Understanding 3D scenes in mixed reality (MR) is crucial for advancing human-computer interaction, especially in MR applications that demand spatial awareness and contextual reasoning. While Vision-Language Models (VLMs) perform well in 2D image interpretation, they struggle to incorporate spatial context from 3D settings, which limits their effectiveness in MR scenarios. To address this issue, we introduce SAMR, a Spatial-Augmented Mixed Reality method designed to enhance VLMs for 3D scene understanding. Our system consists of three key modules. The first module, a spatial-segmented fusion module, uses FastSAM-based segmentation to create objectlevel meshes from head-mounted display (HMD) images. It maps extracted feature points to 3D coordinates through ray casting on the HMD-captured mesh and applies triangular facet fitting. The second module, a multimodal interaction module, combines gestures, gaze, and voice commands to enable intuitive interaction with 3D meshes for annotating prompts. The third module, a VLM integration module, processes data by merging annotated 2D images with user queries to form standardized prompts for the VLM. The VLM then generates responses linked to user-specified object meshes. By enhancing VLMs with spatial context and multimodal capabilities, SAMR greatly improves 3D scene interpretation. We demonstrate SAMR's effectiveness across six key application scenarios: object identification, relationship analysis, distance estimation, targeted object questioning, and cognitive assistance. This approach provides a robust framework for MR applications with AI agents.

Augmented reality (AR) has been widely employed in assembly guidance and maintenance as an excellent visualization tool. On this basis, AR technology combined with visual inspection has become a research topic to realize rapid and intuitive quality inspection while reducing operators' workload. This article proposes a novel multitarget, AR-based, assembly result inspection method, in which detected information is matched with prior knowledge via high-precision registration. First, a multimarker-based global registration method is designed to significantly improve the average registration accuracy in large scenes based on the mutual calibration of a few markers. Second, based on the correlation between the inspection target and its AR twin, an image containing multiple mechanical components is segmented according to the locations, and the local images are matched with the prior knowledge for evaluation. Finally, the inspection method is deployed to AR-based assembly inspection system, and its validity is verified on a rocket cabin imitation platform. Experiments show that the proposed inspection method can accurately segment the multitarget image into several images containing a single target according to the prior locations and can verify the assembly results of the targets, running at 15.1 fps.

This paper constructs a new four-dimensional dissipative chaotic system with coexisting attractors. The system's dynamic behaviors are analyzed through the numerical simulation of the phase portrait, Lyapunov exponent graph, bifurcation diagram , etc. Numerical simulation results indicate that the system has abundant dynamic characteristics, and the polarity of the chaotic signal can be flexibly changed by introducing offset boosting control. By studying the spectral entropy (SE) complexity of the two different initial conditions, an initial condition with higher complexity is selected as the initial state when the system is synchronized. Finally, a backstepping controller is designed to implement the synchronization of the chaotic system. Multisim is used to simulate the system's circuit and field programmable gate arrays (FPGA) are used to implement the system on actual hardware.

The rapid development of renewable energy and adjustable load has brought challenges to the safety and economic operation of power system. In this paper, we propose a generation and load integrated optimal scheduling strategy. The power generation side considers the wind-photovoltaic hybrid power system with battery energy storage system. The user side considers electric vehicles and the adjustable load such as transferable load and interruptible load to participate in scheduling. A scheduling strategy model is established to optimize both the benefits of power generation side and the user side. The multi-objective particle swarm optimization algorithm is used to solve the model. Simulation results based on historical data of a particular region (105.0° E, 35.40° N) show the feasibility of the proposed optimal scheduling strategy.

The nervous system is one of the most complex biological systems, and nervous system disease (NSD) is a major cause of disability and mortality. Extensive evidence indicates that numerous dysregulated microRNAs (miRNAs) are involved in a broad spectrum of NSDs. A comprehensive review of miRNA-mediated regulatory will facilitate our understanding of miRNA dysregulation mechanisms in NSDs. In this work, we summarized currently available databases on miRNAs and NSDs, star NSD miRNAs, NSD spectrum width, miRNA spectrum width and the distribution of miRNAs in NSD sub-categories by reviewing approximately 1000 studies. In addition, we characterized miRNA-miRNA and NSD-NSD interactions from a network perspective based on miRNA-NSD benchmarking data sets. Furthermore, we summarized the regulatory principles of miRNAs in NSDs, including miRNA synergistic regulation in NSDs, miRNA modules and NSD modules. We also discussed computational challenges for identifying novel miRNAs in NSDs. Elucidating the roles of miRNAs in NSDs from a network perspective would not only improve our understanding of the precise mechanism underlying these complex diseases, but also provide novel insight into the development, diagnosis and treatment of NSDs.