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
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
Web application for recommending and monitoring training and nutrition for athletes based on machine learning
No full textCilj završnog rada je razvoj web aplikacije za preporuku i praćenje treninga i prehrane sportaša
koji se bave trčanjem i biciklizmom. Aplikacija nudi korisnicima mogućnost procjene fizičke
spremnosti, na temelju koje se preporučuje individualizirani plan treninga i prehrane. Korisnici
mogu pratiti rezultate svojih treninga putem linijskog dijagrama, dok su konzumirani prehrambeni
proizvodi prikazani po obrocima i datumu. Osim osnovnih funkcionalnosti praćenja treninga i
prehrane, web aplikacija nudi i elemente društvene mreže. Korisnicima je omogućeno
pregledavanje profila drugih korisnika, praćenje rezultata treninga, primanje obavijesti o
treninzima i događanjima organiziranim od strane sportskih zajednica te privatna komunikacija
među korisnicima. Aplikacija je implementirana korištenjem web okvira Django, dok je korisničko
sučelje izrađeno uz pomoć HTML-a, CSS-a, Bootstrapa i JavaScript-a. Na strani poslužitelja
korišteni su SQLite baza podataka i Nutritionix API za upravljanje prehrambenim informacijama.
Razvoj modela uključuje primjenu strojnog učenja za preporuku treninga i prehrane, dok su glavni
algoritmi testirani i vrednovani kroz različite korisničke slučajeve. Dobiveni rezultati potvrdili su
ispravan rad aplikacije i njezinu upotrebljivost za krajnje korisnike.The goal of the final paper is the development of a web application for recommending and
monitoring the training and nutrition of athletes who are involved in running and cycling. The
application offers users the possibility of assessing physical fitness, based on which an
individualized training and nutrition plan is recommended. Users can monitor the results of their
training via a line diagram, while the food products they consume are displayed by portion and
date. In addition to the basic functionality of tracking training and nutrition, the web application
also offers elements of a social network. Users can view other users' profiles, track training results,
receive notifications about training and events organized by sports communities, and communicate
privately with users. The application was implemented using the Django web framework, while
the user interface was created using HTML, CSS, Bootstrap, and JavaScript. On the server side,
the SQLite database and Nutritionix API were used to manage nutritional information. The
development of the model includes the application of machine learning to recommend training and
nutrition, while the main algorithms were tested and evaluated through different user cases. The
obtained results confirmed the correct operation of the application and its usability for end users
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
Web application for recommending and monitoring training and nutrition for athletes based on machine learning
No full textCilj završnog rada je razvoj web aplikacije za preporuku i praćenje treninga i prehrane sportaša
koji se bave trčanjem i biciklizmom. Aplikacija nudi korisnicima mogućnost procjene fizičke
spremnosti, na temelju koje se preporučuje individualizirani plan treninga i prehrane. Korisnici
mogu pratiti rezultate svojih treninga putem linijskog dijagrama, dok su konzumirani prehrambeni
proizvodi prikazani po obrocima i datumu. Osim osnovnih funkcionalnosti praćenja treninga i
prehrane, web aplikacija nudi i elemente društvene mreže. Korisnicima je omogućeno
pregledavanje profila drugih korisnika, praćenje rezultata treninga, primanje obavijesti o
treninzima i događanjima organiziranim od strane sportskih zajednica te privatna komunikacija
među korisnicima. Aplikacija je implementirana korištenjem web okvira Django, dok je korisničko
sučelje izrađeno uz pomoć HTML-a, CSS-a, Bootstrapa i JavaScript-a. Na strani poslužitelja
korišteni su SQLite baza podataka i Nutritionix API za upravljanje prehrambenim informacijama.
Razvoj modela uključuje primjenu strojnog učenja za preporuku treninga i prehrane, dok su glavni
algoritmi testirani i vrednovani kroz različite korisničke slučajeve. Dobiveni rezultati potvrdili su
ispravan rad aplikacije i njezinu upotrebljivost za krajnje korisnike.The goal of the final paper is the development of a web application for recommending and
monitoring the training and nutrition of athletes who are involved in running and cycling. The
application offers users the possibility of assessing physical fitness, based on which an
individualized training and nutrition plan is recommended. Users can monitor the results of their
training via a line diagram, while the food products they consume are displayed by portion and
date. In addition to the basic functionality of tracking training and nutrition, the web application
also offers elements of a social network. Users can view other users' profiles, track training results,
receive notifications about training and events organized by sports communities, and communicate
privately with users. The application was implemented using the Django web framework, while
the user interface was created using HTML, CSS, Bootstrap, and JavaScript. On the server side,
the SQLite database and Nutritionix API were used to manage nutritional information. The
development of the model includes the application of machine learning to recommend training and
nutrition, while the main algorithms were tested and evaluated through different user cases. The
obtained results confirmed the correct operation of the application and its usability for end users
Web application for recommending and monitoring training and nutrition for athletes based on machine learning
No full textCilj završnog rada je razvoj web aplikacije za preporuku i praćenje treninga i prehrane sportaša
koji se bave trčanjem i biciklizmom. Aplikacija nudi korisnicima mogućnost procjene fizičke
spremnosti, na temelju koje se preporučuje individualizirani plan treninga i prehrane. Korisnici
mogu pratiti rezultate svojih treninga putem linijskog dijagrama, dok su konzumirani prehrambeni
proizvodi prikazani po obrocima i datumu. Osim osnovnih funkcionalnosti praćenja treninga i
prehrane, web aplikacija nudi i elemente društvene mreže. Korisnicima je omogućeno
pregledavanje profila drugih korisnika, praćenje rezultata treninga, primanje obavijesti o
treninzima i događanjima organiziranim od strane sportskih zajednica te privatna komunikacija
među korisnicima. Aplikacija je implementirana korištenjem web okvira Django, dok je korisničko
sučelje izrađeno uz pomoć HTML-a, CSS-a, Bootstrapa i JavaScript-a. Na strani poslužitelja
korišteni su SQLite baza podataka i Nutritionix API za upravljanje prehrambenim informacijama.
Razvoj modela uključuje primjenu strojnog učenja za preporuku treninga i prehrane, dok su glavni
algoritmi testirani i vrednovani kroz različite korisničke slučajeve. Dobiveni rezultati potvrdili su
ispravan rad aplikacije i njezinu upotrebljivost za krajnje korisnike.The goal of the final paper is the development of a web application for recommending and
monitoring the training and nutrition of athletes who are involved in running and cycling. The
application offers users the possibility of assessing physical fitness, based on which an
individualized training and nutrition plan is recommended. Users can monitor the results of their
training via a line diagram, while the food products they consume are displayed by portion and
date. In addition to the basic functionality of tracking training and nutrition, the web application
also offers elements of a social network. Users can view other users' profiles, track training results,
receive notifications about training and events organized by sports communities, and communicate
privately with users. The application was implemented using the Django web framework, while
the user interface was created using HTML, CSS, Bootstrap, and JavaScript. On the server side,
the SQLite database and Nutritionix API were used to manage nutritional information. The
development of the model includes the application of machine learning to recommend training and
nutrition, while the main algorithms were tested and evaluated through different user cases. The
obtained results confirmed the correct operation of the application and its usability for end users
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