Search Results (955)

Distributed economic dispatch in AC distribution systems relies heavily on communication networks and is therefore vulnerable to denial-of-service (DoS) attacks. To address this issue, this paper proposes a resilient event-triggered distributed economic dispatch control strategy. Two typical DoS attack scenarios, namely communication-link blocking and node isolation, are first modeled, and an event-triggered distributed economic dispatch controller is then developed to maintain incremental cost consensus and system power balance while reducing communication overhead. Based on Lyapunov stability theory and a linear matrix inequality approach, sufficient conditions for the asymptotic stability of the closed-loop system are derived, tolerable bounds on the frequency and duration of DoS attacks are established, and the absence of Zeno behavior is proved. Simulations on the IEEE 33-bus AC distribution system show that, under load disturbances, dispatch-command variations, and DoS attacks, the proposed strategy can maintain stable system operation, restore dispatch performance after attacks, and reduce communication overhead by 91.86% compared with a fixed-step periodic updating baseline. These results demonstrate the effectiveness and resilience of the proposed method for distributed economic dispatch in AC distribution systems under DoS attacks. Full article

Surrogate models for nuclear reactor calculations typically provide point predictions without quantifying uncertainty, limiting their use in risk-informed applications. While several studies have applied machine learning to reactor physics, systematic evaluation of prediction interval calibration against Monte Carlo statistical uncertainty remains underexplored. This study develops a Gaussian Process regression (GPR) surrogate model that provides both accurate predictions and calibrated uncertainty estimates for the infinite multiplication factor ($k_{\infty }$) of a pressurized water reactor pin-cell. A dataset of 400 OpenMC Monte Carlo simulations was generated using Latin Hypercube Sampling across boron concentration (0–2000 ppm), fuel temperature (600–1200 K), and moderator temperature (500–600 K). The GPR model achieves $R^2=0.9971$ with prediction errors below the Monte Carlo statistical uncertainty ($\mathrm{MAE}/{\sigma }_{\mathrm{MC}}=0.75$), indicating that model accuracy is limited only by inherent training data noise. The key contribution is demonstrating that GPR prediction intervals are well-calibrated, achieving 92.5% coverage for 95% confidence bounds (bootstrap 95% CI: [87.5%, 97.5%], containing the nominal level; binomial test p = 0.297), with mean prediction uncertainty closely matching the Monte Carlo statistical uncertainty (${\sigma }_{\mathrm{GPR}}=0.00192$ vs. ${\sigma }_{\mathrm{MC}}=0.00200$). This near-perfect match suggests the surrogate has captured essentially all deterministic variation, with residual uncertainty attributable to Monte Carlo noise alone. Variance-based sensitivity analysis confirms boron concentration accounts for 99% of output variance. The surrogate preserves physically meaningful reactivity coefficients (Doppler: $-2.1$ pcm/K; boron worth: $-6.1$ pcm/ppm) while providing ${10}^5$-fold computational speedup. The framework is restricted to fresh fuel with fixed enrichment; extension to burnup-dependent scenarios is left for future work. Full article

Background: Trabecular-bone adaptation (TBA) continuously reshapes the trabecular-bone (TB) microstructure at the microscale in response to mechanical loading. While organ-scale adaptation has been extensively studied, the mechanisms governing the evolution of individual trabeculae remain inadequately understood. Methods: This study proposes a new remodelling model: under finite remodelling capacity, surface regions that satisfy mechanostat criteria compete for remodelling events according to the spatial non-uniformity of local mechanical stimulus. This model uses a two-criteria remodelling scheme that combines (i) a mechanostat criterion for bone formation and resorption and (ii) a distance-weighted non-uniformity criterion. The model is implemented with a 2D finite-element framework using a USDFLD subroutine in the Abaqus/Standard software package. Idealised X- and I-shaped trabecular geometries are subjected to controlled bending, compression, and shear load cases to examine loading-dependent morphology evolution. Results: Compared with the corresponding one-criterion models, the two-criteria framework produces a lower fraction of active remodelling surface and a more clearly bounded convergence process. The numerical simulations reproduce characteristic plate-like morphologies of trabeculae under bending and rod-like morphologies under compression, while additional variations in thresholds and loading conditions shift the response towards resorption-biased structures. Conclusions: The results indicate that the mechanostat criterion primarily stabilises the global bone mass, whereas the non-uniformity criterion governs where remodelling is preferentially located on the trabecular surface. The proposed framework therefore provides a microscale and mechanistically interpretable basis for analysing loading-dependent morphological adaptation of individual trabeculae. Full article

