Search Results (3,804)

In-depth analysis of epidemic models, particularly for cholera, is crucial because they serve as significant tools for disease transmission prediction, evaluation of control strategies, and optimization of healthcare resource management. The stochastic models provide increased realism by incorporating environmental uncertainty such as variability in water quality, disparities in access to sanitation, and population mobility. The present work generalizes a deterministic two-patch cholera model to a stochastic framework. We first prove the existence and uniqueness of global solutions, then establish the extinction condition $R_0^{*}<1$ for disease eradication in the long term. A key contribution lies in proving the existence of a unique ergodic stationary distribution when $R_0^{\left(1\right)}>1$ and $R_0^{\left(2\right)}>1$. Furthermore, we derive the stochastic threshold $R_0=max\{R_0^{\left(1\right)},R_0^{\left(2\right)}\}$, which corresponds to the basic reproduction number ${\mathcal{R}}_0=max\{{\mathcal{R}}_0^{\left(1\right)},{\mathcal{R}}_0^{\left(2\right)}\}$. Lastly, numerical simulations are employed to confirm theoretical results. Full article

The rapid expansion of wind and solar generation has significantly increased the share of variable renewable energy in power systems worldwide, introducing new operational challenges. Among these, the simultaneous growth of renewable energy curtailment and persistent blackout risk reveals structural limitations in energy planning and system flexibility. This study conducts a Systematic Literature Review (SLR) following the PRISMA protocol to examine how the scientific literature has addressed the relationship between curtailment, energy security, and optimization strategies in high-renewable power systems. A total of 53 Q1-indexed articles published between 2021 and 2025 were analyzed using bibliometric and qualitative content analysis techniques. The results indicate that curtailment should not be interpreted solely as an operational inefficiency but rather as a potential flexibility asset when integrated with energy storage systems, power-to-X technologies, demand-side management, and stochastic optimization frameworks. The findings also highlight a shift from deterministic planning approaches toward robust and distributionally aware models capable of managing renewable uncertainty. Despite significant advances, geographic imbalances in case studies and limited integration between regulatory mechanisms and technical optimization remain key research gaps. This review contributes by synthesizing mitigation strategies into a structured flexibility framework and by outlining research directions for enhancing reliability in renewable-dominated systems. Full article

This paper proposes a generalized seasonal beta prime autoregressive moving average model with dynamic precision, denoted by BPSARMA, for modeling and forecasting positive-valued seasonal time series. The proposed framework extends the generalized BPARMA model by incorporating stochastic seasonal dynamics in the conditional mean through seasonal autoregressive and moving average components while allowing a flexible autoregressive structure for the conditional precision parameter, thereby accommodating time-varying uncertainty. The model also allows the inclusion of covariates and deterministic seasonal regressors. Parameter estimation is carried out by conditional maximum likelihood, and the main inferential and diagnostic tools are discussed. Monte Carlo simulations are conducted to examine the finite-sample behavior of the estimators and associated inference procedures. The practical usefulness of the proposed approach is illustrated through hydro-environmental time series applications, where its forecasting performance is evaluated using both in-sample and out-of-sample predictive measures. The empirical results indicate that the BPSARMA specification often provides competitive or superior forecasting accuracy relative to competing models, highlighting its usefulness for modeling and prediction in positive seasonal time series. Full article

