RESUMO Este estudo foca no desempenho de vacas para produção de carne, criadas nos pântanos do Paraguai, examinando cinco genótipos bovinos; Brahman, Brangus, Nelore, bem como duas raças locais em risco de extinção. O principal objetivo é identificar e classificar variáveis fenotípicas que incluem variáveis sanguíneas, clínicas, de pelagem e saúde, demonstrando ligação causal com o peso vivo das vacas analisadas. Inicialmente, foram identificadas correlações elevadas entre diferentes variáveis incluídas neste estudo, e, então, utilizando técnicas avançadas de aprendizado de máquina e a aplicação de explicações aditivas de Shapley (SHAP), foi proporcionado um entendimento mais profundo dos fatores fortemente associados à adaptabilidade nestes ambientes, e, portanto, o respectivo desempenho zootécnico. A associação entre o componente genotípico bovino ligado à estação do ano mostrou ser o fator influente mais predominante sobre o peso vivo bovino. Variáveis como comprimento do pelo, hematócrito, fosfatase, fósforo, creatina phosphokinase, creatinina, proteína, cortisol, cálcio e a presença de endoparasitas foram destacadas, demonstrando sua importância hierárquica para a seleção animal. Os modelos de ML são ferramentas eficazes para estabelecer hierarquias de relevância em multivariáveis fenotípicas complexas, o que é crucial em programas de melhoramento genético em diferentes espécies zootécnicas, bem como em ambientes especiais e específicos, como os pântanos. carne Paraguai bovinos Brahman Brangus Nelore extinção sanguíneas clínicas saúde analisadas Inicialmente então SHAP, SHAP , (SHAP) portanto zootécnico pelo hematócrito fosfatase fósforo phosphokinase creatinina proteína cortisol destacadas animal complexas zootécnicas específicos (SHAP

ABSTRACT This study focuses on the performance of cows for meat production raised in the wetlands of Paraguay, examining five cattle genotypes: Brahman, Brangus, and Nelore, as well as two local breeds at risk of extinction. The main objective is to identify and rank phenotypic variables, including blood, clinical, hair, and health variables, demonstrating causal linkage with the live weight of the cows analyzed. Initially, high correlations were identified between different variables included in this study; then, using advanced Machine learning (ML) techniques and the application of Shapley additive explanations (SHAP), a deeper understanding was provided of the factors strongly associated with adaptability in these environments, and, therefore, the respective zootechnical performance. The association between cattle genotypic components linked with the season of the year proved to be the most influential factor on cattle live weight. Variables such as hair length, hematocrit, phosphatase, phosphorus, creatine phosphokinase, creatinine, protein, cortisol, calcium, and the presence of endoparasites were highlighted, demonstrating their hierarchical importance for animal selection. ML models are effective tools for establishing hierarchies of relevance in complex phenotypic multivariable, which is crucial in breeding programs for different zootechnical species and in special and specific environments like wetlands. Paraguay genotypes Brahman Brangus Nelore extinction blood clinical analyzed Initially then (ML SHAP, SHAP , (SHAP) therefore length hematocrit phosphatase phosphorus phosphokinase creatinine protein cortisol calcium highlighted selection multivariable (SHAP

ABSTRACT Knowledge of the complicated correlation between meteorological variables and crop yield is crucial for food security and agricultural sustainability. This study aimed to investigate how incident solar radiation has affected crop production in the Gadarif region of Sudan over the last 41 years. Using a predictive framework, trends in annual incident solar radiation and temporal variations during sorghum and sesame growing seasons were examined and machine learning (ML) with Extreme Gradient Boosting (XGBoost), Boosted Regression Forest (BRF), and K-Nearest Neighbors (K-NN) was used to predict crop yield. Significant relationships between incident solar radiation indicators and crop yields were identified via detrending approaches and correlation analyses. Results indicate a significant inverse correlation between solar radiation and sorghum yield, and a positive correlation between sesame yield and solar radiation. For both sorghum and sesame yield, K-NN was the most accurate model, demonstrating the significance of incident solar radiation and temperature in predicting crop yield. These findings highlight the potential of ML to improve agricultural forecasting models and inform adaptive agricultural practices in the region. In general, this study provides valuable insights into the dynamic relationship between incident solar radiation and crop yield, emphasizing the importance of considering meteorological factors in agricultural planning and management. sustainability 4 years framework (ML XGBoost, XGBoost , (XGBoost) BRF, BRF (BRF) KNearest K Nearest KNN NN (K-NN analyses model general management (XGBoost (BRF

