Search Results (1,701)

Rock cohesion ($c$) and angle of internal friction ($\phi$) are core parameters for rock mass stability analysis and engineering design; however, traditional triaxial tests are costly and time-consuming, limiting their availability in preliminary engineering assessments. To address this limitation, this study develops a machine learning framework that predicts these parameters from easily measurable physical properties, enabling rapid and cost-effective estimation without the need for complex laboratory testing. Based on a total of 199 sets of measured data from four rock types (shale, limestone, quartzite, and quartz-mica schist) in the Himalayan region, this study uses P-wave velocity ($V_p$), density ($\rho$), uniaxial compressive strength (UCS), and tensile strength (TS) as input variables. It employs four models: Support Vector Regression (SVR), Random Forest (RF), Multi-Layer Perceptron (MLP), and extreme gradient boosting (XGBoost) to predict $c$ and $\phi$. Hyperparameters were tuned using grid search and Bayesian optimization. We compared unified modeling with rock-type-specific modeling, performed interpretability analysis using SHapley Additive exPlanations (SHAP), and tested robustness by introducing Gaussian noise. The results show that XGBoost produced the best predictions at$c$ (test set $R^2$ = 0.9901, RMSE = 0.512 MPa), while the Bayesian-optimized SVR model yielded the best results at $\phi$ ($R^2$ = 0.9776, RMSE = 0.744°). Rock-type-specific modeling improved the $R^2$ for limestone at $\phi$ by 0.3541; the SHAP contribution for UCS and TS exceeded 70%; Random Forest demonstrated the best noise resistance, with a decrease in $R^2$ of less than 0.04 under 10% noise. In summary, the strategy proposed in this paper allows for the selection of prediction schemes based on data quality and lithological differences, providing a feasible approach for rapidly obtaining rock strength parameters. Full article

This paper presents an optimized control strategy that enables a grid-connected photovoltaic (PV) system to operate as a static synchronous compensator (PV-STATCOM) to damp power oscillations in the transmission system, using an arithmetic optimization algorithm (AOA). The key contribution of this work is a synchronized, AOA-optimized multi-mode switching approach that includes standard PV operation, Full STATCOM, and Partial STATCOM with ramp-rate recovery, rather than relying solely on PI-gain adjustment. This is accomplished across the complete pre-fault, fault, and post-fault cycle. Under the proposed strategy, the PV system temporarily curtails its real power output when power oscillations arise following a system disturbance, thereby releasing the full inverter capacity for STATCOM operation and, hence, for oscillation damping. Once the oscillations are damped, the PV system ramps its real power back to the pre-disturbance level; at night, the inverter’s full capacity remains available for damping oscillations. The control scheme is implemented with a set of proportional–integral (PI) controllers whose parameters are tuned with the AOA, and its performance is benchmarked against tuning with the cuckoo search (CS) algorithm. Simulation results demonstrate that the AOA-tuned PV-STATCOM significantly improves damping, reduces oscillation amplitudes, maintains the point-of-common-coupling voltage within the low-voltage ride-through envelope, and keeps the system frequency within grid-code limits, thereby ensuring stable grid operation. Compared to a CS-tuned benchmark, the AOA-tuned design keeps the frequency continuously within the grid code band, settles at nominal 50 Hz, and reduces the maximum voltage overshoot from 20% to 15%. Full article

A pursuer and an evader can exploit low-energy, non-Keplerian trajectories in cislunar space, making it difficult to obtain the saddle point for impulsive orbital pursuit–evasion games (OPEG). To address this problem, this paper first establishes a zero-sum differential game model based on the circular restricted three-body problem (CR3BP), where the terminal interception time is taken as the performance objective. The necessary optimality conditions for impulsive maneuvers are then derived using Pontryagin’s Maximum Principle (PMP), which transforms the optimal control problem into multipoint boundary value problems (MPBVPs). Subsequently, to overcome the high sensitivity of the MPBVPs to initial costate vectors in shooting methods, a two-layer hybrid initial-guess strategy combining a genetic algorithm with a time-domain coarse-grid search method is proposed for the single-impulse case. Furthermore, a receding-horizon strategy is introduced to generate the initial impulse sequence guess stage by stage for multiple-impulse cases. Finally, numerical simulations demonstrate that the proposed initial-guess strategy can effectively obtain the Stackelberg equilibrium solution for representative cislunar scenarios, including distant retrograde orbits (DROs) and Halo orbits. Meanwhile, the effects of observation delay and three-dimensional orbital characteristics on the game outcomes are also discussed based on dynamic game theory. Full article

