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 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 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: 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: 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 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,

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.

RESUMO: A qualidade e a produtividade de um vinhedo estão relacionadas com a biomassa do dossel e o vigor foliar, e técnicas de sensoriamento próximo têm sido utilizadas como alternativas aos métodos convencionais para estimar esses parâmetros. O conhecimento do teor de clorofila é fundamental para as avaliações fitossanitárias. No entanto, índices de clorofila também podem ser extraídos de espectros de refletância obtidos para uma ampla gama de aplicações. Nesta perspectiva, foram investigadas as relações entre os índices de clorofila obtidos por medidas diretas e derivados de radiometria de campo, com o objetivo de avaliar a acurácia do teor de clorofila previsto. A investigação foi realizada em plantas da variedade Cabernet Sauvignon, baseando-se em levantamentos diretos de clorofila, espectrorradiometria foliar e na derivação de Índices de Vegetação Hiperespectrais (HVIs), sendo a aquisição de dados realizada em duas fases do ciclo vegetativo. Os resultados das estimativas mostraram que os maiores coeficientes de determinação expressando a correlação entre medições e predições foram obtidas para Chl a e Chl a/Chl b modeladas pelo algoritmo RFR, com valores de R ² tão altos quanto 0,8 e erros quadráticos médios tão baixos quanto 0,093. Com relação aos HVIs, o Photochemical Reflectance Index (PRI) calculado para a segunda data de aquisição, correspondente às folhas que atingiram a senescência, foi o que produziu o maior percentual de explicações de predição. Em conclusão, sugere-se que este estudo pode trazer uma contribuição significativa para o desenvolvimento de técnicas não invasivas de monitoramento de vinhedos. RESUMO parâmetros fitossanitárias entanto aplicações perspectiva campo previsto Sauvignon baseandose baseando se HVIs , (HVIs) vegetativo aChl RFR 08 0 8 0, 0093 093 0,093 PRI (PRI senescência predição conclusão sugerese sugere vinhedos (HVIs 009 09 0,09 00 0,0

ABSTRACT: Quality and yield of a vineyard are related to canopy biomass and leaf vigor, and proximal techniques have been used as alternatives to conventional methods to estimate these parameters. Knowledge on chlorophyll content is crucial to plant health assessments. However, chlorophyll indices can also be extracted from reflectance spectra obtained for an ample range of applications. In this perspective, relations between chlorophyll indices obtained by direct measurements and derived from field radiometry were investigated, with the aim to assess the accuracy of predicted chlorophyll content. The investigation was performed on Cabernet Sauvignon vines, being based on direct chlorophyll surveys, vine leaf spectroradiometry and the derivation of Hyperspectral Vegetation Indices (HVIs), with data acquisition being performed on two stages of the vegetative cycle. Direct chlorophyll data was compared with predicted indices using two machine learning algorithms: Partial Least-Squares Regression (PLSR) and Random Forest Regressor (RFR), using data from reflectance spectra and derived HVIs. The higher correlations between measurements and predictions were obtained for ChlaandChl a/Chl b modeled by the RFR algorithm, with R 2 values as high as 0.8 and Root Mean Squared Errors as low as 0.093. With respect to HVIs, the Photochemical Reflectance Index (PRI) calculated for the second acquisition date, corresponding to leaves reaching senescence was the one which produced the highest percentage of prediction explanations. This study can bring a significant contribution to the development of non-invasive techniques to vine monitoring. ABSTRACT vigor parameters assessments However applications perspective investigated vines surveys HVIs , (HVIs) cycle algorithms LeastSquares Least Squares PLSR (PLSR RFR, (RFR) aChl Chl algorithm 08 0 8 0. 0093 093 0.093 PRI (PRI date explanations noninvasive non invasive monitoring (HVIs (RFR 009 09 0.09 00 0.0

