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 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,

Abstract: In many supervised learning problems, feature selection techniques are increasingly essential across various applications. Feature selection significantly influences the classification accuracy rate and the quality of SVM model by reducing the number of features, remove irrelevant and redundant features. In this paper, we evaluate the performance of twenty feature selection algorithms over four databases. The performance is conducted in term of: classification accuracy rate, Kuncheva’s Stability, Information Stability, SS Stability and SH Stability. To measure the feature selection algorithms, multiple datasets from the UCI Machine Learning Repository are utilized to assess both classification accuracy and stability variations.

Abstract Tree diseases contribute to significant economic and food losses in the agricultural sector. Early detection of phytosanitary problems in trees with non-destructive methods is essential to guarantee sustainable orange production. This study presents the findings of a designed methodology conducted to identify diseased orange trees in an orchard situated in the citrus belt of Mexico, specifically in the Rioverde region of San Luis Potosi. To accomplish this, we captured images using a multispectral camera with very high spatial resolution, which was mounted on an unmanned aerial vehicle. These images were used to construct a georeferenced orthomosaic of the orchard. Six thematic classes were established to distinguish various health levels among the trees. We employed several supervised classification algorithms at the pixel level, including Random Forest (RF), K-Nearest Neighbor (KNN), Spectral Angle Mapper (SAM), Support Vector Machine (SVM), and Maximum Likelihood (ML). Considering the classification accuracy achieved by each algorithm, they can be ranked as follows: Maximum Likelihood (ML) with 88.10%, Support Vector Machine (SVM) with 77.38%, Spectral Angle Mapper (SAM) with 76.19%, K-Nearest Neighbor (KNN) with 64.68%, and Random Forest (RF) with 61.90%. These results successfully identified the phytosanitary status of all the trees in the orchard with an acceptable level of accuracy, providing valuable management information for the grower.

Resumen Las enfermedades de los árboles contribuyen a importantes pérdidas económicas y de alimentos en el sector agrícola. La detección temprana de problemas fitosanitarios en árboles con métodos no destructivos resulta fundamental para garantizar la producción sostenible de naranja. Este trabajo presenta los resultados de una metodología diseñada para la identificación de árboles de naranja enfermos en una huerta ubicada en el cinturón citrícola de México, particularmente en la región de Rioverde, San Luis Potosí. Para ello, se tomaron imágenes con una cámara multiespectral de muy alta resolución espacial instalada en un vehículo aéreo no tripulado con las que se construyó un ortomosaico georreferenciado. Se emplearon seis clases temáticas para identificar los diferentes niveles de sanidad. Se utilizaron diferentes algoritmos de clasificación supervisada a nivel píxel que incluyen Random Forest (RF), K-Nearest Neighbor (KNN), Spectral Angle Mapper (SAM), Support Vector Machine (SVM), y Maximum Likelihood (ML). Considerando la exactitud de clasificación obtenida por cada uno de los algoritmos, se pueden ordenar de la siguiente manera: Maximum Likelihood (ML) con un 88,10%, Support Vector Machine (SVM) con un 77,38%, Spectral Angle Mapper (SAM) con un 76,19%, K-Nearest Neighbor (KNN) con un 64,68% y Random Forest (RF) con un 61,90%. Los resultados permitieron identificar el estado fitosanitario de todos los árboles de la huerta, con una exactitud aceptable y representan información valiosa de manejo para el productor.

Resumen En la exploración geofísica, el análisis del terreno es fundamental para planificar la adquisición de datos de gravimetría y magnetometría. No obstante, en la actualidad dichos análisis requieren el uso supervisado de información secundaria extensa. Por este motivo, en este estudio se propone una metodología que utiliza el aprendizaje profundo para evaluar el impacto de la cobertura vegetal y la topografía en la adquisición geofísica. Se usó una red neuronal artificial perceptrón multicapa para considerar cinco variables terrestres, y una red neural convolucional para la clasificación automatizada de coberturas vegetales sobre imágenes satelitales. A partir de esto, se obtuvieron puntos para la localización de estaciones de levantamiento geofísico que cumplen con un criterio de favorabilidad por accesibilidad, distancias, restricciones por cuerpos de agua y coberturas boscosas. La geometría obtenida se evaluó en la exploración del Granito de Durania en Colombia, y los resultados se analizaron utilizando transectas de adquisición y modelado computacional de anomalías magnéticas y de gravedad. Finalmente, se aplicaron técnicas de interpolación donde el método Ponderación de Distancia Inversa (IDW) mostró la mejor imagen para interpretar la delimitación del cuerpo intrusivo.

