Algoritmos de aprendizaje neurocomputacionales para su implementación hardware

Las redes de neuronas artificiales son un paradigma de aprendizaje y procesamiento automático inspirado en el funcionamiento del sistema nervioso, se emplean en toda tipo de aplicaciones, con lo que van apareciendo nuevas aplicaciones donde la utilización de ordenadores no da una solución de manera. Los sistemas en tiempo real y las redes de sensores son dos de las tecnologías más extendidas donde la utilización de modelos neurocomputacionales requiere un desarrollo en dispositivos y el empleo de técnicas de programación diferente a las convencionales. En este tipo de aplicaciones otros dispositivos hardware como las FPGAs o microcontroladores son más adecuados a la hora de la implementación de redes neuronales artificiales. Los sistemas en tiempo real están sujetos a unas limitaciones temporales y la estructura de las FPGAs permite implementar este tipo de diseños debido a que se diseña el dispositivo a nivel hardware, consiguiendo unos tiempos de respuesta muy acotados, exigidos en una aplicación en tiempo real. En esta línea de investigación existen dos posibles alternativas para este tipo de sistemas: (i) implementaciones específicas de algoritmos conocidos adaptados a los dispositivos hardware; (ii) implementaciones de nuevos algoritmos que se adapten mejor a este dispositivo. Otra tecnología muy importante es las redes de sensores inalámbricas, debido a los avances tecnológicos registrados en la última década sobre la capacidad de los microcontroladores usados en este tipo de aplicaciones. Una motivación de esta tesis es de dotar de inteligencia a las redes de sensores en este tipo de escenarios para proporcionar de una cierta lógica a la toma de decisiones Se busca alcanzar los siguientes objetivos: (i) analizar el algoritmo Backpropagation, desarrollando y evaluando diferentes técnicas de optimización para cada dispositivo; (ii) evaluar una alternativa al algoritmo Backpropagation para poder realizar una implementación hardware maximizando el uso de recursos; (iii) realizar una implementación hardware eficiente del algoritmo C-Mantec para evaluar su posible utilización en aplicaciones de sistemas en tiempo real; y (iv) evaluar una implementación del algoritmo constructivo C-Mantec sobre microcontroladores para su utilización en redes de sensores. Se implementa el algoritmo Backpropagation una FPGA y un microcontrolador. La implementación en FPGA se precisa diseñar con diferentes modificaciones, que se centran en introducir una nueva neurona ``Primera Capa'', incorporar la multiplexación por división en el tiempo del bloque multiplicador, así como tabular e interpolar los valores de la función sigmoidea, permitiendo un reducción de recursos, en media, de un 25,8\% de celdas lógicas y un 50'3\% de bloques específicos. En el caso específico del microcontrolador la modificación del tipo de representación de los datos permite un incremento en la velocidad de cómputo de entre 8 a 18 veces más rápido, además de una reducción importante en la cantidad de memoria utilizada. La implementación ``on-chip'' del algoritmo C-Mantec ha sido realizada de forma específica para su implantación en una placa FPGA, haciendo un análisis del tiempo de cómputo se observa una disminución del tiempo empleado en las implementaciones sobre FPGA en relación a las realizadas sobre PC. Esto es debido a que el tiempo de cómputo en un ordenador crece de forma exponencial y de manera lineal en una FPGA, dando lugar a implementaciones hasta 47 veces más rápidas. La implementación hardware del algoritmo C-Mantec es un 15\% más eficientes en recursos hardware utilizados en comparación a la del algoritmo Backpropagation, permitiendo mayor número de neuronas en la arquitectura. Para finalizar es importante mencionar que el tiempo de aprendizaje de las implementaciones FPGAs en ambos algoritmos es notablemente menor que el tiempo empleado por un PC, siendo la del C-Mantec sustancialmente inferior al del Backpropagation. Además el tiempo de ejecución del modelo es considerablemente inferior para el C-Mantec, lo que supone una ventaja en la fase de explotación del modelo. El algoritmo C-Mantec se ha implementado en la placa Arduino, para lo que se ha modificado el paradigma de representación de datos reduciendo considerablemente la memoria utilizada para el almacenamiento de variables y aumentando la velocidad de procesamiento, debido a que la unidad aritmético lógica en este tipo de representación son más simples. El algoritmo implementado se ha empleado como una red de sensor/actuador en tres casos de estudios con el fin de demostrar la eficiencia y la versatilidad de la aplicación resultante. Los tres casos de estudios seleccionados son problemas definidos en entornos cambiantes, y por lo tanto la toma de decisiones del sensor/actuador ha de adaptarse en consecuencia a los cambios observados, por lo que requieren una reconversión del modelo de red neuronal que controla el proceso de decisión. Los tiempos de reprogramación observados son significativamente bajos en los tres casos de estudio, siendo en consecuencia el consumo de energía del dispositivo también bastante pequeño. Incluso sin una comparación exhaustiva con el caso tradicional en el que el nuevo código tiene que ser transmitido desde una unidad de control central, los resultados hacen evidente una reducción en el gasto energético, cualidad muy importante en este tipo de tecnología debida a la corta duración de las baterías que lo alimentan. Como resultado, se ha demostrado la idoneidad del algoritmo C-Mantec para su aplicación en una tarea compleja utilizando un microcontrolador Arduino UNO. Hoy en día, dada la existencia de dispositivos con mucho más poder de cómputo y recursos que la placa considerada, el presente estudio permite confirmar la potencial aplicación del algoritmo propuesto en tareas reales que necesitan sensores/actuadores. Las futuras líneas de investigación pueden ser muy variadas, algunas de los iniciados son evolucionar la implementación hardware del algoritmo Backpropagation; o analizar la posibilidad de emplear las FPGAs como aceleradoras hardware para simulaciones de sistemas complejos. Además de estudiar otros modelos computacionales con otras reglas de; o analizar la posibilidad de aplicar los sistemas neurocomputacionales en tiempo real en tareas complejas

