Subestación eléctrica transformadora 220/132/20 kV

Proyecto de diseño de una Subestación Eléctrica Transformadora con tres niveles de tensión, 220, 132 y 20 kV. Posee cuatro transformadores de potencia, dos de 80 MVA y dos de 30 MVA. Dos líneas de entrada a 220 kV, dos líneas de salida a 132 kV para el anillo de Zaragoza y seis salidas a 20 kV

Estudio de una instalación fotovoltaica para una explotación ganadera

Se trata del desarrollo de una instalación eléctrica fotovoltaica para una explotación ganadera contemplando distintos escenarios: instalación convencional a red, instalación aislada, instalación parcialmente aislada, vertido de energía a red y una instalación con balance neto. Para ello se han dimensionado las distintas opciones, se ha hecho una valoración económica de cada una de ellas y se ha elegido la opción más idónea para el caso a tratar

Ultra-low loss hybrid ITO/Si thermo-optic phase shifter with optimized power consumption

© 2020 Optical Society of America. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modifications of the content of this paper are prohibited"[EN] Typically, materials with large optical losses such as metals are used as microheaters for silicon based thermo-optic phase shifters. Consequently, the heater must be placed far from the waveguide, which could come at the expense of the phase shifter performance. Reducing the gap between the waveguide and the heater allows reducing the power consumption or increasing the switching speed. In this work, we propose an ultra-low loss microheater for thermo-optic tuning by using a CMOS-compatible transparent conducting oxide such as indium tin oxide (ITO) with the aim of drastically reducing the gap. Using finite element method simulations, ITO and Ti based heaters are compared for different cladding configurations and TE and TM polarizations. Furthermore, the proposed ITO based microheaters have also been fabricated using the optimum gap and cladding configuration. Experimental results show power consumption to achieve a pi phase shift of 10 mW and switching time of a few microseconds for a 50 mu m long ITO heater. The obtained results demonstrate the potential of using ITO as an ultra-low loss microheater for high performance silicon thermo-optic tuning and open an alternative way for enabling the large-scale integration of phase shifters required in emerging integrated photonic applications. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing AgreementMinisterio de Economía y Competitividad (TEC2016-76849); Generalitat Valenciana (PROMETEO/2019/123); Ministerio de Ciencia, Innovación y Universidades (FPU17/04224).Parra Gómez, J.; Hurtado Montañés, J.; Griol Barres, A.; Sanchis Kilders, P. (2020). Ultra-low loss hybrid ITO/Si thermo-optic phase shifter with optimized power consumption. Optics Express. 28(7):9393-9404. https://doi.org/10.1364/OE.386959S93939404287Komma, J., Schwarz, C., Hofmann, G., Heinert, D., & Nawrodt, R. (2012). Thermo-optic coefficient of silicon at 1550 nm and cryogenic temperatures. Applied Physics Letters, 101(4), 041905. doi:10.1063/1.4738989Sun, J., Timurdogan, E., Yaacobi, A., Hosseini, E. S., & Watts, M. R. (2013). Large-scale nanophotonic phased array. Nature, 493(7431), 195-199. doi:10.1038/nature11727Shen, Y., Harris, N. C., Skirlo, S., Prabhu, M., Baehr-Jones, T., Hochberg, M., … Soljačić, M. (2017). 