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

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: Os agentes conversacionais (CAs) percorreram um longo caminho desde a sua primeira aparição na década de 1960. Os avanços tecnológicos contínuos, como a informática estatística e os grandes modelos de linguagem, estão a alcançar uma interação cada vez mais natural e sem esforço entre os seres humanos. O presente estudo realizou uma revisão bibliométrica sobre a utilização de chatbots no contexto educativo, compilando documentos científicos da base de dados Scopus. Os resultados destacam as lacunas de conhecimento na investigação; da mesma forma, múltiplas direções são evidentes para investigações futuras baseadas na aprendizagem colaborativa (collaborative learning), agentes conversacionais encarnados (embodied conversa- tional agents), agentes conversacionais pedagógicos (pedagogical conversational agents), processamento de linguagem natural (natural language processing) e agentes racionais (rational agents). A investigação contribui para a teoria científica sobre chatbots e a sua incidência na educação ao estabelecer uma revisão bibliométrica do tema e mostrar implicações práticas ao destacar oportunidades de investigação e lacunas de conhecimento.

Resumen: Los agentes conversacionales (CAs) han recorrido un largo camino desde su primera aparición en la década de 1960. Avances tecnológicos continuos, como la informática estadística y los grandes modelos del lenguaje, vienen logrando una interacción cada vez más natural y sin esfuerzo entre humanos. El presente estudio realizó una revisión bibliométrica sobre el uso de los chatbots en el contexto educativo, recopilando los documentos científicos de la base de datos Scopus. Los resultados destacan los vacíos de conocimiento de la investigación; asimismo, se evidencian múltiples direcciones para futuras investigaciones basadas en aprendizaje colaborativo (collaborative learning), agentes conversacionales encarnados (embodied conversational agents), agentes conversacionales pedagógicos (pedagogical conversational agents), procesamiento del lenguaje natural (natural language processing) y agentes racionales (rational agents). La investigación contribuye a la teoría científica sobre los chatbots y su incidencia en la educación al establecer un repaso bibliométrico del tema y mostrar implicaciones prácticas al resaltar las oportunidades de investigación y vacíos de conocimiento.

Abstract: Conversational agents (CAs) have come a long way since their first appearance in the 1960s. Ongoing technological advances, such as statistical computing and large-scale language models, have made human-to-human interaction increasingly natural and effortless. The present study conducted a bibliometric re- view of the use of chatbots in educational contexts by collecting scientific papers from the Scopus database. The results highlight the gaps in research knowledge and suggest several directions that can be taken for future research based on collaborative learning, embodied conversational agents, pedagogical conversational agents, natural language processing, and rational agents. The research contributes to the scientific theory of chatbots and their impact on education by providing a bibliometric review of the topic and shows practical implications by highlighting research opportunities and knowledge gaps.

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.

Resumo: Desde a década de 1960, uma série de pesquisas tem questionado a capacidade das escolas para igualar os níveis de aprendizagem entre alunos de diferentes origens sociais. Apesar da diversidade de programas e políticas que buscaram equalizar as diferenças, pesquisas em diversos contextos constataram repetidamente que as diferenças não apenas persistem, mas são reproduzidas nas escolas. Este artigo aborda o problema de uma perspectiva um pouco diferente: seu objetivo é identificar práticas e atitudes de estudantes e docentes associadas a diferenças no desempenho escolar entre estudantes de condição social e econômica mais baixa no Brasil e na Argentina. O objetivo não é entender os mecanismos que reproduzem as diferenças entre os estratos, mas sim aqueles que geram maior desempenho relativo entre os estudantes com menos recursos. Para isso, foi realizada uma análise comparativa dos resultados do teste PISA de 2018 por meio de modelos estatísticos multivariados. Essa análise mostrou que determinados fatores, como a empatia docente, a motivação e perseverança estudantil ou as modalidades de estudo, estão associados ao desempenho. Mas também mostrou que a magnitude da associação varia de acordo com o contexto. Esses resultados contribuem para a elaboração de políticas educacionais que buscam aumentar o desempenho em estratos desfavorecidos, indicando algumas condições que podem favorecê-lo, mas também alertando sobre as dificuldades de extrapolar resultados entre contextos de forma acrítica

