34 research outputs found

    Efficiency analysis of information technology and online social networks management : an integrated DEA-Model assessment

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    This paper analyses the relationship between productive efficiency and online-social-networks (OSN) in Spanish telecommunications firms. A data-envelopment-analysis (DEA) is used and several indicators of business ?social Media? activities are incorporated. A super-efficiency analysis and bootstrapping techniques are performed to increase the model?s robustness and accuracy. Then, a logistic regression model is applied to characterise factors and drivers of good performance in OSN. Results reveal the company?s ability to absorb and utilise OSNs as a key factor in improving the productive efficiency. This paper presents a model for assessing the strategic performance of the presence and activity in OSN

    Modelo de distribución potencial de Leontochir ovallei con datos de sensores remotos

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    [EN] Predicting the potential distribution of short-lived species with a narrow natural distribution range is a difficult task, especially when there is limited field data. The possible distribution of L. ovallei was modeled using the maximum entropy approach. This species has a very restricted distribution along the hyperarid coastal desert in northern Chile. Our results showed that local and regional environmental factors define its distribution. Changes in altitude and microhabitat related to the landforms are of critical importance at the local scale, whereas cloud cover variations associated with coastal fog was the principal factor determining the presence of L. ovallei at the regional level. This study verified the value of the maximum entropy in understanding the factors that influence the distribution of plant species with restricted distribution ranges.[ES] Predecir la distribución potencial de especies de vida corta con un rango de distribución natural restringido es una tarea compleja, especialmente cuando los datos de campo son limitados. La posible distribución de L. ovallei se modeló utilizando la técnica de máxima entropía. Esta especie tiene una distribución muy restringida a lo largo del desierto costero hiperárido del norte de Chile. Nuestros resultados mostraron que los factores ambientales locales y regionales definen su distribución. Los cambios de altitud y el microhábitat relacionados con la forma del terreno son de importancia crítica a escala local, mientras que las variaciones en la cobertura nubosa asociadas con la niebla costera fueron el principal factor que determinó la presencia de L. ovallei a nivel regional. Este estudio verificó el valor de la técnica de máxima entropía en la comprensión de los factores que influyen en la distribución de las especies de plantas con rangos de distribución restringidos.The Master supported this work in Remote Sensing program, Earth Observation Center Hémera and Centre for Genomics, Ecology and Environment (GEMA), Faculty of Sciences, Universidad Mayor.Payacán, S.; Alfaro, F.; Pérez-Martínez, W.; Briceño-De-Urbaneja, I. (2019). Potential distribution model of Leontochir ovallei using remote sensing data. Revista de Teledetección. 0(54):59-69. https://doi.org/10.4995/raet.2019.12792OJS5969054Austin, M. 2007. Species distribution models and ecological theory: A critical assessment and some possible new approaches. Ecological Modellling, 200(1-2), 1-19. https://doi.org/10.1016/j. ecolmodel.2006.07.005Baldwin, R.A. 2009. Use of Maximum Entropy Modeling in Wildlife Research. Entropy, 11(4), 854-866. https://doi.org/10.3390/e11040854.Bartel, R.A., Sexton, J.O. 2009. Monitoring habitat dynamics for rare and endangered species using satellite images and niche-based models. Ecography, 32(5), 888-896. https://doi.org/10.1111/ j.1600-0587.2009.05797.x.Barragán-Barrera, D.C., do Amaral, K.B., ChávezCarreño, P.A., Farías-Curtidor, N., LancherosNeva, R., Botero-Acosta, N., Bueno, P., Moreno, I.B., Bolaños-Jiménez, J., Bouveret, L., Castelblanco-Martínez, D.N., Luksenburg, J.A., Melliger, J., Mesa-Gutiérrez R., de Montgolfier, B., Ramos, E.A., Ridoux, V., Palacios, D.M. 2019. Ecological Niche Modeling of Three Species of Stenella Dolphins in the Caribbean Basin, With Application to the Seaflower Biosphere Reserve. Frontiers in Marine Science, 6(10). https://doi. org/10.3389/fmars.2019.00010.Carvajal, D.E., Loayza, A.P., López, R.P., Toro, P.J., Squeo, F.A. 2014. Growth and early seedling survival of four Atacama Desert shrub species under experimental light and water availability regimes. Revista Chilena de Historia Natural, 87(1), 28. https://doi.org/10.1186/S40693-014- 0028-9.Cereceda, P, Larraín, H., Osses, P., Lázaro, P., García, J.L., Hernández, V. 2000. El factor clima en la floración del desierto en los años "El Niño" 1991 y 1997". Revista de Geografía Norte Grande, 27, 37-52. Accesible at https://repositorio.uc.cl/ bitstream/handle/11534/10433/000313720. pdf?sequence=1&isAllowed=yCONAF. 