomes; confirmation in controlled studies is needed. Full article

Chaotic systems appear in a wide range of natural and engineering contexts, making the design of reliable and flexible control strategies a crucial challenge. This work proposes a robust control scheme based on the Fractional-Order Backstepping Control (FOBC) method for the stabilization of non-commensurate fractional-order chaotic systems subject to bounded uncertainties and external disturbances. The method is developed through a rigorous stability analysis grounded in the Mittag–Leffler function, enabling the step-by-step stabilization of each subsystem. By incorporating fractional-order derivatives into carefully selected Lyapunov candidate functions, the proposed controller ensures global system stability. The performance of the FOBC approach is validated on fractional-order versions of the Duffing–Holmes system and the Rayleigh oscillator, with the results compared against those of a fractional-order PID (FOPID) controller. Numerical evaluations demonstrate the superior performance of the proposed strategy: the error dynamics converge rapidly to zero, the system exhibits strong robustness by restoring state variables to equilibrium quickly after disturbances, and the method achieves low energy dissipation with a high error convergence speed. These quantitative indices confirm the efficiency of FOBC over existing methods. The integration of fractional-order dynamics within the backstepping framework offers a powerful, robust, and resilient approach to stabilizing complex chaotic systems in the presence of uncertainties and external perturbations. Full article

Background: Precise upper limb movements are essential for daily tasks and motor function. Feedforward responses enable anticipatory movement planning, while feedback responses utilize sensory information for real-time corrections. Long-latency reflexes (LLRs) represent rapid feedback responses during unexpected perturbations and are integral in maintaining motor control, yet the factors governing LLRs in the upper extremity remain unclear. Methods: Forty healthy participants with ages ranging from 20 to 45 years (mean = 26.75, and SD = 5.6), performed a unilateral visuomotor elbow flexion and extension task with one arm while following a sinusoidal target at varied resistances and speeds. Task performance was quantified and communicated to participants after each bout. Resistance was randomly released during the flexion phase to trigger a perturbation. Electromyography data from the biceps and triceps muscles were analyzed for the long-latency reflex (LLR) and secondarily for the short-latency reflex (SLR), and voluntary response (VR) phases. Results: In response to unexpected upper extremity perturbations, participants relied on two core strategies. Inhibitory LLRs within the biceps were prominent, emphasizing inhibition to maintain movement stability 50–150 ms post-disturbance. Additionally, volitional control through the triceps allowed participants to regain precision starting from over 150 ms. Participants’ responses to perturbations were dependent on speed and resistance but were not modified with learning across repeated attempts. Conclusions: This study reveals that participants demonstrate both long-latency and volitional responses to counteract perturbations during an upper extremity visuomotor task. These findings highlight that a predominant agonist inhibition strategy emerged during the during unpredictable perturbations of the upper extremity. Understanding these responses may inform rehabilitation and pharmaceutical interventions when treating individuals with neurological conditions that influence motor control. Full article

This paper addresses the path tracking problem of underactuated unmanned surface vessels (USVs) in the presence of unknown external disturbances. A fixed-time adaptive integral sliding mode control (AISMC) method, incorporating a nonlinear disturbance observer (NDO), is proposed. Initially, a three-degree-of-freedom dynamic model of the USV is developed, accounting for external disturbances and model uncertainties. Based on the vessel’s longitudinal and transverse dynamic position errors, a virtual control law is designed to ensure fixed-time convergence, thereby enhancing the position error convergence speed. Next, a fixed-time NDO is introduced to estimate real-time external perturbations, such as wind, waves, and currents. The observed disturbances are fed back into the control system for compensation, thereby improving the system’s disturbance rejection capability. Furthermore, a sliding mode surface is designed using a symbolic function to address the issue of sliding mode surface parameter selection, leading to the development of the adaptive integral sliding mode control strategy. Finally, compared with traditional SMC and PID, the proposed AISMC-NDO offers higher accuracy, faster convergence, and improved robustness in complex marine environments. Full article

