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.

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

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

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.

Abstract: The primary objectives of this research study are to provide a detailed description of machine learning (ML) technology when applied to assessing innovation and to design a model that allows predicting an institution’s degree of innovation. Machine learning technology lacks assumptions or preconceptions and is capable of processing a large amount of data and variables. After data processing, the ML model is built using variables associated with educational context, training is performed, and a web is built to predict the degree of innovation of an educational institution. This model provides an innovation accuracy prediction of 66% and allows assessing the influence of the variables analyzed when predicting the use of active methodologies at a given institution. In conclusion, this approach can open new data analysis techniques supported by ML that complement traditional statistically based approaches.

Resumen: Los principales objetivos de esta investigación son proporcionar una descripción detallada de la tecnología de aprendizaje automático (ML, por sus siglas en inglés) aplicada a la medición de la innovación y diseñar un modelo que permita predecir el grado de innovación en una determinada institución. La tecnología ML carece de suposiciones o preconcepciones y es capaz de procesar una gran cantidad de datos y variables. Tras procesar los datos, se construye el modelo ML empleando variables asociadas al contexto educativo, se realiza el entrenamiento y se construye una web para predecir el grado de innovación de una institución educativa. El modelo proporciona una precisión del 66% en la predicción de la innovación y permite discutir la influencia de las variables analizadas a la hora de predecir el uso de metodologías activas en una institución. En conclusión, este enfoque puede abrir nuevas técnicas de análisis de datos apoyadas en ML que complementen los enfoques tradicionales basados en la estadística.

HIV/AIDS remains one of the world's most significant public health and economic challenges, with approximately 36 million people currently living with the disease. Considerable progress has been made to reduce the impact of HIV/AIDS in the past years through successful multiple HIV/AIDS prevention and treatment interventions. However, barriers such as lack of engagement, limited availability of early HIV-infection detection tools, high rates of HIV/sexually transmitted infections (STIs), barriers to access antiretroviral therapy, lack of innovative resource optimisation and distribution strategies, and poor prevention services for vulnerable populations still exist and substantially affect the attainment of the UNAIDS 95-95-95 targets. A rapid review was conducted from 24 October 2022 to 5 November 2022. Literature searches were conducted in different prominent and reputable electronic database repositories including PubMed, Google Scholar, Science Direct, Scopus, Web of Science, IEEE Xplore, and Springer. The study used various search keywords to search for relevant publications. From a list of collected publications, researchers used inclusion and exclusion criteria to screen and select relevant papers for inclusion in this review. This study unpacks emerging opportunities that can be explored by applying machine learning techniques to further knowledge and understanding about HIV service design, prediction, implementation, and evaluation. Therefore, there is a need to explore innovative and more effective analytic strategies including machine learning approaches to understand and improve HIV service design, planning, implementation, and evaluation to strengthen HIV/AIDS prevention, treatment, and awareness strategies.

It is human nature to see the world as imperfect, because people strive towards improving existing and designing new systems. The education system is one such system which is regarded as being a challenge in South Africa, since it affects the quality of education received by every young South African. The serious nature of dysfunctionality could be attributed to the lack of competent leadership in schools. Learners' education and examination results are being impaired by inadequate teaching, dysfunctional school management, inefficient procedures and poor resources, which foster low educational standards. Leadership practices make a significant contribution to the desired outcomes of a school. Department heads are responsible for educational leadership; however, as they rarely receive leadership training, it is impossible for them to acquire the knowledge and skills to prepare them for their task as educational leaders. There is a dire need for educational leaders who could run a school professionally. The principal and staff should be professional leaders and managers capable of establishing a culture of learning and effectiveness. The purpose of this study was to establish if department heads have the required knowledge and skills to prepare them for their leadership and management role in a secondary school, how they see their competence in this regard, to identify the gaps regarding their preparation as effective educational leaders, and to submit proposals for improving the preparation of department heads for their leadership role. The research methodology was based on quantitative research strategies whereby the research question could accordingly be answered by means of a once-off cross-sectional survey obtained from a random cluster sampling of secondary schools in a district with approximately 70 schools in the Free State province. Self-designed questionnaires, which had to be completed by all the department heads at the schools, were used. A total of 186 department heads participated in the investigation. Ethical approval to conduct the research was obtained from the North-West University. Descriptive statistics and exploratory factor analysis were used to identify latent variables. Cronbach's alpha coefficient was used to determine the reliability of the variables involved. Hierarchical linear models were used to determine relationships among variables. The results showed that the heads of department view training and development as important, but they also indicated that they did not receive management training before their appointment in their position. Finally, suggestions are provided for improving the preparation of department heads.