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Polarization-insensitive mode-independent thermo-optic switch based on symmetric waveguide directional coupler. Optics Express, 27(24), 35385. doi:10.1364/oe.27.035385Atabaki, A. H., Shah Hosseini, E., Eftekhar, A. A., Yegnanarayanan, S., & Adibi, A. (2010). Optimization of metallic microheaters for high-speed reconfigurable silicon photonics. Optics Express, 18(17), 18312. doi:10.1364/oe.18.018312Yu, L., Yin, Y., Shi, Y., Dai, D., & He, S. (2016). Thermally tunable silicon photonic microdisk resonator with transparent graphene nanoheaters. Optica, 3(2), 159. doi:10.1364/optica.3.000159Schall, D., Mohsin, M., Sagade, A. A., Otto, M., Chmielak, B., Suckow, S., … Kurz, H. (2016). Infrared transparent graphene heater for silicon photonic integrated circuits. Optics Express, 24(8), 7871. doi:10.1364/oe.24.007871Yan, S., Zhu, X., Frandsen, L. H., Xiao, S., Mortensen, N. A., Dong, J., & Ding, Y. (2017). Slow-light-enhanced energy efficiency for graphene microheaters on silicon photonic crystal waveguides. Nature Communications, 8(1). doi:10.1038/ncomms14411Xu, Z., Qiu, C., Yang, Y., Zhu, Q., Jiang, X., Zhang, Y., … Su, Y. (2017). Ultra-compact tunable silicon nanobeam cavity with an energy-efficient graphene micro-heater. Optics Express, 25(16), 19479. doi:10.1364/oe.25.019479Lv, J., Yang, Y., Lin, B., Cao, Y., Zhang, Y., Li, S., … Zhang, D. (2019). Graphene-embedded first-order mode polymer Mach–Zender interferometer thermo-optic switch with low power consumption. Optics Letters, 44(18), 4606. doi:10.1364/ol.44.004606Wang, X., Jin, W., Chang, Z., & Chiang, K. S. (2019). Buried graphene electrode heater for a polymer waveguide thermo-optic device. Optics Letters, 44(6), 1480. doi:10.1364/ol.44.001480Lee, D.-J., Kim, H.-M., Kwon, J.-Y., Choi, H., Kim, S.-H., & Kim, K.-B. (2010). Structural and Electrical Properties of Atomic Layer Deposited Al-Doped ZnO Films. Advanced Functional Materials, 21(3), 448-455. doi:10.1002/adfm.201001342Cleary, J. W., Smith, E. M., Leedy, K. D., Grzybowski, G., & Guo, J. (2018). Optical and electrical properties of ultra-thin indium tin oxide nanofilms on silicon for infrared photonics. Optical Materials Express, 8(5), 1231. doi:10.1364/ome.8.001231Ray, S., Banerjee, R., Basu, N., Batabyal, A. K., & Barua, A. K. (1983). Properties of tin doped indium oxide thin films prepared by magnetron sputtering. Journal of Applied Physics, 54(6), 3497-3501. doi:10.1063/1.332415Babicheva, V. E., Kinsey, N., Naik, G. V., Ferrera, M., Lavrinenko, A. V., Shalaev, V. M., & Boltasseva, A. (2013). Towards CMOS-compatible nanophotonics: Ultra-compact modulators using alternative plasmonic materials. Optics Express, 21(22), 27326. doi:10.1364/oe.21.027326Sorger, V. J., Lanzillotti-Kimura, N. D., Ma, R.-M., & Zhang, X. (2012). Ultra-compact silicon nanophotonic modulator with broadband response. Nanophotonics, 1(1), 17-22. doi:10.1515/nanoph-2012-0009Shi, K., Haque, R. R., Zhao, B., Zhao, R., & Lu, Z. (2014). Broadband electro-optical modulator based on transparent conducting oxide. Optics Letters, 39(17), 4978. doi:10.1364/ol.39.004978Hoessbacher, C., Fedoryshyn, Y., Emboras, A., Melikyan, A., Kohl, M., Hillerkuss, D., … Leuthold, J. (2014). The plasmonic memristor: a latching optical switch. Optica, 1(4), 198. doi:10.1364/optica.1.000198Liu, X., Zang, K., Kang, J.-H., Park, J., Harris, J. S., Kik, P. G., & Brongersma, M. L. (2018). Epsilon-Near-Zero Si Slot-Waveguide Modulator. ACS Photonics, 5(11), 4484-4490. doi:10.1021/acsphotonics.8b00945Li, E., Gao, Q., Chen, R. T., & Wang, A. X. (2018). Ultracompact Silicon-Conductive Oxide Nanocavity Modulator with 0.02 Lambda-Cubic Active Volume. Nano Letters, 18(2), 1075-1081. doi:10.1021/acs.nanolett.7b04588Li, E., Gao, Q., Liverman, S., & Wang, A. X. (2018). 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Thermal effect analysis of silicon microring optical switch for on-chip interconnect. Journal of Semiconductors, 38(10), 104004. doi:10.1088/1674-4926/38/10/10400

