Search Results (710)

The traditional secondary control method for virtual synchronous generators suffers from limitations such as slow dynamic response and poor adaptability under varying operating conditions, which significantly affect the reliability and stability of microgrids. To address these issues, this paper proposes an adaptive virtual synchronous generator secondary control method for microgrids based on predefined-time convergence. First, a predefined-time controller is designed, whose convergence time can be preset by the user, thereby resolving the problem of excessively long convergence times for frequency regulation and power sharing. Second, an adaptive inertia damping control method incorporating Gaussian functions is introduced to mitigate frequency fluctuations during disturbances in the microgrid system, effectively suppressing frequency deviations and enhancing microgrid stability. Finally, based on Lyapunov stability theory, the convergence of the proposed control method is rigorously proved, and its feasibility is validated through MATLAB/Simulink simulations. The results demonstrate that the proposed secondary control method reduces the frequency and active power convergence times by 0.98 s and 0.49 s, respectively, compared to traditional virtual synchronous generator secondary control methods. Additionally, it exhibits smaller frequency fluctuation magnitude during disturbances, enabling fast and smooth frequency recovery. Full article

Raw lacquer, a natural polymer derived from the bast of lacquer trees (Toxicodendron vernicifluum), is renowned as the “King of Coatings” due to its exceptional film-forming properties, abrasion resistance, corrosion resistance, and biocompatibility. However, its inherent limitations—including stringent drying conditions, slow curing rates, deep coloration, and difficult application—have severely restricted its modernization and widespread adoption. This review systematically summarizes recent research advances in the modification and application of raw lacquer, focusing on four major modification strategies: (1) Nanocomposite modification—incorporating functional nanofillers such as Al₂O₃, cellulose nanofibrils (CNF), polydopamine (PDA) melanin-like nanoparticles, and SiO₂ to significantly enhance film hardness, compactness, UV-aging resistance, and drying kinetics. (2) Chemical structure modification—employing molecular design strategies including aminoanthraquinone grafting, tung oil blending, water-based emulsification, and terpene/allyl group functionalization to improve hydrophobicity, flexibility, fast-drying properties, and achieve dual photo/oxygen curing. (3) Biomass synergistic composites—utilizing natural polymers such as chitosan and lignin, along with bio-inspired adhesion mechanisms (e.g., PDA), to confer advanced functionalities including antibacterial and antifouling properties. (4) Curing behavior regulation—precisely controlling drying kinetics through inorganic salt ion microenvironment engineering, nonionic surfactants, and salicylaldehyde Schiff base-based driers. Building upon these foundations, this review further expands on the emerging high-value applications of modified lacquer in preventive conservation of cultural heritage, advanced functional coatings (anti-corrosion, super-hydrophobicity, flame retardancy), biomedical materials (hemostasis, antibacterial activity, drug-controlled release, water treatment adsorption), and intelligent responsive flexible electronics. Finally, addressing challenges including weak fundamental research, bottlenecks in green industrialization, and lack of standardization, future development directions are proposed encompassing interdisciplinary innovation, sustainable modification strategies, integration of multifunctional intelligent systems, and big data-driven research paradigms, aiming to provide theoretical guidance and technical references for the high-value utilization and modernization of lacquer resources. Full article

We investigate the coevolutionary dynamics in a predator–prey system with Holling-III functional response. By using the theory of adaptive dynamics, we first classify coevolutionary singular coalitions and then prove the existence of cyclic coevolution. Coupling this cyclic trait evolution with oscillatory population dynamics generates multi-timescale oscillations in population size. These oscillations arise from the interplay of fast ecological and slow evolutionary processes, producing distinct patterns such as frequency-modulated and bursting oscillations. Our results demonstrate that complex oscillations in population size emerge intrinsically from eco-evolutionary feedback loops. Full article

