Search Results (601)

The realization of the value of health big data relies on the coordinated cooperation among patients, the government, and data users. Enhancing the symmetry and balance between patient participation and the compliant use of data by data users is a critical link. This paper constructs a tripartite evolutionary game model and employs MATLAB R2023a simulation to analyze the impact of factors such as initial willingness, compliance costs, and penalties for violations on the strategic choices of the game players and the evolution of the system. The findings reveal that: (1) Patient participation is a key condition for achieving an ideal equilibrium in the system. (2) The data service income from participating in data provision and the costs associated with privacy breaches are critical factors influencing patients’ strategic choices. (3) Penalties for violations are a crucial factor in ensuring that data users choose compliant utilization; however, when compliance costs are high, their constraining effect may be somewhat diminished. (4) Enhancing regulatory efficiency is the future direction for government departments. Based on these findings, countermeasures and suggestions are proposed, including trust building, technological innovation and differentiated supervision, and constructing trusted data spaces, to provide references for health big data governance. Full article

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

As the digital economy advances, information infrastructure has become a core engine for driving green economic transition and optimizing sustainable land systems. However, its heterogeneous governance effects on different types of pollutants and spatial spillover mechanisms remain insufficiently explored. This study draws on the theoretical framework of the dynamic game between scale and technique effects. It utilizes the PSTR model and the SDM to systematically investigate the nonlinear and spatial synergistic impacts of information infrastructure. The analysis covers aggregate information infrastructure and its structural subdivisions, including traditional and new information infrastructure. To ensure empirical rigor, this study introduces a Bartik instrumental variable constructed via the shift share approach and thoroughly eliminates endogeneity interference through the Control Function Approach and a core variable lagging strategy. The empirical research reveals three core findings. Firstly, after crossing the initial extensive scale effect dominated by physical construction, the profound technique effect dominates long-term environmental governance. Secondly, new-type information infrastructure demonstrates a superior capacity for long-term environmental governance and land use efficiency compared to traditional telecommunications. Finally, spatial spillover analysis indicates that although PM2.5 exhibits strong cross-regional physical contagion, the current environmental dividends of information infrastructure remain highly localized due to regional administrative data silos, lacking significant cross-regional synergistic spillover effects. This study provides a solid empirical basis for formulating differentiated digital spatial governance frameworks, breaking interprovincial data factor barriers, and preventing the physical expansion trap of traditional infrastructure. Full article

High-penetration renewable energy significantly increases uncertainty, dynamic network coupling, and the need for secure and coordinated smart-grid dispatch. To address the limitations of conventional forecasting-based and static graph-based methods, this paper proposes a unified dispatch framework that integrates topology-informed dynamic graph learning, privacy-aware multi-agent symbiotic reinforcement learning, and structural causal intervention analysis. The dispatch problem is formulated as a constrained partially observable stochastic game, in which multiple agents coordinate generation adjustment, reserve allocation, and congestion-aware corrective actions under engineering constraints. A physics-informed dynamic graph convolutional module captures both fixed physical topology and stress-dependent operational couplings, while a KL-regularized multi-agent reinforcement learning scheme improves cooperative task allocation under renewable fluctuations. Federated optimization with Rényi differential privacy is introduced to protect sensitive local operational information during training. In addition, a structural causal module provides intervention-based interpretation of how wind variation, load escalation, and line stress affect dispatch cost, congestion risk, and renewable curtailment. Experiments on a public-trace-driven benchmark based on a modified IEEE 30-bus system show that the proposed method achieves the best overall performance among the compared baselines, reducing dispatch-cost RMSE to 3.82, locational-price MAE to 2.95, renewable curtailment to 4.8%, and the constraint-violation rate to 0.30%. Overall, the framework shows favorable performance on the test benchmark, provides post hoc intervention-based interpretation of dispatch outcomes, and is evaluated under a reproducible benchmark construction and assessment protocol. Full article