Intelligent mechanical fault diagnosis plays a key role in maintaining rotating machinery. Although data-driven unsupervised domain adaptation methods have achieved considerable progress, their industrial applications are often restricted by low-quality sensor data. Non-stationary vibration signals and background noise easily corrupt target pseudo-labels, while conventional methods focusing on global statistical matching usually neglect local structures, leading to confirmation bias under dynamic loads. To improve diagnostic reliability, we propose a Noise-Resilient Whitened Domain Adaptation (NRWDA) framework. To handle covariance fluctuations caused by changing working conditions, a Lipschitz-bounded Temporal Whitening (LTW) module is designed as a low-pass filter. An Entropy-guided Prototype Truncation (EPT) mechanism is adopted to discard ambiguous labels and better calibrate semantic centers. In addition, a Dispersion-Adaptive Contrastive Sharpening (DACS) strategy is introduced to dynamically adjust the contrastive temperature based on predictive dispersion, thus tightening decision boundaries. The proposed method is evaluated on CWRU, PU, and MFPT datasets. The PU dataset, featuring fluctuating loads and non-stationary signals, poses a strict test, yet our model maintains its stability even at a 0 dB SNR—a condition where standard approaches usually break down. During the $P0\to P3$ transfer task involving substantial radial force variations, NRWDA secures a 72.36% accuracy and surpasses established baselines. These findings confirm that our technique successfully isolates dependable diagnostic features from corrupted sensor measurements within actual industrial settings. Full article

This study examines the long- and short-run determinants of green employment in Saudi Arabia over the period 1990–2024 using an Autoregressive Distributed Lag (ARDL) bounds testing framework within an error-correction model. Six macroeconomic and structural variables are analyzed: renewable energy capacity, GDP growth, domestic credit, urbanization, foreign direct investment, and the Vision 2030 policy regime shift. Supplementary analyses test the Environmental Kuznets Curve (EKC) hypothesis and map causal relationships using pairwise Granger causality tests. The bounds test indicates long-run cointegration among the variables (F = 8.45, exceeding the 5% I(1) critical bound of 3.61). The model explains 89% of the variation in log green employment (R² = 0.89) and passes standard diagnostic tests for serial correlation, heteroskedasticity, normality, and parameter stability. Three correlates of long-run green employment are identified. The post-2016 dummy used to capture the Vision 2030 regime shift is associated with the largest coefficient in the long-run equation (θ = 1.75, p = 0.008), although this estimate should be interpreted with caution because the dummy absorbs all post-2016 changes, including policy effects, the rapid expansion of renewable capacity, broader institutional reforms, and possibly changes in measurement practices. Renewable energy capacity is the primary continuously measurable driver (θ = 0.145, p = 0.018), with Toda–Yamamoto modified Wald tests indicating a bidirectional predictive relationship between investment and employment. Urbanization exerts a significant positive long-run effect (θ = 0.098, p = 0.001). The error correction term (δ = −0.520, p < 0.001) implies equilibrium reversion with a half-life of approximately one year. The EKC hypothesis is not supported in the Saudi context, suggesting that active decarbonization policy—rather than income-driven structural change alone—is needed for environmental improvement. The findings carry implications for Vision 2030 implementation and for other resource-dependent economies undertaking structural green transitions. Full article

This paper presents a TD3-based reinforcement learning framework for adaptive PID-like control of uncertain dynamical systems. Although proportional–integral–derivative (PID) control remains widely used because of its simplicity, interpretability, and practical effectiveness, fixed-gain PID controllers often experience performance degradation in the presence of external disturbances, parameter variations, and changing operating conditions. To address this limitation, the control task is formulated as a continuous-action reinforcement learning problem in which the observation vector is constructed from PID-related error components, namely the tracking error, its integral, and its derivative. Based on these error-derived observations, a Twin Delayed Deep Deterministic Policy Gradient (TD3) agent learns a bounded continuous control policy through interaction with the environment while preserving a PID-like structural interpretation. The proposed framework is evaluated on a representative mass–spring–damper system under three challenging scenarios: external disturbance, parametric uncertainty, and their simultaneous presence. Its performance is further examined for both constant-reference regulation and sinusoidal reference tracking. The simulation results show that the learned controller achieves stable and accurate tracking, fast transient response, and robust behavior across varying operating conditions. These findings demonstrate the potential of TD3-based reinforcement learning as an effective adaptive PID-like control strategy for uncertain dynamical systems. Full article

