We propose a new meta-heuristic algorithm named Levy flights multi-verse optimizer (LFMVO), which incorporates Levy flights into multi-verse optimizer (MVO) algorithm to solve numerical and engineering optimization problems. The Original MVO easily falls into stagnation when wormholes stochastically re-span a number of universes (solutions) around the best universe achieved over the course of iterations. Since Levy flights are superior in exploring unknown, large-scale search space, they are integrated into the previous best universe to force MVO out of stagnation. We test this method on three sets of 23 well-known benchmark test functions and an NP complete problem of test scheduling for Network-on-Chip (NoC). Experimental results prove that the proposed LFMVO is more competitive than its peers in both the quality of the resulting solutions and convergence speed.

Purpose: This systematic review assesses the effectiveness of face-to-face and online delivery modes of continuing professional development (CPD) for science teachers. It focuses on three aspects: evaluating the effectiveness of these modes, summarizing the literature on the factors influencing them, and conducting a comparative analysis of their advantages. Methods: The research team employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method and the Mixed Methods Appraisal Tool (MMAT) for article quality assessment. A total of 12 articles, selected from a potential 82, were included in the study. Results: This research suggests that the face-to-face and online CPD modes are equally effective and that external factors, such as psychological variables and establishing communication communities, influence their effectiveness. Face-to-face CPD fosters communication communities, while online CPD offers geographical flexibility and cost-saving benefits. Implications: The effectiveness of face-to-face and online CPD relies on external psychosocial factors. Future research should focus on strategies to enhance participants’ communication engagement in online communities. Additionally, it is worth conducting further investigations of the potential relationships between psychosocial variables and the effectiveness of online CPD, along with the impact of digital skills on online CPD.

The research in this paper mainly includes as follows: for the principle of action recognition based on the 3D diffusion model convolutional neural network, the whole detection process is carried out from fine to coarse using a bottom-up approach; for the human skeleton detection accuracy, a multibranch multistage cascaded CNN structure is proposed, and this network structure enables the model to learn the relationship between the joints of the human body from the original image and effectively predict the occluded parts, allowing simultaneous prediction of skeleton point positions and skeleton point association information on the one hand, and refinement of the detection results in an iterative manner on the other. For the combination problem of discrete skeleton points, it is proposed to take the limb parts formed between skeleton points as information carriers, construct the skeleton point association information model using vector field, and consider it as a feature, to obtain the relationship between different skeleton points by using the detection method. It is pointed out that the reorganization problem of discrete skeleton points in multiperson scenes is an NP-Hard problem, which can be simplified by decomposing it into a set of subproblems of bipartite graph matching, thus proposing a matching algorithm for discrete skeleton points and optimizing it for the skeleton dislocation and algorithm problems of human occlusion. Compared with traditional two-dimensional images, audio, video, and other multimedia data, the 3D diffusion model data describe the 3D geometric morphological information of the target scene and are not affected by lighting changes, rotation, and scale transformation of the target and thus can describe the realistic scene more comprehensively and realistically. With the continuous updating of diffusion model acquisition equipment, the rapid development of 3D reconstruction technology, and the continuous enhancement of computing power, the research on the application of 3D diffusion model in the detection and extraction of a human skeleton in sports dance videos has become a hot direction in the field of computer vision and computer graphics. Among them, the feature detection description and model alignment of 3D nonrigid models are a fundamental problem with very important research value and significance and challenging at the same time, which has received wide attention from the academic community.

Staircase-like contours introduced to a video by quantization in flat areas, commonly known as banding, have been a longstanding problem in both video processing and quality assessment communities. The fact that even a relatively small change of the original pixel values can result in a strong impact on perceived quality makes banding especially difficult to be detected by objective quality metrics. In this paper, we study how banding annoyance compares to more commonly studied scaling and compression artifacts with respect to the overall perceptual quality. We further propose a simple combination of VMAF and the recently developed banding index, CAMBI, into a banding-aware video quality metric showing improved correlation with overall perceived quality.

Efficiently using the limited I/O resources provided by automatic test equipment (ATE) to test Network-on-Chip (NoC) is a key issue in NoC test scheduling. Numerous test scheduling approaches for NoC have been proposed to test the embedded cores. Yet