To address load–energy dynamic coupling in heterogeneous unmanned aerial vehicle (UAV) emergency rescue, this paper proposes an energy-coupled heterogeneous UAV task allocation (EC-HUTA) model that explicitly characterizes nonlinear interdependencies among payload, velocity, and power consumption, minimizing aggregate mission costs subject to physical and temporal constraints. To tackle the resulting high-dimensional, nonconvex problem, we introduce a multi-strategy improved stellar oscillation optimizer (MISOO), establishing a closed-loop synergistic system through three coupled stages: (i) evolutionary game-theoretic strategy competition via replicator dynamics for adaptive exploration–exploitation balance; (ii) intuitionistic fuzzy entropy (IFE)-driven dimension-wise parameter control, where IFE calibrates global exploration intensity while dimension-specific crossover probabilities accommodate heterogeneous convergence; and (iii) memory-driven differential escape mechanisms modulated by historical memory parameters to evade local optima. Cross-stage coupling through IFE ensures state information flows across the “strategy selection-refined search-dynamic escape” pipeline. Coupled with a dual-layer encoding scheme, this framework ensures efficient feasible search. Ablation studies validate each mechanism’s contribution. Evaluations on CEC2017 benchmarks demonstrate MISOO’s superior convergence against six metaheuristics. Large-scale earthquake rescue simulations confirm that EC-HUTA/MISOO strictly adheres to nonlinear energy constraints while enhancing task completion and temporal compliance. These results validate the framework’s efficacy for time-critical emergency resource allocation. Full article

For the complex problems of multi-aircraft cooperative game guidance with No-Fly Zone (NFZ) avoidance and cross-task constraint propagation, a deep deterministic policy gradient algorithm with temporal awareness and priority cooperative optimization (TP-MADDPG) is proposed. Based on the three-body cooperative guidance, a new coupled guidance task is formed by adding the NFZ avoidance constraint. At the same time, considering the constraint compatibility problem in dynamic task switching, the cooperative aircraft are modeled as independent agents with differentiated policy networks. First, a nonlinear kinematic model of the three-body game constructed by Evader–Pursuer–Defender is established. And four complex constraint conditions, namely homing guidance, NFZ avoidance, collision avoidance, and cooperative guidance, are modeled separately. Secondly, the Long Short-Term Memory-based (LSTM) Actor–Critic framework is proposed to dynamically capture the evolution patterns of adversarial scenarios by mining hidden correlations in historical state-action sequences. This enables smooth policy transitions between the cooperative guidance phase and subsequent homing guidance phase, effectively addressing the challenges of environmental non-stationarity and temporal task dependencies. Then, a priority-driven adaptive sampling mechanism is proposed along with a heterogeneous roles cooperative reward function to specifically address credit assignment imbalance and sparse reward problems, respectively. The sampling mechanism capitalizes on the efficient retrieval properties of SumTree data structures while integrating bias correction techniques to expedite policy gradient convergence. The reward function utilizes the reward shaping method to formulate cooperative reward components that explicitly capture behavioral correlations among agents. Finally, simulations show that the proposed method significantly outperforms multi-agent reinforcement learning baselines, effectively improving the performance of cooperative game guidance under complex constraints. Full article

Recent advancements in satellite optical reconnaissance have elevated the sun angle to a critical factor in orbital pursuit-evasion games. The stringent imaging constraints imposed by sun angle and relative distance induce non-smoothness in the terminal region of such differential games, significantly complicating equilibrium-solution derivation. To address this challenge, we formulated a novel differential game model where the pursuer minimizes the time-to-entry into the evader’s effective imaging region. We first constructed a sequence of low-dimensional manifolds that collectively cover the terminal region, solving the differential game with this sequence to yield the Nash equilibrium. Subsequently, we approximated the terminal region using a smooth manifold of identical dimensions, enabling a computationally efficient approximate solution. Both methodologies demonstrate broad applicability to orbital differential games featuring non-smooth terminal regions. Simulation results confirm that the approximation error becomes pronounced only under extreme initial sun angles, though this error remains acceptable for practical space reconnaissance applications. Full article

To promote the sustainable development of power battery recycling, this study investigates the strategic interplay between altruistic preferences and channel structure. Addressing divergent interests and the dynamic evolution of recycling scale and brand reputation, a differential game model with two state variables is constructed to analyze four decision modes: resale/agency under selfish/altruistic scenarios. The results reveal that altruistic preferences induce Pareto improvements, reconciling the recycler’s utility with the partner’s profit growth. Notably, altruism acts as a moderating mechanism that reshapes channel advantages, enabling the Resale–Altruistic (RA) mode to surpass the agency mode as the system-wide optimal state. Furthermore, a substitutive compensation effect between altruistic preference and revenue-sharing contracts is identified. This research provides a quantitative framework for optimizing behavioral contract design and governance in battery recycling ecosystems. Full article

