Set Of Keywords Research Articles

Antioxidant Activity of Medicinal Plants and Herbs of North Aegean, Greece: Current Clinical Evidence and Future Perspectives

Background: Medicinal plants and herbs constitute rich sources of flavoring, and aromatic compounds, namely phytochemicals, which have many positive impacts on human health, such as antioxidant, anticancer, antimicrobial, anti-inflammatory, cardioprotective, and neuroprotective properties. Such bioactive compounds may be considered lead compounds that can be introduced in the drug design process to obtain novel drug candidates with better bioavailability and therapeutic efficiency. The Islands of the North Aegean have rich biodiversity and many medicinal plants and herbs with multiple health benefits. Objective: This study aims to summarize and scrutinize the antioxidant activity of medicinal plants and herbs of the North Aegean islands. Methods: A thorough search of the existing literature was performed in the most accurate scientific databases, using a set of effective and relative keywords and including only clinical human studies written in English. Results: Several clinical studies have highlighted the potential antioxidant activity of phytochemicals from plants such as St. John's wort, chamomile, rosemary, spearmint, mastiha, mountain tea, oregano, sage, and thyme, as measured in the blood and saliva of human individuals, after administration of extracts and solutions of these plants. Decreased levels of oxidized lipoproteins, increased activity of antioxidant enzymes, and an enhanced total antioxidant capacity were observed. Conclusions: Current clinical trials remain still limited, and an enhanced series is needed with a better methodology design, larger sample size, longer intervention periods, using controlled groups and randomization to unravel the antioxidant activity of these North Aegean endemic plants in human individuals.

Seq2Set2Seq: A Two-stage Disentangled Method for Reply Keyword Generation in Social Media

Social media produces large amounts of content every day. How to predict the potential influences of the contents from a social reply feedback perspective is a key issue that has not been explored. Thus, we propose a novel task named reply keyword prediction in social media, which aims to predict the keywords in the potential replies in as many aspects as possible. One prerequisite challenge is that the accessible social media datasets labeling such keywords remain absent. To solve this issue, we propose a new dataset, 1 to study the reply keyword prediction in social media. This task could be seen as a single-turn dialogue keyword prediction for open-domain dialogue system. However, existing methods for dialogue keyword prediction cannot be adopted directly, which has two main drawbacks. First, they do not provide an explicit mechanism to model topic complementarity between keywords which is crucial in social media to controllably model all aspects of replies. Second, the collocations of keywords are not explicitly modeled, which also makes it less controllable to optimize for fine-grained prediction since the context information is much less than that in dialogue. To address these issues, we propose a two-stage disentangled framework, which can optimize the complementarity and collocation explicitly in a disentangled fashion. In the first stage, we use a sequence-to-set paradigm via multi-label prediction and determinantal point processes, to generate a set of keyword seeds satisfying the complementarity. In the second stage, we adopt a set-to-sequence paradigm via seq2seq model with the keyword seeds guidance from the set, to generate the more-fine-grained keywords with collocation. Experiments show that this method can generate not only a more diverse set of keywords but also more relevant and consistent keywords. Furthermore, the keywords obtained based on this method can achieve better reply generation results in the retrieval-based system than others.

Exploring the entrepreneurial landscape of university-industry collaboration on public university spin-off creation: A systematic literature review

Research into the factors influencing university-industry collaboration on public university spin-offs creation has focused on management, entrepreneurship, technology and innovation. This research began with a careful systematic literature review of 4427 scientific papers published in the last ten years (2014–2023) and accessible in the prestigious Web of Science core collection. A quantitative methodology was used, complemented by the use of the visual analysis tool Posit PBC™, formerly known as R Cloud Studio. This comprehensive approach facilitated the seamless ingestion of raw data into Biblioshyni, which is a web-based platform specialised in bibliometric analysis. This review has revealed compelling trends, particularly in terms of increasing diversity, the emergence of United Kingdom as a major player, and the central role of university-industry collaboration. Our systematic review identified influential authors in the field, including the prolific contributions of scholars such as D. Radicid, S. Ropers, Y. Li and R. Owen. We also identified important research institutions, such as Utrecht University in Netherlands, Lund University in Sweden and The University of Manchester in the UK. In addition, we have shown that countries such as the United Kingdom, China and USA have made a significant contribution to the volume of publications. The results highlighted a marked increase in the phenomenon of univeristy spin-offs over the past decade, as evidenced by the exponential growth in both publication output and citation rates. This empirical revelation was underpinned by a rigorous exploration of the Web of Science database, using a carefully crafted set of keywords. Thirty-seven pivotal studies were selected for in-depth review through a sophisticated selection process that adhered to the rigorous standards of the PRISMA methodology. The aim of this review is to improve understanding and encourage deeper exploration of spin-off-based public universities through collaboration between academia and industry.