Anaerobic digestion (AD) of agro-industrial residues supports the green energy transition by converting organic matter into renewable biogas. Sugar beet pulp is a highly fermentable feedstock, although its process response may vary with chemical composition. This study examined how chemical composition affects mesophilic biogas-production kinetics of sugar beet pulp prepared under laboratory conditions from surplus sugar beet roots. The roots represented ten sugar beet varieties (A–J), and the prepared pulp was characterised for pH, dry matter, organic dry matter, mineral composition, and the relative shares of simple sugars, polysaccharides, protein, and fibre. Batch digestion tests were performed at 39 °C for 30 days. Production curves were analysed using complementary kinetic models (modified Gompertz and a two-fraction first-order model) to capture the lag phase and the contributions of rapidly and slowly degradable substrate pools. Biogas yields ranged from 126 to 141 m³ Mg⁻¹ fresh matter with 50–55% CH₄, corresponding to 64.3–76.1 m³ CH₄ Mg⁻¹ organic dry matter, while organic matter conversion reached 71.2–82.4%. Varieties enriched in simple sugars exhibited a higher share of the fast-degradable fraction and shorter lag phases, indicating faster onset and stronger methane formation. In contrast, higher fibre contents reduced the slow-fraction rate constant and lowered overall conversion, consistent with hydrolysis-limited degradation of the structural carbohydrate matrix. The mineral ion background, particularly K and Na, indicated moderate ionic buffering and stable operation without inhibition. The novelty of this work lies in integrating detailed compositional profiling with dual kinetic modelling to translate chemical fingerprints into tentative process-relevant implications. These implications include feeding strategy, organic loading control and hydraulic retention time selection, and they require further validation in continuous or semi-continuous AD systems. Full article

GNSS signals are extremely weak at the Earth’s surface and are highly vulnerable to in-band interference, particularly high-dynamic linear frequency-modulated (LFM) jamming, which may lead to receiver loss of lock. Existing anti-jamming techniques struggle to balance real-time constraints with high-precision frequency estimation. This paper proposes a Newton-iteration-enhanced three-spectral-line RIFE algorithm implemented on a heterogeneous FPGA platform (Zynq-7000 SoC). The method performs coarse frequency estimation using the three-spectral-line RIFE to mitigate FFT fence effects, followed by Newton-based quadratic refinement, enabling high estimation accuracy with reduced FFT size. A fast–slow loop architecture is adopted, where the FPGA (PL) performs real-time interference suppression and the ARM (PS) handles system control and parameter updates. Experimental results show that, under static interference, the proposed method achieves a 10.9 dB improvement over direct estimation algorithms. Under chirp interference, it significantly outperforms both direct estimation and conventional iterative methods. In GNSS closed-loop tests, the proposed approach extends the anti-jamming margin to 82 dB J/S. Overall, the proposed method effectively balances estimation accuracy and processing latency, providing a practical solution for GNSS anti-jamming in high-dynamic environments. Full article

Agricultural cold chain logistics is characterized by inherent challenges—product perishability, high carbon emissions, and stringent time windows—which are further exacerbated by dynamic disruptions. Existing methods suffer from slow adaptability, unstable multi-objective convergence, and severe cold-start issues. This work falls within the broad scope of biomimetics—the science of emulating nature’s time-tested strategies to solve complex engineering problems—and bio-inspired data-driven methods and their applications in engineering control, optimization, and artificial intelligence. The proposed H-MODRL framework embodies core biomimetic principles: the Genetic Algorithm (GA) mimics Darwinian natural selection and genetic inheritance, the Sparrow Search Algorithm (SSA) abstracts the cooperative foraging and anti-predation behaviors of sparrow populations in nature, and the Arrhenius-based freshness-decay model captures the biochemical kinetics governing perishable biological products. By synergistically integrating these biological evolution principles, swarm intelligence, and deep learning, the framework tackles real-world logistics complexity in a manner directly inspired by living systems. This study presents a well-organized hybrid optimization framework (H-MODRL) that couples a three-stage hybrid evolutionary mechanism, synergistically integrating heuristic warm-start, evolutionary policy guidance, and deep reinforcement learning decision-making. First, an improved genetic algorithm combined with the earliest deadline first strategy constructs a feasible initial population satisfying hard time-window constraints. Second, a large neighborhood search-enhanced chaotic sparrow search algorithm builds a high-quality elite guidance set for policy learning. Third, a physics-based multi-objective proximal policy optimization model embedded with Arrhenius equation-derived freshness-decay kinetics performs online decision-making. Experiments demonstrate that pre-computed all-pairs shortest paths and an O(1) hash-based dynamic-disruption indexing mechanism support fast online replanning. On heterogeneous simulated terrains based on real Chinese geospatial data, H-MODRL outperforms state-of-the-art algorithms across four objectives—logistics cost, carbon emissions, terminal freshness, and delivery time—while exhibiting compact, low-variance performance distributions, thereby validating its engineering robustness and practical value in complex agricultural cold chain environments. Full article