Alle bestaande stelsels, waaronder onderwysstelsels, verander voortdurend. Die onderwys-stelsel is een van die stelsels in Suid-Afrika wat as 'n uitdaging gesien word, omdat dit die gehalte van onderrig beïnvloed. Die ernstige aard van disfunksionaliteit kan toegeskryf word aan die gebrek aan bevoegde leierskap in skole. Leerders se opvoeding en eksamenuitslae word benadeel deur swak onderrig, disfunksionele skoolbestuur, ondoeltreffende prosedures en swak hulpbronne, wat lae onderwysstandaarde tot gevolg het. Leierskapspraktyke lewer 'n groot bydrae tot die gewenste uitkomste van 'n skool. Departementshoofde is verantwoordelik vir onderrigleierskap, maar aangesien hulle selde leierskapsopleiding ontvang, is dit vir hulle onmoontlik om kennis en vaardighede op te doen om hulle voor te berei vir hulle taak as onderrigleiers. Daar is 'n ernstige behoefte aan onderrigleiers wat 'n skool professioneel kan bestuur. Die skoolhoof en personeel moet professionele leiers en bestuurders wees, sodat 'n kultuur van leer en effektiwiteit geskep kan word. Die doel van hierdie ondersoek was om te bepaal of departementshoofde oor die nodige kennis en vaardighede beskik om die leierskap- en bestuursrol in 'n sekondêre skool te vervul, hoe departementshoofde hulle bevoegdheid in hierdie verband sien, wat die leemtes ten opsigte van die voorbereiding van departementshoofde vir hulle taak/rol as effektiewe onderrigleiers is, en om voorstelle ter verbetering van die voorbereiding van departementshoofde vir hul leierskapsrol te maak. Die navorsingsmetodologie is gebaseer op kwantitatiewe navorsingstrategieë waarvolgens die navorsingsvraag beantwoord kon word deur middel van 'n eenmalige dwarsdeursnitopname wat verkry is met behulp van ewekansige trossteekproefneming by sekondêre skole in 'n distrik in die Vrystaat. Daar is gebruik gemaak van selfontwerpte vraelyste wat deur al die departementshoofde van die betrokke skole voltooi moes word. 'n Totaal van 186 departements-hoofde het aan die ondersoek deelgeneem. Etiese goedkeuring om die studie te onderneem is van die Noordwes-Universiteit verkry. Beskrywende statistiek (frekwensies, persentasies, gemiddeldes en standaardafwykings) en eksploratiewe faktorontleding is gebruik om onderliggende latente veranderlikes te identifiseer. Cronbach se alfa-koëffisiënt is gebruik om die betroubaarheid van betrokke veranderlikes te bepaal. Hiërargiese lineêre modelle is gebruik om verbande met biografiese en demografiese veranderlikes te bepaal. In hierdie studie is bevind dat daar 'n duidelike verband tussen onderrig en leierskap is en dat die meeste van die departementshoofde kwaliteitonderrig belangrik ag en as 'n prioriteit in hul departemente sien. Die departementshoofde in hierdie studie is ook van mening dat die leiding van skoolbestuurspanne die kwaliteit van onderrig in die departement beïnvloed. Departementshoofde vir wie onderrig, leierskap en leerderprestasie belangrik is, heg ook hoë waarde aan opleiding en voorbereiding. In hierdie studie is ook bevind dat departementshoofde oor die algemeen nie bestuursopleiding ontvang het voor hul aanstelling in dié posisie nie. Sommige van die departementshoofde kon staatmaak op die kennis wat hulle opgedoen het tydens hul kollege- of universiteitsopleiding, wat hulle voorberei het vir hulle taak. 'n Klein aantal van die departementshoofde was oortuig dat hul voordiensopleiding nie van waarde was nie en dat hulle nie voorbereid was vir hul rol as beginnerdepartementshoofde nie.

