Abstract Introduction: University dropout is now considered a complex phenomenon that goes beyond the number of students not enrolled and that is continuously growing, especially in the first years of study. Objective: In the present study, a prediction model combining Survival Analysis, Decision Trees, and Random Forest, under the Machine Learning philosophy, is proposed for the early diagnosis of possible factors causing dropout in university students. Methods: The proposal consists of 3 phases: the Survival Analysis that allows estimating the probability of permanence of the student (survival). Phase 2 starts from the probability value obtained in the previous phase and uses it as a response variable in the modeling process based on Decision Trees to establish survival patterns around the variables considered. Finally, in phase 3, the critical variables in the model are identified using the Random Forest. Results: The proposed methodology allowed the design of a prediction model to identify the main segmentation variables in behavior patterns of possible cases of academic dropout. Conclusion: Even though the proposal was developed considering a particular case of a Chilean university, the efficient combination of metaheuristics allows the extrapolation of the methodology to any context and academic reality. However, the conditions and needs of each institution must be considered.

Resumen Introducción: La deserción universitaria se considera actualmente como un fenómeno complejo que va más alla del número de estudiantes no matriculados, y que viene en continuo crecimiento sobre todo en los primeros años de estudio. Objetivo: En el presente estudio se propone un modelo de predicción que combina el Análisis de Supervivencia, Árboles de Decisión y Random Forest, bajo la filosofía de Machine Learning, para el diagnóstico temprano de los posibles factores de la deserción en estudiantes universitarios. Métodos: La propuesta consta de 3 fases: el Análisis de Supervivencia que permite estimar la probabilidad de permanencia del alumno (supervivencia). La fase 2 parte del valor de probabilidad obtenido en la fase anterior y lo utiliza como variable respuesta en el proceso de modelado basado en los árboles de decisión para establecer patrones de supervivencia en torno a las variables consideradas. Finalmente, en la fase 3 se identifican las variables más importantes en el modelo, utilizando Random Forest. Resultados: La metodología propuesta permitió diseñar un modelo de predicción, que identifica las principales variables de segmentación en patrones de comportamiento de posibles casos de deserción académica. Conclusión: Si bien la propuesta fue desarrollada considerando un caso particular de una universidad chilena, la eficiente combinación de la metaheurística permite la extrapolación de la metodología a cualquier contexto y realidad académica. Sin embargo, se deben considerar las condiciones y necesidades de cada institución.

Resumo Introdução: A deserção universitária é atualmente considerada um fenômeno complexo que vai além do número de estudantes não matriculados, e que vem em contínuo crescimento sobretudo nos primeiros anos de estudo. Objetivo: No presente estudo, é proposto um modelo de predição que combina Análise de Sobrevivência, Árvores de Decisão e Random Forest, sob a filosofia de Machine Learning, para o diagnóstico precoce dos possíveis fatores de deserção em estudantes universitários. Métodos: A proposta consiste em 3 fases: a Análise de Sobrevivência que permite estimar a probabilidade de permanência do aluno (sobrevivência). A fase 2 parte do valor de probabilidade obtido na fase anterior e o utiliza como variável resposta no processo de modelagem baseado em árvores de decisão para estabelecer padrões de sobrevivência em torno das variáveis consideradas. Finalmente, na fase 3, as variáveis críticas do modelo são identificadas usando Random Forest. Resultados: A metodologia proposta permitiu desenhar um modelo de previsão, que identifica as principais variáveis de segmentação em padrões de comportamento de possíveis casos de deserção acadêmica. Conclusão: Embora a proposta tenha sido desenvolvida a partir de um caso particular de uma universidade chilena, a combinação eficiente da meta-heurística permite a extrapolação da metodologia para qualquer contexto e realidade acadêmica. No entanto, devem ser consideradas as condições e necessidades de cada instituição.

Abstract The objective of this research is to present a comparative analysis using various lengths of time windows (TW) during emotion recognition, employing machine learning techniques and the portable wireless sensing device EPOC+. In this study, entropy will be utilized as a feature to evaluate the performance of different classifier models across various TW lengths, based on a dataset of EEG signals extracted from individuals during emotional stimulation. Two types of analyses were conducted: between-subjects and within-subjects. Performance measures such as accuracy, area under the curve, and Cohen's Kappa coefficient were compared among five supervised classifier models: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Decision Trees (DT). The results indicate that, in both analyses, all five models exhibit higher performance in TW ranging from 2 to 15 seconds, with the 10 seconds TW particularly standing out for between-subjects analysis and the 5-second TW for within-subjects; furthermore, TW exceeding 20 seconds are not recommended. These findings provide valuable guidance for selecting TW in EEG signal analysis when studying emotions.