Magnetorheological elastomers (MREs) have recently attracted significant attention for the development of adaptive vibration isolators and absorbers. Their ability to tune mechanical properties in response to external excitations makes them promising candidates for semi-active control applications. In this study, the Generalized Maxwell model with three Maxwell branches is employed to predict variations in storage and loss moduli of isotropic MREs operating in shear mode under varying excitation frequencies and magnetic flux densities. A practical semi-active MRE-based seat vibration isolator is proposed, and a multidisciplinary design optimization problem is subsequently formulated to determine the optimal geometrical parameters of the isolator. The objective is to maximize the frequency bandwidth while satisfying constraints on weight, material magnetic saturation, and total volume. The optimization results demonstrate that the proposed adaptive isolator can achieve a significant relative increase in its natural frequency by adjusting the applied magnetic flux density, while maintaining a practical total mass. A post-optimality analysis is also conducted to investigate the influence of the upper bound on the isolator’s mass. The findings reveal a nonlinear relationship between the optimal frequency ratio and the total mass of the isolator. Finally, closed-loop control strategies based on on–off skyhook and PID control are implemented and compared to evaluate the capability of the proposed adaptive isolator to mitigate vibration and shock under varying disturbances. Full article

This study investigates the short-term and long-term impacts of gross domestic product (GDP), inflation, foreign capital flows, trade balance and interest rate on stock market performance in Saudi Arabia for the period 1990–2023. The autoregressive distributed lag (ARDL) approach and error correction model (ECM) are employed to empirically examine the short-run and long-run relationships. The ARDL-ECM technique is effective for analyzing cointegration and assessing adjustment processes. Additionally, impulse response function (IRF) analysis based on the vector autoregression (VAR) model, estimated using these macroeconomic indicators, is applied in this paper. This study provides novel insights and addresses emerging gaps in the literature concerning Saudi Arabia as a developing economy. The long-term relationship in the bounds test results confirms its existence. In the long run, inflation and interest rate exert a statistically significant negative effect on stock market performance, while the trade balance has a significant positive impact. GDP and foreign capital inflows do not exhibit statistically significant long-run effects. Short-run dynamics indicate persistence in stock market performance along with significant effects from inflation and interest rate changes, while GDP and foreign capital inflows remain statistically insignificant in the long-run scenario. Forecast error variance decomposition (FEVD) results show that approximately 68.5% of the variation in market performance is explained by its own shocks, followed by foreign capital flows (16.3%) and inflation (8.4%). While foreign capital flow does not exhibit statistical significance in the ARDL long-run estimates, its contribution in variance decomposition highlights its role as an important source of external shocks. These findings are relevant to various stakeholders, including investors and policymakers. Additionally, policy emphasis should be placed on controlling inflation and maintaining stable interest rates while improving trade balance conditions. Although foreign capital flow does not show a direct long-run effect, its role in influencing market variability suggests the need for a stable and well-regulated investment environment. Full article

We construct a connection-theoretic framework for parallel transport of spectral components along parameter families of signals on the similarity group $\tilde{G}=\mathbb{R}\times \mathrm{SO}\left(2\right)$. Let ${\left\{f_t\right\}}_{t\in I}$ be a signal family that evolves under a $C^1$ group trajectory. The frequency support of the associated scale-rotation transforms produces three Hilbert subbundles over the parameter interval, and the trajectory velocity induces a covariant derivative on each subbundle. The standard spectral viewpoint treats transformation behavior at individual parameter values. Our formulation instead organizes the propagation of spectral components along the entire parameter path and provides closed-form transport operators together with error bounds on each subbundle. We derive three explicit parallel transport formulas. On the equivariant subbundle the transport is an exact isometric translation. On the coupled subbundle, the transport combines log-scale translation with a phase factor $e^{i{n}_0\Delta \theta }$. On the invariant subbundle, the transport is approximate, with the quantitative bound $\parallel {\mathsf{\Pi }}^{\mathrm{inv}}F-F\parallel \le \epsilon |\Delta \tau |\parallel F\parallel$, where ${\mathsf{\Pi }}^{\mathrm{inv}}$ denotes the parallel transport operator on that subbundle. We introduce the notion of non-parallelism rate as a pointwise measure of deviation from parallel evolution, and we prove that cumulative deviation along the path is bounded by the path integral of this quantity. The bound separates into two parts. One part is controlled by trajectory estimation error and reflects geometric mismatch. The other part is controlled by intrinsic appearance variation and reflects non-geometric drift. We also show that regularity transfers from the signal family to the spectral sections, and we establish a discrete transport theorem whose finite-sum error bounds recover the continuous estimates in the small-step limit. The framework provides a quantitative geometric tool for multi-scale feature evolution under continuous scale-rotation transformations. Full article

