Search Results (814)

The rapid growth of electric vehicles (EVs) has created an urgent need for sustainable end-of-life battery management systems. This paper presents a novel AI-driven framework for designing resilient reverse logistics networks that optimize the collection, testing, repurposing, and recycling of EV batteries within a circular economy context. We develop a bi-level optimization model in which the upper level determines strategic facility location decisions under disruption uncertainty, and the lower level employs deep reinforcement learning (DRL) to make dynamic operational decisions including battery routing, State-of-Health (SoH)-based sorting, and inventory management. The model simultaneously optimizes three objectives: total supply chain cost minimization, carbon emission reduction, and resilience maximization. A novel Fuzzy-Robust Stochastic programming approach with Conditional Value-at-Risk (FRS-CVaR) handles hybrid uncertainty from demand variability, supply disruptions, and material price volatility. We propose an enhanced Non-dominated Sorting Genetic Algorithm III (NSGA-III) integrated with Proximal Policy Optimization (PPO) for an efficient solution. The framework is validated through a comprehensive case study of the Gulf Cooperation Council (GCC) region, demonstrating that the AI-driven approach reduces total costs by 18.7%, decreases carbon emissions by 23.4%, and improves supply chain resilience by 31.2% compared to traditional optimization methods. Ablation studies across 10 independent runs with different random seeds confirm the robustness of these findings (95% confidence intervals within ±2.3% for all metrics). Sensitivity analysis reveals that battery SoH prediction accuracy and facility redundancy levels significantly impact network performance. This research contributes to both methodology and practice by providing decision-makers with an intelligent, adaptive tool for sustainable EV battery lifecycle management. Full article

This paper proposes a method based on interval linear robust optimization to address the potential impacts of multiple uncertainties on the operational security of Regional Integrated Energy Systems (RIESs). The model considers the uncertainty in user loads and renewable energy outputs and determines the value ranges of related parameters through statistical analysis to characterize the boundaries of these uncertainties. To transform the stochastic disturbances into a solvable problem, the model introduces energy balance constraints under the worst-case scenario, ensuring that the system remains feasible under extreme conditions. The research framework integrates Nash bargaining theory, demand response mechanisms, and tiered carbon trading policies, constructing a cooperative game model for RIESs to minimize the overall operation cost of the alliance while providing a reasonable revenue distribution scheme. This approach aims to achieve fairness and sustainability in regional cooperation. Simulation results show that the method can effectively reduce the collaborative operation cost and improve the fairness of revenue distribution. To address potential issues of information misreporting and dishonesty in real-world scenarios, the model introduces an adjustable fraud factor in the revenue distribution process to characterize the strategy deviations of participants. Even under potential fraud risks, the mechanism can maintain an optimal revenue structure and lead the participants toward a stable fraud equilibrium, thereby enhancing the robustness and reliability of the overall collaboration. Full article

This paper investigates the optimal portfolio control problem under a stochastic volatility model, whose dynamics are governed by a highly nonlinear Hamilton–Jacobi–Bellman equation. We employ a separable value function and introduce a novel exponential approximation technique to simplify the nonlinear terms of the auxiliary function. The simplified HJB equation is solved numerically using the advanced Fréchet–Newton method, which is known for its rapid convergence properties. We rigorously analyze the numerical outcomes, demonstrating that the iterative sequence converges quickly to the trivial fixed point ($g^{*}=1$) under zero risk and zero excess return conditions. This convergence is mathematically justified through rigorous functional analysis, including the principles of contraction mapping and the Kantorovich theorem, which validate the stability and efficiency of the proposed numerical scheme. The results offer theoretical insight into the behavior of the HJB equation in simplified solution spaces. Full article

Self-cleansing intrusion-tolerant systems mitigate attacker intrusions and control through periodic recovery, thereby enhancing both availability and security. However, vulnerabilities in the control link render these systems susceptible to request forgery attacks. Furthermore, existing research on the modeling and performance analysis of such systems remains insufficient. To address these issues, this paper introduces an authentication mechanism to fortify control link security and employs Generalized Stochastic Petri Nets for system evaluation. We constructed Petri net models for three distinct scenarios: a traditional system, a system compromised by forged controller requests, and a system fortified with authentication mechanism. Subsequently, isomorphic Continuous-Time Markov Chains were derived to facilitate theoretical analysis. Quantitative evaluations were performed by deriving steady-state probabilities and conducting simulations on the PIPE platform. To further assess practicality, we conduct scalability analysis under varying system scales and parameter settings, and implement a prototype in a virtualized testbed to experimentally validate the analytical findings. Evaluation results indicate that authentication mechanism ensures the reliable execution of cleansing strategies, thereby improving system availability, enhancing security, and mitigating data leakage risks. Full article