Resumen El objetivo de esta investigación es presentar un análisis comparativo empleando diversas longitudes de ventanas de tiempo (VT) durante el reconocimiento de emociones, utilizando técnicas de aprendizaje automático y el dispositivo de sensado inalámbrico portátil EPOC+. En este estudio, se utilizará la entropía como característica para evaluar el rendimiento de diferentes modelos clasificadores en diferentes longitudes de VT, basándose en un conjunto de datos de señales EEG extraídas de individuos durante la estimulación de emociones. Se llevaron a cabo dos tipos de análisis: entre sujetos e intra-sujetos. Se compararon las medidas de rendimiento, tales como la exactitud, el área bajo la curva y el coeficiente de Cohen's Kappa, de cinco modelos clasificadores supervisados: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF) y Decision Trees (DT). Los resultados indican que, en ambos análisis, los cinco modelos presentan un mayor rendimiento en VT de 2 a 15 segundos, destacándose especialmente la VT de 10 segundos para el análisis entre los sujetos y 5 segundos intrasujetos; además, no se recomienda utilizar VT superiores a 20 segundos. Estos hallazgos ofrecen una orientación valiosa para la elección de las VT en el análisis de señales EEG al estudiar las emociones.

The structure of climate finance flows from donors (multilateral sources) to recipients (sub-Saharan African countries) was studied. This is the first study to provide a comprehensive network structure of the climate finance flows into Africa, based on the global public climate finance governance system. Network theory and decision tree techniques were employed. The results obtained generally fit the multilateral funding units (MFUs) into two categories: central funding units (CFUs), which simultaneously attend to the climate-related projects of many African countries, and the boundary funding units (BFUs), which cater to only a few countries at once. An isolated region with no BFUs was identified. African countries within this group could be more exposed to climate financial risk as they rely on only the CFUs. In general, with the exception of mitigation REDD (reducing emissions from deforestation and forest degradation) climate finance, a disproportionate distribution of climate themes, with particular reference to adaptation finance, was observed across sub-Saharan Africa. This has real implications for equitable resource allocation of climate funds. The need for African-bred region-wide MFUs is recommended. SIGNIFICANCE: Insights from this analysis expose the presence of contagion effects within the sub-Saharan African climate finance network structure and, consequently, the flow of climate finance-related risks. This knowledge is critical for future planning as it can provide African governments and interested stakeholders with informed evidence upon which they can make reliable and justifiable decisions, such as shaping of sectoral strategies and improving of climate finance flow coordination.

Abstract NDVI elucidates the ecophysiological mechanisms faced by vegetation. With the flexibility and versatility of unmanned aerial vehicles (UAVs), temporal, spatial and spectral resolutions have been useful in supporting decision-making. Here, we modeled diurnal change in the NDVI derived from UAV imagery for individual trees in a natural forest stand. Eight flights were conducted during daylight hours over one day to assess the dynamics of the NDVI of the genera Pinus, Juniperus, and Quercus. The results showed unstable NDVI values over time, with a parabolic quadratic trend in the model. The NDVI reached its maximum around 13:00 h and the values differed significantly according to genus: the highest value was found in Pinus with significant differences presented with Juniperus and Quercus, which showed similar values between them (p=0.533). As a validation strategy, we test the model generated using 124 trees independent of those that were sampled, which strengthened our results in terms of reliability. The similarities of statistical parameters confer a high variability of application of the results in models of simulation of similar forests ecosystems. We recommend to study more spectral indices of vegetation, including calibration of the sensor, particularly in longer-term seasonal studies. We conclude that the NDVI measured using UAV should consider image acquisition time to calibrate the records and thus improve the interpretation of results.

