Development of a prototype plastic space erectable satellite Quarterly report, Dec. 1965 - Feb. 1966

Plastic space erectable satellit

An investigation of the basic properties of irradiated polyethylene memory materials

Properties of irradiated polyethylene memory material

Development of a prototype plastic space erectable satellite Quarterly report, Jun. - Aug. 1966

Copper plated high-density polyethylene film evaluation for space erectable satellite desig

Geometric Inhomogeneous Random Graphs

Real-world networks, like social networks or the internet infrastructure, have structural properties such as their large clustering coefficient that can best be described in terms of an underlying geometry. This is why the focus of the literature on theoretical models for real-world networks shifted from classic models without geometry, such as Chung-Lu random graphs, to modern geometry-based models, such as hyperbolic random graphs. With this paper we contribute to the theoretical analysis of these modern, more realistic random graph models. However, we do not directly study hyperbolic random graphs, but replace them by a more general model that we call \emph{geometric inhomogeneous random graphs} (GIRGs). Since we ignore constant factors in the edge probabilities, our model is technically simpler (specifically, we avoid hyperbolic cosines), while preserving the qualitative behaviour of hyperbolic random graphs, and we suggest to replace hyperbolic random graphs by our new model in future theoretical studies. We prove the following fundamental structural and algorithmic results on GIRGs. (1) We provide a sampling algorithm that generates a random graph from our model in expected linear time, improving the best-known sampling algorithm for hyperbolic random graphs by a factor $O(\sqrt{n})$, (2) we establish that GIRGs have a constant clustering coefficient, (3) we show that GIRGs have small separators, i.e., it suffices to delete a sublinear number of edges to break the giant component into two large pieces, and (4) we show how to compress GIRGs using an expected linear number of bits

Average Distance in a General Class of Scale-Free Networks with Underlying Geometry

In Chung-Lu random graphs, a classic model for real-world networks, each vertex is equipped with a weight drawn from a power-law distribution (for which we fix an exponent $2 < \beta < 3$), and two vertices form an edge independently with probability proportional to the product of their weights. Modern, more realistic variants of this model also equip each vertex with a random position in a specific underlying geometry, which is typically Euclidean, such as the unit square, circle, or torus. The edge probability of two vertices then depends, say, inversely polynomial on their distance. We show that specific choices, such as the underlying geometry being Euclidean or the dependence on the distance being inversely polynomial, do not significantly influence the average distance, by studying a generic augmented version of Chung-Lu random graphs. Specifically, we analyze a model where the edge probability of two vertices can depend arbitrarily on their positions, as long as the marginal probability of forming an edge (for two vertices with fixed weights, one fixed position, and one random position) is as in Chung-Lu random graphs, i.e., proportional to the product of their weights. The resulting class contains Chung-Lu random graphs, hyperbolic random graphs, and geometric inhomogeneous random graphs as special cases. Our main result is that this general model has the same average distance as Chung-Lu random graphs, up to a factor $1+o(1)$. The proof also yields that our model has a giant component and polylogarithmic diameter with high probability

Development of a prototype plastic space erectable satellite Quarterly report, Mar. - May 1966

Mechanical and tensile properties of polyethylene films for prototype plastic space erectable structure

Greedy Routing and the Algorithmic Small-World Phenomenom

The algorithmic small-world phenomenon, empirically established by Milgram's letter forwarding experiments from the 60s, was theoretically explained by Kleinberg in 2000. However, from today's perspective his model has several severe shortcomings that limit the applicability to real-world networks. In order to give a more convincing explanation of the algorithmic small-world phenomenon, we study greedy routing in a more realistic random graph model (geometric inhomogeneous random graphs), which overcomes the previous shortcomings. Apart from exhibiting good properties in theory, it has also been extensively experimentally validated that this model reasonably captures real-world networks. In this model, we show that greedy routing succeeds with constant probability, and in case of success almost surely finds a path that is an almost shortest path. Our results are robust to changes in the model parameters and the routing objective. Moreover, since constant success probability is too low for technical applications, we study natural local patching methods augmenting greedy routing by backtracking and we show that such methods can ensure success probability 1 in a number of steps that is close to the shortest path length. These results also address the question of Krioukov et al. whether there are efficient local routing protocols for the internet graph. There were promising experimental studies, but the question remained unsolved theoretically. Our results give for the first time a rigorous and analytical answer, assuming our random graph model

