Implementation of the PaperRank and AuthorRank indices in the Scopus database

AIRank: An algorithm on evaluating the academic influence of papers based on heterogeneous academic network

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.

Citation Oriented AuthorRank for Scientific Publication Ranking

It is now generally accepted that an article written by influential authors often deserves a higher ranking in information retrieval. However, it is a challenging task to determine an author’s relative influence since information about the author is, much of the time, inaccessible. Actually, in scientific publications, the author is an important metadata item, which has been widely used in previous studies. In this paper, we bring an optimized AuthorRank, which is a topic-sensitive algorithm calculated by citation context, into citation analysis for testing whether and how topical AuthorRank can replace or enhance classical PageRank for publication ranking. For this purpose, we first propose a PageRank with Priors (PRP) algorithm to rank publications and authors. PRP is an optimized PageRank algorithm supervised by the Labeled Latent Dirichlet Allocation (Labeled-LDA) topic model with full-text information extraction. We then compared four methods of generating an AuthorRank score, looking, respectively, at the first author, the last author, the most famous author, and the “average” author (of a publication). Additionally, two combination methods (Linear and Cobb–Douglas) of AuthorRank and PRP were compared with several baselines. Finally, as shown in our evaluation results, the performance of AuthorRank combined with PRP is better (p < 0.001) than other baselines for publication ranking.

Agriculture 5.0: A New Strategic Management Mode for a Cut Cost and an Energy Efficient Agriculture Sector

The farmers’ welfare and its interlinkages to energy efficiency and farm sustainability has attracted global scientific interest within the last few decades. This study examines the contribution of Agriculture 5.0 to the prosperity of the farmers in the post-pandemic era and the gradual transition to an energy-smart farm. To obtain an insight into the attributes of Agriculture 5.0 and the emerging technologies in the field, Bibliometrix analysis with the use of an R package was conducted based on 2000 data consisting of peer-reviewed articles. The data were retrieved from the Scopus database. A bibliometric approach was employed to analyze the data for a comprehensive overview of the trend, thematic focus, and scientific production in the field of Agriculture 5.0 and energy-smart farming. Emerging technologies that are part of Agriculture 5.0 in combination with alternative energy sources can provide cost-effective access to finance, weather updates, remotely monitoring, and future energy solutions for the establishment of smart farms. Keywords such as “renewable energy,” “Internet of Things,” and “emission control” remain the trending keywords. Moreover, thematic analysis shows that “economic and social effects”, “energy efficiency”, “remote sensing”, and “Artificial Intelligence” with their associated components such as “anaerobic digestion”, “wireless sensor network,” “agricultural robots”, and “smart agriculture” are the niche themes of Agriculture 5.0 in combination with green energy sources, which can lead to the cut cost, energy-efficient, and sustainable energy-smart farms.