Solving Scheduling Problems with Genetic Algorithms using a Priority Encoding Scheme

Scheduling problems are very hard computational tasks with several applications in multitude of domains. In this work we solve a practical problem motivated by a real industry situation, in which we apply a genetic algorithm for finding an acceptable solution in a very short time interval. The main novelty introduced in this work is the use of a priority based chromosome codification that determines the precedence of a task with respect to other ones, permitting to introduce in a very simple way all problem constraints, including setup costs and workforce availability. Results show the suitability of the approach, obtaining real time solutions for tasks with up to 50 products.Universidad de Málaga.Campus de Excelencia Internacional Andalucía Tech

Hiperferritinemia y hemocromatosis. Hemocromatosis hereditaria con genotipo mutado hfe c282y

El presente trabajo realiza una revisión sobre la literatura existente relacionada con hiperferritinemia y hemocromatosis. El motivo de dicha revisión reside en el frecuente hallazgo de valores elevados de ferritina de manera rutinaria en atención primaria, y la necesidad de clarificar y asentar conceptos relacionados con el tema. Pretende así facilitar el manejo de esta alteración en la práctica asistencial habitual, informar de una manera generalista tanto de su patogenia como de su enfoque diagnóstico y seguimiento, e ilustrar sobre las patologías de tipo hematológico hereditario

Hierarchical Color Quantization with a Neural Gas Model Based on Bregman Divergences

In this paper, a new color quantization method based on a self-organized artificial neural network called the Growing Hierarchical Bregman Neural Gas (GHBNG) is proposed. This neural network is based on Bregman divergences, from which the squared Euclidean distance is a particular case. Thus, the best suitable Bregman divergence for color quantization can be selected according to the input data. Moreover, the GHBNG yields a tree-structured model that represents the input data so that a hierarchical color quantization can be obtained, where each layer of the hierarchy contains a different color quantization of the original image. Experimental results confirm the color quantization capabilities of this approach.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

DESARROLLO DE HERRAMIENTA DIDÁCTICA EN GEOGEBRA PARA EL ANÁLISIS DE LA RESPUESTA TEMPORAL DE SISTEMAS DE SEGUNDO ORDEN (DEVELOPMENT OF A DIDACTIC TOOL IN GEOGEBRA FOR THE ANALYSIS OF THE TEMPORAL RESPONSE OF SECOND ORDER SYSTEMS)