Abstract: Since the 1960s, a vast array of studies has questioned the ability of schools to equalize learning levels among students from different social backgrounds. Despite various programs and policies aimed at reducing these disparities, research in diverse contexts has repeatedly found that not only do these differences persist, but they are also reproduced within the school system. This article approaches the issue from a slightly different perspective: its objective is to identify practices and attitudes of students and teachers that are associated with differences in school performance among students from lower socioeconomic backgrounds in Brazil and Argentina. The focus is not on understanding the mechanisms that perpetuate differences between social strata, but rather on identifying those that lead to higher relative achievements among students with fewer resources. A comparative analysis of the 2018 PISA test results was conducted using multivariate statistical models. This analysis revealed that certain factors, such as teacher empathy, student motivation and perseverance, and study methods, are associated with academic performance. However, it also showed that the strength of these associations varies depending on the context. These findings contribute to the design of educational policies aimed at improving performance in disadvantaged groups by highlighting conditions that may foster better outcomes, while also cautioning against uncritically extrapolating results across different contexts

Resumen: Desde la década de 1960 en adelante una extensa serie de estudios ha puesto en duda la capacidad de la escuela para igualar los niveles de aprendizaje entre estudiantes de distinto origen social. Pese a la diversidad de programas y políticas que han buscado igualar las diferencias, distintas investigaciones en diversos contextos han encontrado una y otra vez que las diferencias no solo persisten, sino que se reproducen en la escuela. En este artículo se aborda el problema desde una óptica ligeramente diferente: su objetivo es identificar prácticas y actitudes de estudiantes y docentes asociadas a las diferencias de desempeño escolar entre estudiantes de menor nivel económico social en Brasil y Argentina. No se busca comprender los mecanismos que reproducen las diferencias entre estratos, sino aquellos que generan mayores logros relativos en los estudiantes con menos recursos. Se realizó para ello un análisis comparativo de los resultados de las pruebas PISA del 2018 mediante modelos estadísticos multivariados. Este análisis mostró que ciertos factores, como la empatía docente, la motivación y perseverancia estudiantil o las modalidades de estudio se asocian al desempeño. Pero también evidenció que la magnitud de la asociación varía con el contexto. Estos resultados aportan al diseño de políticas educativas que procuren incrementar el desempeño en estratos desfavorecidos, al indicar algunas condiciones que pueden favorecerlo, pero advirtiendo también sobre las dificultades de extrapolar resultados entre contextos de manera acrítica

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.

Abstract Objective: To evaluate the relationship between the perceived usefulness of e-learning, anxiety, physical activity, and eating habits during the COVID-19 lockdown in university students. Methodology: A cross-sectional study involving 102 health undergraduate students (23 men, 79 women, average age 22.32 years). They responded to surveys on the perception of e-learning, eating habits, physical activity, and anxiety. Results: Simple linear regression models show that the perceived usefulness of e-learning predicts trait anxiety (F(1,100) = 9.13, p < 0.01, R 2 = 0.084) and state anxiety (F(1,100) = 11.66, p < 0.01, R 2 = 0.104). In turn, trait anxiety (F(1,100) = 8.35, p = 0.01, R 2 = 0.078) and state anxiety (F(1,100) = 6.55, p < 0.05, R 2 = 0.062) predict healthy eating habits. Conclusions: A positive perception of e-learning decreases anxiety. High levels of anxiety are associated with poorer eating habits. This indicates that reducing anxiety can improve eating habits.

Resumen Objetivo: Evaluar la relación entre la percepción de utilidad del e-learning, ansiedad, actividad física y hábitos alimenticios durante el confinamiento por COVID-19 en estudiantes universitarios. Metodología: Estudio transversal en 102 alumnos de licenciatura en salud (23 hombres, 79 mujeres, edad promedio 22.32 años). Respondieron encuestas sobre la percepción de e-learning, hábitos alimenticios, actividad física y ansiedad. Resultados. Los modelos de regresión lineal simple muestran que la percepción de utilidad de e-learning predice la ansiedad rasgo (F(1,100) = 9.13, p < 0.01, R 2 = 0.084) y estado (F(1,100) = 11.66, p < 0.01, R 2 = 0.104), y a su vez, la ansiedad rasgo (F(1,100) = 8.35, p = .01, R 2 = 0.078) y estado (F(1,100) = 6.55, p < 0.05, R 2 = 0.062) son predictoras de hábitos de alimentación saludable. Conclusiones: La percepción positiva del e-learning disminuye la ansiedad. Niveles altos de ansiedad se asocian con peores hábitos alimenticios. Esto indica que reducir la ansiedad puede mejorar los hábitos alimenticios.

