Gas chromatography coupled to mass spectrometry (GC-MS) has been widely used, and along with other methodologies such as derivatization, is a powerful tool to analyze phytocompounds, including cuticular wax components. The loss of structural information upon fragmentation of waxes makes it difficult to interpret and identify the GC-MS mass spectra of these compounds with the traditional methods, as digital libraries. Therefore, as a new complementary alternative to traditional methods of identifying aliphatic acyclic lipids based on mass spectra, we present the new software WaxAlly in this technical note. WaxAlly is a new software created based on simple algorithms, enabling the rapid recognition by the user of eight aliphatic acyclic lipid classes, including alkanes, alkenes, aldehydes, ketones, esters, and TMS derivatives of free fatty acids, primary and secondary alcohols, and their homologues with carbon chains varying between 10 and 100 carbons. Additionally, the WaxAlly software provides a section for data organization, internet comparison to NIST and PubChem databases, and academic information about mass fragmentation of acyclic lipids. The software has proven to be a very useful complementary technique in identifying plant wax lipid homologues, and new fragmentation patterns of lipid classes can be added in the future to improve the program. GCMS GC MS (GC-MS used derivatization phytocompounds components libraries Therefore note algorithms alkanes alkenes aldehydes ketones esters acids alcohols 1 carbons Additionally organization databases program

ABSTRACT Introduction/framework/objectives: Artificial Intelligence has shown very intense development in recent years, in several areas, with Work Market being no exception. Furthermore, many researchers predict drastic changes in the way it is organized and consequent unemployment and associated economic, social, legal and emotional issues. The aim of this review is to summarize the data that will help to clarify the situation and better guide Occupational Health and Safety professionals. Methodology: This is a Bibliographic Review, initiated through a search carried out in April 2023 in the databases “CINALH plus with full text, Medline with full text, Database of Abstracts of Reviews of Effects, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Cochrane Methodology Register, Nursing and Allied Health Collection: comprehensive, MedicLatina and RCAAP”. Content: Artificial networks function as models with the capacity to store and use data, through simplified application algorithms. They mimic human neural networks; making the best decision, based on the information collected, making it possible to carry out tasks previously considered exclusively human. The machine progressively learns from its mistakes; that is, being able to solve problems for which they were not programmed, based on "experience", examples and analogies. Discussion and Conclusions: The consequences described in the literature regarding the interaction of Artificial Intelligence with Work are not consensual, even though most researchers consider that this will be the most abrupt and serious change faced by humanity, to the extent that it could be possible that the majority of the world's population become unemployed and without a means of subsistence, with all the associated implications, in a much shorter period of time than most can imagine. Some countries value this issue more, which is why they have already started studies or even applied practical measures to try to alleviate these problems. It would be very relevant to publicize the topic to employers, managers, human resources and workers, also evaluating how many companies apply Artificial Inteligence, in what way and how this has changed variables such as productivity, number of jobs and type of tasks; as well as what were the main mistakes made on this journey and what are the best ways to mitigate them, publicizing the process and the conclusions obtained, publishing such information in a magazine in the area.

RESUMO Introdução/enquadramento/objetivos: A Inteligência Artificial tem apresentado um desenvolvimento muito intenso nos últimos anos, em diversas áreas, não sendo o Mercado de Trabalho uma exceção. Para além disso, muitos investigadores preveem mudanças drásticas na forma de este ser organizado e consequente alteração a nível de desemprego e questões económicas, sociais, legais e emocionais associadas. Pretende-se com esta revisão sintetizar os dados que mais ajudarão a elucidar a situação e a melhor orientar os profissionais da Saúde e Segurança Ocupacionais. Metodologia: Trata-se de uma Revisão Bibliográfica, iniciada através de uma pesquisa realizada em abril de 2023 nas bases de dados “CINALH plus with full text, Medline with full text, Database of Abstracts of Reviews of Effects, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Cochrane Methodology Register, Nursing and Allied Health Collection: comprehensive, MedicLatina e RCAAP”. Conteúdo: As redes artificiais funcionam como modelos com capacidade para armazenar e usar dados, através de algoritmos de aplicação simplificada. Eles imitam as redes neuronais humanas; tomando a melhor decisão, em função da informação recolhida, possibilitando a realização de tarefas anteriormente consideradas como exclusivamente humanas. A máquina aprende progressivamente com os seus erros; ou seja, passa a conseguir resolver problemas para os quais não foram programadas, em função da "experiência", exemplos e analogias. Discussão e Conclusões: As consequências descritas na literatura em relação à interação da Inteligência Artificial com o Trabalho não são consensuais, ainda que a maioria dos investigadores considere que isso será a mudança mais brusca e grave enfrentada pela humanidade, na medida em que poderá ser possível que a maioria da população mundial fique desempregada e sem meios de subsistência, com todas as implicações associadas, num prazo muito mais curto do que a maioria poderá imaginar. Alguns países valorizam mais este assunto, pelo que já iniciaram estudos ou até a aplicação de medidas práticas para tentar atenuar esses problemas. Seria muito relevante divulgar o tema perante empregadores, chefias, recursos humanos e trabalhadores, avaliando também quantas empresas aplicam a Inteligência Artificial, de que forma e como tal alterou variáveis como produtividade, número de postos de trabalho e tipo tarefas; bem como quais os principais erros executados nessa caminhada e quais as melhores formas de os atenuar, divulgando o processo e as conclusões obtidas, publicando tais informações em revista da área.

