1,720,955 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
From Concepts to Conditions : Bridging the Gap in AI-Based Maintenance Systems
No full textThe importance of preventing machine failures and reducing costly unplanned downtime has led to extensive research aiming to develop methods that predict maintenance needs. In this context, data-driven and particularly Deep Learning (DL) based methods for anomaly detection, fault diagnosis, and health prognosis have been studied extensively because of their ability to handle the complexity of the sensor data describing the health state of machines. However, many challenging factors exist before it is possible to utilize these methods in practice. These primarily include the lack of labelled failure events, the heterogeneous nature of the data, and the occurrence of multi-component fault scenarios. Currently, most studies ignore these aspects and focus on scenarios limited to a laboratory environment, which means there is a need to develop methods that can be deployed in practice. Therefore, this thesis suggests methods for these challenges and gives insight, aiming to reduce the gap between research-defined scenarios and scenarios found in industrial environments. To achieve this, different areas in the context of DL for Predictive Maintenance (PdM) are examined, including multivariate anomaly detection, fault diagnosis, and Remaining Useful Life (RUL) prediction methods. One of the contributions is a threshold-setting procedure that optimizes anomaly detection models with the user's support and a novel separate scoring method, and outperforms state-of-the-art alternatives for deployments in industrial applications. A published dataset of bearing faults from an industrial environment is also described, which is beneficial when developing and evaluating methods. In addition, a novel DL method for fault diagnosis of bearings using vibration data constructed with knowledge enrichment, time-based contextual enrichment, and a transfer learning technique is suggested. This method can be deployed on any machine without historical faults and outperforms state-of-the-art methods. Lastly, the most significant contribution is a prognostic hybrid framework for multi-component fault scenarios in rotating machines using vibration data that utilizes advancements in methods for anomaly detection, fault diagnosis, and RUL prediction of machines. In summary, this thesis suggests novel methods for PdM adapted for industrial applications that can be used on a general basis and provides insights that lower the gap between research-defined scenarios and scenarios found in industrial environments. Vid tidpunkten för disputationen var följande delarbete opublicerat: delarbete 6 accepterat.At the time of the doctoral defence the following paper was unpublished: paper 6 accepted.</p
From Concepts to Conditions : Bridging the Gap in AI-Based Maintenance Systems
No full textThe importance of preventing machine failures and reducing costly unplanned downtime has led to extensive research aiming to develop methods that predict maintenance needs. In this context, data-driven and particularly Deep Learning (DL) based methods for anomaly detection, fault diagnosis, and health prognosis have been studied extensively because of their ability to handle the complexity of the sensor data describing the health state of machines. However, many challenging factors exist before it is possible to utilize these methods in practice. These primarily include the lack of labelled failure events, the heterogeneous nature of the data, and the occurrence of multi-component fault scenarios. Currently, most studies ignore these aspects and focus on scenarios limited to a laboratory environment, which means there is a need to develop methods that can be deployed in practice. Therefore, this thesis suggests methods for these challenges and gives insight, aiming to reduce the gap between research-defined scenarios and scenarios found in industrial environments. To achieve this, different areas in the context of DL for Predictive Maintenance (PdM) are examined, including multivariate anomaly detection, fault diagnosis, and Remaining Useful Life (RUL) prediction methods. One of the contributions is a threshold-setting procedure that optimizes anomaly detection models with the user's support and a novel separate scoring method, and outperforms state-of-the-art alternatives for deployments in industrial applications. A published dataset of bearing faults from an industrial environment is also described, which is beneficial when developing and evaluating methods. In addition, a novel DL method for fault diagnosis of bearings using vibration data constructed with knowledge enrichment, time-based contextual enrichment, and a transfer learning technique is suggested. This method can be deployed on any machine without historical faults and outperforms state-of-the-art methods. Lastly, the most significant contribution is a prognostic hybrid framework for multi-component fault scenarios in rotating machines using vibration data that utilizes advancements in methods for anomaly detection, fault diagnosis, and RUL prediction of machines. In summary, this thesis suggests novel methods for PdM adapted for industrial applications that can be used on a general basis and provides insights that lower the gap between research-defined scenarios and scenarios found in industrial environments. Vid tidpunkten för disputationen var följande delarbete opublicerat: delarbete 6 accepterat.At the time of the doctoral defence the following paper was unpublished: paper 6 accepted.</p
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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