Abstract This article explores the design and application of machine learning techniques to enhance traditional approaches for solving NP-hard optimization problems. Specifically, it focuses on the Last-Mile Routing Research Challenge (LMRRC), supported by Amazon and MIT, which sought innovative solutions for cargo routing optimization. While the challenge provided travel times and zone identifiers, the dependency on these factors raises concerns about the algorithms’ generalizability to different contexts and regions with standard delivery services registries. To address these concerns, this study proposes personalized cost matrices that incorporate both distance and time models, along with the relationships between delivery stops. Additionally, it presents an improved approach to sequencing stops by combining exact and approximate algorithms, utilizing a customized regression technique alongside fine-tuned metaheuristics and heuristics refinements. The resulting methodology achieves competitive scores on the LMRRC validation dataset, which comprises routes from the USA. By carefully delineating route characteristics, the study enables the selection of specific technique combinations for each route, considering its geometrical and geographical attributes. Furthermore, the proposed methodologies are successfully applied to real-case scenarios of last-mile deliveries in Montevideo (Uruguay), demonstrating similar average scores and accuracy on new testing routes. This research contributes to the advancement of last-mile delivery optimization by leveraging personalized cost matrices and algorithmic refinements. The findings highlight the potential for improving existing approaches and their adaptability to diverse geographic contexts, paving the way for more efficient and effective delivery services in the future.

Resumen Este artículo explora el diseño y aplicación de técnicas de aprendizaje automático para mejorar los enfoques tradicionales y así resolver problemas de optimización NP-hard. En particular, se enfoca en el Last-Mile Routing Research Challenge (LMRRC), apoyado por Amazon y MIT, que buscaba soluciones innovadoras para la optimización de rutas de carga. Si bien el desafío proporcionó tiempos de viaje e identificadores de zona, la dependencia de estos factores plantea preocupaciones sobre la generalización de los algoritmos a diferentes contextos y regiones con registros de servicios de entrega estándar. Para abordar estas interrogantes, este estudio propone matrices de costos personalizadas que incorporan modelos de distancia y tiempo, junto con las relaciones entre las paradas de entrega. Además, presenta enfoques mejorados para la secuenciación de paradas mediante la combinación de algoritmos exactos y aproximados, utilizando técnicas de regresión personalizada junto con metaheurísticas y refinamientos heurísticos ajustados. La metodología resultante logra puntajes competitivos en el conjunto de datos de validación LMRRC, que usa rutas de EE. UU. Al delinear cuidadosamente las características de la ruta, el estudio permite la selección de combinaciones de técnicas específicas para cada ruta, considerando sus atributos geométricos y geográficos. Además, las metodologías propuestas se aplican con éxito a escenarios de casos reales de entregas en Montevideo (Uruguay), demostrando puntajes promedio y precisión similares en nuevas rutas de prueba. Esta investigación contribuye al avance de la optimización de la entrega de última milla al aprovechar matrices de costos personalizadas y refinamientos algorítmicos. Los hallazgos resaltan el potencial para mejorar los enfoques existentes y su adaptabilidad a diversos contextos geográficos, allanando el camino para servicios de entrega más eficientes y efectivos en el futuro.

RESUMEN El objetivo del presente trabajo es ofrecer una estrategia didáctica que contribuya a la solución de problemas matemáticos-profesionales con enfoque formativo agroecológico por los estudiantes de Agronomía. El diagnóstico del proceso de enseñanza-aprendizaje de la Matemática, la revisión de la bibliografía básica y programas de estudio revelaron una insuficiente sistematización didáctico-metodológico en la interrelación de los contenidos de la profesión, la asignatura Matemática y lo contextual agroecológico. La aplicación de la estrategia didáctica propició el interés por el estudio de la Matemática, así como la utilización de modelos y métodos matemáticos en la solución a los problemas objeto de estudio.

ABSTRACT The objective of this work is to offer a didactic strategy that contributes to the solution of mathematical-professional problems with an agroecological formative approach by Agronomy students. The diagnosis of the teaching-learning process of Mathematics, the review of the basic bibliography and study programs revealed an insufficient didactic-methodological systematization in the interrelation of the contents of the profession, the subject Mathematics and the agroecological contextual. The application of the didactic strategy favored the interest in the study of Mathematics, as well as the use of mathematical models and methods in the solution to the problems under study.