Abstract: The purpose of this article is to achieve optimal coordination for timed overcurrent relays modeled in a test system. The traditional adjustment that is usually used in the field is taken as the base criterion to determine the improvements achieved with the implemented optimization methods. Optimal parameters are determined for two timed overcurrent relays modeled on the IEEE 13-node test system using PowerFactory software. The optimization algorithms are coded in Python. PowerFactory links with Python to generate interoperability between the algorithm and the electrical system. Satisfactory results are achieved from the COA and GSA algorithms with a reduction of more than 50% in relay operating times.

Resumen: El objeto de este artículo es conseguir la coordinación óptima para los relés de sobrecorriente temporizados, modelados en un sistema de prueba. El ajuste tradicional que se emplea usualmente en el campo se toma como criterio base para determinar las mejoras conseguidas con los métodos de optimización implementados. Se determinan parámetros óptimos para dos relés de sobrecorriente temporizados modelados en el sistema de prueba de 13 nodos de la IEEE mediante el software PowerFactory. Los algoritmos de optimización son codificados en Python. Gracias a la interfaz que presenta PowerFactoy con Python, cada algoritmo se ejecuta dentro del sistema eléctrico. Se consiguen resultados satisfactorios de los algoritmos COA y GSA con una reducción de más del 50 % en los tiempos de operación de los relés.

Abstract The birth of the liberal system of education in the Roman Empire and its expansion, after the French revolution through pedagogical naturalism along the paths of Western education, its diversification through the new school movement means the installation from the conjunctural to the structural of a soft pedagogy that, with the emergence of emotional intelligences, infantilises education from the devaluation of discipline, effort and study as an arduous and slow process. To this, after the pandemic, online education intensifies and algorithms appear as agile solutions to accelerate and simplify the teaching-learning processes. The desire to make education profitable means the viralisation and hegemony of neoliberal budgets at all levels of education systems, progressively transforming higher education into theme parks at the service of transnationals where gamification replaces pedagogy and didactics replaces epistemologies, emptying universities of knowledge to turn them into higher institutions of entertainment. Re-reading memory not in order to commune with the past, but to analyse the intentions that founded universities as institutions that hosted slow pedagogies, based on study and discipline to dissolve the discourses ascribed to power and to be sources of contextualised and problematic truths, capable of disputing authoritarianisms' status quo through a dialogue of knowledge necessary for the construction of the imaginary of the community.

Resumen El nacimiento del sistema liberal de enseñanza en el imperio romano y su expansión, tras la revolución francesa, a través del naturalismo pedagógico, por las sendas de la educación occidental, su diversificación mediante el movimiento de la escuela nueva, significa la instalación desde lo coyuntural a lo estructural de una pedagogía blanda que, con la emergencia de las inteligencias emocionales, infantiliza la educación desde la devaluación de la disciplina, el esfuerzo y el estudio como un proceso arduo y lento. A ello, tras la pandemia, se intensifica la educación online y aparecen los algoritmos como soluciones ágiles para acelerar y simplificar los procesos de enseñanza-aprendizaje (la educación se vuelve sólo aprendizaje). Los deseos de rentabilizar la educación significan la viralización y hegemonía de los presupuestos neoliberales en todos los niveles de los sistemas educativos, transformando las instituciones de educación superior, progresivamente, en parques temáticos al servicio de las transnacionales donde la gamificación sustituye a la pedagogía y las didácticas a las epistemologías, vaciando las universidades de saberes para convertirlas en instituciones de entretenimiento. Releer el pasado, no para comulgar con él, sino para recuperar las intenciones que fundaron las universidades como instituciones que hospedaron unas pedagogías lentas, basadas en el estudio y la disciplina, capaces de educar sujetos capaces de disolver los discursos adscritos al poder y, ser fuentes de verdades contextualizadas y problemáticas; capaces, en suma, de disputar a los autoritarismos su estatus quo mediante un diálogo de saberes necesario para la construcción del imaginario de lo comunitario.

