Institution
University of Memphis
Education•Memphis, Tennessee, United States•
About: University of Memphis is a education organization based out in Memphis, Tennessee, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 7710 authors who have published 20082 publications receiving 611618 citations. The organization is also known as: U of M.
Topics: Population, Poison control, Fractional calculus, Health care, Cognition
Papers published on a yearly basis
Papers
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TL;DR: In this paper, a framework for the analysis of the optimal levels of corporate social responsibility (CSR) activities in a multi-period supply chain network consisting of manufacturers, retailers, and consumers is presented.
198 citations
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TL;DR: In this paper, the authors discuss seven meta-analytic practices, misconceptions, claims, and assumptions that have reached the status of myths and urban legends (MULs), including issues related to data collection, consequences of choices made in the process of gathering primary-level studies to be included in a meta-analysis, data analysis, and interpretation of results.
Abstract: Meta-analysis is the dominant approach to research synthesis in the organizational sciences. We discuss seven meta-analytic practices, misconceptions, claims, and assumptions that have reached the status of myths and urban legends (MULs). These seven MULs include issues related to data collection (e.g., consequences of choices made in the process of gathering primary-level studies to be included in a meta-analysis), data analysis (e.g., effects of meta-analytic choices and technical refinements on substantive conclusions and recommendations for practice), and the interpretation of results (e.g., meta-analytic inferences about causal relationships). We provide a critical analysis of each of these seven MULs, including a discussion of why each merits being classified as an MUL, their kernels of truth value, and what part of each MUL represents misunderstanding. As a consequence of discussing each of these seven MULs, we offer best-practice recommendations regarding how to conduct meta-analytic reviews.
198 citations
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TL;DR: Adolescents with less prior knowledge about reading strategies performed significantly better on text-based questions if they received iSTART training, and for high-strategy knowledge students, iSTart improved comprehension for bridging–inference questions.
Abstract: This study examines the benefits of reading strategy training on adolescent readers' comprehension of science text. Training was provided via an automated reading strategy trainer called the Interactive Strategy Trainer for Active Reading and Thinking (iSTART), which is an interactive reading strategy trainer that utilizes animated agents to provide reading strategy instruction. Half of the participants were provided with iSTART while the others (control) were given a brief demonstration of how to self-explain text. All of the students then self-explained a text about heart disease and answered text-based and bridging-inference questions. Both iSTART training and prior knowledge of reading strategies significantly contributed to the quality of self-explanations and comprehension. Adolescents with less prior knowledge about reading strategies performed significantly better on text-based questions if they received iSTART training. Conversely, for high-strategy knowledge students, iSTART improved comprehensi...
197 citations
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TL;DR: Information about autism did not affect ratings of either attitudes or behavioral intentions as ascribed to self or others in children's ratings of attitudes and behavioral intentions toward a peer presented with or without autistic behaviors.
Abstract: This study examined children's ratings of attitudes and behavioral intentions toward a peer presented with or without autistic behaviors. The impact of information about autism on these ratings was investigated as well as age and gender effects. Third- and sixth-grade children (N = 233) were randomly assigned to view a video of the same boy in one of three conditions: No Autism, Autism, or Autism/Information. Children at both grade levels showed less positive attitudes toward the child in the two autism conditions. In rating their own behavioral intentions, children showed no differences between conditions. However, in attributing intentions to their classmates, older children and girls gave lower ratings to the child in the autism conditions. Information about autism did not affect ratings of either attitudes or behavioral intentions as ascribed to self or others.
197 citations
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12 May 2002TL;DR: A novel approach inspired by the immune system that allows the application of conventional classification algorithms to perform anomaly detection and produces fuzzy characterization of the normal (or abnormal) space.
Abstract: This paper presents a novel approach inspired by the immune system that allows the application of conventional classification algorithms to perform anomaly detection. This approach appears to be very useful where only positive samples are available to train an anomaly detection system. The proposed approach uses the positive samples to generate negative samples that are used as training data for a classification algorithm. In particular, the algorithm produces fuzzy characterization of the normal (or abnormal) space. This allows it to assign a degree of normalcy, represented by membership value, to elements of the space.
197 citations
Authors
Showing all 7827 results
Name | H-index | Papers | Citations |
---|---|---|---|
James F. Sallis | 169 | 825 | 144836 |
Robert G. Webster | 158 | 843 | 90776 |
Ching-Hon Pui | 145 | 805 | 72146 |
James Whelan | 128 | 786 | 89180 |
Tom Baranowski | 103 | 485 | 36327 |
Peter C. Doherty | 101 | 516 | 40162 |
Jian Chen | 96 | 1718 | 52917 |
Arthur C. Graesser | 95 | 614 | 38549 |
David Richards | 95 | 578 | 47107 |
Jianhong Wu | 93 | 726 | 36427 |
Richard W. Compans | 91 | 526 | 31576 |
Shiriki K. Kumanyika | 90 | 349 | 44959 |
Alexander J. Blake | 89 | 1133 | 35746 |
Marek Czosnyka | 88 | 747 | 29117 |
David M. Murray | 86 | 300 | 21500 |