Línea aérea alta tensión 132Kv derivación SET Aubals

El objeto de este proyecto es el estudio, descripción y valoración para su posterior remodelación de una línea de alta tensión de 132 kV. El objeto del presente proyecto de ejecución es doble: - En el orden administrativo su finalidad es la aportación de los datos necesarios para la Autorización administrativa, la Declaración, en concreto, de Utilidad Pública y la Aprobación del proyecto de Ejecución de la presente línea eléctrica. - En el orden técnico su finalidad es la de informar de las características, así como justificar su adaptación a lo preceptuado en el vigente Reglamento de Líneas Aéreas de Alta Tensión

Trabajo Fin de Máster

Trabajo Fin de Máster, Modalidad A. Máster Universitario en Formación del Profesorado de Educación Secundaria Obligatoria, Bachillerato, Formación Profesional y Enseñanzas de Idiomas, Artísticas y Deportivas. Especialidad Geografía e Historia

Impact of multi-output and stacking methods on feed efficiency prediction from genotype using machine learning algorithms

Feeding represents the largest economic cost in meat production; therefore, selection to improve traits related to feed efficiency is a goal in most livestock breeding programs. Residual feed intake (RFI), that is, the difference between the actual and the expected feed intake based on animal's requirements, has been used as the selection criteria to improve feed efficiency since it was proposed by Kotch in 1963. In growing pigs, it is computed as the residual of the multiple regression model of daily feed intake (DFI), on average daily gain (ADG), backfat thickness (BFT), and metabolic body weight (MW). Recently, prediction using single-output machine learning algorithms and information from SNPs as predictor variables have been proposed for genomic selection in growing pigs, but like in other species, the prediction quality achieved for RFI has been generally poor. However, it has been suggested that it could be improved through multi-output or stacking methods. For this purpose, four strategies were implemented to predict RFI. Two of them correspond to the computation of RFI in an indirect way using the predicted values of its components obtained from (i) individual (multiple single-output strategy) or (ii) simultaneous predictions (multi-output strategy). The other two correspond to the direct prediction of RFI using (iii) the individual predictions of its components as predictor variables jointly with the genotype (stacking strategy), or (iv) using only the genotypes as predictors of RFI (single-output strategy). The single-output strategy was considered the benchmark. This research aimed to test the former three hypotheses using data recorded from 5828 growing pigs and 45,610 SNPs. For all the strategies two different learning methods were fitted: random forest (RF) and support vector regression (SVR). A nested cross-validation (CV) with an outer 10-folds CV and an inner threefold CV for hyperparameter tuning was implemented to test all strategies. This scheme was repeated using as predictor variables different subsets with an increasing number (from 200 to 3000) of the most informative SNPs identified with RF. Results showed that the highest prediction performance was achieved with 1000 SNPs, although the stability of feature selection was poor (0.13 points out of 1). For all SNP subsets, the benchmark showed the best prediction performance. Using the RF as a learner and the 1000 most informative SNPs as predictors, the mean (SD) of the 10 values obtained in the test sets were: 0.23 (0.04) for the Spearman correlation, 0.83 (0.04) for the zero–one loss, and 0.33 (0.03) for the rank distance loss. We conclude that the information on predicted components of RFI (DFI, ADG, MW, and BFT) does not contribute to improve the quality of the prediction of this trait in relation to the one obtained with the single-output strategy.info:eu-repo/semantics/publishedVersio

Machine learning for mortality analysis in patients with COVID-19

This paper analyzes a sample of patients hospitalized with COVID-19 in the region of Madrid (Spain). Survival analysis, logistic regression, and machine learning techniques (both supervised and unsupervised) are applied to carry out the analysis where the endpoint variable is the reason for hospital discharge (home or deceased). The different methods applied show the importance of variables such as age, O2 saturation at Emergency Rooms (ER), and whether the patient comes from a nursing home. In addition, biclustering is used to globally analyze the patient-drug dataset, extracting segments of patients. We highlight the validity of the classifiers developed to predict the mortality, reaching an appreciable accuracy. Finally, interpretable decision rules for estimating the risk of mortality of patients can be obtained from the decision tree, which can be crucial in the prioritization of medical care and resources.This research was funded by Agencia Estatal de Investigación AEI/FEDER Spain, Project PGC2018-095895-B-I00, and Comunidad Autónoma de Madrid, Spain, Project S2017/BMD-368

A machine learning approach to identify groups of patients with hematological malignant disorders

Background and Objective: Vaccination against SARS-CoV-2 in immunocompromised patients with hematologic malignancies (HM) is crucial to reduce the severity of COVID-19. Despite vaccination efforts, over a third of HM patients remain unresponsive, increasing their risk of severe breakthrough infections. This study aims to leverage machine learning's adaptability to COVID-19 dynamics, efficiently selecting patient-specific features to enhance predictions and improve healthcare strategies. Highlighting the complex COVID-hematology connection, the focus is on interpretable machine learning to provide valuable insights to clinicians and biologists. Methods: The study evaluated a dataset with 1166 patients with hematological diseases. The output was the achievement or non-achievement of a serological response after full COVID-19 vaccination. Various machine learning methods were applied, with the best model selected based on metrics such as the Area Under the Curve (AUC), Sensitivity, Specificity, and Matthew Correlation Coefficient (MCC). Individual SHAP values were obtained for the best model, and Principal Component Analysis (PCA) was applied to these values. The patient profiles were then analyzed within identified clusters. Results: Support vector machine (SVM) emerged as the best-performing model. PCA applied to SVM-derived SHAP values resulted in four perfectly separated clusters. These clusters are characterized by the proportion of patients that generate antibodies (PPGA). Cluster 1, with the second-highest PPGA (69.91%), included patients with aggressive diseases and factors contributing to increased immunodeficiency. Cluster 2 had the lowest PPGA (33.3%), but the small sample size limited conclusive findings. Cluster 3, representing the majority of the population, exhibited a high rate of antibody generation (84.39%) and a better prognosis compared to cluster 1. Cluster 4, with a PPGA of 66.33%, included patients with B-cell non-Hodgkin's lymphoma on corticosteroid therapy. Conclusions: The methodology successfully identified four separate patient clusters using Machine Learning and Explainable AI (XAI). We then analyzed each cluster based on the percentage of HM patients who generated antibodies after COVID-19 vaccination. The study suggests the methodology's potential applicability to other diseases, highlighting the importance of interpretable ML in healthcare research and decision-makingPID2021-122347NB-I00, PID2021-127946OB-I0