Abstract: Smart Grids have revolutionized the electricity industry by enabling more efficient control and monitoring of the electricity supply, with a key component being smart meters (SM). These collect information on demand, energy, and harmonic distortion, among others, which must be stored and managed efficiently in a metering data management system (MDMS). The MDMS must ensure that a complete set of data is obtained for use in algorithms to ensure the reliability and quality of the power supply. To address the challenge of management the big data generated by SM, short, medium, and long-term measurement forecasting techniques have been proposed, highlighting the use of artificial intelligence such as Artificial Neural Networks (ANN) and Deep Learning (DL) methods due to their ability to adapt to different input and output variables with various time horizons. In addition, the influence of the diversity of Information and Communication Technologies (ICT) on the update time and data storage in the MDMS is highlighted. In this sense, this work aims to identify which ANN or DL architecture(s) could be more suitable for enterprise, survey, or research applications, demonstrating favorable performance metrics in different scenarios of sampling frequency and typical data update times in Smart Grids. This is relevant due to the need for MDMS to perform multivariate and multi-pass predictions in the short term to complete the information until the information is available or updated.

Resumen: Las Redes Eléctricas Inteligentes de Distribución (REID) han revolucionado la industria eléctrica al permitir un control y monitoreo más eficiente del suministro eléctrico, con un componente clave siendo los medidores inteligentes (SM). Estos recopilan información sobre la demanda, energía, distorsión armónica, entre otros, que deben ser almacenados y gestionados eficientemente en un sistema de gestión de datos de medición (MDMS). El MDMS debe garantizar la obtención de un conjunto completo de datos para su uso en algoritmos que aseguren la confiabilidad y calidad del suministro eléctrico. Para abordar el desafío de gestionar la gran cantidad de datos generados por los SM, se han propuesto diferentes técnicas de predicción de mediciones a corto, mediano y largo plazo, destacando el uso de inteligencia artificial como las Redes Neuronales Artificiales (ANN) y métodos de Aprendizaje Profundo (DL) debido a su capacidad de adaptación a diferentes variables de entrada y salida con diversos horizontes temporales. Además, se destaca la influencia de la diversidad de Tecnologías de la Información y Comunicación (TIC) en el tiempo de actualización y almacenamiento de datos en un MDMS. En este sentido, este trabajo tiene como objetivo identificar qué arquitectura o arquitecturas de ANN o DL podrían ser más adecuadas para aplicaciones en empresas, estudios o investigaciones, demostrando métricas de rendimiento favorables en diferentes escenarios de frecuencia de muestreo y tiempos de actualización de datos típicos en un REID. Esto es relevante debido a la necesidad del MDMS de realizar predicciones multivariables y multipaso a corto plazo para completar la información, hasta que la misma esté disponible o se actualice.

Abstract Introduction: Leptodactylus latinasus and Physalaemus cuqui are sympatric anuran species with similar environmental requirements and contrasting reproductive modes. Climatic configuration determines distribution patterns and promotes sympatry of environmental niches, but specificity/selectivity determines the success of reproductive modes. Species distribution models (SDM) are a valuable tool to predict spatio-temporal distributions based on the extrapolation of environmental predictors. Objectives: To determine the spatio-temporal distribution of environmental niches and assess whether the protected areas of the World Database of Protected Areas (WDPA) allow the conservation of these species in the current scenario and future. Methods: We applied different algorithms to predict the distribution and spatio-temporal overlap of environmental niches of L. latinasus and P. cuqui within South America in the last glacial maximum (LGM), middle-Holocene, current and future scenarios. We assess the conservation status of both species with the WDPA conservation units. Results: All applied algorithms showed high performance for both species (TSS = 0.87, AUC = 0.95). The L. latinasus predictions showed wide environmental niches from LGM to the current scenario (49 % stable niches, 37 % gained niches, and 13 % lost niches), suggesting historical fidelity to stable climatic-environmental regions. In the current-future transition, L. latinasus would increase the number of stable (70 %) and lost (20 %) niches, suggesting fidelity to lowland regions and a possible trend toward microendemism. P. cuqui loses environmental niches from the LGM to the current scenario (25 %) and in the current-future transition (63 %), increasing the environmental sympathy between both species; 31 % spatial overlap in the current scenario and 70 % in the future. Conclusion: Extreme drought events and rainfall variations, derived from climate change, suggest the loss of environmental niches for these species that are not currently threatened but are not adequately protected by conservation units. The loss of environmental niches increases spatial sympatry which represents a new challenge for anurans and the conservation of their populations.

