Institution
University of Central Florida
Education•Orlando, Florida, United States•
About: University of Central Florida is a education organization based out in Orlando, Florida, United States. It is known for research contribution in the topics: Laser & Population. The organization has 18822 authors who have published 48679 publications receiving 1234422 citations. The organization is also known as: UCF.
Topics: Laser, Population, Poison control, Liquid crystal, Nonlinear system
Papers published on a yearly basis
Papers
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TL;DR: In this paper, the authors argue that it is possible to boil down what researchers know about teamwork into five core components that the authors submit as the "Big Five" in teamwork, i.e., team leadership, mutual performance monitoring, backup behavior, adaptability, and team orientation.
Abstract: The study of teamwork has been fragmented through the years, and the findings are generally unable to be used practically. This article argues that it is possible to boil down what researchers know about teamwork into five core components that the authors submit as the “Big Five” in teamwork. The core components of teamwork include team leadership, mutual performance monitoring, backup behavior, adaptability, and team orientation. Furthermore, the authors examine how these core components require supporting coordinating mechanisms (e.g., shared mental modes, closed-loop communication, and mutual trust) and vary in their importance during the life of the team and the team task. Finally, the authors submit a set of propositions for future research.
1,605 citations
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20 Jun 2009TL;DR: A novel method to detect and localize abnormal behaviors in crowd videos using Social Force model and it is shown that the social force approach outperforms similar approaches based on pure optical flow.
Abstract: In this paper we introduce a novel method to detect and localize abnormal behaviors in crowd videos using Social Force model. For this purpose, a grid of particles is placed over the image and it is advected with the space-time average of optical flow. By treating the moving particles as individuals, their interaction forces are estimated using social force model. The interaction force is then mapped into the image plane to obtain Force Flow for every pixel in every frame. Randomly selected spatio-temporal volumes of Force Flow are used to model the normal behavior of the crowd. We classify frames as normal and abnormal by using a bag of words approach. The regions of anomalies in the abnormal frames are localized using interaction forces. The experiments are conducted on a publicly available dataset from University of Minnesota for escape panic scenarios and a challenging dataset of crowd videos taken from the web. The experiments show that the proposed method captures the dynamics of the crowd behavior successfully. In addition, we have shown that the social force approach outperforms similar approaches based on pure optical flow.
1,585 citations
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University of Pittsburgh1, California Pacific Medical Center2, University of Maryland, Baltimore3, Beth Israel Deaconess Medical Center4, National Institutes of Health5, University of Central Florida6, University of Connecticut Health Center7, Wake Forest University8, Columbia University9, Foundation for the National Institutes of Health10
TL;DR: Based on the analyses presented in this series, the final recommended cutpoints for weakness are grip strength <26kg for men and <16kg for women, and for low lean mass, appendicular lean mass adjusted for body mass index <0.789 forMen and women.
Abstract: Background.
Low muscle mass and weakness are common and potentially disabling in older adults, but in order to become recognized as a clinical condition, criteria for diagnosis should be based on clinically relevant thresholds and independently validated. The Foundation for the National Institutes of Health Biomarkers Consortium Sarcopenia Project used an evidence-based approach to develop these criteria. Initial findings were presented at a conference in May 2012, which generated recommendations that guided additional analyses to determine final recommended criteria. Details of the Project and its findings are presented in four accompanying manuscripts.
1,542 citations
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TL;DR: This work investigates the acceleration dynamics of quasi-diffraction-free Airy beams in both one- and two-dimensional configurations and shows that this class of finite energy waves can retain their intensity features over several diffraction lengths.
Abstract: We investigate the acceleration dynamics of quasi-diffraction-free Airy beams in both one- and two-dimensional configurations. We show that this class of finite energy waves can retain their intensity features over several diffraction lengths. The possibility of other physical realizations involving spatiotemporal Airy wave packets is also considered.
1,522 citations
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TL;DR: This article found that transformational leadership is associated with the way followers view their jobs, in terms of Hackman and Oldham's (1976) core job characteristics, such as intrinsic motivation, and goal commitment.
Abstract: Although the effects of transformational leadership on task performance and organizational citizenship behavior (OCB) are well-documented, the mechanisms that explain those effects remain unclear. We propose that transformational leadership is associated with the way followers view their jobs, in terms of Hackman and Oldham’s (1976) core job characteristics. Results of our study support a structural model whereby indirect effects supplement the direct effects of transformational leadership on task performance and OCB through the mechanisms of job characteristics, intrinsic motivation, and goal commitment. Additional analyses revealed that transformational leadership relationships were significantly stronger for followers who perceived highquality leader-member exchange.
1,517 citations
Authors
Showing all 19051 results
Name | H-index | Papers | Citations |
---|---|---|---|
Gang Chen | 167 | 3372 | 149819 |
Kevin M. Huffenberger | 138 | 402 | 93452 |
Eduardo Salas | 129 | 711 | 62259 |
Akihisa Inoue | 126 | 2652 | 93980 |
Allan H. MacDonald | 119 | 926 | 56221 |
Hagop S. Akiskal | 118 | 565 | 50869 |
Richard P. Van Duyne | 116 | 409 | 79671 |
Jun Wang | 106 | 1031 | 49206 |
Mubarak Shah | 106 | 614 | 56738 |
Larry L. Hench | 103 | 491 | 55633 |
Michael Walsh | 102 | 963 | 42231 |
Wei Liu | 102 | 2927 | 65228 |
Demetrios N. Christodoulides | 100 | 704 | 51093 |
Paul E. Spector | 99 | 325 | 52843 |
Eric A. Hoffman | 99 | 809 | 36891 |