The Graduate Student Evolutionary Algorithm (GSEA) has demonstrated promising optimization capability in several engineering tasks; however, its performance may deteriorate when dealing with high-dimensional and complex multimodal problems due to insufficient adaptive search behavior, weak diversity preservation, and stagnation during later optimization stages. To alleviate these limitations, this paper proposes a Multi-Strategy Improved Graduate Student Evolutionary Algorithm (MIGSEA) for numerical optimization and artistic image multi-threshold segmentation. First, an adaptive mentor-guided learning mechanism is introduced to dynamically regulate the influence of mentors and peers throughout the optimization process, enabling a more effective transition from global exploration to local exploitation. Second, an elite–random cooperative learning strategy is designed to combine high-quality solution guidance with stochastic perturbation, thereby improving population diversity and enhancing the ability to escape local optima. Third, a stagnation-aware local refinement mechanism is developed to activate adaptive neighborhood search when the optimization process becomes trapped, which further accelerates convergence and improves solution precision. To verify the effectiveness of the proposed algorithm, MIGSEA is evaluated on the IEEE CEC2017 and CEC2020 benchmark suites and compared with 11 advanced metaheuristic algorithms under identical experimental conditions. Experimental results demonstrate that MIGSEA achieves competitive optimization accuracy, convergence speed, robustness, and statistical superiority in most benchmark functions. Furthermore, MIGSEA is applied to Otsu-based artistic image multi-threshold segmentation using multiple benchmark images with different threshold levels. Quantitative evaluation based on PSNR, FSIM, and SSIM, together with visual analysis, confirms that the proposed method can generate more accurate and visually consistent segmentation results than existing competitors. Overall, the proposed MIGSEA provides an effective and robust optimization framework for both benchmark optimization and practical image segmentation applications. Full article

The increasing integration of renewable generation introduces highly stochastic operating conditions, substantially enlarging the operating space and posing severe computational challenges for traditional iterative power flow solvers. To address this, we propose a Physics-Informed Graph Attention Network (PI-GAT) for fast and physically consistent power flow assessment in power systems with high renewable penetration. PI-GAT represents buses and branches as graph-structured inputs and employs edge-aware multi-head attention to adaptively capture electrical interactions between connected nodes. By embedding AC power flow equations as residuals in the training loss, PI-GAT promotes physical consistency, improving nodal power balance consistency even under high renewable variability and N−1 contingency scenarios. Experimental results on IEEE 30-bus and 118-bus systems demonstrate that PI-GAT reduces active and reactive power mismatches by up to approximately 62% across the two benchmark systems relative to the edge-aware GAT baseline. This improvement in physical consistency is accompanied by a modest increase in point-wise voltage and phase-angle errors. Moreover, PI-GAT achieves substantial inference-time speedups over conventional numerical solvers, especially under batched multi-scenario inference. These findings indicate that PI-GAT provides a reliable and efficient surrogate model for real-time security assessment and contingency screening in power systems with high penetration of renewable generation. Full article

This study evaluates a lightweight, edge-deployable artificial intelligence pipeline to assist, not replace, trained human readers in the classification of RPR test reactions. Two separate and non-directly comparable experimental configurations were investigated: a binary task (Reactive vs. Non-Reactive) using 243 original images and a ternary task (Reactive, Minimally Reactive, Non-Reactive) using a distinct dataset of 293 original images. Because the datasets were acquired using a single device and laboratory protocol, and because deterministic augmentation generates highly correlated transformations rather than independent clinical samples, the reported results should be interpreted as preliminary internal evidence of feasibility rather than proof of clinical generalizability. In the augmented internal test evaluation, the binary model achieved 99.98% accuracy (25,137/25,200), while the ternary model achieved 91.12% accuracy (14,417/15,822). In the original-image deployment evaluation, binary performance remained 100% (58/58) across FP32, FP16, and INT8; ternary performance was preserved under FP32/FP16 at 95.24% (80/84) but decreased to 76.19% (64/84) after INT8 quantization. An additional stochastic augmentation experiment for ternary INT8 deployment restored performance to 95.24% (80/84) and 0.9444 Macro-F1, but external validation remains mandatory before any clinical adoption. Full article

