An official website of the United States government
In recent years, driven by Industry 4.0 wave, academic research has focused on the science, engineering, and enabling technologies for intelligent and cyber manufacturing. Using a network science and data mining-based Keyword Co-occurrence Network (KCN) methodology, this work analyzes the trends in data science topics in the manufacturing literature over the past two decades to inform the researchers, educators, industry leaders of knowledge trends in intelligent manufacturing. It studies the evolution of research topics and methods in data science, Internet of Things (IoT), cloud computing, and cyber manufacturing. The KCN methodology is applied to systematically analyze the keywords collected from 84,041 papers published in top-tier manufacturing journals between 2000 and 2020. It is not practically feasible to review this large body of literature through tradition manual approaches like systematic review and scoping review to discover insights. The results of network modeling and data analysis reveal important knowledge components and structure of the intelligent and cyber manufacturing literature, implicit the research interests switch and provide the insights for industry development. This paper maps the high frequency keywords in the recent literature to nine pillars of Industry 4.0 to help manufacturing community identify research and education directions for emerging technologies in intelligent manufacturing.
more »
« less
Analysis of pain research literature through keyword Co-occurrence networks
Pain is a significant public health problem as the number of individuals with a history of pain globally keeps growing. In response, many synergistic research areas have been coming together to address pain-related issues. This work reviews and analyzes a vast body of pain-related literature using the keyword co-occurrence network (KCN) methodology. In this method, a set of KCNs is constructed by treating keywords as nodes and the co-occurrence of keywords as links between the nodes. Since keywords represent the knowledge components of research articles, analysis of KCNs will reveal the knowledge structure and research trends in the literature. This study extracted and analyzed keywords from 264,560 pain-related research articles indexed in IEEE, PubMed, Engineering Village, and Web of Science published between 2002 and 2021. We observed rapid growth in pain literature in the last two decades: the number of articles has grown nearly threefold, and the number of keywords has grown by a factor of 7. We identified emerging and declining research trends in sensors/methods, biomedical, and treatment tracks. We also extracted the most frequently co-occurring keyword pairs and clusters to help researchers recognize the synergies among different pain-related topics.
more »
« less
- Award ID(s):
- 1838796
- PAR ID:
- 10533971
- Editor(s):
- Feng, Mengling
- Publisher / Repository:
- the Public Library of Science
- Date Published:
- Journal Name:
- PLOS Digital Health
- Volume:
- 2
- Issue:
- 9
- ISSN:
- 2767-3170
- Page Range / eLocation ID:
- e0000331
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
In recent years, driven by Industry 4.0 wave, academic research has focused on the science, engineering, and enabling technologies for intelligent and cyber manufacturing. Using a network science and data mining-based Keyword Co-occurrence Network (KCN) methodology, this work analyzes the trends in data science topics in the manufacturing literature over the past two decades to inform the researchers, educators, industry leaders of knowledge trends in intelligent manufacturing. It studies the evolution of research topics and methods in data science, Internet of Things (IoT), cloud computing, and cyber manufacturing. The KCN methodology is applied to systematically analyze the keywords collected from 84,041 papers published in top-tier manufacturing journals between 2000 and 2020. It is not practically feasible to review this large body of literature through tradition manual approaches like systematic review and scoping review to discover insights. The results of network modeling and data analysis reveal important knowledge components and structure of the intelligent and cyber manufacturing literature, implicit the research interests switch and provide the insights for industry development. This paper maps the high frequency keywords in the recent literature to nine pillars of Industry 4.0 to help manufacturing community identify research and education directions for emerging technologies in intelligent manufacturing.more » « less
-
With the ever-increasing abundance of biomedical articles, improving the accuracy of keyword search results becomes crucial for ensuring reproducible research. However, keyword extraction for biomedical articles is hard due to the existence of obscure keywords and the lack of a comprehensive benchmark. PubMedAKE is an author-assigned keyword extraction dataset that contains the title, abstract, and keywords of over 843,269 articles from the PubMed open access subset database. This dataset, publicly available on Zenodo, is the largest keyword extraction benchmark with sufficient samples to train neural networks. Experimental results using state-of-the-art baseline methods illustrate the need for developing automatic keyword extraction methods for biomedical literature.more » « less
-
Glutamatergic dysfunction has been implicated in the pathophysiology of multiple conditions including epilepsy, chronic pain, post-traumatic stress disorder (PTSD), and premenstrual dysphoric disorder (PMDD), raising interest in potential ways of modifying glutamate in the nervous system. Emerging research has suggested an interactive effect between sex hormones and glutamatergic neurotransmission. The objective of this paper was to review existing literature on the mechanism of interaction between sex hormones and glutamatergic neurotransmission, as well as to explore what is known about these interactions in various neurological and psychiatric conditions. This paper summarizes knowledge regarding mechanisms for these effects, and glutamatergic response to direct modulation of sex hormones. Research articles were identified via scholarly databases including PubMed, Google Scholar, and ProQuest. Articles were included if they were original research from peer-reviewed academic journals that dealt with glutamate, estrogen, progesterone, testosterone, neurosteroids, glutamate and sex hormone interactions, or the potential impact of glutamate and sex hormone interactions in the following conditions: chronic pain, epilepsy, PTSD, and PMDD. Current evidence suggests that sex hormones can directly modulate glutamatergic neurotransmission, with specific protective effects against excitotoxicity noted for estrogens. An effect of monosodium glutamate consumption on sex hormone levels has also been demonstrated, suggesting a possible bidirectional effect. Overall, there is a good deal of evidence suggesting a role for sex hormones, and specifically for estrogens, in the modulation of glutamatergic neurotransmission.more » « less
-
One social mechanism by which marginalization is enacted is via ostracism. Recent research has demonstrated ostracism's impact on physical health, but little is known about the relationship between accumulated lifetime experiences of ostracism and pain. Despite recent calls for added attention to social modulation of pain and social indicators of pain disparities, the impact of specific social factors on pain—including those of ostracism—are not well understood. Results of laboratory studies on the effects of acute ostracism experiences on pain sensitivity have been mixed. However, these studies have not considered lived and repeated experiences of ostracism, and primarily included single static measures of pain sensitivity. Additionally, inclusion and representation of the relationship between ostracism experiences and pain among people with minoritized identities are lacking in the current literature. In this study, we explored accumulated lifetime experiences of ostracism as a potential contributing factor to enhanced pain and one social mechanism by which societal inequity may create and maintain inequity in pain. We extracted measures of lifetime experiences of ostracism from six studies focused on social factors and (non-chronic) pain conducted between 2016 and 2020 ( n = 505 adults). To retain and examine diversity within the sample, we used moderation and within-group analyses. Results indicate that greater experiences of lifetime ostracism are associated with lower cold pain tolerance, but not other pain measures, in the whole sample. Moderation and within-group analyses reveal opposing patterns of results between populations included in the extant literature (White participants, convenience samples) and those under-represented in the scientific literature (racialized groups, community samples). This study provides an example of a diversity science approach to examining social indicators of pain, illustrates the limited generalizability of previous studies on ostracism and pain, and highlights the need for increased representation and inclusion to understand mechanisms of pain and inequity.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1371/journal.pdig.0000331
