13,470 research outputs found
Investigations of toughening mechanisms of epoxy resins
Composite material technology was applied to the solid rocket booster by the development of a carbon filament-epoxy resin case which yields a net increase of 4000 lbs. in payload in the shuttle. The question of reusability of the new composite tanks has not yet been answered and will depend on the toughness of the matrix resin. The present study was aimed at providing conditions whereby test specimens of the epoxy resin (EPON/85) and curing agents of systematically varied structures could be produced in a controlled manner. Three sets of conditions were found that might allow the isolation of the structural effects on toughness from the cure effects. The kinetic methods leading to the determination of these conditions are described
Evolution des classes ASA (2002-2012)
Contexte: Le score ASA, initialement creĢeĢ en 1941 comme outil statistique est actuellement utiliseĢ dans un grand nombre d'eĢtudes afin de preĢdire le risque opeĢratoire d'une intervention pour un patient donneĢ. Or l'utilisation de ce score comme stratifi- cateur de risque opeĢratoire est controverseĢe. La raison principale aĢ cette controverse reĢside dans la simpliciteĢ du score qui, selon certains auteurs, n'est pas suffisamment complet pour preĢdir un eĢveĢnement aussi plurifactoriel qu'un outcome d'une opeĢration chirurgicale.
MeĢthodologie: Dans notre eĢtude sont compris tous les patients opeĢreĢs dans le baĢtiment principal du Centre Hospitalier Universitaire Vaudois (CHUV) entre 2002 et 2012, soit 75'260 patients. 8'437 patients ont eĢteĢ exclus en raisons de donneĢes manquantes. Nous avons utiliseĢ des reĢgressions lineĢaires aĢ une variable pour eĢtudier l'eĢvolution des caracteĢristiques deĢmographiques de notre eĢchantillon au cours du temps. Nous avons utiliseĢ le Student's T test afin de tester l'association de la classe ASA et de l'aĢge avec la survenue de complications per-opeĢratoires.
ReĢsultats: Nous constatons dans la population eĢtudieĢe une augmentation significa- tive (P< 00.5) de patients aĢgeĢs mais eĢgalement de patients avec un score ASA eĢleveĢ (P< 00.5 ASA III, P<005 ASA IV). L'augmentation de l'aĢge n'augmente pas la prob- abliteĢ d'effectuer une complication seĢveĢre, dont le deĢceĢs, mais augmente uniquement le risque de deĢvelopper une complication leĢgeĢre. Une augmentation de classe ASA entraiĢne statistiquement (P< 0.05) un risque supeĢrieur d'effectuer une complication leĢgeĢre ou seĢveĢre. Nous retrouvons parmis les aneĢstheĢsistes du CHUV, une variabiliteĢ lors de la classification de patients standardiseĢs selon la deĢfinition internationale du score ASA.
Conclusion: Nous montrons que durant ces dix dernieĢres anneĢes, de plus en plus de patients aĢgeĢs et en mauvaise santeĢ ont eĢteĢ opeĢreĢs au CHUV. De plus, nos reĢsul- tats confirment le lien qui existe entre un score ASA eĢleveĢ et le risque important de deĢvelopper des complications per-opeĢratoires. Nous confirmons donc l'indication aĢ mieux monitorer et superviser un patient avec un mauvais status physique
Large-scale V/STOL testing
Several facets of large-scale testing of V/STOL aircraft configurations are discussed with particular emphasis on test experience in the Ames 40- by 80-Foot Wind Tunnel. Examples of powered-lift test programs are presented in order to illustrate tradeoffs confronting the planner of V/STOL test programs. Large-scale V/STOL wind-tunnel testing can sometimes compete with small-scale testing in the effort required (overall test time) and program costs because of the possibility of conducting a number of different tests with a single large-scale model where several small-scale models would be required. The benefits of both high- or full-scale Reynolds numbers, more detailed configuration simulation, and number and type of onboard measurements are studied
The FastMap Algorithm for Shortest Path Computations
We present a new preprocessing algorithm for embedding the nodes of a given
edge-weighted undirected graph into a Euclidean space. The Euclidean distance
between any two nodes in this space approximates the length of the shortest
path between them in the given graph. Later, at runtime, a shortest path
between any two nodes can be computed with A* search using the Euclidean
distances as heuristic. Our preprocessing algorithm, called FastMap, is
inspired by the data mining algorithm of the same name and runs in near-linear
time. Hence, FastMap is orders of magnitude faster than competing approaches
that produce a Euclidean embedding using Semidefinite Programming. FastMap also
produces admissible and consistent heuristics and therefore guarantees the
generation of shortest paths. Moreover, FastMap applies to general undirected
graphs for which many traditional heuristics, such as the Manhattan Distance
heuristic, are not well defined. Empirically, we demonstrate that A* search
using the FastMap heuristic is competitive with A* search using other
state-of-the-art heuristics, such as the Differential heuristic
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