A clearer understanding of research streams and players involved in efforts to address the sustainability of global food and agricultural systems is needed to clarify the current state of scientific knowledge and form collaborations to pursue future research directions. This study presents new insights into this issue through a scientometric process involving a case study of technologies for extending fruit shelf-life. The text mining software was utilized to analyze 3,131 Web of Science-indexed articles published between 2000 and 2020 as a means to glean the conceptual structure of current knowledge and conduct a social network analysis to explore scientific and publication activity. The findings were mapped onto a strategic diagram of research productivity and collaboration between players at the national, organizational, and individual levels. This research’s main findings highlight that research on shelf-life technology is in continuous development, and academic institutions from China, Spain, and the U.S. are the core national players in this field. The results provide insights for further investigation to strengthen co-research and technological development programs in other fields. Researchers who are exploring networking opportunities can use the model and process presented as a guideline for identifying emerging and future research trends and formulating strategies.
For FULL-TEXT see https://doi.org/10.1155/2022/7120662
Author(s): Jakkrit Thavorn ,Veera Muangsi, Chupun Gowanit, Nongnuj Muangsin
Organization(s): Chulalongkorn University
Source: The Scientific World Journal
In order to reveal the status and trends of chemical treatment for wastewater, the patents analysis from both structured and unstructured data was performed in this study. 35,838 patents recorded in the Derwent Innovation Index database were adopted. The results showed that China was the country with the largest number of patents in the field, and the United States was the main exporter of international technology flows. Chemical processes combined with biological and physical processes was the mainstream, and ozonation and electrochemical treatment were the major single technologies. Technology evolution path generally showed the transition from biological process-combined chemical treatment to electrochemical treatment and finally to physical process-combined chemical treatment. Furthermore, future trends were revealed from both patents and papers. It demonstrated that efficient removal of ammonia nitrogen, green water treatment agents and resourcezation of wastewater were the key innovation directions, and technologies with regard to efficient use of energy (including photocatalytic technology and microbial fuel cell) were the main research hotspots. Overall, this study provided a comprehensive understanding for the research and application of chemical treatment for wastewater technologies.
Author(s): Xiangzhou Sun, Lili Jin, Fengyao Zhou, Kai Jin, Laichun Wang, Xuxiang Zhang, Hongqiang Ren, Hui Huang
Organization(s): Nanjing University
Profitable companies that used data analytics have a double gain in cost reduction, demand prediction, and decision-making. However, using data analysis in non-profit organisations (NPOs) can help understand and identify more patterns of donors, volunteers, and anticipated future cash, gifts, and grants. This article presents a bibliometric study of 2673 to discover the use of data analytics in different NPOs and understand its contribution. We characterise the associations between data analysis techniques and NPOs using, Bibliometrics R tool, a co-term analysis and scientific evolutionary pathways analysis, as well as identify the research topic changes in this field throughout time. The findings revealed three key conclusions may be drawn from the findings: (1) In the sphere of NPOs, robust and conventional statistical methods-based data analysis procedures are dominantly common at all times; (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. The research findings can leverage future research and add value to the existing literature on the subject of data analytics.
For FULL-TEXT https://doi.org/10.20517/jsegc.2022.09
Author(s): Idrees Alsolbi, Mengjia Wu, Yi Zhang, Sudhanshu Joshi, Manu Sharma, Siamak Tafavogh, Ashish Sinha, Mukesh Prasad
Organization(s): University of Technology Sydney, Commonwealth Bank Health Society
Source: Journal of Smart Environments and Green Computing
The intervention of hydrogen of renewable origin, green H2, in the energy mix of a country is a topic of great interest today as it is emerging as an energy vector conducive to the decarbonization of the economy and the energy transition from a fossil energy system to one based on renewable energies . Thus, in the context of the Hydrogen Economy, green H2 is produced, stored, transported and distributed for use as energy vector or chemical input. The diversity of pathways of production, with different levels of scientific advancement and technological maturity, motivates the realization of this work, which proposes to carry out a systematic bibliographic review, supported by bibliometric methods, of the scientific literature and the registration of patents on the production of green H2, in the period 2010-
For EXTENDED ABSTRACT https://hyceltec2022.com.ar/wp-content/uploads/2022/07/O-4H-Posso.pdf
Author(s): F. Posso1, A. Aguilera, M. Galeano
Organization(s): Universidad de Santander, Universidad Nacional de Asunción
Source: 8th Symposium on Hydrogen, Fuel Cells and Advanced Batteries (HYCELTEC 2022)
Chemical–mechanical caries removal (CMCR) products are in constant evolution and were recommended during the COVID-19 pandemic as substitutes for conventional caries removal. The aim of this paper is to characterize the worldwide scientific literature about CMCR products, over the years, by means of a critical review. An electronic search was performed on Medline/PubMed, Scopus, Web of Science, Cochrane Library, Lilacs, and Embase up to November 2020. Year, journal, country of authors, and type of study were the data extracted from the retrieved studies. Additional data of the clinical studies and systematic reviews were investigated. For results, 2221 records were identified, 397 selected. 2011–2020 period concentrates higher number of publications (n = 169), in the Journal of Dental Research (n = 51), developed in Brazil (n = 45) and India (n = 44). Most studies were in vitro (n = 211) and clinical trials (n = 101). Carisolv™ (n = 48) and Papacarie Duo Gel™ (n = 33) were the most used products, prescript in isolated usage (n = 101), and compared with drills (n = 77). CMCR were more studied in primary teeth (n = 78), receiving glass ionomer cement (GIC) (n = 51) as restorative material. The most evaluated outcomes were time spent (n = 48) and pain (n = 41). Clinical application of CMCR takes more time than other techniques, but can also reduce patient anxiety, pain, and need for anesthesia. Our conclusion is nn vitro and clinical studies with CMCR products have been increasing, mostly carried out in developing countries, evaluating Carisolv™ and Papacarie Duo Gel™. Clinical studies tend to evaluate the time spent and pain compared to drills for removing caries in primary teeth, posteriorly restored with GIC. CMCR clinical application reduces anxiety, pain, and need for anesthesia, despite increase treatments’ time.