Abstract In geophysical exploration, terrain analysis is fundamental for planning the gravity and magnetic surveys. However, current analyses necessitate supervised use of extensive secondary information. Consequently, this study proposes a methodology that leverages deep learning to assess the impact of vegetation coverage and topography in geophysical acquisition. An artificial neural network multilayer perceptron was used to consider five terrain-related variables, while a convolutional neural network is used for automated classification of vegetation cover on satellite imagery. From this, geophysical acquisition points are obtained, adhering to criteria involving accessibility favourability, distances, restrictions imposed by bodies of water, and forested cover. The obtained geometry was tested in the exploration of the Granito de Durania in Colombia, and the results were analysed using acquisition transects and computational modelling of gravity and magnetic anomalies. Interpolation techniques were applied, with the Inverse Distance Weighting (IDW) method yielding the most informative map for interpreting the delineation of the intrusive body.

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

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

ABSTRACT The environmental crisis due to climate change has forced many States to direct efforts towards environmental transition to reduce the probability of occurrence of a situation with a negative impact on their population or environment. Peru is no exception. In this sense, the need arises to identify and categorize its districts according to a certain socio-environmental risk. Faced with this challenge, a multistage quantitative methodology was developed and implemented, which made use of both machine learning (supervised and unsupervised) and spatial econometrics. The results of this methodology, visualized through emerging risk indix, evidenced the existence of 165 districts considered socio-environmental risk zones (SERZ, in Spanish known as ZRS), mostly located in the coastal strip. Finally, it is concluded that the pattern and replicability of urban development model in Peru is currently not coherent with efforts towards environmental conservation and preservation.

RESUMEN La crisis ambiental por el cambio climático ha obligado a muchos Estados a dirigir esfuerzos hacia la transición medioambiental para reducir la probabilidad de ocurrencia de una situación con un impacto negativo sobre su población o medioambiente. El Perú no es la excepción. En tal sentido, surge la necesidad de identificar y categorizar sus distritos según un determinado riesgo socioambiental. Ante tal reto, se construyó e implementó una metodología cuantitativa multietápica, la cual hizo uso tanto del aprendizaje automatizado (supervisado y no supervisado) como de la econometría espacial. Los resultados de la metodología, visualizados a través de índices de riesgo emergentes, evidenciaron la existencia de 165 distritos considerados zonas con riesgo socioambiental (ZRS), ubicados en su mayoría en la franja costera. Finalmente, se concluye que el patrón y replicabilidad del modelo de desarrollo urbanístico en el Perú actualmente no es coherente con los esfuerzos de conservación y preservación del medioambiente.

Abstract The problem that guided this research was: what is found in the articles published in the journal Bolema in the 2013-2017 five-year period when looking for the term learning? To answer this question, we carried out a survey in which the selection of articles had as a search trigger the expressions aprend and learn, representative of the word learning. In possession of these documents, we assume the interpretative procedures indicated by the Content Analysis. As for the results, the first movements with the corpus showed four characteristics of Mathematics learning: who learns; what you learn; in what context do you learn and how do you learn, emphasizing the following subjects of learning: Elementary School students, Mathematics undergraduates and teachers who teach Mathematics. Such subjects learn about mathematical concepts, mathematical content, and teaching practice. This fact can be noticed in contexts such as Mathematics Education approaches/trends, in practical communities, in supervised internships, and with educational products through Problem Solving, Mathematics Investigation, and Information and Communication Technologies (ICT). The second movement that emerged from the first analysis revealed the association of learning with teaching, and who stands out to teach these subjects is the researcher and the research teacher. was 20132017 2013 2017 2013-201 fiveyear five year question documents Analysis results learns students concepts content practice approachestrends approaches trends approaches/trends communities internships Solving Investigation ICT. ICT . (ICT) teacher 2013201 201 2013-20 (ICT 201320 20 2013-2 20132 2 2013-

