Tracking the Current Rise of Chinese Pharmaceutical Bionanotechnology

Background: The Context and Purpose of the Study Over the last decade China has emerged as a major producer of scientific publications, currently ranking second behind the US. During that time Chinese strategic policy initiatives have placed indigenous innovation at the heart of its economy while focusing internal R&D investments and the attraction of foreign investment in nanotechnology as one of their four top areas. China’s scientific research publication and nanotechnology research publication production has reached a rank of second in the world, behind only the US. Despite these impressive gains, some scholars argue that the quality of Chinese nanotech research is inferior to US research quality due to lower overall times cited rates, suggesting that the US is still the world leader. We combine citation analysis, text mining, mapping, and data visualization to gauge the development and application of nanotechnology in China, particularly in biopharmananotechnology, and to measure the impact of Chinese policy on nanotechnology research production. Results, the main findings Our text mining-based methods provide results that counter existing claims about Chinese nanotechnology research quality. Due in large part to its strategic innovation policy, China’s output of nanotechnology publications is on pace to surpass US production in or around 2012.A closer look at Chinese nanotechnology research literature reveals a large increase in research activity in China’s biopharmananotechnology research since the implementation in January, 2006 of China’s Medium & Long Term Scientific and Technological Development Plan Guidelines for the period 2006-2020 (“MLP”). Conclusions Since the implementation of the MLP, China has enjoyed a great deal of success producing bionano research findings while attracting a great deal of foreign investment from pharmaceutical corporations setting up advanced drug discovery operations. Given the combination of current scientific production growth as well as economic growth, a relatively low scientific capacity, and the ability of its policy to enhance such trends, China is in some sense already the new world leader in nanotechnology. Further, the Chinese national innovation system may be the new standard by which other national S&T policies should be measured

Mining Scholarly Data for Fine-Grained Knowledge Graph Construction

Knowledge graphs (KG) are large network of entities and relationships, tipically expressed as RDF triples, relevant to a specific domain or an organization. Scientific Knowledge Graphs (SKGs) focus on the scholarly domain and typically contain metadata describing research publications such as authors, venues, organizations, research topics, and citations. The next big challenge in this field regards the generation of SKGs that also contain a explicit representation of the knowledge presented in research publications. In this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods for extracting entities and relationships from research publications and then integrates them in a KG. More specifically, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, ii) describe an approach for integrating entities and relationships generated by these tools, iii) analyse an automatically generated Knowledge Graph including 10, 425 entities and 25, 655 relationships derived from 12, 007 publications in the field of Semantic Web, and iv) discuss how Deep Learning methods can be applied to overcome some limitations of the current techniques

Accelerating COVID-19 research with graph mining and transformer-based learning

In 2020, the White House released the, "Call to Action to the Tech Community on New Machine Readable COVID-19 Dataset," wherein artificial intelligence experts are asked to collect data and develop text mining techniques that can help the science community answer high-priority scientific questions related to COVID-19. The Allen Institute for AI and collaborators announced the availability of a rapidly growing open dataset of publications, the COVID-19 Open Research Dataset (CORD-19). As the pace of research accelerates, biomedical scientists struggle to stay current. To expedite their investigations, scientists leverage hypothesis generation systems, which can automatically inspect published papers to discover novel implicit connections. We present an automated general purpose hypothesis generation systems AGATHA-C and AGATHA-GP for COVID-19 research. The systems are based on graph-mining and the transformer model. The systems are massively validated using retrospective information rediscovery and proactive analysis involving human-in-the-loop expert analysis. Both systems achieve high-quality predictions across domains (in some domains up to 0.97% ROC AUC) in fast computational time and are released to the broad scientific community to accelerate biomedical research. In addition, by performing the domain expert curated study, we show that the systems are able to discover on-going research findings such as the relationship between COVID-19 and oxytocin hormone

Applying a text mining framework to the extraction of numerical parameters from scientific literature in the biotechnology domain