Resumen El NDVI dilucida los mecanismos ecofisiológicos a los que se enfrenta la vegetación. Gracias a la flexibilidad y versatilidad de los vehículos aéreos no tripulados (VANT), las resoluciones temporales, espaciales y espectrales han sido útiles para apoyar la toma de decisiones. En este trabajo se modelizó el cambio diurno en el NDVI derivado de imágenes de VANT para árboles individuales en un rodal forestal natural incoetáneo. Se realizaron ocho vuelos durante las horas diurnas a lo largo de un día para evaluar la dinámica del NDVI de los géneros Pinus, Juniperus y Quercus. Los resultados mostraron valores de NDVI inestables en el tiempo, con una tendencia cuadrática parabólica en el modelo. El NDVI alcanzó su máximo alrededor de las 13:00 h y los valores difirieron significativamente según el género: el valor más alto se encontró en Pinus con diferencias significativas presentadas con Juniperus y Quercus, que mostraron valores similares entre ellos (p=0.533). Como estrategia de validación, probamos el modelo generado utilizando 124 árboles independientes de los muestreados, lo que reforzó nuestros resultados en términos de confiabilidad. Las similitudes de los parámetros estadísticos confieren una alta variabilidad de aplicación de los resultados en modelos de simulación de ecosistemas forestales similares. Recomendamos estudiar más índices espectrales de vegetación, incluyendo la calibración del sensor, particularmente en estudios estacionales de más largo plazo. Concluimos que el NDVI medido mediante VANT debe considerar el tiempo de adquisición de la imagen para calibrar los registros y así mejorar la interpretación de los resultados.

Abstract: In this paper, we present an in-depth analysis leveraging several renowned machine learning techniques, including Snap Random Forest, XGBoost, Extra Trees, and Snap Decision Trees, to characterize comorbidity factors influencing the Mexican population. Distinct from existing literature, our study undertakes a comprehensive exploration of algorithms within a defined search space, conducting experiments ranging from coarse to fine granularity. This approach, coupled with machine learning-driven feature enhancement, enables us to deeply characterize the factors most significantly affecting COVID-19 mortality rates within the Mexican demographic. Contrary to other studies, which obscure the identification of primary factors for local populations, our findings reveal that geographical factors such as residence location hold greater significance than even comorbidities, indicating that socioeconomic factors play a pivotal role in the survival outcomes of the Mexican population. This research not only contributes to the targeted understanding of COVID-19 mortality drivers in Mexico but also highlights the critical influence of socioeconomic determinants, offering valuable insights for public health strategies and policy formulation.

Abstract Purpose: This study aims to examine foreign direct investment (FDI) factors and develops a rational framework for FDI inflow in Western European countries such as France, Germany, the Netherlands, Switzerland, Belgium and Austria. Design/methodology/approach: Data for this study were collected from the World development indicators (WDI) database from 1995 to 2018. Factors such as economic growth, pollution, trade, domestic capital investment, gross value-added and the financial stability of the country that influence FDI decisions were selected through empirical literature. A framework was developed using interpretable machine learning (IML), decision trees and three-stage least squares simultaneous equation methods for FDI inflow in Western Europe. Findings: The findings of this study show that there is a difference between the most important and trusted factors for FDI inflow. Additionally, this study shows that machine learning (ML) models can perform better than conventional linear regression models. Research limitations/implications: This research has several limitations. Ideally, classification accuracies should be higher, and the current scope of this research is limited to examining the performance of FDI determinants within Western Europe. Practical implications: Through this framework, the national government can understand how investors make their capital allocation decisions in their country. The framework developed in this study can help policymakers better understand the rationality of FDI inflows. Originality/value: An IML framework has not been developed in prior studies to analyze FDI inflows. Additionally, the author demonstrates the applicability of the IML framework for estimating FDI inflows in Western Europe.

ABSTRACT This study aimed to evaluate the use of decision trees to select sows based on the production parameters of parity order (PO) 1 sows from a commercial herd. Data were collected at a piglet production unit with a capacity of housing 5,500 sows in collective pens. Piglet production and sow culling information was collected from PO1 and PO2 sows. The period from January 2017 to March 2020 was analyzed. The correlation analysis was used to identify the influence of the production parameters on sow culling after exploring the database using the graphical analysis and descriptive statistics. The ANOVA was applied to evaluate differences in the response variables between culled and unculled sows. Two models were proposed using the decision tree method: model 1 referred to sow culling, and model 2 comprised the total number of liveborn piglets (TBA). The calculated value was close to 0, although the correlations of the production parameters with culling were statistically significant. The mean number of weaned piglets was higher for unculled sows in PO1 (p < 0.05). The number of weaned piglets, total number of liveborn piglets, and weaning-service interval did not differ in the unculled and culled sows in PO2 (p > 0.05). Using a confusion matrix as a metric tool, the decision tree method used in this study provided consistent results for this database, indicating its possible use for decision-making in sow selection. PO (PO herd 5500 5 500 5,50 pens 201 202 analyzed statistics TBA. TBA . (TBA) 0 significant p 0.05. 005 0.05 05 0.05) weaningservice weaning service tool decisionmaking making selection 550 50 5,5 20 (TBA 00 0.0 55 5, 0.

