Diseño e Implementaci¡on de una antena en THz para un fotomezclador basado en nanocontactos

[ES] Este trabajo consiste en el diseño y fabricación de una antena de banda ancha para el rango de THz, entre 300GHz y 3THz. La antena se utilizará en fuentes de THz (fotomezcladores). La tecnología utilizada combina técnicas tanto en el rango de las microondas como en el de fotónica.[EN] This work consists of designing and fabricating a broadband antenna in the Terahertz range, between 300 GHz and 3 THz. The antenna will be used in Terahertz sources (photomixers). The used technology combines both work in microwave and photonic range.Laín Rubio, V. (2019). Diseño e Implementaci¡on de una antena en THz para un fotomezclador basado en nanocontactos. http://hdl.handle.net/10251/12462

d8···d10 RhI···AuI Interactions in Rh 2,6-Xylylisocyanide Complexes with [Au(CN)2]: Bond Analysis and Crystal Effects

Producción CientíficaThe well known [RhL4]n(anion)n structures, with RhI···RhI d8···d8 interactions, are replaced by others with RhI···AuI d8···d10 interactions such as [{RhL4}{Au(CN)2}] (L = 2,6-Xylylisocyanide) or [{RhL4}{Au(CN)2} {RhL4}{Au2(CN)3}·4(CHCl3)]∞ when the anion is [Au(CN)2]–. Orbital (Rh···Au), coulombic, and inter-unit π-π aryl stacking interactions stabilize these crystal structures.Ministerio de Economía, Industria y Competitividad (grant CTQ2017-89217-P)Junta de Castilla y León (project VA038G18

Generalized (κ, µ)-space forms and d-homothetic deformations

We study the Da-homothetic deformations of generalized (κ, µ)- space forms. We prove that the deformed spaces are again generalized (κ, µ)-space forms in dimension 3, but not in general, although a slight change in their definition would make them so. We give infinitely many examples of generalized (κ, µ)-space forms of dimension 3

The curvature tensor of almost cosymplectic and almost Kenmotsu ( κ, μ, ν ) -space

We study the Riemann curvature tensor of (κ, µ, ν)-spaces when they have almost cosymplectic and almost Kenmotsu structures, giving its writing explicitly. This leads to the definition and study of a natural generalisation of the contact metric (κ, µ, ν)-spaces. We present examples or obstruction results of these spaces in all possible cases.Ministerio de Educación y Ciencia (MEC). EspañaPlan Andaluz de Investigación (Junta de Andalucía

The curvature tensor of almost cosymplectic and almost Kenmotsu (κ,μ,ν)-spaces

We study the Riemann curvature tensor of (κ,μ, ν)-spaces when they have almost cosymplectic and almost Kenmotsu structures, giving its writing explicitly. This leads to the definition and study of a natural generalisation of the contact metric (κ,μ, ν)-spaces. We present examples or obstruction results of these spaces in all possible cases.Ministerio de Educación, Cultura y DeporteJunta de Andalucí

