Search Results (2,957)

Background: Acute lung injury (ALI) and its severe form, acute respiratory distress syndrome (ARDS), remain major causes of morbidity and mortality, yet no targeted pharmacological therapy is available. Excessive neutrophil and macrophage infiltration drives reactive oxygen species (ROS) production and cytokine release, leading to alveolar–capillary barrier disruption and fatal respiratory failure. Methods: We applied an integrative multi-omics strategy combining single-cell transcriptomics, peripheral blood proteomics, and lung tissue proteomics in a lipopolysaccharide (LPS, 10 mg/kg)-induced mouse ALI model to identify key signaling pathways. Harpagide, an iridoid glycoside identified from our natural compound screen, was evaluated in vivo (40 and 80 mg/kg) and in vitro (0.1–1 mg/mL). Histopathology, oxidative stress markers (SOD, GSH, and MDA), cytokine levels (IL-6 and IL-1β), and signaling proteins (HIF-1α, p-PI3K, p-AKT, Nrf2, and HO-1) were quantitatively assessed. Direct target engagement was probed using surface plasmon resonance (SPR), the cellular thermal shift assay (CETSA), and 100 ns molecular dynamics (MD) simulations. Results: Multi-omics profiling revealed robust activation of HIF-1, PI3K/AKT, and glutathione-metabolism pathways following the LPS challenge, with HIF-1α, VEGFA, and AKT as core regulators. Harpagide treatment significantly reduced lung injury scores by ~45% (p < 0.01), collagen deposition by ~50%, and ROS accumulation by >60% relative to LPS (n = 6). The pro-inflammatory cytokines IL-6 and IL-1β were reduced by 55–70% at the protein level (p < 0.01). Harpagide dose-dependently suppressed HIF-1α and p-AKT expression while enhancing Nrf2 and HO-1 levels (p < 0.05). SPR confirmed direct binding of Harpagide to HIF-1α (KD = 8.73 µM), and the CETSA demonstrated enhanced thermal stability of HIF-1α. MD simulations revealed a stable binding conformation within the inhibitory/C-TAD region after 50 ns. Conclusions: This study reveals convergent immune–microenvironmental regulatory mechanisms across cellular and tissue levels in ALI and demonstrates the protective effects of Harpagide through multi-pathway modulation. These findings offer new insights into the pathogenesis of ALI and support the development of “one-drug, multilayer co-regulation” strategies for systemic inflammatory diseases. Full article

Accurate prediction of photovoltaic performance hinges on resolving the electron density in the P-region and the hole density in the N-region. Motivated by this need, we present a comprehensive assessment of a meshless global radial basis function (RBF) collocation strategy for the steady current continuity equation, covering a one-dimensional two-region P–N junction and a two-dimensional single-region problem. The study employs Gaussian (GA) and generalized multiquadric (GMQ) bases, systematically varying shape parameter and node density, and presents a detailed performance analysis of the meshless method. Results map the accuracy–stability–computation-time landscape: GA achieves faster convergence but over a narrower stability window, whereas GMQ exhibits greater robustness to shape-parameter variation. We identify stability plateaus that preserve accuracy without severe ill-conditioning and quantify the runtime growth inherent to dense global collocation. A utopia-point multi-objective optimization balances error and computation time to yield practical node-count guidance; for the two-dimensional case with equal weighting, an optimum of 19 intervals per side emerges, largely insensitive to the RBF choice. Collectively, the results establish global RBF collocation as a meshless, accurate, and systematically optimizable alternative to conventional mesh-based solvers for high-fidelity carrier-density prediction in P-N junctions, thereby enabling more reliable performance analysis and design of photovoltaic devices. Full article