Family's socioeconomic and demographic factors on elements of children's dental and oral health: A scoping review.

Family plays a significant role in children's dental and oral health (DOH) elements, such as children's DOH knowledge and practice, the development of children's dental fear and anxiety (DFA), children's dental visits, and children's DOH status. The study aims to address the interrelationship of these elements based on individual and familial socioeconomic and demographic (SED) attributes. Given the possibility that the COVID-19 epidemic might alter oral health and disrupt dental care, a systematic literature search from the Scopus and Web of Science library database was limited to the 2017-2019 peer-reviewed published literature, which includes quantitative studies that investigated at whether SED status contributed to children's DOH elements. Four sets of keywords are combined in both library database literature searches. Using the PRISMA-ScR Checklist as a reference, we conduct this scoping review. A total of 15 studies were included. Studies were from Brazil, Iran, China, India, Indonesia, Peru, Portugal, and UK. The socioeconomic characteristics include parental education, income, employment, assets and home ownership, type of residence, and health insurance coverage. The demographic characteristics include parent's age, children's age, the number of family members, and family type. There were 13 studies observing parent's education, seven examining family income, four identifying parental employment, three measuring family assets, six assessing the type of residence, and one recording health insurance coverage. Few studies assessed socioeconomic proxies such as school type, free school lunches, and social networks. In addition, race, the number of household members, the number of children, and family type were measured in at least one of the literature. Family SED characteristics may define a pathway to children's DOH elements.

Development of central bank digital currencies: a bibliometric analysis

PurposeWe aim to conduct a bibliometric analysis that explores and maps quantitative data of the emerging field of central bank digital currencies in science and its implications in practice. We seek to clarify the underlying research structures and streams of the new phenomena, and our motivation is the rising number of pilots between governments seeking to implement different types of central bank digital currency.Design/methodology/approachWe designed the unique set of keywords to explore ongoing projects on central bank digital currencies and the evolution of scientific thought on the topic. We conducted a descriptive analysis and an evaluating bibliometric analysis on the timeline from 2018 to April 18, 2023 and investigated 76 articles in the Web of Science database and 152 articles in the Scopus database using VOSviewer.FindingsWe highlight three main directions of discourse on central bank digital currencies in economics using authors keyword analysis, that are: (1) cash, (2) monetary policy and (3) financial stability. We conducted a map-based text analysis of the abstracts and identified the following main streams of discussion in the field: (1) policy-related research on financial systems, (2) a comprehensive review of the design and features of central bank digital currencies and (3) research on the impact of central bank digital currencies on the banking system.Originality/valueThe unique set of keywords allows us to continue the discourse on central bank digital currencies including implications of ongoing governmental projects on the topic and provide directions for future research. We brought the focus on the impact of central bank digital currencies on the banking sector and the new possible order for cash, deposits and payments.

A systematic review of the use of WeChat in teaching Chinese as a foreign language

Abstract Over the years, social media have gained popularity as tools for language teaching. The social media platform WeChat has been employed for teaching Chinese as a foreign language (TCFL). While studies have investigated the use of WeChat for TCFL, no systematic reviews have been conducted to provide an overview of existing research. This review article examines empirical studies on WeChat in TCFL, summarising their main characteristics, findings, and critically assessing the quality and relevance of the available evidence. Scopus and Web of Science were used as primary databases for this review. Relevant studies were identified by using keyword sets such as “WeChat Chinese language”, “WeChat Mandarin”, “WeChat Hanyu” and “WeChat Putonghua”. The review identified the contexts, research methods and themes of the selected articles. The main themes include language learning, social interactions, learner agency and motivation. The paper also discusses the implications of using WeChat for TCFL and highlights areas for future research. It is hoped that the insights provided here will inform the design and implementation of future courses that use WeChat or other technology-assisted applications for language teaching.

Anti-obesity properties of phytochemicals: Highlighting their molecular mechanisms against obesity.