Behavioral deviations from subjective expected utility theory, most famously captured by the Allais paradox and the Ellsberg paradox, have inspired extensive theoretical and experimental research into risk and ambiguity preferences. While the existing analyze these paradoxes independently, little work explores how such heterogeneously biased agents interact in networked strategic environments. Our paper fills this gap by modeling a convergent Markov–network game between Allais-type and Ellsberg-type players, each endowed with fully enriched loss matrices that reflect their distinct probabilistic and ambiguity attitudes. We define convergent priors as those inducing a spectral radius of <1 in iterated enriched matrices, ensuring iterative convergence under a matrix-based update rule. Players minimize their losses under these priors in each iteration, converging to an equilibrium where no further updates are feasible. We analyze this convergence under three learning regimes—homophily, heterophily, and type-neutral randomness—each defined via distinct neighborhood learning dynamics. To validate the equilibrium, we construct a risk-neutral measure by transforming losses into payoffs and derive a riskless rate of return representing players’ subjective indifference to risk. This applies risk-neutral pricing logic to behavioral matrices, which is novel. This framework unifies paradox-type decision makers within a networked Markovian environment (stochastic adjacency matrix), extending models of dynamic learning and providing a novel equilibrium characterization for heterogeneous, ambiguity-averse agents in structured interactions. Full article

Post-earthquake fires often cause more severe losses than the earthquakes themselves, highlighting the critical role of water distribution networks (WDNs) in mitigating fire risks. This study proposed an improved assessment framework for the post-earthquake firefighting functionality of WDNs. This framework integrates a WDN firefighting simulation model into a cloud model-based assessment method. By combining seismic damage and firefighting scenarios, the simulation model derives sample values of the functional indexes through Monte Carlo simulations. These indexes integrate the spatiotemporal characteristics of the firefighting flow and pressure deficiencies to assess a WDN’s capability to control fire and address fire hazards across three dimensions: average, severe, and prolonged severe deficiencies. The cloud model-based assessment method integrates the sample values of functional indexes with expert opinions, enabling qualitative and quantitative assessments under stochastic–fuzzy conditions. An illustrative study validated the efficacy of this method. The flow- and pressure-based indexes elucidated functionality degradation owing to excessive firefighting flow and the diminished supply capacity of a WDN, respectively. The spatiotemporal characteristics of severe flow and pressure deficiencies demonstrated the capability of firefighting resources to manage concurrent fires while ensuring a sustained water supply to fire sites. This method addressed the limitations of traditional quantitative and qualitative assessment approaches, resulting in more reliable outcomes. Full article

Wood flooring manufacturers face complex challenges in dynamically allocating resources across multi-channel markets, characterized by channel conflicts, demand uncertainty, and long-term cumulative effects of decisions. Traditional static optimization or myopic approaches struggle to address these intertwined factors, particularly when critical market states like brand reputation and customer base cannot be precisely observed. This paper establishes a systematic and theoretically grounded online decision framework to tackle this problem. We first model the problem as a Partially Observable Stochastic Dynamic Game. The core innovation lies in introducing an unobservable market position vector as the central system state, whose evolution is jointly influenced by firm investments, inter-channel competition, and macroeconomic randomness. The model further captures production lead times, physical inventory dynamics, and saturation/cross-channel effects of marketing investments, constructing a high-fidelity dynamic system. To solve this complex model, we propose a hierarchical online learning and control algorithm named L-BAP (Lyapunov-based Bayesian Approximate Planning), which innovatively integrates three core modules. It employs particle filters for Bayesian inference to nonparametrically estimate latent market states online. Simultaneously, the algorithm constructs a Lyapunov optimization framework that transforms long-term discounted reward objectives into tractable single-period optimization problems through virtual debt queues, while ensuring stability of physical systems like inventory. Finally, the algorithm embeds a game-theoretic module to predict and respond to rational strategic reactions from each channel. We provide theoretical performance analysis, rigorously proving the mean-square boundedness of system queues and deriving the performance gap between long-term rewards and optimal policies under complete information. This bound clearly quantifies the trade-off between estimation accuracy (determined by particle count) and optimization parameters. Extensive simulations demonstrate that our L-BAP algorithm significantly outperforms several strong baselines—including myopic learning and decentralized reinforcement learning methods—across multiple dimensions: long-term profitability, inventory risk control, and customer service levels. Full article