This study investigates the hydrothermal modification of commercial titanium dioxide (TiO₂) in the presence of a natural licorice root extract (Glycyrrhiza glabra L.), serving as a stabilizing and growth-modulating agent. The experimental framework combines hydrothermal treatment in a Teflon-lined autoclave with subsequent thermal calcination to elucidate the structural, morphological, and chemical evolution of the material. The plant-based extract significantly influences particle assembly during synthesis, fostering the formation of an initial organic–inorganic hybrid system that results in enhanced morphological homogeneity compared to pristine TiO₂. Thermal analyses (TGA and DSC) demonstrated the progressive decomposition of the organic components with increasing temperature, yielding a thermally stable, predominantly inorganic material at 600 °C. Scanning Electron Microscopy (SEM) observations confirmed a more uniform particle distribution in the modified samples. X-ray diffraction (XRD) patterns corroborated that the primary crystalline phase of TiO₂ remains intact across all conditions, with structural variations limited to peak definition and long-range organization. Furthermore, FTIR spectroscopy supported the preservation of characteristic TiO₂ vibrational features while indicating a gradual depletion of weakly bound surface species following thermal treatment. In conclusion, these findings demonstrate that natural extracts can effectively function as growth-modulating agents, steering material organization without altering its intrinsic chemical properties. This approach aligns with the principles of Green Chemistry and the circular economy, highlighting the potential of renewable plant-based resources as functional additives for the sustainable processing of inorganic materials. Rather than seeking to outperform commercial benchmarks, this work establishes a viable and low-environmental-impact strategy for morphological and structural modulation. Full article

To improve grape target perception and picking-point positioning for intelligent harvesting robots, this study develops a vision-based method for orchard grape detection and harvesting-point localization. The method is intended to address missed detections, insufficient recognition accuracy, and unsatisfactory peduncle segmentation caused by illumination variation, occlusion, and interference from branches and leaves in complex orchard scenes. For grape cluster and peduncle detection, a lightweight YOLOv7-derived model, termed YOLO-FES, was established. In this model, FasterNet and SCConv were introduced to refine the backbone and neck structures, and the EMA mechanism was incorporated to lower parameter complexity and computational cost while improving detection performance. For suspended grape structure association and peduncle extraction, the GJK algorithm was combined with nearest-neighbor rectangular discrimination, and an improved YOLACT-based peduncle segmentation network, named M-YOLACT, was constructed. With the integration of the MLCA mechanism and the Mish activation function, accurate peduncle segmentation was achieved. In addition, a stereo depth camera was employed to obtain two-dimensional picking-point information and further recover the corresponding three-dimensional spatial coordinates. Experimental results showed that the mAP@0.5 of YOLO-FES for grape clusters and peduncles reached 95.37%. For grape peduncle segmentation, the mAP@0.5 values of the bounding boxes and masks produced by M-YOLACT reached 95.73% and 94.36%, respectively. The proposed method achieved an overall harvesting success rate of 89.2%, with an average time consumption of 11 s for a single harvesting operation. By integrating deep-learning-based detection and segmentation with binocular-vision localization, this study provides a practical technical solution and useful reference for the visual system design of grape-harvesting robots. Full article

Despite extensive research on soil organic carbon in karst regions, the synergistic changes in multiple carbon fractions and their stabilization mechanisms across a complete rocky desertification gradient remain poorly understood. To clarify how soil carbon pools and their drivers change during karst rocky desertification, we selected Kaiyuan City, Yunnan Province, China, as the study area. Total carbon (TC), soil organic carbon (SOC), and their related fractions, including particulate organic carbon (POC), mineral-associated organic carbon (MAOC), iron-bound organic carbon (Fe-OC), calcium-bound organic carbon (Ca-OC), and soil carbon isotopic composition (δ¹³C), were analyzed under different degrees of rocky desertification. SOC and TC followed a nonlinear pattern: increasing from no to potential desertification, decreasing at light and moderate stages, and rising again at the severe stage, indicating a phased response rather than a monotonic decline. POC was lowest under no rocky desertification and increased significantly after desertification occurred, reaching its maximum at the severe stage. MAOC peaked at the potential stage. With increasing rocky desertification severity, POC/SOC increased from no to moderate stages and then slightly decreased, whereas MAOC/SOC generally decreased. Fe-OC and Ca-OC were lowest under no desertification and increased after desertification occurred, pointing to enhanced mineral protection. Soil δ¹³C values under moderate and severe desertification were higher than under no, potential, and light desertification, implying intensified decomposition and a relative increase in C₄ plants. Mean weight diameter (MWD) and geometric mean diameter (GMD) did not differ significantly among rocky desertification stages (p > 0.05). In contrast, fractal dimension (FD) differed significantly only between the light and moderate stages (p < 0.05). Correlation and redundancy analyses showed that soil water content, bulk density, and porosity were the key factors driving variation in SOC and its fractions. These findings provide both a theoretical basis and practical guidance for soil restoration and ecological management in karst faulted basins affected by rocky desertification. Full article

