Abstract: This research aims to carry out a systematic review of the available literature about smart irrigation systems. It will be focused on systems using artificial intelligence techniques in urban and rural agriculture for soil crops to identify those that are currently being used or can be adapted to urban agriculture. To this end, a modified PRISMA 2020 method is applied, and three search equations are formulated. From those filters, and after a screening process, 170 articles are obtained. These articles are analyzed through VantagePoint, a text processing software. After this, they are taken through a detailed analysis phase in which 50 sources are selected as the most relevant to be read and analyzed by topic. Finally, the different phases of the analysis are used to draw conclusions that might be interesting for researchers working in this specific field or for the general public interested in rural and urban agriculture and its automation.

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Author(s):  David Vallejo-Gómez, Marisol Osorio, Carlos A. Hincapié

Organization(s): Universidad Pontificia Bolivariana

Source: Agronomy

Year: 2023

Profitable organisations that applied data analytics have obtained a double-digit improvement in reducing costs, predicting demands, and enhancing decision-making. However, in nonprofit organisations (NPOs), applying data analysis can interpret and discover more patterns of donors, volunteers, and forecasting future funds, gifts and grants. To uncover the usage of data analytics in different NPOs and understand its contribution, this article presents a bibliometric analysis of 2673 related publications to reveal the research landscape of data analytics applied in NPOs. Through a co-term analysis and scientific evolutionary pathways analysis, we profile the associations between data analysis techniques and NPOs and additionally identify the research topic changes in this field over time. The results yield us three major insights: (1) Robust and classic statistical methods-based data analysis techniques are dominantly prevalent in the NPOs field through all the time; (2) Healthcare and public affairs are two crucial sectors that involve data analytics to support decision-making and problem-solving; (3) Artificial Intelligence (AI)-based data analytics is a recently emerging trending, especially in the healthcare-related sector; however, it is still at an immature stage, and more efforts are needed to nourish its development.

https://doi.org/10.1007/978-981-19-1520-8_61

Author(s): Idrees Alsolbi, Mengjia Wu, Yi Zhang, Siamak Tafavogh, Ashish Sinha, Mukesh Prasad
Organization(s): University of Technology Sydney
Source: Pattern Recognition and Data Analysis with Applications. Lecture Notes in Electrical Engineering
Year: 2022

Citespace and other visualization software were used to analyze the knowledge graph of relevant pieces of literature on GM research in the past decade, and to sort out the number trend, core authors, research institutions, number of core journals published, and keyword co-occurrence graph of core research literature in gm research in the past decade. The analysis shows that the research interest in TRANSGENIC has changed in recent ten years. The research interest in the United States, China, and other countries is similar to the contribution of the sun, moon, and stars, while the major institutions led by the University of Chinese Academy of Sciences, China Agricultural University, and Harvard University are more willing to publish their research results in Plos One. Research focuses on transgenic rice, Alzheimer’s disease, biochemistry, and molecular biology, in addition, future research will focus on transgenic plants, Alzheimer’s disease, and other aspects.

For FULL-TEXT https://francis-press.com/uploads/papers/jfHv6ZHtppG1V424TPabJAXZz4nc4ehd7jgn3rIv.pdf

Author(s): Yongkang Duan
Organization(s): Sichuan University
Source:  Academic Journal of Humanities & Social Sciences
Year: 2022

The use of artificial neural network (ANN) is growing significantly, and their areas of application are varied. In this case, the main aim of the study is to present an overall view of trends and research carried out in ANNs specifically in management science. To this aim, the data of publications about ANN in the field of management through Scopus database have been analyzed. Documents in the field of management science composed by: Business, Management and Accounting; Decision Sciences; Econometrics and Finance; and Social Sciences published from 2000 to 2019 have been obtained and downloaded. Then, text-mining and network analysis software have been applied to gather, clean, analyze and visualize article data. Thus, it has been found that the pioneer country in this research area is China, followed by the USA and India. The study allows to conclude that in the field of management science, ANNs are mostly used for: logistic regression, prediction, classification, forecasting, modelling, data mining and clustering, among others. In addition, it has also been found that the most used neural network is the convolutional neural network (CNN).

https://link.springer.com/chapter/10.1007/978-3-030-95967-8_18

Author(s): M. Jaca-Madariaga, E. Zarrabeitia, R.M. Rio-Belver, I. Álvare
Organization(s): University of the Basque Country (UPV/EHU)
Source: Ensuring Sustainability: Lecture Notes in Management and Industrial Engineering (Springer)
Year: 2022

The appearance of a novel coronavirus in late 2019 radically changed the community of researchers working on coronaviruses since the 2002 SARS epidemic. In 2020, coronavirus-related publications grew by 20 times over the previous two years, with 130,000 more researchers publishing on related topics. The United States, the United Kingdom and China led dozens of nations working on coronavirus prior to the pandemic, but leadership consolidated among these three nations in 2020, which collectively accounted for 50% of all papers, garnering well more than 60% of citations. China took an early lead on COVID-19 research, but dropped rapidly in production and international participation through the year. Europe showed an opposite pattern, beginning slowly in publications but growing in contributions during the year. The share of internationally collaborative publications dropped from pre-pandemic rates; single-authored publications grew. For all nations, including China, the number of publications about COVID track closely with the outbreak of COVID-19 cases. Lower-income nations participate very little in COVID-19 research in 2020. Topic maps of internationally collaborative work show the rise of patient care and public health clusters-two topics that were largely absent from coronavirus research in the two Forthcoming, PLoS One 2 years prior to 2020. Findings are consistent with global science as a self-organizing system operating on a reputation-based dynamic.

