يعرض 1 - 20 نتائج من 950 نتيجة بحث عن '"Alvarez, Francisco J"', وقت الاستعلام: 0.88s تنقيح النتائج
  1. 1
    Book
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    Academic Journal

    المصدر: Anales de Geografía de la Universidad Complutense; Vol. 44 No. 2 (2024); 417-447 ; Anales de Geografía de la Universidad Complutense; Vol. 44 Núm. 2 (2024); 417-447 ; 1988-2378 ; 0211-9803

    وصف الملف: application/pdf

    Relation: https://revistas.ucm.es/index.php/AGUC/article/view/97586/4564456570303; Abarca-Álvarez, F. J., Méndez, C., Torres-Parejo, U., & García-Arias, M. A. (2022). Mejora de la toma de decisiones en la asistencia humanitaria mediante el uso de metodologías del campo de la Inteligencia Artificial. In La transversalidad de la investigación en comunicación (pp. 587-609). Dykinson. Alpízar, F., Saborío-Rodríguez, M., Martínez-Rodríguez, M. R., Viguera, B., Vignola, R., Capitán, T., & Harvey, C. A. (2020). Determinants of food insecurity among smallholder farmer households in Central America: recurrent versus extreme weather-driven events. Regional Environmental Change, 20, 1-16. Andrée, B. P. J. (2022). Machine Learning Guided Outlook of Global Food Insecurity Consistent with Macroeconomic Forecasts. World Bank Policy Research Working Papers. Andrée, B. P. J., Chamorro, A., Kraay, A., Spencer, P., & Wang, D. (2020). Predicting food crises. World Bank Policy Research Working Paper 9412. Aurino, E. (2014). Selecting a core set of indicators for monitoring global food security: A methodological proposal. FAO food and nutrition series. Backer, D., & Billing, T. (2021). Validating famine early warning systems network projections of food security in Africa, 2009–2020. Global Food Security, 29, 100510. Backer, D., & Billing, T. (2024). Forecasting the prevalence of child acute malnutrition using environmental and conflict conditions as leading indicators. World Development, 176, 106484. Berry, E. M., Dernini, S., Burlingame, B., Meybeck, A., & Conforti, P. (2015). Food security and sustainability: can one exist without the other?. Public health nutrition, 18(13), 2293-2302. Bitew, F. H., Sparks, C. S., & Nyarko, S. H. (2022). Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia. Public health nutrition, 25(2), 269-280. Browne, C., Matteson, D. S., McBride, L., Hu, L., Liu, Y., Sun, Y., . & Barrett, C. B. (2021). Multivariate random forest prediction of poverty and malnutrition prevalence. PloS one, 16(9), e0255519. Busker, T. S., van den Hurk, B., de Moel, H., van den Homberg, M., van Straaten, C., Odongo, R. A., & Aerts, J. C. (2023). Predicting Food-Security Crises in the Horn of Africa Using Machine Learning. Authorea Preprints. Christensen, C., Wagner, T., & Langhals, B. (2021). Year-independent prediction of food insecurity using classical and neural network machine learning methods. Ai, 2(2), 244-260. Deléglise, H., Interdonato, R., Bégué, A., d’Hôtel, E. M., Teisseire, M., & Roche, M. (2022). Food security prediction from heterogeneous data combining machine and deep learning methods. Expert Systems with Applications, 190, 116189. Devereux, S., Sabates-Wheeler, R., & Longhurst, R. (Eds.). (2012). Seasonality, rural livelihoods and development. New York, NY, USA:: Earthscan. El Naqa, I., & Murphy, M. J. (2015). What is machine learning? (pp. 3-11). Springer International Publishing. FAO, FIDA, OMS, PMA y UNICEF. (2023). El estado de la seguridad alimentaria y la nutrición en el mundo 2023. Urbanización, transformación de los sistemas agroalimentarios y dietas saludables a lo largo del continuo rural-urbano. Roma, FAO. https://doi.org/10.4060/cc3017es FAO (1996). Rome Declaration on Food Security and World Food Summit Plan of Action. Rome: FAO. FAO (2009). Declaration of the World Food Summit on Food Security. Rome: FAO. Foini, P., Tizzoni, M., Martini, G., Paolotti, D., & Omodei, E. (2023). On the forecastability of food insecurity. Scientific Reports, 13(1), 2793. Frankenberger, T. R., & Verduijn, R. (2011). Integrated Food Security Phase Classification (IPC); End of Project Evaluation. Rome: FAO. https://www.ipcinfo.org/fileadmin/user_upload/ipcinfo/docs/1_IPC_Glob_Proj_Eval_04_11_Report.pdf FSIN & Global Network Against Food Crises (2021). Global Report on Food Crises 2021. Rome. https://www.fsinplatform.org/global-report-food-crises-2021 FSIN & Global Network Against Food Crises. (2023). Global Report on Food Crises 2023. Rome. https://www.fsinplatform.org/global-report-food-crises-2023 FSIN & Global Network Against Food Crises. (2024). Global Report on Food Crises 2024. Rome. https://www.fsinplatform.org/report/global-report-food-crises-2024/ Gao, C., Fei, C. J., McCarl, B. A., & Leatham, D. J. (2020). Identifying Vulnerable households using machine learning. Sustainability, 12(15), 6002. Herteux, J., Räth, C., Baha, A., Martini, G., & Piovani, D. (2023). Forecasting Trends in Food Security: a Reservoir Computing Approach. arXiv preprint arXiv:2312.00626. Hoddinott, J. (1999). Choosing outcome indicators of household food security. International Food Policy Research Institute. Hossain, M., Mullally, C., & Asadullah, M. N. (2019). Alternatives to calorie-based indicators of food security: An application of machine learning methods. Food policy, 84, 77-91. IPC Global Partners. (2019). The Integrated Food Security Phase Classification Technical Manual Version 3.0. Rome: FAO. https://www.ipcinfo.org/fileadmin/user_upload/ipcinfo/docs/IPC_Technical_Manual_3_Final.pdf Jones, A. D., Ngure, F. M., Pelto, G., & Young, S. L. (2013). What are we assessing when we measure food security? A compendium and review of current metrics. Advances in nutrition, 4(5), 481-505. Kaut, J., Bakker, E., van Uffelen, G. J., Cruijssen, F., & Malkowsky, C. (2022). From insight to foresight: using data to improve food and nutrition outcomes in protracted food crises in the Horn of Africa (No. WCDI-22-217). Wageningen Centre for Development Innovation. Krishnamurthy, P. K., Choularton, R. J., & Kareiva, P. (2020a). Dealing with uncertainty in famine predictions: How