Abstract (Introduction): The current cultivation of trees is insufficient to cover the national demand for wood, in this way small producers become an alternative solution to this situation, since despite having small areas, in the country there are abundant farms with these conditions; However, the current forest management scheme does not meet adequate standards for the incorporation of these small producers. (Objective): To present an adaptive forest management scheme (AFMS) to the needs of the small farmer, in addition, to demonstrate the institutional availability to change according to expert criteria. (Methodology): 1) the factors that influence the decision to adopt an agricultural or forestry production system were identified; 2) a profile of an ideal productive system that met the interests and preferences of the small producer was made, which was compared with the characteristics of agricultural and forestry production; 3) the proposal of an EMFA adjusted to the preferences and interest of the producer was made, which was also compared with the ideal profile; 4) three proposed change actions were presented to experts, obtaining different degrees of possibility of changes in the EMFA. (Results): The discrepancy of the forest management scheme for the cultivation of trees with the perspective that a small producer has on an ideal production system was evidenced, as well as the substantial improvement that an EMFA would have in the valuation of the small producer and the institutional availability to change the EMFA according to the perspective of the small producers. (Conclusions): The adjustment of the forest management scheme allows a greater adoption of the activity by the small producer, but it was recognized that any proposal that is made would be successful only if forest governance makes it more flexible and allows change.

Resumen (Introducción): El cultivo actual de árboles es insuficiente para cubrir la demanda nacional de madera, de este modo, los pequeños productores se tornan como una alternativa de solución ante esta situación, ya que pese a contar con áreas pequeñas, en el país son abundantes las fincas con estas condiciones; sin embargo, el esquema actual de manejo forestal no cumple con estándares adecuados para la incorporación de estos pequeños productores. (Objetivo): Presentar un esquema de manejo forestal adaptativo (EMFA) a las necesidades del pequeño productor, además de evidenciar la disponibilidad institucional al cambio, según el criterio experto. (Metodología): 1) se identificaron los factores que influyen en la decisión de adopción de un sistema productivo agrícola o forestal; 2) se realizó un perfil de un sistema productivo ideal que cumpliera con los intereses y preferencias del pequeño productor, el cual se confrontó con las características de la producción agrícola y forestal; 3) se efectuó la propuesta de un EMFA ajustado a las preferencias e interés del productor, que fue también confrontada con el perfil ideal; 4) se expusieron tres acciones de cambio propuestas ante expertos, obteniendo diferentes grados de posibilidad de cambios en el EMFA. (Resultados): Se evidenció la discrepancia del esquema de manejo forestal para el cultivo de árboles con la perspectiva que un pequeño productor tiene sobre un sistema de producción ideal, asimismo la mejora sustancial que un EMFA tendría en la valoración del pequeño productor y la disponibilidad institucional al cambio del EMFA, según la perspectiva de los pequeños productores. (Conclusiones): El ajuste del esquema de manejo forestal permite una mayor adopción del cultivo de árboles por parte del pequeño productor, pero se reconoció que cualquier propuesta que se realice lograría ser exitosa si la gobernanza forestal flexibiliza y posibilita el cambio.

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 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.

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 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.

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

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.

RESUMO O presente trabalho objetivou avaliar o desempenho de algoritmos de aprendizado de máquinas na predição da resistência à compressão de argamassas. A base de dados foi criada através de uma busca bibliográfica de mais de 50 referências que foram catalogadas para conter dados de dosagens de argamassa com ou sem adição de resíduos de construção e demolição (RCD). O conjunto de dados avaliado passou por um pré-processamento de integração dos dados de resíduo de construção e demolição, e normalização. Como normalização optou-se pelo uso da técnica z-score. Em seguida, os algoritmos de Aprendizado de Máquina (AM): regressões linear e polinomial, árvores de decisão, ensembles e redes neurais foram utilizados para a predição da resistência à compressão. O conjunto de dados foi separado em 80% para treino e validação e 20% para teste. A validação cruzada empregada foi do tipo k-fold com 10 divisões no subconjunto de treino. Avaliando o desempenho dos modelos o algoritmo tipo ensemble Gradient Boosting apresentou o melhor desempenho quando comparado aos demais, atingindo um valor superior a 90% no coeficiente de determinação. Por fim, conclui-se que o AM é uma ferramenta prática importante na predição da resistência à compressão de argamassas. Além disso, o modelo de inteligência artificial foi prototipado para uso da comunidade científica e técnica em uma versão web disponível através do framework Streamlit da linguagem Python. argamassas 5 RCD. RCD . (RCD) préprocessamento pré processamento optouse optou se zscore. zscore z score. score z-score seguida (AM) polinomial decisão 80 20 teste kfold k fold 1 demais 90 determinação fim concluise conclui disso Python (RCD (AM 8 2 9

ABSTRACT The present work aimed to evaluate the performance of machine learning algorithms in predicting the compressive strength of mortars. The database was created through a bibliographic search of more than 50 references that were cataloged to contain data on mortar dosages with or without the addition of construction waste. The dataset used in the experiments underwent preprocessing, which included the integration of construction and demolition waste data and normalization. For normalization, the z-score technique was chosen. Then, the algorithms, linear regressions, decision trees, ensembles, and neural networks were used to predict compressive strength. The dataset was separated into 80% for training and validation and 20% for testing. The cross-validation was of the k-fold type with ten divisions in the training subset. Evaluating the performance of the models, the ensemble Gradient Boosting algorithm showed the best performance when compared to the others, reaching a value greater than 89% in the coefficient of determination. Finally, it is concluded that Machine Learning (ML) is a practical calculation tool for predicting the compressive strength of mortars. Furthermore, the artificial intelligence model was prototyped for the scientific and technical community use in a web version available through the Python Streamlit framework. mortars 5 preprocessing normalization zscore z score chosen Then regressions trees ensembles 80 20 testing crossvalidation cross kfold k fold subset models others 89 determination Finally ML (ML Furthermore framework 8 2