This data descriptor presents CINES-REC-CITY, an open synthetic dataset providing high-resolution load profiles for energy community research. The dataset represents a typical German urban district with 70 apartments across eight multi-family buildings, including diverse socioeconomic characteristics. Three main components are provided at 15 min resolution for a full year: non-controllable residential electricity consumption for all apartments, charging profiles for 17 battery electric vehicles with trip information, and heat pump operation data for both variable-speed and hysteresis-controlled ground-source systems. All profiles were generated using validated bottom-up stochastic simulation models accounting for realistic user behavior, mobility patterns, and thermal building physics. The modular structure allows for selective combination of components, enabling investigation of different technology penetration scenarios. The dataset serves as a reference benchmark for reproducible research, allowing for direct comparison of optimization approaches, business models, and control strategies using identical underlying consumption patterns. It is suitable for techno-economic analysis, algorithm development for flexible load control, and grid impact assessment. All data is provided in CSV format with weather data for consistent extensions. Full article

Underground mine production scheduling under uncertainty is a complex and multi-field coupling system project. In this study, underground mine production scheduling seeks to determine the optimal start time of extraction-related projects, with the objectives of maximizing net present value, minimizing makespan, and maximizing resource utilization rate. The Copula function is adopted to formulate the correlation between uncertain project duration and cost and generate a set of stochastic scenarios. Then, the K-means algorithm classifies the scenarios into multiple scenario families, and the SBR algorithm is adopted to perform scenario reduction. Moreover, a rank choice function-based hyper-heuristic algorithm is extended to solve the multi-objective optimization model, which makes an excellent balance among the three objective functions. For determining the optimal scheduling plan, the cross-efficiency DEA algorithm is used to evaluate the archive set, sort the optimal solution, and guide the next iteration. The computational case verifies the effectiveness and efficiency of the multi-objective underground mine scheduling model, stochastic scenario and technical and hyper-heuristic algorithm. Full article

A single user-invariant tensor, kinematically impossible for any human finger to produce, bypasses bidirectional long short-term memory (BiLSTM) continuous-authentication defenders with numerically identical structure across four independently trained generators. We arrive at this finding by training generative adversarial networks against BiLSTM defenders on 51 users across three independent random seeds, with the data partition held fixed, to test the prevailing assumption that successful generative attacks must reproduce the victim’s kinematic behavior. Aggregate attack success rate varies from 31.4% to 45.1% across seeds, a 13.7 percentage-point spread arising purely from optimization stochasticity, demonstrating how unreliable single-seed reporting is as an estimator of the true attack surface. A four-group descriptive stratification shows that 8% of users are attacked across all three seeds, 31% are consistently safe, and 61% exhibit seed-dependent outcomes. Classifier accuracy on zero-effort impostors does not predict adversarial vulnerability (Spearman $\rho =-0.058$, permutation $p=0.688$), whereas intra-user behavioral variance does ($\rho =+0.351$, permutation $p=0.012$, Bonferroni-corrected). The mechanism is not behavioral emulation but convergence to an Adversarial Skeleton Key, a tensor located in an unregularized region of the BiLSTM’s decision surface that the network reliably maps to acceptance, despite lying many standard deviations outside any genuine human distribution. The mimicry-centric evaluation paradigm underestimates the real threat surface. Input-space plausibility must be treated as a defensive layer rather than a preprocessing concern. Full article

Self-organized criticality (SOC) describes a class of dynamical systems that may evolve toward statistically critical states characterized by scale-free avalanche-like events. In this work, we study an SOC-inspired discrete toy model and examine the avalanche-size statistics generated by local stochastic interactions. The aim is to explore whether a minimal avalanche model can reproduce statistical features that are formally reminiscent of multiscale turbulent phenomenology. We present a mathematical formulation of the toy model, analyze its numerical avalanche-size distribution, and discuss its relation to concepts of scaling, intermittency, and energy cascades in turbulence. The comparison with Navier–Stokes turbulence is therefore interpreted as a qualitative and statistical analogy, not as a physically complete correspondence. The results suggest that SOC-inspired toy models can provide a useful exploratory framework for understanding heavy-tailed activity and multiscale organization. Full article