Abstract: Currently, Ecuadorian electrical system operation is experiencing several technical challenges, such as: 1) neighboring electrical systems integration in the Andean Electrical Interconnection System (SINEA) and 2) primary energy sources diversification. Under these new operating conditions, dynamic safety may be compromised by frequency stability issues. Based on frequency regulation, the objective is to use control mechanisms and available generation reserves to respond suitably to the electrical system's demands. Within this context, it becomes imperative to devise methodologies that ensures fast and accurate responses of power-frequency control equipment, such as speed regulators employed for Primary Frequency Regulation in generators. Given this foundation, an approach is presented relying on heuristic optimization methodologies, for the development of a tool that helps to compute the parametric identification and adjustment of speed controllers. Using measurements obtained from the proposed methodology, the developed tool was able to effectively perform the identification and validation of the speed regulation system of the CCS power plant as well as to propose new parameters that improve the response of the identified system.

Resumen: En la actualidad, la operación del sistema eléctrico ecuatoriano experimenta varios desafíos técnicos, tales como: 1) la integración de sistemas eléctricos vecinos en el Sistema de Interconexión Eléctrica Andina (SINEA) y 2) la diversificación de las fuentes primarias de energía. Bajo estas nuevas condiciones operativas, la seguridad dinámica puede ser afectada por problemas de estabilidad de frecuencia. Con base en la regulación de frecuencia se busca, a través de mecanismos de control y reservas de generación disponible, responder en forma adecuada a las exigencias del sistema eléctrico. Bajo este contexto, es necesario el desarrollo de metodologías que permitan la respuesta rápida y precisa de los equipos de control potencia - frecuencia, tales como los reguladores de velocidad, los cuales actúan de forma local en los generadores para realizar la Regulación Primaria de Frecuencia. Con estos antecedentes, se propone una metodología, basada en métodos heurísticos de optimización, para el desarrollo de una herramienta que permita realizar la identificación paramétrica y ajuste de los reguladores de velocidad. Con las mediciones obtenidas en base a la metodología propuesta, la herramienta desarrollada logró de manera efectiva realizar la identificación y validación del sistema de regulación de velocidad de la central CCS así como proponer nuevos parámetros que mejoran la respuesta del sistema identificado.

Abstract: The present work focuses on the optimization of maximum power tracking (MPPT) in photovoltaic systems by employing a new technique based on linear regression. The main purpose is to develop an MPPT algorithm using linear regression techniques, which involves identifying and tracking the maximum power point of solar panels more accurately. The proposed algorithm is developed in MATLAB/Simulink software and validated through experimental tests. Subsequently, the application of the algorithm is extended to an electrical network modeled and simulated in HYPERSIM software, this tool allows a more detailed analysis of the instantaneous dynamics of electrical and control variables in complex systems when varying factors such as temperature and irradiation. The innovative contribution of this work is not only limited to the improvement of MPPT algorithms in photovoltaic systems. It is highlighted that, although the term MPPT is mainly associated with solar PV, the optimization of available power is also relevant for other renewable energy sources. Optimizing the conversion of solar energy into usable electricity not only increases the cost-effectiveness and sustainability of these systems, but also highlights the key role in the transition to a more sustainable electricity supply. Abstract (MPPT techniques accurately MATLABSimulink MATLAB Simulink tests Subsequently irradiation that PV sources costeffectiveness cost effectiveness supply

Resumen: El presente trabajo se enfoca en la optimización del seguimiento de máxima potencia (MPPT) en sistemas fotovoltaicos mediante el empleo de una nueva técnica basada en regresión lineal. El propósito principal es desarrollar un algoritmo de MPPT que utilice técnicas de regresión lineal, lo que implica identificar y seguir con mayor exactitud el punto de máxima potencia de los paneles solares. El algoritmo propuesto se desarrolla en el software MATLAB/Simulink, validándose a través de pruebas experimentales. Posteriormente, se extiende la aplicación del algoritmo a una red eléctrica modelada y simulada en el software HYPERSIM, esta herramienta permite un análisis más detallado de la dinámica instantánea de las variables eléctricas y de control en sistemas complejos al variar factores como la temperatura e irradiación. La contribución innovadora de este trabajo no solo se circunscribe a la mejora de los algoritmos de MPPT en sistemas fotovoltaicos. Se destaca que, aunque el término MPPT se asocie principalmente con la energía solar fotovoltaica, la optimización de la potencia disponible también es relevante para otras fuentes de energía renovable. La optimización de la conversión de energía solar en electricidad utilizable no solo aumenta la rentabilidad y sostenibilidad de estos sistemas, sino que también destaca el papel fundamental en la transición hacia un suministro eléctrico más sostenible. Resumen (MPPT lineal solares MATLABSimulink MATLAB Simulink MATLAB/Simulink experimentales Posteriormente HYPERSIM irradiación fotovoltaica renovable sostenible

