Development of a prototype plastic space erectable satellite Quarterly report, Sep. - Nov. 1965

Test program for cap section mesh fabrication in prototype space erectable satellite developmen

Development of a prototype plastic space erectable satellite

Prototype erectable communications satellite of spherical design using plastic memory effec

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

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

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. La autora principal agradece a la Universidad Mayor por la oportuni-dad de desarrollar esta investigación; en especial a Idania Briceño por sus valiosos comentarios y Waldo Pérez, por su apoyo en las campañas de terreno.Medina, N.; Vidal, P.; Cifuentes, R.; Torralba, J.; Keusch, F. (2018). Evaluación del estado sanitario de individuos de Araucaria araucana a través de imágenes hiperespectrales. Revista de Teledetección. (52):41-53. https://doi.org/10.4995/raet.2018.10916SWORD415352Adamczyk, J., Osberger, A. 2015. Red-edge vegetation indices for detecting and assessing disturbances in Norway spruce dominated mountain forests. International Journal of Applied Earth Observation and Geoinformation, 37, 90-99. https://doi.org/10.1016/j.jag.2014.10.013Alonzo, M., Bookhagen, B., Roberts, D. A. 2014. Urban tree species mapping using hyperspectral and lidar data fusion. Remote Sensing of Environment, 148, 70-83. https://doi.org/10.1016/J.RSE.2014.03.018Ángel, Y. 2012. Metodología para identificar cultivos de coca mediante análisis de parámetros red edge y espectroscopia de imágenes. Tesis magister, Universidad Nacional de Colombia, Colombia.Armesto, J., Villagrán, C., Arroyo, M. 1996. Ecología de los bosques nativos de Chile (Vol. 1). Santiago de Chile: Editorial Universitaria.Awad, M. M. 2018. Forest mapping: a comparison between hyperspectral and multispectral images and technologies. Journal of Forestry Research, 29(5), 1395-1405 https://doi.org/10.1007/s11676-017- 0528-yBaldeck, C. A., Asner, G. P., Martin, R. E., Anderson, C. B., Knapp, D. E., Kellner, J. R., Wright, S. J. 2015. Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy. PLOS ONE, 10(7), e0118403. https://doi.org/10.1371/journal.pone.0118403Birth, G., McVey, G. 1968. Measuring the color of growing turf with a reflectance spectrophotometer. Agronomy Journal, 60(6), 640-643. https://doi. org/10.2134/agronj1968.00021962006000060016xBorràs, J., Delegido, J., Pezzola, A., Pereira, M., Morassi, G., Camps-Valls, G. 2017. Clasificación de usos del suelo a partir de imágenes Sentinel-2. Revista de Teledetección, 48, 55-66. https://doi.org/10.4995/raet.2017.7133Centro del Clima y la Resiliencia (CR2). 2018. Explorador Climático. http://explorador.cr2.cl/ Último acceso: 28 de noviembre, 2018.Chen, J. M. 1996. Evaluation of vegetation indices and a modified simple ratio for boreal applications. Canadian Journal of Remote Sensing, 22(3), 229-242. https://doi.org/10.1080/07038992.1996.10855178Cho, M. A., Skidmore, A. K. 2006. A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method. Remote sensing of environment, 101(2), 181-193. https://doi.org/10.1016/j.rse.2005.12.011Cho, M. A., Debba, P., Mutanga, O., Dudeni-Tlhone, N., Magadla, T., Khuluse, S. A. 2012. Potential utility of the spectral red-edge region of SumbandilaSat imagery for assessing indigenous forest structure and health. International Journal of Applied Earth Observation and Geoinformation, 16, 85-93.Clark, M. L., Roberts, D. A. 2012. Species-Level Differences in Hyperspectral Metrics among Tropical Rainforest Trees as Determined by a Tree-Based Classifier. Remote Sensing, 4(6), 1820-1855. https:// doi.org/10.3390/rs4061820CONAF (Corporación Nacional Forestal, CL). 2008. Catastro de los Recursos Vegetacionales Nativos de Chile, Región del Bíobio, Chile.Dalponte, M., Bruzzone, L., Gianelle, D. 2012. Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data. Remote Sensing of Environment, 123, 258-270. https://doi.org/10.1016/J.RSE.2012.03.013Dalponte, M., Orka, H. O., Gobakken, T., Gianelle, D., Naesset, E. 2013. 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. Accesible en: http:// www.aet.org.es/revistas/revista29/Revista-AET-29-7. pdf Último acceso: 28 de noviembre, 2018.Kokaly, R., Despain, D., Clark, R., Livo, K. 2003. Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data. Remote sensing of environment, 84(3), 437-456. https://doi.org/10.1016/S0034-4257(02)00133-5Landis, J., Koch, G. 1977. The measurement of observeragreement for categorical data. Biometrics. 