1997. Corporación Nacional Forestal, Plan de Manejo Parque Nacional Llanos de Challe. Documento de Trabajo Nº 250. Mieres, G. (Ed.) Santiago, 129 pp.Ćorović, J, Popović, M., Cogălniceanu, D., Carretero, M.A., Crnobrnja-Isailović, J. 2018. Distribution of the meadow lizard in Europe and its realized ecological niche model. Journal of Natural History, 52(29-30), 1909-1925. https://doi.org/10.1080/002 22933.2018.1502829.Chavez, P.S. 1996. Image-based atmospheric corrections: Revisited and improved. Photogrammetric Engineering and Remote Sensing, 62(9), 1025-1036.Chávez, R.O., Moreira-Muñoz, A., Galleguillos, M., Olea, M., Aguayo, J., Latín, A., Aguilera-Betti, I., Muñoz, A.A., Manríquez H. 2019. GIMMS NDVI time series reveal the extent, duration, and intensity of "blooming desert" events in the hyper-arid Atacama Desert, Northern Chile. International Journal Applied Earth Observation and Geoinformation, 76, 193-203. https://doi.org/10.1016/j.jag.2018.11.013.Dormann, C.F., Elith, J., Bacher, S., Buchmann, C., Carls, G., Carré, G., Marquéz, J.R., Gruber, B., Lafourcade, B., Leitão, P.J., Münkemüller, T., McClean, C., Osborne, P.E., Reineking, B., Schröder, B., Skidmore, A.K., Zurell, D., Lautenbach, S. 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36(1), 27-46. https://doi.org/10.1111/j.1600- 0587.2012.07348.x.Errázuriz, A.M., Hanisch, M. 1995. Horizonte 7: Historia y Geografía. Editorial Andrés Bello. https:// books.google.com.br/books?id=fB2TTLzH_5IC (accessed 07 February 2017).Franklin, J. 2010. Ecology, Biodiversity and Conservation. In Mapping Species Distributions: Spatial Inference and Prediction, Frontmatter, pp. I-Viii, Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511810602.Garreaud, R., Rutllant, J.A., Fuenzalida, H. 2002. Coastal Lows along the Subtropical West Coast of South America: Mean Structure and Evolution. Monthly Weather Review, 130, 75-88. https://doi. org/10.1175/1520-0493(2002)1302.0.CO;2.Garreaud, R., Barichivich, J., Christie, D.A. and Maldonado, A. 2008. Interannual variability of the coastal fog at Fray Jorge relict forests in semiarid Chile. Journal of Geophysical Research, 113, G04011. https://doi.org/10.1029/2008JG000709.Giannakopoulos, A., Vasileiou, N.G.C., Gougoulis, D.A., Cripps, P.J., Ioannidi, K.S., Chatzopoulos, D.C., Billinis, C., Mavrogianni, V.S., Petinaki, E., Fthenakis, G.C. 2019. Use of geographical information system and ecological niche modelling for predicting potential space distribution of subclinical mastitis in ewes. Veterinary Microbiology, 228, 119-128. https://doi.org/10.1016/j.vetmic.2018.11.021.González, B.A., Samaniego, H., Marín, J.C., Estades, C.F. 2013. Unveiling Current Guanaco Distribution in Chile Based upon Niche Structure of Phylogeographic Lineages: Andean Puna to Subpolar Forests. PLoS ONE, 8(11), e78894. https://doi.org/10.1371/journal.pone.0078894.Haughian, S.R., Clayden, S.R., Cameron, R. 2018. On the distribution and habitat of Fuscopannaria leucosticta in New Brunswick, Canada. Écoscience, 26(2), 99-112. https://doi.org/10.1080/11956860.2 018.1526997.Hawk, A.M. 2017. Habitat modeling of a rare endemic Trillium Species (Trillium Simile Gleason): a comparison of the methods maxent and domain for modeling rare species-rich habitat. In Biology Department. Vol. Master of Science in Biology (Dissertation). Western Carolina University, USA.Hernández, P.A., Graham, C.H., Master, L.L., Albert, D.L. 2006. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29, 773-785. https://doi.org/10.1111/j.0906- 7590.2006.04700.x.Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jaarvis, A. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal Climatology, 25(15), 1965-1978. https://doi.org/10.1002/joc.1276.Jafari, A., Zamani-Ahmadmahmoodi, R., Mirzaei, R. 2018. Persian leopard and wild sheep distribution modeling using the Maxent model in the Tang-eSayad protected area, Iran. Mammalia, 83(1), 84- 96. https://doi.org/10.1515/mammalia-2016-0155.Juliá, C., Montecinos, S., Maldonado, A. 2008. Características climáticas de la Región de Atacama, In Libro Rojo de la Flora Nativa y de Los Sitios Prioritarios para su Conservación: Región de Atacama, Squeo, F.A., Arancio, G., Gutiérrez, J.R., (Eds.), pp. 3: 25-42, Ediciones Universidad de La Serena, La Serena, Chile.Kafley, H., Khadka, M., Sharma, M. 2009. Habitat Evaluation and Suitability Modeling of Rhinoceros unicornis in Chitwan National Park, Nepal: A Geospatial Approach, In XII World Forestry Congress, 18-23 October, Buenos Aires, Argentina.Kottek, M., Grieser, J., Beck, C., Rudolf, B., Rubel, F. 2006. World Map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift, 15(3), 259-263. https://doi.org/10.1127/0941- 2948/2006/0130.Kumar, S., Stohlgren, T.J. 2009. Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia. Journal of Ecology and The Natural Environment, 1(4), 94-98. Accesible at https://pdfs. semanticscholar.org/66b8/8f62b73226934f6f7216 efbcf31ac1e7ef61.pdf.Larraín, H., Velásquez, F., Cereceda, P., Espejo, R., Pinto, R., Osses, P., Schemenauer, R.S. 2002. Fog measurements at the site "Falda Verde" north of Chañaral compared with other fog stations of Chile. Atmospheric Research, 64(1-4), 273-284. https://doi.org/10.1016/S0169-8095(02)00098-4.Latorre, C., González, A.L., Quade, J., Fariña, J.M., Pinto, R., Marquet, P.A. 2011. Establishment and formation of fog-dependent Tillandsia landbeckii dunes in the Atacama Desert: Evidence from radiocarbon and stable isotopes. Journal of Geophysical Research, 116, G03033. https://doi.org/10.1029/2010JG001521.Marini, M.