1,720,994 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
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A Hybrid Asymptotic-Numerical Method for Inertial Migration in Microfluidic Devices
Full text linkInertial microfluidic devices manipulate nano-liter volumes of fluids, allowing for precise control of individual cells and small particles. These devices consist of channels and chambers whose geometry exploits interaction between fluid flow and particles to manipulate particles of different sizes. This exploitation is a physical phenomenon called inertial migration which causes particles to migrate across streamlines when suspended in high velocity flow. Inertial microfluidics has found many academic and medical applications, including trapping of rare cancer cells from blood samples, flow cytometry, and genomic analysis. However, rational design of microfluidic devices has been held back from its full potential by a lack of quantitative modeling of inertial migration forces. In this thesis I will describe a novel hybrid asymptotic-numerical method, in which particles are accurately modeled as singularities in a linearized flow field to rapidly calculate inertial migration forces. Improvements to asymptotics and numerical techniques allow for large particles to be resolved without any refinement needed to the numerical mesh, leading to an average computation time of 20 seconds per time point on a work station. A particular focus of our study will be the design of micro-centrifuges, notches that capture large particles from inertial streams, allowing for preferential capture and identification of rare circulating cancer cells. Particle capture and retention in microcentrifuges relies on complex entry patterns of streamlines in the background flow. Adding particles creates a moving geometry problem that is hard to simulate using existing numerical methods. Since microcentrifuges motivate our methods development, we analyze them extensively, but our methods are generally applicable for the study of inertial microfludiic devices with Reynolds numbers between 1 and 200. Throughout, results are validated against high speed video and particle tracking is used to elucidate previously unknown 3D dynamics
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Information Theoretic and Statistical Models for Spatial Transportation Networks: Total Mixing Entropy on Optimal Fluid Flow Networks and Time Dependent Stochastic Block Models
Full text linkThis thesis contains two studies about models on organized spatial transport networks. The first introduces a new objective function aimed at understanding the ability that networked organisms such as fungi and slime molds to mix and efficiently disperse nuclei and molecular cues via advective currents. The second develops a novel type of stochastic block model to multilayer networks expressing model human transportation. It is a statistically based model that aims to classify parts of the network based on the function they serve for commuters. Our specific application is to bicycle share networks in different urban communities. Although these models are applied to disparate subjects they are connected from a mathematical point of view and illuminate a central theme in general tranpsport networks in two contrasting lights.The first part of the thesis is inspired by the work of Murray [Mur26], who hypothesized that the geometries of blood flow networks are optimized to minimize the friction of flows through the network for a given total investment in the material that makes up the network. In the spirit of Murray, we hypothesize that biological networks, maximize their performance of objectives that are beneficial to the organism's existence while respecting constraints. We are inspired by experimental observations that show that the filamentous fungus \textit{Neurospora crassa} mixes nuclei and the slime mold \textit{Physarum polycephalum} mixes signals it receives from its environment on the distribution of food sources [AAP17]. We use the concept of information entropy to describe how advection currents within the network carry information. We construct a probability space for the signals passing through the network and write down a novel objective function, called the total negative mixing entropy, representing each node receiving the most mixed collection of signals. Put another way, to maximize its ability to adapt to stimuli, each node receives the most even distribution of signals from the other nodes within the network. We then define optimal networks to be ones minimizing a cost function that is the sum of the total negative mixing entropy and of fluid dissipation. A constraint assuming a fixed amount of energy used for network upkeep is assumed. Using original optimization techniques, that we describe in this paper, we numerically calculate optimal networks under different assumptions on the driving force of the flow, the underlying topology and the total material cost function. From our numerical results we identify highlight results about the structure of optimal networks, which we are then able to prove rigorously. The proofs involve constructions and computations that illluminate how energy efficient fluid transport flows are connected to mix signals and the effects that Murray's law has on features such as whether the networks posess loops. In the second part of the thesis, we define two new types of time-dependent stochastic block models for Bicycle-Sharing