Search Results (1,730)

This article presents the synthesis, implementation, and experimental study of a PI-based adaptive actor–critic displacement volume controller of an axial-piston pump intended for open-loop circuit hydraulic drive systems. The proposed control structure combines a conventional PI actor with an adaptive critic that estimates the infinite-horizon cost through Bellman-error minimization. By using the tracking error and its integral as actor inputs, the controller avoids the need for an accurate plant model while retaining a compact and practically implementable structure. The adaptive laws are derived using gradient-based learning, and a Lyapunov-based analysis establishes closed-loop stability for sufficiently small adaptation gains. The controller is implemented in a fixed-step Simulink^® environment and deployed on a rapid prototyping platform with real-time communication to an industrial microcontroller and proportional valve amplifier. The experimental results obtained under four fixed loading conditions and dynamic load variations demonstrate a stable operation, bounded critic behavior, and a near-zero Bellman error during learning. Comparative tests against a classical PI controller, a Lyapunov-based model reference adaptive controller, and a generic actor–critic scheme show that the proposed PI-based actor–critic achieves the lowest performance index and the shortest settling times in most cases. Full article

Continouous unmanned aerial vehicle (UAV) missions are fundamentally limited by Lithium-Polymer (LiPo) battery endurance under intermittent and power-constrained renewable energy conditions. This paper proposes an integrated energy management and charging framework for a photovoltaic (PV)-powered mobile station equipped with a hybrid energy storage system (HESS) and an automated battery replacement (ABR) mechanism. A lexicographic priority-based allocator sequentially serves ABR actuation, multi-slot LiPo charging, and Brushless DC (BLDC) propulsion, while the HESS compensates for PV intermittency. At the charging level, a constraint-aware constant current–constant voltage (CC–CV) strategy is enhanced by an adaptive neuro-fuzzy inference system (ANFIS) trained on optimization-derived labels using battery temperature and its rate of change, thus enabling anticipatory thermal current derating with smooth, discontinuity-free control action. Anti-windup proportional–integral (PI) regulation and bumpless mode transfer ensure stable CC-to-CV transitions. An event-triggered emergency mode accelerates battery readiness via a max-first selection policy. Comparative simulations against a PSO/DE-optimized PID benchmark over a full diurnal PV cycle demonstrate that the ANFIS controller reduces the CC-mode current tracking root-mean-square error (RMSE) by up to 96.9%, delivers higher charge throughput, and lowers battery degradation proxies, including SOC-weighted thermal dose and equivalent full cycles (EFC). The proposed framework reliably sustains continuous charge–swap–recharge logistics under fluctuating renewable generation. Full article

To evaluate the influence of near-field ground scattering on ultra-wideband (UWB) fuze performance, this paper presents an efficient operator-expansion time-domain physical optics (OE-TD-PO) framework. This method extends conventional far-field TD-PO to electrically large, dispersive rough surfaces under near-field excitation. By leveraging the local plane wave approximation (LPA) and time-domain Kirchhoff approximation (KA), the complex scattering process is decomposed into independent element-wise responses, which reduces the coupling between geometry and wave propagation. The scattering physics of each facet are represented using closed-form material and geometric operators. The material operator accounts for frequency-dependent dispersion and polarimetric reflection, while the geometric operator models intra-facet delay spread in the time domain. An excitation-order expansion of the transient dipole radiation formula is introduced to decouple the source waveform from spatial facet loops, yielding radiation, induction, and static components corresponding to the derivative, proportional, and integral terms of the excitation signal. This decoupling reduces computational complexity while preserving physical fidelity. Validated against analytical and numerical benchmarks, the proposed method effectively quantifies terrain-induced ranging biases and initiation reliability, providing a rigorous basis for adaptive error compensation and gain control in UWB fuzes across diverse environments. Full article