Abstract Pedagogical models are the key to responding to the difficulties that students face during their teaching-learning process; likewise, their study contributes to the improvement of pedagogical practices. This comparative research addresses the problem of identifying the most effective educational model between hybrid and face-to-face in higher education, focused on an educational experience. Its objective is to evaluate and compare both pedagogical scenarios to improve educational practices. A quantitative, descriptive and transversal approach was adopted, using an instrument specifically designed to measure variables related to the conduct, monitoring and evaluation of the learning process. The results reveal better achievement in the experimental group (hybrid), with 100% approval and a higher average grade than the control group (in-person). The conclusions show the greater effectiveness of the hybrid model in the context evaluated, highlighting its contribution to technological adaptation in education and it’s potential to optimize learning. This study contributes to originality by directly comparing these models in a real university context, providing valuable evidence for pedagogical decision-making.

Resumen Los modelos pedagógicos son la pieza clave para responder a las dificultades que los estudiantes enfrentan durante su proceso de enseñanza-aprendizaje, asimismo, su estudio contribuye al mejoramiento de las prácticas pedagógicas. Esta investigación comparativa aborda la problemática de identificar el modelo educativo más efectivo entre el híbrido y el presencial en la enseñanza superior, focalizada en una experiencia educativa. Su objetivo es evaluar y comparar ambos escenarios pedagógicos para mejorar prácticas educativas. Se adoptó un enfoque cuantitativo, de tipo descriptivo y transversal, utilizando un instrumento diseñado específicamente para medir variables relacionadas con la conducción, seguimiento y evaluación del proceso de aprendizaje. Los resultados revelan un mejor aprovechamiento en el grupo experimental (híbrido), con una aprobación del 100% y calificación promedio superior al grupo de control (presencial). Las conclusiones muestran la mayor efectividad del modelo híbrido en el contexto evaluado, resaltando su contribución a la adaptación tecnológica en la educación y su potencial para optimizar el aprendizaje. Este estudio aporta a la originalidad al comparar directamente estos modelos en un contexto universitario real, proporcionando evidencia valiosa para la toma de decisiones pedagógicas.

Resumo Os modelos pedagógicos são a chave para responder às dificuldades que os alunos enfrentam durante o seu processo de ensino-aprendizagem; da mesma forma, o seu estudo contribui para a melhoria das práticas pedagógicas; Esta pesquisa comparativa aborda o problema de identificar o modelo educacional mais eficaz entre o híbrido e o presencial no ensino superior, focado em uma experiência educacional. Seu objetivo é avaliar e comparar ambos os cenários pedagógicos para melhorar as práticas educativas. Adotou-se uma abordagem quantitativa, descritiva e transversal, utilizando um instrumento especificamente concebido para medir variáveis relacionadas à condução, monitoramento e avaliação do processo de aprendizagem. Os resultados revelam melhor aproveitamento no grupo experimental (híbrido), com 100% de aprovação e nota média superior ao grupo controle (presencial). As conclusões mostram a maior eficácia do modelo híbrido no contexto avaliado, destacando a sua contribuição para a adaptação tecnológica na educação e o seu potencial para otimizar a aprendizagem. Este estudo contribui para a originalidade ao comparar diretamente estes modelos num contexto universitário real, fornecendo evidências valiosas para a tomada de decisões pedagógicas.

Abstract The analysis of the production of learners of a foreign language is crucial for defining the parameters by which it is acquired. Therefore, the development and use of learner corpora are indispensable. The Covid-19 pandemic brought about changes in teaching methodology. During this period, due to the impossibility of conducting in-person classes and the necessity for students to submit their work electronically for correction, it became possible to compile a corpus using the essays of Chinese learners studying Spanish at Nankai University (China). This article aims to detail the steps for creating a textual corpus, its compilation, and the tools to be used for its analysis, specifically the program AntConc. After the creation and analysis of the corpus, it is concluded that, by using the corpus, one can identify the deficiencies and difficulties faced by students, which allows for the formation of linguistic learning models and the improvement of existing ones. Moreover, its incorporation in the classroom enables the individualization of students’ linguistic learning and the generation of theories about language use.