The widespread adoption of Location-Based Services (LBSs) has significantly enhanced daily convenience, yet the upload and storage of user trajectory data pose substantial privacy leakage risks. Addressing the limitations of existing privacy protection methods in achieving personalized adaptation and balancing privacy with utility, this paper proposes a personalized privacy protection strategy for location trajectories based on weighted Kullback–Leibler (KL) divergence. The approach first employs a Markov transition matrix to model user movement patterns, utilizes quadtree-based dynamic grid partitioning for adaptive encoding of the state space, and introduces sensitivity scores weighted by dwell duration and visit frequency to identify critical privacy-sensitive points. It then develops an exponential decay perturbation mechanism combining regularization parameters and distortion thresholds to preserve trajectory spatial usability while protecting sensitive transitions. By quantifying privacy leakage through weighted KL divergence and measuring data utility via distortion metrics, a linearly weighted composite index is constructed, enabling personalized parameter optimization via grid search. Experimental results on the real-world Geolife dataset demonstrate that compared to three differential privacy baselines, this method reduces privacy leakage (measured by weighted KL divergence), improves POI Recall rates, and decreases average geographic errors. Paired t-tests confirm that all improvements are statistically significant (p < 0.001) with large effect sizes, validating its effectiveness and superiority in balancing privacy protection and data usability. Full article

Planning renewable energy bases with chronological source-grid-storage simulation makes exhaustive capacity screening impractical. This study develops a phase-adaptive constrained active-sampling framework whose core mechanism is a simulator-verified phase switch: a probability-of-feasibility-weighted lower confidence bound (PoF-LCB) directs the search until the first verified feasible plan is found, after which constrained expected improvement (CEI) directs economic refinement, supplemented by bounded optimal-neighborhood and constraint-boundary ranking refinements. Gaussian-process surrogates decide only the evaluation order; the reported objective and all four engineering constraints—photovoltaic curtailment, loss-of-load energy, capacity credit, and flexibility scarcity—are verified exclusively by the original 8760 h simulator. In a paired 2 × 2 factorial experiment over 30 common initial designs on a 125-candidate pool, the phase switch raised exact-optimum recovery from 24/30 to 30/30 (exact McNemar p = 0.03125), and the full rule maintained 30/30 under two unseen profile seeds where CEI achieved 24/30 and 22/30. The full rule further recovered the exact optimum of a 1224-candidate pool in 30/30 runs within 20 evaluations and of a six-variable 729-point grid within 75 evaluations, using roughly 2–10% of the exhaustive simulation budget. Constraint-slack and guard-band reporting, candidate-domain audits, and repeated wall-clock measurements turn the recommendation into auditable planning decisions, with all evidence drawn from a reproducible synthetic benchmark. Full article

Screen content images (SCIs) exhibit complex structural heterogeneity, rendering traditional statistics-based natural scene image quality assessment (NR-IQA) metrics ineffective. Although deep learning models achieve high prediction accuracy, their prohibitive computational demands preclude deployment in latency-sensitive industrial scenarios. While existing handcrafted lightweight SCI-IQA metrics reduce computational overhead, most rely on unsegmented global feature pooling or holistic edge statistics (e.g., edge histograms or Fisher vector coding), thereby diluting locally critical text-edge distortions in vast homogeneous backgrounds. To address this limitation, we propose an ultra-lightweight, deep-learning-free NR-IQA framework centered on human visual masking. Unlike existing lightweight methods, our approach explicitly employs dual-scale Canny edge operators to partition SCIs into edge-sensitive and flat background regions. Guided by this visual prior, structural degradations and micro-compression textures are extracted region-wise using Sobel gradients and uniform local binary patterns (LBPs) and aggregated with global Commission Internationale de I’Eclairage L*a*b*(CIELAB) color statistics into a compact 60-dimensional descriptor. A grid-search-optimized Support Vector Regression (SVR) maps these features to subjective quality scores. Extensive cross-validation on the SIQAD and SCID datasets demonstrates that our metric outperforms existing handcrafted lightweight SCI metrics and traditional NSS models, while achieving accuracy competitive with representative full-reference metrics. Consuming only 79.3 ms per image on a standard CPU, it offers a practical accuracy–efficiency trade-off for resource-constrained periodic quality monitoring. Full article