Á., Barbet-Massin, M., Martínez, J., Prestes, N.P., Jiguet, F. 2010. Applying ecological niche modelling to plan conservation actions for the Red-spectacled Amazon (Amazona pretrei). Biological Conservation, 143(1), 102-112. https://doi.org/10.1016/j.biocon.2009.09.009.MINSEGPRES (Ministerio Secretaría General de la Presidencia). 2008. Decreto Supremo N° 50/2008. Aprueba y oficializa nómina para el segundo proceso de clasificación de especies según su estado de conservación, Santiago, Chile.Morales, N. 2012. Modelos de distribución de especies: Software Maxent y sus aplicaciones en Conservación. Revista Conservación Ambiental, 2(1), 1-5. Accesible at https://issuu.com/ fundacionecomabi/docs/revista_conservaci__n_ ambiental_mas.Muñoz, C. 1973. Chile: Plantas en extinción. Editorial Universitaria, Santiago, Chile. 248 p.Muñoz-Schick, M., Sierra, T. 2006. Ficha de antecedentes de especie: Leontochir ovallei, in Documento de trabajo, Proceso Nacional de Clasificación de Especies. Comisión Nacional del Medio Ambiente (CONAMA).Phillips, S.J., Dudík, M., Schapire, R.E. 2004. A maximum entropy approach to species distribution modeling. In Proceedings of the Twenty-First International Conference on Machine Learning (ICML), 4-8 July, Banff, Alberta Canada, p.83. https://doi.org/10.1145/1015330.1015412.Phillips, S.B., Aneja, V.P., Kang, D., Arya, S.P. 2006. Modelling and analysis of the atmospheric nitrogen deposition in North Carolina. International Journal of Global Environmental Issues, 6(2-3), 231-252. https://doi.org/10.1504/IJGENVI.2006.010156Pliscoff, P., Fuentes-Castillo, T. 2011. Modelación de la distribución de especies y ecosistemas en el tiempo y en el espacio: una revisión de las nuevas herramientas y enfoques disponible. Revista de Geografía Norte Grande, 48, 61-79. https://doi.org/10.4067/S0718-34022011000100005.QGIS Development Team. 2017. QGIS Geographic Information System, Version 2.18 Brighton. Open Source Geospatial Foundation Project. Accesible at http://www.qgis.org.R Core Team. 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/.Rundel, P.W., Dillon, M.O., Palam, B., Mooney, H.A., Gulmon, S.L., Ehleringer, J.R. 1991. The Phytogeography and Ecology of the Coastal Atacama and Peruvian Deserts. Aliso: A Journal of Systematic and Evolutionary Botany, 13(1/2), 1-49. https://doi.org/10.5642/aliso.19911301.02.Sarma, B., Baruah, P., Tanti B. 2018. Habitat distribution modeling for reintroduction and conservation of Aristolochia indica L. - a threatened medicinal plant in Assam, India. Journal Threatened Taxa, 10(11), 12531-12537. https://doi.org/10.11609/jott.3600.10.11.12531-12537.Sarricolea, P., Herrera-Ossandon, M.J., MeseguerRuiz, Ó. 2017. Climatic regionalisation of continental Chile. Journal of Maps, 13(2), 66-73.https://doi.org/10.1080/17445647.2016.1259592.Shiv, P., Samant, S.S., Lal, M., Ram J. 2019. Population Assessment and Habitat Distribution Modelling of High Value Corylus jacquemontii for in situ Conservation in the State of Himachal Pradesh, India. Proceedings of the Indian National Science Academy, 85(1), 275-289. https://doi.org/10.16943/ ptinsa/2018/49507.Squeo, F.A., Arancio, G., Gutiérrez, J.R. 2008. Libro rojo de la Flora Nativa y de los Sitios Prioritarios para su Conservación: Región de Atacama. Editorial Universidad de La Serena, La Serena, Chile, pp. 25-42.Tang, J., Li, J., Lu, H., Lu, F.,d Lu, B. 2018. Potential distribution of an invasive pest, Euplatypus parallelus, in China as predicted by Maxent. Pest Management Science, 75, 1630-1637. https://doi. org/10.1002/ps.5280.Thapa, A., Wu, R., Hu, Y., Nie, Y., Sing, P.B., Khatiwada, J.R., Yan, L., Gu, X., Wei, F. 2018. Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling. Ecology and Evolution, 8(21), 10542-10554. https://doi.org/10.1002/ece3.4526.Thompson, M.V., Palma, B., Knowles, J.T., Holbrook, N.M. 2003. Multi-annual climate in Parque Nacional Pan de Azúcar, Atacama Desert, Chile. Revista Chilena de Historia Natural, 76(2), 235-254. https://doi.org/10.4067/S0716- 078X2003000200009.Urbina-Cardona, J.N., Flores-Villela, O. 2010. Ecological-Niche Modeling and Prioritization of Conservation-Area Networks for Mexican Herpetofauna. Conservation Biology, 24(4), 1031-1041. https://doi.org/10.1111/j.1523- 1739.2009.01432.x.Wang, W.C., Lo, N.J., Chang, W.I., Huang, K.Y. 2012. Modeling spatial distribution of a rare and endangered plant species (Brainea insignis) in Central Taiwan", in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B7, 241-246. https://doi.org/10.5194/isprsarchivesXXXIX-B7-241-2012.Wilson, A.M., Jetz, W. 2016. 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    Business models in the Smart Grid: challenges, opportunities and proposals for prosumer profitability.