Beta diversity represents the degree of variation in species composition between plant communities, and is thus an important indicator of the spatial distribution of biodiversity within regions. Patterns of beta diversity are shaped by deterministic processes relating to environmental conditions and species interactions, or by stochastic processes that include speciation, extinction, and dispersal limitation. Knowledge of the mechanisms that generate and maintain beta diversity is important and can inform management strategies for the conservation of biodiversity. The study aimed to assess the influence of environmental gradients on beta diversity in the grassland Biome of South Africa by comparing plant species composition between selected protected areas within the biome. Similarity in species composition between the protected areas was compared with the Jaccard index (β_J). In addition, constrained (CCA) and unconstrained (DCA) ordination, variation partitioning, and linear regression were used to analyse species turnover along environmental gradients. Jaccard similarity values were low, indicating high species turnover. There was an average of only 9% species composition similarity between the protected areas. Composition similarity decreased significantly with geographical distance between protected areas, but it increased significantly with mean annual temperature and assumed a hump-shaped pattern with mean annual rainfall. In general, geographic and climatic factors each explained approximately 20% of the variation in species composition. The patterns of beta diversity between the study locations suggest an interplay of both stochastic and deterministic processes in shaping community structure and composition, with environmental filtering as possibly one of the major drivers. Full article

Surface-enhanced Raman scattering (SERS) offers a powerful route for biomolecular detection because it combines molecular specificity with high sensitivity, rapid optical readout, and multiplexing capability. In real biological samples, however, analytical performance is rarely determined by signal enhancement alone. Biofluids such as serum, plasma, saliva, urine, and interstitial fluid contain complex biomolecular mixtures that interfere with target capture, spectral response, and data interpretation. A practical SERS biosensor must therefore localize targets, stabilize spectral responses, tolerate matrix-induced variation, and convert complex spectra into reliable analytical information. This review discusses recent progress in SERS biosensing from an integrated system perspective, with particular focus on artificial intelligence/machine learning (AI/ML)-assisted interpretation. Direct label-free SERS provides chemically transparent readouts but is limited by stochastic adsorption, hotspot heterogeneity, and spectral variation in complex samples. Bio-recognition interfaces improve target localization, while signal-transduction strategies based on nanotags, immunoassays, clustered regularly interspaced short palindromic repeats (CRISPR) systems, nanozymes, and lateral-flow formats decouple molecular recognition from spectral generation. Digital SERS further improves measurement robustness by converting fluctuating intensities into countable, event-based outputs. AI/ML-assisted analysis can support full-spectrum classification, calibration transfer, explainability, and patient-level decision-making. We frame AI/ML-assisted SERS biosensing as an integrated architecture connecting substrate design, interface engineering, signal transduction, digital measurement, and clinical validation. Future progress will depend as much on validation-ready workflows as on plasmonic enhancement itself, especially for systems intended to operate across different samples, instruments, and clinical settings. Full article

The sign of the maximal almost sure Lyapunov exponent determines the stability of stochastic systems, while its numerical computation for three-dimensional linear Stratonovich stochastic differential equations remains challenging due to the failure of classical two-dimensional strategies. The spherical angular motion of 3D systems produces a Fokker–Planck equation with intractable mixed partial derivatives, preventing conventional analytical solutions. This paper develops a unified computational framework for three-dimensional linear Stratonovich stochastic systems using analytical derivation for degenerate cases and physics-informed neural network (PINN) approximation for general non-degenerate scenarios. For degenerate systems, we reduce the coefficient matrix to a lower triangular form via orthogonal transformation and establish tight upper bounds based on the logarithmic growth property of the Wiener process, yielding closed-form expressions for the maximal almost sure Lyapunov exponent under all parameter sign configurations. For non-degenerate systems, we reformulate the Fokker–Planck equation in spherical coordinates and construct a customized PINN with trigonometric encoding to enforce periodic boundary conditions. The network is trained by joint loss functions of equation residuals, boundary constraints and normalization consistency, and the converged stationary density is substituted into the Furstenberg–Khasminskii formula to calculate the exponent via Gauss–Legendre quadrature. Monte Carlo simulations confirm the accuracy and robustness of the proposed method, which reliably identifies the sign of the maximal almost sure Lyapunov exponent even in near-critical regimes. Numerical experiments on a 3D stochastic Hopf bifurcation model show that noise negatively shifts the bifurcation point, with the offset linearly proportional to the squared noise intensity. This work extends Lyapunov stability analysis from two-dimensional to three-dimensional linear Stratonovich stochastic systems, offering an effective tool for stability evaluation of general three-dimensional stochastic dynamical models. Full article