Pharmaceutical supply chains face high inventory and stockout risks because of short product shelf lives and volatile demand. To enhance coordination efficiency and reduce drug waste, this study examines a two-echelon supply chain comprising a manufacturer and multiple medical institutions. We built a joint ordering and transshipment optimization model that simultaneously incorporates shelf-life constraints, the first-in–first-out (FIFO) policy, inventory capacity limits, and peer-level transshipment. Under deterministic and stochastic demand, we solved the model using Bayesian optimization and Monte Carlo simulation. The results show that moderate inventory transshipment effectively mitigates risk from demand uncertainty and increases total supply-chain profit; under stochastic demand, the optimal strategy relies more heavily on coordinated transshipment to reduce excess inventory and near-expiry waste. Full article

This paper examines an optimal investment–consumption problem in a setting where the financial environment is influenced by both stochastic factors and delayed effects. The investor, endowed with Constant Relative Risk Aversion (CRRA) preferences, allocates wealth between a risk-free asset and a single risky asset. The short rate follows a Vas$\check{\mathrm{i}}$ček-type term structure model, while the risky asset price dynamics are driven by a delayed Heston specification whose variance process evolves according to a Cox–Ingersoll–Ross (CIR) diffusion. Delayed dependence in the wealth dynamics is incorporated through two auxiliary variables that summarize past wealth trajectories, enabling us to recast the naturally infinite-dimensional delay problem into a finite-dimensional Markovian framework. Using Bellman’s dynamic programming principle, we derive the associated Hamilton–Jacobi–Bellman (HJB) partial differential equation and demonstrate that it generalizes the classical Merton formulation to simultaneously accommodate delay, stochastic interest rates, stochastic volatility, and consumption. Under CRRA utility, we obtain closed-form expressions for the value function and the optimal feedback controls. Numerical illustrations highlight how delay and market parameters impact optimal portfolio allocation and consumption policies. Full article

Background/Objectives: Fluoroscopy-guided procedures are widely used across surgical and interventional specialties but expose operators to ionizing radiation with associated stochastic and deterministic effects. The aim is to characterize occupational radiation exposure, evaluate the effectiveness of shielding strategies, assess long-term cancer risks, and identify compliance patterns. Methods: This structured narrative review summarizes evidence on operator dose, shielding effectiveness, compliance with protective practices, and long-term cancer risk. A search of PubMed, Scopus, Embase, and Web of Science (limited to January 2000–March 2024) identified 62 records; 27 full texts were reviewed, and 16 studies met the inclusion criteria. Results: Across studies, unshielded chest exposure averaged 0.08–0.11 mSv per procedure, and eye exposure averaged 0.04–0.05 mSv. Lead aprons reduced exposure by about 90% at 0.25 mm and 99% at 0.5 mm, thyroid collars reduced neck dose by 60–70%, and lead glasses reduced ocular dose 2.5–4.5-fold. Compliance with aprons and thyroid collars was high, whereas lead glasses and lower-body shielding were inconsistently used. Limited epidemiologic data suggested a higher cancer burden in exposed surgeons, and Biologic Effects of Ionizing Radiation (BEIR) VII–based modeling projected increased lifetime risks of solid cancers in chronically exposed operators. Conclusions: Protective equipment substantially reduces operator dose, but exposure variability and inconsistent shielding practices persist and justify standardized monitoring, stronger enforcement of radiation safety protocols, and longitudinal studies. Full article

Against the backdrop of China’s dual-carbon goals, traditional road transportation has relatively high carbon emissions and is in urgent need of a low-carbon transition. The intermittency of photovoltaic (PV) power generation and the stochastic nature of electric vehicle (EV) charging demand introduce significant uncertainty for PV-energy storage-charging systems in highway service areas. Existing approaches often struggle to balance economic efficiency and reliability. This study develops a min-max-min robust optimization model for a full-route PV-energy storage-charging system. A box uncertainty set is used to characterize uncertainties in PV output and EV load, and a tunable uncertainty parameter is introduced to regulate risk. The model is solved using a column-and-constraint generation (C&CG) algorithm that decomposes the problem into a master problem and a subproblem. Strong duality, combined with a big-M formulation, enables an alternating iterative solution between the master problem and the subproblem. Simulation results demonstrate that the proposed algorithm attains the optimal solution and, relative to deterministic optimization, achieves a desirable trade-off between economic performance and robustness. Full article

