The widespread adoption of electric vehicles (EVs) has posed significant challenges to the security of distribution grid loads. To address issues such as increased grid load fluctuations, rising user charging costs, and rapid load surges around midnight caused by uncoordinated nighttime charging of household electric vehicles in communities, this paper first models electric vehicle charging behavior as a Markov Decision Process (MDP). By improving the state-space sampling mechanism, a continuous space mapping and a priority mechanism are designed to transform the charging scheduling problem into a continuous decision-making framework while optimizing the dynamic adjustment between state and action spaces. On this basis, to achieve synergistic load forecasting and charging scheduling decisions, a forecast-augmented deep reinforcement learning method integrating Gated Recurrent Unit and Twin Delayed Deep Deterministic Policy Gradient (GRU-TD3) is proposed. This method constructs a multi-objective reward function that comprehensively considers time-of-use electricity pricing, load stability, and user demands. The method also applies a single-objective pre-training phase and a model-specific importance-sampling strategy to improve learning efficiency and policy stability. Its effectiveness is verified through extensive comparative and ablation validation. The results show that our method outperforms several benchmarks. Specifically, compared to the Deep Deterministic Policy Gradient (DDPG) and Particle Swarm Optimization (PSO) algorithms, it reduces user costs by 11.7% and the load standard deviation by 12.9%. In contrast to uncoordinated charging strategies, it achieves a 42.5% reduction in user costs and a 20.3% decrease in load standard deviation. Moreover, relative to single-objective cost optimization approaches, the proposed algorithm effectively suppresses short-term load growth rates and mitigates the “midnight peak” phenomenon.
In the field of contemporary textile pattern design, floral patterns are widely favored due to their diversity and aesthetic appeal. However, due to the wide variety of flowers and their complex morphological structures, accurately presenting their characteristics and aesthetic appeal through computer-aided design techniques, such as 3D modeling, poses certain challenges. Based on this, the study proposes a graphic editing algorithm that combines 3D data with a directional bounding box method. On this basis, a textile pattern design system is established. The results show that the proposed algorithm performs exceptionally well. Experimental results on the Oxford 102 Flower Dataset indicate that the proposed algorithm achieves a mean squared error of 0.25, a root mean squared error of 0.001, and a mean absolute error of 0.0017. In addition, the method for extracting contour sampling points based on the front and side angles of flowers maintains flower contour integrity over 85% throughout the experimental process, reaching 100% at most. These findings suggest that the proposed method can comprehensively capture flower-contour information and generate two-dimensional design drawings with high accuracy, thereby improving the efficiency of textile pattern design.
The construction and dynamic analysis of chaotic maps based on discrete memristors have laid the foundation for the application of memristor theory in discrete-time systems, digital circuits, embedded systems, and other fields. This paper designs a hybrid memristor-coupled circuit to derive a memristive map. At first, by integrating two distinct types of memristor electronic components into a nonlinear circuit, and then a corresponding memristive oscillator model is derived using nonlinear circuit theory and expressed through differential equations. Furthermore, a memristive map model is obtained via linear transformation. In addition, employing nonlinear analysis methods, the dynamics of the memristive map are investigated under different configurations of electronic component branches. Results indicate that the proposed memristive map can exhibit both chaotic and various periodic behaviors depending on the branch parameters. These findings confirm that embedding memristor components into nonlinear circuits provides an effective approach for constructing memristive map models, thereby enriching the theoretical foundation of memristive map modeling.
IntroductionHappiness has become an important public health and policy concern, and physical activity is increasingly viewed as a relevant lifestyle-related factor. Against this background, this study examines whether the association between sports club participation (SCP) and happiness depends on active participation rather than nominal membership alone. Its contribution is to distinguish formal membership from actual engagement in sports clubs and to examine whether the SCP-happiness association is statistically linked to two behavioral dimensions of physical activity: leisure-time sports participation (LTSP) and venue-based exercise (VBE).MethodsUsing data from the 2023 Chinese General Social Survey, the analysis distinguishes among non-members, inactive members, and active members. Ordinary least squares regression, ordered logit models, propensity score matching, and bootstrap-based parallel mediation analysis were applied to examine the association between SCP and happiness and the statistical indirect pathways through LTSP and VBE.ResultsThe results show that active SCP is positively associated with happiness (β = 0.151, p < 0.001), whereas inactive membership is not. After LTSP and VBE are introduced, the coefficient for active SCP declines but remains significant (β = 0.098, p < 0.05). Both LTSP and VBE are positively associated with happiness and statistically mediate the association between active SCP and happiness.DiscussionThese findings suggest that the relevance of sports clubs for happiness is not attached to formal membership alone, but is mainly observed when membership involves active engagement and greater participation in physical activity. Given the cross-sectional design, the findings should be interpreted as associational rather than causal evidence.