Obesity is a complex, chronic and inflammatory disease that affects more than one-third of the world's population, leading to a higher incidence of diabetes, dyslipidemia, metabolic syndrome, cardiovascular diseases, and some types of cancer. Several phytochemicals are used as flavoring and aromatic compounds, also exerting many benefits for public health. This study aims to summarize and scrutinize the beneficial effects of the most important phytochemicals against obesity. Systematic research of the current international literature was carried out in the most accurate scientific databases, e.g., Pubmed, Scopus, Web of Science and Google Scholar, using a set of critical and representative keywords, such as phytochemicals, obesity, metabolism, metabolic syndrome, etc. Several studies unraveled the potential positive effects of phytochemicals such as berberine, car-vacrol, curcumin, quercetin, resveratrol, thymol, etc., against obesity and meta-bolic disorders. Mechanisms of action include inhibition of adipocyte differentia-tion, browning of the white adipose tissue, inhibition of enzymes such as lipase and amylase, suppression of inflammation, improvement of the gut microbiota, and downregulation of obesity-inducing genes. In conclusion, multiple bioactive compounds-phytochemicals exert many beneficial effects against obesity. Future molecular and clinical studies must be performed to unravel the multiple molecu-lar mechanisms and anti-obesity activities of these naturally occurring bioactive compounds.

An Overview of Mathematical Methods Applied in the Biomechanics of Foot and Ankle–Foot Orthosis Models

Ankle–foot orthoses (AFOs) constitute medical instruments designed for patients exhibiting pathological gait patterns, notably stemming from conditions such as stroke, with the primary objective of providing support and facilitating rehabilitation. The present research endeavors to conduct a comprehensive review of extant scholarly literature focusing on mathematical techniques employed for the examination of AFO models. The overarching aim is to gain deeper insights into the biomechanical intricacies underlying these ankle–foot orthosis models from a mathematical perspective, while concurrently aiming to advance novel models within the domain. Utilizing a specified set of keywords and their configurations, a systematic search was conducted across notable academic databases, including ISI Web of Knowledge, Google Scholar, Scopus, and PubMed. Subsequently, a total of 23 articles were meticulously selected for in-depth review. These scholarly contributions collectively shed light on the utilization of nonlinear optimization techniques within the context of ankle–foot orthoses (AFOs), specifically within the framework of fully Cartesian coordinates, encompassing both kinematic and dynamic dimensions. Furthermore, an exploration of a two-degree-of-freedom AFO design tailored for robotic rehabilitation, which takes into account the interplay between foot and orthosis models, is delineated. Notably, the review article underscores the incorporation of shape memory alloy (SMA) elements in AFOs and overviews the constitutive elastic, viscoelastic, and hyperelastic models. This comprehensive synthesis of research findings stands to provide valuable insights for orthotists and engineers, enabling them to gain a mathematical understanding of the biomechanical principles underpinning AFO models and fostering the development of innovative AFO designs.

Skaitmeninių metodų taikymas praeities kelionių tyrimuose

The massive digitisation of written historical sources, optical character recognition (OCR) of texts, and their online availability in recent decades have created new opportunities and challenges for historical research. The digital humanities research model presented in this paper is based on the information organisation paradigm and the application of digital technology-based methods in studying ancient travels. The model has been developed and tested using the materials of the project “Homo Viator: Travel Space and Travellers’ Experiences in Early Modern Lithuania”. The main problem of the research is related to the fact that one of the essential sources of information about ancient travel are egodocuments (letters, diaries, memoirs, etc.) that contain journeys described alongside other important life events of a particular person. However, travel descriptions form only a small part of a text of a given egodocument and are unevenly distributed among different egodocuments. Therefore, given the size of the text of the egodocuments and their collections and the number of egodocuments published in different languages, researching them as sources in only one aspect (travel) requires a significant amount of human and time resources. A similar problematic situation exists with other sources of knowledge on ancient travel: a massive number of documents published in digital form (including OCR), their texts are voluminous, and the text fragments related to travel, country descriptions, ancient travel routes, travel and mobility infrastructure, and travellers’ experiences are relatively small and scattered throughout the source text. The research model described in the paper is divided into two steps: (i) collection of the corpus of OCR source texts; (ii) collection of empirical data using a dictionary-based computer-aided [or assisted] qualitative text analysis method implemented with the MaxQDA software. The collection of the source text corpus is carried out by applying the general principles and methods of online search of scientific publications. The corpus comprises authentic, published sources relevant to the study (letters, diaries, memoirs, etc.) and scholarly publications about them, thus forming two blocks of text - sources and literature. The literature block is used as additional material for a more precise selection and interpretation of the source texts. A key element for applying a dictionary-based computer-aided [or assisted] qualitative text analysis method is a high-quality dictionary that accurately describes the concepts (categories) relevant to the research. Considering the specificity of the sources (the languages used in the sources and their translations), a multilingual dictionary (Lithuanian-Polish-English-Russian-German) was compiled. The structure of the dictionary consists of six concepts (categories) related to ancient travel: (i) journey (general description), (ii) road and its infrastructure (bridges, fords, etc.), (iii) means of transportation, (iv) resting and accommodation places (towns, villages, taverns, post offices, etc.), (v) people encountered on the way (inn-keepers, highwaymen, guides, etc.), and (vi) food of the journey. A set of keywords and phrases describes each concept. In the last stage of the study, the research model was tested. The testing showed that the model could solve the above problems that arose during the project.

