Winthrop Part of $20 Million Federal Grant to Research Complex Materials

This project is for development of intelligently designed optical and magnetic materials, polymers and stimuli-responsive materials. These advanced materials will benefit commercial applications such as lasers, water treatment and regenerative medicine. Planned over five years, the initiative will involve eight Winthrop faculty members who will participate a state-wide consortium

A machine learning taxonomic classifier for science publications

Dissertação 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 evolução na produção de ciência, associada à crescente colaboração interdomínios do conhecimento e à também crescente coautoria de trabalhos permanece suportada por processos de classificação manual, subjetiva e sujeita a interpretações erradas. A própria taxonomia na qual assenta esse mesmo processo de classificação não é consensual, com organismos estatais a recorrerem a taxonomias que não acompanham as alterações nas áreas científicas, e indexadores/repositórios que procuram acompanhar essas mesmas alterações. Verificamos uma realidade distinta do espectável e que os domínios onde são registados os trabalhos científicos podem facilmente estar desenquadrados. A taxonomia hoje aplicada pelos organismos governamentais, como o caso do organismo que regulamenta a produção científica em Portugal, não é suficiente, é limitadora, e promove a classificação em domínios aproximados do desejado, logo com grande potencial para erro. Um processo de classificação automática com base em algoritmos de machine learning apresenta-se como uma possível solução para o problema da subjetividade na classificação, e embora não resolva a questão do desenquadramento da taxonomia utilizada, é apresentada neste trabalho como uma possibilidade comprovada. Neste trabalho propomos uma taxonomia de classificação, bem como nós desenvolvemos um processo baseado em machine learning algoritmos para resolver o problema de classificação. Apresentamos ainda um conjunto de direções para trabalhos futuros para uma classificação cada vez mais representativa da evolução nas ciências, que não pretende ser hermética, mas flexível e talvez cada vez mais baseada em fenómenos e não apenas em disciplinas

Influence of high-energy electron irradiation on the transport properties of La_{1-x}Ca_{x}MnO_{3} films (x \approx 1/3)

The effect of crystal lattice disorder on the conductivity and colossal magnetoresistance in La_{1-x}Ca_{x}MnO_{3} (x \approx 0.33) films has been examined. The lattice defects are introduced by irradiating the film with high-energy (\simeq 6 MeV) electrons with a maximal fluence of about 2\times 10^{17} cm^{-2}. This comparatively low dose of irradiation produces rather small radiation damage in the films. The number of displacements per atom (dpa) in the irradiated sample is about 10^{-5}. Nethertheless, this results in an appreciable increase in the film resistivity. The percentage of resistivity increase in the ferromagnetic metallic state (below the Curie tempetature T_{c}) was much greater than that observed in the insulating state (above T_{c}). At the same time irradiation has much less effect on T_{c} or on the magnitude of the colossal magnetoresistance. A possible explanation of such behavior is proposed.Comment: RevTex, 22 pages, 3 Postscript figures, submitted to Eur. Phys. J.

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

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

Pmma fiber viscoelasticity in extremely low frequency regime

We are reporting on the viscoelasticity of PMMA based Fiber Bragg Grating (FBG) strain sensors when exposed to repeated sequences of long term strain and relaxation with various duty-cycles and frequencies much smaller than 1 Hz. Monitoring the FBG wavelength and how it follows the applied strain, we have shown that after being strained up to 1%, the fiber will rapidly contract elastically to a certain amount after which a viscous-dominated relaxation takes place. The amount of elastic versus viscous relaxation depends both on the level of applied strain and on the duration of the strain. For a big duration of the strain with respect to relaxation, this fast relaxation wavelength range stays almost the same. However, with increasing relaxation duration and keeping the same strain duration, elastic relaxation wavelength range will be shortened for up to 18% (1%) when strained for 0.5% (1%), which could, in certain applications, influence the sensitivity range of sensors based on plastic fibers