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Martin, TN, United States

The University of Tennessee at Martin , located in Martin, Tennessee in the United States, is one of the five campuses of the University of Tennessee system. Other campuses include the flagship campus in Knoxville, the Chattanooga campus, the Center for the Health science in Memphis, and the Space Institute in Tullahoma. Prior to the acquisition of Lambuth University in Jackson by University of Memphis in 2011, UTM was the only public four-year university in West Tennessee outside of Memphis.UT Martin is featured in U.S. News & World Report top-tier ranking for southern master’s institutions in the 2013 edition of America’s Best Colleges. The Princeton Review also named UT Martin “A Best Southeastern College” for 2013 and among the nation’s “Best Value” colleges and universities in the book The Best Value Colleges: 2012 Edition ; And, for the sixth consecutive year, UT Martin is listed among America’s 100 Best College Buys, a listing by Institutional Research and Evaluation, Inc.UTM operates a large experimental farm and several satellite centers in West Tennessee. Wikipedia.


Ray D.L.,University of Tennessee at Martin
American Biology Teacher | Year: 2016

Students are almost universally interested in animals, and especially endotherms, including mammals and birds. According to Bergmann's rule, endotherms that live in colder climates at higher latitudes are larger than those living in warmer climates. As with most biological principles, hands-on investigation will provide a better understanding of why size is important in endotherm thermal regulation. One easily observable aspect of this principle is that larger organisms have a lower ratio of body surface area to total body volume. This affects how efficiently they can retain or radiate heat, which can be easily tested in the laboratory using commonly available materials. In this activity, simple models of endotherms of different sizes are used to assess the effects of body size on heat loss. © 2016 National Association of Biology Teachers. Source


Goyret J.,University of Tennessee at Martin | Goyret J.,Cornell University | Yuan M.L.,Cornell University
Integrative and Comparative Biology | Year: 2015

As a goal-directed behavior, foraging for nectar functions on the basis of a sequence of innate stereotyped movements mainly regulated by sensory input. The operation of this inherited program is shaped by selective pressures acting on its efficiency, which is largely dependent upon the way the system handles sensory information. Flowers offer a wealth of signals, from odors acting as distant attractants, to colors eliciting approximation and feeding responses, to textures guiding feeding responses toward a reservoir of nectar. Thus, animals use different signals in the regulation of particular motor outputs. Nevertheless, the use of these sensory signals can be user-specific (e.g. species, motivation, experience, learning) as well as context-dependent (e.g. spatiotemporal patterns of stimulation, availability of signals, multimodal integration). The crepuscular/nocturnal hawkmoths Manduca sexta experience a wide range of illuminations during their foraging activity, which raises the question of how these environmental changes might affect the use of two important floral signals, odor and visual display. In a flight cage, we explored the use of these signals under different illuminances. Under conditions of starlight and crescent moonlight, moths showed very low levels of responsiveness to unscented feeders (artificial flowers). However, responsiveness was recovered either by increasing illumination, or by offering olfactory signals. Additionally, we recorded a bias toward white over blue feeders under dim conditions, which disappeared with increasing illumination. We discuss how this kind of experimental manipulation may provide insights to the study of how innate behavioral programs, and their underlying neural substrates, overcome selective forces imposed by the uncertainty of natural, ever-changing environments. © 2015 Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology 2015. This work is written by US Government employees and is in the public domain in the US. Source


Lei M.,Zhejiang Sci-Tech University | Li P.G.,Zhejiang Sci-Tech University | Li L.H.,University of Tennessee at Martin | Tang W.H.,Zhejiang Sci-Tech University
Journal of Power Sources | Year: 2011

A highly ordered Pt-free Fe-N-C catalyst is synthesized through a hydrogen bonding-assisted self-assembly route. The catalyst has a porous structure with an average pore size of 5.5 nm and a large surface area of 416 m2 g-1, making it highly active in oxygen reduction. Cells assembled with the synthesized catalyst perform significantly better than those assembled with amorphous Fe-N-C cathode catalysts. The maximum powers of cells assembled from the highly ordered and amorphous catalysts are 252 and 60 mW cm -2, respectively. © 2010 Elsevier B.V. All rights reserved. Source


DeVito J.,University of Tennessee at Martin
Differential Geometry and its Application | Year: 2014

We classify all compact simply connected biquotients of dimensions 4 and 5. In particular, all pairs of groups (G, H) and embeddings H→. G × G giving rise to a particular biquotient are classified. © 2014 Elsevier B.V. Source


Xu H.,University of Tennessee at Martin | Jagannathan S.,Missouri University of Science and Technology
IEEE Transactions on Neural Networks and Learning Systems | Year: 2015

The stochastic optimal control of nonlinear networked control systems (NNCSs) using neuro-dynamic programming (NDP) over a finite time horizon is a challenging problem due to terminal constraints, system uncertainties, and unknown network imperfections, such as network-induced delays and packet losses. Since the traditional iteration or time-based infinite horizon NDP schemes are unsuitable for NNCS with terminal constraints, a novel time-based NDP scheme is developed to solve finite horizon optimal control of NNCS by mitigating the above-mentioned challenges. First, an online neural network (NN) identifier is introduced to approximate the control coefficient matrix that is subsequently utilized in conjunction with the critic and actor NNs to determine a time-based stochastic optimal control input over finite horizon in a forward-in-time and online manner. Eventually, Lyapunov theory is used to show that all closed-loop signals and NN weights are uniformly ultimately bounded with ultimate bounds being a function of initial conditions and final time. Moreover, the approximated control input converges close to optimal value within finite time. The simulation results are included to show the effectiveness of the proposed scheme. © 2012 IEEE. Source

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