Resumen El análisis de sistemas de segundo orden dentro del campo de la ingeniería de control tiene una gran relevancia, ya que habitualmente la mayor parte de los sistemas pueden ser aproximados a un sistema de orden dos. Con la finalidad de contribuir al proceso de enseñanza/aprendizaje se desarrolló una herramienta didáctica mediante el software GeoGebra, la cual proporciona un análisis de sistemas de este tipo y su respuesta. La herramienta desarrollada calcula y despliega de forma gráfica la respuesta temporal del sistema ante una entrada escalón unitario así como una serie de parámetros. Palabras Clave: Sistemas de segundo orden, Función de transferencia, Parámetros, GeoGebra. Abstract The analysis of second-order systems within the field of control engineering is of great importance, since usually most systems can be approximated to an order two system. In order to contribute to the teaching/learning process, a teaching tool was developed using the GeoGebra software, which provides an analysis of this type of system and its response. The developed tool calculates and displays graphically the temporal response of the system to a unitary step input as well as a series of parameters. Keywords: Second order systems, Transfer function, Parameters, GeoGebra

Exploring the limits of anaerobic biodegradability of urban wastewater by AnMBR technology

[EN] Anaerobic membrane bioreactors (AnMBRs) can achieve maximum energy recovery from urban wastewater (UWW) by converting influent COD into methane. The aim of this study was to assess the anaerobic biodegradability limits of urban wastewater with AnMBR technology by studying the possible degradation of the organic matter considered as non-biodegradable as observed in aerobic membrane bioreactors operated at very high sludge retention times. For this, the results obtained in an AnMBR pilot plant operated at very high SRT (140 days) treating sulfate-rich urban wastewater were compared with those previously obtained with the system operating at lower SRT (29 to 70 days). At 140 days SRT the organic matter biodegraded by the AnMBR system accounted for 64.4% of the influent COD (45.9% was removed by sulfate reducing bacteria (SRB), and only 18.5% was converted into methane, highlighting the strong competition between SRB and methanogenic archaea (MA) when treating sulfate-rich wastewater). Almost half of the methane produced (46%) was dissolved in the permeate and most of it was recovered by a degassing membrane. The organic matter biodegraded by the AnMBR system was similar to the influent anaerobic biodegradability determined by wastewater characterization assays (68.5% of the influent COD), indicating that nearly all the influent's biodegradable organic matter had been removed. This percentage of degraded COD was similar to that obtained in previous studies working at 70 days SRT, showing that the limit of anaerobic biodegradability was already reached in this SRT. The organic matter considered as non-biodegradable according to wastewater characterization assays therefore was not seen to degrade in the AnMBR pilot plant, even at very high SRT. Once the biodegraded COD is close to the influent's anaerobic biodegradability, increasing the SRT is not justified as it only leads to higher operational costs for the same biogas production. These findings support the use of mathematical models for AnMBR design since they accurately represent the behaviour of these systems in a wide range of operating conditions.This research project was supported by the Spanish Ministry of Economy and Competitiveness (MINECO, Project CTM2014-54980-C2-2-R). The authors are also grateful for the support received from the Generalitat Valenciana via CPI-16-155 fellowships.Seco Torrecillas, A.; Mateo-Llosa, O.; Zamorano-López, N.; Sanchis-Perucho, P.; Serralta Sevilla, J.; Martí Ortega, N.; Borrás Falomir, L.... (2018). Exploring the limits of anaerobic biodegradability of urban wastewater by AnMBR technology. Environmental Science: Water Research & Technology. 4(11):1877-1887. https://doi.org/10.1039/c8ew00313kS18771887411Li, W.-W., & Yu, H.