Resumen Introducción: Leptodactylus latinasus y Physalaemus cuqui son especies de anuros simpátricos con requerimientos ambientales similares y modos reproductivos contrastantes. La configuración climática determina los patrones de distribución y promueve la simpatría de los nichos ambientales, pero la especificidad/selectividad determina el éxito de los modos reproductivos. Los modelos de distribución de especies (MDE) son una herramienta valiosa para predecir distribuciones espacio-temporales basadas en la extrapolación de predictores ambientales. Objetivos: Determinar la distribución espacio-temporal de los nichos ambientales y evaluar si las áreas protegidas de la base de Datos Mundial de Áreas Protegidas (DMAP) permiten la conservación de estas especies en el escenario actual y futuro. Métodos: Aplicamos diferentes algoritmos para predecir la distribución y superposición espacio-temporal de nichos ambientales de L. latinasus y P. cuqui dentro de América del Sur en el último máximo glacial (UGM), Holoceno medio, actual y futuro. Evaluamos el estado de conservación de ambas especies con las unidades de conservación de la DMAP. Resultados: Todos los algoritmos aplicados mostraron un alto rendimiento para ambas especies (TSS = 0.87, AUC = 0.95). Las predicciones de L. latinasus mostraron amplios nichos ambientales desde LGM hasta el escenario actual (49 % de nichos estables, 37 % de nichos ganados y 13 % de nichos perdidos), sugiriendo fidelidad histórica por regiones climático-ambientales estables. En la transición actual-futura L. latinasus incrementaría la cantidad de nichos estables (70 %) y perdidos (20 %), sugiriendo fidelidad por regiones de tierras bajas y la posible tendencia hacia el microendemismo. P. cuqui pierde nichos ambientales desde el LGM al escenario actual (25 %) y en la transición actual-futura (63 %), incrementando la simpatría ambiental entre ambas especies; 31 % de superposición espacial en el escenario actual y 70 % en el futuro. Conclusión: Los eventos de sequía extrema y las variaciones de precipitaciones, derivados del cambio climático, sugieren la pérdida de nichos ambientales para estas especies, actualmente no se encuentran amenazadas, pero no están adecuadamente protegidas por las unidades de conservación. La pérdida de nichos ambientales aumenta la simpatría espacial que representa un nuevo desafío para estos anuros y la conservación de sus poblaciones.

Abstract Introduction: Coryphaena hippurus is a species of commercial interest with a high migratory capacity, a characteristic that places it in tropical and subtropical environments, preferring areas with a temperature range between 21 and 30 °C and salinity close to 31 ppt. Although the population trend of C. hippurus is stable, the fishing of this resource is increasing and occupies important positions in the economy of the Eastern Tropical Pacific coastal countries, which demonstrates the need to design and strengthen conservation strategies for the adequate use of this resource. Given its location in tropical and subtropical environments, its availability and distribution could be affected by climate change. Objective: To analyze the current and future potential distribution of C. hippurus under climate change scenarios. Methods: Ten algorithms were used to model the potential distribution and current habitat suitability index of C. hippurus as a function of sea surface temperature, current salinity and velocity, and these results were then projected under the most extreme climate change scenario. Results: There were good performances with all the algorithms used, but the model generated with BIOCLIM (AUC: 0.89) was chosen because it also solves the problem of spatial and temporal bias found in the records of the species. The region of greatest habitat suitability for C. hippurus matches the oceanic fronts of the Eastern Tropical Pacific. Under future conditions of extreme climate change, the species distribution pattern indicates a contraction, relocation, and expansion of habitat south of the equator. Conclusions: Under conditions of extreme climate change, the distribution model for C. hippurus suggests a process of topicalization of marine ecosystems in the Eastern Tropical Pacific by 2100.