For low-speed heavy-load steering of electric forklifts, conventional three-loop proportional–integral (PI) control employs a fixed saturation limit on the position-loop output. Consequently, the maximum allowable speed cannot be adjusted according to load variations. Under light-load conditions, the steering motor speed is excessively constrained, which wastes the available voltage margin. Under heavy-load conditions, the allowable speed may exceed the voltage limit, thereby causing voltage saturation. Moreover, load-torque feedforward compensation is commonly adopted to improve load-carrying capability. However, at medium and high speeds, excessive feedforward action may cause voltage saturation and current-vector offset. This can lead to loss of control of the steering motor. To address these issues, a voltage-limit-constrained dynamic saturation and load-torque feedforward control strategy is proposed for electric forklift steering systems. First, fuzzy PI control is adopted in the position loop. Then, considering the nearly identical direct-axis and quadrature-axis inductances of a surface-mounted permanent magnet synchronous motor (PMSM), the direct-axis current is set to zero. An analytical expression of the maximum safe speed is derived with the quadrature-axis current as the only independent variable. Based on this expression, a dynamic saturation limit is designed for the position-loop output. Finally, a reduced-order disturbance observer (DOB) is utilized to estimate the equivalent load torque in real time. The current feedforward gain is dynamically regulated according to the voltage margin. This compensates for torque limitation caused by speed-loop saturation while preventing voltage saturation. A Simulink simulation platform is developed using a forklift as the case study. The results demonstrate that, compared with the conventional three-loop PI controller, the proposed strategy reduces the no-load 180° step-response time by 30%. Under heavy-load and large-angle steering conditions, the voltage margin is maintained at approximately 10%. Full article

Photovoltaic (PV) generation and electric vehicle (EV) charging infrastructure are changing the dynamic behavior of current power systems, especially in terms of voltage stability and LVRT capabilities. In this work, 50% PV penetration on a modified Kundur two-area power system was tested to mitigate transient instability under severe fault circumstances. With PV units running at unity power factors under steady-state conditions, 50% PV penetration was defined relative to the system’s total active load demand. A steady-state power-flow study ensured generation–load balance before MATLAB/Simulink dynamic simulations. Controllable reactive power compensation was used as an EV aggregator on Bus 7. We constructed and evaluated a genetic algorithm (GA)-optimized fractional-order proportional–integral–derivative (FOPID) controller with a traditional PID controller utilizing identical optimization conditions. An inter-area tie-line critical three-phase fault was applied and removed after 100 ms to evaluate system performance. While the GA-PID controller increased transient performance, it did not restore system stability. Instead, the GA-FOPID controller provided superior dynamic support by restoring Bus 7 voltage to 0.9–1.1 pu within 250 ms after fault clearance and maintaining about 95% LVRT compliance. The suggested controller also reduced rotor angle oscillations and enhanced inter-area damping. Fractional-order control increased EV aggregators’ reactive power response during transient shocks. Thus, in renewable-energy-dominated power systems, the GA-FOPID-controlled EV support technique may improve voltage stability and LVRT compliance. Full article

Ascophyllum nodosum extracts (ANE) are widely used biostimulants associated with improvements in plant growth, productivity, nutrient acquisition, and abiotic stress tolerance. However, the molecular mechanisms linking extract composition to plant signaling and physiological responses remain incompletely resolved. ANE contains a complex mixture of bioactive constituents, including polysaccharides, osmolytes, phenolic compounds, and phytohormone-like molecules. Their composition varies according to biomass source, environmental conditions, and extraction methodology, contributing to variability in biological activity. Current evidence suggests that ANE functions mainly as a signaling modulator rather than a direct nutrient source. ANE treatment has been associated with early cellular responses, including cytosolic Ca²⁺ influx, reactive oxygen species (ROS) generation, and mitogen-activated protein kinase (MAPK)-associated signaling events. However, many proposed mechanisms remain unresolved, and a considerable proportion of the available mechanistic evidence originates from studies using purified ANE-derived polysaccharides or related elicitor systems. ANE-associated responses include modulation of nutrient transport, primary metabolism, hormonal regulation, transcriptional reprogramming, and stress-responsive pathways, contributing to improved root development, nutrient acquisition, and defense-related responses. Nevertheless, limited knowledge of receptor-mediated perception mechanisms, signaling hierarchies, and extract-dependent variability continues to constrain mechanistic understanding and reproducibility. Future research should prioritize receptor identification, bioassay-guided fractionation, integrated multi-omics approaches, and improved standardization of extraction and formulation procedures. These advances will be essential for establishing robust mechanistic models and supporting the development of evidence-based ANE biostimulants for sustainable crop production. Full article