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    Considering that non-renewable energy resources are dwindling, the smart grid turns out to be one of the most promising and compelling systems for the future of energy. Not only does it combine efficient energy consumption with avant-garde technologies related to renewable energies, but it is also capable of providing several beneficial utilities, such as power monitoring and data provision. When smart grid end users turn into prosumers, they become arguably the most important value creators within the smart grid and a decisive agent of change in terms of electricity usage. There is a plethora of research and development areas related to the smart grid that can be exploited for new business opportunities, thus spawning another branch of the so-called ?green economy? focused on turning smart energy usage into a profitable business. This paper deals with emerging business models for smart grid prosumers, their strengths and weaknesses and puts forward new prosumer-oriented business models, along with their value propositions

    Un modelo no paramétrico de evaluación de la eficiencia y la gestión de las redes sociales virtuales : una aplicación a las empresas del sector de las telecomunicaciones en España

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    Este artículo analiza la relación entre la eficiencia productiva y las Redes Sociales Virtuales (RSV) en las empresas de telecomunicaciones en España. En una primera etapa, se aplica el análisis envolvente de datos (DEA) incorporando varios indicadores de actividad ?Social Media?. En una segunda etapa, se utiliza una regresión logística para caracterizar las empresas eficientes. Los resultados muestran que la capacidad de absorción y utilización de las RSV es un factor determinante en la mejora de la eficiencia productiva. La utilización combinada y las distintas capacidades de gestión de las RSV permiten identificarlas como un recurso heterogéneo. Este trabajo presenta un modelo para la evaluación del desempeño estratégico al abordar su presencia y actividad en RSV. ABSTRACT. This paper analyzes the relationship between the productive efficiency and the Online Social Networks - OSN in the Spanish telecommunications firms. First, a data envelopment analysis (DEA) is used and several indicators of business "Social Media" activities are incorporated. In a second stage, a logistic regression model regression is applied to characteri ze the efficient enterprises. Results show that the company's ability to absorb and utilize this OSN is a key factor in improving the productive efficiency. These results on the combined use and different management capabilities of OSN point to a definitio n of OSN as a heterogeneous resource. This paper presents a model for assessing the strategic performance to address their presence and activity in OSN

    Estimation of the subsidence around the trace of the San Ramón Chile fault, using the SBAS DInSAR technique through TerraSAR-X images

