191 research outputs found

    NASA multidisciplinary research grant

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    Research is discussed in the multidisciplinary areas of space and planetary science; materials and radiation; systems, instrumentation, and structures; and technology and man. Highlights are identified as an alpha-recoil track method of archeological dating; infrared astronomical telescope; reaction rates data, semiconductor radiation detectors, and analysis of time-dependent systems; Gunn effect devices for microwave generation and detection, mode-locked lasers, and radiation theory; and the application of a satellite communication system to educational development. Detectors to be flown on Apollo 16 to measure heavy particle flux in the solar wind and to be part of the HEAO-A experiment on extremely heavy nuclei in cosmic rays were developed. The impact of the multidisciplinary research on university activities is described, and individual departmental reports are included

    Particle reacceleration by compressible turbulence in galaxy clusters: effects of reduced mean free path

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    Direct evidence for in situ particle acceleration mechanisms in the inter-galactic-medium (IGM) is provided by the diffuse Mpc--scale synchrotron emissions observed from galaxy clusters. It has been proposed that MHD turbulence, generated during cluster-cluster mergers, may be a source of particle reacceleration in the IGM. Calculations of turbulent acceleration must account self-consistently for the complex non--linear coupling between turbulent waves and particles. This has been calculated in some detail under the assumption that turbulence interacts in a collisionless way with the IGM. In this paper we explore a different picture of acceleration by compressible turbulence in galaxy clusters, where the interaction between turbulence and the IGM is mediated by plasma instabilities and maintained collisional at scales much smaller than the Coulomb mean free path. In this regime most of the energy of fast modes is channeled into the reacceleration of relativistic particles and the acceleration process approaches a universal behaviour being self-regulated by the back-reaction of the accelerated particles on turbulence itself. Assuming that relativistic protons contribute to several percent (or less) of the cluster energy, consistent with the FERMI observations of nearby clusters, we find that compressible turbulence at the level of a few percent of the thermal energy can reaccelerate relativistic electrons at GeV energies, that are necessary to explain the observed diffuse radio emission in the form of giant radio halos.Comment: 8 pages, 3 figures. Accepted in MNRAS (October 28, 2010

    Differences between journals and years in the proportions of students, researchers and faculty registering Mendeley articles

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    This article contains two investigations into Mendeley reader counts with the same dataset. Mendeley reader counts provide evidence of early scholarly impact for journal articles, but reflect the reading of a relatively young subset of all researchers. To investigate whether this age bias is constant or varies by narrow field and publication year, this article compares the proportions of student, researcher and faculty readers for articles published 1996-2016 in 36 large monodisciplinary journals. In these journals, undergraduates recorded the newest research and faculty the oldest, with large differences between journals. The existence of substantial differences in the composition of readers between related fields points to the need for caution when using Mendeley readers as substitutes for citations for broad fields. The second investigation shows, with the same data, that there are substantial differences between narrow fields in the time taken for Scopus citations to be as numerous as Mendeley readers. Thus, even narrow field differences can impact on the relative value of Mendeley compared to citation counts

    The Research Space: using the career paths of scholars to predict the evolution of the research output of individuals, institutions, and nations

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    In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps

    A machine learning taxonomic classifier for science publications

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    Disserta√ß√£o de mestrado integrado em Engineering and Management of Information SystemsThe evolution in scientific production, associated with the growing interdomain collaboration of knowledge and the increasing co-authorship of scientific works remains supported by processes of manual, highly subjective classification, subject to misinterpretation. The very taxonomy on which this same classification process is based is not consensual, with governmental organizations resorting to taxonomies that do not keep up with changes in scientific areas, and indexers / repositories that seek to keep up with those changes. We find a reality distinct from what is expected and that the domains where scientific work is recorded can easily be misrepresentative of the work itself. The taxonomy applied today by governmental bodies, such as the one that regulates scientific production in Portugal, is not enough, is limiting, and promotes classification in areas close to the desired, therefore with great potential for error. An automatic classification process based on machine learning algorithms presents itself as a possible solution to the subjectivity problem in classification, and while it does not solve the issue of taxonomy mismatch this work shows this possibility with proved results. In this work, we propose a classification taxonomy, as well as we develop a process based on machine learning algorithms to solve the classification problem. We also present a set of directions for future work for an increasingly representative classification of evolution in science, which is not intended as airtight, but flexible and perhaps increasingly based on phenomena and not just disciplines.A evolu√ß√£o na produ√ß√£o de ci√™ncia, associada √† crescente colabora√ß√£o interdom√≠nios do conhecimento e √† tamb√©m crescente coautoria de trabalhos permanece suportada por processos de classifica√ß√£o manual, subjetiva e sujeita a interpreta√ß√Ķes erradas. A pr√≥pria taxonomia na qual assenta esse mesmo processo de classifica√ß√£o n√£o √© consensual, com organismos estatais a recorrerem a taxonomias que n√£o acompanham as altera√ß√Ķes nas √°reas cient√≠ficas, e indexadores/reposit√≥rios que procuram acompanhar essas mesmas altera√ß√Ķes. Verificamos uma realidade distinta do espect√°vel e que os dom√≠nios onde s√£o registados os trabalhos cient√≠ficos podem facilmente estar desenquadrados. A taxonomia hoje aplicada pelos organismos governamentais, como o caso do organismo que regulamenta a produ√ß√£o cient√≠fica em Portugal, n√£o √© suficiente, √© limitadora, e promove a classifica√ß√£o em dom√≠nios aproximados do desejado, logo com grande potencial para erro. Um processo de classifica√ß√£o autom√°tica com base em algoritmos de machine learning apresenta-se como uma poss√≠vel solu√ß√£o para o problema da subjetividade na classifica√ß√£o, e embora n√£o resolva a quest√£o do desenquadramento da taxonomia utilizada, √© apresentada neste trabalho como uma possibilidade comprovada. Neste trabalho propomos uma taxonomia de classifica√ß√£o, bem como n√≥s desenvolvemos um processo baseado em machine learning algoritmos para resolver o problema de classifica√ß√£o. Apresentamos ainda um conjunto de dire√ß√Ķes para trabalhos futuros para uma classifica√ß√£o cada vez mais representativa da evolu√ß√£o nas ci√™ncias, que n√£o pretende ser herm√©tica, mas flex√≠vel e talvez cada vez mais baseada em fen√≥menos e n√£o apenas em disciplinas

    Large-Scale Analysis of the Accuracy of the Journal Classification Systems of Web of Science and Scopus

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    Journal classification systems play an important role in bibliometric analyses. The two most important bibliographic databases, Web of Science and Scopus, each provide a journal classification system. However, no study has systematically investigated the accuracy of these classification systems. To examine and compare the accuracy of journal classification systems, we define two criteria on the basis of direct citation relations between journals and categories. We use Criterion I to select journals that have weak connections with their assigned categories, and we use Criterion II to identify journals that are not assigned to categories with which they have strong connections. If a journal satisfies either of the two criteria, we conclude that its assignment to categories may be questionable. Accordingly, we identify all journals with questionable classifications in Web of Science and Scopus. Furthermore, we perform a more in-depth analysis for the field of Library and Information Science to assess whether our proposed criteria are appropriate and whether they yield meaningful results. It turns out that according to our citation-based criteria Web of Science performs significantly better than Scopus in terms of the accuracy of its journal classification system
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