Scientific publications are the main vehicle to disseminate information in the field of biotechnology for wastewater treatment. Indeed, the new research paradigms and the application of high-throughput technologies have increased the rate of publication considerably. The problem is that manual curation becomes harder, prone-to-errors and time-consuming, leading to a probable loss of information and inefficient knowledge acquisition. As a result, research outputs are hardly reaching engineers, hampering the calibration of mathematical models used to optimize the stability and performance of biotechnological systems. In this context, we have developed a data curation workflow, based on text mining techniques, to extract numerical parameters from scientific literature, and applied it to the biotechnology domain. A workflow was built to process wastewater-related articles with the main goal of identifying physico-chemical parameters mentioned in the text. This work describes the implementation of the workflow, identifies achievements and current limitations in the overall process, and presents the results obtained for a corpus of 50 full-text documents

Analysing recent augmented and virtual reality developments in tourism

Purpose Virtual reality (VR) and augmented reality (AR) are two technological breakthroughs that stimulate reality perception. Both have been applied in tourism contexts to improve tourists’ experience. This paper aims to frame both AR and VR developments during the past 15 years from a scientific perspective. Design/methodology/approach This study adopts a text mining and topic modelling approach to analyse a total of 1,049 articles for VR and 406 for AR. The papers were selected from Scopus, with the title, abstract and keywords being extracted for the analysis. Formulated research hypotheses based on relevant publications are then evaluated to assess the current state of the broader scope of the large sets of literature. Findings Most of research using AR is based on mobile technology. Yet, wearable devices still show few publications, a gap that is expected to close in the near future. There is a lack of research adopting Big Data/machine learning approaches based on secondary data. Originality/value As both AR and VR technologies are becoming more mature, more applications to tourism emerge. Scholars need to keep pace and fill in the research gaps on both domains to move research forward.info:eu-repo/semantics/acceptedVersio

Generating knowledge graphs by employing Natural Language Processing and Machine Learning techniques within the scholarly domain

The continuous growth of scientific literature brings innovations and, at the same time, raises new challenges. One of them is related to the fact that its analysis has become difficult due to the high volume of published papers for which manual effort for annotations and management is required. Novel technological infrastructures are needed to help researchers, research policy makers, and companies to time-efficiently browse, analyse, and forecast scientific research. Knowledge graphs i.e., large networks of entities and relationships, have proved to be effective solution in this space. Scientific knowledge graphs focus on the scholarly domain and typically contain metadata describing research publications such as authors, venues, organizations, research topics, and citations. However, the current generation of knowledge graphs lacks of an explicit representation of the knowledge presented in the research papers. As such, in this paper, we present a new architecture that takes advantage of Natural Language Processing and Machine Learning methods for extracting entities and relationships from research publications and integrates them in a large-scale knowledge graph. Within this research work, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, ii) describe an approach for integrating entities and relationships generated by these tools, iii) show the advantage of such an hybrid system over alternative approaches, and vi) as a chosen use case, we generated a scientific knowledge graph including 109,105 triples, extracted from 26,827 abstracts of papers within the Semantic Web domain. As our approach is general and can be applied to any domain, we expect that it can facilitate the management, analysis, dissemination, and processing of scientific knowledge

Capturing the complexity of COVID-19 research: Trend analysis in the first two years of the pandemic using a bayesian probabilistic model and machine learning tools