For FULL-TEXT https://doi.org/10.1007/s40368-022-00726-6
Author(s): T. F. Souza, M. L. Martins, M. B. Magno, J. M. Vicente-Gomila, A. Fonseca-Gonçalves, L. C. Maia
Organization(s): Universidade Federal do Rio de Janeiro (UFRJ), ESIC Business and Marketing School
Source: European Archives of Paediatric Dentistry
This study investigates the effect of regulatory uncertainty on the translation of scientific discovery on emerging research topics to technical applications in science-driven industry. Our empirical analysis using the case of the US Federal Drug and Food Administration’s release of the report on the regulatory approach to nanomedicine in 2007 shows that; (1) the regulatory uncertainty decelerated the translation of nanomedicine research to technical applications, (2) this effect was particular for the nanomedicine research on emerging topics in the field. Our further analysis suggested that the effect of the regulatory uncertainty originated from the suppressed business activities in the field where the regulatory uncertainty presents. Contributions to the literature on the relationship between governmental regulation and innovation and the implication for science policymakers are discussed.
Author(s): Seokbeom Kwon, Jan Youtie, Alan Porter, Nils Newman
Organization(s): Sungkyunkwan University, Georgia Institute of Technology, Search Technology
Source: Academy of Management Proceedings Volume 2022 [Best Papers], [Journal of Technology Transfer (submitted)]
The spring up of new & emerging technologies brings a lot of innovation opportunities for society, which enables technology opportunities analysis attracts increasing attention by both industry and academia recently. This study proposes a hybrid approach which integrates topic modeling, sentiment analysis, patent mining and expert judgment to identify technological topics and the potential development opportunities. In order to illustrate how the approach is validated and optimized, and to present its potential to contribute technical intelligence for research and development management, we apply the hybrid approach to analyze a set of 9883 DII records that involved dye sensitized solar cell research. The main contributions of this study include three-fold. First, we distinguished the terms in the different parts of DII patent documents when utilizing them to recognize technical topics. Second, we utilized the terms extracted from the Advantage and the Use part to identify topics on technical problems and applications, and proposed a probability-based topic relation measurement method to identify the relationships of the technical problems and applications with the core sub-technologies. Third, we introduced both topic modeling and sentiment analysis to support technical topic analysis.
For FULL-TEXT of manuscript https://eeke-workshop.github.io/2022/submissions/EEKE2022_paper_8.pdf
Author(s): Tingting Ma, Ruiping Cheng, Hongshu Chen, XiaoZhou
Organization(s): Beijing Wuzi University, Beijing Institute of Technology, Xidian University
Source: 3rd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2022)
To what extent is military technology innovation emergent? This study answers this question by applying an emergence detection algorithm to roughly 300,000 technical terms extracted from military technology patents granted from 1980 to 2019. Emergence – instances of sudden and rapid growth of a technical term within the military patent corpus – is found to vary greatly over time. Military technology innovation during the period of 1996-2008 is found to be highly emergent. This period was found to be characterized by high organization-type diversity; non-traditional vendors, traditional defense contractors, large civilian-facing firms, and individuals generated military patents containing many novel emergent technical terms. However, in recent years, military technology innovation has exhibited markedly less emergence. The period of low emergence is characterized by reduced contributions by non-traditional vendors, defense prime contractors, and individual inventors to military patents containing emergent terms. These observations suggest that policies attempting to ensure a healthy defense innovation ecosystem should seek organization-type diversity and may benefit from employing promotion strategies targeted at distinct organization types.
Author(s): Jon Schmid
Source: Defense and Peace Economics
Rare genetic diseases affect millions of people worldwide. Most of them are caused by defective genes that impair quality of life and can lead to premature death. As genetic therapies aim to fix or replace defective genes, they are considered the most promising treatment for rare genetic diseases. Yet, as these therapies are still under development, it is still unclear whether they will be successful in treating these diseases. This study aims to address this gap by assessing researchers’ opinions on the future of genetic therapies for the treatment of rare genetic diseases. We conducted a global cross-sectional web-based survey of researchers who recently authored peer-reviewed articles related to rare genetic diseases. We assessed the opinions of 1430 researchers with high and good knowledge about genetic therapies for the treatment of rare genetic diseases. Overall, the respondents believed that genetic therapies would be the standard of care for rare genetic diseases before 2036, leading to cures after this period. CRISPR-Cas9 was considered the most likely approach to fixing or replacing defective genes in the next 15 years. The respondents with good knowledge believed that genetic therapies would only have long-lasting effects after 2036, while those with high knowledge were divided on this issue. The respondents with good knowledge on the subject believed that non-viral vectors are more likely to be successful in fixing or replacing defective genes in the next 15 years, while most of the respondents with high knowledge believed viral vectors would be more successful. Overall, the researchers who participated in this study expect that in the future genetic therapies will greatly benefit the treatment of patients with rare genetic diseases.
For FULL-TEXT https://journals.sagepub.com/doi/pdf/10.1177/26330040221100840
Author(s): Luiza Amara Maciel Braga , Carlos Gilbert Conte Filho, Fabio Batista Mota
Organization(s): Oswaldo Cruz Foundation, Fluminense Federal University, Federal University of Santa Maria
Source: Therapeutic Advances in Rare Disease
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).
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)