Structural neuroimaging of social cognition in progressive non-fluent aphasia and behavioral variant of frontotemporal dementia

Social cognition impairments are pervasive in the frontotemporal dementias (FTD). These deficits would be triggered by (a) basic emotion and face recognition processes as well as by (b) higher level social cognition (e.g., theory of mind, ToM). Both emotional processing and social cognition impairments have been previously reported in the behavioral variant of FTD (bvFTD) and also in other versions of FTDs, including primary progressive aphasia. However, no neuroanatomic comparison between different FTD variants has been performed. We report selective behavioral impairments of face recognition, emotion recognition, and ToM in patients with bvFTD and progressive non-fluent aphasia (PNFA) when compared to controls. Voxel-based morphometry (VBM) shows a classical impairment of mainly orbitofrontal (OFC), anterior cingulate (ACC), insula and lateral temporal cortices in patients. Comparative analysis of regional gray matter related to social cognition deficits (VBM) reveals a differential pattern of fronto-insulo-temporal atrophy in bvFTD and an insulo-temporal involvement in PNFA group. Results suggest that in spite of similar social cognition impairments reported in bvFTD and PNFA, the former represents an inherent ToM affectation whereas in the PNFA these deficits could be related to more basic processes of face and emotion recognition. These results are interpreted in the frame of the fronto-insulo-temporal social context network model (SCNM).Fil: Couto, Juan Blas Marcos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Favaloro; ArgentinaFil: Manes, Facundo Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Favaloro; ArgentinaFil: Montañés, Patricia. Pontificia Universidad Javeriana; ColombiaFil: Matallana, Diana. Pontificia Universidad Javeriana; ColombiaFil: Reyes, Pablo. Pontificia Universidad Javeriana; ColombiaFil: Velasquez, Marcela. Universidad Favaloro; ArgentinaFil: Yoris Magnago, Adrián Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Favaloro; ArgentinaFil: Báez Buitrago, Sandra Jimena. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Favaloro; ArgentinaFil: Ibáñez Barassi, Agustín Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Favaloro; Argentina. Universidad Diego Portales; Chil

Plk1 overexpression induces chromosomal instability and suppresses tumor development

Polo-like kinase 1 (Plk1) is overexpressed in a wide spectrum of human tumors, being frequently considered as an oncogene and an attractive cancer target. However, its contribution to tumor development is unclear. Using a new inducible knock-in mouse model we report here that Plk1 overexpression results in abnormal chromosome segregation and cytokinesis, generating polyploid cells with reduced proliferative potential. Mechanistically, these cytokinesis defects correlate with defective loading of Cep55 and ESCRT complexes to the abscission bridge, in a Plk1 kinase-dependent manner. In vivo, Plk1 overexpression prevents the development of Kras-induced and Her2-induced mammary gland tumors, in the presence of increased rates of chromosome instability. In patients, Plk1 overexpression correlates with improved survival in specific breast cancer subtypes. Therefore, despite the therapeutic benefits of inhibiting Plk1 due to its essential role in tumor cell cycles, Plk1 overexpression has tumor-suppressive properties by perturbing mitotic progression and cytokinesis.We are indebted to Stephen Taylor for the Sgo1 antibody. We thank Simone Kraut, Jessica Steiner, and the DKFZ light microscopy unit for excellent technical assistance. The results published here are in part based on data generated by TCGA pilot project (https://cancergenome.nih.gov/established by the NCI and the National Human Gen- ome Research Institute. The data were retrieved through dbGaP authorization (accession no. phs000178.v9.p8). S.V.V. was supported by the Marie Curie Network Ploidynet, funded by the European Union Seventh Framework Programme (FP7/2007–2013) under Grant Agreement #316964. L.S. is supported by a postdoctoral fellowship from Funda- cion Ramon Areces. Work in the R.S. laboratory was supported by an ERC starting grant (#281614), Marie Curie PCIG09-GA-2011 –293745 and the Howard Hughes Medical Institute. G.d.C. is funded by AECC Scientific Foundation (LABAE16017DECA). Work in the M.M. laboratory was supported by grants from the MINECO (SAF2015 –69920-R cofunded by ERDF-EU), Worldwide Cancer Research (WCR no. 150278), and Comunidad de Madrid (iLUNG-CM; B2017/BMD3884). The CNIO is a Severo Ochoa Center of Excellence (MINECO award SEV-2015-0510).S