This paper proposes a novel prescribed-time formation tracking control farmework of multi-fixed-wing UAVs under external disturbance and actuator failures. As the complexity of aerial missions intensifies, achieving precise position and attitude tracking within a user-defined upper bound of settling time becomes a paramount challenge for intelligent swarm systems. Unlike traditional finite or fixed-time methods, where convergence depends on initial states or suffers from conservative estimation, the proposed approach ensures stability within a prescribed time independent of initial conditions. A key innovation is the introduction of a piecewise reference convergence differential function. This mechanism eliminates the need for state transitions, thereby reducing computational complexity while ensuring smooth tracking without control surface chattering across the entire mission. Additionally, a prescribed-time sliding mode disturbance observer is developed to provide precise and timely compensation for external disturbances and actuator faults. Rigorous Lyapunov analysis proves that all closed-loop signals are bounded and the tracking errors converge to a small neighborhood of zero within the predefined time. Numerical simulations demonstrate that, under time-varying disturbances and actuator faults, the disturbance estimation errors converge within 4 s, while both attitude and velocity tracking errors converge within 6 s, achieving fast transient response and high tracking accuracy. The UAV swarm successfully maintains the desired formation during aggressive maneuvers, including speed variations, climbing, and diving. These results verify that the proposed method provides a computationally efficient, robust, and high-precision solution for time-critical formation control of fixed-wing UAV swarms under complex uncertainties. Full article

This paper presents a novel Adaptive Sliding Mode Control (ASMC) strategy for nonlinear multivariable systems subjected to parameter uncertainties and external disturbances. The proposed control scheme guarantees robust and smooth state convergence via an adaptive mechanism that dynamically adjusts the switching gain. Unlike conventional SMC techniques, this adaptive formulation effectively mitigates the chattering phenomenon through a continuously updated boundary layer and eliminates the need for prior knowledge of the uncertainty bounds. The effectiveness of the synthesized controller is validated on an autonomous electric vehicle (AEV) platform, a system characterized by strong dynamic coupling. MATLAB/Simulink (version 2022b) simulations are conducted under various operational scenarios, including load variations and strict trajectory tracking. Comparative results with a traditional SMC demonstrate superior convergence, significant chattering reduction, and an optimized energy consumption profile, leading to a 22% reduction in equivalent CO₂ emissions. This approach provides a viable and energy-efficient control framework for modern autonomous EVs. Full article

Preventive geohazard assessment in heritage landscapes presents a methodological challenge, as environmental processes rarely operate within clearly bounded states. Instead, they evolve gradually across space and time and are often only partially observable. Conventional cumulative indices based on linear aggregation and fixed classification thresholds provide operational clarity but may suppress gradual activation, interaction effects, and uncertainty that are critical for preventive heritage management. This study develops a fuzzy geohazard assessment approach that extends cumulative hazard modeling through graded representation and uncertainty-aware aggregation. Environmental variables are represented as spatial fuzzy sets, allowing hazard conditions to be expressed as degrees of activation rather than discrete classes. Hazard-specific activation is derived through rule-based fuzzy inference, while cumulative geohazard conditions are synthesized using a weighted fuzzy γ aggregation operator that balances conjunctive behavior with precautionary disjunctive amplification. The approach is implemented within a Geographic Information System (GIS) environment and demonstrated in Parrhasian Heritage Park, a mountainous heritage landscape in Southern Greece. Results show that cumulative geohazard patterns respond systematically to variations in the precautionary parameter γ, enhancing transitional zones where multiple hazards coexist at moderate activation levels while preserving spatial continuity. Sensitivity analysis indicates that cumulative activation patterns remain structurally stable under moderate variations in membership calibration, supporting preventive GIS-based decision making. Full article