Search Results (22)

To address the challenges faced by virtual power plants (VPPs) in uncertain market environments and complex distribution networks, including strong market coupling, difficulty in multi-resource coordination, and strict safety constraints, this paper proposes a Hierarchical Hybrid Intelligent Framework (H2IF). The proposed framework integrates a market-aware meta-game mechanism, a topology-embedded graph attention coordination method, and a risk-aware soft/hard constraint safety mechanism to achieve economically optimal dispatch of VPPs in complex dynamic scenarios. By explicitly modeling competitive market interactions, the proposed method enhances strategy robustness; by exploiting grid topology priors, it improves multi-agent coordination capability; and by combining differentiable projection with risk-constrained optimization, it jointly ensures operational safety and revenue stability. Simulation results on a modified IEEE 33-bus system demonstrate that H2IF outperforms mainstream deep reinforcement learning methods and rule-based dispatch strategies in overall performance. In the 24 × 300-step testing scenario, H2IF achieves an average single-episode operating cost of 38.23 k$, which is 28.9%, 40.4%, and 26.5% lower than those of MADDPG, SAC, and the rule-based method, respectively, while also yielding the lowest constraint violation level. Ablation studies further verify the effectiveness of each key module in improving profit, reducing operating costs, enhancing tracking performance, and strengthening safety. The results indicate that the proposed method enables coordinated optimization of economy, safety, and robustness for VPP dispatch under uncertain market and operating conditions. Full article

Castrate-resistant prostate cancer (PCa) is a critical situation in which many patients will relapse. Hormonal androgen deprivation therapy (HADT) is the gold standard of care when a patient relapses, following primary surgical or radiation therapy. Usually, the benefits from HADT are poor and recurrent disease after HADT treatment is termed castrate-resistant prostate cancer (CRPC), which is in most cases fatal. The therapeutic regimens for CRPC include chemotherapy with docetaxel, immunotherapy agent sipuleucel-T, the taxane cabazitaxel, the CYP17 inhibitor abiraterone acetate and the androgen receptor (AR) antagonist enzalutamide. Thus, it is imperative to study the inherent property of prostate cancer cells, to resist therapy and reconsider the therapeutic protocols (continuous v’s intermittent). We make use of a hybrid mathematical model which consists of an extension of a very potent ordinary differential equation (ODE) Baez–Kuang model, combined with two Game Theory components: the Minority Game for adaptive behavior and the Axelrod model for heterogeneity behavior. Our study suggests that increasing tumor adaptability, through Minority Game dynamics, improves short-term prostatic-specific antigen (PSA) control and stabilizes therapy cycles. However, this comes at the cost of driving the tumor to a homogeneous, androgen-independent (AI) state, which is therapy-resistant. Conversely, maintaining heterogeneity, via Axelrod dynamics, sustains a mixed population, with androgen-dependent (AD) cells persisting longer and potentially delaying resistance emergence. Full article

Rapid urbanization increasingly threatens urban cultural heritage. While buffer zones are crucial for mitigating external pressures, conventional delineation relies on value-based or geometric rules, overlooking parcel-scale heterogeneous externalities. This study addresses this gap by proposing a parcel-based, risk–value coupling framework that delineates heritage buffer zones and supports differentiated land-use regulations. In this study, “negative-impact risk” is operationalized as a composite proxy of cumulative urban development pressures that may increase the likelihood and potential severity of adverse externalities on heritage settings, rather than a full hazard–exposure–vulnerability risk model. And we construct a multi-source indicator system with 12 parcel-level indicators to characterize negative impact risk and heritage value, and adopt a hybrid weighting strategy integrating an AHP, entropy weighting, and game-theoretic combination to reconcile expert judgement and data-driven heterogeneity. To address uncertainty in multi-criteria evaluation, a cloud model maps indicator sets into discrete management levels. The framework is applied to the Pingjiang Historic District in Suzhou, China, using 121 land parcels as decision units. Results show that the approach identifies spatial risk–value patterns and delineates an operational buffer prioritizing parcels with elevated coupled scores. Compared with a fixed-distance buffer, it achieves greater coverage of high-risk parcels while maintaining a smaller regulatory scope. The parcel classification is then translated into tiered planning controls, including development intensity limits, land-use rules, and monitoring priorities. The framework integrates risk management and heritage conservation to support uncertainty-aware, proactive, and transferable zoning decisions. Full article