33, 159-174. https://doi.org/10.2307/2529310Liang S. 2005. Quantitative Remote Sensing of Land Surfaces. New Jersey, A John Wiley & Sons.Liu, L., Coops, N. C., Aven, N. W, Pang, Y. 2017. Mapping urban tree species using integrated airborne hyperspectral and LiDAR remote sensing data. Remote Sensing of Environment, 200, 170-182. https://doi.org/10.1016/J.RSE.2017.08.010Melendo-Vega, J. R., Martín, M. P., Vilar del Hoyo, L., Pacheco-Labrador, J., Echavarría, P., Martínez-Vega, J. 2017. Estimación de variables biofísicas del pastizal en un ecosistema de dehesa a partir de espectroradiometría de campo e imágenes hiperespectrales aeroportadas. Revista de Teledetección, 48, 13-28. https://doi.org/10.4995/raet.2017.7481Ministerio del Medio Ambiente. 2008. Ficha de especie: Araucaria araucana (Molina) K. Koch. Inventario nacional de especies de Chile. http://especies. mma.gob.cl/CNMWeb/Web/WebCiudadana/ficha_ indepen.aspx?EspecieId=240&Version=1 Último acceso:20 de Mayo, 2017.Naidoo, L., Cho, M. A., Mathieu, R., Asner, G. 2012. Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment. ISPRS Journal of Photogrammetry and Remote Sensing, 69, 167-179. https://doi.org/10.1016/J.ISPRSJPRS.2012.03.005Ojeda, N., Sandoval, V., Soto, H., Casanova, J., Herrera, M., Morales, L., Espinosa, A., San Martín, J. 2011. Discriminación de bosques de Araucaria araucana en el Parque Nacional Conguillío, centro-sur de Chile, mediante datos Landsat TM. Bosque (Valdivia), 32(2), 113-125. https://doi.org/10.4067/S0717-92002011000200002Peñuelas, J., Filella, I., Biel, C., Serrano, L., Save, R. 1993. The reflectance at the 950-970 nm region as an indicator of plant water status. International journal of remote sensing, 14(10), 1887-1905. https://doi.org/10.1080/01431169308954010Premoli, A., Quiroga, P., Gardner, M. 2013. Araucaria araucana. The IUCN Red List of Threatened Species 2013: e.T31355A2805113. Último acceso: 15 de Marzo, 2017, de https://doi.org/10.2305/IUCN. UK.2013-1.RLTS.T31355A2805113.enRoig, M. 2010. Identificación y clasificación de formaciones forestales mediante imágenes hiperespectrales aéreas. Tesis Escuela de ingeniería forestal. Universidad Mayor de Chile, 76 p.Roujean, J., Breon, M. 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote sensing of Environment, 51(3), 375-384. https://doi.org/10.1016/0034- 4257(94)00114-3Rouse, W., Haas, H., Schell, J., Deering, D. 1974. Monitoring vegetation systems in the great plains with ERTS. Third ERTS Symposium, NASA SP-351 I: 309-317.Shafri, H., Hamdan, N. 2009. Hyperspectral Imagery for Mapping Disease Infection in Oil Palm Plantation Using Vegetation Indices and Red Edge Techniques. American Journal of Applied Sciences, 6(6), 1031. https://doi.org/10.3844/ajassp.2009.1031.1035Shafri, H., Salleh, M., Ghiyamat, A. 2006. Hyperspectral remote sensing of vegetation using red edge position techniques. American Journal of Applied Sciences, 3(6), 1864-1871. https://doi.org/10.3844/ajassp.2006.1864.1871Shi, Y., Skidmore, A. K., Wang, T., Holzwarth, S., Heiden, U., Pinnel, N., Zhu, X., Heurich, M. 2018. Tree species classification using plant functional traits from LiDAR and hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 73, 207-219. https://doi.org/10.1016/J.JAG.2018.06.018Sims, D., Gamon, J. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote sensing of environment, 81(2), 337-354. https://doi.org/10.1016/S0034-4257(02)00010-XSmith, K., Steven, M., Colls, J. 2004. Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks. Remote sensing of environment, 92(2), 207-217. https://doi.org/10.1016/j.rse.2004.06.002Somers, B., Verbesselt, J., Ampe, E. M., Sims, N., Verstraeten, W. W., Coppin, P. 2010. Spectral mixture analysis to monitor defoliation in mixedaged Eucalyptus globulus Labill plantations in southern Australia using Landsat5-TM and EO1Hyperion data. International Journal of Applied Earth Observation and Geoinformation, 12(4), 270- 277. https://doi.org/10.1016/J.JAG.2010.03.005Torralba, J. 2012. Generación de algoritmo para la identificación de alerce (Fitzroya cupressoides) mediante análisis de imágenes hiperespectrales en el lago Tagua-Tagua, X Región, Chile. Proyecto final de Grado en Ingeniería Forestal y del Medio Natural, Universidad Castilla-La Mancha, 95 p.Vogelmann, J., Rock, B., Moss, D. 1993. Red edge spectral measurements from sugar maple leaves. Remote sensing, 14(8), 1563-1575. https://doi. org/10.1080/01431169308953986Willis, K. 2015. Remote sensing change detection for ecological monitoring in United States protected areas. Biological Conservation, 182, 233-242. https://doi.org/10.1016/j.biocon.2014.12.006Wright, C., Gallant, A. 2007. Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data. 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Weekly low-dose treatment with intravenous iron sucrose maintains iron status and decreases epoetin requirement in iron-replete haemodialysis patients

