1,721,035 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
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
An accurate and scalable direction-splitting solver for flows laden with non-spherical rigid bodies
No full textFlows laden with rigid bodies are ubiquitous in both industrial and natural environments. A comprehensive knowledge of particle-laden flows facilitates the effective functioning of numerous industrial processes. However, in order to accurately resolve flows laden with (1000) particles in a computational model, (10⁹) spatial variables are required. Numerical computing of these variables on parallel platforms over (1000) cores is challenging. We begin this dissertation by proposing a scalable Direction-Splitting solver for flows containing non-spherical moving and fixed rigid bodies. We enhance the discretizations of advection and divergence terms in order to correctly capture the sharp changes in flow that occur in the vicinity of a fluid-solid interface. We rigorously verify the effectiveness of the numerical scheme through a comprehensive comparison of our outcomes with numerous benchmark simulations documented in the literature. We demonstrate the accuracy, speed, and scalability up to 6,400 cores. By expanding the functionality of our solver to incorporate intricate three-dimensional geometries defined by a collection of triangles in Standard Triangle Language (STL) files, we enhance the solver suitability for geometries that depict industrial processes. We also demonstrate that using an STL file does not affect the spatial accuracy of the computed flow field. Following this, we analyze the impact of a wall on the pairwise interaction of two spheres using our solver. Our calculations are verified against data from the literature in order to establish the accuracy of the simulations. We propose a Fourier Predictive Model to represent the modulations in the streamwise and spanwise components of force using the data produced by our fast and accurate solver. The model exhibits exceptional performance, as evidenced by the coefficient of determination of ~ 0.99. Next, we examine the effect of particle shape on the hydrodynamic forces and torques in a random array of Platonic polyhedrons. In order to illustrate the effect of particle sphericity, we depict the variation of forces and torques by comparing our outcomes to a random array of spheres. The effect of sphericity on a random array is illustrated through the use of sophisticated models, including a probability-driven model and physics-inspired neural networks.Applied Science, Faculty ofChemical and Biological Engineering, Department ofGraduat
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
Modeling hydrodynamic interactions in non-spherical particle-laden flows : numerical simulations and machine learning models
No full textParticle-laden flows, involving solid particles suspended in a fluid, are prevalent in many natural and industrial applications. These flows manifest rich flow physics and pose unique modeling challenges. The dynamics of the two phases are strongly coupled, giving rise to spatio-temporal nonlinear interactions. This multi-scale nature poses significant challenges for achieving accurate two-way coupling in real-world applications. The key contributions of this PhD dissertation are twofold: (i) to deepen the understanding of the hydrodynamic forces driving the particle-fluid momentum transport in these flows, particularly focusing on non-spherical particles and (ii) to advance closure law modeling of these forces by integrating physical insights derived from high-fidelity numerical simulations into tailored architectures of Machine Learning (ML) methodologies. We first investigate the influence of shape using superquadric geometries, which allow controlled deviations from sphericity. We show that particle shape, orientation, and Reynolds number significantly affect the flow and hydrodynamic forces. Extending this to pairwise interactions, we show that neighboring particle arrangements drive local force disturbances. These interactions exhibit periodicity, which we capture using a Fourier series, offering a compact and interpretable modeling framework. We leverage ML to develop a multi-scale approach to modeling higher-order interactions. Building on a hierarchical model, we incorporate quaternary interactions and explicitly account for global and local heterogeneities. This significantly improves predictive accuracy, especially for streamwise drag, surpassing lower-order interaction models. Further, we introduce a novel attention-based Graph Neural Network architecture. This model captures complex spatial, geometric, and relational dependencies, demonstrating superior performance in predicting streamwise forces at low Reynolds numbers and volume fractions. While challenges remain in predicting transverse forces and torques at higher Reynolds numbers, this work lays the groundwork for data-driven multi-scale modeling of particle-laden flows. Overall, this dissertation offers new insights into the interplay of particle shape, flow structure, and hydrodynamic forces, advancing both our understanding and predictive capabilities in particle-laden suspensions.Applied Science, Faculty ofChemical and Biological Engineering, Department ofGraduat
Numerical methods for biological flows laden with deformable capsules and solid particles
No full textBiological fluids such as blood accomplish many vital tasks in the human body, including carrying oxygen and nutrients to tissues, regulating internal temperature and pH, or transporting white blood cells to infected areas. A better understanding of these fluids can provide insight into many pathologies such as the formation of aneurysms and the effect of sickle cell disease on the flow of red blood cells, as well as help design efficient diagnosis tools on microfluidic devices. Such fluids are composed of a continuous viscous phase and suspended bodies, including rigid particles and deformable membranes enclosing an inner fluid, referred to as capsules. In this thesis, we develop numerical tools aiming to simulate cell-resolved biological fluids such as blood. In a first part, we focus on the dispersed solid phase, a field known as granular mechanics. In this context, we implement a contact force able to accurately model static assemblies of granular media. After extensive validation, we use this contact model in a purely granular setting to study avalanches of entangled particles. Our numerical results are compared to experiments and show very good qualitative and quantitative agreement. Moreover, we present a variety of novel avalanching behaviors, as well as an intermittent regime in which reproducibility is lost. After analyzing the microstructure of granular assemblies in this regime, we conclude that it likely arises from mesoscale clusters of particles. In a second part, we concentrate on flowing biological capsules. We develop an adaptive front-tracking method which enables simulations of capsules in very large geometries for a wide range of Reynolds number. We validate our solver extensively and we show excellent qualitative and quantitative agreement with the literature. We then study the dynamics of capsules flowing through a sharp corner, a commonly encountered geometry in microfluidic devices. We analyze the trajectory, normalized velocity and area variations of the capsules and we show that in our case of strong confinement, the capsules interact weakly unless they are located very close to each other. Finally, we present and implement a fully Eulerian alternative method to simulate flowing capsules, and we highlight its advantages and limits.Science, Faculty ofMathematics, Department ofGraduat
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