From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management

[EN] The information that crops offer is turned into profitable decisions only when efficiently managed. Current advances in data management are making Smart Farming grow exponentially as data have become the key element in modern agriculture to help producers with critical decision-making. Valuable advantages appear with objective information acquired through sensors with the aim of maximizing productivity and sustainability. This kind of data-based managed farms rely on data that can increase efficiency by avoiding the misuse of resources and the pollution of the environment. Data-driven agriculture, with the help of robotic solutions incorporating artificial intelligent techniques, sets the grounds for the sustainable agriculture of the future. This paper reviews the current status of advanced farm management systems by revisiting each crucial step, from data acquisition in crop fields to variable rate applications, so that growers can make optimized decisions to save money while protecting the environment and transforming how food will be produced to sustainably match the forthcoming population growth.This research article is part of a project that has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 737669.Sáiz Rubio, V.; Rovira Más, F. (2020). From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management. Agronomy. 10(2):1-21. https://doi.org/10.3390/agronomy10020207S121102Himesh, S. (2018). Digital revolution and Big Data: a new revolution in agriculture. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources, 13(021). doi:10.1079/pavsnnr201813021Digital Agriculture: Improving Profitabilityhttps://www.accenture.com/_acnmedia/accenture/conversion-assets/dotcom/documents/global/pdf/digital_3/accenture-digital-agriculture-point-of-view.pdfDigital Farming: What Does It Really Mean?http://www.cema-agri.org/publication/digital-farming-what-does-it-really-meanAgriculture Needs to Attract More Young Peoplehttp://www.gainhealth.org/knowledge-centre/worlds-farmers-age-new-blood-neededGenerational Renewalhttps://enrd.ec.europa.eu/enrd-thematic-work/generational-renewal_enWhat is IoT in Agriculture? Farmers Aren’t Quite Sure Despite $4bn US Opportunity—Reporthttps://agfundernews.com/iot-agriculture-farmers-arent-quite-sure-despite-4bn-us-opportunity.htmlPrecision Agriculture Yields Higher Profits, Lower Riskshttps://www.hpe.com/us/en/insights/articles/precision-agriculture-yields-higher-profits-lower-risks-1806.htmlTzounis, A., Katsoulas, N., Bartzanas, T., & Kittas, C. (2017). Internet of Things in agriculture, recent advances and future challenges. Biosystems Engineering, 164, 31-48. doi:10.1016/j.biosystemseng.2017.09.007From Dirt to Data: The Second Green Revolution and IoT. Deloitte insightshttps://www2.deloitte.com/insights/us/en/deloitte-review/issue-18/second-green-revolution-and-internet-of-things.html#endnote-sup-9Big Data: The Next Frontier for Innovation, Competition, and Productivity | McKinseyhttps://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/big-data-the-next-frontier-for-innovationWolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017). Big Data in Smart Farming – A review. Agricultural Systems, 153, 69-80. doi:10.1016/j.agsy.2017.01.023Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23-37. doi:10.1016/j.compag.2017.09.037How Big Data Will Change Agriculturehttps://proagrica.com/news/how-big-data-will-change-agriculture/Big Data Coordination Platform. Proposal to the CGIAR Fund Councilhttps://cgspace.cgiar.org/handle/10947/4303Zambon, I., Cecchini, M., Egidi, G., Saporito, M. G., & Colantoni, A. (2019). Revolution 4.0: Industry vs. Agriculture in a Future Development for SMEs. Processes, 7(1), 36. doi:10.3390/pr7010036How AI Is Transforming Agriculturehttps://www.forbes.com/sites/cognitiveworld/2019/07/05/how-ai-is-transforming-agriculture/Bechar, A., & Vigneault, C. (2016). Agricultural