Rhythm and articulation are essential for expressive guitar performance. Existing tools provide basic beat cues, whereas beginners often struggle to align with these cues when playing complex techniques, such as strumming and muting. Informed by a formative study with five instructors and grounded in embodied learning theories, we present FretFlow, a haptic vest-based tool that simulates common instructional practices to guide learners through physical interactions like tapping. The key to FretFlow is its design space that maps rhythmic and articulation patterns in various playing techniques to distinct haptic patterns, enabling authoring of haptic scores. FretFlow further dynamically adapts haptic intensity based on learners’ real-time performance accuracy, accompanied by multimodal guidance across haptic, visual, and audio channels. We iteratively refined haptic designs across two rounds with 46 participants, followed by a two-week user study with 20 beginners. Results show that FretFlow improves learners’ rhythmic accuracy and expressive performance.

In many animal species, courtship-related social behavior is essential for mating success. Given the diversity and complexity of animal courtship-related social behavior, it is challenging to automatically recognize courtship-related social behavior. Zebrafish has emerged as an important animal model for studying courtship behavior. However, to the best of our knowledge, no research has been conducted on the automated recognition of zebrafish courtship-related social behavior. Therefore, this work builds a benchmark dataset of zebrafish courtship-related social behavior and proposes a zebrafish courtship-related social behavior automated recognition (ZCSBAR) framework. The ZCSBAR framework consists of three functional modules, including pose estimation, long-time identity tracking and social behavior recognition. The experimental results show that the ZCSBAR achieves high performance in zebrafish social behavior recognition, specifically the identity recognition index IDF1 reaches 0.902 and the average behavior recognition accuracy reaches 0.859. These findings highlight the potential of ZCSBAR for quantifying the courtship-related social behavior in the fields of animal behavior and reproduction.

The 2025 Los Angeles fire devastated over 40,500 acres, destroying vast neighborhoods and cultural landmarks. In response to the global grief caused by this tragedy, Memoverse, a spatial WebAR social platform, was prototyped to enable users to revisit 3D scans of lost sites and share memories through text, images, and videos. By reconstructing community memory through immersive spatial experiences, Memoverse fosters healing and connection after profound loss. Memoverse demonstrates the potential of extended reality (XR) technologies in addressing collective trauma and preserving cultural heritage in the wake of disaster.

Many-objective optimization problems (MaOPs) pose a big challenge to the traditional Pareto-based multiobjective evolutionary algorithms (MOEAs). As the number of objectives increases, the number of mutually nondominated solutions explodes and MOEAs become invalid due to the loss of Pareto-based selection pressure. Indicator-based many-objective evolutionary algorithms (MaOEAs) have been proposed to address this issue by enhancing the environmental selection. Indicator-based MaOEAs are easy to implement and of good versatility, however, they are unlikely to maintain the population diversity and coverage very well. In this article, a new indicator-based MaOEA with boundary protection, namely, MaOEA-IBP, is presented to relieve this weakness. In MaOEA-IBP, a worst elimination mechanism based on the${I}_{{\epsilon }^{+}}$indicator and boundary protection strategy is devised to enhance the balance of population convergence, diversity, and coverage. Specifically, a pair of solutions with the smallest${I}_{{\epsilon }^{+}}$value are first identified from the population. If one solution dominates the other, the dominated solution is eliminated. Otherwise, one solution is eliminated by the boundary protection strategy. MaOEA-IBP is compared with four indicator-based algorithms (i.e.,${I}_{{{ {SDE}}}^{+}}$, SRA, MaOEAIGD, and ARMOEA) and other five state-of-the-art MaOEAs (i.e., KnEA, MaOEA-CSS, 1by1EA, RVEA, and EFR-RR) on various benchmark MaOPs. The experimental results demonstrate that MaOEA-IBP can achieve competitive performance with the compared algorithms.

Summary The manufacturing characteristics of the manufacturing industry make the processing process more complex and difficult to control. How to have more effective production management and scheduling is the key link for enterprises to ensure balanced production. The problem of production scheduling and improvement of production efficiency is one of the core contents of manufacturing production management. It plays an important role in optimizing resource utilization, saving production costs and improving work efficiency. First, combined with the production characteristics of the manufacturing industry, the preliminary search ability for the basic genetic algorithm (GA) is high. The characteristics of poor search ability and strong ability of global optimization are in the late period, and the Particle Swarm Optimization (PSO) has the characteristics of poor early search ability, strong late search ability, and easy to fall into local optimum. In order to obtain better scheduling results, we make the two algorithms complementary to each other, and propose an improved genetic algorithm to solve the manufacturing efficiency problem. It is verified that the algorithm has better search performance. Second, aiming at the dynamic characteristics of manufacturing in manufacturing and processing, using the multi‐module technology of Internet information technology, the Internet information technology production planning model was constructed, and the functions, structural models, and interactive of each module were studied. Finally, the shortest path is solved by optimizing the genetic algorithm. Experiments show that, based on the improved genetic algorithm with rapid convergence, it can effectively improve the manufacturing efficiency.