The aerial game environment is complex. To enhance mission success rates, UAVs must comprehensively consider threats from various directions and distances, as well as autonomous evasion maneuver decision-making methods for multiple UAV platforms, rather than solely focusing on threats from specific directions and distances or decision-making methods for fixed UAV platforms. Accordingly, this study proposes an autonomous missile evasion maneuver decision-making method for UAVs, suitable for multi-scenario and multi-platform transferable mission requirements. A three-dimensional UAV-missile pursuit-evasion model is established, along with state-space, hierarchical maneuver action space and reward function models for autonomous missile evasion. The auto-regressive multi-hybrid proximal policy optimization (ARMH-PPO) algorithm is proposed for this model, integrating autoregressive network structures and utilizing long short-term memory (LSTM) networks to extract temporal features. Drawing on exploration curriculum learning principles, temporal fusion of process and event reward functions is implemented to jointly guide the agent’s learning process through human experience and strategy exploration. Additionally, a proportion integration differentiation (PID) method is introduced to control the UAV’s maneuver execution, reducing the coupling between maneuver control quantities and the simulation object. Simulation experiments and result analysis demonstrate that the proposed algorithm ranks first in both average reward value and average evasion success rate metrics, with the average evasion success rate approximately 8% higher than the second-ranked algorithm. In the three initial scenarios where the missile is positioned laterally, head-on, and tail-behind the UAV, the UAV’s missile evasion success rates are 95%, 70%, and 85%, respectively. Multi-platform simulation results demonstrate that the decision model constructed in this paper exhibits a certain degree of multi-platform transferability. Full article

As global warming intensifies, extreme weather phenomena such as heatwaves, flash droughts, torrential floods, cold waves, and blizzards are becoming increasingly frequent. Against this backdrop, traditional static food security assessment methods fail to capture the dynamic transmission patterns of agricultural productivity risks and their regional heterogeneity. Therefore, it is imperative to reconstruct a resilience analysis paradigm for food production systems, dynamically investigate the mechanisms through which climate change affects China’s agricultural productivity and discern the interactive effects between technological evolution and climate constraints. This will provide theoretical foundations for building a climate-resilient food security system. Accordingly, this study establishes a multidimensional resilience measurement index system for China’s grain productivity by integrating agricultural factor elasticity analysis with disaster impact response modeling. Through production function decomposition and hybrid forecasting models, we reveal the evolutionary patterns of China’s grain productivity under climate risk shocks and trace the transmission pathways of risk fluctuations. Key findings indicate the following: (1) Extreme climate events exhibit significant negative correlations with grain production, with drought and flood impacts demonstrating pronounced regional heterogeneity. (2) A dynamic game relationship exists between agricultural technological progress and climate risk constraints, where the marginal contribution of resource efficiency improvements to productivity growth shows diminishing returns. (3) Climate-sensitive factors vary substantially across agricultural zones: Northeast China faces dominant cold damage, North China experiences drought stress, while South China contends with humid-heat disasters as primary regional risks. Consequently, strengthening foundational agricultural infrastructure and optimizing regionally differentiated risk mitigation strategies constitute critical pathways for enhancing food security resilience. (4) Future research should leverage higher-resolution, county-level data and incorporate a wider range of socio-economic variables to enhance granular understanding and predictive accuracy. Full article

This study develops a multi-agent evolutionary game model that incorporates both retailers and heterogeneous logistics providers, extending beyond prior dyadic models that typically isolate either channel choice or logistics competition. By comparing scenarios with and without the BOPS channel, the framework captures the dynamic interactions between retailers and logistics providers. The results show that introducing In-Store Pickup significantly increases market demand and retailer revenue by reducing consumer waiting time, but it also produces a revenue crowding effect for slow logistics providers. For fast providers, the impact depends on their ability to adjust service quality: lowering service levels helps retain market share, while efficiency improvement enhances profitability. Furthermore, consumer product valuation plays a critical role in driving retailers toward dual-provider or hybrid strategies. The methodological innovation lies in integrating heterogeneous logistics service differentiation with channel strategy selection into a unified evolutionary game framework. The study contributes by proposing a dynamic “efficiency threshold–channel selection” mechanism, offering both theoretical advancement in omnichannel retailing research and managerial insights for retailers and logistics providers seeking to optimize logistics capabilities and channel collaboration. Full article

The issue of in-orbit optimal safe surrounding control for service satellite (SSat) formation against a huge unknown target satellite (TSat) with specific structures is solved by using relative measurements only, and an optimal cooperative safe surrounding (OCSS) hybrid controller achieving both target tracking (TT) and configuration tracking (CT) is proposed corresponding to the two equal sub-objectives. Facing the challenges caused by incomplete information of the TSat, by using relative measurements only, the initial-condition-free boundaries are constructed by an arctan-based state transformation to directly constrain the target tracking error to perform prescribed transient and steady-state behaviors. Based on the shared TT control law, optimal collision-free CT controllers for all SSats are further solved via a nonzero-sum differential game, where the collision threat from all SSats and target structures are modeled by a novel circumscribed-sphere model. Finally, the effectiveness and advantages of the proposed OCSS control technique is verified by simulation results. Full article