Resumen El análisis de la producción de los aprendientes de una lengua extranjera resulta vital para definir los parámetros por los que ésta se adquiere. Es por ello que resulta indispensable la elaboración y utilización de corpus de aprendientes. La pandemia de covid-19 provocó cambios en la metodología docente. Durante la misma, debido a la imposibilidad de impartir las clases de manera presencial y al estar el alumno obligado a enviar sus trabajos en formato electrónico para su corrección, fue posible la compilación de un corpus empleando los trabajos de aprendientes chinos que estudian español en la Universidad de Nankai (China). El objetivo de este artículo es detallar los pasos para la creación de un corpus textual, su compilación y las herramientas a emplear para su análisis, específicamente el programa AntConc. Después de la creación y análisis del corpus, se concluye que utilizando el corpus se pueden identificar las carencias y dificultades a las que se enfrentan los estudiantes, lo que permite conformar modelos de aprendizaje lingüístico y mejorar los ya existentes. Además, su incorporación en el aula permite individualizar el aprendizaje lingüístico del alumnado y generar teorías sobre el uso que se hace del lenguaje.

Abstract Introduction: The use of point-of-care ultrasound (PoCUS) has gained relevance in medical practice, improving diagnostic capacity and patient safety. However, in Uruguay, PoCUS training for internal medicine physicians is limited. The objective is to achieve the implementation of PoCUS training in Internal Medicine postgraduate courses. Methodology: A course-workshop was created divided into modules by clinical systems and protocols. Bibliography and audiovisual material were provided to participants. Wireless portable ultrasound machines and voluntary models were used. Diagnostic knowledge tests, structured objective clinical evaluations, and satisfaction surveys were conducted. Results: 30 of the 32 participants completed and passed the course. The satisfaction survey showed a high evaluation of the course objectives, logistics, and instructors. There was a statistically significant improvement in PoCUS knowledge between the pre and post test (54.72% vs. 82.5%). The final assessment had an average passing rate of 92%, with only one exception requiring retake. Discussion: PoCUS training in Internal Medicine is perceived as valuable, but its development is limited in Uruguay. The implementation of a structured course demonstrated improvements in knowledge and high levels of satisfaction. It was compared with other clinical skills courses with similar results. The learning curve for different organs varies, highlighting the need for continued monitoring. Conclusions: High levels of satisfaction were obtained from participants with positive knowledge and skills results. We believe that it is important to continue investigating the learning curve after the course.

Resumen: Introducción: El uso de la ultrasonografía en el punto de atención (PoCUS) ha ganado relevancia en la práctica médica, mejorando la capacidad diagnóstica y la seguridad de los pacientes. Sin embargo, en Uruguay, la formación en PoCUS para médicos internistas es limitada. El objetivo es lograr la implementación de la formación de PoCUS en los posgrados de Medicina Interna. Metodología: Se creó un curso-taller dividido en módulos por sistemas y protocolos clínicos. Se proporcionó bibliografía y material audiovisual a los participantes. Se utilizaron ecógrafos portátiles inalámbricos y modelos voluntarios. Se realizaron pruebas de conocimiento diagnósticas, evaluaciones clínicas objetivas estructuradas y encuestas de satisfacción. Resultados: 30 de los 32 participantes completaron y aprobaron el curso. La encuesta de satisfacción mostró una alta evaluación de los objetivos del curso, la logística y los instructores. Hubo una mejora estadísticamente significativa en el conocimiento de PoCUS entre el pre y post test (54.72% vs. 82.5%). La evaluación final tuvo una media de aprobación del 92%, con una sola excepción que requirió recuperación. Discusión: La formación en PoCUS en Medicina Interna es percibida como valiosa, pero su desarrollo es limitado en Uruguay. La implementación de un curso estructurado demostró mejoras en conocimientos y altos niveles de satisfacción. Se comparó con otros cursos de habilidades clínicas con resultados similares. La curva de aprendizaje para diferentes órganos varía, lo que destaca la necesidad de un seguimiento continuo. Conclusiones: Se obtuvieron altos niveles de satisfacción de los participantes con resultados de conocimientos y destrezas positivos. Creemos que es de importancia seguir investigando la curva de aprendizaje posterior al curso.