Networks. The model assumes that network can be modeled by a random process based on partitioning the possible origins and destinations into blocks. The blocks in our model express the roles that the stations play in relation to the entire network and trips are assumed to be generated by a mixture of time dependent commute patterns occuring between the blocks. The only parameter of our model that is chosen by the practitioner is the number of different blocks .The block to block commute patterns are represented in a array, and the commute patterns are not assumed to be equal if the order of the pair of blocks is changed to take into account direction of flows. It can be viewed as a degree corrected model in that there are multiplicative terms for each station representing their importance within each block. The commute patterns and degree-correction terms are inferred parameters, optimized using gradient descent. We derive both a continuous and discrete version of this model.We apply our models to Los Angeles, Manhattan, New York and San Francisco bike-share communities. The results reveal crisp divisions of home and work communities as defined by the preference of commuters to use bikes if we use two blocks. The models also reveal other relevant functional regions, such as parks, leisure commutes, and broad communities representing micro-cosms where riders mostly do not exit. With increasing blocks reveals more roles detecting new functional roles as well as refining roles with less blocks, such as breaking a geographical community into its home-work commute roles. How to choose the number of blocks is touched on, although we do not reach a definitive result with regards to that
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
Microvascular Hydrodynamics: Structure and Adaptation Principles
Full text linkMicrovasculature structures vary drastically from species to species, and from organs to organs. Different structures signify inclinations of distinct blood flow perfusion features: uniform, or localized? Robust, or efficient? Like the vertebrate tissues having preferred types of vasculature systems that emphasize different traits, in the course of my research, I chose two contrasting systems to be studied by virtue of their specific features: mammalian cerebral cortex microvasculature, and zebrafish embryo trunk microvasculature. For mammalian cerebral microvasculature, considering the distinguished hierarchical construction, and the complex, dense nature of the capillary bed perfusing brain tissue, a model that abstracts the structure while revealing the relationship between blood perfusion and network properties would be extremely helpful; in contrast, zebrafish embryo trunk microvasculature is by itself a simple structure, but being an embryo, its hemodynamic features still undergo developments, and the network would adapt accordingly, which provides an excellent model to study microvascular network adaptation. Specifically, in different mammalian cortices, I found that the dense, parallel penetrating vessels perfusing the cerebral cortex -- arterioles and venules, are consistently in imbalanced ratios. Whether and how the arteriole-venule arrangement and ratio affect the efficiency of energy delivery to the cortex has never been asked before. I show by mathematical modeling and analysis of the mapped mouse sensory cortex that the perfusive efficiency of the network is predicted to be limited by low flow regions produced between pairs of arterioles or pairs of venules. Increasing either arteriole or venule density decreases the size of these low flow regions but increases their number, setting an optimal ratio between arterioles and venules that closely matches that observed across mammalian cortical vasculature. Low flow regions are reshaped in complex ways by changes in vascular conductance, creating geometric challenges for matching cortical perfusion with neuronal activity.Within the zebrafish trunk, tuning of vessel radii ensures red blood cells are delivered at equal rates across tens of microvessels. How do vessels find optimal radii? Vessels are known to adapt their radii to maintain the shear stress from blood flow at the vessel wall at a set point. Yet models of adaptation purely on the basis of average shear stress have not, until now, been able to produce complex loopy networks that resemble real microvascular systems. The shear stress on real vessel endothelia peaks sharply when a red blood cell passes through the vessel. I show that if vessel shear stress set points are cued to the stress peaks, then stable shear-stress-based adaptation is possible. Model networks that respond to peak stresses alone can quantitatively reproduce the observed zebrafish trunk microvasculature, including its adaptive trajectory when hematocrit changes. My work reveals the potential for mechanotransduction alone to generate stable hydraulically tuned microvascular networks. When parts of the zebrafish network -- the anastomoses in the distant trunk that connects the artery and the vein directly -- are amputated, a localization of blood flow at the zebrafish tail is observed in my adaptation model, which is verified through experiments. This discovery highlights a specific structure's function, which can only be identified under network adaptation, and shows the significance of taking adaptation into account when evaluating a vascular structure's hemodynamic functions
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