Objectives: Treadmill walking is often employed for tightly controlled gait and energetics research, but growing evidence suggests that treadmill-based metabolic and biomechanical measurements may not directly reflect the ecologically valid mode of overground walking. While many previous studies focused on older adults, much less is known about how treadmill walking influences gait energetics and spatiotemporal parameters in young healthy adults across matched speeds. We investigated energy expenditure, metabolic cost of walking and spatiotemporal gait parameters in healthy young adults walking overground and on a treadmill at three speeds (slow—1.0, comfortable—1.3, fast—1.5 m/s). Our hypothesis was that at the comfortable speed, treadmill and overground energetics and gait parameters would be comparable. However, at slow and fast speeds, there would be a significant energetic penalty, accompanied by significant differences in spatiotemporal parameters. Methods: Twenty young participants (10 males and 10 females) completed a randomized cross-over walking protocol with a minimum of ten minutes treadmill familiarization at 1.3 m/s. Breath-by-breath oxygen consumption ($\dot{V}{O}_2$) and Respiratory Exchange Ratio were measured using a portable indirect calorimetry system and gait parameters were calculated from Inertial Measurement Units. Gross and net energy expenditures, costs of walking, cadence, average step and stride lengths, and walk ratio were calculated. A three-way mixed ANOVA was used for primary statistical analyses. Results: Treadmill walking was characterized by higher gross and net energy expenditures and metabolic costs (p < 0.001, η_p² = 0.6) across all speeds compared to overground. It was also characterized by faster cadence and shorter average step and stride lengths (p < 0.001, η_p² = 0.9). Additionally, there was an effect of sex (p = 0.01, η_p² = 0.3) on the gait parameters, with females exhibiting a faster cadence and shorter average step and stride lengths than males. Conclusions: Our findings show that treadmill walking imposes a medium-to-large metabolic penalty even in healthy young adults, with compensatory gait adaptations, possibly reflecting increased stabilization demands and altered neuromuscular control strategies. These results underscore the limits of generalizing treadmill derived gait data to overground walking and we caution against the uncritical use of treadmills, especially while trying to understand ecologically relevant human walking mechanics and energetics. Full article

This paper presents a capability-aware frequency control strategy for microgrids comprising a ramp-rate-limited synchronous generator (SG) and a bounded inverter-based resource (IBR). In contrast to conventional droop and virtual inertia methods, the proposed design activates IBR support according to whether the required power-rate exceeds the ramp-rate capability of synchronous generation. A smooth activation mechanism detects when the required power-ramp demand exceeds the SG ramp-rate limit. The IBR is then engaged to supply the excess ramping requirement while providing additional damping through frequency-deviation feedback. A two-timescale model is formulated, where the IBR power-tracking dynamics evolve on a fast boundary-layer timescale. In contrast, the SG regulation loop evolves on a slow electromechanical timescale. Using singular perturbation theory combined with Lyapunov and input-to-state stability (ISS) analysis, local practical stability of the closed-loop system is established for sufficiently fast IBR dynamics. The proposed framework yields a physically interpretable coordination mechanism that exploits the fast response of IBR without introducing artificial inertia or frequency-domain disturbance splitting. Full article