RESUMO O conhecimento da complicada correlação entre as variáveis meteorológicas e o rendimento das culturas é crucial para a segurança alimentar e a sustentabilidade agrícola. Este estudo está centrado na investigação de como a radiação solar incidente afetou a produção agrícola na região de Gadarif, no Sudão, nos últimos quarenta anos. Usando uma estrutura preditiva, a pesquisa avalia tendências recentes na radiação solar incidente anual, examina variações temporais durante as estações de cultivo de sorgo e gergelim e utiliza técnicas de aprendizado de máquina para prever o rendimento das culturas. Além disso, ML, incluindo Extreme Gradient Boosting (XGBoost), Boosted Regression Forest (BRF) e K-Nearest Neighbours (K-NN), foram empregados para previsão de rendimento. Através de abordagens de redução de tendências e análises de correlação, foram identificadas relações significativas entre os indicadores de radiação solar incidente e o rendimento das culturas. Os resultados indicam uma correlação inversa substancial entre a radiação solar e a produção de sorgo, enquanto a produção de gergelim demonstra uma correlação positiva com a radiação solar. Tanto para o rendimento do sorgo como do gergelim, o K-NN surge como o modelo mais preciso, mostrando a importância da radiação solar incidente e da temperatura na previsão do rendimento das culturas. Estas descobertas destacam o potencial da aprendizagem de máquina para melhorar os modelos de previsão agrícola e informar as práticas agrícolas adaptativas na região. Em geral, este estudo fornece informações valiosas sobre a relação dinâmica entre a radiação solar incidente e o rendimento das culturas, enfatizando a importância de considerar fatores meteorológicos no planeamento e gestão agrícola. Gadarif Sudão anos preditiva anual disso ML XGBoost, XGBoost , (XGBoost) BRF (BRF KNearest K Nearest KNN, KNN NN (K-NN) preciso geral (XGBoost (K-NN

Resumen: Se trata de estudiar, en una primera aproximación, el impacto del cambio en el modo de producción como consecuencia de la telemática y el uso del aprendizaje de las máquinas y la inteligencia artificial en muchas de las aplicaciones y procesos repetitivos en los sistemas sociales de producción y distribución de bienes. Se utilizó el método del construccionismo sistémico, que, en definitiva, parte de la base de que los datos no son datos, sino construcciones con supuestos teóricos inevitables, y de que la lengua es el sistema en el que se construyen los lenguajes que son un límite general para obtener y comunicar información en la sociedad. Esto conduce a una teoría de las acciones algo diferente de las de Parsons y Luhmann, que lleva al concepto de praxemas, y a todo un nuevo análisis de la sociedad y su derecho. Resumen estudiar aproximación bienes sistémico definitiva inevitables Luhmann praxemas derecho

Abstract: The aim is to study, in a first approximation, the impact of the change in the production mode as a result of telematics and the use of machine learning and artificial intelligence in many of the applications and repetitive processes in social systems of production and distribution of goods. The method of systemic constructionism was used, which, in short, assumes that data are not data - they are constructions with unassailable theoretical assumptions and that language is the system in which the general limit languages are constructed to obtain and communicate information in society. This leads to a theory of actions somewhat different from those of Parsons and Luhmann - leading to the concept of praxemas, and to a whole new analysis of society and its law. Abstract study approximation goods used short praxemas law

Resumo: Objetiva-se estudar, em primeira aproximação, o impacto da mudança do modo de produção por efeito da telemática e do emprego do machine learning e inteligência artificial em grande parte das aplicações e processos repetitivos nos sistemas sociais de produção e distribuição de mercadorias. Utilizou-se o método do construcionismo-sistêmico que, em síntese, assume que os dados não são dados – são construções com pressupostos teóricos inafastáveis em que a língua é o sistema no qual se constroem as linguagens, limite geral da obtenção e da comunicação de informações na sociedade. Disso advém uma teoria das ações diferente daquelas de Parsons e Luhmann – conduzindo ao conceito de praxemas e, a toda uma nova analítica da sociedade e de seu direito. Resumo Objetivase Objetiva estudar aproximação mercadorias Utilizouse Utilizou construcionismosistêmico construcionismo sistêmico síntese linguagens direito