complex events affect food security early warning skill in the Greater Horn of Africa. Global Food Security, 26, 100374. Krishnamurthy R, P. K., Fisher, J. B., Schimel, D. S., & Kareiva, P. M. (2020b). Applying tipping point theory to remote sensing science to improve early warning drought signals for food security. Earth's Future, 8(3), e2019EF001456. Laney, D., and L. Kart. 2012. Emerging Role of the Data Scientist and the Art of Data Science. Report G00227058. Stamford, CT: Gartner, Inc. https://www.gartner.com/en/documents/1955615 Lentz, E. C., Michelson, H., Baylis, K., & Zhou, Y. (2019). A data-driven approach improves food insecurity crisis prediction. World Development, 122, 399-409. Martini, G., Bracci, A., Riches, L., Jaiswal, S., Corea, M., Rivers, J., . & Omodei, E. (2022). Machine learning can guide food security efforts when primary data are not available. Nature Food, 3(9), 716-728. Meerza, S. I. A., Meerza, S. I. A., & Ahamed, A. (2021). Food insecurity through machine learning lens: identifying vulnerable households. Selected Paper prepared for presentation at the 2021 Agricultural & Applied Economics Association Annual Meeting, Austin, TX, August 1 – August. https://ageconsearch.umn.edu/nanna/record/314072/files/Abstracts_21_06_15_22_32_25_65__173_216_85_243_0.pdf?withWatermark=0&withMetadata=0&version=1&registerDownload=1 Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019). Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences, 116(44), 22071-22080. Norberg, J. (2017). Progress: Ten reasons to look forward to the future. Simon and Schuster. Oakford, S. (2019). Deaths before data. Significance, 16(1), 29-31. Qasrawi, R., Hoteit, M., Tayyem, R., Bookari, K., Al Sabbah, H., Kamel, I., . & Al-Halawa, D. A. (2023). Machine learning techniques for the identification of risk factors associated with food insecurity among adults in Arab countries during the COVID-19 pandemic. BMC public health, 23(1), 1805. Sotelo Pérez I., Sotelo Pérez, M. y Sotelo Navalpotro J. A. (2023). Análisis geográfico regional de la “huella hídrica” española: Bases para la planificación turística. Cuadernos de turismo, ISSN 1139-7861, Nº. 51 (Ejemplar dedicado a: Enero - Junio), págs. 349-383 Sotelo Pérez I. y Sotelo Navalpotro J. A. (2022). Aspectos científicos del estudio del Medio Ambiente, en el contexto del Espacio Geográfico, desde el ámbito del Estado Constitucional de Derecho y el Estado Jurisprudencial de Derecho. Observatorio Medioambiental, 25, 65-90. UNISDR (United Nations International Strategy for Disaster Reduction). 2017. Report of the open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction. Geneva: UNISDR. https://www.preventionweb.net/files/50683_oiewgreportenglish.pdf. Uskov, V. L., Bakken, J. P., Shah, A., Krock, T., Uskov, A., Syamala, J., & Rachakonda, R. (2019). Smart learning analytics: conceptual modeling and agile engineering. In Smart Education and e-Learning 2018 5 (pp. 3-16). Springer International Publishing. Villacis, A., Badruddoza, S., Mayorga, J., & Mishra, A. K. (2022). Using machine learning to test the consistency of food insecurity measures. Selected Paper prepared for presentation at the 2021 Agricultural & Applied Economics Association Annual Meeting, Austin, TX, August 1 – August Westerveld, J. J., van den Homberg, M. J., Nobre, G. G., van den Berg, D. L., Teklesadik, A. D., & Stuit, S. M. (2021). Forecasting transitions in the state of food security with machine learning using transferable features. Science of the Total Environment, 786, 147366. Zhou, Y. (2020). Three essays on machine learning and food security. Doctoral dissertation, University of Illinois at Urbana-Champaign. Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019). Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences, 116(44), 22071-22080. Norberg, J. (2017). Progress: Ten reasons to look forward to the future. Simon and Schuster. Oakford, S. (2019). Deaths before data. Significance, 16(1), 29-31. Qasrawi, R., Hoteit, M., Tayyem, R., Bookari, K., Al Sabbah, H., Kamel, I., . & Al-Halawa, D. A. (2023). Machine learning techniques for the identification of risk factors associated with food insecurity among adults in Arab countries during the COVID-19 pandemic. BMC public health, 23(1), 1805. Sotelo Pérez I., Sotelo Pérez, M. y Sotelo Navalpotro J. A. (2023). Análisis geográfico regional de la “huella hídrica” española: Bases para la planificación turística. Cuadernos de turismo, ISSN 1139-7861, Nº. 51 (Ejemplar dedicado a: Enero - Junio), págs. 349-383 Sotelo Pérez I. y Sotelo Navalpotro J. A. (2022). Aspectos científicos del estudio del Medio Ambiente, en el contexto del Espacio Geográfico, desde el ámbito del Estado Constitucional de Derecho y el Estado Jurisprudencial de Derecho. Observatorio Medioambiental, 25, 65-90. UNISDR (United Nations International Strategy for Disaster Reduction). 2017. Report of the open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction. Geneva: UNISDR. https://www.preventionweb.net/files/50683_oiewgreportenglish.pdf. Uskov, V. L., Bakken, J. P., Shah, A., Krock, T., Uskov, A., Syamala, J., & Rachakonda, R. (2019). Smart learning analytics: conceptual modeling and agile engineering. In Smart Education and e-Learning 2018 5 (pp. 3-16). Springer International Publishing. Villacis, A., Badruddoza, S., Mayorga, J., & Mishra, A. K. (2022). Using machine learning to test the consistency of food insecurity measures. Selected Paper prepared for presentation at the 2021 Agricultural & Applied Economics Association Annual Meeting, Austin, TX, August 1 – August Westerveld, J. J., van den Homberg, M. J., Nobre, G. G., van den Berg, D. L., Teklesadik, A. D., & Stuit, S. M. (2021). Forecasting transitions in the state of food security with machine learning using transferable features. Science of the Total Environment, 786, 147366. Zhou, Y. (2020). Three essays on machine learning and food security. Doctoral dissertation, University of Illinois at Urbana-Champaign.; https://revistas.ucm.es/index.php/AGUC/article/view/97586