Prevalence of tuberculosis infection among patients with Takayasu arteritis: a meta-analysis of observational studies

To clarify the risk of tuberculosis (TB) infection in patients with Takayasu arteritis (TAK). In this study, we conducted a comprehensive search across multiple databases, including PubMed, Web of Science, Embase, Cochrane, and Medline, from the inception of the Literature Library to May 16, 2023. Using a specific set of keywords, including “Takayasu Arteritis”, “Tuberculosis”, and “Mycobacterium tuberculosis”, the main objective of this search was to identify all relevant observational studies, including case-control studies, cohort studies, and cross-sectional studies, that report the prevalence of TB in individuals diagnosed with TAK. Two independent evaluators rigorously screened the studies, extracted data, and assessed the study quality using the Joanna Briggs Institute (JBI) critical appraisal tools. Statistical analyses were conducted using R software version 4.3.0, which allowed for the synthesis of prevalence and subgroup analyses. Subgroup analyses were stratified based on quality scores, World Health Organization regional categorizations, and TB categories. Assessment of publication bias was performed using a funnel plot. The study included a total of 30 studies with 5548 participants. The findings showed that individuals with TAK exhibited an average prevalence of TB infection at 31.27% (95% CI 20.48–43.11%). Significantly, the prevalence of TB infection demonstrated notable regional disparities, ranging from 16.93% (95% CI 7.71–28.76%) in the Western Pacific Region to 63.58% (95% CI 35.70–87.66%) in the African Region. Moreover, the study revealed that patients with TAK displayed a high prevalence of latent TB infection (LTBI) at 50.01% (95% CI 31.25–68.77%) and active TB at 14.40% (95% CI 9.03–20.68%). The high heterogeneity observed in the data highlights significant variability in TB infection rates among the populations studied, with the African Region exhibiting the highest rates. The study concludes that there is a high prevalence of TB infection in the TAK population, with regional variations. Consideration should be given to implementing rigorous TB screening measures and preventive interventions specifically tailored for the TAK population.

Review of agile project management research through systematic literature network analysis approach

In the present digital era, the concepts of Agile project methodology or Agile methods have transformed technology as perceived in learning organizations. During the COVID-19 pandemic, the Agile methods domain has gained considerable attention from practitioners and researchers. While past review studies have provided valuable insights into Agile methods research, none have addressed the trends, structure, and opportunities in this domain. Therefore, this research aims to conduct a systematic review of Agile project management or Agile methods using a novel research methodology called Systematic Literature Network Analysis (SLNA). A corpus of fifty research articles from 2006 to 2023 is extracted from the Web of Science (WoS) database. The bibliometric technique has been applied to systematically collected documents. The research findings highlight that the Agile project method domain gained considerable attention during and after the COVID-19 pandemic, with most research conducted in developed countries. The outcomes suggest that future studies should focus on people and technical factors and explore different frameworks and variables to strengthen the field of Agile project methodology. These findings are valuable for future researchers, practitioners, and industry personnel. Emphasize people and technical factors while implementing Agile methods, fostering effective collaboration, and utilizing appropriate technical resources. Investigate emerging trends in Agile project management, including the impact of remote work, and conduct cross-country studies to understand the applicability of Agile methods across different contexts. The impact of Agile project management methods on society includes fostering adaptability and resilience in organizations, promoting efficient collaboration and innovation, and facilitating the successful delivery of projects that meet the evolving needs of society. A broader set of keywords, a longer time frame, and additional online databases could have resulted in a greater number of articles being analysed.