The production and leasing of electric construction machinery play a critical role in the low-carbon transition. However, from a multi-cycle dynamic perspective, there is a lack of targeted research on how to enhance electric goodwill and AI-enabled maintenance service levels while maximizing enterprise profits. To fill this gap, this study incorporates AI-enabled O&M effort, R&D technology, AI-enabled maintenance effort, and advertising effort into a long-term dynamic framework to examine optimal decisions for the manufacturer and the lessor. We assume that the information in the leasing supply chain is symmetric, that the marginal profits of the manufacturer and the lessor are fixed parameters, and that the AI-enabled maintenance service effort level and the electric goodwill are taken as state variables. We develop differential game models across four decision cases: centralized (Case C), decentralized (Case D), unilateral cost-sharing contract (Case U), and bilateral cost-sharing contract (Case B). Results demonstrate monotonic state variable trajectories. Both Case U and Case B can achieve supply chain coordination, with the profit-sharing mechanism in Case B proving superior. In addition, the optimal cost-sharing proportion depends on the relative sizes of the manufacturer’s and the lessor’s marginal profits in both Case U and Case B. The AI-enabled maintenance service plays a significant role in enhancing equipment reliability and supply chain resilience. In addition, the impacts of key parameters on optimal decision variables, state variables, profits, and coordination of the leasing supply chain are comprehensively discussed. 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

Background: research suggests that adolescents with a migrant background may be particularly vulnerable to behavioral addictions, including problematic gaming and Internet use. Methods: we compared Italian (ITA) and non-Italian (WIC) students on Internet Gaming Disorder (IGD) and Internet Addiction (IA) and examined whether coping strategies and interpersonal-relationship quality were associated with these outcomes, using robust linear models estimated with the GENLIN procedure in IBM SPSS Statistics 31 and regression-based models on observed variables. A total of 535 students (64.5% female; aged 9–18) completed the Video Games Addiction Questionnaire (VGA), the Internet Addiction Test (IAT), the Children’s Coping Strategies Checklist–Revised (CCSC), and the Assessment of Interpersonal Relations (AIR). Results: robust generalized linear models showed that WIC adolescents reported significantly higher IGD levels than their Italian peers, while no differences emerged for IA. Gender differences were evident only in unadjusted models, with males reporting higher IGD and females higher IA; however, these effects were not significant once age and nationality were considered simultaneously. Age was positively associated with IA but not with IGD. Avoidance coping was associated with higher levels of both IGD and IA, whereas active coping was negatively associated with IGD. Relationship quality was not associated with IGD but showed protective effects for IA: better relationships with mothers and with both male and female peers were associated with lower IA scores. Overall, the findings highlight that IGD and IA follow partially distinct developmental patterns. Migrant background emerged as a specific vulnerability factor for IGD, while IA appears more closely linked to age-related processes, coping styles, and interpersonal-relationship quality. Conclusions: the results call for differentiated prevention and intervention approaches targeting the distinct etiological mechanisms of each problematic behavior, focusing on coping and migration-related stress and belonging for IGD, and on strengthening coping repertoires and relational resources for IA. Full article

Online ride-hailing platforms increasingly rely on differentiated incentive mechanisms to regulate driver participation and balance supply and demand. However, drivers’ adaptive responses to such incentives introduce dynamic feedback and uncertainty that static equilibrium models fail to capture. This study develops a dual-layer Stackelberg–evolutionary game framework in which the platform acts as a strategic leader setting the order allocation rates and prices, while heterogeneous drivers adapt their working-hour strategies through evolutionary dynamics. Using operational data from Ningbo, China, we calibrated the demand elasticity and driver cost parameters and identified endogenous fatigue-cost thresholds that govern regime shifts in strategy dominance. Simulation results show that uniform incentives tend to drive the system toward single-strategy lock-in, whereas differentiated order allocation and pricing effectively sustain multi-strategy coexistence and mitigate extreme supply polarization. The findings reveal how platform-led differentiation reshapes the evolutionary fitness landscape of drivers, providing actionable guidance for incentive design aimed at stabilizing supply structures, improving platform revenue, and protecting driver welfare. Full article