To address active power fluctuations in power grids induced by high renewable energy penetration and overcome the limitations of existing probabilistic power flow (PPF) methods that ignore generator frequency regulation constraints, this paper proposes a segmented stochastic power flow modeling method and an efficient analytical framework that incorporates the actions and capacity constraints of regulation units. Firstly, a dual dynamic piecewise linear power injection model is established based on “frequency deviation interval stratification and unit limit-reaching sequence ordering,” clarifying the hierarchical activation sequence of “loads first, followed by conventional units, and finally automatic generation control (AGC) units” along with the coupled adjustment logic upon reaching limits, thereby accurately reflecting the actual frequency regulation process. Subsequently, this model is integrated with the State-Independent Linearized Power Flow (DLPF) model to develop a segmented stochastic power flow framework. For the first time, a deep integration of unscented transformation (UT) and regulation-aware power allocation is achieved, coupled with the Nataf transformation to handle correlations among random variables, forming an analytical framework that balances accuracy and computational efficiency. Case studies on the New England 39-bus system demonstrate that the proposed method yields results highly consistent with those of Monte Carlo simulations while significantly enhancing computational efficiency. The DLPF model is validated to be applicable under scenarios where voltage remains within 0.95–1.05 p.u., and line transmission power does not exceed 85% of rated capacity, exhibiting strong robustness against parameter fluctuations and capacity variations. Furthermore, the method reveals voltage distribution patterns in wind-integrated power systems, providing reliable support for operational risk assessment in grids with high shares of renewable energy. Full article

This paper quantifies the steady-state voltage-compliance impact of ultra-fast electric vehicle (EV) charging on the IEEE 33-bus radial distribution feeder. Four practical scenarios are examined by combining two penetration levels (6 and 12 charging points, i.e., ≈20% and ≈40% of PQ buses) with two charger ratings (1 MW and 350 kW per point). Candidate buses for EV station integration are selected through a nodal voltage–reactive sensitivity ranking ($\partial V/\partial Q$), prioritizing electrically robust locations. To capture realistic operating uncertainty, a 24-hour quasi-static time-series power-flow assessment is performed using Monte Carlo sampling ($N=100$), jointly modeling residential-demand variability and stochastic EV charging activation. Across the four cases, the worst-hour minimum voltage (uncompensated) ranges from 0.803 to 0.902 p.u., indicating a persistent under-voltage risk under dense and/or high-power charging. When the expected minimum-hourly voltage violates the 0.95 p.u. limit, a closed-form, sensitivity-guided reactive compensation is computed at the critical bus, and the power flow is re-solved. The proposed mitigation increases the minimum-voltage trajectory by approximately 0.03–0.12 p.u. (about 3.0–12.0% relative to 1 p.u.), substantially reducing the depth and duration of violations. The maximum required reactive support reaches 6.35 Mvar in the most stressed case (12 chargers at 1 MW), whereas limiting the unit charger power to 350 kW lowers both the severity of under-voltage and the compensation requirement. Overall, the Monte Carlo V–Q sensitivity framework provides a lightweight and reproducible tool for probabilistic voltage-compliance assessment and targeted steady-state mitigation in EV-rich radial distribution networks. Full article

This study investigates the permeability evolution in floor fault damage zones under stress–seepage–damage coupling, with a focus on water inrush risks caused by confined water upward conduction during deep mining. A stochastic fracture geometry model of the fault damage zone was developed using the discrete fracture network (DFN) model and the Monte Carlo method. Based on geological data from a mining area in Shandong, a multiphysics-coupled numerical model under mining-induced conditions was established with COMSOL Multiphysics. The simulations visually reveal the dynamic evolution of damage propagation patterns in the floor strata during working face advancement. Results indicate that the damage zone stabilizes after the working face advances to 80 m, with its morphology exhibiting strong spatial correlation to regions of high seepage velocity. Moreover, increasing confined water pressure plays a critical role in driving flow field evolution. Full article

Existing inspection systems for railway point machines often suffer from high latency and poor interpretability, which impedes the real-time detection of critical mechanical anomalies, thereby increasing the risks of derailment and leading to cascading schedule delays. Addressing these challenges, this study proposes a lightweight computer vision-based detection framework deployed on the RK3588S edge platform. First, to overcome the accuracy degradation of segmentation networks on constrained edge NPUs, a Sensitivity-Aware Mixed-Precision Quantization and Heterogeneous Scheduling (SMPQ-HS) strategy is proposed. Second, a Multimodal Semantic Diagnostic Framework is constructed. By integrating geometric engagement depths—calculated via perspective rectification—with visual features, a Hard-Constrained Knowledge Embedding Paradigm is designed for the Qwen2.5-VL model. This approach constrains the stochastic reasoning of the Qwen2.5-VL model into standardized diagnostic conclusions. Experimental results demonstrate that the optimized model achieves an inference speed of 38.5 FPS and an mIoU of 0.849 on the RK3588S, significantly outperforming standard segmentation models in inference speed while maintaining high precision. Furthermore, the average depth-estimation error remains approximately 3%, and the VLM-based fault identification accuracy reaches 88%. Overall, this work provides a low-cost, deployable, and interpretable solution for intelligent point machine maintenance under edge-computing constraints. Full article