Resumo A problemática que orientou esta pesquisa foi: o que se encontra nos artigos publicados no periódico Bolema no quinquênio de 2013-2017 quando se procura pelo termo aprendizagem? Para responder a tal questão realizamos um levantamento em que a seleção dos artigos teve como disparador de busca as expressões aprend e learn, representativas da palavra aprendizagem. De posse desses documentos assumimos os procedimentos interpretativos indicados pela Análise de Conteúdo. Quanto aos resultados, os primeiros movimentos com o corpus evidenciaram quatro categorias: quem aprende; o que aprende; em que contexto aprende e como aprende, enfatizando os seguintes sujeitos da aprendizagem: alunos do Ensino Fundamental, licenciandos em Matemática e professores que ensinam Matemática. Tais sujeitos aprendem sobre conceito matemático, conteúdo matemático e prática docente. Pode-se perceber tal fato em contextos como as abordagens/tendências da Educação Matemática, em comunidades práticas, no estágio supervisionado e como produto educacional por meio da Resolução de Problemas, da Investigação Matemática e das Tecnologias de Informação e Comunicação (TIC). O segundo movimento que emergiu da primeira análise revelou a associação da aprendizagem com o ensino, e quem se destaca para ensinar esses sujeitos é o pesquisador e o professor pesquisador. foi 20132017 2013 2017 2013-201 learn Conteúdo resultados categorias Fundamental docente Podese Pode abordagenstendências abordagens tendências práticas Problemas TIC. TIC . (TIC) ensino 2013201 201 2013-20 (TIC 201320 20 2013-2 20132 2 2013-

Con el avance de la tecnología, el aprendizaje en teleconsultas pasó a formar parte de la graduación médica actual. Esta investigación presenta las representaciones de un grupo de alumnos de un curso de medicina localizado en la capital del estado de São Paulo sobre sus experiencias con la realización de teleconsultas sincronizadas supervisadas durante una pasantía realizada en un ambulatorio escuela, durante el segundo trimestre de 2021. Se traba de una investigación cualitativa que utilizó el software IRaMuTeQ® y Análisis de Contenido. Estos alumnos puntúan obstáculos para la utilización de las teleconsultas, destacando preocupaciones con la humanización, seguridad de la persona atendida y calidad de la consulta, pero visualizan potencialidades tales como la facilidad de acceso y la comodidad. Reconocen la importancia de la práctica durante su graduación en un mundo cada vez más tecnológico, pero presentan inquietudes relacionadas a la pérdida del examen físico y a la mercantilización de la medicina.

Com o avançar da tecnologia, passou a fazer parte da graduação médica atual o aprendizado em teleconsultas. Esta pesquisa apresenta as representações de um grupo de alunos de um curso de Medicina localizado na capital do estado de São Paulo sobre suas experiências com a realização de teleconsultas síncronas supervisionadas durante estágio realizado em um ambulatório-escola, durante o segundo semestre de 2021. Trata-se de pesquisa qualitativa que utilizou o software IRaMuTeQ® e “Análise do Conteúdo”. Esses educandos pontuam entraves para a utilização das teleconsultas, destacando preocupações com a humanização, segurança da pessoa atendida e qualidade da consulta, mas visualizam potencialidades tais como a facilidade de acesso e a comodidade. Reconhecem a importância da prática durante sua Graduação em um mundo cada vez mais tecnológico, mas trazem inquietações relacionadas à perda do exame físico e à mercantilização da Medicina.

With the progress of technology, teleconsultation has become part of medical degree courses. This article presents the representations of a group of medical students from a university in the capital of the state of São Paulo regarding their experiences with supervised synchronous teleconsultations during an internship in a teaching clinic in the second semester of 2021. We conducted a qualitative study using the software IRaMuTeQ® and content analysis. The students highlighted barriers to the use of teleconsultation, emphasizing concerns with humanization, patient safety and consultation quality, but also perceived strengths such as ease of access and convenience. They recognize the importance of practice on the degree course in an ever more technological world, but raise concerns about the lack of physical examination and the commodification of medicine.