Implementing hybrid carbon policies is crucial for supply chains’ low-carbon transition. However, the downstream retailer is often passive in low-carbon strategies, leading to fair issues that may influence the decision-making of channel members. Therefore, this study integrates green technology, remanufacturing, retailer’s fairness concerns, low-carbon preference, and hybrid carbon policies into a manufacturer-led supply chain through differential game theory. Then, the equilibrium solutions for each member are analyzed under the centralized case and decentralized case involving a cost-sharing contract for low-carbon promotion. Our results show that centralized decision-making can optimize both the economic and environmental performances of channel members; retailer’s fairness concerns can enhance low-carbon promotional efforts and the cost-sharing ratio for such initiatives, but do not impact low-carbon production efforts. Additionally, a threshold exists on the relationship between retailer’s fairness concerns and the cost-sharing ratio; increased low-carbon preference motivates more efforts in low-carbon production and promotion. Moreover, stricter carbon policies motivate the manufacturer to increase low-carbon efforts, but the retailer tailors its low-carbon promotional strategy according to the unit carbon emissions of products to maintain an adequate level of low-carbon goodwill. Full article

This study proposes DtMin, a novel protocol for implementing data minimization principles in medical information sharing between healthcare providers (HCPs) and electronic health record providers (EHRPs). DtMin utilizes a multi-type encryption approach, combining attribute-based encryption (ABE) and hybrid encryption techniques. The protocol classifies patient data attributes into six categories based on sensitivity, consent status, and sharing requests. It then applies differential encryption methods to ensure only the intersection of patient-consented and EHRP-requested attributes is shared in decipherable form. DtMin’s security is formally analyzed and proven under the ICR-DB and ICR-IS security games. Performance analysis demonstrates efficiency across various data volumes and patient numbers. This study explores the integration of DtMin with advanced cryptographic techniques such as lattice-based ABE and lightweight ABE variants, which can potentially enhance its performance and security in complex healthcare environments. Furthermore, it proposes strategies for integrating DtMin with existing healthcare information systems and adapting it to future big data environments processing over 100,000 records. These enhancements and integration strategies position DtMin as a scalable and practical solution for implementing data minimization in diverse healthcare settings, from small clinics to large-scale health information exchanges. Full article

In the rapidly evolving platform economy, the competition between platform-owned products and third-party offerings is intensifying. This study examines the entry and pricing strategies of dominant e-commerce platforms such as Amazon and JD Mall which sell both platform-owned and third-party products. We use a complete information game model to analyze the strategic interactions between these platforms and third-party sellers, focusing on cost discrepancies and limited entry capabilities, areas previously underexplored. Our key findings include the following: (1) Platforms with dominant power can restrict third-party product pricing. (2) Increased consumer influence by the platform can reduce competition between platform-owned and third-party products. (3) Platforms prioritize high-value-product markets when entry capabilities are limited. (4) Commission-based revenue models are generally more efficient than entry fees. (5) Regulatory bans on hybrid models do not necessarily enhance social welfare; differentiated taxation on various revenue sources may be more effective. This study contributes by developing a comprehensive game-theoretic model to simulate strategic interactions, analyze pricing competition and entry strategies under cost asymmetry and capacity constraints, and provide theoretical guidance for regulatory policies. Full article

Considering that the nonlinearity and uncertainty of the microgrid model complicate the derivation and design of the optimal controller, an adaptive dynamic programming (ADP) algorithm is designed to solve the model-free non-zero-sum game. By combining the advantages of policy iteration and value iteration, an optimal learning control scheme based on hybrid iteration is constructed to provide stringent real power sharing for the nonlinear and coupled microgrid systems with N-distributed generations. First, using non-zero-sum differential game strategy, a novel distributed secondary voltage recovery consensus optimal control protocol is built using a hybrid iteration method to realize the voltage recovery of microgrids. Then, the data of the system state and input are gathered along a dynamic system trajectory and a data-driven optimal controller learns the game solution without microgrid physics information, enhancing convenience and efficiency in practical applications. Furthermore, the convergence analysis is given in detail, and it is proved that the control protocol can converge to the optimal solution so as to ensure the stability of the voltage recovery of the microgrid system. Convergence analysis proves the convergence of the the protocol to the optimal solution, ensuring voltage recovery stability. Simulation results validate the feasibility and effectiveness of the proposed scheme. Full article

