Institution
United States Department of the Army
Government•Arlington, Virginia, United States•
About: United States Department of the Army is a government organization based out in Arlington, Virginia, United States. It is known for research contribution in the topics: Poison control & Population. The organization has 32668 authors who have published 42453 publications receiving 947075 citations. The organization is also known as: DA & U.S. Department of the Army.
Topics: Poison control, Population, Laser, Signal, Virus
Papers published on a yearly basis
Papers
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TL;DR: Based on the experimental results on the test data, the neural networks trained under the ML_EMO_HEV framework are effective in predicting roadway type and traffic congestion levels, predicting driving trends, and learning optimal engine speed and optimal battery power from DP.
Abstract: In this series of two papers, we present our research on intelligent energy management for hybrid electric vehicles (HEVs). These two papers cover the modeling of power flow in HEVs, the mathematical background of optimization in energy management in HEVs, a machine learning framework that combines dynamic programming (DP) with machine learning to learn about roadway-type- and traffic-congestion-level-specific energy optimization, machine learning algorithms, and real-time quasi-optimal control of energy flow in an HEV. This first paper presents our research on machine learning for optimal energy management in HEVs. We will present a machine learning framework ML_EMO_HEV developed for the optimization of energy management in an HEV, machine learning algorithms for predicting driving environments, and the generation of an optimal power split for a given driving environment. Experiments are conducted based on a simulated Ford Escape Hybrid vehicle model provided by Argonne National Laboratory's Powertrain Systems Analysis Toolkit (PSAT). Based on the experimental results on the test data, we can conclude that the neural networks trained under the ML_EMO_HEV framework are effective in predicting roadway type and traffic congestion levels, predicting driving trends, and learning optimal engine speed and optimal battery power from DP.
160 citations
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TL;DR: The breakpoint appears to occur at a constant sweetness (that is, constant sensory) level, and the sweetness of sucrose solutions and sweetened food conform to different functions.
Abstract: Sweetness and the pleasantness of sweetness of sucrose solutions and sweetened food conform to different functions. Sweetness rises with concentration, whereas pleasantness first rises and then decreases. The breakpoint appears to occur at a constant sweetness (that is, constant sensory) level.
160 citations
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TL;DR: In this paper, a wavelet-transform-based power management for hybrid electric vehicles (HEV) with multiple on-board energy sources and energy storage systems including a battery, a fuel cell, and an ultra-capacitor is proposed.
160 citations
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TL;DR: The study of multilevel phenomena in organizations involves a complex interplay between methods and statistics on one hand and theory development on the other as mentioned in this paper, and this interplay can be seen as a form of "theory development".
Abstract: The study of multilevel phenomena in organizations involves a complex interplay between methods and statistics on one hand and theory development on the other. In this introduction, the authors pro...
160 citations
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TL;DR: An axenic amastigote drug screening system is established using a Leishmania mexicana strain and data suggest that for the compounds tested, susceptibility is intrinsic to the parasite stage, which contradicts previous hypotheses that suggested that the activities of antimonial agents against intracellular amastsigotes were solely a function of the macrophage.
Abstract: Currently available primary screens for selection of candidate antileishmanial compounds are not ideal. The choices include screens that are designed to closely reflect the situation in vivo but are labor-intensive and expensive (intracellular amastigotes and animal models) and screens that are designed to facilitate rapid testing of a large number of drugs but do not use the clinically relevant parasite stage (promastigote model). The advent of successful in vitro culture of axenic amastigotes permits the development of a primary screen which is quick and easy like the promastigote screen but still representative of the situation in vivo, since it uses the relevant parasite stage. We have established an axenic amastigote drug screening system using a Leishmania mexicana strain (strain M379). A comparison of the 50% inhibitory concentration (IC50) drug sensitivity profiles of M379 promastigotes, intracellular amastigotes, and axenic amastigotes for six clinically relevant antileishmanial drugs (sodium stibogluconate, meglumine antimoniate, pentamidine, paromomycin, amphotericin B, WR6026) showed that M379 axenic amastigotes are a good model for a primary drug screen. Promastigote and intracellular amastigote IC50s differed for four of the six drugs tested by threefold or more; axenic amastigote and intracellular amastigote IC50s differed by twofold for only one drug. This shows that the axenic amastigote susceptibility to clinically used reference drugs is comparable to the susceptibility of amastigotes in macrophages. These data also suggest that for the compounds tested, susceptibility is intrinsic to the parasite stage. This contradicts previous hypotheses that suggested that the activities of antimonial agents against intracellular amastigotes were solely a function of the macrophage.
160 citations
Authors
Showing all 32680 results
Name | H-index | Papers | Citations |
---|---|---|---|
David L. Kaplan | 177 | 1944 | 146082 |
Russel J. Reiter | 169 | 1646 | 121010 |
Donald G. Truhlar | 165 | 1518 | 157965 |
Jie Liu | 131 | 1531 | 68891 |
Martin A. Green | 127 | 1069 | 76807 |
William J. Kraemer | 123 | 755 | 54774 |
Steven J. Jacobsen | 123 | 662 | 62716 |
Roger H Unger | 121 | 493 | 48035 |
Thomas C. Quinn | 120 | 827 | 65881 |
John B. Holcomb | 120 | 733 | 53760 |
Stephen Mann | 120 | 669 | 55008 |
Bette T. Korber | 117 | 392 | 49526 |
Thomas G. Ksiazek | 113 | 398 | 46108 |
John R. Anderson | 112 | 538 | 84725 |
Stanley I. Rapoport | 107 | 696 | 45793 |