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    [EN] Chile is one of the countries with the highest seismicity in the world and is affected by three types of seismogenic sources; interplate, intraplate and superficial or cortical intraplate. In this context, in the eastern sector of the city of Santiago, capital of Chile, the Falla San Ramón (FSR) is located. It is a cortical seismogenic source, which threatens its habitants and the various economic activities that are located in that sector, geological hazards such as earthquakes and mass removals. In relation to the above, this study aims to identify and establish the subsidence areas in a longitudinal strip of the Santiago mountain front and its impact on the neighboring communes to the FSR trace during the years 2011 to 2017. To do this, The DInSAR technique was used with the Small Baseline Subset (SBAS) algorithm through a time series of images from the TerraSAR-X (TSX) satellite. The results show subsidence zones, with average displacements ranging from -13.11 mm to +9.89 mm, with an average annual speed rate of -2.19 to +1.65 mm/year.[ES] Chile es uno de los países con mayor sismicidad en el mundo y es afectado por tres tipos de fuentes sismogénicas: interplaca, intraplaca e intraplaca superficial o cortical. En este contexto, en el sector oriente de la ciudad de Santiago, capital de Chile, se localiza la Falla San Ramón (FSR). Se trata de una fuente sismogénica cortical que amenaza a sus habitantes y a las diversas actividades económicas que se ubican en ese sector, generando peligros geológicos como seísmos y remociones en masa. En relación con lo anterior, este estudio tiene por objetivo identificar y establecer las áreas de subsidencias en una franja longitudinal del frente cordillerano de Santiago y su impacto sobre las comunas aledañas a la traza de la FSR durante los años 2011 a 2017. Para ello, se utilizó la técnica DInSAR con el algoritmo Small Baseline Subset (SBAS) mediante una serie de tiempo de imágenes del satélite TerraSAR-X (TSX). Los resultados muestran zonas de subsidencia, con desplazamientos promedio que van desde los -13,11 mm hasta los +9,89 mm, con una tasa de velocidad anual promedio de -2,19 hasta +1,65 mm/año.Este artículo es parte del trabajo para obtener el grado de Magíster en Teledetección de la Facultad de Ciencias de la Universidad Mayor de Chile. El autor agradece a la Universidad por su incondicional apoyo. En especial agradecer a Marcela Vivanco, Walter Tapia, y especialmente al Dr. Dominique Derauw del Centro Espacial de Lieja, Bélgica. Finalmente, al Centro Aeroespacial Alemán DLR a través del convenio PROPOSAL ID: GEO3642 suscrito para la utilización de las imágenes TerraSAR-X.Lamperein-Polo, P.; Vidal-Páez, P.; Pérez-Martínez, W. (2022). Estimación de la subsidencia en torno a la traza de la falla de San Ramón Chile, mediante la técnica SBAS DInSAR usando imágenes TerraSAR-X. Revista de Teledetección. 0(59):87-102. https://doi.org/10.4995/raet.2022.15640OJS87102059Abidin, H.Z., Andreas, H., Gumilar, I., Sidiq, T.P., Fukuda, Y.. 2013. Land subsidence in coastal city of Semarang ( Indonesia ): characteristics , impacts and causes.Geomatics, Natural Hazards and Risk, 4(3), 226-240. https://doi.org/10.1080/19475705.2012.692336Ammirati, J.B., Vargas, G., Rebolledo, S., Abrahami, R., Potin, B., Leyton, F., Ruiz, S. 2019. The Crustal Seismicity of the Western Andean Thrust (Central Chile, 33°-34° S ): Implications for Regional Tectonics and Seismic Hazard in the Santiago Area. Bulletin of the Seismological Society of America 109(5), 1985- 1999. https://doi.org/10.1785/0120190082Armijo, R., Rauld, R., Thiele, R., Vargas, G., Campos, J., Lacassin, R., and Kausel, E. 2010. The West Andean Thrust , the San Ramón Fault , and the seismic hazard for Santiago, Chile. Tectonisc, 29(2), 1-34. https://doi.org/10.1029/2008TC002427Berardino, P., Fornaro, G., Lanari, R., Sansosti, E. 2002. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. In IEEE Transactions on Geoscience and Remote Sensing, 40(11), 2375-2383. https://doi.org/10.1109/TGRS.2002.803792Braun, A. (2020). Sentinel-1 Toolbox: DEM generation with Sentinel-1 Workflow and challenges. ESA online publications, 27 p.Cabral-Cano, E., Osmanoglu, B., Dixon, T., Wdowinski, S., Demets, C., Cigna, F., Díaz-Molina, O. 2010. Subsidence and fault hazard maps using PSI and permanent GPS networks in central Mexico. In Land Subsidence, Associated Hazards and the Role of Natural Resources Development (p. 255-259). (IAHS-AISH Publication; Vol. 339).Cakir, Z., Akoglu, A.M., Belabbes, S., Ergintav, S., Meghraoui, M. 2005. Creeping along the Ismetpasa section of the North Anatolian fault (Western Turkey): Rate and