Abstract Introduction: Chronic kidney disease (CKD) and metabolic syndrome (MS) are recognized as public health problems which are related to overweight and cardiometabolic factors. The aim of this study was to develop a model to predict MS in people with CKD. Methods: This was a prospective cross-sectional study of patients from a reference center in São Luís, MA, Brazil. The sample included adult volunteers classified according to the presence of mild or severe CKD. For MS tracking, the k-nearest neighbors (KNN) classifier algorithm was used with the following inputs: gender, smoking, neck circumference, and waist-to-hip ratio. Results were considered significant at p < 0.05. Results: A total of 196 adult patients were evaluated with a mean age of 44.73 years, 71.9% female, 69.4% overweight, and 12.24% with CKD. Of the latter, 45.8% had MS, the majority had up to 3 altered metabolic components, and the group with CKD showed statistical significance in: waist circumference, systolic blood pressure, diastolic blood pressure, and fasting blood glucose. The KNN algorithm proved to be a good predictor for MS screening with 79% accuracy and sensitivity and 80% specificity (area under the ROC curve – AUC = 0.79). Conclusion: The KNN algorithm can be used as a low-cost screening method to evaluate the presence of MS in people with CKD. Introduction (CKD (MS factors Methods crosssectional cross sectional Luís MA Brazil tracking knearest k nearest (KNN inputs gender smoking circumference waisttohip hip ratio 005 0 05 0.05 19 4473 44 73 44.7 years 719 71 9 71.9 female 694 69 4 69.4 1224 12 24 12.24 latter 458 45 8 45.8 components pressure glucose 79 80 area 0.79. 079 0.79 . 0.79) Conclusion lowcost low cost 00 0.0 1 447 7 44. 71. 6 69. 122 2 12.2 45. 07 0.7 0. 12.

Resumo Introdução: A doença renal crônica (DRC) e a síndrome metabólica (SM) são reconhecidas como problemas de saúde pública relacionados ao excesso de peso e a fatores cardiometabólicos. O objetivo deste estudo foi desenvolver um modelo para prever a SM em pessoas com DRC. Métodos: Este foi um estudo transversal prospectivo de pacientes de um centro de referência em São Luís, MA, Brasil. A amostra incluiu voluntários adultos classificados de acordo com a presença de DRC leve ou grave. Para o rastreamento da SM, o algoritmo de classificação k-nearest neighbors (KNN) foi utilizado com os seguintes dados: sexo, tabagismo, circunferência do pescoço e relação cintura-quadril. Os resultados foram considerados significativos com p < 0,05. Resultados: Foram avaliados 196 pacientes adultos com média de idade de 44,73 anos, 71,9% do sexo feminino, 69,4% com sobrepeso e 12,24% com DRC. Desses últimos, 45,8% apresentaram SM, a maioria tinha até 3 componentes metabólicos alterados, e o grupo com DRC apresentou significância estatística em: circunferência da cintura, pressão arterial sistólica, pressão arterial diastólica e glicemia de jejum. O algoritmo KNN comprovou ser um bom preditor para a triagem de SM com acurácia e sensibilidade de 79% e especificidade de 80% (área sob a curva ROC – AUC = 0,79). Conclusão: O algoritmo KNN pode ser usado como um método de triagem de baixo custo para avaliar a presença de SM em pessoas com DRC. Introdução (DRC (SM cardiometabólicos Métodos Luís MA Brasil grave knearest k nearest (KNN dados tabagismo cinturaquadril. cinturaquadril cintura quadril. quadril cintura-quadril 005 0 05 0,05 Resultados 19 4473 44 73 44,7 anos 719 71 9 71,9 feminino 694 69 4 69,4 1224 12 24 12,24 últimos 458 45 8 45,8 alterados sistólica jejum 79 80 área 0,79. 079 0,79 . 0,79) Conclusão 00 0,0 1 447 7 44, 71, 6 69, 122 2 12,2 45, 07 0,7 0, 12,

Resumen El desarrollo de proyectos mineros en etapas de exploración y prefactibilidad conlleva desafíos relacionados a la heterogeneidad e incertidumbre geológica y de procesamiento debido a la variabilidad en sus atributos primarios y de respuesta. Por lo tanto, el objetivo de esta investigación radica en elaborar el modelo geometalúrgico del índice de trabajo de Bond. Para esto se desarrollaron modelos de regresión lineal en Jupyter Notebook considerando 790 muestras de resistencia a la compresión uniaxial de la roca (UCS), litologías, zonas minerales e índice de trabajo de Bond (BWi) en un depósito pórfido cuprífero. Los resultados indican relación directamente proporcional de tipo lineal entre el BWi y UCS, generando modelamientos con desempeños aceptables de R2 entre 0,76 a 0,90. Además, las litologías y zonas mineralizadas en el depósito estudiado son características relevantes relacionadas a la conminución. Finalmente, de acuerdo a la competencia de la roca, esta se clasifica como tipo media a dura, dura y muy dura en las zonas de óxidos, mixtos y sulfuros primarios respectivamente.

