With the rapid development of satellite technology, communication satellites have become an indispensable part of modern infrastructure. They serve as a key pillar for the future integrated communication satellite network (CSN). However, the increasing number of communication satellites presents significant challenges for data transmission between the satellite and ground station. This article focuses on transmitting communication data by scheduling resources for communication tasks. The goal of data transmission scheduling optimization in CSN (DTSOCSN) is to design a scheduling scheme that maximizes task profit across satellite–ground links, considering the constraints of the two working modes of communication satellites. To solve DTSOCSN, a mixed-integer programming model is developed, which incorporates various constraints such as the conditions for feed switching operation and the limitations of task execution windows. Based on the complexity of the problem, we propose an evolutionary algorithm with memory guidance (MGEA). The algorithm takes into account the memory dependence of Caputo fractional-order differential and innovatively designs a crossover operator, called Caputo crossover (CX). This crossover method uses the genetic information stored in memory to guide the crossover operation of the next generation, thereby forming a smooth optimization path and improving the search efficiency of the algorithm. In addition, an elite opposition-based heuristic initialization method and a tracking variation strategy are designed to enhance the algorithm’s ability to find high-quality initial solutions and perform local optimization. Experimental validation proceeds in two stages: first, multiscale simulations demonstrate MGEA’s superior performance over existing mainstream algorithms in task profit, convergence speed, resource utilization, and search efficiency. Second, to verify the contribution of the CX operator, it is integrated into several classical algorithms for comparative testing on benchmark problems. The results consistently show that algorithms using the CX operator achieve significant performance advantages compared with those relying on traditional crossover operators. This study not only provides an effective solution for DTSOCSN but also offers new idea for solving other types of satellite scheduling problems.

Class-incremental event detection (CIED) is essential for real-world information extraction systems, which must continually recognize new event types without forgetting past knowledge. The main challenge lies in balancing stability and adaptability under data imbalance. Existing methods often underuse the hierarchical and syntactic structures of language, and thus limit the generalization capacity. We propose HPLLM, a hyperbolic prompt-enhanced large language model framework, motivated by the observation that both embedding distributions and dependency graphs in event datasets exhibit hyperbolic properties. HPLLM integrates two key components: (1) Hyperbolic LoRA fine-tuning, enabling geometry-aware parameter adaptation for hierarchical semantics; and (2) Hyperbolic Adaptive Graph Diffusion Convolution (HADC), which encodes syntactic dependencies into structure-aware prompts for LLMs. Together, these techniques strengthen semantic discrimination, reduce forgetting, and improve adaptation across incremental stages. Extensive experiments on ACE2005 and MAVEN demonstrate that HPLLM consistently surpasses state-of-the-art baselines in macro-F1, achieving stronger retention of old knowledge and better generalization to new event types. In particular, the model shows clear gains on rare categories with few training mentions, demonstrating its robustness in imbalanced and few-shot regimes.

Due to the multiple types of objects and the uncertainty of their geometric structures and scales in indoor scenes, the position and pose estimation of point clouds of indoor objects by mobile robots has the problems of domain gap, high learning cost, and high computing cost. In this paper, a lightweight 6D pose estimation method is proposed, which decomposes the pose estimation into a viewpoint and the in-plane rotation around the optical axis of the viewpoint, and the improved PointNet+⁣+ network structure and two lightweight modules are used to construct a codebook, and the 6d pose estimation of the point cloud of the indoor objects is completed by building and querying the codebook. The model was trained on the ShapeNetV2 dataset, and reports the ADD-S metric validation on the YCB-Video and LineMOD datasets, reaching 97.0% and 94.6% respectively. The experiment shows that the model can be trained to estimate the 6d position and pose of the unknown object point cloud with lower computation and storage cost, and the model with fewer parameters and better real-time performance is superior to other high-recision methods.

In the era of big data and industry 4.0, the mode of human resource management (HRM) will be changed. As the role of talents rises, enterprises need to pay more attention to human capital. Meanwhile, intelligent production also urges intelligent HRM. Based on the accumulation of data, enterprises should use big data and artificial intelligence technologies to analyze employees, predict the future, and support businesses. Thus, the paper attempts to put forward ideas on data-driven solution to the promotion issue in HRM, and focus on the influence from the position of organization. The data come from a state-owned enterprise in China. Here the features of organizational position are chosen to analyze employee promotion and forecast employee prospect. From the analysis based on statistics and networks, as well as the prediction based on machine learning, we find that structural position plays a more critical role than geographic position. Besides, employees can benefit from working in the place where special experience is available, where mobility is more stable, or where resource is more abundant. But organizations should be concerned with the fair development of workers. The experimental results also validate that the prediction model is practical and effective.

In this paper, a bilateral tele-rehabilitation system capable of providing haptic feedback to both the therapist and the patient is proposed. As internet is used for communication in the proposed system, the problem of time delay must be solved, which may give rise to loss of transparency and even instability. To address the problem, a control strategy is designed, which could deal with not only constant time delay, but also time-varying delay. The feasibility of the proposed method is verified by experiments.

This paper investigates the control algorithm of an exoskeleton for hand rehabilitation, which can realize the active, passive, and assisted rehabilitation motion. The active mode is accomplished with the force control algorithm during which the resistance is compensated in free space and the virtual interactive force is rendered to the finger in constraint space. The passive mode is realized by the position controller given the desired motion trajectory. The assisted mode is implemented in the customized positions by switching between the active and passive modes according to the predefined action procedure. The experiments are conducted to verify the proposed method, and the results show that the different rehabilitation motion are successfully accomplished and the maximum joint position error is less than 1.2 degree, which satisfies the requirement in hand rehabilitation application. The results demonstrate the validity of the proposed method.