Michigan State University

About: Michigan State University is a education organization based out in East Lansing, Michigan, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 60109 authors who have published 137074 publications receiving 5633022 citations. The organization is also known as: MSU & Michigan State.

PDFgetX2: a GUI-driven program to obtain the pair distribution function from X-ray powder diffraction data

Abstract: The pair distribution function (PDF) reveals directly in real space the interatomic distances in a material. Recent applications have proved the PDF technique to be a powerful local-structure probe of nanostructured materials (Egami & Billinge, 2003; Billinge & Kanatzidis, 2004), in addition to its traditional use to study liquids and glasses (Furukawa, 1962; Wright, 1998). The experimental PDF is obtained by a sine Fourier transformation of the total-scattering structure function S(Q), where Q is the magnitude of the scattering vector. To obtain S(Q) from raw scattering intensities, many corrections have to be made to account for various instrument and sample effects. Adding to this complexity, most existing X-ray data PDF analysis software is menu driven and not particularly user-friendly. Reproducing an earlier data analysis has been dif®cult because data processing parameters are usually kept in the notebook other than with the data. Cross-platform compatibility has also been an issue.

Fake News Detection on Social Media: A Data Mining Perspective

Abstract: Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of "fake news", i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ineffective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users' social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.

Tracing cosmic evolution with clusters of galaxies

Abstract: The most successful cosmological models to date envision structure formation as a hierarchical process in which gravity is constantly drawing lumps of matter together to form increasingly larger structures. Clusters of galaxies currently sit atop this hierarchy as the largest objects that have had time to collapse under the influence of their own gravity. Thus their appearance on the cosmic scene is also relatively recent. Two features of clusters make them uniquely useful tracers of cosmic evolution. First, clusters are the biggest things whose masses we can reliably measure because they are the largest objects to have undergone gravitational relaxation and entered into virial equilibrium. Mass measurements of nearby clusters can therefore be used to determine the amount of structure in the universe on scales of ${10}^{14}--{10}^{15}{M}_{\astrosun{}}$, and comparisons of the present-day cluster mass distribution with the mass distribution at earlier times can be used to measure the rate of structure formation, placing important constraints on cosmological models. Second, clusters are essentially ``closed boxes'' that retain all their gaseous matter, despite the enormous energy input associated with supernovae and active galactic nuclei, because the gravitational potential wells of clusters are so deep. The baryonic component of clusters therefore contains a wealth of information about the processes associated with galaxy formation, including the efficiency with which baryons are converted into stars and the effects of the resulting feedback processes on galaxy formation. This article reviews our theoretical understanding of both the dark-matter component and the baryonic component of clusters, providing a context for interpreting the flood of new cluster observations that are now arriving from the latest generation of x-ray observatories, large optical surveys, and measurements of cluster-induced distortions in the spectrum of the cosmic microwave background.

Comet 81P/Wild 2 under a microscope.

Abstract: The Stardust spacecraft collected thousands of particles from comet 81P/Wild 2 and returned them to Earth for laboratory study. The preliminary examination of these samples shows that the nonvolatile portion of the comet is an unequilibrated assortment of materials that have both presolar and solar system origin. The comet contains an abundance of silicate grains that are much larger than predictions of interstellar grain models, and many of these are high-temperature minerals that appear to have formed in the inner regions of the solar nebula. Their presence in a comet proves that the formation of the solar system included mixing on the grandest scales.

Ecotoxicological Risk Assessment for Roundup ® Herbicide

Abstract: Glyphosate-based weed control products are among the most widely used broad-spectrum herbicides in the world. The herbicidal properties of glyphosate were discovered in 1970, and commercial formulations for nonselective weed control were first introduced in 1974 (Franz et al. 1997). Formulations of glyphosate, including Roundup® Herbicide (RU)1 (Monsanto Company, St. Louis, MO), have been extensively investigated for their potential to produce adverse effects in nontarget organisms. Governmental regulatory agencies, international organizations, and others have reviewed and assessed the available scientific data for glyphosate formulations and independently judged their safety. Conclusions from three major organizations are publicly available and indicate RU can be used with minimal risk to the environment (Agriculture Canada 1991; USEPA 1993a; WHO 1994). Several review publications are available on the fate and effects of RU or glyphosate in the environment (Carlisle and Trevors 1988;Smith and Oehme 1992 ; Malik et al. 1989;Rueppel et al. 1977; Sullivan and Sullivan 1997;Forestry Canada, 1989). In addition, several books have been published about the environmental and human health considerations of glyphosate and its formulations (Grossbard and Atkinson 1985; Franz et al. 1997). In addition, RU and other glyphosate formulations have been selected for use in a number of weed control programs for state and local jurisdictions in the United States. Many of these uses require that ecological risk assessments be conducted in the form of Environmental Impact Statements or Environmental Assessments. These documents are comprehensive and specific to local use situations. Documents are available for risk assessments in Texas, Washington, Oregon, Pennsylvania, New York, Virginia, and other states (USDA 1989;USDA 1992;USDA 1996;USDA 1997;USDI 1989; Washington State DOT 1993).