Cardiovascular implications of inflammatory bowel disease: An updated review.

Emerging data highlights the heightened risk of atherosclerotic cardiovascular diseases (ASCVD) in patients with chronic inflammatory disorders, particularly those afflicted with inflammatory bowel disease (IBD). This review delves into the epidemiological connections between IBD and ASCVD, elucidating potential underlying mechanisms. Furthermore, it discusses the impact of current IBD treatments on cardiovascular risk. Additionally, the cardiovascular adverse effects of novel small molecule drugs used in moderate-to-severe IBD are investigated, drawing parallels with observations in patients with rheumatoid arthritis. This article aims to comprehensively evaluate the existing evidence supporting these associations. To achieve this, we conducted a meticulous search of PubMed, spanning from inception to August 2023, using a carefully selected set of keywords. The search encompassed topics related to IBD, such as Crohn's disease and ulcerative colitis, as well as ASCVD, including coronary artery disease, cardiovascular disease, atrial fibrillation, heart failure, conduction abnormalities, heart blocks, and premature coronary artery disease. This review encompasses various types of literature, including retrospective and prospective cohort studies, clinical trials, meta-analyses, and relevant guidelines, with the objective of providing a comprehensive overview of this critical intersection of inflammatory bowel disease and cardiovascular health.

Augmented Reality Marketing Implementation in Greek SMEs - A SWOT Analysis

Abstract Augmented Reality (AR) has established itself as a transformative tool in the domain of marketing, enabling both immersive and interactive experiences for consumers. The inherent capability of AR to overlay digital content onto the real world provides marketers with unparalleled opportunities to connect with their target audience. For the effective integration of Augmented Reality Marketing (ARM) within Greek Small and Medium-sized Enterprises (SMEs), it's essential to capitalize on inherent strengths, address potential weaknesses, exploit existing opportunities, and strategically navigate anticipated threats. Fundamental to the successful incorporation of ARM in this context is the development of a carefully crafted strategy, enriched by a deep understanding of the local market dynamics. From a methodological standpoint, this research draws upon a set of predetermined keywords sourced from renowned databases such as Google, Google Scholar, Semantic Scholar, Science Direct, and Scopus. Using secondary data, the collated information was analyzed using the SWOT framework, facilitating a thorough examination of strengths, weaknesses, opportunities, and threats. To achieve this, a SWOT analysis was systematically employed, offering a comprehensive view of the potential for integrating augmented reality marketing within Greek SMEs. Keywords: Augmenting Reality Marketing, Digital Marketing, SMEs, Swot Analysis, Greece, Greek enterprises.

From Text to Insight: A Natural Language Processing-Based Analysis of Topics and Trends in Neurosurgery.

Neurosurgical research is a rapidly evolving field, with new research topics emerging continually. To provide a clearer understanding of the evolving research landscape, our study aimed to identify and analyze the prevalent research topics and trends in Neurosurgery. We used BERTopic, an advanced natural language processing-based topic modeling approach, to analyze papers published in the journal Neurosurgery . Using this method, topics were identified based on unique sets of keywords that encapsulated the core themes of each article. Linear regression models were then trained on the topic probabilities to identify trends over time, allowing us to identify "hot" (growing in prominence) and "cold" (decreasing in prominence) topics. We also performed a focused analysis of the trends in the current decade. Our analysis led to the categorization of 12 438 documents into 49 distinct topics. The topics covered a wide range of themes, with the most commonly identified topics being "Spinal Neurosurgery" and "Treatment of Cerebral Ischemia." The hottest topics of the current decade were "Peripheral Nerve Surgery," "Unruptured Aneurysms," and "Endovascular Treatments" while the cold topics were "Chiari Malformations," "Thromboembolism Prophylaxis," and "Infections." Our study underscores the dynamic nature of neurosurgical research and the evolving focus of the field. The insights derived from the analysis can guide future research directions, inform policy decisions, and identify emerging areas of interest. The use of natural language processing in synthesizing and analyzing large volumes of academic literature demonstrates the potential of advanced analytical techniques in understanding the research landscape, paving the way for similar analyses across other medical disciplines.