robots for field operations: Concepts and components. Biosystems Engineering, 149, 94-111. doi:10.1016/j.biosystemseng.2016.06.014Bechar, A., & Vigneault, C. (2017). Agricultural robots for field operations. Part 2: Operations and systems. Biosystems Engineering, 153, 110-128. doi:10.1016/j.biosystemseng.2016.11.004Ramin Shamshiri, R., Weltzien, C., A. Hameed, I., J. Yule, I., … E. Grift, T. (2018). Research and development in agricultural robotics: A perspective of digital farming. International Journal of Agricultural and Biological Engineering, 11(4), 1-11. doi:10.25165/j.ijabe.20181104.4278Farming 4.0: The Future of Agriculture?https://www.euractiv.com/section/agriculture-food/infographic/farming-4-0-the-future-of-agriculture/Ag Tech Deal Activity More Than Tripleshttps://www.cbinsights.com/research/agriculture-farm-tech-startup-funding-trends/AI, Robotics, And the Future of Precision Agriculturehttps://www.cbinsights.com/research/ai-robotics-agriculture-tech-startups-future/VineScout European Projectwww.vinescout.euPrecision Farming: A New Approach to Crop Managementhttp://agpublications.tamu.edu/pubs/eng/l5177.pdfZhang, N., Wang, M., & Wang, N. (2002). Precision agriculture—a worldwide overview. Computers and Electronics in Agriculture, 36(2-3), 113-132. doi:10.1016/s0168-1699(02)00096-0MIAO, Y., MULLA, D. J., & ROBERT, P. C. (2018). An integrated approach to site-specific management zone delineation. Frontiers of Agricultural Science and Engineering, 0(0), 0. doi:10.15302/j-fase-2018230Klassen, S. P., Villa, J., Adamchuk, V., & Serraj, R. (2014). Soil mapping for improved phenotyping of drought resistance in lowland rice fields. Field Crops Research, 167, 112-118. doi:10.1016/j.fcr.2014.07.007Khanal, S., Fulton, J., & Shearer, S. (2017). An overview of current and potential applications of thermal remote sensing in precision agriculture. Computers and Electronics in Agriculture, 139, 22-32. doi:10.1016/j.compag.2017.05.001Aravind, K. R., Raja, P., & Pérez-Ruiz, M. (2017). Task-based agricultural mobile robots in arable farming: A review. Spanish Journal of Agricultural Research, 15(1), e02R01. doi:10.5424/sjar/2017151-9573Roldán, J. J., Cerro, J. del, Garzón‐Ramos, D., Garcia‐Aunon, P., Garzón, M., León, J. de, & Barrientos, A. (2018). Robots in Agriculture: State of Art and Practical Experiences. Service Robots. doi:10.5772/intechopen.69874Gonzalez-de-Santos, P., Ribeiro, A., Fernandez-Quintanilla, C., Lopez-Granados, F., Brandstoetter, M., Tomic, S., … Debilde, B. (2016). Fleets of robots for environmentally-safe pest control in agriculture. Precision Agriculture, 18(4), 574-614. doi:10.1007/s11119-016-9476-3What’s Slowing the Use of Robots in the Ag Industry?https://www.therobotreport.com/whats-slowing-the-use-of-robots-in-the-ag-industry/Bogue, R. (2016). Robots poised to revolutionise agriculture. Industrial Robot: An International Journal, 43(5), 450-456. doi:10.1108/ir-05-2016-0142Features & Benefits OZ Weeding Robothttps://www.naio-technologies.com/en/agricultural-equipment/weeding-robot-oz/Robotics for Sustainable Broad-Acre Agriculturehttps://www.researchgate.net/publication/283722961_Robotics_for_Sustainable_Broad-Acre_AgricultureThe Ultimate Guide to Agricultural Roboticshttps://www.roboticsbusinessreview.com/agriculture/the_ultimate_guide_to_agricultural_robotics/Kweon, G., Lund, E., & Maxton, C. (2013). Soil organic matter and cation-exchange capacity sensing with on-the-go electrical conductivity and optical sensors. Geoderma, 199, 80-89. doi:10.1016/j.geoderma.2012.11.001Agricultural Robots—Present and Future Applications (Videos Included)https://emerj.com/ai-sector-overviews/agricultural-robots-present-future-applications/Köksal, Ö., & Tekinerdogan, B. (2018). Architecture design approach for IoT-based farm management information systems. Precision Agriculture, 20(5), 