Abstract Introduction Cardiovascular disease (CVD) is the lead-ing cause of death globally, with a high proportion of hospitalizations and costs. In view of this, it is essential to understand the main CVDs in patients admitted to hospital emergency services and the role of physiotherapists, in order to plan and direct health services, and to denote participation and encourage specific physiotherapy training in the context of tertiary care. Objective To outline the profile of cardiovascular emergencies and to evaluate physiotherapy in adult patients in the emergency department of a hospital in the interior of the state of São Paulo. Methods This was an observational study which analyzed 1,256 on-call records over a period of eight months. The data collected included age, gender, cardiovascular diagnostic hypothesis and physiotherapy treatment carried out. Results A total of 75 patients with cardiovascular emergencies were included, the most prevalent of which were: heart failure (n = 21), acute coronary syndrome (n = 14), acute myocardial infarction (n = 13), bradyarrhythmia (n = 6) and hypertensive crisis (n = 5). Regarding physiotherapeutic actions and their applications, the most frequent were invasive mechanical ventilation management (n = 34), lung re-expansion maneuvers (n = 17), orotracheal intubation assistance (n = 17), non-invasive mechanical ventilation (n = 14), bronchial hygiene maneuvers (n = 12), kinesiotherapy (n = 10) and sedation (n = 10). Conclusion Heart failure and acute coronary syndrome were the cardiovascular diseases that caused the most admissions to the hospital emergency department and that the procedures with an emphasis on the respiratory system were the most applied. CVD (CVD leading lead ing globally costs this physiotherapists care Paulo 1256 1 256 1,25 oncall call months age gender out 7 n 21, 21 , 21) 14, 14 14) 13, 13 13) 6 5. 5 . 5) applications 34, 34 34) reexpansion re expansion 17, 17 17) noninvasive non 12, 12 12) 10 10. applied 125 25 1,2 2 3 1,

Resumo Introdução As doenças cardiovasculares (DCV) repre-sentam a principal causa de morte global, destacando-se em internações e gastos. Diante disso, é essencial compreender as principais DCV em pacientes admitidos em serviços de emergência hospitalar e a atuação do fisioterapeuta para planejamento e direcionamento dos serviços de saúde e para denotar a participação e incentivar formações fisioterapêuticas específicas no contexto da atenção terciária. Objetivo Traçar o perfil de emergências cardiovasculares e avaliar a atuação fisioterapêutica em pacientes adultos de serviço de emergência de um hospital no interior do estado de São Paulo. Métodos Trata-se de um estudo observacional, em que foram analisadas 1.256 fichas de passagem de plantão, no período de oito meses. Os dados coletados foram idade, sexo, hipótese diagnóstica cardiovascular e tratamento fisioterapêutico realizado. Resultados Foram incluídos 75 pacientes que apresentavam o perfil de emergências cardiovasculares, sendo as mais prevalentes: insuficiência cardíaca (n = 21), síndrome corona-riana aguda (n = 14), infarto agudo do miocárdio (n = 13), bradarritmia (n = 6) e crise hipertensiva (n = 5). Em relação à atuação fisioterapêutica e suas aplicações, as mais frequentes foram manejo da ventilação mecânica invasiva (n = 34), manobras de reexpansão pulmonar (n = 17), auxílio a intubação orotraqueal (n = 17), ventila-ção mecânica não invasiva (n = 14), manobras de higiene brônquica (n = 12), cinesioterapia (n = 10) e sedestação (n = 10). Conclusão A insuficiência cardíaca e a síndrome coronária aguda foram as doenças cardiovasculares que mais ocasionaram internação no serviço de emergência hospitalar e as condutas com ênfase no aparelho respiratório foram as mais aplicadas. (DCV representam repre sentam global destacandose destacando se gastos disso terciária Paulo Tratase Trata observacional 1256 1 256 1.25 plantão meses idade sexo realizado 7 prevalentes n 21, 21 , 21) coronariana corona riana 14, 14 14) 13, 13 13) 6 5. 5 . 5) aplicações 34, 34 34) 17, 17 17) ventila ção 12, 12 12) 10 10. aplicadas 125 25 1.2 2 3 1.