Less payment and higher efficiency: A verifiable, fair and forward-secure range query scheme using blockchain

Searchable symmetric encryption (SSE) has recently been under the spotlight in a cloud data storage system due to its high efficiency. SSE allows a client to outsource his private data while maintaining data searchability. To ensure the correctness of query results, verifiable SSE has attracted a lot of attention from both academia and industry to enable reliable encrypted search over the untrusted cloud server. However, most traditional verifiable SSE schemes focus on point queries instead of range queries. Moreover, no effective countermeasures are available to prevent clients from maliciously rejecting the correct result for denying the payment. In this paper, we take the first step to study verifiable range queries with fairness and forward security. First, to support efficient range query over numerical values, we propose Prefix Tree-like Keyword Set (PTKS), a novel data structure to largely reduce the number of search tokens. Specifically, the number of search tokens is up to a logarithmic value (e.g., 7) of the query range width (e.g., [0, 1000]) with PTKS. Then, we propose a double-layer verification mechanism including client-side verification and on-chain verification via the smart contract on the blockchain to achieve cost-effective fair verification. In addition, our range query scheme supports forward-secure update operations. Based on these, we propose a blockchain-based verifiable, fair and forward-secure range query scheme. Finally, extensive experiments demonstrate the efficiency of our scheme.

Mindful minds and entrepreneurial spirits in higher education: a scoping review

IntroductionMindfulness at Higher Education Institutions (HEIs) may enhance personal development, learning, and entrepreneurial thinking. Thus, this scoping review investigates the effects of mindfulness on HEI entrepreneurship education, focusing on teaching, learning, and entrepreneurial intention.MethodTo identify relevant articles for inclusion, the study used a predetermined set of keywords and a descriptive search algorithm in six electronic databases. The process of study selection adhered to the principles outlined in the Preferred Reporting of Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and resulted in the inclusion of eleven (11) studies. Said studies spanned several nations and employed various research methods, with an emphasis on quantitative approaches.ResultsResults indicated that mindfulness did not have a direct impact on lecturers’ commitment to teaching entrepreneurship. Nevertheless, mindfulness appeared to indirectly impact teaching outcomes by influencing other variables, such as readiness for change. From an educational processes and outcomes perspective, mindfulness was found to improve the entrepreneurship learning environment and enhance students’ entrepreneurial orientation. The latter included students’ intentions to develop or participate in environmental and socially responsible entrepreneurial ventures.DiscussionThe present study advances our understanding of the relationship between mindfulness, entrepreneurship teaching and students’ entrepreneurial orientation in higher education settings. Nevertheless, it also demonstrates a lack of comprehension of the exact mechanisms at play, and therefore highlights the need for further research in this scientific area. By gaining a broader awareness of the impact of mindfulness on entrepreneurship education, education professionals and decision-makers can improve the design of programmes to cultivate the entrepreneurial orientation and skills necessary for students’ success in a rapidly changing business environment.Systematic Review RegistrationThe review process has been duly registered with the Open Science Framework (OSF) and given the identifier DOI 10.17605/OSF.IO/YJTA3.

Production of the Scaffold Using 3D Bioprinting Applied to the Biomedical Area: A Bibliometric Study

Tissue Engineering is an ascent area with clinical applications through combining cells and biomaterials to reconstitute organs and tissues using scaffolds. These are defined as porous, temporary, biodegradable threedimensional supports used to mimic the structure of the extracellular matrix and stimulate specific cellular responses at the molecular level. Among the existing technologies for producing scaffolds, 3D bioprinting stands out as one of the most promising technologies that enable layer-by-layer deposition with precise control of the spatial arrangement of functional components. One of the most used biomaterials for the fabrication of scaffolds for application in Tissue Engineering is poly(lactic acid), PLA, a biodegradable, biocompatible, and non-toxic polyester, whose main application is in the regeneration and replacement of bone tissue. In this way, the present article aims to obtain a comprehensive view of the current scenario of PLA scaffolds obtained by 3D bioprinting for biomedical applications through bibliometric analysis. The research was conducted in the Web of Science database between August 16 and 17, 2022. The following set of keywords was used: "scaffolds" AND "PLA" AND "3D printing" AND "biomedical applications" OR "biomedical application," resulting in 2,351 publications in the last five years. The VOSviewer software was used as a bibliometric analysis tool to visualize country networksand keywords on the topic studied. Based on the results, it was possible to observe that China, India, and the USA are the countries that published the most on the subject, and Chinese institutions and authors are also responsible for a considerable part of the publication of documents. There was also an increase in publications in the last five years, reinforcing the research interest that strengthens Tissue Engineering.