926-958. doi:10.1007/s11119-018-09624-8Rovira-Más, F., & Sáiz-Rubio, V. (2013). Crop Biometric Maps: The Key to Prediction. Sensors, 13(9), 12698-12743. doi:10.3390/s130912698Oliver, M. A., & Webster, R. (2014). A tutorial guide to geostatistics: Computing and modelling variograms and kriging. CATENA, 113, 56-69. doi:10.1016/j.catena.2013.09.006Adamchuk, V. ., Hummel, J. ., Morgan, M. ., & Upadhyaya, S. . (2004). On-the-go soil sensors for precision agriculture. Computers and Electronics in Agriculture, 44(1), 71-91. doi:10.1016/j.compag.2004.03.002Cossell, S., Whitty, M., Liu, S., & Tang, J. (2016). Spatial Map Generation from Low Cost Ground Vehicle Mounted Monocular Camera. IFAC-PapersOnLine, 49(16), 231-236. doi:10.1016/j.ifacol.2016.10.043N. Zhang, & R. K. Taylor. (2001). APPLICATIONS OF A FIELD LEVEL GEOGRAPHIC INFORMATION SYSTEM (FIS) IN PRECISION AGRICULTURE. Applied Engineering in Agriculture, 17(6). doi:10.13031/2013.6829Runquist, S., Zhang, N., & Taylor, R. K. (2001). Development of a field-level geographic information system. Computers and Electronics in Agriculture, 31(2), 201-209. doi:10.1016/s0168-1699(00)00155-1Granular Farm Management Software, Precision Agriculture, Agricultural Softwarehttps://granular.ag/Capterra. Farm Management Softwarewww.capterra.comTop 9 Farm Management Software—Compare Reviews, Features, Pricing in 2019https://www.predictiveanalyticstoday.com/top-farm-management-software/Srivastava, P. K., & Singh, R. M. (2016). GIS based integrated modelling framework for agricultural canal system simulation and management in Indo-Gangetic plains of India. Agricultural Water Management, 163, 37-47. doi:10.1016/j.agwat.2015.08.025Giusti, E., & Marsili-Libelli, S. (2015). A Fuzzy Decision Support System for irrigation and water conservation in agriculture. Environmental Modelling & Software, 63, 73-86. doi:10.1016/j.envsoft.2014.09.020Asfaw, D., Black, E., Brown, M., Nicklin, K. J., Otu-Larbi, F., Pinnington, E., … Quaife, T. (2018). TAMSAT-ALERT v1: a new framework for agricultural decision support. Geoscientific Model Development, 11(6), 2353-2371. doi:10.5194/gmd-11-2353-2018https://dssat.netNavarro-Hellín, H., Martínez-del-Rincon, J., Domingo-Miguel, R., Soto-Valles, F., & Torres-Sánchez, R. (2016). A decision support system for managing irrigation in agriculture. Computers and Electronics in Agriculture, 124, 121-131. doi:10.1016/j.compag.2016.04.003Kumar, A., Sah, B., Singh, A. R., Deng, Y., He, X., Kumar, P., & Bansal, R. C. (2017). A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renewable and Sustainable Energy Reviews, 69, 596-609. doi:10.1016/j.rser.2016.11.191Rupnik, R., Kukar, M., Vračar, P., Košir, D., Pevec, D., & Bosnić, Z. (2019). AgroDSS: A decision support system for agriculture and farming. Computers and Electronics in Agriculture, 161, 260-271. doi:10.1016/j.compag.2018.04.001Rose, D. C., Sutherland, W. J., Parker, C., Lobley, M., Winter, M., Morris, C., … Dicks, L. V. (2016). Decision support tools for agriculture: Towards effective design and delivery. Agricultural Systems, 149, 165-174. doi:10.1016/j.agsy.2016.09.009Colaço, A. F., & Molin, J. P. (2016). Variable rate fertilization in citrus: a long term study. Precision Agriculture, 18(2), 169-191. doi:10.1007/s11119-016-9454-9Nawar, S., Corstanje, R., Halcro, G., Mulla, D., & Mouazen, A. M. (2017). Delineation of Soil Management Zones for Variable-Rate Fertilization. Advances in Agronomy, 175-245. doi:10.1016/bs.agron.2017.01.003Fountas, S., Carli, G., Sørensen, C. G., Tsiropoulos, Z., Cavalaris, C., Vatsanidou, A., … Tisserye, B. (2015). Farm management information systems: Current situation and future perspectives. Computers and Electronics in Agriculture, 115, 40-50. doi:10.1016/j.compag.2015.05.011Precision Agriculture in Europe: Legal, Social and Ethical Considerations—Think Tankhttp://www.europarl.europa.eu/thinktank/en/document.html?reference=EPRS_STU(2017)60320

