Redes neuronales artificiales en conducción de calor multidimensional transitorio

Este artículo ilustra la aplicabilidad de las Redes Neuronales Artificiales en la predicción de latemperatura de algunos fenómenos de conducción de calor multidimensional transitorio. Seplantean dos casos en una dimensión con condición inicial constante y condiciones de frontera,para uno, de Dirichlet y para el otro, convectivas. Con estas mismas condiciones, se abordangeometrías en dos y tres dimensiones y se desarrollan sus soluciones analiticas para obtener lospatrones de entrada y salida utilizados en elposterior entrenamiento, verificación y generalizaciónde las redes neuronales artificiales. Para predecir la temperatura de los casos estudiados a partirde variables espaciales y temporales mediante la inteligencia artificial, Redes NeuronalesArtificiales, se empleó el Perceptrón multicapa con conexiones hacia adelante, función deactivación tangente hiperbólica para los nodos de la(s) capa(s) oculta(s) y lineal para el nodode salida, algoritmo de aprendizaje Levenberg - Marquardt y raíz de la suma de los cuadrados ypreprocesamiento rango como normalizaciones de las variables de entrada y salidarespectivamente. Una vez determinadas las especificaciones se llevaron a cabo las etapas dedesalTollo: entrenamiento, verificación y generalización de las redes de cada caso de conducciónde calor considerado empleando diversas configuraciones con el fin de seleccionar la másadecuada de acuerdo a los criterios: convergencia en el entrenamiento, capacidad degeneralización y simplicidad en su estructura.Neural networks to prediet the multidimensional unsteady-state temperature projile in a solidhave been used; convective and Dirichlet boundmy conditions for the mathematical model wereapplied to salve the model. For computer simulations several neural networks following theMultilayer Perceptron architecture were trained using the Levenberg-Marquardt algorithm. Resultsshowed an excellent agreement between numerical solutions afthe mathematical model and theneural network predictions

Redes neuronales artificiales en conducción de calor multidimensional transitorio

Este artículo ilustra la aplicabilidad de las Redes Neuronales Artificiales en la predicción de latemperatura de algunos fenómenos de conducción de calor multidimensional transitorio. Seplantean dos casos en una dimensión con condición inicial constante y condiciones de frontera,para uno, de Dirichlet y para el otro, convectivas. Con estas mismas condiciones, se abordangeometrías en dos y tres dimensiones y se desarrollan sus soluciones analiticas para obtener lospatrones de entrada y salida utilizados en elposterior entrenamiento, verificación y generalizaciónde las redes neuronales artificiales. Para predecir la temperatura de los casos estudiados a partirde variables espaciales y temporales mediante la inteligencia artificial, Redes NeuronalesArtificiales, se empleó el Perceptrón multicapa con conexiones hacia adelante, función deactivación tangente hiperbólica para los nodos de la(s) capa(s) oculta(s) y lineal para el nodode salida, algoritmo de aprendizaje Levenberg - Marquardt y raíz de la suma de los cuadrados ypreprocesamiento rango como normalizaciones de las variables de entrada y salidarespectivamente. Una vez determinadas las especificaciones se llevaron a cabo las etapas dedesalTollo: entrenamiento, verificación y generalización de las redes de cada caso de conducciónde calor considerado empleando diversas configuraciones con el fin de seleccionar la másadecuada de acuerdo a los criterios: convergencia en el entrenamiento, capacidad degeneralización y simplicidad en su estructura.Neural networks to prediet the multidimensional unsteady-state temperature projile in a solidhave been used; convective and Dirichlet boundmy conditions for the mathematical model wereapplied to salve the model. For computer simulations several neural networks following theMultilayer Perceptron architecture were trained using the Levenberg-Marquardt algorithm. Resultsshowed an excellent agreement between numerical solutions afthe mathematical model and theneural network predictions

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Redes neuronales artificiales en conducción de calor multidimensional transitorio