Abstract This research is undertaken, motivated by the need to unravel the progression of learning models, which have been adapting to meet the demands of society in its constant dynamics of fluctuation and transformation. The aim of this work is to systematically examine the evolution of learning models, highlighting the paradigmatic changes that have favored the transition from traditional learning approaches to more innovative and transdisciplinary proposals. To achieve this, a bibliographic analysis is carried out, supported by the hermeneutic method for the contextual interpretation of literature and discourses, in order to unravel the complexities inherent in the evolution of learning models. The results highlight the limiting influence of the simplicity paradigm in traditional educational formation and the need for a shift towards dialogicity and disciplinary collaboration and integration to advance towards complexity. It concludes by arguing in favor of adopting the complexity paradigm, advocating for a transdisciplinary epistemology and an interstructuring approach to learning, which allow for integral human development. Recognizing human complexity in teaching and learning demands a radical transformation of education towards more holistic and transformative practices, essential for building an equitable and just society.

Resumen Esta investigación se emprende motivada por la necesidad de desentrañar la progresión de los modelos de aprendizaje, los cuales se han ido adaptando para responder a las demandas de la sociedad en su dinámica constante de fluctuación y transformaciones. El objetivo de este trabajo es examinar de forma sistemática la evolución de los modelos de aprendizaje, destacando los cambios paradigmáticos que han favorecido la transición de enfoques de aprendizaje tradicionales hacia propuestas más innovadoras y transdisciplinarias. Para lograrlo, se lleva a cabo un análisis bibliográfico apoyado en el método hermenéutico para la interpretación contextual de la literatura y los discursos, a fin de desentrañar las complejidades inherentes en la evolución de los modelos de aprendizaje. Los resultados resaltan la influencia limitante del paradigma de la simplicidad en la formación educativa tradicional y la necesidad de un giro hacia la dialogicidad y la colaboración e integración disciplinaria para avanzar hacia la complejidad. Se concluye argumentando a favor de la adopción del paradigma de la complejidad, abogando por una epistemología transdisciplinaria y un enfoque de aprendizaje interestructurante, que permitan el desarrollo humano integral. El reconocimiento de la complejidad humana en la enseñanza y el aprendizaje exige una transformación radical de la educación hacia prácticas más holísticas y transformadoras, esenciales para construir una sociedad equitativa y justa.

Energy consumption from the residential sector forms a large portion of the electricity grid demand. The growing accessibility of residential load profile data presents an opportunity for improved residential load forecasting and the implementation of demand-side management (DSM) strategies. Machine learning is a tool well-suited for predicting stochastic processes, such as residential power usage due to human behavior. Long short-term memory (LSTM) recurrent neural networks are especially suited for predicting time-series data such as electrical load profiles. This paper investigates the impact of LSTM hyperparameters to the predictive performance of models, which include the tradeoffs associated with training data size, horizon ratios, model fidelity, prediction horizon and computational intensity. This paper provides a framework to evaluate the choice of LSTM hyperparameters for understanding trade-offs in a practical application of load profile predictions for the context of Grid-interactive Efficient Buildings (GEBs).

Higher Education needs to be reimagined considering the exciting developments within virtual reality and artificial intelligence. As a new generation, Generation Alpha, better known as Gen Alpha,1 is preparing for higher education, and educators are challenged to create opportunities to meet their needs. In this study, we explore an existing paradigm, scenario planning, but within an unfamiliar context - the future of higher education. We pose the question: What are the possible future scenarios within higher education that will appeal to the Gen Alpha student and a millennial parent in a post-pandemic world? As a result, we present a scenario matrix with four learning and teaching models forecast to be successful in 2030 in higher education institutions. These four learning and teaching models are based on two axes: Social connect vs social disconnect in learning and teaching and a single career option or multiple career options as the end goal for the student. Respondents of this longitudinal study are millennial parents of Gen Alpha students. Results indicate that the three main themes that will influence the learning and teaching models of the future are learning spaces and places, glass learning, and techceptance. In line with the three themes, the three major findings were that (1) millennial parents are open to hybrid learning approaches, (2) learning on screens is important, but it should be regulated and based on set standards, and lastly (3) millennial parents are accepting technology (techceptance) as a supportive tool and learning place in higher education. Overall, there was a positive shift in perspectives on higher education regardless of the impact of the pandemic. The outcomes of the research will assist higher educational institutions to strategically align the learning and teaching offerings with the needs and resources of the future student, namely Gen Alpha.