Abstract: The identification of seeds from native species is a complex assessment due to the high Brazilian biodiversity and varied characteristics between species. The objective was to apply different machine learning classifiers associated with image analysis to identify seeds of forest species. In total, 155 native species belonging to 42 botanical families were analyzed. In addition, to determine the appropriate machine learning classifier, five supervised learning classification techniques were implemented: decision trees (DT), artificial neural networks (ANN), k-nearest neighbors (k-NN), Naive-Bayes classifier (NBC) and support vector machine (SVM), which had their performance evaluated. For modeling, 66% of the seeds’ morphobiometric data were used to train the classifiers, while 34% were reserved for validation. The classifiers are promising tools for identifying species from seed images. The decision tree (DT) classifier showed greater accuracy for correct species identification (82.8%), followed by ANN (81.7%), k-NN (81.7%), NBC (81.1%) and SVM (78.7%). Therefore, it is possible to identify seeds of native species from images and machine learning with a satisfactory accuracy rate. Finally, the decision tree classifier is recommended. Abstract total 15 4 analyzed addition implemented DT, DT , ANN, (ANN) knearest k nearest kNN, kNN NN (k-NN) NaiveBayes Naive Bayes (NBC SVM, (SVM) evaluated modeling 66 34 validation (DT 82.8%, 828 82.8% 82 8 (82.8%) 81.7%, 817 81.7% 81 7 (81.7%) 81.1% 811 1 (81.1% 78.7%. 787 78.7% . 78 (78.7%) Therefore rate Finally recommended (ANN (k-NN (SVM 6 3 82.8 (82.8% 81.7 (81.7% 81.1 (81.1 78.7 (78.7% 82. (82.8 81. (81.7 (81. 78. (78.7 (82. (81 (78. (82 (8 (78 ( (7

Resumo: A identificação de sementes de espécies nativas é uma avaliação complexa devido a elevada biodiversidade brasileira e características variadas entre as espécies. Objetivou-se aplicar diferentes classificadores de aprendizado de máquina associado à análise de imagens para identificar sementes de espécies florestais. Foram analisadas 155 espécies nativas pertencentes a 42 famílias botânicas. Para determinar o classificador de aprendizado de máquina adequado, cinco técnicas de classificação por aprendizado supervisionado foram implementadas: árvores de decisão (DT), redes neurais artificiais (ANN), k-vizinhos mais próximos (k-NN), classificador Naive-Bayes (NBC) e máquina de vetores de suporte (SVM), os quais tiveram seu desempenho avaliados. Para modelagem, 66% dos dados morfobiométricos das sementes foram usados para treinamento dos classificadores, enquanto 34% foram reservados para validação. Os classificadores são ferramentas promissoras para a identificação de espécies a partir das imagens de sementes. O classificador por árvores de decisão (DT) apresentou maior acurácia para identificação correta das espécies (82,8%), seguido dos classificadores ANN (81,7%), k-NN (81,7%), NBC (81,1%) e SVM (78,7%). Portanto, é possível realizar a identificação de sementes de espécies nativas a partir de imagens e aprendizado de máquina com taxa satisfatória de acurácia. Recomenda-se o classificador por árvores de decisão. Resumo Objetivouse Objetivou se florestais 15 4 botânicas adequado implementadas DT, DT , ANN, (ANN) kvizinhos k vizinhos kNN, kNN NN (k-NN) NaiveBayes Naive Bayes (NBC SVM, (SVM) avaliados modelagem 66 34 validação (DT 82,8%, 828 82,8% 82 8 (82,8%) 81,7%, 817 81,7% 81 7 (81,7%) 81,1% 811 1 (81,1% 78,7%. 787 78,7% . 78 (78,7%) Portanto Recomendase Recomenda (ANN (k-NN (SVM 6 3 82,8 (82,8% 81,7 (81,7% 81,1 (81,1 78,7 (78,7% 82, (82,8 81, (81,7 (81, 78, (78,7 (82, (81 (78, (82 (8 (78 ( (7