Resumen Introducción: Coryphaena hippurus es una especie migratoria de interés comercial, que se encuentra en ambientes tropicales y subtropicales, prefiriendo zonas con un rango de temperatura entre 21 y 30 °C y salinidad cercana a 31 ppt. Aunque la tendencia poblacional del dorado es estable, la pesquería de este recurso está aumentando y ocupa importantes posiciones en la economía de países costeros del Pacífico Oriental Tropical, evidenciándose la necesidad de diseñar y fortalecer estrategias de conservación y mejorar el aprovechamiento de este recurso. Dada su ubicación en ambientes tropicales y subtropicales, la disponibilidad y distribución de este recurso podría verse afectado a futuro por el cambio climático. Objetivo: Analizar la distribución potencial actual y futura de C. hippurus bajo condiciones de cambio climático. Métodos: Se emplearon 10 algoritmos para modelar la distribución potencial e idoneidad de hábitat actual para C. hippurus a partir de la temperatura superficial del mar, la salinidad y velocidad de las corrientes, posteriormente se proyectaron estos resultados según el escenario de cambio climático más extremo. Resultados: Hubo buenos desempeños con todos los algoritmos empleados, pero se escogió el modelo generado con BIOCLIM (AUC: 0.89) pues además resuelve el inconveniente del sesgo espacial y temporal hallado en los registros de la especie. La región de mayor idoneidad de hábitat para C. hippurus coincide con los frentes oceánicos del Pacífico Oriental Tropical. En condiciones futuras de cambio climático extremo, el modelo de distribución de la especie indica una contracción, reubicación y expansión de hábitat hacia el sur de la línea ecuatorial. Conclusiones: En condiciones de cambio climático extremo, el modelo de distribución para C. hippurus sugiere un proceso de tropicalización de los ecosistemas marinos en el Pacífico Oriental Tropical para el año 2100.

Abstract The use of artificial intelligence (AI) in laboratory medicine (LM) has led to a qualitative leap in the diagnosis of diseases that afflict humans. The development of robots for measurement, calculation and prediction has increased the reliability, validity and reproducibility of AI diagnostic tests, leading to an easy choice of such technology in the clinical laboratory. However, AI in LM entails several ethical reflections that need to be considered. The incipient technology under development, the presence of cognitive biases in algorithms and data, the uncertainty of robot performance, technological limitations, the threat to privacy, and the absence of a legal framework open ethical conflicts that lacerate human equity, safety, and autonomy. The technological imperative of AI in LM must not overcome responsibility, nor infringe on the dignity of the person.

Resumen El uso de la Inteligencia artificial (IA) en la Medicina de laboratorio (ML) ha provocado un salto cualitativo en el diagnóstico de enfermedades que aquejan al ser humano. El desarrollo de robots para la medición, cálculo y predicción ha aumentado la confiabilidad, validez y reproducibilidad de las pruebas diagnósticas con IA, induciendo a una fácil elección de dicha tecnología en el laboratorio clínico. Sin embargo, la IA en la ML entraña una serie de reflexiones éticas que deben considerarse. La incipiente tecnología en desarrollo, la presencia de sesgos cognitivos en los algoritmos y datos, la incertidumbre del funcionamiento del robot, las limitaciones tecnológicas, la amenaza a la privacidad y la ausencia de un marco legal abren conflictos éticos que laceran la equidad, la seguridad y la autonomía del hombre. El imperativo tecnológico de la IA en la ML no debe superar la responsabilidad, ni atentar contra la dignidad de la persona.

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

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

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

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

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

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

Autonomous electric vehicles use camera sensors for vision-based steering control and detecting both roads and objects. In this study, road and object detection are combined, utilizing the YOLOv8x-seg model trained for 200 epochs, achieving the lowest segmentation loss at 0.53182. Simulation tests demonstrate accurate road and object detection, effective object distance measurement, and real-time road identification for steering control, successfully keeping the vehicle on track with an average object distance measurement error of 2.245 m. Route planning for autonomous vehicles is crucial, and the A-Star algorithm is employed to find the optimal route. In real-time tests, when an obstacle is placed between nodes 6 and 7, the A-Star algorithm can reroute from the original path (5, 6, 7, 27, and 28) to a new path (5, 6, 9, 27, and 28). This study demonstrates the vital role of sensor fusion in autonomous vehicles by integrating various sensors. This study focuses on sensor fusion for object-road detection and path planning using the A* algorithm. Real-time tests in two different scenarios demonstrate the successful integration of sensor fusion, enabling the vehicle to follow planned routes. However, some route nodes remain unreachable, requiring occasional driver intervention. These results demonstrate the feasibility of sensor fusion with diverse tasks in third-level autonomous vehicles.

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.