BACKGROUND. Neonatal cholestatic jaundice is a common paediatric condition with a paucity of recent local data and a notable absence of locally influenced diagnostic algorithms. OBJECTIVES. To describe the causes of cholestatic jaundice, investigations conducted and patient outcomes. METHODS. This was a retrospective file review of 96 patients presenting to the Specialist Clinic at Rahima Moosa Mother and Child Hospital in Johannesburg between 1 January 2014 and 31 December 2020. Clinical features, serum biochemistry at presentation, diagnostic investigations (haematological, radiological and histological), diagnosis and outcome were analysed during data collection. RESULTS. Ninety-six patients were included in the study. The median age of jaundice onset was at 2 months, while the median age of presentation was 3.3 months. Causes of jaundice fell into three main categories: 38 had biliary atresia (BA); 24 had another specific diagnosis; and 34 remained without an underlying diagnosis (idiopathic neonatal hepatitis). An overall mortality rate of 26% was noted at a median (interquartile range) age of 13 (5 - 24) months and it was highest in the BA group (n= 14; 56%). CONCLUSION. The evaluation of neonatal jaundice requires a wide differential and expeditious referral to optimise outcomes and avoid complications. Many patients remained undiagnosed, and the overall prognosis was poor. The authors recommend the development of a locally relevant diagnostic protocol to minimise delays in the identification, diagnosis and treatment of neonatal jaundice.

Resumen El uso de wavelets adaptadas para el reconocimiento de patrones es muy atractivo por la multiescalaridad de la transformada wavelet. Sin embargo, el buen desempeño de estos algoritmos en la detección de patrones depende fuertemente de la construcción de los filtros adaptados al patrón de interés. La Transformada Shapelet Discreta II (9) (DST-II) es un algoritmo inspirado en la transformada wavelet, que permite el diseño de filtros a la medida para la detección de patrones en señales unidimensionales. La construcción de estos filtros requiere la solución de un sistema de ecuaciones no lineales, que según (9) se puede efectuar mediante cualquier método iterativo. Esta investigación presenta un novedoso y exhaustivo estudio numérico que demuestra el impacto de la elección del método numérico adecuado para la solución del sistema no lineal en la DST-II. La eficacia de los filtros estimados repercute en el desempeño de esta transformada en la detección de patrones. Los mejores resultados se obtienen al combinar el método de Newton con preiteración mediante el algoritmo de continuación. La convergencia alcanzada para el 55, 37% de los patrones sugiere que la DST-II podría ser adecuada para patrones con formas específicas, de utilidad en aplicaciones sobre señales biomédicas.

Abstract The use of adapted wavelets for pattern recognition is very attractive because of the multiscalarity of the wavelet transform. However, the good performance of these algorithms in pattern detection strongly depends on the construction of the filters adapted to the pattern of interest. The Discrete Shapelet Transform II (9) (DST-II) is an algorithm inspired by the wavelet transform, which allows the design of tailored filters for pattern detection in one-dimensional signals. The construction of these filters requires the solution of a system of nonlinear equations, which according to (9) can be performed by any iterative method. This research presents a novel and comprehensive numerical study that demonstrates the impact of the choice of the appropriate numerical method for the solution of the nonlinear system in DST-II. The efficiency of the estimated filters has an impact on the performance of this transform in pattern detection. The best results are obtained by combining Newton’s method with preiteration using the continuation algorithm. The convergence achieved for 55,37% of the patterns suggests that DST-II could be suitable for patterns with specific shapes, useful in biomedical signal applications.

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.