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 The objective of this research was to evaluate the diversity, horizontal structure and vertical structure of tree species in three types of forest vegetation in the Adolfo Ruiz Cortines ejido in the south of the state of Durango, Mexico. The data analyzed corresponds to 665 circular sites of 0.1 ha; 56 sites correspond to pine forests (BP), 511 to pine oak forests (BPQ) and 98 to pine oak forests (BQP). For each site, the name of the species, the normal diameter (>7.5 cm) and the total height (m) of the trees were recorded. For the tree species in each type of vegetation, their diversity was determined using the Shannon index, their dominance with the Simpson index and their specific richness using the Margalef index. Subsequently, a test of over similarity or difference in the diversity-abundance Hutchenson's t was carried out to compare the diversity of species between the vegetation types using the Shannon index, to know if there are significant statistical differences between the diversity of species of the types of vegetation. To know the horizontal structure, the importance value index (IVI) was obtained, which is a hierarchical index that measures the dominance of the species through percentage values, which are obtained with the abundance Ai, dominance Di and frequency Fi of the species. species, these ecological parameters were analyzed with the non-parametric Kruskal-Wallis test to determine if there are significant statistical differences. The vertical structure was determined with the species vertical distribution index A. Hutchenson's t test demonstrated that there are significant statistical differences in the tree diversity of the forest types. According to the horizontal structure, only the absolute frequencies recorded significant statistical changes between the three vegetation types (BP-BPQ-BQP). The values of the vertical index of species A indicated that for the three types of vegetation there is a non-uniform plant community in terms of height, presenting a greater number of individuals in the lower stratum, followed by the middle stratum, to finally have a smaller amount in the higher altitude stratum. The information generated in this research provides a frame of reference on how the tree structure is formed in the vegetation types of the ejido, as well as the basis for future decision-making on forest management plans.

Resumen El objetivo de la presente investigación fue evaluar la diversidad, estructura horizontal y estructura vertical de especies arbóreas en tres tipos de vegetación forestal en el ejido Adolfo Ruíz Cortines al sur del estado de Durango, México. Los datos analizados corresponden a 665 sitios circulares de 0.1 ha; 56 sitios correspondientes a bosques de pino (BP), 511 a bosques de pino encino (BPQ) y 98 a bosques de encino pino (BQP). Para cada sitio se registró el nombre de la especie, el diámetro normal (>7.5 cm) y la altura total (m) de los árboles. Para las especies, en cada tipo de vegetación, se determinó su diversidad mediante el índice de Shannon, dominancia con el índice de Simpson y riqueza específica utilizando el índice de Margalef. Se realizó una prueba de similitud o diferencia en la diversidad-abundancia t de Hutchenson para comparar la diversidad de especies entre los tipos de vegetación utilizando el índice de Shannon, para conocer si existen diferencias estadísticas significativas entre la diversidad de especies de los tipos de vegetación. Para conocer la estructura horizontal se obtuvo el índice de valor de importancia (IVI), el cual es un índice jerárquico que mide la dominancia de las especies mediante valores porcentuales, los cuales son obtenidos con la abundancia A i , dominancia D i y frecuencia F i de las especies, dichos parámetros ecológicos se analizaron con la prueba no paramétrica de Kruskal-Wallis para determinar si existen diferencias estadísticas significativas. La estructura vertical se determinó con el índice de distribución vertical de especies A. La prueba de t de Hutchenson demostró que existen diferencias estadísticas significativas en la diversidad arbórea de los tipos de bosque. De acuerdo con la estructura horizontal, sólo las frecuencias absolutas registraron cambios estadísticos significativos entre los tres tipos de vegetación (BP-BPQ-BQP). Los valores del índice vertical de especies A, indicaron que para los tres tipos de vegetación se presenta una comunidad vegetal no uniforme en cuanto a sus alturas, presentando un mayor número de individuos en el estrato inferior, seguido del estrato medio, para finalmente tener una cantidad menor en el estrato de altura mayor. La información generada en esta investigación proporciona un marco de referencia sobre cómo se conforma la estructura arbórea en los tipos de vegetación del ejido, así como la base para futuras tomas de decisión sobre planes de manejo forestal.

ABSTRACT The prediction of traits allows the breeder to guide strategies to select and accelerate the progress of genetic improvement. The objective of this work was to determine the best prediction approach and establish a network with better predictive power for white oat using methodologies based on artificial intelligence, and machine learning. Seventy-eight white oat genotypes were evaluated. The design was randomized blocks with three replications. The models were evaluated with and without fungicide, and prediction models were established using four sets of experiments. The grain yield was used as a response trait the others as explanatory traits. The coefficient of determination was considered to evaluate the proposed methodologies. The importance of the traits was assessed through the impact of destructuring or disturbing the information of a given input on the estimation of R2. For machine learning, decision trees, bagging, random forest, and boosting were used. The traits indicated to assist in decision-making are plant height, leaf rust severity, and lodging percentage. The R2 ranged from 30.14% - 96.45% and 10.57% - 94.61% for computational intelligence and machine learning, respectively. A high estimate of the coefficient of determination, which was larger than the other estimates, was obtained using the bagging technique. improvement learning Seventyeight Seventy eight replications fungicide experiments R trees forest decisionmaking making height severity percentage 3014 30 14 30.14 9645 96 45 96.45 1057 10 57 10.57 9461 94 61 94.61 respectively estimates technique 301 3 1 30.1 964 9 4 96.4 105 5 10.5 946 6 94.6 30. 96. 10. 94.