For FULL-TEXT https://doi.org/10.1371/journal.pone.0261624

Author(s): Caroline Wagner, Xiaojing Cai, Yi Zhang, Caroline Fry
Organization(s): The Ohio State University, Zhejiang University, University of Technology Sydney, University of Hawai’i at Mānoa
Source: PLoS One
Year: 2022

The objective of this study was to identify the profile of academic research on blended learning in the world and propose a research agenda for the topic. Recent literature has reported good results in both student performance and satisfaction in blended learning (Dziuban et al., 2004). However, there is still much to investigate and learn about BL because it is a recent development. We analysed the profile of international publications on blended learning in management and business from 2001 to 2021. We identified “when, who, where and what” was published on the subject, singling out the authors and journals with the greatest impact based on the h-index and CiteScore (Scopus), as well as exploring the cooperation between countries. The volume of research has been increasing over the past twenty years, although there are only a few authors, institutions and reference journals contributing to the topic’s consolidation and the countries conducting the most joint research in coauthoring networks account for the largest volume of publications, authors and impact journals. We suggest a future research agenda and highlight the contributions made to executive and management education. The results indicate that the number of publications is growing, and the management and business area is the one that contributes the most, with the countries that produce in co-authorship also providing the most publications.

Author(s): Sheila Serafim-Silva, Renata Giovinazzo Spers; Luiz Vázquez-Suárez, Camilo Peña Ramírez
Organization(s): University of Sao Paulo (USP), University of Oviedo
Source: International Journal of Professional Business Review
Year: 2022

For FULL-TEXT https://doi.org/10.26668/businessreview/2022.v7i1.291

Highlights
• The science and technology semantic linkage integration model improves the identification of linked topics in science and technology (LTSTs).

• Simple fusion and link prediction form a twofold model to identify topics and implicit semantics.

• Term co-occurrence networks of basic and applied research are fused.

• The fusion expands topic networks and enhances their semantic associations.

• LTST chains identified by connected LTST terms provide micro granularity.

Linked topics in science and technology (LTSTs) can provide new avenues for technological innovation and are a key step in the transition from basic to applied research. This paper proposes a science and technology semantic linkage integration model for discovering LTSTs. Particularly, the integrative model fuses the term co-occurrence networks of basic and applied research, which expands the completeness of topic networks by enhancing the semantic characteristics of these networks. It is found that link prediction can further reinforce the semantic association of topic terms in networks between basic and applied topics. Simple fusion explicitly linked the topic terms, which can be used as automatic seed marking for subsequent link prediction to identify implicit linking of topic terms. Furthermore, an application to the gene-engineered vaccines field depicted that newly predicted implicit relations can effectively identify LTSTs. The results also show that implicit semantic recognition of LTSTs can be enhanced through simple fusion, while the recognition of LTST can be improved through link prediction. Therefore, the proposed model can assist experts to identify LTSTs that cannot be recognized through simple fusion.

https://doi.org/10.1016/j.joi.2022.101265

Author(s): Haiyun Xu, Zenghui Yue, Hongshen Pang, Ehsan Elahi, Jing Li, Lu Wang
Organization(s): Shandong University of Technology, Jining Medical University, Shenzhen University, Chinese Academy of Sciences
Source: Journal of Informetrics
Year: 2022

Collaboration is one of the most important contributors to scientific advancement and a crucial aspect of an academic’s career. However, the explosion in academic publications has, for some time, been making it more challenging to find suitable research partners. Recommendation approaches to help academics find potential collaborators are not new. However, the existing methods operate on static data, which can render many suggestions less useful or out of date. The approach presented in this paper simulates a dynamic network from static data to gain further insights into the changing research interests, activities and co-authorships of scholars in a field–all insights that can improve the quality of the recommendations produced. Following a detailed explanation of the entire framework, from data collection through to recommendation modelling, we provide a case study on the field of information science to demonstrate the reliability of the proposed method, and the results provide empirical insights to support decision-making in related stakeholders—e.g., scientific funding agencies, research institutions and individual researchers in the field.

https://doi.org/10.1007/s11192-021-04164-x

Author(s): Lu Huang, Xiang Chen, Yi Zhang, Yihe Zhu, Suyi Li, Xingxing Ni
Organization(s): Beijing Institute of Technology, University of Technology Sydney
Source: Scientometrics
Year: 2021