Deep learning-based software bug classification

Context:Accurate classification of bugs can help accelerate the bug triage process, code inspection, and repair activities. In this context, many machine learning techniques have been proposed to classify bugs. The expressive power of deep learning could be used to further improve classification. Objective:We propose a novel deep learning-based bug classification approach. Methods:We first build a bug taxonomy with eight bug classes, each characterized by a set of keywords. Subsequently, we heuristically annotate a moderately large set (∼1.36M) of software bug resolution reports using an earth-mover distance technique based on the keywords. Finally, we use four attention-based classification techniques to classify these curated bugs. Results:Our experiments on a carefully collected dataset indicate that our proposed technique achieved a mean F1-Score of 84.78% and a mean macro-average ROC of 98.25%. Conclusion:Our proposed approach was observed to outperform the existing techniques by 16.88% on an average in terms of F1-Score for the considered dataset.

Select and Augment: Enhanced Dense Retrieval Knowledge Graph Augmentation

Injecting textual information into knowledge graph (KG) entity representations has been a worthwhile expedition in terms of improving performance in KG oriented tasks within the NLP community. External knowledge often adopted to enhance KG embeddings ranges from semantically rich lexical dependency parsed features to a set of relevant key words to entire text descriptions supplied from an external corpus such as wikipedia and many more. Despite the gains this innovation (Text-enhanced KG embeddings) has made, the proposal in this work suggests that it can be improved even further. Instead of using a single text description (which would not sufficiently represent an entity because of the inherent lexical ambiguity of text), we propose a multi-task framework that jointly selects a set of text descriptions relevant to KG entities as well as align or augment KG embeddings with text descriptions. Different from prior work that plugs formal entity descriptions declared in knowledge bases, this framework leverages a retriever model to selectively identify richer or highly relevant text descriptions to use in augmenting entities. Furthermore, the framework treats the number of descriptions to use in augmentation process as a parameter, which allows the flexibility of enumerating across several numbers before identifying an appropriate number. Experiment results for Link Prediction demonstrate a 5.5% and 3.5% percentage increase in the Mean Reciprocal Rank (MRR) and Hits@10 scores respectively, in comparison to text-enhanced knowledge graph augmentation methods using traditional CNNs.

35 years of grey system theory in economics and education

PurposeThe Grey System Theory (GST) is an emerging area of research within artificial intelligence. Since its founding in 1982, it has seen a lot of multidisciplinary applications. In just a short period, it has garnered some considerable strengths. Based on the 1987–2021 data collected from the Web of Science (WoS), the current study reports the advancement of the GST.Design/methodology/approachResearch papers utilizing the GST in the fields of economics and education were retrieved from the Web of Science (WoS) platform using a set of predetermined keywords. In the final stage of the process, the papers that underwent analysis were manually chosen, with selection criteria based on the information presented in the titles and abstracts.FindingsThe study identifies prominent authors, institutions, publications and journals closely associated with the subject. In terms of authors, two major clusters are identified around Liu SF and Wang ZX, while the institution with the highest number of publications is Nanjing University of Aeronautics and Astronautics. Moreover, significant keywords, trends and research directions have been extracted and analyzed. Additionally, the study highlights the regions where the theory holds substantial influence.Research limitations/implicationsThe study is subject to certain limitations stemming from factors such as the language employed in the chosen literature, the papers included within the Web of Science (WoS) database, the designation of works categorized as “articles” in the database, the specific selection of keywords and keyword combinations, and the meticulous manual process employed for paper selection. While the manual selection process itself is not inherently limiting, it demands a greater investment of time and meticulous attention, contributing to the overall limitations of the study.Practical implicationsThe significance of the study extends not only to scholars and practitioners but also to readers who observe the development of emerging scientific disciplines.Originality/valueThe analysis of trends revealed a growing emphasis on the application of GST in diverse domains, including supply chain management, manufacturing and economic development. Notably, the emergence of COVID-19 as a new research focal point among GST scholars is evident. The heightened interest in COVID-19 can be attributed to its global impact across various academic disciplines. However, it is improbable that this interest will persist in the long term, as the pandemic is gradually brought under control.