1,720,957 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Interpolations spatio-temporelles (4D) basées sur des Graph Neural Network Dynamiques (GNN-D) et applications au stockage géologique de déchets radioactifs
No full textA deep disposal center for radioactive waste, such as Andra's Cigéo project requires continuous, long-term monitoring. This is made possible by a multitude of sensors. However, due to the environmental constraints associated with storage (i.e. radioactivity, the mechanical convergence of galleries, etc.), this network of sensors is prone to degradation over time. Therefore, it is crucial to ensure the consistency of the data collected to guarantee effective monitoring of the center. This means not only identifying sensor failures but also replacing erroneous values with reliable predictions. Graph Neural Networks (GNNs) are suitable tools for these tasks, as they enable accurate representation of system physics and take into account the local topology of the sensor network. In our research, we use data from Andra's underground research laboratory. Specifically, we are using data from an experiment that involved heating a high-activity (HA) cell demonstrator using heating resistors. This setup simulated the heating of radioactive waste on an HA cell. By introducing synthetic errors into this dataset, we trained Graph Neural Networks (GNNs) that leverage both current and historical sensor responses to assess sensor integrity. Additionally, we trained GNNs to generate predictions that could replace the responses of failed sensors, starting from the moment of failure. These GNNs are based on a forward integration mechanism and have been assessed on fundamental thermal simulation problems to evaluate their efficiency and limitations. The architecture of each GNN has been optimized through hyperparameter analysis. Given the large number of variables involved, we proposed a novel method for optimizing GNN architecture based on rating systems. Finally, we compared the performance of the best GNNs with traditional machine learning methods to demonstrate their effectiveness.Un centre de stockage profond de déchêts radioactifs comme le projet Cigéo de l'Andra requiert une surveillance continue sur le long terme. Cette surveillance est possible grâce à une multitudes de capteurs. Cependant, en raison des contraintes environementales associées au stockage (radioactivité, convergence mécanique des galeries, etc.) ce réseau de capteurs est sujet à une dégradation au fil du temps. Il est alors crucial d'assurer la cohérence des données recueillies pour garantir la surveillance du centre. Pour ce faire, il faut non seulement identifier les défaillances de capteurs mais aussi remplacer les valeurs erronées par des prédictions cohérentes. Les Graph Neural Networks (GNN) sont des outils adaptés pour ces tâches car ils permettent de représenter précisément la physique du système et prennent en compte la topologie locale du réseau de capteurs. Dans nos travaux, nous utilisons des données issues du laboratoire de recherche souterrain de l'Andra. En particulier, celles d'une expérience de chauffe d'un démonstrateur de cellule haute-activité (HA) par des résistances chauffantes. Ce qui permet d'imiter la chauffe d'une alvéole HA par des déchêts radioactifs. En ajoutant des erreurs synthétiques à ces données, nous avons entrainé des GNN qui utilisent les réponses capteurs (présentes et passées) pour détecter les défaillances capteurs. A l'aide des mêmes données, nous avons entrainés des GNN effectuant une prédiction au niveau des capteurs défaillants, à partir de l'instant de la panne. Ces GNN se basent sur un mécanisme d'intégration temporelle et ont été étudiés sur des problèmes de simulation thermique élémentaires afin d'évaluer leur efficacité ainsi que leurs limites. L'architecture de chacun des GNN a été optimisée par le biais d'un analyse hyper-paramétrique. En raison du grand nombre de variables, nous avons proposé une nouvelle méthode d'optimisation de l'architecture des GNN basée sur les systèmes de classement. Enfin, nous avons comparé les meilleurs GNN à des méthodes d'apprentissage machine traditionnelles afin de prouver leur efficacité
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe 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
Interpolations spatio-temporelles (4D) basées sur des Graph Neural Network Dynamiques (GNN-D) et applications au stockage géologique de déchets radioactifs
No full textA deep disposal center for radioactive waste, such as Andra's Cigéo project requires continuous, long-term monitoring. This is made possible by a multitude of sensors. However, due to the environmental constraints associated with storage (i.e. radioactivity, the mechanical convergence of galleries, etc.), this network of sensors is prone to degradation over time. Therefore, it is crucial to ensure the consistency of the data collected to guarantee effective monitoring of the center. This means not only identifying sensor failures but also replacing erroneous values with reliable predictions. Graph Neural Networks (GNNs) are suitable tools for these tasks, as they enable accurate representation of system physics and take into account the local topology of the sensor network. In our research, we use data from Andra's underground research laboratory. Specifically, we are using data from an experiment that involved heating a high-activity (HA) cell demonstrator using heating resistors. This setup simulated the heating of radioactive waste on an HA cell. By introducing synthetic errors into this dataset, we trained Graph Neural Networks (GNNs) that leverage both current and historical sensor responses to assess sensor integrity. Additionally, we trained GNNs to generate predictions that could replace the responses of failed sensors, starting from the moment of failure. These GNNs are based on a forward integration mechanism and have been assessed on fundamental thermal simulation problems to evaluate their efficiency and limitations. The architecture of each GNN has been optimized through hyperparameter analysis. Given the large number of variables involved, we proposed a novel method for optimizing GNN architecture based on rating systems. Finally, we compared the performance of the best GNNs with traditional machine learning methods to demonstrate their effectiveness.Un centre de stockage profond de déchêts radioactifs comme le projet Cigéo de l'Andra requiert une surveillance continue sur le long terme. Cette surveillance est possible grâce à une multitudes de capteurs. Cependant, en raison des contraintes environementales associées au stockage (radioactivité, convergence mécanique des galeries, etc.) ce réseau de capteurs est sujet à une dégradation au fil du temps. Il est alors crucial d'assurer la cohérence des données recueillies pour garantir la surveillance du centre. Pour ce faire, il faut non seulement identifier les défaillances de capteurs mais aussi remplacer les valeurs erronées par des prédictions cohérentes. Les Graph Neural Networks (GNN) sont des outils adaptés pour ces tâches car ils permettent de représenter précisément la physique du système et prennent en compte la topologie locale du réseau de capteurs. Dans nos travaux, nous utilisons des données issues du laboratoire de recherche souterrain de l'Andra. En particulier, celles d'une expérience de chauffe d'un démonstrateur de cellule haute-activité (HA) par des résistances chauffantes. Ce qui permet d'imiter la chauffe d'une alvéole HA par des déchêts radioactifs. En ajoutant des erreurs synthétiques à ces données, nous avons entrainé des GNN qui utilisent les réponses capteurs (présentes et passées) pour détecter les défaillances capteurs. A l'aide des mêmes données, nous avons entrainés des GNN effectuant une prédiction au niveau des capteurs défaillants, à partir de l'instant de la panne. Ces GNN se basent sur un mécanisme d'intégration temporelle et ont été étudiés sur des problèmes de simulation thermique élémentaires afin d'évaluer leur efficacité ainsi que leurs limites. L'architecture de chacun des GNN a été optimisée par le biais d'un analyse hyper-paramétrique. En raison du grand nombre de variables, nous avons proposé une nouvelle méthode d'optimisation de l'architecture des GNN basée sur les systèmes de classement. Enfin, nous avons comparé les meilleurs GNN à des méthodes d'apprentissage machine traditionnelles afin de prouver leur efficacité
- …