Abstract The development of mining projects in the exploration and pre-feasibility stages involves challenges related to geological and processing heterogeneity and uncertainty due to variability in their primary and response attributes. Therefore, the objective of this research is to develop the geometallurgical model of the Bond work index. For this purpose, linear regression models were developed in Jupyter Notebook considering 790 samples of uniaxial compressive strength of rock (UCS), lithologies, mineral zones and Bond work index (BWi) in a porphyry copper deposit. The results indicate a directly proportional linear relationship between BWi and UCS, generating modeling with acceptable R2 performances between 0,76 and 0,90. In addition, the lithologies and ore zones in the deposit studied are relevant characteristics related to comminution. Finally, according to rock competence, the rock is classified as medium to hard, hard and very hard in the oxide, mixed and primary sulfide zones, respectively.

Abstract: This study addresses the application of the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology in the analysis of dissolved gases in oil of power transformers, being this a critical component in electrical systems. The adoption of this six-phase structured method allowed a comprehensive evaluation of the condition of the transformer units of the Ecuadorian electrical system based on the analysis of investment and expansion data of the sector, as well as the study of 1 099 DGA (Dissolved Gas Analysis) profiles obtained from a population of 153 transformers located in the different regions of continental Ecuador. The findings described in this work have the potential to significantly improve investment and maintenance strategies and policies. In addition, the adoption of automation techniques in the DGA classification process is proposed, using supervised learning models to enhance the reliability and efficiency of the public energy service. The results suggest that this approach not only improves the diagnosis within the maintenance activities, but also provides a solid basis to draw a roadmap towards a predictive asset management, resulting in a substantial improvement of the reliability of the national power system.

Resumen: Este estudio aborda la aplicación de la metodología Cross-Industry Standard Process for Data Mining (CRISP-DM) en el análisis de los gases disueltos en aceite de transformadores de potencia, siendo este un componente crítico en los sistemas eléctricos. La adopción de este método estructurado de seis fases permitió evaluar de forma integral la condición de las unidades de transformación del sistema eléctrico ecuatoriano a partir del análisis de datos de inversión y expansión del sector, así como del estudio de 1 099 perfiles DGA (Dissolved Gas Analysis) obtenidos de una población de 153 transformadores ubicados en las distintas regiones de Ecuador continental. Los hallazgos descritos en este trabajo tienen el potencial de mejorar significativamente las estrategias y políticas de inversión y mantenimiento. Además, se propone la adopción de técnicas de automatización en el proceso de clasificación DGA, utilizando modelos de aprendizaje supervisado para potenciar la confiabilidad y eficiencia del servicio público de energía. Los resultados sugieren que este enfoque no solo mejora el diagnóstico dentro de las actividades de mantenimiento, sino que también proporciona una base sólida para trazar una hoja de ruta hacia una gestión predictiva de los activos, lo que se traduce en una mejora sustancial de la confiabilidad del sistema eléctrico nacional.

Livestock farms in Kenya face pressure to increase productivity amid rising global population. Cattle farming dominates, but small to medium-sized farms struggle with cattle insemination. Currently, visual observation is used for heat detection, with farmers maintaining farm journals. Modern methods utilizing sensors to improve estrus prediction are time-consuming, costly and need constant internet connection. This research proposes a novel approach-the use of an on-controller machine learning algorithm- for estrus prediction in cattle. Motion and temperature data was collected from two zero-grazed multiparous Holstein Friesian cows in Kiambu County, Kenya for 11 months. The data was cleaned and stored. Movement intensity profiles were derived by root-mean-squaring directional accelerometer values and averaging this over time. Validation was performed by observing cow behavior for indicators such as restlessness, mounting, and vulva swelling, with farmer predictions documented in their records. The collected data was then used to train a machine learning algorithm, with several models tested, and a neural network emerged as the best fit. The TensorFlow library facilitated the implementation of the algorithm on a microcontroller, allowing for the development of an animal tag featuring the ML algorithm. Results demonstrated 83.9% sensitivity, 89.0% specificity and 89.5% accuracy in detecting estrus, compared to farmer's visual observation, which had only 37% sensitivity. These findings underscore the potential to integrate machine learning into Precision Livestock Farming for estrus prediction, with prediction occurring directly on the animal tag offline. This integration holds promise for farmers, notably heightened insemination success rates, without necessitating significant financial investment.