This paper describes a kind of linear quadratic uncertain stochastic hybrid differential game system grounded in the framework of subadditive measures, in which the system dynamics are described by a hybrid differential equation with Wiener–Liu noise and the performance index function is quadratic. Firstly, we introduce the concept of hybrid differential games and establish the Max–Min Lemma for the two-player zero-sum game scenario. Next, we discuss the analysis of saddle-point equilibrium strategies for linear quadratic hybrid differential games, addressing both finite and infinite time horizons. Through the incorporation of a generalized Riccati differential equation (GRDE) and guided by the principles of the Itô–Liu formula, we prove that that solving the GRDE is crucial and serves as both a sufficient and necessary precondition for identifying equilibrium strategies within a finite horizon. In addition, we also acquire the explicit formulations of equilibrium strategies in closed forms, alongside determining the optimal values of the cost function. Through the adoption of a generalized Riccati equation (GRE) and applying a similar approach to that used for the finite horizon case, we establish that the ability to solve the GRE constitutes a sufficient criterion for the emergence of equilibrium strategies in scenarios extending over an infinite horizon. Moreover, we explore the dynamics of a resource extraction problem within a finite horizon and separately delve into an $H_{\infty }$ control problem applicable to an infinite horizon. Finally, we present the conclusions. Full article

Considering the uncertainty of game duration and periodic seasonal fluctuation, an n-player switched pollution-control differential game is modeled to investigate a sustainable and adaptive strategy for players. Based on the randomness of game duration, two scenarios are considered in this study. In the first case, the game duration is a random variable, $T_f$, described by the shifted exponential distribution. In the second case, we assumed that players’ equipment is heterogeneous, and the i-th player’s equipment failure time, $T_{fi}$, is described according to the shifted exponential distribution. The game continues until a player’s equipment breaks down. Thus, the game duration is defined as $T_f=min\{T_{f1},\dots ,T_{fn}\}$. To achieve the goal of sustainable development, an environmentally sustainable strategy and its corresponding condition are defined. By using Pontryagin’s maximum principle, a unique control solution is obtained in the form of a hybrid limit cycle, the state variable converges to a stable hybrid limit cycle, and the total payoff of all players increases and then converges. The results indicate that the environmentally sustainable strategy in the n-player pollution-control cooperative differential game with switches and random duration is a unique strategy that not only ensures profit growth but also considers environmental protection. Full article

This paper proposes an improved algorithm applied to path planning for the inspection of unmanned aerial vehicles (UAVs) in urban pipe corridors, which introduces a collaborative game between spherical vector particle swarm optimization (SPSO) and differential evolution (DE) algorithms. Firstly, a high-precision 3D grid map model of urban pipe corridors is constructed based on the actual urban situation. Secondly, the cost function is formulated, and the constraints for ensuring the safe and smooth inspection of UAVs are proposed to transform path planning into an optimization problem. Finally, a hybrid algorithm of SPSO and DE algorithms based on the Nash bargaining theory is proposed by introducing a cooperative game model for optimizing the cost function to plan the optimal path of UAV inspection in complex urban pipe corridors. To evaluate the performance of the proposed algorithm (GSPSODE), the SPSO, DE, genetic algorithm (GA), and ant colony optimization (ACO) are compared with GSPSODE, and the results show that GSPSODE is superior to other methods in UAV inspection path planning. However, the selection of algorithm parameters, the difference in the experimental environment, and the randomness of experimental results may affect the accuracy of experimental results. In addition, a high-precision urban pipe corridors scenario is constructed based on the RflySim platform to dynamically simulate the optimal path planning of UAV inspection in real urban pipe corridors. Full article

In this paper, we solve a stochastic linear quadratic tracking problem. The controlled dynamical system is modeled by a system of linear Itô differential equations subject to jump Markov perturbations. We consider the case when there are two decision-makers and each of them wants to minimize the deviation of a preferential output of the controlled dynamical system from a given reference signal. We assume that the two decision-makers do not cooperate. Under these conditions, we state the considered tracking problem as a problem of finding a Nash equilibrium strategy for a stochastic differential game. Explicit formulae of a Nash equilibrium strategy are provided. To this end, we use the solutions of two given terminal value problems (TVPs). The first TVP is associated with a hybrid system formed by two backward nonlinear differential equations coupled by two algebraic nonlinear equations. The second TVP is associated with a hybrid system formed by two backward linear differential equations coupled by two algebraic linear equations. Full article