Reservoir effectiveness in marine shales is controlled not only by pore volume but also by pore-fluid occurrence, pore–throat connectivity, and mineral–organic matter coupling. In this study, the Permian Gufeng Formation shales from the Enshi area, western Hubei, South China, were investigated through an integrated analysis of total organic carbon (TOC), X-ray diffraction (XRD)-based mineral composition and lithofacies, low-field nuclear magnetic resonance (NMR), scanning electron microscopy (SEM), micro-computed tomography (Micro-CT), and entropy-weighted technique for order preference by similarity to an ideal solution (TOPSIS) evaluation. The TOC content ranges from 1.60% to 21.38% and shows clear vertical differentiation, with moderate but variable enrichment in the lower interval, reduced organic matter abundance in the middle interval, and pronounced organic enrichment in the upper interval. Mineral compositions demonstrate an upward transition from a mixed siliceous–carbonate system to a dominantly siliceous shale system. NMR results reveal strong heterogeneity in porosity, NMR-derived permeability, T2cutoff, bound-fluid saturation, and free-fluid saturation. Based on saturated and centrifuged T2 spectra, four descriptive reservoir response types were identified: short-T2-dominated micropore-bound response, intermediate-T2-dominated movable-fluid response, long-T2-enriched but low-efficiency response, and NMR-inferred enhanced mobility composite response. SEM observations show diverse pore types, including organic-matter-related pores, dissolution pores, interparticle pores, mineral-edge pores, pyrite intercrystalline pores, and local microfracture-like pores. Micro-CT results indicate that micrometer-scale pore bodies are commonly isolated, demonstrating that pore abundance or pore size alone cannot determine reservoir effectiveness. TOC mainly controls pore generation potential, whereas siliceous minerals, pore–throat connectivity, movable fluid proportion, and local fractures exert stronger controls on effective reservoir development. The most favorable reservoir responses are concentrated in the upper high-organic siliceous shale interval from A33 to A42, with local enhanced responses in A16 and A21. These results provide an integrated framework for evaluating reservoir heterogeneity and favorable intervals in complex marine shale systems. Full article

The vertical landing of quadrotor unmanned aerial vehicles (UAVs) involves highly transient impact dynamics that generate significant vibrations on the UAV body, particularly under uncertain touchdown conditions such as uneven terrain, asymmetric ground contact, and high-impact landing. In this study, a robust semi-active vibration control framework is proposed for a quadrotor UAV equipped with a four-point soft landing gear system. The UAV is modeled as a three-degree-of-freedom rigid body including heave, pitch, and roll motions, while each landing gear leg is represented by an equivalent spring-damper mechanism with adaptively controllable damping characteristics. To evaluate the effectiveness of the proposed framework, PID (Proportional–Integral–Derivative), GA-PID (Genetic Algorithm-Based Proportional–Integral–Derivative), Fuzzy–PID (Fuzzy Logic-Based Proportional–Integral–Derivative), and ANFIS-PID (Adaptive Neuro-Fuzzy Inference System-Based Proportional–Integral–Derivative) controllers are comparatively investigated under five different landing scenarios. The nonlinear touchdown dynamics are implemented in the MATLAB/Simulink environment using a state-space-based simulation model. The results demonstrate that intelligent adaptive control methods significantly improve landing stability and vibration attenuation compared to the conventional PID controller. Among all methods, the ANFIS-PID controller achieved the best overall performance. Under the most severe landing condition, the peak vertical displacement was reduced from 0.114 m to 0.025 m, while the maximum pitch and roll angles decreased from approximately 11° to nearly 2°. Additionally, the settling time was reduced from nearly 10 s to below 3 s. Full article