extent from InSAR. Earth and Planetary Science Letters, 238(1-2), 225-234. https://doi.org/10.1016/j.epsl.2005.06.044Casu F., Manzo M., Lanari, R. 2006. A quantitative assessment of the SBAS algorithm performance for surface deformation retrieval from DInSAR data. Remote Sensing of Environment, 102 (3-4), 195-210. https://doi.org/10.1016/j.rse.2006.01.023Costantini, M. 1998. A novel phase unwrapping method based on network programming. In IEEE Transactions on Geoscience and Remote Sensing, 36(3), 813-821. https://doi.org/10.1109/36.673674Crosetto, M., Tscherning, C.C., Crippa, B., Castillo, M. 2002. Subsidence monitoring using SAR interferometry: Reduction of the atmospheric effects using stochastic filtering. Geophysical Research Letters, 29(9), 26-1-26-4. https://doi.org/10.1029/2001GL013544Fernandez, P., Irigaray, C., Jimenez, J., El Hamdouni, R., Crosetto, M., Monserrat, O., Chacon, J. 2009. First delimitation of areas affected by ground deformations in the Guadalfeo River Valley and Granada metropolitan area (Spain) using the DInSAR technique. Engineering Geology, 105(1-2), 84-101. https://doi.org/10.1016/j.enggeo.2008.12.005Ferretti, A., Monti-guarnieri, A., Milano, P. 2007. InSAR Principles - Guidelines for SAR Interferometry Processing and Interpretation SAR Interferometry Processing and Interpretation. ESA Publications, TM-19 . ISBN 92-9092-233-8.Galloway, D.L., Hoffmann, J. 2007. The application of satellite differential SAR interferometry- derived ground displacements in hydrogeology. Hydrogeology Journal, 15, 133-154. https://doi.org/10.1007/s10040-006-0121-5Hanssen, R.F. 2001. Radar interferometry: Data Interpretation and Error Analysis (Vol. 2). Springer Science, Dordrecht, 308 p. https://doi.org/10.1007/0- 306-47633-9Hermosilla Díaz, D.E. 2016. Interferometría Radar de Apertura Sintética (INSAR) aplicada al Estudio del movimiento de ladera aledaña al Volcán Calbuco con la ayuda de imágenes Sentinel 1A. Tesis para optar al Título de Ingeniero en Aviación Comercial, Academia de Ciencias Aeronáuticas, Universidad Federico Santa María, Chile, 82 p.INE. 2017. Resultados Censo de Población y Vivienda 2017. Instituto Nacional de Estadísticas, Santiago,Chile.Lagios, E., Sakkas, V., Papadimitriou, P., Parcharidis, I., Damiata, B.N., Chousianitis, K., Vassilopoulou, S. 2007. Crustal deformation in the Central Ionian Islands (Greece): Results from DGPS and DInSAR analyses (1995-2006). Tectonophysics, 444(1-4), 119-145. https://doi.org/10.1016/j.tecto.2007.08.018Lakhote, A., Thakkar, M.G., Kandregula, R.S., Jani, C., Kothyari, G.C., Chauhan, G., Bhandari, S. 2020. Estimation of active surface deformation in the eastern Kachchh region, western India: Application of multi-sensor DInSAR technique. Quaternary International, 575-576, 130-140. https://doi.org/10.1016/j.quaint.2020.07.010Osmanoglu, B., Dixon, T.H., Wdowinski, S., Cabral-Cano, E., Jiang, Y. 2010. Mexico City subsidence observed with Persistent Scatterer InSAR. International Journal Applied Earth Observation and Geoinfomation, 13(1), 1-12. https://doi.org/10.1016/j.jag.2010.05.009Parcharidis, I., Kokkalas, S., Fountoulis, I., and Foumelis, M. 2009. Detection and Monitoring of Active Faults in Urban Environments: Time Series Interferometry on the Cities of Patras and Pyrgos (Peloponnese, Greece). Remote Sensing, 1(4), 676-696. https://doi.org/10.3390/rs1040676Rauld, R. 2011. Deformación cortical y peligro sísmico asociado a la falla San Ramón en el frente cordillerano de Santiago, Chile Central (33° S). 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Tesis para Optar al Grado de Magíster en Aplicaciones Espaciales de Alerta y Respuesta Temprana a Emergencias, Universidad Nacional de Córdoba,Argentina, IG-CONAE/UNC.Solano-Rojas, D., Cabral-Cano, E., Hernández-Espriú, A., Wdowinski, S., Demets, C., Salazar-Tlaczani, L., Falorni, G., Bohane, A. 2015. La relación de subsidencia del terreno InSAR-GPS y el abatimiento del nivel estático en pozos de la zona Metropolitana de la Ciudad de México. Boletín de la Sociedad Geológica Mexicana, 67(2).https://doi.org/10.18268/BSGM2015v67n2a10Tobita, M., Nishimura, T., Kobayashi, T., Hao, K.X., Shindo, Y. 2011. Estimation of coseismic deformation and a fault model of the 2010 Yushu earthquake using PALSAR interferometry data. Earth and Planetary Science Letters, 307(3-4), 430-438. https://doi.org/10.1016/j.epsl.2011.05.017Valenzuela, G. (1978). Suelo de Fundación de Santiago. Instituto de Investigaciones Geológicas, 33(Boletín N°33), 21.Vargas, G., Rebolledo, S. 2015. 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    Respuesta del tomate (Solanum lycopersicum L.) al consumo hídrico, área foliar y rendimiento con respecto al número de tallos