Background and Objectives: Changes in the environment and physiology may be associated with an increased or decreased risk of cancer. Our study aims to evaluate the strength and the direction of the selection acting on oncosuppressor genes in association with phenotypic changes. Methods: We calculated the relative evolutionary rate (RER) using the converge method and linear regression on branches of phylogenetic trees. The association between changes in the evolutionary rate of oncosuppressors (DNA repair and cell cycle control genes) and trait selection was studied. The evolutionary rates of single oncosuppressor genes and pathways were evaluated. We studied two types of traits: those that are characteristic of vertebrates, such as homeothermy (the ability to maintain a constant body temperature), flight, and amnions; and those that are characteristic of mammals, such as high body mass and lifespan, an underground lifestyle, and hibernation. The analysis included 19,445 genes; 100 vertebrates and 46 mammalian species. We studied ancestral branches individually and all the clades having a trait. Results: Oncosuppressor genes accelerated in association with the ability to fly; p-value = 0.03 (positive or relaxed negative selection) and decelerated in homeothermic species; p-value = 0.04 (stabilizing selection). DNA repair genes were significantly accelerated in ancestral branches and in all clades of amniotic, homeothermic, and high-body-mass mammals (p-value < 0.05, FDR correction). Cell cycle control genes were under stabilizing selection in homeothermic animals, high-body-mass, long-lived, and underground mammals (p-value < 0.05, FDR correction). Data on the evolution of oncosuppressors are crucial for understanding the origin of cancer and will be important for future studies of tumor pathogenesis, pathomorphosis, and microevolution. Conclusions: The selection of traits associated with changes in cancer risk leads to positive/relaxed negative and stabilizing selection of oncosuppressor genes. Full article

To address the challenges of insufficient response speed and robustness in optical attitude control systems under highly dynamic disturbances and internal uncertainties, a composite control strategy is proposed in this study. By integrating the proposed piecewise sliding control (P-SMC) with the improved active disturbance rejection control (ADRC), this strategy achieves complementary performance, which can not only suppress the disturbance but also converge to a bounded region fast. Under highly dynamic disturbances, the improved extended state observer (ESO) based on the EKF achieves rapid response with amplified state observations, and the Nonlinear State Error Feedback (NLSEF) generates a compensation signal to actively reject disturbances. Simultaneously, the robust sliding mode control (SMC) suppresses the effects of system nonlinearity and uncertainty. To address chattering and overshoot of the conventional SMC, this study proposes a novel P-SMC law which applies distinct reaching functions across different error bands. Furthermore, the key parameters of the composite scheme are globally optimized using the particle swarm optimization (PSO) algorithm to achieve Pareto-optimal trade-offs between tracking accuracy and disturbance rejection robustness. Finally, MATLAB simulation experiments validate the effectiveness of the proposed strategy under diverse representative disturbances. The results demonstrate improved performance in terms of response speed, overshoot, settling time and control input signals smoothness compared to conventional control algorithms (ADRC, C-ADRC, T-SMC-ADRC). The proposed strategy enhances the stability and robustness of optical attitude control system against internal uncertainties of system and sensor measurement noise. It achieves bounded-error steady-state tracking against random multi-source disturbances while preserving high real-time responsiveness and efficiency. Full article

Background and aim: Patient satisfaction is a critical indicator of healthcare quality, shaping treatment adherence, continuity of care, and the allocation of resources. The Short Assessment of Patient Satisfaction (SAPS) is a brief, reliable tool that is widely used internationally, but no validated Arabic version currently exists. Therefore, this study aimed to translate, culturally adapt, and validate the SAPS into Arabic for use in primary care clinics. Methods: We conducted a cross-sectional validation study at general practice clinics of a tertiary academic hospital in Riyadh, Saudi Arabia (June–August 2025). Consecutive Arabic-speaking patients aged 18–80 were recruited post-visit and completed a self-administered electronic survey including the Arabic Short Assessment of Patient Satisfaction (Ar-SAPS), PSQ-18, and PDRQ-9, as well as demographic and visit variables. Psychometric testing included internal consistency, test–retest reliability, construct validity, and factor analysis. Results: A total of 273 participants enrolled in our study. The Ar-SAPS demonstrated good reliability (Cronbach’s α = 0.789; McDonald’s ω = 0.882) and moderate test–retest stability (ICC = 0.634, p < 0.0001). Factor analysis supported a primarily unidimensional structure, with the first factor explaining 60.2% of variance. Most inter-item correlations were moderate to strong, except for item 6. Convergent validity was supported by significant correlations with the Arabic PDRQ-9 (r = 0.623, p < 0.001, CI [0.532, 0.713]) and PSQ-18 (r = 0.662, p < 0.001, CI [0.531, 0.793]), confirming consistency with established measures of patient satisfaction. Furthermore, it demonstrated excellent discriminative ability, with areas under the curve of 0.965 for overall satisfaction and 0.955 for willingness to recommend. Conclusion: The Ar-SAPS is valid and reliable for use to assess patient satisfaction. Full article