Stimulation of gastrointestinal antibody to Shiga toxin by orogastric immunization in mice

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141579/1/imcb69.pd

Evaluation of the health status of Araucaria araucana trees using hyperspectral images

Revista oficial de la Asociación Española de Teledetección[EN] The Araucaria araucana is an endemic species from Chile and Argentina, which has a high biological, scientific and cultural value and since 2016 has shown a severe affection of leaf damage in some individuals, causing in some cases their death. The purpose of this research was to detect, from hyperspectral images, the individuals of the Araucaria species (Araucaria araucana (Molina and K. Koch)) and its degree of disease, by isolating its spectral signature and evaluating its physiological state through indices of vegetation and positioning techniques of the inflection point of the red edge, in a sector of the Ralco National Reserve, Biobío Region, Chile. Seven images were captured with the HYSPEX VNIR-1600 hyperspectral sensor, with 160 bands and a random sampling was carried out in the study area, where 90 samples of Araucarias were collected. In addition, from the remote sensing techniques applied, spatial data mining was used, in which Araucarias were classified without symptoms of disease and with symptoms of disease. A 55.11% overall accuracy was obtained in the classification of the image, 53.4% in the identification of healthy Araucaria and 55.96% in the identification of affected Araucaria. In relation to the evaluation of their sanitary status, the index with the best percentage of accuracy is the MSR (70.73%) and the one with the lowest value is the SAVI (35.47%). The positioning technique of the inflection point of the red edge delivered an accuracy percentage of 52.18% and an acceptable Kappa index.[ES] La Araucaria araucana es una especie endémica de Chile y Argentina, presenta un alto valor biológico, científico, cultural y desde el año 2016 ha evidenciado una severa afección del daño foliar en algunos individuos, causando en ciertos casos su muerte. Esta investigación tiene por objetivo detectar a partir de imágenes hiperespectrales, los individuos de la especie Araucaria (Araucaria araucana (Molina y K. Koch)) y su grado de afección, mediante el aislamiento de su firma espectral y la evaluación de su estado sanitario mediante índices de vegetación y técnicas de posicionamiento del punto de inflexión del red edge, en un sector de la Reserva Nacional Ralco, Región del Biobío, Chile. Se capturaron siete imágenes con el sensor hiperespectral HYSPEX VNIR-1600, con 160 bandas y se realizó un muestreo aleatorio en el área de estudio, donde se recolectaron 90 muestras de Araucarias. Además, de las técnicas de teledetección aplicadas, se utilizó minería de datos espaciales, que permitió clasificar las Araucarias con y sin síntomas de afección. Se logró un 55,11% de exactitud global en la clasificación de la imagen, un 53,4% en la identificación de Araucarias sanas y un 55,96% en la identificación de Araucarias afectadas. En relación a la evaluación de su estado sanitario, el índice con mejor porcentaje de exactitud es el MSR (70,73%) y el con menor porcentaje de exactitud es el SAVI (35,47%). La técnica de posicionamiento del punto de inflexión del red edge entregó un porcentaje de exactitud de 52,18% y un índice de Kappa aceptable.Este artículo se ha realizado en el contexto de fin de grado del Magíster en Teledetección, Facultad de Ciencias de la Universidad Mayor y en el mar-co del Proyecto “Prospección fitosanitaria para determinar los niveles de afección de daño foliar en bosques de Araucaria araucana de las regiones del Biobío, Araucanía y Los Ríos, 2017/ID: 633-32-LE16, financiado por la Corporación Nacional Forestal (CONAF) de Chile. 