Resumen La inteligencia artificial (IA) es una tecnología que ha revolucionado numerosos campos, incluyendo el proceso penal. En la actualidad, se están desarrollando sistemas de IA capaces de analizar grandes cantidades de datos, lo que puede ayudar a los tribunales a tomar decisiones más informadas y precisas. El presente artículo tiene como objetivo analizar el papel de la inteligencia artificial como herramienta de mejora y optimización de los procesos penales en Ecuador. A través de una metodología de revisión bibliográfica se recabó toda la información tendiente a entender el uso de la IA en el proceso penal para aumentar la eficiencia y eficacia del proceso penal, así como la identificación de patrones en grandes conjuntos de datos. Como resultado de la presente investigación, se pudieron evidenciar, entre otros, sistemas basados en IA para estudiar a los sospechosos más probables y a los patrones de delito para prevenir futuros delitos. Se evidenció que el uso de la IA en el proceso penal también plantea preocupaciones en cuanto a la privacidad y la seguridad de los datos, así como preguntas sobre cómo la IA afectaría la toma de decisiones judiciales, ya que algunos temen que los algoritmos de IA sean sesgados o discriminatorios.

Abstract Artificial intelligence (AI) is a technology that has revolutionized numerous fields, including criminal proceedings. AI systems capable of analyzing large amounts of data are currently being developed, which can help courts make more informed and accurate decisions. This article aims to analyze the role of artificial intelligence as a tool for improving and optimizing criminal processes in Ecuador. Through a bibliographic review methodology, all the information aimed at understanding the use of AI in the criminal process was collected to increase the efficiency and effectiveness of the criminal process, as well as the identification of patterns in large data sets. As a result of this investigation, it was possible to demonstrate, among others, AI-based systems to study the most likely suspects and crime patterns to prevent future crimes. It is evident that the use of AI in criminal proceedings also raises concerns regarding privacy and data security, as well as questions about how AI would affect judicial decision-making, as some fear that AI algorithms are biased or discriminatory.

Abstract Assembly lines are a production mechanism that has historically presented economic and quality benefits in organizations. However, different problems arise during their execution, among them, the issue of balancing assembly lines with multi-manned stations. This situation occurs more frequently in industrial organizations that manufacture products of medium and large dimensions, compared to a single worker per workstation model. Despite this, we find a greater tendency to study the second case in the literature. In contrast, few studies refer to the first, in addition, exhaustive search methods such as linear programming have encountered barriers due to computational complexity, so research on this problem has focused on using heuristics looking for more efficient algorithms in order to solve it. Therefore, the present work proposes a genetic algorithm that, to our knowledge, has not been used in the search to minimize the number of workers and the number of workstations for the balancing model of assembly lines with multi-manned stations. In addition, a new cost function is proposed that weights the number of workstations and workers, punishing solutions with high idle times to avoid their selection. The results of the proposed algorithm are evaluated by comparing test instances presented in the literature. The algorithm is available at <https://github.com/juanseck/GAMmALB>.

Resumen Las líneas de ensamble son un mecanismo de producción que históricamente ha presentado beneficios económicos y de calidad en las organizaciones; sin embargo, diferentes problemas se presentan durante su ejecución. Entre estos destaca el problema del modelo de balanceo de líneas de ensamble con estaciones multi-tripuladas, el cual suele presentarse con mayor frecuencia en las organizaciones industriales que fabrican productos de dimensiones medianas y grandes, en comparación con el modelo de un solo tripulante por estación de trabajo; a pesar de ello, en la literatura encontramos una mayor tendencia a estudiar el segundo caso. Pocos estudios hacen referencia al primero. Adicionalmente, los métodos de búsqueda exhaustiva como los de programación lineal han encontrado barreras debido a la complejidad computacional, por lo cual las investigaciones sobre este problema se han centrado en utilizar heurísticos para su solución, en la búsqueda de algoritmos que sean cada vez más eficientes. Por lo anterior, el presente trabajo propone un algoritmo genético que en nuestro conocimiento no se ha utilizado en la búsqueda por minimizar el número de trabajadores y el número de estaciones de trabajo para el modelo de balanceo de líneas de ensamble con estaciones multi-tripuladas. Además, se propone una nueva función de costos la cual pondera el número de estaciones de trabajo y trabajadores, castigando soluciones con tiempos muertos altos para evitar su selección. Los resultados del algoritmo propuesto son evaluados mediante la comparación de instancias de prueba presentadas en la literatura. El algoritmo está disponible en <https://github.com/juanseck/GAMmALB>.