Optimal sizing of hybrid renewable energy systems for desert camps is a multi-objective problem that must account for cost, reliability, component degradation, and uncertainty. This paper introduces an improved multi-objective neural architecture search (IMONAS) framework for hybrid nanogrid sizing in the desert environment of Hafr Al-Batin, Saudi Arabia. The framework combines neural optimization, stochastic uncertainty modeling, and explicit battery degradation modeling, a combination not addressed in the reviewed studies for this application. Six test cases are examined by varying uncertainty assumptions, battery degradation, and the annual duration of uncertain operation. For each case, IMONAS provides Pareto-front solutions that specify the photovoltaic, diesel generator, battery autonomy, and inverter choices while minimizing the cost of energy (COE) and the loss of power supply probability (LPSP). IMONAS is compared with the original MONAS and five other multi-objective optimization methods. In addition to visual Pareto-front comparisons, the assessment uses Pareto-dominance indicators, namely the C-metric and an aggregated score derived from pairwise C-metric comparisons across the algorithms and cases. The results provide a validated sizing framework for remote arid-region nanogrids under uncertainty and battery degradation. Full article

The increasing penetration of renewable energy sources introduces significant variability, low-inertia behaviour, and operational uncertainty into modern power systems, resulting in frequent frequency deviations and degraded dynamic stability. Conventional Load Frequency Control (LFC) approaches based on fixed-parameter PID controllers often exhibit limited disturbance rejection capability under nonlinear and stochastic operating conditions. This study proposes an enhanced LFC framework that integrates a PID controller optimised using the Flower Pollination Algorithm (FPA) with support from a Unified Power Flow Controller (UPFC) and a Redox Flow Battery (RFB) to improve frequency regulation, damping, and robustness in renewable-integrated low-inertia power systems. This study developed a MATLAB/Simulink single-area power system model comprising governor, turbine, and generator-load dynamics to evaluate controller performance under a 0.01 pu step disturbance, stochastic load variations, renewable energy fluctuations, and ±20% parameter uncertainty conditions. The FPA optimally tuned the PID controller gains using the Integral Time Absolute Error criterion to enhance transient response and disturbance rejection capability. Comparative analyses were conducted against conventional PID and fuzzy-based controllers using settling time, overshoot, RMS deviation, ITAE, and mean frequency deviation indices. Simulation results demonstrate that the proposed FPA–PID + UPFC framework significantly outperforms the conventional PID controller by achieving approximately 66.6% settling-time reduction, 72.1% RMS reduction, and 75.5% ITAE reduction. The proposed framework reduced settling time from 18.46 s to 6.16 s and substantially improved damping performance under stochastic disturbances. The coordinated integration of the UPFC and RFB further enhanced transient stability through dynamic power-flow regulation and rapid active-power compensation during disturbances. Sensitivity analysis under parameter uncertainty and stochastic operating conditions confirmed stable and reliable operation under stochastic disturbances and parameter uncertainty conditions. The proposed architecture, therefore, provides an effective, practically applicable solution for secondary frequency regulation in renewable-rich smart grids, low-inertia transmission systems, microgrids, and future distributed power networks. Full article