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Tree Species Classification in Boreal Forests With Hyperspectral Data. IEEE Transactions on Geoscience and Remote Sensing, 51(5), 2632- 2645. https://doi.org/10.1109/TGRS.2012.2216272Dawson, T. P., Curran, P. J. 1998. A new technique for interpolating red edge position. International Journal of Remote Sensing, 19(11), 2133−2139.https://doi. org/10.1080/014311698214910Drake, F. 2004. Uso sostenible en bosques de Araucaria araucana (Mol.) K. Koch; aplicación de modelos de gestión. Tesis doctoral, Escuela Técnica Superior de Ingenieros Agrónomos y de Montes, Universidad de Córdoba, Córdoba, España.Fassnacht, F. E., Latifi, H., Ghosh, A., Joshi, P. K., Koch, B. 2014. Assessing the potential of hyperspectral imagery to map bark beetle-induced tree mortality. Remote Sensing of Environment, 140, 533-548.https:// doi.org/10.1016/j.rse.2013.09.014Fassnacht, F. E., Stenzel, S., Gitelson, A. A. 2015. Non-destructive estimation of foliar carotenoid content of tree species using merged vegetation indices. Journal of Plant Physiology, 176, 210-217. https://doi.org/10.1016/J.JPLPH.2014.11.003Gholizadeh, A., Mišurec, J., Kopačková, V., Mielke, C., Rogass, C. 2016. Assessment of Red-Edge Position Extraction Techniques: A Case Study for Norway Spruce Forests Using HyMap and Simulated Sentinel-2 Data. Forests, 7(226), 1-17. https://doi.org/10.3390/f7100226Guyot, G., Baret, F., Major, D. 1988. High spectral resolution: Determination of spectral shifts between the red and the near infrared. International Archives of Photogrammetry and Remote Sensing, 11(750-760).Hakkenberg, C. R., Peet, R. K., Urban, D. L., Song, C. 2018. Modeling plant composition as community continua in a forest landscape with LiDAR and hyperspectral remote sensing. Ecological Applications, 28(1), 177- 190. https://doi.org/10.1002/eap.1638Hall, M. A. 1998. Correlation-based feature subset selection for machine learning. Thesis degree of doctor, University of Waikato, New Zealand.Hermosilla, T., Wulder, M. A., White, J. C., Coops, N. C., Hobart, G. W. 2015. An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sensing of Environment, 158, 220-234. https://doi.org/10.1016/j.rse.2014.11.005Horler, D., Dockray, M., Barber, J. 1983. The red edge of plant leaf reflectance. International Journal of Remote Sensing, 4(2), 273-288. https://doi.org/10.1080/01431168308948546Huete, A. R. 1988. A soil-adjusted vegetation index (SAVI). Remote sensing of environment, 25(3), 295- 309. https://doi.org/10.1016/0034-4257(88)90106-XJeffrey, A. 1985. Mathematics for Engineers and Scientists. Wokingham, UK: Van Nostrand Reinhold.Kemerer, A., Mari, N., Di Bella, C., Rebella, C. 2008. Comparación de técnicas de clasificación de cultivos a partir de información Multi E Hyperespectral. Revista de Teledetección, 29, 67-72. 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Human viruses:discovery and emergence

There are 219 virus species that are known to be able to infect humans. The first of these to be discovered was yellow fever virus in 1901, and three to four new species are still being found every year. Extrapolation of the discovery curve suggests that there is still a substantial pool of undiscovered human virus species, although an apparent slow-down in the rate of discovery of species from different families may indicate bounds to the potential range of diversity. More than two-thirds of human viruses can also infect non-human hosts, mainly mammals, and sometimes birds. Many specialist human viruses also have mammalian or avian origins. Indeed, a substantial proportion of mammalian viruses may be capable of crossing the species barrier into humans, although only around half of these are capable of being transmitted by humans and around half again of transmitting well enough to cause major outbreaks. A few possible predictors of species jumps can be identified, including the use of phylogenetically conserved cell receptors. It seems almost inevitable that new human viruses will continue to emerge, mainly from other mammals and birds, for the foreseeable future. For this reason, an effective global surveillance system for novel viruses is needed