RESUMEN El arándano es un fruto que posee una fuente importante de componentes bioactivos beneficiosos para la dieta humana, como las antocianinas y fenoles totales, que se ven alterados por el uso de temperaturas altas durante el procesamiento. El objetivo de este estudio fue evaluar el uso de redes neuronales artificiales en la optimización de la concentración de sacarosa y el tiempo para el pretratamiento osmótico de arándanos de la variedad Biloxi, con la finalidad de retener la mayor cantidad de antocianinas y componentes fenólicos totales en la elaboración posterior de mermelada. Se utilizó redes neuronales artificiales del tipo feedfoward, con algoritmo de entrenamiento de Backpropagation con ajuste de pesos de Levenberg-Marquardt para lograr la combinación óptima predicha que maximice la retención de estos componentes bioactivos. El modelo logró su mejor rendimiento con 11 neuronas en la capa oculta, logrando un coeficiente R2 de 0,98 y un error cuadrático medio de 4,76; lo que indica una gran capacidad de generalización. Las redes neuronales artificiales permitieron obtener la mejor combinación de los factores experimentales -concentración de sacarosa (1,64 M) y tiempo (211,52 min)- que maximizaron los contenidos de antocianinas monoméricas totales (70,98 mg cianidina-3-O-glucósido 100 g-1) y fenoles totales (110,54 mg AGE g-1) presentes en mermelada. En cambio, mediante optimización de respuesta se obtuvo que la combinación de factores experimentales que maximizó las antocianinas totales (71,59 mg cianidina-3-O-glucósido 100 g-1 de mermelada) fue 1,54 M de sacarosa y 232,73 min y para fenoles totales (111,06 mg GAE g-1 de mermelada) 1,79 M y 196,36 min. El uso de redes neuronales artificiales es una excelente alternativa para modelar fenómenos, en comparación con los métodos tradicionales.

ABSTRACT Blueberries are a fruit that is an important source of bioactive components beneficial to the human diet, such as anthocyanins and total phenolics, which are altered by the use of high temperatures during processing. This study aimed to evaluate the use of artificial neural networks in the optimization of sucrose concentration and time for the osmotic pre-treatment of blueberries of the Biloxi variety, to retain the greatest amount of anthocyanins and total phenolics in the subsequent preparation of jam. Artificial neural networks of the feedforward type were used, with a Backpropagation training algorithm with Levenberg-Marquardt weight adjustment, to achieve the optimal predicted combination that maximizes the retention of these bioactive components. The model achieved its best performance with 11 neurons in the hidden layer, achieving an R2 coefficient of 0.98 and a mean square error of 4.76, indicating a strong ability for generalization. Artificial neural networks allowed to obtain the best optimal combination of predicted multiple responses of factors consisting of a sucrose concentration of 1.64 M and a time of 211.52 min, which retained a higher content of total monomeric anthocyanins with 70.98 mg cyanidin-3-O-glucoside 100 g-1 of jam and total phenolics with 110.54 mg GAE g-1 of jam. On the other hand, through single-response optimization was obtained that the combination of experimental factors that maximized total anthocyanins (71.59 mg cyanidin-3-O-glucoside 100 g-1 of jam) was 1.54 M of sucrose and 232.73 min and for total phenols (111.06 mg GAE g-1 of jam) 1.79 M of sucrose and 196.36 min. The use of artificial neural networks is an excellent alternative for modeling phenomena, compared to traditional methods.

ABSTRACT Aeration is carried out by blowing external air into the silo, with the aim to keep the temperature in the mass of stored grains at safe levels. In the present study, the energy efficiency of aeration of stored sunflower grains was estimated, and a model was proposed and tested to estimate the energy efficiency of aeration, using different algorithms in supervised and unsupervised machine learning. The objective of the work was to develop a Web application based on data mining and modeling with machine learning. The database was composed of information on the average temperature at the height of the sensors, average temperature of the silo, external ambient temperature, occurrence of aeration, if there was cooling, heating and direct heating during aeration, and the energy efficiency of the aeration process. The model for estimating the energy efficiency of the aeration process proved to be efficient, identifying that the energy efficiency was 97.78% during the aeration of stored sunflower grains. Among the classifier algorithms tested, Support Vector Machine (SVM-Poly) showed the best metrics and indicators, hence being recommended for implementation in system development networks capable of predicting the aeration status of stored grains. silo levels study estimated learning sensors cooling efficient 9778 97 78 97.78 SVMPoly SVM Poly (SVM-Poly indicators 977 9 7 97.7 97.