Buried-pipeline leakage poses significant safety risks, yet traditional CFD (Computational Fluid Dynamics) simulations are too slow for real-time diagnosis. This study integrates machine learning with interval sampling to develop a fast and interpretable prediction method. From 1.4 billion CFD-generated data points, 140 million representative samples were extracted via 1:10 interval sampling. Using 17 physical features as inputs, we trained and compared XGBoost, LightGBM, and a Multi-Layer Perceptron (MLP). The MLP model demonstrated exceptional performance (R² (R-squared) = 0.9988, RMSE (Root Mean Square Error) = 0.0153), significantly outperforming the tree-based models (R² ≈ 0.93). Three independent sampling runs confirmed its robustness (R² coefficient of variation~0%). SHAP (Shapley Additive Explanations) analysis identified spatial coordinates and leak aperture as the most critical factors, while also revealing the nonlinear influence of soil particle size. This approach offers a high-precision, interpretable, and efficient surrogate model for buried-pipeline leakage warning systems. Full article

Phishing and malicious websites are a serious threat on the internet. Attackers use fake links to trick users and steal their private information. Detecting these links is difficult because attackers change their tricks often. Many old methods cannot detect new or hidden threats. Some recent models use deep learning (DL), but they are large, slow, and hard to use in real-time systems. In this paper, we present a lightweight and accurate model called MiniLM-CNNLSTM. It combines a small transformer model (MiniLM) with a hybrid DL network using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) layers. The transformer learns the meaning of URLs. The CNN finds important patterns. The LSTM captures the order of characters. We also add handcrafted features that help the model detect tricky URLs. We test our method on two public datasets: the Phishing Site URLs dataset and the Malicious URLs dataset from Kaggle. We use 3-fold cross-validation and early stopping to ensure fair and stable results. The MiniLM-CNN-LSTM model outperformed previous benchmarks by achieving an average three-fold cross-validation accuracy of 98.98%, a precision of 98.63%, a recall of 98.29%, an F1-score of 98.46%, and a false positive rate of 0.68%. The proposed model has a higher accuracy, precision, recall, F1-score and a lower false positive rate, which enhances the accuracy by 1.88, precision by 3.77, recall by 4.17 and decreases the false positive rate by 61.58% compared with the strongest baseline (Distil BERT + CNN-LSTM), showing significant practical improvements. The results show that our approach is fast, small, and highly effective. It can detect phishing and malicious links with high accuracy. This makes it a good choice for real-time security systems like browsers, email filters, or firewalls. Full article

With the rapid electrification of urban transportation and increasing penetration of renewable energy, maintaining frequency stability in smart-city multi-microgrids (MMG) systems increasingly depends on coordinated vehicle-to-grid (V2G) flexibility. However, existing load frequency control strategies typically treat electric vehicles (EVs) as homogeneous resources and overlook the impacts of charging-infrastructure diversity, user mobility constraints, and extreme weather conditions on regulation availability. To address these challenges, this study proposes a weather-adaptive intelligent load frequency control strategy for smart-city MMG considering heterogeneous charging stations and energy requirements of EV users. Fast and slow charging infrastructures are modeled separately to reflect their distinct regulation characteristics, while time-varying charging and discharging margins are derived from travel demand, parking duration, and state-of-charge preferences and further adjusted under extreme weather scenarios. Based on these dynamic constraints, an enhanced multi-agent soft actor–critic (MA-SAC) controller coordinates micro gas turbines and charging stations for distributed frequency regulation. Simulations demonstrate MA-SAC outperforms PID, Fuzzy, and MA-DDPG methods, achieving a 98.51% frequency excellent rate normally and 91.47% during extreme weather. It reduces maximum deviations by up to 80% versus PID, while preserving user travel requirements. The proposed framework provides a practical pathway for integrating electrified mobility into resilient smart-city MMG frequency regulation. Full article