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    Academic Journal

    المساهمون: Gobierno de Arag?n, Agencia Estatal de Investigaci?n, Horizon 2020 Framework Programme, Campus Iberus

    المصدر: Inorganic Chemistry ; volume 64, issue 1, page 255-267 ; ISSN 0020-1669 1520-510X

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    Academic Journal

    المساهمون: Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), Ministerio de Ciencia e Innovación (España), Gobierno de Aragón, Universidad de Zaragoza, Banco Santander, ALBA Synchrotron

    وصف الملف: application/pdf

    Relation: #PLACEHOLDER_PARENT_METADATA_VALUE#; info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-106184GB-I00/ES/UNA APROXIMACION DIFERENTE PARA ENTENDER Y CONTROLAR LA CATALISIS/; info:eu-repo/grantAgreement/AEI//RED2018-102387-T; info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-139318NB-I00/ES/ENTENDIENDO LOS FACTORES QUE CONTROLAN LA REACTIVIDAD EN GRUPOS PRINCIPALES/; info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PGC2018-099383-B-I00/ES/CATALIZADORES ORGANOMETALICOS PARA LA TRANSFORMACION SOSTENIBLE DE CO2 Y NH3 EN PRODUCTOS QUIMICOS DE ALTO VALOR AÑADIDO/; info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-126212OB-I00/ES/CATALIZADORES BASADOS EN METALES ABUNDANTES EN LA CORTEZA TERRESTRE PARA SISTEMAS SOSTENIBLES DE ALMACENAMIENTO DE HIDROGENO/; Publisher's version; Gómez-España, Alejandra; García-Orduña, Pilar; Lahoz, Fernando J.; Fernández, Israel; Fernández-Álvarez, Francisco J.; 2024; Supporting Information: Rhodium complexes with a pyridine-2-yloxy-silyl-based N,Si-ligand: Bonding situation and activity as alkene hydrogenation catalysts [Dataset]; American Chemical Society; https://doi.org/10.1021/acs.organomet.3c00498; https://doi.org/10.1021/acs.organomet.3c00498; Sí; Organometallics 43(3): 402-413 (2024); http://hdl.handle.net/10261/358273