RESUMO A aeração é realizada por meio da insuflação do ar externo para dentro do silo, tendo como objetivo manter a temperatura da massa de grãos armazenados em níveis seguros. No presente estudo foi estimada a eficiência energética da aeração de grãos de girassol armazenados, assim como proposto e testado um modelo de estimativa da eficiência energética da aeração, utilizando diferentes algoritmos no aprendizado de máquinas supervisionado e não supervisionado. O objetivo no trabalho foi desenvolver uma aplicação Web a partir da mineração e modelagem dos dados com o aprendizado de máquinas. O banco de dados foi composto pelas informações da temperatura média do nível dos sensores, temperatura média do silo, temperatura ambiente externa, ocorrência de aeração, se houve resfriamento, aquecimento e aquecimento direto durante aeração, e a eficiência energética do processo de aeração. O modelo de estimativa da eficiência energética do processo de aeração demonstrou-se eficiente, identificando que durante a aeração de grãos de girassol armazenados a eficiência energética foi de 97,78%. Dentre os algoritmos classificadores testados na Máquina de Vetores de Suporte (SVM-Poly) apresentou as melhores métricas e indicadores, sendo recomendado para implementação em redes de desenvolvimento de sistemas capaz de predizer o status da aeração de grãos armazenados. silo seguros sensores externa resfriamento demonstrouse demonstrou eficiente 9778 97 78 97,78% SVMPoly SVM Poly (SVM-Poly indicadores 977 9 7 97,78 97,7 97,

SUMMARY: In the present study, it is aimed to reveal the performances of different classification algorithms in sex determination from first cervical vertebra, that is, atlas measurements. The classification success of 4 different machine learning algorithms was comparatively examined for the purpose of sex determination by evaluating 22 atlas measurements on cone beam computed tomography (CBCT) images of 145 female and 145 male adults. Logistic regression (LR), classification and regression tree (CART), support vector machine (SVM) and neural network (NN) algorithms were used for sex diagnosis. Area under the ROC curve (AUC), classification accuracy (CA), F1-ratio, Precision and Recall indexes were used for model performances. Except for 2 measurements, there was a significant difference between men and women in terms of 20 other parameters (p<0.05). The adjusted effects of these parameters on sex determination were examined with multivariate models and algorithms, and the success of all 4 algorithms was quite good. The success of the NN algorithm (Accuracy 91.3 %; 0.87 Specificity, 0.85 Sensitivity) in correctly classifying male and female was the highest, followed by the LR algorithm (Accuracy 90.9 %; 0.86 Specificity, 0.83 Sensitivity). It was found that the machine learning algorithm applied to the variables of the atlas gave high accuracy regarding sex and the NN model was highly effective in sex determination. In addition, a large morphometric database of atlas was presented in our results.

RESUMEN: El objetivo de estudio fue revelar el desempeño de diferentes algoritmos de clasificación en la determinación del sexo a partir de la primera vértebra cervical, el atlas. El éxito de la clasificación de 4 algoritmos diferentes de aprendizaje automático se examinó comparativamente con el fin de determinar el sexo mediante la evaluación de 22 mediciones de atlas en imágenes de tomografía computarizada de haz cónico (CBCT) de 145 mujeres y 145 hombres adultos. Para el diagnóstico de sexo se utilizaron algoritmos de clasificación y árbol de regresión (CART), máquina de vectores de soporte (SVM) y redes neuronales (NN). Para el rendimiento del modelo se utilizaron el área bajo la curva ROC (AUC), la precisión de clasificación (CA), la relación F1, los índices de precisión y recuperación. Excepto en dos mediciones, hubo una diferencia significativa entre hombres y mujeres en otros 20 parámetros (p<0,05). Los efectos ajustados de estos parámetros en la determinación del sexo se examinaron con modelos y algoritmos multivariados, y el éxito de los cuatro algoritmos fue bastante bueno. El éxito del algoritmo NN (Precisión 91,3 %; 0,87 Especificidad, 0,85 Sensibilidad) en clasificar correctamente a hombres y mujeres fue el más alto, seguido por el algoritmo LR (Precisión 90,9 %; 0,86 Especificidad, 0,83 Sensibilidad). Se encontró que el algoritmo de aprendizaje automático aplicado a las variables del atlas dio una alta precisión con respecto al sexo, y el modelo NN fue altamente efectivo en la determinación del sexo. Además, en nuestros resultados se presentó una importante base de datos morfométricos del atlas.

SUMMARY: The determination of sex differences in anatomical structures is critical in establishing gold standard morphometric data in basic medical sciences, and in surgical and internal sciences in selecting the right area during invasive intervention and applying the correct intervention methodology appropriate to the area. The aim of this study is to determine the sex difference using Machine learning (ML) algorithms and Artificial neural networks (ANN) with parameters obtained from basilar artery. The study was performed on computed tomography angiography images of 63 women and 94 men. The following parameters were measured on the images: initial width of the right vertebral artery, initial width of the left vertebral artery, termination width of the right vertebral artery, termination width of the left vertebral artery, basilar artery width, and basilar artery length. The measurements were used in ML algorithms and ANN input to determine sex differences. As a result of the study, a sex difference rate of 0.84 was determined with the ML algorithms Random Forest (RF), Quadratic Discriminant Analysis (QDA), Extra Tree Classifier (ETC) and 0.84 with the Multilayer Perceptron Classifier (MLCP) algorithm of ANN. As a result of the study, sex difference was found with an accuracy rate of 0.84 using ML algorithms and ANN with parameters obtained from basilar artery. In this context, we think that this study will shed light on basic and clinical medical sciences.