Iterative learning contouring control (ILCC) improves contouring accuracy in multi-axis motion systems via the equivalent contour error formulation. However, its convergence strongly depends on the learning gain. Large gains may induce overly aggressive updates and local divergence, degrading performance, whereas small gains lead to slow convergence. Moreover, contour error convergence is typically non-uniform along the trajectory, and local divergence may still occur despite global convergence, particularly near error saturation regions. To address these issues, a fuzzy inference mechanism is integrated into the online ILCC framework, yielding an online ILCC with fuzzy-regulated convergence parameters (online ILCCf), enabling adaptive regulation of the learning gain. Two regulation strategies are developed: (i) online ILCCf_i, an independent multi-parameter regulation scheme; and (ii) online ILCCf_u, a unified single-parameter regulation scheme. The fuzzy mechanism adaptively adjusts the convergence parameters online according to the instantaneous magnitude of the equivalent contour error. Experimental results on a six-axis industrial robot demonstrate fast convergence while maintaining satisfactory contouring performance. Among all comparison cases, online ILCCf_i achieves the best performance, reducing the RMS position error from $7.26\times {10}^{-1}$ mm to $5.93\times {10}^{-2}$ mm and the RMS orientation error from $6.95\times {10}^{-4}$ rad to $5.64\times {10}^{-5}$ rad, without oscillation or local divergence. Further simulations confirm robustness under model uncertainty and measurement noise. Full article

In this work, we explore a piezoelectric neuron model in deterministic perturbations and stochastic forcing due to its use in mechanically driven sensing systems and neuromorphic sensor design. The model comprises of fast activation and slow recovery behaviors and constitutes a multiscale excitable system, converting external mechanical perturbations into nonlinear electrical responses. We initially examine the deterministic dynamics with phase-space reconstruction, basin of attraction mapping, return map analysis and sensitivity to initial conditions. These findings demonstrate stable limit-cycle oscillations and high nonlinear sensitivity that are crucial to high-resolution sensing and signal amplification. Stochastic forcing is added in order to include realistic environmental effects, and solved numerically with the Euler-Maruyama scheme. Time-series statistics, phase portraits, and recurrence quantification analysis are used to analyze the resulting ensemble dynamics, making it possible to characterize the variability and loss of predictability caused by noise. Comparison of deterministic and stochastic regimes indicates that the intensity of noise can considerably alter the firing patterns and recurrence structures. Full article

Accurately estimating the state of health (SOH) of lithium-ion batteries remains challenging for battery management systems. Traditional data-driven methods, such as long short-term memory (LSTM), lack physical interpretability and often fail to generalize across varying operating conditions. To address this, a physics-informed SP-LSTM framework is proposed that integrates the single particle model (SPM) with a bidirectional LSTM network. A hybrid optimization strategy combining particle swarm optimization and the limited-memory Broyden–Fletcher–Goldfarb–Shanno with bounds (L-BFGS-B) is first used to identify key SPM parameters, which are then combined with macro external features (charging time, discharge energy, IC peak) to form a seven-dimensional fusion vector. A dual-stream Bi-LSTM architecture separately models fast-varying macro trends and slow-varying physical parameters, achieving robust SOH mapping. Validated on the NASA PCoE dataset, the proposed SP-LSTM achieves a root mean square error (RMSE) of 0.0136 and a mean absolute error (MAE) of 0.0089 on an independent test set (B0018), outperforming the baseline LSTM by 38.2% in RMSE. Noise robustness tests (0–3% voltage noise) and Sobol global sensitivity analysis further confirm its stability and interpretability. By embedding electrochemical priors into the data-driven pipeline, this work provides a practical physics-data collaborative framework for accurate and trustworthy battery SOH estimation. Full article

To improve the speed regulation performance of permanent magnet synchronous motor (PMSM) drive systems, a composite control strategy consisting of an improved sliding mode controller (ISMC) and an adaptive Luenberger load torque observer (ALLTO) is proposed. The ISMC is constructed based on a novel sliding mode reaching law (NSMRL). The proposed NSMRL overcomes the slow convergence and chattering problems of conventional reaching laws by introducing system state variables and a nonlinear adaptive function, ensuring rapid convergence with reduced chattering. In parallel, the ALLTO is developed to estimate and compensate load disturbances in real time, where its bandwidth is adaptively adjusted according to the speed error to achieve fast response and high estimation accuracy without degrading steady-state performance. Experimental results demonstrate that the proposed control scheme significantly improves the dynamic response and disturbance rejection capability of PMSM drive systems. Full article