To address the inherent limitations of the traditional Aquila Optimizer (AO)—specifically slow convergence, susceptibility to local optima, and limited high-dimensional adaptability—this study proposes a multi-strategy Improved Aquila Optimizer algorithm. Key enhancements include the integration of a logarithmically decaying tangent flight factor to optimize high-dimensional solution distributions, and a dual-layer t-distribution adaptive perturbation model to dynamically regulate search density. Additionally, to solve path-planning problems under strict constraints, we incorporate a prior feasible region initialization, a continuous-to-discrete mapping correction, and a local fine-search mechanism for trajectory smoothing. The proposed Improved Aquila Optimizer algorithm is systematically evaluated against the original AO and six popular algorithms (PSO, SSA, GWO, DBO, DE, and GA) across 23 benchmark functions, the CEC2017 suite, and multi-scale grid maps. The results demonstrate that the Improved Aquila Optimizer algorithm achieves an order-of-magnitude improvement in convergence reliability. By prioritizing absolute search stability and robustness in high-dimensional tasks, the proposed algorithm attains an optimal balance between convergence quality and practical engineering efficiency, proving exceptionally effective in complex path-planning scenarios. Full article

The large-scale integration of photovoltaic generation into distribution grids has introduced significant operational challenges, including voltage excursions, reverse power flows, and increased variability. Battery energy storage systems (BESSs) offer a versatile solution by providing coordinated active- and reactive-power support. However, their scheduling in active distribution networks is challenging because of the non-convex alternating-current (AC) power-flow equations, the nondifferentiability of battery-degradation modeling, and uncertainty in renewable generation and demand. This paper proposes a two-stage methodology for the day-ahead operation of BESSs in ADNs. In the first stage, parallel particle swarm optimization (PPSO) determines the hourly active- and reactive-power schedules of the BESS units. In the second stage, a matrix-based multi-period AC power flow based on successive approximations evaluates the schedules and verifies voltage, thermal, converter-capability, and state-of-charge (SoC) constraints. A rainflow-counting degradation model is incorporated into the objective function to account for cycling and calendar aging costs. The methodology is assessed through ablation analyses comparing active-power-only and coordinated P–Q dispatches, degradation-unaware and degradation-aware scheduling, and serial and parallel PSO implementations. It is validated on modified 33-, 69-, and 136-node systems under deterministic and uncertainty-based operating conditions, including 100 demand and PV-generation scenarios. PPSO is compared with parallel versions of the adaptive Jaya algorithm (AJAYA), genetic algorithm (GA), multi-verse optimizer (MVO), salp swarm algorithm (SSA), grey wolf optimizer (GWO), and vortex search algorithm (VSA), using operating-cost reduction, computational time, solution variability, feasibility indicators, BESS lifetime, and weekly cost analysis. Additionally, exact one-sided Wilcoxon signed-rank tests with Holm adjustment are used to assess the statistical significance of the economic differences between PPSO and the benchmark methods. Results show that PPSO provides the lowest or most competitive operating costs and the shortest computational time in the evaluated cases, while all network and storage constraints remain satisfied. Full article

Wireless sensing networks increasingly extend into obstacle-prone deployments, where physical blockage degrades reliability and open propagation exposes transmission activity. Intelligent reflecting surfaces (IRSs) establish programmable paths around obstacles while passive elements remain constrained by severe cascaded attenuation. To address the tradeoff between reliability and covertness, we propose an unmanned aerial vehicle (UAV) -assisted active-IRS architecture under probabilistic line-of-sight and non-line-of-sight propagation conditions that accounts for direct leakage from the transmitter to the warden together with residual jammer cancellation and always-on IRS circuit noise under a finite output power budget. Furthermore, bidirectional Kullback–Leibler analysis identifies the reverse divergence as the tighter restriction and converts the covertness requirement into conservative gain bounds under warden location uncertainty and relative phase uncertainty conditions between the direct and aggregate reflected fields. Subsequently, closed-form phase control for calibrated equal-gain elements and gain monotonicity reduce the joint design to an exhaustive search over the prescribed placement grid. The numerical results demonstrate a SINR advantage over passive reflection and single-element relaying across the evaluated settings. The finite-array and hardware analyses show that gain back-off enforces a prescribed covert-outage limit while direct leakage and residual self-interference remain explicitly controlled. Overall, the framework provides a transparent basis for reliable covert sensing through UAV-assisted active reflection. Full article