RESUMO O objetivo deste estudo retrospectivo é determinar se o lactato sanguíneo (LB) e o lactato peritoneal (PL) estão correlacionados com a frequência cardíaca, a relação BL/PL e a gravidade da desidratação em cavalos com cólica que morreram ou sobreviveram 24 horas após a admissão hospitalar, e construir árvores de decisão destinadas a prever a mortalidade a curto prazo utilizando-se algoritmos de aprendizagem automática. Foram recuperados prontuários (n = 339) do banco de dados do Hospital Veterinário da Faculdade de Medicina Veterinária e Zootecnia da Universidade de São Paulo (FMVZ-USP) de cavalos internados com cólica. As correlações entre variáveis de interesse foram investigadas por meio do coeficiente de correlação de Pearson ou de Spearman. O nível de significância foi estabelecido em 5% (P<0,05). Os algoritmos utilizados pertencem ao ambiente WEKA e foram comparados pelo teste t pareado (P<0,05). Os valores de lactato peritoneal correlacionaram-se forte e negativamente (P<0,05) com a relação BL:PL, e moderada e positivamente (P< 0,05) com o percentual de desidratação. Duas árvores de decisão com mais de 80% de precisão na previsão de mortalidade foram construídas e podem ser usadas para informar os clínicos sobre a probabilidade de sobrevivência além das primeiras 24 horas de hospitalização. LB (LB PL (PL cardíaca BLPL BL 2 hospitalar utilizandose utilizando automática n 339 FMVZUSP FMVZ USP (FMVZ-USP Spearman 5 P<0,05. P005 P P<0,05 . 0 05 correlacionaramse correlacionaram (P<0,05 BLPL, PL, BL:PL P< (P 0,05 005 80 hospitalização 33 P00 P<0,0 (P<0,0 0,0 00 8 3 P0 P<0, (P<0, 0, P<0 (P<0

ABSTRACT The objective of this retrospective study was to determine whether blood lactate (BL) and peritoneal lactate (PL) are correlated with heart rate, BL-to-PL ratio and dehydration severity in horses with colic that died within or survived beyond 24 hours of hospital admission, and to construct decision trees aimed at predicting short-term mortality using machine learning algorithms. Medical records (n = 339) from the database of the Veterinary Hospital of the School of Veterinary Medicine and Animal Science, University of São Paulo (FMVZ-USP) of horses admitted with colic were retrieved. Correlations between variables of interest were investigated using the Pearson’s or the Spearman’s correlation coefficient. The level of significance was set at 5% (p<0.05). Algorithms used belong to the WEKA environment and were compared using the paired t test (p<0.05). Peritoneal lactate values were strongly and negatively (p<0.05) correlated with BL:PL ratio, and moderately and positively (p<0.05) correlated with percentage of dehydration. Two decision trees with more than 80% accuracy in predicting mortality were constructed and can be used to inform clinicians about the probability of survival beyond the first 24 hours of hospitalization. BL (BL PL (PL rate BLtoPL 2 admission shortterm short term algorithms n 339 Science FMVZUSP FMVZ USP (FMVZ-USP retrieved Pearsons Pearson s Spearmans Spearman coefficient 5 p<0.05. p005 p p<0.05 . 0 05 (p<0.05 BLPL 80 hospitalization 33 p00 p<0.0 (p<0.0 8 3 p0 p<0. (p<0. p<0 (p<0 p< (p< (p

ABSTRACT Wind energy is an important renewable source that has been considerably developed recently. In order to obtain successful 24-h lead-time wind power forecasts for operational and commercial uses, a combination of physical and statistical models is desirable. In this paper, a hybrid methodology that employs a numerical weather prediction model (Weather Research and Forecasting) and a neural network (NN) algorithm is proposed and assessed. The methodology is applied to a wind farm in northwestern Mexico, a region with high wind potential where complex geography adds large uncertainty to wind energy forecasts. The energy forecasts are then evaluated against actual on-site power generation over one year and compared with two reference models: decision trees (DT) and support vector regression (SVR). The proposed method exhibits a better performance with respect to the reference methods, showing an hourly normalized mean absolute percentage error of 6.97%, which represents 6 and 13 percentage points less error in wind power forecasts than with DT and SVR methods, respectively. Under strong synoptic forcing, the NN wind power forecast is not very accurate, and novel approaches such as hierarchical algorithms should be employed instead. Overall, the proposed model is capable of producing high-quality wind power forecasts for most weather conditions prevailing in this region and demonstrates a good performance with respect to similar models for medium-term wind power forecasts.