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    The objective of this study was to analyze tomato responses to water requirements (evaluated by means of balance lysimeters), leaf area, yield, quality and its relationship with weather, depending on the number of stems. The work was carried out in a greenhouse under hydroponic conditions. Tezontle (Stuff) was used as a substrate and a drip irrigation system was installed. The experiment consisted of three treatments, with one (T1), two (T2) and three (T3) stems per plant. The daily crop evapotranspiration was 0.30 L m-2 in the initial stage, up to 4.41, 4.77 and 6.0 L m-2, in the stage of maximum demand for T1, T2 and T3. The gross volume applied throughout the cycle was 352.2, 388.4 and 434.7 L m-2 for T1, T2 and T3, with productivities of 49, 41 and 36 kg m3 and yields of 20, 18 and 16 kg m-2 for T1, T2 and T3. Regarding quality parameters in size, T1 was the best, with 69, 23, 8 and 1% fruits of first, second, third and small fruits per plant respectively. The meteorological variables such as; temperature, wind, relative humidity, vapor pressure deficit and atmospheric water potential determined the consumption of water and nutrients in crops and are variables for irrigation scheduling.El objetivo de este estudio fue analizar las respuestas del jitomate a los requerimientos hídricos (evaluado por medio de lisímetros de balance), área foliar, rendimiento, calidad y su relación con el tiempo atmosférico, en función del número tallos. El trabajo se realizó en un invernadero en condiciones de hidroponía. Se utilizó tezontle como sustrato y un sistema de riego por goteo. El experimento consistió en tres tratamientos, con uno (T1), dos (T2) y tres (T3) tallos por planta. El consumo por evapotranspiración diaria del cultivo fue 0,30 L m-2 en la etapa inicial, hasta 4,41, 4,77 y 6,0 L m-2 en la etapa de máxima demanda para T1, T2 y T3. El volumen bruto aplicado durante todo el ciclo fue 352,2; 388,4 y 434,7 L m-2 para T1, T2 y T3, con productividades de 49, 41 y 36 kg m3 y rendimientos de 20, 18 y 16 kg m-2 para T1, T2 y T3. Con relación a los parámetros de calidad en tamaño, el T1 fue mejor, con 69, 23, 8 y 1% frutos de primera, segunda, tercera y frutos pequeños por planta. Las variables meteorológicas como temperatura, viento, humedad relativa, déficit de presión de vapor y potencial hídrico atmosférico determinan el consumo de agua y nutrimentos en los cultivos y son variables para calendarización del riego