-Q. (2011). From wastewater to bioenergy and biochemicals via two-stage bioconversion processes: A future paradigm. Biotechnology Advances, 29(6), 972-982. doi:10.1016/j.biotechadv.2011.08.012Shin, C., & Bae, J. (2018). Current status of the pilot-scale anaerobic membrane bioreactor treatments of domestic wastewaters: A critical review. Bioresource Technology, 247, 1038-1046. doi:10.1016/j.biortech.2017.09.002EEA , Performance of water utilities beyond compliance (Technical report No. 5/2014) , Luxemburg , 2014Martin, I., Pidou, M., Soares, A., Judd, S., & Jefferson, B. (2011). Modelling the energy demands of aerobic and anaerobic membrane bioreactors for wastewater treatment. Environmental Technology, 32(9), 921-932. doi:10.1080/09593330.2011.565806JEISON, D., & VANLIER, J. (2007). Cake formation and consolidation: Main factors governing the applicable flux in anaerobic submerged membrane bioreactors (AnSMBR) treating acidified wastewaters. Separation and Purification Technology, 56(1), 71-78. doi:10.1016/j.seppur.2007.01.022Pretel, R., Robles, A., Ruano, M. V., Seco, A., & Ferrer, J. (2014). The operating cost of an anaerobic membrane bioreactor (AnMBR) treating sulphate-rich urban wastewater. Separation and Purification Technology, 126, 30-38. doi:10.1016/j.seppur.2014.02.013Cashman, S., Ma, X., Mosley, J., Garland, J., Crone, B., & Xue, X. (2018). Energy and greenhouse gas life cycle assessment and cost analysis of aerobic and anaerobic membrane bioreactor systems: Influence of scale, population density, climate, and methane recovery. Bioresource Technology, 254, 56-66. doi:10.1016/j.biortech.2018.01.060Ozgun, H., Dereli, R. K., Ersahin, M. E., Kinaci, C., Spanjers, H., & van Lier, J. B. (2013). A review of anaerobic membrane bioreactors for municipal wastewater treatment: Integration options, limitations and expectations. Separation and Purification Technology, 118, 89-104. doi:10.1016/j.seppur.2013.06.036Lin, H., Peng, W., Zhang, M., Chen, J., Hong, H., & Zhang, Y. (2013). A review on anaerobic membrane bioreactors: Applications, membrane fouling and future perspectives. Desalination, 314, 169-188. doi:10.1016/j.desal.2013.01.019Giménez, J. B., Martí, N., Ferrer, J., & Seco, A. (2012). Methane recovery efficiency in a submerged anaerobic membrane bioreactor (SAnMBR) treating sulphate-rich urban wastewater: Evaluation of methane losses with the effluent. Bioresource Technology, 118, 67-72. doi:10.1016/j.biortech.2012.05.019Glória, R. M., Motta, T. M., Silva, P. V. O., Costa, P. da, Brandt, E. M. F., Souza, C. L., & Chernicharo, C. A. L. (2016). STRIPPING AND DISSIPATION TECHNIQUES FOR THE REMOVAL OF DISSOLVED GASES FROM ANAEROBIC EFFLUENTS. Brazilian Journal of Chemical Engineering, 33(4), 713-721. doi:10.1590/0104-6632.20160334s20150291Scherer, E., & Wichmann, K. (2000). Treatment of Groundwater Containing Methane - Combination of the Processing Stages Desorption and Filtration. Acta hydrochimica et hydrobiologica, 28(3), 145-154. doi:10.1002/1521-401x(200003)28:33.0.co;2-vD. Schippers and R.Schotsman , Recovery and beneficial use of water-based methane, Water21 , 2010 , pp. 34–35Crone, B. C., Garland, J. L., Sorial, G. A., & Vane, L. M. (2016). Significance of dissolved methane in effluents of anaerobically treated low strength wastewater and potential for recovery as an energy product: A review. Water Research, 104, 520-531. doi:10.1016/j.watres.2016.08.019Cookney, J., Mcleod, A., Mathioudakis, V., Ncube, P., Soares, A., Jefferson, B., & McAdam, E. J. (2016). Dissolved methane recovery from anaerobic effluents using hollow fibre membrane contactors. Journal of Membrane Science, 502, 141-150. doi:10.1016/j.memsci.2015.12.037Hatamoto, M., Yamamoto, H., Kindaichi, T., Ozaki, N., & Ohashi, A. (2010). Biological oxidation of dissolved methane in effluents from anaerobic reactors using a down-flow hanging sponge reactor. Water Research, 44(5), 1409-1418. doi:10.1016/j.watres.2009.11.021Pretel, R., Robles, A., Ruano, M. V., Seco, A., & Ferrer, J. (2013). Environmental impact of submerged anaerobic MBR (SAnMBR) technology used to treat urban wastewater at different temperatures. Bioresource Technology, 149, 532-540. doi:10.1016/j.biortech.2013.09.060Lubello, C., Caffaz, S., Gori, R., & Munz, G. (2009). A modified Activated Sludge Model to estimate solids production at low and high solids retention time. Water Research, 43(18), 4539-4548. doi:10.1016/j.watres.2009.08.001L. Cabrera , F.García-Usach , J.Ribes , A.Seco , J. J.Morenilla , F.Llavador and J.Ferrer , Estudio de la producción de fangos en bioreactores de membranas aerobios