Thermal runaway of lithium-ion batteries involves rapid heat release, gas generation, and multiphase transport, but their interaction inside a cell remains difficult to resolve. A multiscale computational fluid dynamics model was developed for a single 18650 cell by coupling microscale reaction kinetics, mesoscale interfacial heat transfer, and macroscale gas–liquid transport with a stationary porous-solid energy balance. The model describes internal temperature and the evolution of carbon dioxide, oxygen, water vapour, and hydrogen fluoride while examining the effects of porosity and the modelled dimethyl carbonate mass fraction. The medium-to-fine grid difference in carbon dioxide mass fraction was approximately 0.16%. Time steps of 0.01, 0.001, and 0.0001 s produced mass fractions of 0.0564, 0.0617, and 0.0618, respectively. Increasing the solvent mass fraction and porosity primarily shortened the induction period, while the peak temperature and terminal species levels remained similar. A quadratic response surface fitted to the simulation database was searched using grey wolf, genetic, and particle swarm methods. Grey wolf and particle swarm gave candidate times to peak temperature of about 238.8 s, whereas the genetic method gave 237.2 s, a difference of 1.6 s (0.67%). Particle swarm reached the high-response region within fewer iterations, while grey wolf maintained broader exploration. The proposed model connects reaction kinetics with macroscopic temperature and species evolution and clarifies how electrolyte composition and porous structure regulate the time scale of thermal runaway. Full article

Advances in artificial intelligence models offer promising approaches for intelligent convection initiation (CI) identification—a critical step in severe weather nowcasting—thereby driving demand for high-quality, long-term labeled datasets. Accordingly, this study develops a CI identification technique using quality-controlled, gridded composite reflectivity data from the weather radar and CMA global atmospheric reanalysis wind data over central-eastern China (2018–2023) to construct a labeled dataset. The proposed CI identification method integrates a “forward-time search and backward-time verification” strategy, which involves three key steps: screening grid points with absent or weak convection; monitoring these points for convective development within 30 min; and finally, confirming the first occurrence of convection. Additionally, quality control is applied to eliminate the influence of outliers and anomalous radar data. The resulting dataset constructed from 829 severe convective processes comprises ~25.6 million grid points labeled for CI occurrences at one or more lead times of 10, 20, or 30 min to resolve spatiotemporal evolution. Of these, 71.90% are accompanied by surface weather phenomena. This study provides a reliable dataset to support the learning of intelligent identification and nowcasting models for CI events. Furthermore, based on this dataset, the spatiotemporal distribution characteristics of warm-season CI over central-eastern China are delineated. Full article

Electricity theft remains a critical threat to power distribution infrastructure globally, with annual losses exceeding USD 89 billion and non-technical loss rates reaching 40% in developing economies. While machine learning has emerged as the dominant analytical approach for automated theft detection in smart grid environments, the field lacks a unifying framework that connects algorithm selection to the operational realities of Distribution System Operators (DSOs). Existing reviews catalogue methods and report benchmark metrics without addressing how detection paradigm selection should be aligned with data maturity, regulatory requirements, computational constraints, and institutional capacity. This review addresses that gap by systematically analysing 90 peer-reviewed studies published between 2015 and 2025, identified through structured multi-database searches, screened against explicit eligibility criteria, and graded with a formal five-criterion quality rubric, through a unified adversarial time-series formulation that provides a consistent analytical lens across all major learning paradigms. The analysis covers supervised ensemble methods, unsupervised and semi-supervised anomaly detection, deep learning architectures, including convolutional neural networks, long short-term memory networks and Transformer models, graph neural networks, federated learning, and explainable artificial intelligence. Key findings reveal that no single paradigm achieves optimality across all deployment dimensions simultaneously, that gradient boosting methods deliver near state-of-the-art performance with significantly lower computational overhead than deep learning, and that hybrid architectures achieve AUC-ROC scores of 0.95 to 0.98 on benchmark datasets but require complementary governance mechanisms to satisfy regulatory defensibility requirements. A lifecycle-aligned deployment framework and a layered detection architecture are proposed, offering practitioners a structured pathway from early AMI rollout through to advanced smart grid deployment. The principal outcomes of the review are a formal characterisation of which component of the detection problem each learning paradigm estimates, quality-graded and harmonised benchmark performance ranges, and a quantified illustrative analysis indicating that the proposed layered architecture can improve inspection productivity by roughly an order of magnitude at a fixed field budget. Four priority research challenges are identified: real-time edge detection, continual learning, multi-modal data fusion, and standardised benchmarking. Full article

