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Abstract:Abstract:Hydropower has superior storage capacity and speedy response to meet sudden fluctuations in electricity demand. According to a recent estimate by International Energy Agency (IEA), its generation capacity could double by 2050. Nevertheless, no great picture of academic knowledge exists in relation to hydropower. This study was undertaken to reveal the academic landscape in this field by analysing the citation network of papers published in academic journals. We collected 7,521 target papers from the W… Show more
“…After clustering the network, visualisation is converted to intuitively infer a relation among these clusters. We use a large graph layout (LGL) based on a force-direct layout algorithm (Adai et al, 2004) 31 32 . This layout can display the largest connected component of the network to generate coordinates for nodes in two dimensions.…”
ObjectivesHorizon-scanning for innovative technologies that might be applied to medical products and require new assessment approaches/regulations will help to prepare regulators, allowing earlier access to the product for patients and an improved benefit/risk ratio. In this study, we focused on the field of AI-based medical image analysis as a retrospective example of medical devices, where many products have recently been developed and applied. We proposed and validated horizon-scanning using citation network analysis and text mining for bibliographic information analysis.Methods and analysisResearch papers for citation network analysis which contain “convolutional*” OR “machine-learning” OR “deep-learning” were obtained from Science Citation Index Expanded (SCI-expanded) in the Web of Science (WoS). The citation network among those papers was converted into an unweighted network with papers as nodes and citation relationships as links. The network was then divided into clusters using the topological clustering method and the characteristics of each cluster were confirmed by extracting a summary of frequently cited academic papers, and the characteristic keywords, in the cluster.ResultsWe classified 119,553 publications obtained from SCI and grouped them into 36 clusters. Hence, it was possible to understand the academic landscape of AI applications. The key articles on AI-based medical image analysis were included in one or two clusters, suggesting that clusters specific to the technology were appropriately formed. Based on the average publication year of the constituent papers of each cluster, we tracked recent research trends. It was also suggested that significant research progress would be detected as a quick increase in constituent papers and the number of citations of hub papers in the cluster.ConclusionWe validated that citation network analysis applies to the horizon-scanning of innovative medical devices and demonstrated that AI-based electrocardiograms and electroencephalograms can lead to the development of innovative products.Article SummaryStrengths and limitations of this studyCitation network analysis can provide an academic landscape in the investigated research field, based on the citation relationship of research papers and objective information, such as characteristic keywords and publication year.It might be possible to detect possible significant research progress and the emergence of new research areas through analysis every several months.It is important to confirm the opinions of experts in this area when evaluating the results of the analysis.Information on patents and clinical trials for this analysis is currently unavailable.
“…After clustering the network, visualisation is converted to intuitively infer a relation among these clusters. We use a large graph layout (LGL) based on a force-direct layout algorithm (Adai et al, 2004) 31 32 . This layout can display the largest connected component of the network to generate coordinates for nodes in two dimensions.…”
ObjectivesHorizon-scanning for innovative technologies that might be applied to medical products and require new assessment approaches/regulations will help to prepare regulators, allowing earlier access to the product for patients and an improved benefit/risk ratio. In this study, we focused on the field of AI-based medical image analysis as a retrospective example of medical devices, where many products have recently been developed and applied. We proposed and validated horizon-scanning using citation network analysis and text mining for bibliographic information analysis.Methods and analysisResearch papers for citation network analysis which contain “convolutional*” OR “machine-learning” OR “deep-learning” were obtained from Science Citation Index Expanded (SCI-expanded) in the Web of Science (WoS). The citation network among those papers was converted into an unweighted network with papers as nodes and citation relationships as links. The network was then divided into clusters using the topological clustering method and the characteristics of each cluster were confirmed by extracting a summary of frequently cited academic papers, and the characteristic keywords, in the cluster.ResultsWe classified 119,553 publications obtained from SCI and grouped them into 36 clusters. Hence, it was possible to understand the academic landscape of AI applications. The key articles on AI-based medical image analysis were included in one or two clusters, suggesting that clusters specific to the technology were appropriately formed. Based on the average publication year of the constituent papers of each cluster, we tracked recent research trends. It was also suggested that significant research progress would be detected as a quick increase in constituent papers and the number of citations of hub papers in the cluster.ConclusionWe validated that citation network analysis applies to the horizon-scanning of innovative medical devices and demonstrated that AI-based electrocardiograms and electroencephalograms can lead to the development of innovative products.Article SummaryStrengths and limitations of this studyCitation network analysis can provide an academic landscape in the investigated research field, based on the citation relationship of research papers and objective information, such as characteristic keywords and publication year.It might be possible to detect possible significant research progress and the emergence of new research areas through analysis every several months.It is important to confirm the opinions of experts in this area when evaluating the results of the analysis.Information on patents and clinical trials for this analysis is currently unavailable.