Resumo Introdução: O uso da ultrassonografia no local de atendimento (PoCUS) tem ganhado relevância na prática médica, melhorando a capacidade diagnóstica e a segurança do paciente. Contudo, no Uruguai, o treinamento PoCUS para internistas é limitado. O objetivo é concretizar a implementação da formação PoCUS para os cursos de pós-graduação em Medicina Interna. Metodologia: Foi criado um curso-oficina dividido em módulos por sistemas e protocolos clínicos. Bibliografia e material audiovisual foram fornecidos aos participantes. Foram utilizados aparelhos de ultrassom portáteis sem fio e modelos voluntários. Foram realizados testes diagnósticos de conhecimento, avaliações clínicas objetivas estruturadas e pesquisas de satisfação. Resultados: 30 dos 32 participantes concluíram e foram aprovados no curso. A pesquisa de satisfação mostrou alta avaliação dos objetivos do curso, logística e instrutores. Houve uma melhoria estatisticamente significativa no conhecimento do PoCUS entre o pré e o pós-teste (54,72% vs. 82,5%). A avaliação final teve média de aprovação de 92%, com uma única exceção que exigiu recuperação. Discussão: A formação PoCUS em Medicina Interna é considerada valiosa, mas o seu desenvolvimento é limitado no Uruguai. A implementação de um curso estruturado demonstrou melhorias no conhecimento e elevados níveis de satisfação. Foi comparado com outros cursos de habilidades clínicas com resultados semelhantes. A curva de aprendizado para diferentes órgãos varia, destacando a necessidade de monitoramento contínuo. Conclusões: Altos níveis de satisfação dos participantes foram obtidos com resultados positivos em conhecimentos e habilidades. Acreditamos que é importante continuar investigando a curva de aprendizagem pós-curso.

SUMMARY: Human Macroscopic Anatomy (MHA) is the foundation of clinical knowledge for health professions. Its learning involves understanding the spatial location of body structures, the anatomical reference position, international anatomical terminology and the literacy involved in its study. The objective is to promote an understanding of the AMH through an anatomical and interactive model, which in this case is exemplified by the heart and its adjacent structures. For the proposal for the development of the anatomical model, seven phases were adopted: identification of a deficiency, search for a possible solution, definition of an objective for the construction of an anatomical model, in-depth theoretical review, proposal for creation with graphic design software, search and selection of materials and preparation of a guide for the construction of the manipulable anatomical model in 360°. Software was exported to an easy-to-print two-dimensional polygonal format, then the materials were chosen; Finally, the 360° anatomical model was assembled and the respective guides were prepared. The construction of anatomical models that are three-dimensional, favors the Teaching- Learning (E-L) processes by involving the student in building their knowledge, thereby allowing them to observe, identify, differentiate and spatially locate the anatomical structures through artistic pieces.

RESUMEN: La Anatomía Macroscópica Humana (AMH) sienta las bases del saber clínico de las profesiones de la salud. Su aprendizaje implica la comprensión de la ubicación espacial de las estructuras corporales, la posición anatómica de referencia, la terminología anatómica internacional y la literacidad implicada en su estudio. El objetivo es favorecer la comprensión de la AMH a través de un modelo anatómico e interactivo, que para este caso se ejemplifica con el corazón y sus estructuras adyacentes. Para la propuesta de desarrollo del modelo anatómico se adoptaron siete fases: identificación de una falencia, búsqueda de una posible solución, definición de un objetivo para la construcción de un modelo anatómico, revisión teórica profunda, propuesta de creación con un software de diseño gráfico, búsqueda y selección de materiales y elaboración de guía para la construcción del modelo anatómico manipulable en 360°. El software se exportó a un formato poligonal bidimensional fácil de imprimir, después se eligió los materiales; finalmente, se ensambló el modelo anatómico manipulable en 360° y se elaboraron las guías respectivas. La construcción de modelos anatómicos favoreció los procesos de Enseñanza - Aprendizaje (E-A) al involucrar al estudiante en la construcción de su conocimiento que le permitió observar, identificar, diferenciar y ubicar espacialmente las estructuras anatómicas mediante piezas artísticas.