Background. Haemodialysis patients need sustained treatment with intravenous iron because iron deficiency limits the efficacy of recombinant human epoetin therapy in these patients. However, the optimal intravenous iron maintenance dose has not been established yet. Methods. We performed a prospective multicentre clinical trial in iron-replete haemodialysis patients to evaluate the efficacy of weekly low-dose (50 mg) intravenous iron sucrose administration for 6 months to maintain the iron status, and to examine the effect on epoetin dosage needed to maintain stable haemoglobin values in these patients. Fifty patients were enrolled in this prospective, open-label, single arm, phase IV study. Results. Forty-two patients (84%) completed the study. After 6 months of intravenous iron sucrose treatment, the mean ferritin value showed a tendency to increase slightly from 405 ± 159 at baseline to 490 ± 275 µg/l at the end of the study, but iron, transferrin levels and transferrin saturation did not change. The haemoglobin level remained stable (12 ± 1.1 at baseline and 12.1 ± 1.5 g/dl at the end of the study). The mean dose of darbepoetin alfa could be reduced from 0.75 to 0.46 µg/kg/week; epoetin alfa was decreased from 101 to 74 IU/kg/week; and the mean dose of epoetin beta could be reduced from 148 to 131 IU/kg/week at the end of treatment. Conclusions. A regular 50 mg weekly dosing schedule of iron sucrose maintains stable iron stores and haemoglobin levels in haemodialysed patients and allows considerable dose reductions for epoetins. Low-dose intravenous iron therapy may represent an optimal approach to treat the continuous loss of iron in dialysis patient

Salmonella enteritidis meningitis in a first time diagnosed AIDS patient: Case report

We describe a patient with salmonella enteritidis meningitis and unknown HIV infection

Impression management and retrospective sense-making in corporate annual reports: banks' graphical reporting during the global financial crisis

This study investigates two potentially complementary reporting scenarios in annual reports: reactive impression management and retrospective sense-making. It examines stock market performance graphs in European listed banks? annual reports before and during the global financial crisis. Our results indicate that banks reacted to the global financial crisis by omitting stock market performance graphs from the annual report and from its most prominent sections. On the other hand, banks reduced favorable distortions and favorable performance comparisons. No significant evidence of retrospective sense-making is found. Overall, the findings are consistent with impression management incorporating human cognitive biases, with companies preferring misrepresentation by omission over misrepresentation by commission. Under high public scrutiny, banks appear to seek to provide a more favorable view by concealing negative information rather than by favorable distortions or comparisons. The study contributes to the development of impression management theories. It uses a psychological interpretation that incorporates human cognitive biases, rather than adopting a purely economically based perspective