“…Q is an evaluation function of the degree of coupling within a cluster and between clusters, and is given as follows: Fig. 1 Steps of clustering and making academic landscape based on citation network [ 29 , 30 ]. This Figure has been published in reference [ 18 ].…”
Section: Citation Network Analysismentioning
confidence: 99%
“… Steps of clustering and making academic landscape based on citation network [ 29 , 30 ]. This Figure has been published in reference [ 18 ].…”
Section: Citation Network Analysismentioning
confidence: 99%
“…After clustering the network, visualization was converted to intuitively infer relationships among these clusters. We use a large graph layout (LGL), which is based on a force-direct layout algorithm [ 29 , 30 ]. This layout can display the largest connected component of the network to generate coordinates for nodes in two dimensions.…”
Certain innovative technologies applied to medical product development require novel evaluation approaches and/or regulations. Horizon scanning for such technologies will help regulators prepare, allowing earlier access to the product for patients and an improved benefit/risk ratio. This study investigates whether citation network analysis and text mining of scientific papers could be a tool for horizon scanning in the field of immunology, which has developed over a long period, and attempts to grasp the latest research trends. As the result of the analysis, the academic landscape of the immunology field was identified by classifying 90,450 papers (obtained from PubMED) containing the keyword “immune* and t lymph*” into 38 clusters. The clustering was indicative of the research landscape of the immunology field. To confirm this, immune checkpoint inhibitors were used as a retrospective test topic of therapeutics with new mechanisms of action. Retrospective clustering around immune checkpoint inhibitors was found, supporting this approach. The analysis of the research trends over the last 3 to 5 years in this field revealed several candidate topics, including ARID1A gene mutation, CD300e, and tissue resident memory T cells, which shows notable progress and should be monitored for future possible product development. Our results have demonstrated the possibility that citation network analysis and text mining of scientific papers can be a useful objective tool for horizon scanning of life science fields such as immunology.
“…Worldwide, electricity production from hydro power could double by 2050 to reach 7000 TWh, and the main part of this growth will be coming from large hydropower plants (HPP) in emerging economies and developing countries [1,2]. In Switzerland, hydro power contributes to 60% of the total power generation [3], with an average of 36 TWh of produced electricity per year [4].…”
A B S T R A C THydropower is the central pillar of the current Swiss electricity mix. The Swiss energy transition will profoundly change the electricity mix by transitioning a third of electricity generation from nuclear towards renewable energy sources mainly. This backbone role of hydropower is hindered by the current Europe-wide sluggish economic situation, characterized by subsidized new renewables energies which distort electricity prices, a partially liberalized market and power production overcapacity. In this difficult context, a strong and comprehensive political support through long-term policy will be of utmost importance to accompany hydropower across this transformation. This article explores various subsidy instruments, before using the general framework of "System Engineering" to base a precise recommendation for Swiss hydropower. The recommended mixture of investment contribution with discount reclamation appears the most appropriate instrument. Also, the short time horizon means that the subsidization can be quickly terminated if necessary. In parallel, it is a mature instrument since it is already used for other new renewable energies. The discount model allows reclaiming subsidies in case of an excess profitability. The strength of this study is its robust, comprehensive and allencompassing methodology which can be replicated to other cases (for instance, other technologies or energy sources) and other countries.
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