1,720,954 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
Machine Learning Driven Optimization of Complex Turbulent Flows
Get PDFOptimizing complex turbulent flows presents a difficult challenge due to the non-linear, chaotic nature of turbulence. Most fluid flows found in nature or in engineering applications are turbulent, prompting the need for a design optimization method that can quickly and efficiently handle this complexity. Incorporating machine learning and artificial intelligence can greatly accelerate traditionally computationally-intensive methods of optimizing these flows. The aim of this thesis is to showcase the development of a continuously-learning, machine-learning driven optimization method. This is coupled with computational fluid dynamics (CFD) and is applied to various engineering design problems to demonstrate its potential as a powerful engineering design tool.
The first case study presented is the optimization of the exit turbulence field in a gas turbine combustor simulator. Combustor turbulence in a gas turbine engine greatly influences the efficiency of the downstream high pressure turbine stage. Studies have shown that combustor turbulence can result in a 1.3% reduction in stage efficiency of the turbine. This is a staggering number when considering the impact on engine fuel efficiency, with improvements of 0.1% generally garnering substantial research funding. With an optimized design, I am able to achieve substantial improvements in stage efficiency, as well as identify the specific aspects of combustor geometry that contribute to this finding. The second case study presented is the optimization of airflow patterns in a dental office to minimize infection spread. The placement of the air supply and return vents is analyzed to produce optimal circulation within the building and decrease infection potential for dental personnel and patients.
Two more cases are presented as tutorial cases for the packaged optimizer and to expand functionality of the optimization scheme, named the Multi-fidelity Integrated Learning Optimization, or MILO. The third case is a simplified heat exchanger, where the cooling air temperature, velocity, and fan speed is adjusted to target a desired outlet water temperature. Uncertainty in the inlet conditions and fan speed is propagated throughout the optimization, showing the potential success of this tool when used in real-world industrial applications where operating conditions may vary. The fourth case introduces multi-objective optimization of a fire suppression system where both the mass flow rate of the water and the amount of solid burned are minimized. This demonstration of a multi-objective problem radically expands the number of engineering design problems that MILO can be applied to. In all cases presented in this thesis, there is a substantial improvement in design performance despite the numerous design constraints applied, as well as an average 88% savings in the number of CPU hours needed to reach an optimized solution.</p
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
Machine Learning Driven Optimization of Complex Turbulent Flows
Get PDFOptimizing complex turbulent flows presents a difficult challenge due to the non-linear, chaotic nature of turbulence. Most fluid flows found in nature or in engineering applications are turbulent, prompting the need for a design optimization method that can quickly and efficiently handle this complexity. Incorporating machine learning and artificial intelligence can greatly accelerate traditionally computationally-intensive methods of optimizing these flows. The aim of this thesis is to showcase the development of a continuously-learning, machine-learning driven optimization method. This is coupled with computational fluid dynamics (CFD) and is applied to various engineering design problems to demonstrate its potential as a powerful engineering design tool.
The first case study presented is the optimization of the exit turbulence field in a gas turbine combustor simulator. Combustor turbulence in a gas turbine engine greatly influences the efficiency of the downstream high pressure turbine stage. Studies have shown that combustor turbulence can result in a 1.3% reduction in stage efficiency of the turbine. This is a staggering number when considering the impact on engine fuel efficiency, with improvements of 0.1% generally garnering substantial research funding. With an optimized design, I am able to achieve substantial improvements in stage efficiency, as well as identify the specific aspects of combustor geometry that contribute to this finding. The second case study presented is the optimization of airflow patterns in a dental office to minimize infection spread. The placement of the air supply and return vents is analyzed to produce optimal circulation within the building and decrease infection potential for dental personnel and patients.
Two more cases are presented as tutorial cases for the packaged optimizer and to expand functionality of the optimization scheme, named the Multi-fidelity Integrated Learning Optimization, or MILO. The third case is a simplified heat exchanger, where the cooling air temperature, velocity, and fan speed is adjusted to target a desired outlet water temperature. Uncertainty in the inlet conditions and fan speed is propagated throughout the optimization, showing the potential success of this tool when used in real-world industrial applications where operating conditions may vary. The fourth case introduces multi-objective optimization of a fire suppression system where both the mass flow rate of the water and the amount of solid burned are minimized. This demonstration of a multi-objective problem radically expands the number of engineering design problems that MILO can be applied to. In all cases presented in this thesis, there is a substantial improvement in design performance despite the numerous design constraints applied, as well as an average 88% savings in the number of CPU hours needed to reach an optimized solution.</p
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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