Abstract Professional learning has garnered considerable attention from researchers, as it forms a fundamental aspect of teachers’ professional development. Such learning encompasses a range of essential elements, including acquiring knowledge about curriculum content, teaching methods, core values, attitudes intrinsic to the teaching profession, and the nuances of professional culture. Notably, supervised training stands as a significant context for the professional learning of prospective teachers, offering direct exposure to the teaching profession and opportunities for critical reflection on theoretical and practical experiences. In this study, we undertake an investigation of the professional learning experiences of prospective teachers within the Early Years of Elementary Education, employing a qualitative and interpretative approach. To achieve this objective, we conducted in-depth research 3 with ten female students enrolled in an Education Undergraduate Program at the Federal University of the Fronteira Sul, Erechim Campus. Our focus was on examining their learning encounters during supervised training. The empirical data for the study encompassed narratives shared by the ten interns during sessions dedicated to teaching in Elementary School, alongside textual productions prepared by the students throughout their training. The analysis of the gathered data has revealed noteworthy learning outcomes concerning teaching in various dimensions. Firstly, it shed light on the process of lesson planning, characterized by its reflective nature, theoretical underpinnings, and comprehensive approach to devising learning pathways for students. Secondly, the investigation illuminated the development of teaching skills, an intricate fusion of multiple elements that collectively facilitate effective classroom practices. Lastly, the study elucidated the dynamics of the school environment, perceived as an interactive, dynamic, pluralistic, and adaptable context conducive to the realization of successful teaching endeavors. researchers content methods values culture Notably objective indepth depth Sul Campus School dimensions Firstly planning nature underpinnings Secondly skills practices Lastly environment interactive dynamic pluralistic endeavors

Resumo As aprendizagens profissionais têm interessado pesquisadores por constituírem uma das bases do desenvolvimento profissional de professores. Aprendizagens sobre o conteúdo curricular a ser ensinado, didática, valores e atitudes inerentes à profissão docente e aspectos da cultura profissional têm sido frequentemente associadas ao desenvolvimento profissional de professores. Um importante contexto de aprendizagens profissionais para futuros professores é o estágio supervisionado, concebido como contato direto com a profissão, de modo a refletir sobre as experiências teórico-práticas. Neste trabalho, dedicamo-nos a investigar, na perspectiva qualitativa e interpretativa de pesquisa, as aprendizagens profissionais de futuros professores dos anos iniciais do ensino fundamental. Para tanto, realizamos uma investigação[^3] com dez acadêmicas de um curso de licenciatura em pedagogia da Universidade Federal da Fronteira Sul (UFFS), Campus Erechim, examinando as aprendizagens realizadas no âmbito do estágio supervisionado. O material empírico do estudo constitui-se das narrativas de dez estagiárias, produzidas nos encontros de partilha de experiências sobre a docência nos anos iniciais, assim como das produções textuais das acadêmicas, elaboradas durante o estágio. A análise evidenciou aprendizagens relacionadas à docência nos seguintes aspectos: planejamento do ensino, que caracterizou o processo reflexivo, sustentado teoricamente e ampliado temporalmente, de planejar percursos de aprendizagem para os alunos; desenvolvimento do ensino, concretizado na interseção entre os diversos elementos que produzem as condições para a realização do trabalho em sala de aula; e a dinâmica escolar, compreendida como contexto interativo, dinâmico, plural e plástico de concretização da docência. ensinado didática supervisionado teóricopráticas. teóricopráticas teórico práticas. práticas teórico-práticas dedicamonos dedicamo investigar pesquisa fundamental tanto investigação^3 investigação3 investigação ^3 3 investigação[^3 UFFS, UFFS , (UFFS) Erechim constituise constitui se estagiárias reflexivo temporalmente alunos aula escolar interativo dinâmico investigação^ ^ investigação[^ (UFFS investigação[