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    Academic Journal

    المساهمون: Universidad de Sevilla. Departamento de Fisiología, National Institutes of Health. United States, Junta de Andalucía, Ministerio de Ciencia e Innovación (MICIN). España

    Relation: Brain Structure and Function, 228 (3-4), 967-984.; R01 NS111969; R21 NS114839; P20_00529; PGC2018-094654-B-100; PID2021-124300NB-I00; https://doi.org/10.1007/s00429-023-02635-w; https://idus.us.es/handle//11441/151199

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    Academic Journal
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    Academic Journal

    مصطلحات موضوعية: Ia afferent, Microglia, Motoneuron, Neonates, Nerve injury, Plasticity

    وصف الملف: application/pdf

    Relation: Agencia Estatal de Investigación RTI2018-096386-B-I00; Agencia Estatal de Investigación SAF2017-84464-R; Ministerio de Sanidad y Consumo CB06/05/1105; Ministerio de Economía, Industria y Competitividad RD16/0011/0014; eNeuro; Vol. 10 (february 2023); https://ddd.uab.cat/record/272463; urn:10.1523/ENEURO.0436-22.2023; urn:oai:ddd.uab.cat:272463; urn:pmcid:PMC9948128; urn:pmc-uid:9948128; urn:pmid:36759186; urn:oai:pubmedcentral.nih.gov:9948128; urn:oai:egreta.uab.cat:publications/c3fee3ba-497f-4eef-bd99-441a7e7018c4

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    Academic Journal

    المساهمون: Institut Català de la Salut, Martínez-Díaz I, Martos N, Llorens-Cebrià C, Vergara A, Jacobs-Cachá C, Soler MJ Grup de Recerca de Nefrologia i Trasplantament Renal, Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain. Vall d’Hebron Hospital Universitari, Barcelona, Spain. Álvarez FJ, Bedard PW Travere Therapeutics, Inc., San Diego, CA, USA, Vall d'Hebron Barcelona Hospital Campus

    المصدر: Scientia

    وصف الملف: application/pdf

    Relation: International Journal of Molecular Sciences;24(4); https://doi.org/10.3390/ijms24043427; info:eu-repo/grantAgreement/ES/PEICTI2021-2023/PI21%2F01292; Martínez-Díaz I, Martos N, Llorens-Cebrià C, Álvarez FJ, Bedard PW, Vergara A, et al. Endothelin Receptor Antagonists in Kidney Disease. Int J Mol Sci. 2023 Feb 8;24(4):3427.; https://hdl.handle.net/11351/9204; 000939220800001

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    Academic Journal

    المساهمون: Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), Diputación General de Aragón, Gobierno de Aragón, Ministerio de Ciencia e Innovación (España), Universidad de Zaragoza, Banco Santander, European Commission

    وصف الملف: application/pdf

    Relation: #PLACEHOLDER_PARENT_METADATA_VALUE#; info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-126212OB-I00/ES/CATALIZADORES BASADOS EN METALES ABUNDANTES EN LA CORTEZA TERRESTRE PARA SISTEMAS SOSTENIBLES DE ALMACENAMIENTO DE HIDROGENO/; Publisher's version; The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI 10.1039/D3DT00744H; https://doi.org/10.1039/D3DT00744H; Sí; Dalton Transactions 52(20): 6722-6729 (2023); http://hdl.handle.net/10261/345877

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    Conference

    مصطلحات موضوعية: Iridium-silicon bond, Structure

    وصف الملف: application/pdf

    Relation: Publisher's version; Sí; XXXVIII Reunión Bienal de la Real Sociedad Española de Química (2022); http://hdl.handle.net/10261/284734

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    Academic Journal

    وصف الملف: application/pdf

    Relation: info:eu-repo/grantAgreement/ES/DGA-FSE/E42-20R; info:eu-repo/grantAgreement/ES/MICINN/PGC2018-099383-B-I00; info:eu-repo/grantAgreement/ES/MINECO-AEI-FEDER/RED2018-102387-T; info:eu-repo/grantAgreement/ES/MINECO/PID2019-106184GB-I00; http://zaguan.unizar.es/record/130334

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    Academic Journal