SUMMARY: There exists a scarcity of neurocognitive research that has successfully used electrophysiology as an objective tool to assess different learning modalitiesthat are commonly used in anatomy. The objective of this study was to explore the trend in the expression of brain waves in medical students when using different didactic strategies in learning anatomy. An exploratory, observational, cross-sectional study was conducted on 22 first- semester medical students in anatomy. Using didactic strategies with different pedagogical tools such as biological models (human skulls and pig hearts) and/or digital application (Visible Body software), and different evaluation methods, students were grouped into three groups: "EOR1" students who learned using a skull and were evaluated with it; "EOV1" students who learned using a pig heart and were evaluated with the Visible Body software; and "EOV2" for students who learned through the Visible Body software and were evaluated with it. During the evaluation, each student was monitored in real-time using an electroencephalogram and the Emotiv EPOC brain-computer interface system to record the electro-physiological activity of beta (B), theta (T), and gamma (G) waves. The data obtained (the trend of the electrical activity of each wave and its relationship with students' performance in each didactic strategy) were analyzed using Pearson's correlation coefficient. Groups EOR1 and EOV1 had satisfactory results in their evaluations and showed a predominance of B waves (x=67.33) and T waves (x=57.67). Additionally, a strongly positive correlation (r=0.82) was found between the presence of B waves and the duration of T waves. Students who used three-dimensional biological models as a pedagogical tool showed a predominance of B and T waves, which are associated with sensorimotor and exploratory learning necessary for studying anatomy.

RESUMEN: Son escasos los estudios de neurociencia cognitiva que permitan comprender, de manera objetiva, las manifestaciones electrofisiológicas de los estudiantes de medicina expuestos a diferentes herramientas pedagógicas utilizadas en el área de anatomía. El objetivo de este trabajo fue explorar la tendencia en la expresión de las ondas cerebrales de estudiantes de medicina, al utilizar diferentes estrategias didácticas en el aprendizaje de la anatomía. Se realizó un estudio exploratorio, observacional, de corte transversal en 22 estudiantes de medicina de primer semestre en anatomía. Utilizando estrategias didácticas con diferentes herramientas pedagógicas; como modelos biológicos (cráneos humanos y corazones porcinos) y/o aplicación digital (software Visible Body (SVB)), y diferente método de evaluación, se agruparon los estudiantes en tres grupos: “EOR1” estudiantes que aprendieron usando un cráneo y se evaluaron con este, “EOV1” estudiantes que aprendieron usando un corazón porcino y fueron evaluados con el SVB, y “EOV2” para los estudiantes que aprendieron a través del SVB y fueron evaluados con este. Durante la evaluación, cada estudiante fue monitorizado en tiempo real, utilizando un electroencefalograma y el sistema interfaz cerebro - computadora Emotiv EPOC para registrar la actividad electrofisiológica de las ondas beta (B), theta (T) y gamma (G). Los datos obtenidos (tendencia de la actividad eléctrica de cada onda y su relación con el desempeño de los estudiantes en cada estrategia didáctica) fueron analizados usando el índice de correlación de Pearson. Los grupos EOR1 y EOV1, tuvieron resultados satisfactorios en sus evaluaciones y presentaron predominio de ondas B (X=67,33) y T (X=57,67), además, se evidenció una correlación fuertemente positiva (r=0,82) entre la presencia de B y el tiempo de permanencia de T. Los estudiantes que utilizaron modelos biológicos tridimensionales como herramienta pedagógica presentaron predominio de las ondas B y T, asociadas con aprendizajes sensoriomotores y de tipo exploratorio necesarios para el estudio en anatomía.

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.

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.

Malawi's economy heavily depends on agriculture, including both commercial and subsistence farming. Smallholder and small-medium enterprises leading the production of tomatoes in Malawi cannot satisfy local demand due to problems such as pests, diseases, unstable markets, and high costs. Many farmers lack the expertise to effectively manage these threats. To address the problem of tomato leaf disease identification, this research aimed to develop an automated system for tomato leaf disease detection by utilizing data augmentation techniques, MobileNetV3, and Convolutional Neural Network algorithms. We trained models on secondary data collected from the public PlantVillage dataset and tested the resultant classifiers on primary data of local farm images. The experimental results demonstrate that both models tested better on the PlantVillage dataset. Additionally, with an accuracy of 92.59% and a loss of 0.2805, the pre-trained MobileNetV3 model conventionally performs better than a CNN model. However, when tested on the primary field dataset, the models did not meet expectations for generalization, with the pre-trained MobileNetV3 achieving an accuracy of 9.2%, and loss of 12.91 and the CNN achieving an accuracy of 10.14% and loss of 8.11. The experiments aided in showing that the models trained on the PlantVillage dataset are not as effective when applied in real-world scenarios. Further improvements are needed to enhance the models' generalization in real-world scenarios.