RESUMEN: La determinación de las diferencias de sexo en las estructuras anatómicas es fundamental para establecer datos morfométricos de gold estándar, en las ciencias médicas básicas y en las ciencias quirúrgicas e internas para seleccionar el área correcta durante la intervención invasiva y aplicar la metodología de intervención correcta y apropiada. El objetivo de este estudio fue determinar la diferencia de sexo utilizando algoritmos de aprendizaje automático (ML) y redes neuronales artificiales (ANN) con parámetros obtenidos de la arteria basilar. El estudio se realizó sobre imágenes de angiografía por tomografía computarizada de 63 mujeres y 94 hombres. Se midieron en las imágenes los siguientes parámetros: ancho inicial de la arteria vertebral derecha, ancho inicial de la arteria vertebral izquierda, ancho de terminación de la arteria vertebral derecha, ancho de terminación de la arteria vertebral izquierda, ancho de la arteria basilar y longitud de la arteria basilar. Las mediciones se utilizaron en algoritmos de ML y entradas de ANN para determinar las diferencias de sexo. Como resultado del estudio, se determinó una tasa de diferencia de sexo de 0,84 con los algoritmos ML Random Forest (RF), Análisis Discriminante Cuadrático (QDA), Extra Tree Classifier (ETC) y 0,84 con el algoritmo Multilayer Perceptron Classifier (MLCP) de ANA. Como resultado del estudio, se encontró una diferencia de sexo con una tasa de precisión de 0,84 utilizando algoritmos ML y ANN con parámetros obtenidos de la arteria basilar. En este contexto, pensamos que este estudio será de utilidad en las ciencias médicas básicas y clínicas.

The concept of digital transformation (DT) has revolutionized different facets of society including institutions of learning, where it has continued to influence teaching, learning and research. Over time and with the continued technological advancements in biological science disciplines, it has become clear that the increased convergence between these disciplines and digital systems application will become more apparent. For example, the advent and application of next-generation sequencing (NGS), machine learning (ML), artificial intelligence (AI) and other automated biological processes have afforded unique huge data sets with high societal impact. As such, digitalization of the bioeconomy remains necessary. The digitalization of the biosciences has birthed emerging concepts like "digital biology", "engineered biology" and "bio-revolution" and involve computer-centered biosciences. These are built around the integration of scientific data, modeling at several levels, as well as networked science. Nonetheless, it is still plagued by information derivation policies and technicality issues. Although, digital biology has seen a major boost in recent years with the emergence of new and updated digital biology databases and platforms for advanced computational modelling, and connection and visualization of bio-data at a molecular level, more efforts are needed from governmental stakeholders (policy makers), industry and academia to enhance the impact of computational automation and digital transformation in bioscience activities. Hence, a steady upscale in digitalization of the biosciences would ensure bioprocess development, increase teaching, research efficiency and productivity, birth more innovative future applications for various industries, and thereby impact several aspects of life. This article presents how selected biological science disciplines have been digitally transformed through data analytics and how the concept could be further explored in advancing teaching, learning and research.

This research presents the implementation and evaluation of machine learning models to predict solar irradiance (W/m²). The objective is to provide valuable insights for making informed decisions regarding solar system design and financing. A thorough exploratory data analysis was conducted on the Southern African Universities Radiometric Network (SAURAN) data collected at the University of Pretoria's station to gain insights into the patterns of solar irradiance over the past 10 years. Python's functions and libraries are utilized extensively for conducting exploratory data analysis, model implementation, model testing, forecasting, and data visualization. Random Forest (RF), k-Nearest Neighbors (KNN), Feedforward Neural Network (FFNN), Support Vector Regression (SVR), and extreme Gradient Boosting models (XGBoost) are implemented and evaluated. The KNN model was found to be superior achieving a relative Root Mean Squared Error (RMSE), relative Mean Absolute Error (MAE), and R-Squared (R²) of 5.77%, 4.51% and 0.89 respectively on testing data. The variable importance analysis revealed that temperature (°C) exerted the greatest influence on predicting solar irradiance, accounting for 44% of the predictive power. The KNN model is suitable to inform solar systems design and financing decisions. Directions for future studies are identified and suggestions for areas of exploration are provided to contribute to the advancement of solar irradiance predictions.