To address the limitations of traditional controllers for the considered six-degree-of-freedom multi-thruster underwater robot under strong nonlinearities and environmental disturbances, this paper proposes a particle swarm optimization–radial basis function–proportional–integral–derivative (PSO-RBF-PID) control algorithm. The proposed method combines the nonlinear identification capability of the RBF neural network, the global optimization capability of PSO, and the stable closed-loop structure of PID control, thereby enabling adaptive parameter tuning and disturbance compensation. Unlike existing PSO-PID- and RBF-based controllers, the proposed method combines offline global optimization and online adaptive gain tuning within a unified control framework. Although the framework is modular and can be extended to underwater robotic systems with different degrees of freedom by redefining the state vector, controller channels, and thrust allocation matrix, the present study validates the method through a six-degree-of-freedom multi-thruster underwater robot case study. Comparative simulations were conducted under the same model, disturbance conditions, sampling settings, and evaluation indices for six controllers: PID, cascade PID, fuzzy PID, FOPID, PSO-PID, and PSO-RBF-PID. For the considered 6-DOF multi-thruster underwater robot, PSO-RBF-PID achieved the best overall performance in steady-state error, settling time, overshoot, and IAE. This improvement is mainly attributed to the combination of PSO-based offline optimization and RBF-based online adaptive compensation. Full article

Osteoporotic vertebral fractures (OVFs) are the most common fragility fractures, representing a substantial burden on healthcare systems worldwide. Although up to 30% of OVFs may be clinically silent, a subset of patients experiences an unfavorable course, developing painful pseudoarthrosis/nonunion, progressive vertebral collapse, and even neurological compromise. While initial OVF management is typically nonoperative, a considerable proportion of patients ultimately require surgical intervention. However, clear and universally accepted surgical indications are lacking, rendering clinical decision-making complex and highly individualized. In this context, evaluating the spine’s ability to withstand physiological loads in the presence of potential instability is a critical step in the treatment algorithm. Nevertheless, spinal stability remains a dynamic and multifactorial concept that requires comprehensive assessment integrating both clinical and radiological parameters. This narrative review synthesizes the current state-of-the-art literature on the assessment of stability in OVFs, with particular clinical emphasis on clinical applicability. It revisits classical trauma-derived concepts and adapts them to the specific context of OVFs. We examine the respective roles of radiography, CT and MRI in evaluating fracture characteristics and spinal stability and summarize the main clinical and radiological markers. Furthermore, we distinguish between predictors of fracture progression and indirect indicators of established or evolving instability. Finally, we review current classification systems and outline general treatment considerations, focusing on how imaging findings may guide clinical decision-making in OVFs. Overall, this review provides a comprehensive framework of key imaging and clinical features that should be systematically assessed to estimate the risk of spinal instability. Full article

Portable industrial X-ray flaw detectors operating in outdoor environments predominantly rely on small diesel generators for power supply. However, the inherent grid frequency drift of such weak grids induces critical phase-shift mismatches in conventional fixed-delay controllers, leading to voltage loss-of-control. This study aims to develop a robust, frequency-adaptive power drive system to overcome these operational challenges. A dynamic zero-crossing capture mechanism is proposed to extract real-time grid frequency variations, enabling instantaneous phase-shift feedforward compensation. This mechanism is integrated with an adaptive incremental proportional–integral–derivative (PID) controller that utilizes grid-condition recognition to dynamically schedule gains and neutralize frequency disturbances. Furthermore, a linear voltage soft-start strategy is incorporated to coordinate downstream constant-current regulation, preventing inrush currents. Concurrently, an offline downtime perception mechanism executes autonomous stepped-voltage conditioning to prevent cold high-voltage breakdowns. Simulation and hardware experimental results demonstrate that under continuous generator frequency drift, the adaptive control maintains a steady-state voltage error below 1%, suppresses the voltage ripple factor to 1.11%, and limits tube current fluctuations to 4.2%. The proposed system effectively mitigates weak-grid instability, ensuring reliable high-voltage generation and extending component lifespan for demanding non-destructive testing (NDT) applications. Full article