con elevados valores de tiempo de retención celular, Fangos y lodos , 2009 , vol. 7 , pp. 1–3Giménez, J. B., Robles, A., Carretero, L., Durán, F., Ruano, M. V., Gatti, M. N., … Seco, A. (2011). Experimental study of the anaerobic urban wastewater treatment in a submerged hollow-fibre membrane bioreactor at pilot scale. Bioresource Technology, 102(19), 8799-8806. doi:10.1016/j.biortech.2011.07.014Robles, Á., Durán, F., Ruano, M. V., Ribes, J., Rosado, A., Seco, A., & Ferrer, J. (2015). Instrumentation, control, and automation for submerged anaerobic membrane bioreactors. Environmental Technology, 36(14), 1795-1806. doi:10.1080/09593330.2015.1012180R. E. Moosbrugger , M. C.Wentzel , G. A.Ekama and G. R.Marais , Simple Titration Procedures to Determine H2CO3 * Alkalinity And Short-chain Fatty Acids In Aqueous Solutions Containing Known Concentrations Of Ammonium, Phosphate And Sulphide Weak Acid/Bases. WRC Report No. TT 57/92, UCT Research Report W 74 , 1992Metcalf & Eddy, Inc. , G.Tchobanoglous , F.Burton and H.David Stensel , Wastewater Engineering: Treatment and Reuse , McGraw-Hill Education , 2002D. A. Stahl and R.Amann , in Nucleic Acid Techniques in Bacterial Systematics, Sequencing and Hybridization Techniques in Bacterial Systematics , 1991 , pp. 205–248Crocetti, G., Murto, M., & Björnsson, L. (2006). An update and optimisation of oligonucleotide probes targeting methanogenic Archaea for use in fluorescence in situ hybridisation (FISH). Journal of Microbiological Methods, 65(1), 194-201. doi:10.1016/j.mimet.2005.07.007Daims, H., Brühl, A., Amann, R., Schleifer, K.-H., & Wagner, M. (1999). The Domain-specific Probe EUB338 is Insufficient for the Detection of all Bacteria: Development and Evaluation of a more Comprehensive Probe Set. Systematic and Applied Microbiology, 22(3), 434-444. doi:10.1016/s0723-2020(99)80053-8C. W. Gellings and K. E.Parmenter , Energy efficiency in fertilizer production and use. In Knowledge for Sustainable Development , Encyclopedia of Life Support Systems (EOLSS), Eolss Publisher , Oxford , 2004 , vol. II , pp. 419–450J. B. Giménez , Estudio del tratamiento anaerobio de aguas residuales urbanas en biorreactores de membrana (Doctoral Thesis) , Universitat de València , Valencia , 2014Giménez, J. B., Martí, N., Robles, A., Ferrer, J., & Seco, A. (2014). Anaerobic treatment of urban wastewater in membrane bioreactors: evaluation of seasonal temperature variations. Water Science and Technology, 69(7), 1581-1588. doi:10.2166/wst.2014.069Robles, A., Ruano, M. V., Ribes, J., & Ferrer, J. (2012). Sub-critical long-term operation of industrial scale hollow-fibre membranes in a submerged anaerobic MBR (HF-SAnMBR) system. Separation and Purification Technology, 100, 88-96. doi:10.1016/j.seppur.2012.09.010Robles, A., Ruano, M. V., Ribes, J., & Ferrer, J. (2013). Factors that affect the permeability of commercial hollow-fibre membranes in a submerged anaerobic MBR (HF-SAnMBR) system. Water Research, 47(3), 1277-1288. doi:10.1016/j.watres.2012.11.055Ferrer, J., Pretel, R., Durán, F., Giménez, J. B., Robles, A., Ruano, M. V., … Seco, A. (2015). Design methodology for submerged anaerobic membrane bioreactors (AnMBR): A case study. Separation and Purification Technology, 141, 378-386. doi:10.1016/j.seppur.2014.12.018Regueiro, L., Veiga, P., Figueroa, M., Alonso-Gutierrez, J., Stams, A. J. M., Lema, J. M., & Carballa, M. (2012). Relationship between microbial activity and microbial community structure in six full-scale anaerobic digesters. Microbiological Research, 167(10), 581-589. doi:10.1016/j.micres.2012.06.002Khan, M. A., Patel, P. G., Ganesh, A. G., Rais, N., Faheem, S. M., & Khan, S. T. (2018). Assessing Methanogenic Archaeal Community in Full Scale Anaerobic Sludge Digester Systems in Dubai, United Arab Emirates. The Open Microbiology Journal, 12(1), 123-134. doi:10.2174/1874285801812010123Reyes, M., Borrás, L., Seco, A., & Ferrer, J. (2014). Identification and quantification of microbial populations in activated sludge and anaerobic digestion processes. Environmental Technology, 36(1), 45-53. doi:10.1080/09593330.2014.934745Shin, C., McCarty, P. L., Kim, J., & Bae, J. (2014). Pilot-scale temperate-climate treatment of domestic wastewater with a staged anaerobic fluidized membrane bioreactor (SAF-MBR). Bioresource Technology, 159, 95-103. doi:10.1016/j.biortech.2014.02.06