The growing flexibility of load dispatching in modern smart grids has exposed critical limitations in conventional capacity pricing mechanisms, which calculate charges based solely on monthly maximum demand without distinguishing when peak demand occurs. This approach fails to reflect the temporal value of capacity and provides insufficient incentives for demand-side optimization. To address these challenges, this paper proposes a time-of-use (TOU) capacity pricing method that integrates user load characteristics to enable more equitable cost allocation and optimized electricity consumption patterns. The methodology employs K-means clustering analysis of user load profiles to partition pricing periods, accurately capturing differential capacity value across temporal intervals. We validate the clustering approach through the elbow method and silhouette analysis, confirming k = 3 as optimal and demonstrating K-means superiority over hierarchical and density-based alternatives. This data-driven approach ensures that period delineation reflects actual consumption patterns of commercial and industrial users. A capacity cost allocation model is established using the Shapley value method, incorporating maximum demand in each designated period while maintaining revenue neutrality for the grid operator. The 80% load simultaneity factor is empirically validated using 12 months of Shanghai industrial data (May 2023–April 2024). A Stackelberg game-based pricing model for TOU capacity tariffs is developed, incentivizing users to deploy energy storage systems and optimize charging strategies. We prove game convergence theoretically and demonstrate equilibrium achievement within 3–5 iterations across diverse initialization scenarios. Energy storage capacity is optimized by sector (3.5–6.5% of peak demand) rather than uniformly, and realistic battery self-discharge rates (0.006%/hour) are incorporated. Case study analysis using real operational data from 11 commercial and industrial sub-sectors in Shanghai demonstrates effectiveness. Extended to 12 months with seasonal analysis, results show the proposed strategy reduces the peak-to-valley difference ratio by 2.4% [95% CI: 1.9%, 2.9%], p < 0.001; increases the system load factor by 1.3% [95% CI: 0.9%, 1.7%], p < 0.001; and achieves reductions in users’ total capacity costs of 3.6% [95% CI: −4.2%, −3.0%], p < 0.001. Comparative analysis shows the proposed method significantly outperforms simple TOU (improvement +1.2 pp) and peak-responsibility pricing (improvement +0.6 pp). Monte Carlo robustness analysis (1000 scenarios) confirms performance stability under demand uncertainty. This research provides theoretical foundations and practical methodologies for capacity cost allocation, offering valuable insights for policymakers and utilities seeking to enhance demand-side response mechanisms and improve power resource allocation efficiency. Full article

The study uses the emerging science of Mind Genomics to study the prevalence of extremist thought in random samples of online research panel participants, first with 212 respondents in August 2021, and then with another group of 200 respondents in August 2025. The two studies presented each respondent with a unique set of 24 vignettes, comprising 2–4 statements that could be construed as extremist (e.g., Vaccines are a way for the government to control people). Respondents rated the likelihood that either they, their family, or both agreed with the statements in the vignette or disagreed with the statements in the vignette. The respondents were deconstructed by regression modeling and clustering to show the prevalence of agreement with the statements across different types of people (age, gender, political leaning, year) and across mind-sets. The data suggest that respondents easily differentiated the statements and that the distribution of responses to extremist statements did not dramatically change when President Trump succeeded President Biden. The approach is presented as a new way to investigate sensitive topics by creating sets of test stimuli, answers to which cannot be “gamed”. Given all the news and near-news circulating in the fragmented media, this research offers a clear, if complex, view of attitudes and any changes which may have occurred between the Biden and second Trump administrations. Full article

Real-time strategy (RTS) games provide a cognitively demanding and ecologically valid context for investigating workload dynamics in human–computer interaction (HCI). This multimodal study (HRV, NASA-TLX, behavior, interviews) examined multitasking, visual complexity, and decision pressure in 36 novice RTS players. High multitasking significantly increased subjective workload (total raw-TLX: from 22.50 ± 14.65 to 36.47 ± 20.19, p < 0.001) and prolonged completion time (from 317.17 ± 37.26 s to 354.92 ± 50.70 s, p < 0.001). Decision pressure elevated subjective workload (total raw-TLX: from 20 to 28, p = 0.008) without affecting performance. Although HRV did not consistently differentiate experimental conditions at the group level, it showed stable individual-level associations with perceived workload—both in expected directions (e.g., LF power positively correlated with total raw-TLX across four experiments, r = 0.28–0.53, all p < 0.05) and in inverse relationships that deviate from conventional stress models (e.g., stress index negatively correlated with total raw-TLX, r = −0.34 to −0.40, all p < 0.01). These findings suggest that autonomic responses in complex interactive environments may reflect dynamic engagement processes rather than uniform stress activation, supporting multimodal cognitive load assessment and offering transferable insights for interface design and workload evaluation in demanding HCI contexts. Full article