Wavelength stability of distributed feedback (DFB) semiconductor lasers in dense wavelength division multiplexing (DWDM) systems hinges on sub-millikelvin temperature regulation, a task complicated by the nonlinear, multi-node dynamics of the thermoelectric cooler (TEC) and the purely reactive nature of conventional proportional–integral–derivative (PID) control. We present a physics-informed neural network (PINN) built around a residual correction architecture for hybrid feedforward–feedback TEC temperature control. Rather than penalizing physics-residual violations in the loss function, the architecture wires a simplified one-node thermal model directly into the network graph as a frozen baseline. A trainable branch then learns only the residual mismatch. Temporal lag features are appended to the input so that the network can reconstruct unmeasured internal thermal states from the cold-side temperature history, which proves essential for overcoming the partial-observability bottleneck inherent in multi-node TEC packages. Ablation experiments on a high-fidelity three-node TEC simulator show that all model variants (PINN, physics-feature-augmented NN, and pure NN) exceed R² = 0.993 when trained on the full dataset, yet the PINN’s advantage becomes pronounced under data scarcity. At a 3% training budget, it reaches R² = 0.966 versus 0.930 for the pure NN, implying an approximately 5.4× reduction in the data needed to reach a given accuracy target. In closed-loop validation, the PINN+PID hybrid settles 60% faster than standalone PID. Tracking RMSE drops by 69%, and peak disturbance deviation falls by 74%, across step, multi-setpoint, and current-perturbation scenarios. All results reported here are obtained in simulations. Experimental validation on physical DFB-TEC hardware is left to future work. Full article

The performance of the aircraft anti-skid braking system is critical to the ground operational safety of an aircraft. Conventional Pressure Bias Modulation (PBM) can suffer from deep skidding under low runway friction coefficients or low aircraft speeds. To address these issues, a composite control strategy based on Gaussian Quantum Particle Swarm Optimization (GQPSO) is proposed. This strategy employs the GQPSO algorithm for offline Proportional–Integral–Derivative (PID) parameter optimization, followed by real-time adaptive scheduling through a lookup table to accommodate varying speed domains and runway conditions. Simultaneously, by integrating the main-wheel dynamics model and friction characteristics, a runway identification function based on a Back Propagation Neural Network (BPNN) is designed to provide runway status information. The stability of the controller is verified via phase-plane analysis and Monte Carlo simulation. Subsequently, comparative Hardware-in-the-Loop (HIL) tests are conducted among PBM, PSO-PID, and the proposed GQPSO-PID controller under various runway conditions. The experimental results demonstrate that this composite controller can adapt to different speed domains and runway conditions, stably track the target slip ratio, effectively suppress skidding, and significantly improve braking efficiency, as well as exhibiting excellent robustness and control performance. Full article