RESUMEN La energía eólica es una importante fuente de energía renovable que se ha desarrollado considerablemente en los últimos años. Con el fin de obtener pronósticos de energía eólica de alta precisión con 24 horas de anticipación para usos operativos y comerciales, se necesita una combinación de modelos físicos y estadísticos. En este artículo se propone y evalúa una metodología híbrida que emplea un modelo numérico (Weather Research and Forecasting) y un algoritmo de red neuronal (NN). La metodología se aplica a un parque eólico en el noroeste de México, una región con alto potencial eólico donde la compleja geografía agrega gran incertidumbre a los pronósticos de energía eólica. Los pronósticos de energía se evalúan usando la generación de energía real en el sitio de interés durante un año y se comparan con dos modelos de referencia: árboles de decisión (DT, por su sigla en inglés) y regresión de vectores de soporte (SVR). El método propuesto muestra un mejor desempeño respecto a los métodos de referencia, mostrando un error absoluto medio porcentual horario de 6.97%, lo que representa 6 y 13 puntos porcentuales menos de error en los pronósticos de energía eólica que con los métodos DT y SVR, respectivamente. El pronóstico de energía eólica NN no es muy preciso cuando tiene un fuerte forzamiento sinóptico, por lo que en su lugar deben emplearse enfoques novedosos como los algoritmos jerárquicos. En general, el modelo propuesto tiene la capacidad de producir pronósticos de energía eólica de alta calidad para la mayoría de las condiciones climáticas que prevalecen en esta región, y de- muestra un buen desempeño respecto a modelos similares para pronósticos de energía eólica a mediano plazo.

Abstract: The COVID-19 outbreak commenced in Wuhan, China, in December 2019 and swiftly disseminated worldwide. On March 11, 2020, the World Health Organization (WHO) formally designated the COVID-19 outbreak as a global pandemic [1]. This highly contagious disease, which has also started to spread among young people, necessitates the implementation of policies to avert the collapse of hospitals due to shortages in beds, mechanical ventilators, and intensive care units. Using the record from March 2020 to May 31 2021, (before the arrival of vaccines in Mexico) of the website of the General Directorate of Epidemiology of the Ministry of Health of the Government of the Mexican Republic, with more than seven million people associated with COVID-19, a model is built with twelve characteristics that predicts the risk of death associated with COVID-19, applying Supervised Learning Algorithms such as Linear Regression, Naive Bayes, Decision Trees and Random Forests. The results of the machine learning algorithms show a performance of 87%. Subsequently, the model was tested again with 402,116 COVID-19-associated patients from the month of June, achieving an accuracy of 91%, surpassing the model proposed in [3]. When performing data cleaning, we observed that the twelve variables are not correlated, unlike what the authors showed in [4], where data cleaning was not carried out. This analysis can aid in designing strategies and policies to combat its spread and prevent mortality, as well as assist hospitals in prioritizing the care of higher-risk patients.

Abstract: The examination of student attrition within higher education is a dynamic field that seeks to tackle the complex task of preventing dropout occurrences and formulating effective retention strategies. This challenge becomes particularly pertinent within the realm of Science, Technology, Engineering, and Mathematics (STEM) disciplines. In the pursuit of these objectives, this research endeavors to assess prevailing data mining methodologies, specifically focusing on Decision Trees (DT), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANN) – all of which are widely employed for the prediction of student attrition. The study is conducted on a comprehensive dataset encompassing engineering students from a prominent Mexican university, with a specific emphasis on the application of variable selection through Recursive Feature Elimination (RFE) and addressing class imbalance via Synthetic Minority Over-sampling Technique (SMOTE). The outcomes of this investigation conspicuously identify Random Forest as the most optimal predictive model, yielding an impressive accuracy rate of 98%. Additionally, the research underscores the effectiveness of RFE in discerning influential variables. Furthermore, to provide complex insights and decision support, the study harnesses the Local Interpretable Model-Agnostic Explanations (LIME) technique to expound upon the factors that wield significant impact. This multifaceted analysis contributes to the advancement of strategies for enhancing student retention within STEM disciplines.

