1,721,017 research outputs found
Multi-analytical Approach for the Characterisation of Ancient Mineral Fibres: Tracing the Use of Asbestos in the Italic Peninsula
No full textThis paper presents the multi-analytical approach performed by microscopic analysis using Optical and Scanning Electron Microscope (SEM-EDX) and Raman spectroscopy. These techniques allow us to recognise asbestos fibres and precisely characterise their mineral nature. Using Raman micro-spectroscopy, which is often used to discriminate tiny mineral fibres, it was possible to precisely characterise the mineral composition of the asbestos fibres from ancient central-southern Italy by comparing their manufacture through the identification of the specific asbestos phase.
The results obtained highlight the extraction activity of the mineral fibres and the use of long hair-like fibres to manufacture textile objects in the ancient Italic Peninsula
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
Application of Machine Learning Algorithms to Predict the Condensation Heat Transfer Coefficient Inside Microfin Tubes
No full textFor many years, Heat Transfer specialists have collected data in the lab to enable the calculation of heat transfer coefficients and pressure drops in various systems. Empirical and semi-empirical correlations have typically been developed to estimate such quantities over a wider range of operating and design conditions and to enable the design of cooling and heating devices. However, with this approach, a significant amount of prior knowledge is needed to select the input variables used in the regression. Unfortunately, when it comes two phase systems, this prior knowledge is very difficult to acquire, owing the complexity of the underlying phenomena. Machine Learning algorithms offer a novel and promising approach, as they leverage from large data-sets collected in the lab, and attempt to predict key quantities with a more limited knowledge of the system itself. Nevertheless, there are still several questions open on the use of these algorithms to explain heat transfer data, in particular around model overfitting and extrapolability of the estimations. As a result, it is important to further investigate these methods and develop guidelines to formulate ML models correctly, that is, to appropriately define training and testing set, to avoid model overfitting and to test extrapolation capabilities. This article aims to (i) showcase the benefits and limitations of the use of Machine Learning algorithms (namely Random Forest and Deep Neural Networks) in regressing heat transfer data; (ii) compare the results to semi-empirical correlations; (iii) establish the relative importance of each dimensional variable in the explanation of the heat transfer coefficient; (iv) assess the difference in the regression performance of using non-dimensional numbers commonly used in the design instead of dimensional variables and (v) testing extrapolability to unseen data. A case study showing the estimation of the condensation heat transfer coefficients in micro-fin tubes using a database of 4, 333 data points is presented to illustrate the above procedures and objectives
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
- …