Con el avance de la tecnología, el aprendizaje en teleconsultas pasó a formar parte de la graduación médica actual. Esta investigación presenta las representaciones de un grupo de alumnos de un curso de medicina localizado en la capital del estado de São Paulo sobre sus experiencias con la realización de teleconsultas sincronizadas supervisadas durante una pasantía realizada en un ambulatorio escuela, durante el segundo trimestre de 2021. Se traba de una investigación cualitativa que utilizó el software IRaMuTeQ® y Análisis de Contenido. Estos alumnos puntúan obstáculos para la utilización de las teleconsultas, destacando preocupaciones con la humanización, seguridad de la persona atendida y calidad de la consulta, pero visualizan potencialidades tales como la facilidad de acceso y la comodidad. Reconocen la importancia de la práctica durante su graduación en un mundo cada vez más tecnológico, pero presentan inquietudes relacionadas a la pérdida del examen físico y a la mercantilización de la medicina. tecnología actual escuela 2021 IRaMuTeQ Contenido humanización consulta comodidad tecnológico 202 20 2

With the progress of technology, teleconsultation has become part of medical degree courses. This article presents the representations of a group of medical students from a university in the capital of the state of São Paulo regarding their experiences with supervised synchronous teleconsultations during an internship in a teaching clinic in the second semester of 2021. We conducted a qualitative study using the software IRaMuTeQ® and content analysis. The students highlighted barriers to the use of teleconsultation, emphasizing concerns with humanization, patient safety and consultation quality, but also perceived strengths such as ease of access and convenience. They recognize the importance of practice on the degree course in an ever more technological world, but raise concerns about the lack of physical examination and the commodification of medicine. technology courses 2021 IRaMuTeQ analysis humanization quality convenience world medicine 202 20 2

Com o avançar da tecnologia, passou a fazer parte da graduação médica atual o aprendizado em teleconsultas. Esta pesquisa apresenta as representações de um grupo de alunos de um curso de Medicina localizado na capital do estado de São Paulo sobre suas experiências com a realização de teleconsultas síncronas supervisionadas durante estágio realizado em um ambulatório-escola, durante o segundo semestre de 2021. Trata-se de pesquisa qualitativa que utilizou o software IRaMuTeQ® e “Análise do Conteúdo”. Esses educandos pontuam entraves para a utilização das teleconsultas, destacando preocupações com a humanização, segurança da pessoa atendida e qualidade da consulta, mas visualizam potencialidades tais como a facilidade de acesso e a comodidade. Reconhecem a importância da prática durante sua Graduação em um mundo cada vez mais tecnológico, mas trazem inquietações relacionadas à perda do exame físico e à mercantilização da Medicina. tecnologia ambulatórioescola, ambulatórioescola ambulatório escola, escola ambulatório-escola 2021 Tratase Trata se IRaMuTeQ Análise Conteúdo. Conteúdo . Conteúdo” humanização consulta comodidade tecnológico 202 20 2

ABSTRACT. Invasive species threaten crops and ecosystems worldwide. Therefore, we sought to understand the relationship between the geographic distribution of species populations and the characteristics of seeds using new techniques such as seed image analysis, multivariate analysis, and machine learning. This study aimed to characterize Leucaena leucocephala (Lam.) de Wit. seeds from spatially dispersed populations using digital images and analyzed their implications for genetic studies. Seed size and shape descriptors were obtained using image analysis of the five populations. Several analyses were performed including descriptive statistics, principal components, Euclidean distance, Mantel correlation test, and supervised machine learning. This image analysis technique proved to be efficient in detecting biometric differences in L. leucocephala seeds from spatially dispersed populations. This method revealed that spatially dispersed L. leucocephala populations had different biometric seed patterns that can be used in studies of population genetic divergence. We observed that it is possible to identify the origin of the seeds from the biometric characters with 80.4% accuracy (Kappa statistic 0.755) when we applied the decision tree algorithm. Digital imaging analysis associated with machine learning is promising for discriminating forest tree populations, supporting management activities, and studying population genetic divergence. This technique contributes to the understanding of genotype-environment interactions and consequently identifies the ability of an invasive species to spread in a new area, making it possible to track and monitor the flow of seeds between populations and other sites. ABSTRACT worldwide Therefore Lam. Lam (Lam. Wit statistics components distance test L divergence 804 80 4 80.4 Kappa 0.755 0755 0 755 algorithm activities genotypeenvironment genotype environment area sites (Lam 8 80. 0.75 075 75 0.7 07 7 0.

