Electrical stimulation (ES) has been widely applied in sports science, primarily in long-term training assistance and post-exercise recovery. However, its role during exercise, particularly in high-intensity running, remains underexplored. This study introduces a wearable Acupoint Electrical Stimulation (AES) system to modulate systemic physiological states during exercise.Complementary animal experiments validated the mechanism, showing that the stimulation protocol significantly modulates the balance of inhibitory and excitatory neurotransmitters (e.g., gamma-aminobutyric acid (GABA) and 5-hydroxytryptamine(5-HT)) in the hypothalamus. Twenty-four healthy participants completed a 400-m sprint trial with continuous monitoring of running speed, stride frequency, heart rate, and recovery dynamics. Results demonstrated that AES applied during running significantly reduced heart rate and enhanced post-run recovery efficiency, without disrupting natural running mechanics. These findings highlight AES as a promising integrative strategy for improved physiological efficiency, representing a departure from conventional muscle-targeted paradigms and offering practical implications for sports training and rehabilitation engineering.

In this approach, we present an efficient topology and geometry optimization of triply periodic minimal surfaces (TPMS) based porous shell structures, which can be represented, analyzed, optimized and stored directly using functions. The proposed framework is directly executed on functions instead of remeshing (tetrahedral/hexahedral), and this framework substantially improves the controllability and efficiency. Specifically, a valid TPMS-based porous shell structure is first constructed by function expressions. The porous shell permits continuous and smooth changes of geometry (shell thickness) and topology (porous period). The porous structures also inherit several of the advantageous properties of TPMS, such as smoothness, full connectivity (no closed hollows), and high controllability. Then, the problem of filling an object's interior region with porous shell can be formulated into a constraint optimization problem with two control parameter functions. Finally, an efficient topology and geometry optimization scheme is presented to obtain optimized scale-varying porous shell structures. In contrast to traditional heuristic methods for TPMS, our work directly optimize both the topology and geometry of TPMS-based structures. Various experiments have shown that our proposed porous structures have obvious advantages in terms of efficiency and effectiveness.

We propose a robust method for detecting features on triangular meshes by combining normal tensor voting with neighbor supporting. Our method contains two stages: feature detection and feature refinement. First, the normal tensor voting method is modified to detect the initial features, which may include some pseudo features. Then, at the feature refinement stage, a novel salient measure deriving from the idea of neighbor supporting is developed. Benefiting from the integrated reliable salient measure feature, pseudo features can be effectively discriminated from the initially detected features and removed. Compared to previous methods based on the differential geometric property, the main advantage of our method is that it can detect both sharp and weak features. Numerical experiments show that our algorithm is robust, effective, and can produce more accurate results. We also discuss how detected features are incorporated into applications, such as feature-preserving mesh denoising and hole-filling, and present visually appealing results by integrating feature information.

Two algorithms for evaluation of regular quad-mesh interpolatory subdivision surfaces are proposed. Algorithms are designed based on the parametric m-ary decomposition and construction of matrix sequence. The weights of the control points on the initial mesh can be obtained, through direct computation of the basic function values by multiplying the finite matrix sequence corresponding to the decomposition number sequence. Algorithm-I is based on 2D subdivision masks while the other is based on tensor-product. Numerical experiments show that algorithms are efficient with low storage cost.