    Respuesta del tomate (Solanum lycopersicum L.) al consumo hídrico, área foliar y rendimiento con respecto al número de tallos en invernadero

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    The objective of this study was to analyze tomato responses to water requirements (evaluated by means of balance lysimeters), leaf area, yield, quality and its relationship with weather, depending on the number of stems. The work was carried out in a greenhouse under hydroponic conditions. Tezontle (Stuff) was used as a substrate and a drip irrigation system was installed. The experiment consisted of three treatments, with one (T1), two (T2) and three (T3) stems per plant. The daily crop evapotranspiration was 0.30 L m-2 in the initial stage, up to 4.41, 4.77 and 6.0 L m-2, in the stage of maximum demand for T1, T2 and T3. The gross volume applied throughout the cycle was 352.2, 388.4 and 434.7 L m-2 for T1, T2 and T3, with productivities of 49, 41 and 36 kg m3 and yields of 20, 18 and 16 kg m-2 for T1, T2 and T3. Regarding quality parameters in size, T1 was the best, with 69, 23, 8 and 1% fruits of first, second, third and small fruits per plant respectively. The meteorological variables such as; temperature, wind, relative humidity, vapor pressure deficit and atmospheric water potential determined the consumption of water and nutrients in crops and are variables for irrigation scheduling.El objetivo de este estudio fue analizar las respuestas del jitomate a los requerimientos hídricos (evaluado por medio de lisímetros de balance), área foliar, rendimiento, calidad y su relación con el tiempo atmosférico, en función del número de tallos. El trabajo se realizó en un invernadero en condiciones de hidroponía. Se utilizó tezontle como sustrato y un sistema de riego por goteo. El experimento consistió en tres tratamientos, con uno (T1), dos (T2) y tres (T3) tallos por planta. El consumo por evapotranspiración diaria del cultivo fue 0,30 L m-2 en la etapa inicial, hasta 4,41, 4,77 y 6,0 L m-2 en la etapa de máxima demanda para T1, T2 y T3. El volumen bruto aplicado durante todo el ciclo fue 352,2; 388,4 y 434,7 L m-2 para T1, T2 y T3, con productividades de 49, 41 y 36 kg m3 y rendimientos de 20, 18 y 16 kg m-2 para T1, T2 y T3. Con relación a los parámetros de calidad en tamaño, el T1 fue mejor, con 69, 23, 8 y 1% frutos de primera, segunda, tercera y frutos pequeños por planta. Las variables meteorológicas como temperatura, viento, humedad relativa, déficit de presión de vapor y potencial hídrico atmosférico determinan el consumo de agua y nutrimentos en los cultivos y son variables para calendarización del riego.Fil: Mendoza-Pérez, Cándido. Colegio de Postgraduados. Campus Montecillo (México)Fil: Ramírez-Ayala, Carlos. Colegio de Postgraduados. Campus Montecillo (México)Fil: Ojeda-Bustamante, Waldo. Instituto Mexicano de Tecnología del Agua (Morelos, México).Fil: Trejo, Carlos. Colegio de Postgraduados. Campus Montecillo (México)Fil: López-Ordaz, Anselmo. Texcoco (México). Colegio de PostgraduadosFil: Quevedo-Nolasco, Abel. Colegio de Postgraduados. Campus Montecillo (México)Fil: Martínez Ruiz, Antonio. Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (México

    Construyendo el capital intelectual en la gestión del conocimiento para el aprendizaje en una administración pública española [Building the intellectual capital in the Knowledge Management for learning in a Spanish Public Administration]

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    Las organizaciones públicas son grandes productoras y consumidoras de conocimiento. La Gestión del Conocimiento y su utilización inteligente permiten generar valor público en sus actuaciones. El conocimiento es el conjunto de nociones y habilidades por las cuales los miembros de una organización pública atienden y completan su trabajo. Uno de los primeros pasos hacia una Gestión Inteligente del Conocimiento en una Administración Pública Forestal es la construcción y definición del capital intelectual mediante la socialización del conocimiento en el que se muestra el “saber hacer” de la organización. Esta comunicación presenta los resultados y conclusiones del Curso “Gestión y Política Forestal en la Administración General del Estado en España” como una experiencia innovadora y pionera de construcción del capital intelectual en la Administración Forestal del Estado dentro de un sistema de Gestión del Conocimiento para el aprendizaje. Por último, se indican los siguientes pasos y factores a tener en cuenta para la valoración del capital intelectual en una Administración Pública Forestal dentro de Sistema de Gestión del Conocimiento. [Public organizations are great producers and consumers of knowledge. The Knowledge Management and its intelligent use allow to generate public value in its actions. Knowledge is the set of notions and skills by which the members of a public organization attend and complete their work. One of the first steps towards an Intelligent Management of Knowledge in a Public Forest Administration is the construction and definition of intellectual capital through the socialization of knowledge in which the organization's "know-how" is shown. This communication presents the results and conclusions of the course "Forest Management and Policy in the General State Administration in Spain" as an innovative and pioneering experience of the construction of intellectual capital in the State Forestry Administration within a system of Knowledge Management for the learning. Finally, the following steps and factors to be taken into account for the valuation of intellectual capital in a Public Forest Administration within the Knowledge Management System

    SPOT and GPRS drifting buoys for HF Radar calibration

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    Traditional drifting buoys have been designed to measure the surface currents at a nominal depth of 15m with drogues of 6m height. Herein, in order to assess the performance of HF Radars two designs of Lagrangian drifting buoys have been developed and targeted to provide the vertically averaged velocity of the currents in the frst 2 and 0.5 meters of the water column. These are the layer heights of the HF Radars of RAIA observatory. The buoys were made with standard materials and of-the-shelf electronics, to keep costs as low as possible.Peer Reviewe

    Model type II regression for lagrangian validation of HF radar velocities in the NW Iberian Peninsula

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    Two designs of lagrangian low-cost drifting buoys have been developed in order to monitor the ocean surface dynamics in the North-west Iberian Peninsula and provide ground-truth observations that can be used to assess the performance of High Frequency (HF) Radars of RAIA observatory from 2020 to 2022. Since regression model type I, which is typically used in buoy-HF radar antennas validations, does not consider the presence of errors in the observations from both instruments, regression model type II was proposed to instrument intercomparison. Furthermore, a new metric was developed to better assess both model types regressions in lagrangian validations.Peer Reviewe
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