Evaluation of papers’ academic influence is a hot issue in the field of scientific research management. Academic big data provides a data treasure with the coexistence of different types of academic entities, which can be used to evaluate academic influence from a more macro and comprehensive perspective. Based on academic big data, a heterogeneous academic network composed of links within and between three types of academic entities (authors, papers and venues) is constructed. In addition, a new academic influence ranking algorithm, AIRank, is proposed to evaluate papers’ academic influence. Different from the existing academic influence ranking algorithms, AIRank has made innovations in the following two aspects. (1) AIRank distinguishes the influence transmission intensity between different node pairs. Different from the strategy of evenly distributing influence among different node pairs, AIRank quantifies the intensity of influence transmission between node pairs based on investigating the citation emotional attribute, semantic similarity and academic quality differences between node pairs. Based on the intensity characteristics, AIRank realises the distribution and transmission of influence among different node pairs. (2) AIRank incorporates the influence transmission from heterogeneous neighbours in evaluating papers’ influence. According to the academic influence of author nodes and venue nodes, AIRank fine-tunes the iteration formula of paper influence to obtain the ranking of papers under the joint influence of homogeneous and heterogeneous neighbours. Experimental results show that, compared with the ranking results based on citation frequency and PageRank algorithm, AIRank algorithm can produce more differentiated and reasonable academic influence ranking results.
In forest variety registration, visual traits of the plants appearance are widely used to discern different tree species. The new recognition system of leaf image strategy which based on neural network established to administrate a hierarchical list of leaf images, some sorts of edge detection can be performed to identify the individual tokens of every image and the frame of the leaf can be got to differentiate the tree species. An approach based on back-propagation neuronal network is proposed and the programming language for the implementation is also given by using Java. The numerical simulations results have shown that the proposed leaf strategy is effective and feasible.
A novel hybrid robust control design method is investigated for a three-axis-rotational maneuver and vibration stabilization of a spacecraft with flexible appendages. Based on the backstepping , a robust attitude control law is derived to control the attitude motion of spacecraft. For actively suppressing the induced vibration, strain rate feedback control methods are presented by using piezoelectric materials as additional sensors and actuators bonded on the surface of the flexible appendages. Numerical simulations demonstrate the feasibility and effectiveness of the control strategy. Index Terms -Flexible spacecraft; Backstepping; Attitude maneuvering.( )Then we introduce the following new variables:
According to the characteristics of massive, multi-source, heterogeneous, and rapid growth of book literature data information from the perspective of the metaverse, in order to meet the requirements of efficient management and rapid retrieval such as standardized storage, effective extraction, and scientific library construction for unstructured massive and heterogeneous book in-formation, this study focuses on the normalization of multi-source heterogeneous massive book data, the construction of a warehouse model for book data in the metaverse perspective, and the query and optimization of book data. Systematic research and implementation were conducted to solve the problem of how to process, manage, and query multi-source heterogeneous massive book data in the metaverse, improving the utilization value and query efficiency of the data. This study utilized the semi-structured features of book text data to construct an extraction rule model for heterogeneous book data, and effectively extracted massive heterogeneous book information. Based on the HBase distributed storage structure and parallel computing technology, the storage scheme has been optimized and query efficiency has been improved to ensure efficient management and retrieval of massive heterogeneous book data. The experimental results show that compared with traditional methods, there are significant improvements in multiple aspects such as the accuracy and recall rate of book text data extraction, the management methods and query efficiency of book information.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.