Active distribution networks (ADNs) require battery energy storage system (BESS) scheduling strategies that reduce operating costs while preserving electrical feasibility and battery lifetime. This paper proposes a parallel adapted JAYA-based methodology for the day-ahead coordinated active and reactive power dispatch of BESS units in this type of grid. The novelty of this research lies in four key contributions: (i) the coordinated optimization of active and reactive power from BESS converters, exploiting their full capabilities for both energy management and voltage support; (ii) the integration of battery degradation costs within the optimization framework, preventing short-term economic strategies that accelerate aging; (iii) the implementation of a parallel adapted JAYA algorithm (AJAYA) with stagnation control and population reactivation mechanisms to enhance solution quality and convergence; and (iv) a comprehensive assessment under both deterministic and uncertainty-based operating conditions, providing a realistic validation of the proposed approach. Our model minimizes conventional generation, DER operation and maintenance, and BESS degradation costs while subject to power balance, distributed energy resource limits, voltage and current constraints, converter capacity, and state of charge (SoC) requirements. Each solution is encoded as BESS active/reactive power setpoints and evaluated through a multi-period AC power flow based on the successive approximations method, including SoC verification and a penalized fitness function. The methodology was validated in modified 33- and 69-node ADNs under deterministic and uncertainty scenarios (based on the conditions observed in Colombia), and it was benchmarked against the population-based genetic algorithm (PGA), the multiverse optimizer (MVO), the salp swarm algorithm (SALPS), the grey wolf optimizer (GWO), and the vortex search algorithm (VSA). According to the results, AJAYA outperformed the comparison methods, providing the best economic performance and exhibiting a robust behavior, with standard deviations below 0.06% and processing times below 0.05 h within a 24-h scheduling horizon. These findings demonstrate that the proposed framework constitutes an AC-feasible and degradation-aware academic contribution and a practical decision-support tool for operators and BESS owners, enabling a cost-effective and reliable BESS scheduling that preserves battery lifetime while improving network operation. Therefore, this research addresses the critical need for advanced energy management strategies that balance short-term economic benefits, technical feasibility, and long-term asset sustainability in modern distribution networks. Full article

Spectral-library matching is a widely used approach for compound annotation in mass spectrometry (MS)-based metabolomics, yet annotation performance is influenced by spectrum preprocessing, parameter selection, and similarity-measure choice. We present PyCompound, an open-source Python package for spectral-library matching with flexible preprocessing, parameter optimization, and diverse similarity measures for both nominal-resolution and high-resolution mass spectrometry data. PyCompound implements six preprocessing procedures, nineteen similarity measures, including the newly developed Rényi entropy similarity in this work for spectral-library matching, user-defined composite similarity scores, and automated parameter optimization using grid search and differential evolution. The software supports common spectral formats and is accessible through a Python API, command-line interface, and interactive Shiny application. The software was validated using public GNPS LC-MS/MS and WebNIST GC-MS datasets, where the newly developed Rényi entropy similarity demonstrated annotation performance comparable to that of the established Shannon and Tsallis entropy similarity measures. PyCompound provides a flexible platform for practical compound annotation through configurable preprocessing workflows, diverse similarity measures, and automated parameter optimization. Full article

Path planning for power-line inspection requires asset-specific clearance from conductors, towers, and insulators, together with temporal separation from moving obstacles. Existing methods often optimize path length or a soft risk objective without showing that a single, explicit feasibility predicate is enforced during both search and postprocessing. We propose PL-RiskPlanner, a constraint-consistent framework that applies asset- and obstacle-specific checks before admitting grid edges, ranks the admitted edges using a power-line risk field, predicts moving-object positions at estimated UAV arrival times, and applies the same sampled verifier during smoothing. The contribution lies not in a new or faster A* search rule but in a power-line-specific feasible-set formulation that remains consistent across search, shortcutting, and final evaluation. A bounded margin accounts for uncertainty in velocity estimates. We evaluate eight executable planners across six synthetic scenario families and conduct controlled experiments on obstacle density, component ablation, risk weights, route crossings, prediction noise, and equal verifier budgets. Across 1800 nominal PL-RiskPlanner trials, no safety flag was recorded by the independent evaluator (Wilson 95% upper bound, 0.21%), and the mean verified-path length was 110.64 m. In controlled route crossings, the no-prediction variant produced 222 flags in 300 trials, whereas no safety flag was recorded for the complete planner. At velocity-noise level 0.4, the uncertainty margin changed the flag count from 203 to 47 and increased the mean simulator-reference dynamic clearance from −0.27 to 2.23 m. A 3600-trial equal-verifier-budget audit quantifies the resulting completion–computation trade-off without equating algorithm-specific iteration counters. Although the 50,400 trial records are specific to the stated simulator, they provide a reproducible protocol for safety-constrained inspection-path planning. Full article