ABSTRACT For some years now, the authority has encountered resistance in the educational context; Teaching work is questioned and respect is no longer granted spontaneously. This situation has a negative impact on coexistence and school learning. This article aimed to analyze teaching authority, its conceptualization, characteristics and factors that favor or hinder its construction. The PRISMA method was used to search and select suitable works for the stated objective. The databases used were Scopus , Scielo and Google Scholar. After the selection, 24 works remained to be reviewed. The results of the research showed that both students and teachers recognize that the type of authority that predominates in the educational context is vertical and horizontal. Furthermore, teachers are recognized as authorities by their students, mainly due to two factors: their conceptual and methodological knowledge and the closeness, interest and respect they demonstrate in the classroom. This study concluded that, although authority is still necessary in the educational field, teachers are aware that the openness to greater student participation and the abandonment of vertical models favor the recognition of their authority.

RESUMO Há alguns anos, a autoridade encontra resistência no contexto educacional, o trabalho docente é questionado e o respeito não é mais concedido espontaneamente. Esta situação tem um impacto negativo na convivência e na aprendizagem escolar. Este estudo teve como objetivo realizar uma revisão de artigos acadêmicos que abordam o tema autoridade docente, sua conceituação, características e fatores que favorecem ou dificultam sua construção. O método PRISMA foi utilizado para pesquisar e selecionar trabalhos adequados ao objetivo declarado. As bases de dados utilizadas foram Scopus, Scielo e Google Acadêmico. Após a seleção, restaram 24 trabalhos para serem avaliados. Os resultados da pesquisa foram que tanto alunos quanto professores reconhecem que o tipo de autoridade que predomina no contexto educacional é vertical e horizontal. Além disso, os professores são reconhecidos como autoridades pelos seus alunos principalmente devido a dois fatores: o seu conhecimento conceitual e metodológico e a proximidade, interesse e respeito que demonstram em sala de aula. Este estudo concluiu que, embora a autoridade ainda seja necessária no campo educacional, os professores têm consciência de que a abertura para uma maior participação dos alunos e o abandono dos modelos verticais favorecem o reconhecimento da sua autoridade.

RESUMEN Desde hace algunos años, la autoridad ha encontrado resistencia en el contexto educativo; se cuestiona la labor docente y el respeto ya no es concedido espontáneamente. Esta situación repercute negativamente en la convivencia y el aprendizaje escolar. Este artículo tuvo como objetivo analizar la autoridad docente, su conceptualización, características y factores que favorecen o dificultan su construcción. Se utilizó el método PRISMA para la búsqueda y selección de trabajos idóneos para el objetivo planteado. Las bases de datos que se emplearon fueron Scopus, Scielo y Google Académico. Después de la selección quedaron para ser revisados 24 trabajos. Los resultados de la investigación arrojaron que, tanto estudiantes como docentes reconocen que el tipo de autoridad que predomina en el contexto educativo es la vertical y la horizontal. Además, los maestros son reconocidos como autoridad por sus estudiantes, principalmente por dos factores: su saber conceptual y metodológico y por la cercanía, interés y respeto que demuestran en aula. Este estudio concluyó que, si bien la autoridad no deja de ser necesaria en el ámbito educativo, los docentes son conscientes de que la apertura a una mayor participación del estudiantado y el abandono de modelos verticales favorecen el reconocimiento de su autoridad.