Myocardial injury determination improves risk stratification and predicts mortality in COVID-19 patients

Background: Despite being associated with worse prognosis in patients with COVID-19, systematic determination of myocardial injury is not recommended. The aim of the study was to study the effect of myocardial injury assessment on risk stratification of COVID-19 patients.Methods: Seven hundred seven consecutive adult patients admitted to a large tertiary hospital with confirmed COVID-19 were included. Demographic data, comorbidities, laboratory results and clinical outcomes were recorded. Charlson comorbidity index (CCI) was calculated in order to quantify the degree of comorbidities. Independent association of cardiac troponin I (cTnI) increase with outcomes was evaluated by multivariate regression analyses and area under curve. In addition, propensity-score matching was performed to assemble a cohort of patients with similar baseline characteristics.Results: In the matched cohort (mean age 66.76 ± 15.7 years, 37.3% females), cTnI increase above the upper limit was present in 20.9% of the population and was associated with worse clinical outcomes, including all-cause mortality within 30 days (45.1% vs. 23.2%; p = 0.005). The addition of cTnI to a multivariate prediction model showed a significant improvement in the area under the time-dependent receiver operating characteristic curve (0.775 vs. 0.756, DC-statistic = 0.019; 95% confidence interval 0.001–0.037). Use of renin–angiotensin–aldosterone system inhibitors was not associated with mortality after adjusting by baseline risk factors.Conclusions: Myocardial injury is independently associated with adverse outcomes irrespective of baseline comorbidities and its addition to multivariate regression models significantly improves their performance in predicting mortality. The determination of myocardial injury biomarkers on hospital admission and its combination with CCI can classify patients in three risk groups (high, intermediate and low) with a clearly distinct 30-day mortality

New-onset atrial fibrillation during COVID-19 infection predicts poor prognosis

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has led toa paradigm shift in healthcare worldwide. Little is known about the impact on the cardiovascularsystem, and the incidence and consequences of new onset of atrial fibrillation (AF) in infected patientsremain unclear. The aim of this study was to analyze the cardiovascular outcomes of patients with newonset AF and coronavirus disease 2019 (COVID-19) infection.Methods: This observational study analyzed a sample of 160 consecutive patients hospitalized due toCOVID-19. A group with new-onset AF (n = 12) was compared with a control group (total: n = 148,sinus rhythm: n = 118, previous AF: n = 30). New-onset AF patients were significantly older andhypertensive, as well as presenting more frequently with a history of acute coronary syndrome andrenal dysfunction. This group showed a higher incidence of thromboembolic events (41.7% vs. 4.1%;p < 0.001), bleeding (33.3% vs. 4.7%, p = 0.005), a combined endpoint of thrombosis and death(58.3% vs. 19.6%, p = 0.006) and longer hospital stays (16.4 vs. 8.6 days, p < 0.001), with no differences in all-cause mortality.Results: In multivariate analysis, adjusted by potential confounding factors, new-onset AF demonstrateda 14.26 odds ratio for thromboembolism (95% confidence interval 2.86–71.10, p < 0.001).Conclusions: New-onset AF in COVID-19 patients presumably has a notable impact on prognosis.The appearance of new-onset AF is related to worse cardiovascular outcomes, considering it as an independent predictor of embolic events. Further studies are needed to identify patients with COVID-19at high risk of developing “de novo” AF, provide early anticoagulation and minimize the embolic risk ofboth entities