Neural networks to prediet the multidimensional unsteady-state temperature projile in a solidhave been used; convective and Dirichlet boundmy conditions for the mathematical model wereapplied to salve the model. For computer simulations several neural networks following theMultilayer Perceptron architecture were trained using the Levenberg-Marquardt algorithm. Resultsshowed an excellent agreement between numerical solutions afthe mathematical model and theneural network predictions.Este artículo ilustra la aplicabilidad de las Redes Neuronales Artificiales en la predicción de latemperatura de algunos fenómenos de conducción de calor multidimensional transitorio. Seplantean dos casos en una dimensión con condición inicial constante y condiciones de frontera,para uno, de Dirichlet y para el otro, convectivas. Con estas mismas condiciones, se abordangeometrías en dos y tres dimensiones y se desarrollan sus soluciones analiticas para obtener lospatrones de entrada y salida utilizados en elposterior entrenamiento, verificación y generalizaciónde las redes neuronales artificiales. Para predecir la temperatura de los casos estudiados a partirde variables espaciales y temporales mediante la inteligencia artificial, Redes NeuronalesArtificiales, se empleó el Perceptrón multicapa con conexiones hacia adelante, función deactivación tangente hiperbólica para los nodos de la(s) capa(s) oculta(s) y lineal para el nodode salida, algoritmo de aprendizaje Levenberg - Marquardt y raíz de la suma de los cuadrados ypreprocesamiento rango como normalizaciones de las variables de entrada y salidarespectivamente. Una vez determinadas las especificaciones se llevaron a cabo las etapas dedesalTollo: entrenamiento, verificación y generalización de las redes de cada caso de conducciónde calor considerado empleando diversas configuraciones con el fin de seleccionar la másadecuada de acuerdo a los criterios: convergencia en el entrenamiento, capacidad degeneralización y simplicidad en su estructura

Environmental and societal factors associated with COVID-19-related death in people with rheumatic disease: an observational study

Published by Elsevier Ltd.Background: Differences in the distribution of individual-level clinical risk factors across regions do not fully explain the observed global disparities in COVID-19 outcomes. We aimed to investigate the associations between environmental and societal factors and country-level variations in mortality attributed to COVID-19 among people with rheumatic disease globally. Methods: In this observational study, we derived individual-level data on adults (aged 18-99 years) with rheumatic disease and a confirmed status of their highest COVID-19 severity level from the COVID-19 Global Rheumatology Alliance (GRA) registry, collected between March 12, 2020, and Aug 27, 2021. Environmental and societal factors were obtained from publicly available sources. The primary endpoint was mortality attributed to COVID-19. We used a multivariable logistic regression to evaluate independent associations between environmental and societal factors and death, after controlling for individual-level risk factors. We used a series of nested mixed-effects models to establish whether environmental and societal factors sufficiently explained country-level variations in death. Findings: 14 044 patients from 23 countries were included in the analyses. 10 178 (72·5%) individuals were female and 3866 (27·5%) were male, with a mean age of 54·4 years (SD 15·6). Air pollution (odds ratio 1·10 per 10 μg/m3 [95% CI 1·01-1·17]; p=0·0105), proportion of the population aged 65 years or older (1·19 per 1% increase [1·10-1·30]; p<0·0001), and population mobility (1·03 per 1% increase in number of visits to grocery and pharmacy stores [1·02-1·05]; p<0·0001 and 1·02 per 1% increase in number of visits to workplaces [1·00-1·03]; p=0·032) were independently associated with higher odds of mortality. Number of hospital beds (0·94 per 1-unit increase per 1000 people [0·88-1·00]; p=0·046), human development index (0·65 per 0·1-unit increase [0·44-0·96]; p=0·032), government response stringency (0·83 per 10-unit increase in containment index [0·74-0·93]; p=0·0018), as well as follow-up time (0·78 per month [0·69-0·88]; p<0·0001) were independently associated with lower odds of mortality. These factors sufficiently explained country-level variations in death attributable to COVID-19 (intraclass correlation coefficient 1·2% [0·1-9·5]; p=0·14). Interpretation: Our findings highlight the importance of environmental and societal factors as potential explanations of the observed regional disparities in COVID-19 outcomes among people with rheumatic disease and lay foundation for a new research agenda to address these disparities.MAG is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (grant numbers K01 AR070585 and K24 AR074534 [JY]). KDW is supported by the Department of Veterans Affairs and the Rheumatology Research Foundation Scientist Development award. JAS is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (grant numbers K23 AR069688, R03 AR075886, L30 AR066953, P30 AR070253, and P30 AR072577), the Rheumatology Research Foundation (K Supplement Award and R Bridge Award), the Brigham Research Institute, and the R. Bruce and Joan M. Mickey Research Scholar Fund. NJP is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (T32-AR-007258). AD-G is supported by grants from the Centers for Disease Control and Prevention and the Rheumatology Research Foundation. RH was supported by the Justus-Liebig University Giessen Clinician Scientist Program in Biomedical Research to work on this registry. JY is supported by grants from the National Institutes of Health (K24 AR074534 and P30 AR070155).info:eu-repo/semantics/publishedVersio