Marine biological invasions are rapidly reshaping Mediterranean ecosystems, with growing consequences for biodiversity and fisheries. This study investigates recent changes in the composition of commercially important crustacean assemblages along the south-eastern coast of Sicily (central Mediterranean), focusing on penaeid shrimps (Penaeus aztecus and Penaeus kerathurus) and stomatopods (Erugosquilla massavensis and Squilla mantis). Field surveys were conducted during the fishing seasons of 2021 and 2025 at major landing sites and markets (Portopalo di Capo Passero, Syracuse and Catania), using standardized subsampling protocols applied to catches obtained by trammel nets and bottom trawls. Species composition was quantified through repeated sampling events, and temporal differences were analyzed using non-parametric tests and binomial generalized linear models, incorporating year and fishing gear as explanatory variables. Quantitative data were complemented by local ecological knowledge derived from structured interviews with professional fishers. Across the four-year interval, both taxonomic groups exhibited a pronounced shift in species dominance. The proportion of the invasive shrimp P. aztecus increased from approximately 20% in 2021 to over 80% in 2025, while the invasive stomatopod E. massavensis rose from about 2% to nearly 90% of total landings. These changes were statistically significant and independent of fishing gear. Fishers’ perceptions closely mirrored the quantitative trends, confirming the rapid replacement of native species by non-indigenous taxa and highlighting emerging socio-economic implications for local fisheries. Our findings document a rapid shift in the composition of commercial crustacean landings in Sicilian coastal waters, with invasive species becoming the dominant component of catches within a few years. This study underscores the need for adaptive fisheries management and integrated monitoring frameworks capable of responding to accelerating biological invasions in Mediterranean marine ecosystems. Full article

Background: While trefoil factor 3 (TFF3) has been linked to cardiovascular disease, its role in peripheral artery disease (PAD) remains largely unexplored. In this prospective study, we assessed three pre-selected circulating biomarkers and found that TFF3 demonstrated the strongest association with the presence of PAD. Building on this finding, we integrated plasma TFF3 concentrations with clinical characteristics to construct predictive models aimed at identifying individuals with PAD and estimating their risk of major adverse limb events (MALE) over a two-year follow-up period. Methods: A total of 476 individuals were prospectively recruited, including 312 patients with PAD and 164 controls without PAD. At study entry, circulating concentrations of TFF3, oncostatin M (OSM), and brain-derived neurotrophic factor (BDNF) were quantified, and all participants were subsequently monitored for a two-year period. The primary endpoint was the occurrence of MALE within two years, comprising acute limb ischemia, major amputation, or lower extremity revascularization by either open surgical or endovascular approaches. PAD diagnosis served as the secondary outcome and was established by an ankle–brachial index (ABI) ≤ 0.9 or toe–brachial index (TBI) ≤ 0.67 in the presence of reduced or absent pedal pulses. For predictive model development, the cohort was randomly divided into training (70%) and testing (30%) sets. A random forest algorithm incorporating clinical variables and plasma TFF3 levels was developed and optimized using 10-fold cross-validation. Model discrimination was quantified using the area under the receiver operating characteristic curve (AUROC). For prognostic evaluation, patients were classified into low- and high-risk groups based on the optimal ROC-derived probability threshold of 0.60, and MALE-free survival between groups was assessed using Cox proportional hazards regression. Results: Among the three candidate biomarkers evaluated, only TFF3 demonstrated a significant association with PAD. Patients with PAD exhibited higher circulating TFF3 concentrations than those without PAD (7.27 ± 3.36 vs. 5.89 ± 2.67 pg/mL; p < 0.001), whereas OSM and BDNF showed no significant differences between groups. Over the two-year follow-up period, MALE occurred in 28 patients (9%). Predictive models combining plasma TFF3 measurements with clinical variables achieved strong performance for both PAD detection and 2-year MALE risk estimation, yielding AUROCs of 0.79 and 0.85, respectively. Furthermore, patients classified as high risk by the model experienced a significantly increased hazard of MALE during follow-up (HR 1.12, 95% CI 1.10–1.19; p = 0.003). Variable importance analysis revealed that TFF3 was the most influential predictor of MALE, followed by age and smoking history. Conclusions: Combining plasma TFF3 levels with readily available clinical characteristics enabled the development of a predictive model with good discriminatory ability for both PAD diagnosis and estimation of 2-year MALE risk. Such an approach may enhance risk stratification by identifying patients at elevated risk earlier in their disease course, thereby informing decisions related to vascular testing, referral for specialist evaluation, and implementation of targeted treatment strategies. Full article