Partial shading in photovoltaic (PV) systems causes multiple local maximum power points (LMPPs), complicating tracking and reducing energy efficiency. Conventional maximum power point tracking (MPPT) methods, such as Perturb and Observe (P&O), often fail because of oscillations and entrapment at local maxima. To address these shortcomings, this study proposes a hybrid MPPT strategy combining artificial neural networks (ANNs) and the P&O algorithm to enhance tracking accuracy under partial shading while maintaining implementation simplicity. The research employs a detailed PV cell model in MATLAB/Simulink (2019b) that incorporates dynamic shading to simulate non-uniform irradiance. Within this framework, an ANN trained with the Levenberg–Marquardt algorithm predicts global maximum power points (GMPPs) from voltage and irradiance data, guiding and accelerating subsequent P&O operation. In the hybrid system, the ANN predicts the maximum power points (MPPs) to provide initial estimates, after which the P&O fine-tunes the duty cycle optimization in a DC-DC converter. The proposed hybrid ANN–P&O MPPT method achieved relative improvements of 15.6–49% in tracking efficiency, 16–20% in stability, and 14–54% in convergence speed compared with standalone P&O, depending on the irradiance scenario. This research highlights the potential of ANN-enhanced MPPT systems to maximize energy harvest in PV systems facing shading variability. Full article

Background/Objectives: Swing leg resistance may stimulate propulsive force, required for forward progression and leg swing, in post-stroke patients. To assess the potential of swing leg resistance in rehabilitation, more knowledge is needed on how this unilateral manipulation affects gait. Therefore, we explored the bilateral effects of a unilateral swing leg resistance on muscle activity, kinematics, and kinetics of gait in able-bodied individuals. Methods: Fourteen able-bodied participants (8 female, aged 20.7 ± 0.8 years, BMI 23.5 ± 1.9) walked on an instrumented treadmill at 0.28 m/s, 0.56 m/s, and 0.83 m/s with and without unilateral swing leg resistance provided by a weight (0 kg, 0.5 kg, 1.25 kg, and 2 kg) attached to the leg through a pulley system. Propulsion and braking forces, swing time, step length, transverse ground reaction torques, and muscle activity in the gluteus medius (GM), biceps femoris (BF), rectus femoris (RF), vastus medialis (VM), medial gastrocnemius (MG), and soleus (SOL) were compared between conditions. Statistical analyses were performed using repeated measures ANOVAs, with a significance level of 5%. Results: Peak propulsive force and propulsive duration increased bilaterally, while peak braking force decreased bilaterally with unilateral swing leg resistance. In addition, the swing time of the perturbed leg increased with swing leg resistance. Muscle activity in the perturbed leg (GM, BF, RF, VM, MG) and the unperturbed leg (GM, BF, VM, MG, SOL) increased. Only in the BF (perturbed leg, late swing) and MG (unperturbed leg, early stance) did the muscle activity decrease with swing leg resistance. No adaptations in step length and transverse ground reaction torques were observed. Specific effects were enhanced by gait speed. Conclusions: Unilateral swing leg resistance can evoke effects that might stimulate the training of propulsion. A study in post-stroke patients should be conducted to test whether prolonged exposure to unilateral swing leg resistance leads to functional training effects. Full article

In order to enhance the tracking accuracy and disturbance rejection capability in the speed loop of an optoelectronic tracking servo control system, a parameter self-adjusting disturbance rejection generalized predictive control method (STGPC) based on a continuous-time model is proposed in this paper. First, a dynamic model of the servo turntable system is established, and a linear extended state observer (LESO) is designed to perform real-time estimation of internal and external disturbances in the system. Second, a generalized predictive control law incorporating the predictive model, performance metrics, and rolling optimization is systematically derived, where the reference trajectory is generated by a tracking differentiator and the system state is provided in real time by the LESO. Furthermore, a gradient descent method is innovatively introduced to achieve adaptive adjustment in the predictive time domain, and the stability of the closed-loop system is rigorously proven based on Lyapunov theory. Finally, simulation experiments were conducted to verify the tracking performance, disturbance rejection performance, and time-domain parameter self-adjustment effects of the control method. Simulation results show that compared with PID control and traditional linear generalized predictive control (LGPC), the proposed STGPC method reduces speed tracking residuals by 73.79% and 51.04%, respectively, enhances disturbance suppression capability for speed vibration disturbances by 50.55% and 47.55%, respectively, and enhances compensation capability for friction torque disturbances by 68.03% and 59.33%, respectively. The system demonstrates outstanding velocity tracking accuracy and disturbance rejection while exhibiting good robustness against system parameter perturbations. Full article