Optimally positioning renewable-based distributed generation (DG) units is vital for mitigating technical challenges in active distribution networks (ADNs). With the goal of achieving technical goals such as reduced losses and mitigated unstable voltage, two available optimization methods have been combined for positioning wind-energy DGs: grey wolf optimization (GWO) and opposition-based learning (OBL), which tries out opposite possibilities for each assessed population, thus addressing GWO’s susceptibility to becoming stuck in local optima. This new fusion technique enhances the algorithm’s scrutiny of each area under consideration and reduces the likelihood of premature convergence. Results show that, compared with standard GWO, the proposed OBL-GWO reduced active power losses by up to 95.16%, improved total voltage deviation (TVD) by 99.7%, and increased the minimum bus voltage from 0.907 p.u. to 0.994 p.u. In addition, the voltage stability index (VSI) was also enhanced by nearly 30%. The proposed methodology outperformed both standard GWO on the IEEE 33-bus test system and comparable techniques reported in the literature consistently. By accounting for the uncertainty in wind generation, load demand, and future growth, this framework offers a more reliable and practical planning approach that better reflects real operating conditions. 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

With the rapid development of renewable energy and the continuous pursuit of efficient energy utilization, distributed photovoltaic power generation has been widely used in village-level microgrids. As a key platform connecting distributed photovoltaics with users, energy storage systems play an important role in alleviating the imbalance between supply and demand in VMG. However, current energy storage systems rely heavily on lithium batteries, and their frequent charging and discharging processes lead to rapid lifespan decay. To solve this problem, this study proposes a hybrid energy storage system combining supercapacitors and lithium batteries for VMG, and designs a hybrid energy storage scheduling strategy to coordinate the “source–load–storage” resources in the microgrid, effectively cope with power supply fluctuations and slow down the life degradation of lithium batteries. In order to give full play to the active support ability of supercapacitors in suppressing grid voltage and frequency fluctuations, the scheduling optimization goal is set to maximize the sum of the virtual inertia time constants of the supercapacitor. In addition, in order to efficiently solve the high-complexity model, the reason for choosing the snow goose algorithm is that compared with the traditional mathematical programming methods, which are difficult to deal with large-scale uncertain systems, particle swarm optimization, and other meta-heuristic algorithms have insufficient convergence stability in complex nonlinear problems, SGA can balance global exploration and local development capabilities by simulating the migration behavior of snow geese. By improving the convergence effect of SGA and constructing a multi-objective SGA, the effectiveness of the new algorithm, strategy and model is finally verified through three cases, and the loss is reduced by 58.09%, VMG carbon emissions are reduced by 45.56%, and the loss of lithium battery is reduced by 40.49% after active support optimization, and the virtual energy inertia obtained by VMG from supercapacitors during the scheduling cycle reaches a total of 0.1931 s. Full article

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

Long-range rotating wrap-around-fin rockets may exhibit non-convergent conical motion at high Mach numbers, causing increased drag, reduced range, and potential flight instability. This study employs the implicit dual time-stepping method to solve the unsteady Reynolds-averaged Navier–Stokes (URANS) equations for simulating the flow field around a high aspect ratio wrap-around-fin rotating rocket at supersonic speeds. Validation of the numerical method in predicting aerodynamic characteristics at small angles of attack is achieved by comparing numerically obtained side force and yawing moment coefficients with experimental data. Analyzing the rocket’s angular motion process, along with angular motion equations, reveals the necessary conditions for the yawing moment to ensure stability during angular motion. Shape optimization is performed based on aerodynamic coefficient features and flow field structures at various angles of attack and Mach numbers, using the yawing moment stability condition as a guideline. Adjustments to parameters such as tail fin curvature radius, tail fin aspect ratio, and body aspect ratio diminish the impact of asymmetric flow induced by the wrap-around fin on the lateral moment, effectively resolving issues associated with near misses and off-target impacts resulting from dynamic instability at high Mach numbers. Full article

