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Institution

Michigan State University

EducationEast Lansing, Michigan, United States
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.


Papers
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Journal ArticleDOI
TL;DR: The combination of bio-fibers such as kenaf, hemp, flax, jute, henequen, pineapple leaf fiber, and sisal with polymer matrices from both nonrenewable and renewable resources to produce composite materials that are competitive with synthetic composites requires special attention as discussed by the authors.
Abstract: Sustainability, industrial ecology, eco-efficiency, and green chemistry are guiding the development of the next generation of materials, products, and processes. Biodegradable plastics and bio-based polymer products based on annually renewable agricultural and biomass feedstock can form the basis for a portfolio of sustainable, eco-efficient products that can compete and capture markets currently dominated by products based exclusively on petroleum feedstock. Natural/Biofiber composites (Bio-Composites) are emerging as a viable alternative to glass fiber reinforced composites especially in automotive and building product applications. The combination of biofibers such as kenaf, hemp, flax, jute, henequen, pineapple leaf fiber, and sisal with polymer matrices from both nonrenewable and renewable resources to produce composite materials that are competitive with synthetic composites requires special attention, i.e., biofiber–matrix interface and novel processing. Natural fiber–reinforced polypropylene composites have attained commercial attraction in automotive industries. Natural fiber—polypropylene or natural fiber—polyester composites are not sufficiently eco-friendly because of the petroleum-based source and the nonbiodegradable nature of the polymer matrix. Using natural fibers with polymers based on renewable resources will allow many environmental issues to be solved. By embedding biofibers with renewable resource–based biopolymers such as cellulosic plastics; polylactides; starch plastics; polyhydroxyalkanoates (bacterial polyesters); and soy-based plastics, the so-called green bio-composites are continuously being developed.

1,921 citations

Journal ArticleDOI
TL;DR: This paper justifies a parsimonious interdisciplinary typology and relates trust constructs to e-commerce consumer actions, defining both conceptual-level and operational-level trust constructs.
Abstract: Trust is a vital relationship concept that needs clarification because researchers across disciplines have defined it in so many different ways. A typology of trust types would make it easier to compare and communicate results, and would be especially valuable if the types of trust related to one other. The typology should be interdisciplinary because many disciplines research e-commerce. This paper justifies a parsimonious interdisciplinary typology and relates trust constructs to e-commerce consumer actions, defining both conceptual-level and operational-level trust constructs. Conceptual-level constructs consist of disposition to trust (primarily from psychology), institution-based trust (from sociology), and trusting beliefs and trusting intentions (primarily from social psychology). Each construct is decomposed into measurable subconstructs, and the typology shows how trust constructs relate to already existing Internet relationship constructs. The effects of Web vendor interventions on consumer behaviors are posited to be partially mediated by consumer trusting beliefs and trusting intentions in the e-vendor.

1,910 citations

Journal ArticleDOI
TL;DR: In this article, the authors address Ronkko and Evermann's criticisms of the Partial Least Squares (PLS) approach to structural equation modeling and conclude that PLS should continue to be used as an important statistical tool for management and organizational research, as well as other social science disciplines.
Abstract: This article addresses Ronkko and Evermann’s criticisms of the partial least squares (PLS) approach to structural equation modeling. We contend that the alleged shortcomings of PLS are not due to problems with the technique, but instead to three problems with Ronkko and Evermann’s study: (a) the adherence to the common factor model, (b) a very limited simulation designs, and (c) overstretched generalizations of their findings. Whereas Ronkko and Evermann claim to be dispelling myths about PLS, they have in reality created new myths that we, in turn, debunk. By examining their claims, our article contributes to reestablishing a constructive discussion of the PLS method and its properties. We show that PLS does offer advantages for exploratory research and that it is a viable estimator for composite factor models. This can pose an interesting alternative if the common factor model does not hold. Therefore, we can conclude that PLS should continue to be used as an important statistical tool for management and organizational research, as well as other social science disciplines.

1,906 citations

Journal ArticleDOI
TL;DR: A camera model that accounts for major sources of camera distortion, namely, radial, decentering, and thin prism distortions is presented and a type of measure is introduced that can be used to directly evaluate the performance of calibration and compare calibrations among different systems.
Abstract: A camera model that accounts for major sources of camera distortion, namely, radial, decentering, and thin prism distortions is presented. The proposed calibration procedure consists of two steps: (1) the calibration parameters are estimated using a closed-form solution based on a distribution-free camera model; and (2) the parameters estimated in the first step are improved iteratively through a nonlinear optimization, taking into account camera distortions. According to minimum variance estimation, the objective function to be minimized is the mean-square discrepancy between the observed image points and their inferred image projections computed with the estimated calibration parameters. The authors introduce a type of measure that can be used to directly evaluate the performance of calibration and compare calibrations among different systems. The validity and performance of the calibration procedure are tested with both synthetic data and real images taken by tele- and wide-angle lenses. >

1,896 citations

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors presented 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.
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 ine ective 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.

1,891 citations


Authors

Showing all 60636 results

NameH-indexPapersCitations
David Miller2032573204840
Anil K. Jain1831016192151
D. M. Strom1763167194314
Feng Zhang1721278181865
Derek R. Lovley16858295315
Donald G. Truhlar1651518157965
Donald E. Ingber164610100682
J. E. Brau1621949157675
Murray F. Brennan16192597087
Peter B. Reich159790110377
Wei Li1581855124748
Timothy C. Beers156934102581
Claude Bouchard1531076115307
Mercouri G. Kanatzidis1521854113022
James J. Collins15166989476
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023250
2022752
20217,041
20206,870
20196,548
20185,779