Resumen El presente artículo explora las causas de la inclusión financiera entre la población mexicana. Con datos de la Encuesta Nacional de Inclusión Financiera (ENIF) (INEGI, 2021) desarrolla dos modelos de aprendizaje automático con el objetivo de identificar a individuos que forman parte del sis tema financiero. Estos modelos son evaluados valiéndose tanto de metodologías de inteligencia artificial como de pruebas estadísticas de significancia tradicionales. Los hallazgos sugieren que factores como nivel educativo, ingreso mensual, preferencias orientadas hacia el futuro sobre las presentes, capacidad de ahorro y acceso a teléfonos inteligentes son impulsores significativos que aumentan la probabilidad de inclusión financiera. En consecuencia, existe un potencial para la implementación de políticas públicas dirigidas a incentivar a los individuos para que adopten voluntariamente servicios financieros formales.

Abstract The current article explores the causes of financial inclusion among the Mexican population. It leverages data from the Encuesta Nacional de Inclusión Financiera (ENIF) (INEGI, 2021) to develop two machine learning models aimed at identifying individuals who are part of the financial system. These models are assessed using both artificial intelligence methodologies and traditional statistical significance tests. The findings suggest that factors such as education level, monthly income, future-oriented behavioral preferences over present ones, saving capacity, and access to smartphones are significant drivers that enhance the likelihood of financial inclusion. Consequently, there is a potential for implementing public policies to incentivize individuals to voluntarily adopt formal financial services.

Resumen Este trabajo examina las tendencias en los precios de medicamentos antirretrovirales y su impac to en la salud pública, centrándose en el contexto mexicano en 2019. Por medio de técnicas de aprendizaje automático, el estudio analiza las fluctuaciones en los precios de los medicamentos antirretrovirales, con base en un conjunto de datos que comprende 15,220 observaciones de medicamentos antirretrovirales (ARV) adquiridos entre 2016 y 2019, con énfasis particular en el año 2019. Los resultados indican que los “bosques aleatorios” demostraron la mayor precisión en la predicción de cambios de precios, seguidos por K-Nearest Neighbors o KNN (algoritmo de k vecinos más próximos) y la regresión logística. Se identificaron factores significativos que afectan los precios de adquisición, como el tipo de medicamento y la duración de la estrategia de adqui sición. Además, el estudio profundiza en las consideraciones presupuestarias, evaluando las im plicaciones financieras de estas fluctuaciones de precios. Estos hallazgos destacan la efectividad de iniciativas como los enfoques de adquisición consolidada y la integración de medicamentos más nuevos y rentables en los protocolos de tratamiento, lo que conduce a ahorros significativos y mejor acceso para las personas que viven con VIH/sida. Es importante destacar que este artículo se centra en analizar un esquema específico de adquisición de medicamentos antirretrovirales. Los estudios futuros ampliarán este análisis para abarcar un espectro más amplio de esquemas de adquisición de medicamentos, proporcionando así una comprensión más completa de la di námica de fijación de precios y sus implicaciones para la salud pública. Además, la investigación en curso perfeccionará la metodología e integrará fuentes de datos adicionales para mejorar la precisión y profundidad del análisis.

Abstract This paper investigates trends in antiretroviral medication prices and their impact on public health in Mexico during 2019. Using three machine learning models developed in Python (logistic regression, random forest, and K-Nearest Neighbors or KNN), this study discerns increasing or decreasing patterns in antiretroviral (ARV) drug price fluctuations using a dataset comprising 15,220 observations of ARV drugs acquired between 2016 and 2019. Results indicate that random forests exhibited the highest precision in predicting price changes, followed by KNN and logistic regression. Significant factors affecting acquisition prices, such as drug type and duration of procurement strategy, were identified. In addition to analyzing price trends, the paper explores the budgetary considerations associated with these fluctuations, providing insights into the financial implications for healthcare systems and stakeholders. It is important to note that this paper focuses on a specific ARV pharmaceutical purchasing scheme. Moreover, the study emphasizes the creation of a unified and detailed medication price database, highlighting the significant effort invested in compiling complete and comprehensive information from various sources. This study’s findings underscore the effectiveness of initiatives such as consolidated purchasing approaches and the integration of newer, cost-effective medications into treatment protocols. These initiatives have led to significant cost savings in antiretroviral medication procurement, contributing to improved access for individuals living with HIV/AIDS. Overall, the research highlights the importance of data-driven approaches and strategic planning in optimizing pharmaceutical purchasing processes and ensuring sustainable access to essential medications for public health interventions.