Metaheuristics have proven to be effective in solving large-scale combinatorial problems by combining global exploration with local exploitation, all within a reasonably short time. The balance between these phases is crucial to avoid slow or premature convergence. We propose binary variants of the Arithmetic Optimization Algorithm for the set cover problem, integrating a two-step binarization scheme based on transfer functions with binarization rules and a greedy repair operator to ensure feasibility. We evaluate the proposed solution using forty-five instances from OR-Beasley and compare it with representative approaches, including genetic algorithms, path-relinking strategies, and Lagrangian-based heuristics. The quality of the solution is evaluated using relative percentage deviation and stability with the coefficient of variation. The results show competitive deviations and consistently low variation, confirming that our approach is a robust alternative with a solid balance between exploration and exploitation. Full article

The increasing integration of renewable energy sources (RES) and rising energy demand have created challenges in maintaining stability in interconnected power systems, particularly in terms of frequency, voltage, and tie-line power. While traditional load frequency control (LFC) and automatic voltage regulation (AVR) strategies have been widely studied, they often fail to address the complexities introduced by RES and nonlinear system dynamics such as boiler dynamics, governor deadband, and generation rate constraints. This study introduces the Arithmetic Optimization Algorithm (AOA)-optimized PI(1+DD) controller, chosen for its ability to effectively optimize control parameters in highly nonlinear and dynamic environments. AOA, a novel metaheuristic technique, was selected due to its robustness, efficiency in exploring large search spaces, and ability to converge to optimal solutions even in the presence of complex system dynamics. The proposed controller outperforms classical methods such as PI, PID, I–P, I–PD, and PI–PD in terms of key performance metrics, achieving a settling time of 7.5 s (compared to 10.5 s for PI), overshoot of 2.8% (compared to 5.2% for PI), rise time of 0.7 s (compared to 1.2 s for PI), and steady-state error of 0.05% (compared to 0.3% for PI). Additionally, sensitivity analysis confirms the robustness of the AOA-optimized controller under ±25% variations in turbine and speed control parameters, as well as in the presence of nonlinearities, demonstrating its potential as a reliable solution for improving grid performance in complex, nonlinear multi-area interconnected power systems. Full article

In global cold regions and seasonal frozen soil areas, frost heave failure of rock slopes severely endangers infrastructure safety, particularly along China’s Sichuan–Tibet and Qinghai–Tibet Railways. To address this, a meshless numerical model based on the smoothed particle hydrodynamics (SPH) method was developed to simulate progressive frost heave and fracture of water-saturated fissured rock masses—its novelty lies in avoiding grid distortion and artificial crack path assumptions of FEM as well as complex parameter calibration of DEM by integrating the maximum tensile stress criterion (with a binary fracture marker for particle failure), thermodynamic phase change theory (classifying fissure water into water, ice-water mixed, and ice particles), and the equivalent thermal expansion coefficient method to quantify frost heave force. Systematic simulations of fissure parameters (inclination angle, length, number, and row number) revealed that these factors significantly shape failure modes: longer fissures and more rows shift failure from strip-like to tree-like/network-like, more fissures accelerate crack coalescence, and larger inclination angles converge stress to fissure tips. This study clarifies key mechanisms and provides a theoretical/numerical reference for cold region rock slope stability control. 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

Aiming at the problems of traditional calibrated visual servo systems relying on precise model calibration and the high training cost and low efficiency of online reinforcement learning, this paper proposes a Multi-Network Mean Delayed Deep Deterministic Policy Gradient Algorithm with Behavior Cloning (MN-MD3+BC) for uncalibrated visual adaptive control of robotic arms. The algorithm improves upon the Twin Delayed Deep Deterministic Policy Gradient (TD3) network framework by adopting an architecture with one actor network and three critic networks, along with corresponding target networks. By constructing a multi-critic network integration mechanism, the mean output of the networks is used as the final Q-value estimate, effectively reducing the estimation bias of a single critic network. Meanwhile, a behavior cloning regularization term is introduced to address the common distribution shift problem in offline reinforcement learning. Furthermore, to obtain a high-quality dataset, an innovative data recombination-driven dataset creation method is proposed, which reduces training costs and avoids the risks of real-world exploration. The trained policy network is embedded into the actual system as an adaptive controller, driving the robotic arm to gradually approach the target position through closed-loop control. The algorithm is applied to uncalibrated multi-degree-of-freedom robotic arm visual servo tasks, providing an adaptive and low-dependency solution for dynamic and complex scenarios. MATLAB simulations and experiments on the WPR1 platform demonstrate that, compared to traditional Jacobian matrix-based model-free methods, the proposed approach exhibits advantages in tracking accuracy, error convergence speed, and system stability. Full article