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Additive Manufacturing in Biomedical Engineering
Published in Atul Babbar, Ankit Sharma, Vivek Jain, Dheeraj Gupta, Additive Manufacturing Processes in Biomedical Engineering, 2023
Vidyapati Kumar, Chander Prakash, Atul Babbar, Shubham Choudhary, Ankit Sharma, Amrinder Singh Uppal
The applications mentioned in the preceding section make use of a variety of AM techniques. For the hard-to-find application areas for particular processes, a search was conducted first using ISO/ASTM terminology with a mix of AM medical application keywords in a specific class and then using trade names or other widely used names for the processes, such as the manufacturer name. The goal was to locate at least a few instances for each category, as well as to figure out which regions lacked certain applications and procedures, and why. Scopus, Web of Science and Google Scholar were the databases utilized for the search. There has been earlier research on PBF of metallic implants [105–106], AM of medical equipment [93], biomaterials in medical AM [107], and medical phantoms and regenerated tissue and organ applications using AM [108], to name a few. Previous research has either failed to classify AM processes or has only looked at a single one. Various optimization [109–113] and selection strategies [114] may also be utilized to ensure optimum biomaterial selection and manufacture for application as implants, scaffolds and prosthetics devices. Some studies simply looked at the materials that were used, while others only looked at the applications themselves, with no mention of the AM procedures or materials. The search emphasis was then moved to additional processes and applications when at least three specific application classes and processes were discovered. Table 8.3 shows some specific search terms.
Role of Artificial Intelligence in the Era of COVID-19 to Improve Hospital Management
Published in Adarsh Garg, D. P. Goyal, Global Healthcare Disasters, 2023
The respiratory illness caused by the novel SARS-CoV-2 virus known as COVID-19. It has become pandemic and is a challenge all over the world. The most crucial challenge of this pandemic is the management of COVID-19 patients’ urgency of critical respiratory care. Based on the need of this situation, an AI-based model was developed to enhance the critical care of COVID-19 patients. A review of available literature was carried out like PubMed, Google Scholar, Web of Science, etc. More and more clinicians and engineers are working rigorously on a vaccine, testing facilities, and monitoring systems. This chapter highlights the opportunities gained through the use of AI methods for diagnosis and prognosis system. Major efforts of the healthcare system to fight COVID-19 using AI-based decision-making system would support in management of the critically ill patients with COVID-19 more efficiently. By gathering, categorizing, and studying of clinical information from the large number of patients are approaching to diagnosis and decide toward treatment process.
Bibliometric and Visualized Analysis of Water Footprint Research
Published in Yeqiao Wang, Fresh Water and Watersheds, 2020
Peili Duan, Lijie Qin, Peng Yin
The relevant documents were retrieved from the Science Citation Index Expanded (SCI-Expanded) and Social Science Citation Index (SSCI) of the Web of Science database. The document types include article, review, proceedings paper, meeting abstract, editorial material, letter, book chapter, note, news item, book review. Only articles published in international journals were collected in this study, because they represented the majority of documents with complete research results and outcomes. The research of water footprint is developed on the basis of virtual water. Therefore, the time span of the study was from 1993 to 2018, since the concept of virtual water was put forward in 1993. And the search items were being set as “water footprint” or “virtual water.” After searching, a total of 8,118 articles downloaded on March 16, 2019, were retrieved as the data for this study.
A semantic similarity analysis of Internet of Things
Published in Enterprise Information Systems, 2018
Chun Kit Ng, Chun Ho Wu, Kai Leung Yung, Wai Hung Ip, Tommy Cheung
In this research, the representative research papers related to IoT were retrieved from Web of Science. Web of Science is a world’s leading online platform connecting many high-reputation scientific databases for providing scientific paper search and discovery, and scientific citation indexing service. This platform covers over 12,500 high quality journals, 170,000 conference proceedings and 70,000 scholarly books from more than 250 disciples (Thomson Reuters 2016). It is trusted by approximately 7,000 worldwide scholarly institutions. More importantly, it provides a useful function to list the searching result sequentially based on citation counts. Such function enables user to sort out the highly cited articles efficiently. In the next step, a set of search keywords were defined. The main keywords to search IoT-related studies are ‘internet of things’ and ‘IoT’. However, some papers without these two main keywords may include IoT-related content. For instance, before the raising of IoT paradigm, RFID and M2M related studies shaped the concept of IoT. When IoT emerges, cloud and NFC are two of the main components mentioned together with IoT in many studies. Consequently, the set of keywords is defined as KEYWORDS = {‘internet of things’, ‘IoT’, ‘machine-to-machine’, ‘M2M’, ‘radio frequent identification’, ‘RFID’, ‘near field communication’, ‘NFC’, ‘cloud’} after reviewing the development and components of IoT. By using these keywords, 1457 results and 7029 total number of citations to all the results could be yielded from 2004 to 1 November 2015 in Web of Science.
Construction and demolition waste research: a bibliometric analysis
Published in Architectural Science Review, 2019
Huanyu Wu, Jian Zuo, George Zillante, Jiayuan Wang, Hongping Yuan
Compared to other databases (e.g. PubMed, Scopus and Google Scholar), Web of Science™ (WoS) has a comprehensive coverage in science, technology, social sciences, arts and humanities (Falagas et al. 2008). As a result, WoS has been widely adopted in bibliometric studies (i.e. Qiao, Kristoffersson, and Randrup 2018; Siva et al. 2016). WoS was also chosen for article searching and filtering in our study based on two considerations: (1) the WoS has a good coverage of articles in both waste management and waste recycling techniques; and (2) by checking the journal list in previous review articles regarding C&D waste, it is found that most of the journals are included in the WoS database.
Systematic Literature Review of Virtual Reality Intervention Design Patterns for Individuals with Autism Spectrum Disorders
Published in International Journal of Human–Computer Interaction, 2022
All searches were conducted in March, 2020. Databases searched for this systematic review were Web of Science, PubMed, Scopus, IEEE Xplore, ERIC, and Google Scholar. Web of Science is a subscription-based scientific indexing platform that provides comprehensive citation data across many disciplines. The Web of Science Core Collection is made up of six databases. PubMed is a database maintained by the National Center for Biotechnology Information and includes more than 30 million citations primarily related to life sciences and biomedical topics. Scopus is an abstract and citation database launched by Elsevier. Scopus includes book series, trade journals, and journals related to life sciences, social sciences, health sciences, and the physical sciences. IEEE Xplore is a digital library that provides access to over five million publications related to electrical engineering, computer science, and electronics. More than 20,000 new documents are added to the IEEE Xplore library every month. The ERIC Collection is an online library that was established in 1966. ERIC is currently sponsored by the Institute of Education Sciences by the United States Department of Education. It includes various types of publications including journal articles, books, conference papers, technical reports, dissertations, and more. The ERIC dataset includes over 1.5 million records which are largely available in Adobe PDF format. Google Scholar is a free search engine that indexes full text and metadata of the literature including peer-reviewed manuscripts, conference papers, dissertations, preprints, technical reports, patents, and other scholarly works. Google Scholar is estimated to include approximately 400 million documents in its index. Google Scholar was also searched as it has emerged as being the go-to academic search tool for many in the field due to its ease-of-use and convenience (Gusenbauer & Haddaway, 2020). However, while Google Scholar’s immense dataset serves as a multidisciplinary collection of knowledge, it lacks many of the features required for conducting a systematic search such as the ability to specify tailored queries with high recall and precision. Therefore, this search engine was only used as a secondary tool to seek literature that may have been missed in the full systematic review of the electronic databases (Gusenbauer & Haddaway, 2020; Haddaway et al., 2015).