Proximal sensing mapping method to generate field maps in vineyards

[EN] An innovative methodology to generate vegetative vigor maps in vineyards (Vitis vinifera L.) has been developed and pre-validated. The architecture proposed implements a Global Positioning System (GPS) receiver and a computer vision unit comprising a monocular charge-coupled device (CCD) camera equipped with an 8-mm lens and a pass-band near-infrared (NIR) filter. Both sensors are mounted on a medium-size conventional agricultural tractor. The synchronization of perception (camera) and localization (GPS) sensors allowed the creation of globally-referenced regular grids, denominated universal grids, whose cells were filled with the estimated vegetative vigor of the monitored vines. Vine vigor was quantified as the relative percentage of vegetation automatically estimated by the onboard algorithm through the images captured with the camera. Validation tests compared spatial differences in vine vigor with yield differentials along the rows. The positive correlation between vigor and yield variations showed the potential of proximal sensing and the advantages of acquiring top view images from conventional vehicles.Sáiz Rubio, V.; Rovira Más, F. (2013). Proximal sensing mapping method to generate field maps in vineyards. Agricultural Engineering International: CIGR Journal. 15(2):47-59. http://hdl.handle.net/10251/102750S475915

Efecto de la intensidad y el momento de la defoliación sobre el crecimiento de Populus alba y Salix babylonica x Salix alba

Insect defoliations have detrimental impacts on timber production in commercial tree plantations. The effect of intensity and timing of defoliation on the growth of two commercial salicaceae was assessed at plantations located in the Delta of the Paraná River, Argentina. Experimental trees were randomly selected from two 1-year-old plantations of the most common clones planted in forestry production, Populus alba ´Villafranca´ (¨I-58/57¨) and Salix babylonica x Salix alba (¨A-250/33¨). We used a pre-post design to evaluate the effect of five intensities of manual defoliation (i.e. 100 %, 75 %, 50 %, 25 %, and 0 % as control) applied in four different times during the growing season (i.e. October, November, December and January) on tree height and diameter at breast height (DBH). Results indicated that manual defoliation negatively affected the growth of the studied poplar and willow clones in both height and diameter, and that the magnitude of the effect depended on the intensity and timing of defoliation. Willows were only affected by defoliation conducted during the spring (October and November); complete defoliation caused the highest reduction in growth (46 % reduction in height and 62 % in DBH compared to the control). Manual defoliation of poplars had a significant effect on growth at any time during the springsummer; trees subjected to 100 % defoliation showed the highest growth reductions (up to 76 % in height and 88 % in DBH compared to control). This study indicated that commercial poplars were less tolerant to defoliation than willows.Las defoliaciones causadas por insectos tienen impactos perjudiciales sobre la producción de madera en plantaciones comerciales de árboles. El efecto de la intensidad de defoliación sobre el crecimiento en altura y diámetro de dos salicáceas de uso comercial fue evaluado en plantaciones del Delta del Río Paraná, Argentina. Los árboles experimentales fueron elegidos al azar de plantaciones de un año de edad de clones comúnmente utilizados en la producción forestal, Populus alba ´Villafranca´ (¨I-58/57¨) y Salix babylonica x Salix alba (¨A-250/33¨). Mediante un diseño antes-después fue evaluado el efecto de cinco intensidades de defoliación manual (100 %, 75 %, 50 %, 25 %, y 0 % como control) aplicadas en cuatro épocas de la estación de crecimiento (octubre, noviembre, diciembre y enero) sobre la altura y el diámetro a la altura del pecho (DAP) de los árboles. Según los resultados, la defoliación manual afectó negativamente el crecimiento de los clones estudiados, en diámetro y altura; la magnitud del efecto dependió de la intensidad y del momento de la defoliación. Salix sp. solo fue afectado por defoliaciones de primavera (octubre y noviembre); la defoliación completa causó las mayores reducciones en el crecimiento (46 % en altura y 62 % en DAP). La defoliación en Populus sp. afectó el crecimiento en cualquier momento de primavera-verano; los árboles completamente defoliados mostraron las mayores reducciones en el crecimiento (hasta 76 % en altura y 88 % en DAP). Este estudio indica que Populus sp. de uso comercial fue menos tolerante a la defoliación que Salix sp.Fil: Rubio, Alejandra. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Loetti, Verónica. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Bellocq, Maria Isabel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Null pseudo-isotropic Lagrangian surfaces

In this paper we will show that a Lagrangian, Lorentzian surface M 2 1 in a complex pseudo space form Mf2 1 (4c) is pseudo-isotropic if and only if M is minimal. Next we will obtain a complete classification of all Lagrangian, Lorentzian surfaces which are lightlike pseudo-isotropic but not pseudo-isotropic.Ministerio de Economía y CompetitividadFondo Europeo de Desarrollo Regiona

Análisis de la extracción y validación bilingüe de terminología con el programa informático Multiterm Extract

Catorzenes Jornades de Foment de la Investigació de la FCHS (Any 2008-2009