Regarding Susana Mendes, this work was funded by national funds through FCT - Fundação para a Ciência e a Tecnologia, I.P., under the project MARE (UIDB/04292/2020 and UIDP/04292/2020) and the project LA/P/0069/2020 granted to the Associate Laboratory ARNET.Publications about COVID-19 have occurred practically since the first outbreak. Therefore, studying the evolution of the scientific publications on COVID-19 can provide us with information on current research trends and can help researchers and policymakers to form a structured view of the existing evidence base of COVID-19 and provide new research directions. This growth rate was so impressive that the need for updated information and research tools become essential to mitigate the spread of the virus. Therefore, traditional bibliographic research procedures, such as systematic reviews and meta-analyses, become time-consuming and limited in focus. This study aims to study the scientific literature on COVID-19 that has been published since its inception and to map the evolution of research in the time range between February 2020 and January 2022. The search was carried out in PubMed extracting topics using text mining and latent Dirichlet allocation modeling and a trend analysis was performed to analyze the temporal variations in research for each topic. We also study the distribution of these topics between countries and journals. 126,334 peerreviewed articles and 16 research topics were identified. The countries with the highest number of scientific publications were the United States of America, China, Italy, United Kingdom, and India, respectively. Regarding the distribution of the number of publications by journal, we found that of the 7040 sources Int. J. Environ. Res. Public Health, PLoS ONE, and Sci. Rep., were the ones that led the publications on COVID-19. We discovered a growing tendency for eight topics (Prevention, Telemedicine, Vaccine immunity, Machine learning, Academic parameters, Risk factors and morbidity and mortality, Information synthesis methods, and Mental health), a falling trend for five of them (Epidemiology, COVID-19 pathology complications, Diagnostic test, Etiopathogenesis, and Political and health factors), and the rest varied throughout time with no discernible patterns (Therapeutics, Pharmacological and therapeutic target, and Repercussion health services).info:eu-repo/semantics/publishedVersio

Global Trends of Educational Data Mining in Online Learning

Educational data mining (EDM) in online learning involves data mining techniques to analyze data from online environments to gain insights into student behavior, performance, and engagement. This study explored EDM in online learning publication trends and focuses. It involved a bibliometric analysis of 615 scholarly works related to EDM in online learning as recorded in Scopus, the largest peer-reviewed citation database, on February 1, 2023. The study examined EDM in online learning publications regarding its evolution and distribution, key focus areas, impact and performance, and prominent authors and collaborations in the last decade, in which the timespan is the period from 2012 to 2022. This bibliometric analysis shows that EDM in online learning is a dynamic area of scientific research as related publications grow steadily throughout the years and involve worldwide collaborations. The study reveals current research trends, offering valuable insights for future researchers to guide their investigations in this field

Past, present, and future of pro-environmental behavior in tourism and hospitality: a text-mining approach

Scholars have been interested in examining what drives pro-environmental behavior. However, only a few scientific studies have been devoted to analyzing and understanding the pro-environmental behavior of those that are on vacation. Therefore, the current paper contributes to the existing literature by employing a text-mining approach to conduct a full-text analysis of 210 articles and (1) describes pro-environmental conceptualization, (2) presents the important topics and studies that have emerged from the literature, and (3) suggests directions for future research. The eight core topics that were uncovered contributed to discussion of the content of publications, related theories, core constructs, methodologies, main authors, and journals. The paper shows that the literature on pro-environmental behavior uses more quantitative than qualitative approaches and uses structural equations or regression analysis to explore the data. The findings also show that researchers tend to employ well-known theories arising from psychology, sociology, and biology.info:eu-repo/semantics/acceptedVersio

Non-charismatic waterbodies and ecosystem disservices: Mine pit lakes are underrepresented in the literature

Pit lakes are one of the greatest legacies of open-cut mining. Despite the potential hazards of these lakes, they represent newly formed ecosystems with great scientific and ecological potential. Although thousands of pit lakes occur on every inhabited continent, with more being created, the microbial ecology of pit lakes is relatively under-researched. We evaluated the current state of microbial research in pit lakes by performing a Web of Science search and creating a literature database. Study lakes were categorized according to location and water quality (pH and conductivity) which is a key community and environmental concern. Research technology employed in the study was also categorized. We compared research effort in lakes, rivers, and streams which are the more “charismatic” inland aquatic ecosystems. Pit lake publications on microbes from 1987 to 2022 (n = 128) were underrepresented in the literature relative to rivers and streams (n = 321) and natural lakes (n = 948). Of the 128 pit lake publications, 28 were within the field of geochemistry using indirect measures of microbial activity. Most pit lake microbial research was conducted in a few acidic lakes in Germany due to social pressure for remediation and government initiative. Relatively few studies have capitalized on emerging technology. Pit lake microbial research likely lags other more charismatic ecosystems given that they are viewed as performing “ecosystem disservices,” but this is socially complex and requires further research. Improving understanding of microbial dynamics in pit lakes will allow scientists to deliver safer pit lakes to communities