Abstract Objectives: characterize, in a sociodemographic and clinical perspective, pacients who underwent videoendoscopy of swallowing and had oropharyngeal dysphagia during their hospital stay. Furthermore, it is also intended to find strategies to improve the quality of the response to pacientes in need of videoendoscopy of swallowing. Case study: this study is based on a sample collected in Centro Hospitalar Universitário Santo António, between january 2019 and september 2021, including. Materials and Methods: the clinical data and image studies from 157 pacients were retrospectively analyzed. After careful selection, all cases that did not meet the inclusion criteria were excluded from the study. Thus, 143 patients were selected for analysis. Results and conclusions: this study identifies determining factors of FOIS level variability, detecting severe patients in a preliminary, pre-VED phase, reducing waiting time by over 70%, and consequently improving service quality in all its dimensions.

Resumo Objetivos: caracterizar, do ponto de vista sociodemográfico e clínico, os doentes submetidos a videoendoscopia da deglutição que, no decurso do seu internamento, apresentaram quadro de disfagia orofaríngea; encontrar estratégias para melhorar a qualidade da resposta aos doentes que necessitem de recorrer a videoendoscopia da deglutição. Desenho do Estudo: este estudo tem por base uma amostra recolhida no Centro Hospitalar Universitário Santo António, entre janeiro de 2019 e setembro de 2021, inclusive. Material e Métodos: foram analisados retrospetivamente os processos clínicos e estudos imagiológicos de 157 doentes. Após seleção cuidada, foram excluídos do estudo todos os casos que não cumpriram os critérios de inclusão. Assim, obtiveram-se para análise 143 doentes. Resultados e Conclusões: este estudo identifica fatores determinantes da variabilidade do nível FOIS, detetando doentes graves numa fase preliminar, pré-VED, diminuindo o tempo de espera em mais de 70%, e, consequentemente, melhorando a qualidade do serviço em todas as suas dimensões.

Abstract Digital Welfare State in Ecuador: Citizen Datafication and Machine Learning in the Construction and Management of Poverty In the era of digital governance, the increasing use of Big Data and Artificial Intelligence (AI) in social protection systems has emerged as a prominent concern. The state's efforts to promote citizen datafication and the implementation of AI for managing and providing social services and benefits are guided by principles of neutrality, objectivity, and technological efficiency. Nevertheless, these technologies can not only improve the effectiveness of social benefits allocation, but also have the potential of controlling, monitoring, sanctioning, and excluding beneficiaries. In light of the prevailing techno-optimism evident in the design of contemporary social policy, particularly with regard to its allocation mechanisms, this study draws upon insights from Science, Technology, and Society Studies to shed light on how data systems and AI technologies transform the notion of poverty and impact the definition of beneficiaries and non-beneficiaries. This approach focuses on unraveling the political dimension of data systems and AI technologies that are emerging as mediators in the relationship between the state and citizens. Through the case study of Ecuador's Social Registry, the aim is to enrich the critical examination of digital welfare states in the Latin American region, analyzing their implications for society and their potential to produce or exacerbate inequality and exclusion; challenging deterministic views of technological change and public policy design.

Resumen En la era de la gobernanza digital, la utilización creciente de datos y tecnologías de Big Data e Inteligencia Artificial (IA) en los sistemas de protección social se ha convertido en un asunto relevante. Los esfuerzos del Estado por impulsar la datificación ciudadana y la implementación de la IA para la gestión y provisión de servicios y beneficios sociales están vehiculizados por las ideas de neutralidad, objetividad y eficiencia tecnológica. No obstante, estas tecnologías no solo pueden mejorar la efectividad en la asignación de beneficios sociales; sino que también tienen el potencial de controlar, monitorear, sancionar y excluir a los beneficiarios. Ante el tecno-optimismo que marca el diseño de la política social contemporánea, específicamente sus mecanismos de asignación, este trabajo apuesta por los Estudios de la Ciencia, Tecnología y Sociedad para dar luz sobre cómo los sistemas de datos y las técnicas de IA transforman la noción de pobreza e impactan en la definición de beneficiarios y no beneficiarios. Este enfoque se centra en desentrañar la dimensión política -de la politics- de los sistemas de datos y las tecnologías de la IA que comienzan a mediar la relación entre el Estado y los ciudadanos. A través del estudio del caso ecuatoriano del Registro Social, el objetivo recae en nutrir el examen crítico de los Estados de Bienestar Digital en la región latinoamericana, analizando sus implicaciones en la sociedad y su potencial para producir o amplificar desigualdad y exclusión; desafiando las visiones deterministas del cambio tecnológico y del diseño de políticas públicas.