Air Force Institute of Technology Research Report 2012

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Air Force Institute of Technology Research Report 2011

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

NASA Tech Briefs, August 1994

Topics covered include: Computer Hardware; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences; Books and Reports

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Impacts des polluants métalliques sur l'abeille : de la colonie au cerveau

Les abeilles sont des pollinisateurs essentiels. Une pléthore de facteurs de stress environnementaux, tels que les produits agrochimiques, a été identifiée comme contribuant à leur déclin mondial. En particulier, ces facteurs de stress altèrent les processus cognitifs impliqués dans les comportements fondamentaux. Jusqu'à présent, cependant, on ne sait pratiquement rien de l'impact de l'exposition à des métaux lourds, dont la toxicité est avérée chez de nombreux organismes. Pourtant, leurs émissions mondiales résultant des activités humaines ont élevé leurs concentrations bien au-dessus des niveaux naturels dans l'air, le sol, l'eau et la flore, exposant ainsi les abeilles à tous les stades de leur vie. Le but de ma thèse était d'examiner les effets de la pollution métallique sur l'abeille domestique en utilisant une approche multi-échelle, du cerveau à la colonie, en laboratoire et sur le terrain. J'ai d'abord observé que les abeilles exposées à une gamme de concentrations de trois métaux communs (arsenic, plomb et zinc) en laboratoire étaient incapables de percevoir et éviter des concentrations usuelles, néanmoins nocives, de ces métaux dans leur nourriture. J'ai ensuite exposé de façon chronique des colonies à des concentrations réalistes de plomb dans la nourriture et démontré que la consommation de ce métal altérait la cognition et le développement morphologique des abeilles. Comme les polluants métalliques se trouvent souvent dans des mélanges complexes dans l'environnement, j'ai exploré l'effet des cocktails de métaux, montrant que l'exposition au plomb, à l'arsenic ou au cuivre seul était suffisante pour ralentir l'apprentissage et perturber le rappel de la mémoire, et que les combinaisons de ces métaux induisaient des effets négatifs additifs sur ces deux processus cognitifs. J'ai finalement étudié l'impact de l'exposition naturelle aux polluants métalliques dans un environnement contaminé, en collectant des abeilles à proximité d'une ancienne mine d'or, et montré que les individus des populations les plus exposées aux métaux présentaient des capacités d'apprentissage et de mémoire plus faibles, et des altérations de leur développement conduisant à une réduction de la taille de leur cerveau. Une analyse plus systématique des abeilles non exposées a révélé une relation entre la taille de la tête, la morphométrie du cerveau et les performances d'apprentissage dans différentes tâches comportementales, suggérant que l'exposition aux polluants métalliques amplifie ces variations naturelles. Ainsi, mes résultats suggèrent que les abeilles domestiques sont incapables d'éviter l'exposition à des concentrations réalistes de métaux qui sont préjudiciables au développement et aux fonctions cognitives, et appellent à une révision des niveaux environnementaux considérés comme "sûrs". Ma thèse est la première analyse intégrée de l'impact de plusieurs polluants métalliques sur la cognition, la morphologie et l'organisation cérébrale chez l'abeille, et vise à encourager de nouvelles études sur la contribution de la pollution métallique dans le déclin signalé des abeilles, et plus généralement, des insectes.Honey bees are crucial pollinators. A plethora of environmental stressors, such as agrochemicals, have been identified as contributors to their global decline. Especially, these stressors impair cognitive processes involved in fundamental behaviours. So far however, virtually nothing is known about the impact of metal pollutants, despite their known toxicity to many organisms. Their worldwide emissions resulting from human activities have elevated their concentrations far above natural baselines in the air, soil, water and flora, exposing bees at all life stages. The aim of my thesis was to examine the effects of metallic pollution on honey bees using a multiscale approach, from brain to colonies, in laboratory and field conditions. I first observed that bees exposed to a range of concentrations of three common metals (arsenic, lead and zinc) in the laboratory were unable to perceive and avoid, low, yet harmful, field-realistic concentrations of those metals in their food. I then chronically exposed colonies to field-realistic concentrations of lead in food and demonstrated that consumption of this metal impaired bee cognition and morphological development, leading to smaller adult bees. As metal pollutants are often found in complex mixtures in the environment, I explored the effect of cocktails of metals, showing that exposure to lead, arsenic or copper alone was sufficient to slow down learning and disrupt memory retrieval, and that combinations of these metals induced additive negative effects on both cognitive processes. I finally investigated the impact of natural exposure to metal pollutants in a contaminated environment, by collecting bees in the vicinity of a former gold mine, and showed that individuals from populations most exposed to metals exhibited lower learning and memory abilities, and development impairments conducing to reduced brain size. A more systematic analysis of unexposed bees revealed a relationship between head size, brain morphometrics and learning performances in different behavioural tasks, suggesting that exposure to metal pollutants magnifies these natural variations. Hence, altogether, my results suggest that honey bees are unable to avoid exposure to field-realistic concentrations of metals that are detrimental to development and cognitive functions; and call for a revision of the environmental levels considered as 'safe'. My thesis is the first integrated analysis of the impact of several metal pollutants on bee cognition, morphology and brain structure, and should encourage further studies on the contribution of metal pollution in the reported decline of honey bees, and more generally, of insects