Resumen Objetivo: Aplicar un modelo de análisis de redes neuronales artificiales (RNA) para identificar las variables que predicen el liderazgo asignado y el éxito académico en egresados de seis generaciones de la carrera de Medicina. Método: Estudio analítico, retrospectivo y comparativo. Participaron 1434 egresados. Se envió un cuestionario por correo electrónico que incluyó el consentimiento de participación voluntaria. Se realizó análisis estadístico multivariado mediante RNA del tipo perceptrón multicapa, árboles de decisión y análisis de impulsores. Resultados: Las RNA identificaron siete variables independientes que predijeron el éxito profesional y ocho para el liderazgo en los médicos egresados. Los árboles de decisión identificaron diferencias significativas en las variables desempeño profesional (p = 0.000), edad (p = 0.005) y actividades de educación continua (p = 0.034) relacionadas con el éxito profesional, y para el liderazgo las variables sexo (p = 0.000), promedio en el bachillerato (p = 0.042), realizar práctica clínica en el servicio social (p = 0.002) y actividades de educación continua (p = 0.011). Conclusiones: Las RNA identificaron las principales variables independientes predictoras del éxito profesional y el liderazgo de los egresados. El estudio abre dos líneas de investigación poco estudiadas con las técnicas de RNA en el área de la medicina.

Abstract Objective: To apply an artificial neural networks analysis (ANN) model to identify variables that predict assigned leadership and academic success in graduates of six generations of medical school. Method: Analytical, retrospective, comparative study. A total of 1434 graduates participated. A questionnaire was sent to them by e-mail including a voluntary participation consent. A multivariate statistical analysis using multi-layer perceptron ANN, decision trees and driver analysis was performed. Results: The ANN identified seven independent variables that predicted professional success and eight for leadership in medical graduates. The decision trees identified significant differences in the variables professional performance (p = 0.000), age (p = 0.005) and continuing education activities (p = 0.034) related to professional success, and for leadership the variables gender (p = 0.000), high school grades (p = 0.042), performing clinical practice during the social service year (p = 0.002) and continuing education activities (p = 0.011). Conclusions: The ANN identified the main independent predictor variables of professional success and leadership of the graduates. This study opens up two new lines of research little studied with the techniques of in the area of medicine.

Resumen: Este artículo presenta un enfoque novedoso para amortiguar de forma adaptativa las oscilaciones de baja frecuencia mediante un clasificador de árboles de decisión que utilizan como entradas las señales de frecuencia y potencia de las barras de generación sensadas por el sistema de monitoreo de área amplia (WAMS). El ajuste adaptativo de los estabilizadores de sistemas de potencia (PSS) convencionales (p.ej., PSS1A, PSS2A, PSS2B), que se encuentran disponibles en el sistema, se realiza al modificar los parámetros óptimos de sintonización del PSS, mediante el análisis de los modos críticos de los escenarios operativos utilizando el algoritmo de optimización heurística de mapeo media- varianza (MVMO). Estos parámetros por escenario pueden ser seleccionados mediante un clasificador inteligente basado en árboles de decisión con el objetivo de adaptar la sintonización del PSS a las condiciones de operación. Dicho proceso se implementa en PowerFactory de DIgSILENT mediante la herramienta de análisis modal, se determinan los modos críticos, para el caso de simulación. Mientras en tiempo real, los modos críticos son obtenidos por medio del algoritmo de identificación modal establecido con la matriz Pencil de las señales WAMS. La metodología planteada se aplica al sistema New York-New England de 66-Barras, obteniéndose la mejor respuesta dinámica del sistema con los PSS adaptativos a las diferentes condiciones operativas del sistema. Además, se compara la respuesta con el PSS4B que se caracteriza por monitorear varias frecuencias de los modos de oscilación presentes en la operación del sistema y así presentar los beneficios a la contribución propuesta.

Abstract: This paper presents a novel approach for adaptively damping low-frequency electromechanical oscillations via the application of decision trees that uses as inputs frequency and power signals of generation buses monitored by the wide-area measurement system (WAMS). This methodology can be applied for adaptive tuning of conventional Power System Stabilizers (e.g., PSS1A, PSS2A, PSS2B), generally available in actual power systems. It is done by adjusting the PSS tuning parameters by analyzing critical oscillation modes for different operational scenarios using the Mean-Variance Mapping Optimization (MVMO) heuristic algorithm. An intelligent classifier selects the PSS’s optimal parameters for each scenario based on Decision Trees with the objective of adapting the PSS tuning to the operating conditions. This process uses PowerFactory of DIgSILENT and critical modes are determined using modal analysis for simulation. In real-time, binding modes are derived by modal identification algorithm established with matrix Pencil of WAMS-signals. Such a methodology is applied to the 66-Bus New York-New England test power system, showing an excellent dynamic response in adapting PSS tuning to the different power system conditions. In addition, the response is compared with the PSS4B, which is characterized by monitoring various frequencies of the oscillation modes present in the system operation, thus presenting the benefits of proposed contribution.