According to the international standard ISO 14687:2019 for hydrogen fuel quality, the maximum allowable concentration of water in hydrogen for use in refueling stations and storage systems must not exceed 5 µmol/mol. Therefore, an adsorption purification process following the electrolyzer is necessary. This study numerically investigates the adsorption of water and the corresponding water loading on zeolite 13X BFK, based on the mass flows entering the adsorption column from three 5 MW electrolyzers coupled to a 15 MW offshore wind turbine. As the mass flow is influenced by wind speed, a direct comparison between realistic wind speeds and adsorption loading is presented. The presented numerical discretization of the model also accounts for perturbations in wind speed and, consequently, mass flows. In addition, adsorption isobars were measured for water on zeolite 13X BFK within the required pressure and temperature range. The measured data was utilized to fit parameters to the Langmuir–Freundlich isotherm. Full article

With the increasing penetration of renewable energy in power systems, the multi-objective optimal scheduling of microgrids has become increasingly complex. Traditional optimization methods face limitations when addressing high-dimensional, nonlinear, and multi-constrained models. This study proposes a multi-objective optimal scheduling method for microgrids based on the Tuned Dung Beetle Optimization (TDBO) algorithm, aiming to simultaneously minimize operational and environmental costs while satisfying a variety of physical and engineering constraints. The proposed TDBO algorithm integrates multiple strategic mechanisms—including task allocation, spiral search, Lévy flight, opposition-based learning, and Gaussian perturbation—to significantly enhance global exploration and local exploitation capabilities. On the modeling side, a high-dimensional decision-making model is developed, encompassing photovoltaic systems, wind turbines, diesel generators, gas turbines, energy storage systems, and grid interaction. A dual-objective scheduling framework is constructed, incorporating operational economics, environmental sustainability, and physical constraints of the equipment. Simulation experiments conducted under typical scenarios demonstrate that TDBO outperforms both the improved particle swarm optimization (IPSO) and the original DBO in terms of solution quality, convergence speed, and result stability. Simulation results demonstrate that, compared with benchmark algorithms, the proposed TDBO achieves a 2.24–6.18% reduction in average total cost, improves convergence speed by 27.3%, and decreases solution standard deviation by 18.8–23.5%. These quantitative results highlight the superior optimization accuracy, efficiency, and robustness of TDBO in multi-objective microgrid scheduling. The results confirm that the proposed method can effectively improve renewable energy utilization and reduce system operating costs and carbon emissions, and holds significant theoretical value and engineering application potential. Full article

This study presents an improved variant of the greater cane rat algorithm (GCRA), called adaptive and global-guided greater cane rat algorithm (AGG-GCRA), which aims to alleviate some key limitations of the original GCRA regarding convergence speed, solution precision, and stability. GCRA simulates the foraging behavior of the greater cane rat during both mating and non-mating seasons, demonstrating intelligent exploration capabilities. However, the original algorithm still faces challenges such as premature convergence and inadequate local exploitation when applied to complex optimization problems. To address these issues, this paper introduces four key improvements to the GCRA: (1) a global optimum guidance term to enhance the convergence directionality; (2) a flexible parameter adjustment system designed to maintain a dynamic balance between exploration and exploitation; (3) a mechanism for retaining top-quality solutions to ensure the preservation of optimal results.; and (4) a local perturbation mechanism to help escape local optima. To comprehensively evaluate the optimization performance of AGG-GCRA, 20 separate experiments were carried out across 26 standard benchmark functions and six real-world engineering optimization problems, with comparisons made against 11 advanced metaheuristic optimization methods. The findings indicate that AGG-GCRA surpasses the competing algorithms in aspects of convergence rate, solution precision, and robustness. In the stability analysis, AGG-GCRA consistently obtained the global optimal solution in multiple runs for five engineering cases, achieving an average rank of first place and a standard deviation close to zero, highlighting its exceptional global search capabilities and excellent repeatability. Statistical tests, including the Friedman ranking and Wilcoxon signed-rank tests, provide additional validation for the effectiveness and importance of the proposed algorithm. In conclusion, AGG-GCRA provides an efficient and stable intelligent optimization tool for solving various optimization problems. Full article