In this paper, we investigate the synergistic effect of a fixed punishment probability and a Q-learning-based update mechanism in the spatial public goods game (SPGG) to explore the effects of punishment intensity and punishment cost on the evolution of cooperation. In the model, individuals can choose among three strategies: cooperation, defection, and punishment. Punishers impose sanctions on defectors with a fixed probability and bear the corresponding costs. Strategy updates are governed by a Q-learning algorithm, in which individuals dynamically adjust their strategy preferences based on the payoff they receive as an immediate reward. Through large-scale Monte Carlo simulations, we found that increasing punishment intensity significantly enhances cooperation, whereas increasing punishment cost suppresses the viability of punishers and undermines the stability of cooperation. Furthermore, the range of immediate reward also modulates evolutionary outcomes: when individuals use only a single round of payoff as the learning feedback, the system tends to maintain a high level of cooperation; in contrast, when individuals use five rounds of payoffs as the learning feedback, the evolution of cooperation becomes more dependent on punishment intensity, requiring a higher intensity to suppress defection effectively. These findings may help us uncover the intrinsic mechanisms of cooperative evolution under the coupling of a fixed punishment probability and Q-learning, offering a new perspective on the effectiveness of punishment institutions in structured populations and providing insights into incentive design in multi-agent cooperative systems.
Generative AI (GAI) is increasingly mediating tourism service encounters, reshaping GAI failure recovery within a broader tourist–employee–GAI service configuration, in which GAI serves as the failure source, employees act as recovery agents, and tourists are the end recipients. Drawing on Mind Perception Theory, we theorize and test a post-failure recovery tactic in which employees intentionally foreground anthropomorphic aspects of the failing GAI during recovery interactions. Across six scenario-based experiments and two on-site simulation studies (N = 2061), results provide convergent evidence that employee-led disclosure of GAI anthropomorphism (vs. control) improves recovery outcomes for both tourists and employees through mirrored mechanisms. Specifically, it increases tourist forgiveness by elevating perceived GAI experience and increases state employee satisfaction by elevating perceived GAI agency. These benefits weaken under high tourist anger or employees' perceptions of tourist anger and are further contingent on tourists' AI expertise and employees’ competitive versus collaborative mindset. The findings advance tourism research by offering parallel dyadic evidence on GAI failure recovery beyond a tourist-centric lens, extending Mind Perception Theory through stakeholder-contingent effects of perceived experience and perceived agency, and repositioning anthropomorphism in recovery as a co-produced mechanism jointly enacted through employee framing and available GAI interface affordances.
This research examines how biospheric values can encourage green behavior in the public sector by analyzing attitude-based and generative leadership based mechanisms and environmental extra-role behavior. The data were gathered in a multi-source and multi-wave research on 348 subordinates and 56 supervisors of public sector organizations. We used bootstrapping tests, mediation, and moderation analyses in SmartPLS-SEM. The results indicate that biospheric values, generative concern, environmental attitudes, and generative leadership are significant predictors of environmental extra-role behavior in public sector organizations. Grounded in the natural resource-based view, it rigorously tests its hypotheses through numerous mediating and moderating variables. This study is particularly significant as it provides actionable insights for public sector leaders seeking to instill sustainable practices within their organizations. It contributes to a growing body of knowledge on effective strategies for promoting sustainability in organizational contexts by demonstrating the impact of generative leadership on pro-environmental behaviors. Beyond the public sector, this study offers valuable policy implications to industries, scholars, and consultants who focus on environmental sustainability practices.
The objective of medical image fusion is to discern and amalgamate complementary features extracted from multimodal medical images, producing fused representations that are more interpretable and informative. This enhancement facilitates higher diagnostic accuracy and efficiency. While Transformer-based fusion methods excel at capturing long-range dependencies and enable parallel computation, their computational cost rises sharply with increasing input dimensions, often reaching quadratic complexity. To address this challenge, we propose a novel spatial-frequency domain fusion network, termed as SFMamba, which exploits a frequency transformation in conjunction with the Mamba model to fully leverage both spatial and frequency information. An efficient Mamba branch incorporates a spatial-frequency state-space (SFSS) model, reducing computational burden to linear or near-linear complexity. The selective-band feature extraction (SBFE) branch is constructed using a discrete wavelet pyramid, designed to capture multi-scale frequency components consistently across source images. To dynamically and effectively fuse multi-modal complementary information, we introduce a multi-domain feature fusion (MDFF) module that elevates fusion performance. Training is conducted with a multi-teacher learning strategy (MTLS) that integrates pre-trained convolutional neural networks and transformer-based fusion methods to generate multiple pseudo-labels, guiding the network to inherit fused knowledge from these priors. Extensive experiments demonstrate that SFMamba achieves state-of-the-art performance in both subjective and objective evaluations. The code for SFMamba is available at https://github.com/DZSYUNNAN/SFMamba .
In this paper, we present a stabilized high-order exponential integrator for solving the regularized logarithmic Schrödinger equation, which simultaneously satisfies discrete analogues of the mass and energy conservation laws. The key ingredient of our method is first to reformulate the original system into a stabilized exponential supplementary variable system by combining the exponential supplementary variable method with the stabilized method, and then the reformulated system is discretized by using the sine pseudo-spectral approximation for spatial discretization and the high-order prediction-correction Lawson Runge-Kutta method for temporal discretization, respectively. The proposed method is computationally efficient, temporally high-order accurate, and conserves both mass and energy in the discrete setting. Finally, numerical results demonstrate the remarkable accuracy and numerical stability of our newly developed methods in comparison with the existing high-order structure-preserving methods in the literature.