Abstract Introduction: This study aimed to assess the learning curves of peribulbar anesthesia and estimate the number of blocks needed to attain proficiency. Methods: Anonymized records of sequential peribulbar blocks performed by first-year anesthesia residents were analyzed. The block sequential number and the outcomes were extracted from each record. Success was defined as a complete sensory and motor block of the eye, and failure was defined as an incomplete block requiring supplemental local anesthetic injections or general anesthesia. Learning curves using the LC-CUSUM method were constructed, aiming for acceptable and unacceptable failure rates of 10% and 20%, and 10% probability of type I and II errors. Simulations were used to determine the proficiency limit h0. Residents whose curves reached h0 were considered proficient. The Sequential Probability Ratio Test Cumulative Sum Method (SPRT-CUSUM) was used for follow-up. Results: Thirty-nine residents performed 2076 blocks (median = 52 blocks per resident; Interquartile Range (IQR) [range] = 27–78 [4–132]). Thirty residents (77%) achieved proficiency after a median of 13 blocks (13–24 [13–24]). Conclusions: The LC-CUSUM is a robust method for detecting resident proficiency at peribulbar anesthesia, defined as success rates exceeding 90%. Accordingly, 13 to 24 supervised double-injection peribulbar blocks are needed to attain competence at peribulbar anesthesia. Introduction Methods firstyear first year analyzed record eye LCCUSUM LC CUSUM constructed 10 20 20% errors h proficient SPRTCUSUM SPRT (SPRT-CUSUM followup. followup follow up. up follow-up Results Thirtynine nine 207 5 IQR (IQR range [range 2778 27 78 27–7 4–132. 4132 4–132 . 4 132 [4–132]) 77% 77 (77% 1 13–24 1324 (13–2 13–24. [13–24]) Conclusions 90 90% Accordingly 2 doubleinjection double injection 277 7 27– 413 4–13 [4–132] (77 13–2 (13– [13–24] 9 41 4–1 [4–132 (7 13– (13 [13–24 4– [4–13 ( (1 [13–2 [4–1 [13– [4– [13 [4 [1 [

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

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

Abstract Dental fillings, frequently used in dentistry to address various dental tissue issues, may pose problems when not aligned with the anatomical contours and physiology of dental and periodontal tissues. Our study aims to detect the prevalence and distribution of normal and overhanging filling restorations using a deep CNN architecture trained through supervised learning, on panoramic radiography images. A total of 10480 fillings and 2491 overhanging fillings were labeled using CranioCatch software from 2473 and 1850 images, respectively. After the data obtaining phase, validation (80%), training 10%), and test-groups (10%) were formed from images for both labelling. The YOLOv5x architecture was used to develop the AI model. The model’s performance was assessed through a confusion matrix and sensitivity, precision, and F1 score values of the model were calculated. For filling, sensitivity is 0.95, precision is 0.97, and F1 score is 0.96; for overhanging were determined to be 0.86, 0.89, and 0.87, respectively. The results demonstrate the capacity of the YOLOv5 algorithm to segment dental radiographs efficiently and accurately and demonstrate proficiency in detecting and distinguishing between normal and overhanging filling restorations. issues tissues learning 1048 249 247 185 respectively phase 80%, 80 80% , (80%) 10%, 10 10% 10%) testgroups test groups (10% labelling YOLOvx YOLOv x models s F calculated 095 0 95 0.95 097 97 0.97 0.96 096 96 086 86 0.86 089 89 0.89 087 87 0.87 104 24 18 8 (80% 1 (10 09 9 0.9 08 0.8 2 (80 (1 0. (8 (