Resumen La corrosión es un problema recurrente en la industria del gas natural, debido a la presencia de gases corrosivos como el CO2 y el H2S que en presencia de agua pueden atacar el acero y producir daños y fugas que generen accidentes y contaminación, es por ello que la predicción de la tendencia del gas natural hacia la corrosión es fundamental para desarrollar estrategias de inhibición y mitigación de este proceso. El objetivo del estudio fue desarrollar una Red Neuronal Artificial (RNA) entrenada con datos de composición de muestras de gas natural para predecir la tendencia corrosiva, como método alternativo que pueda ser utilizado como una herramienta predictiva eficiente. La RNA se entrenó con una muestra de 46 gases naturales y 11 componentes por cada una, además de la presión y la temperatura como condiciones operacionales, lo que representa un total de 598 datos de entrenamiento de la RNA, además se utilizaron 8 muestras adicionales para la validación externa del modelo. La RNA que desarrolló fue dentro de los principios de la estadística Bayesiana, con una arquitectura de Perceptrón Multicapa. Se obtuvo que la RNA es capaz de clasificar correctamente el 95,65% de la tendencia corrosiva de las muestras, siendo aquellas con tendencia no corrosiva las que presentaron el menor porcentaje de predicción (75%). La predicción de las muestras externas siguió la tendencia esperada, con menor capacidad para clasificar a las no corrosivas, y las muestras con posible corrosión y corrosivas fueron predichas en 80%. Se concluye que la RNA es una herramienta eficiente para la predicción de la tendencia corrosiva del gas natural en presencia de CO2 y su efectividad depende de que se utilicen muestras con todos los componentes necesarios para entrenar la misma, además puede ser mejorada al introducir otros parámetros como la cantidad de agua presente.

Abstract Corrosion is a recurrent problem in the natural gas industry, due to the presence of corrosive gases such as CO2 and H2S that in the presence of water can attack steel and produce damages and leaks that generate accidents and contamination, that is why the prediction of the tendency of natural gas towards corrosion is essential to develop strategies to inhibit and mitigate this process. The objective of the study was to develop an Artificial Neural Network (ANN) trained with composition data of natural gas samples to predict the corrosive tendency, as an alternative method that can be used as an efficient predictive tool. The ANN was trained with a sample of 46 natural gases and 11 components for each one, in addition to pressure and temperature as operational conditions, representing a total of 598 training data of the ANN, also 8 additional samples were used for external validation of the model. The ANN that developed was within the principles of Bayesian statistics, with a Multilayer Perceptron architecture. It was obtained that the ANN is able to correctly classify 95.65% of the corrosive tendency of the samples, being those with non-corrosive tendency the ones that presented the lowest prediction percentage (75%). The prediction of the external samples followed the expected trend, with lower capacity to classify the non-corrosive ones, and the samples with possible corrosion and corrosive were predicted in 80%. It is concluded that ANN is an efficient tool for predicting the corrosive tendency of natural gas in the presence of CO2 and its effectiveness depends on using samples with all the necessary components to train it, and it can be improved by introducing other parameters such as the amount of water present.

Resumen: Durante la construcción, los recursos no controlados impactan el desempeño del proyecto. La Gestión del Valor Ganado (GVG) es un método ampliamente utilizado para la gestión de proyectos basado en el control de tiempos y costos. Los avances en las Tecnologías de la Información (TI) brindan opciones para mejorar el método GVG. El GVG es un método a nivel de proyecto que excluye detallar el comportamiento de los parámetros del proyecto a nivel de operaciones de construcción, se manejan en términos económicos agregados a lo largo del tiempo. Por lo tanto, este trabajo estudia la mejora del GVG utilizando TI para expresar el manejo de variables operativas. Este artículo utiliza un proyecto de construcción de carreteras como caso de estudio, para evaluar tres enfoques (es decir, Red Bayesiana (RB), Red Neuronal Artificial (RNA) y EVM-RNA Híbrida) como opciones de mejora para el método GVG. Se encontró que la RNA proporciona la mejor mejora de los resultados del GVG. El uso de la RNA y los parámetros del proyecto mejora el manejo del GVG. Al tener una mayor efectividad en los pronósticos, se espera que mejore la calidad y disponibilidad de los datos para la toma de decisiones, una condición que a su vez puede mejorar la agilidad y adaptabilidad de los resultados del proyecto tal como está construido. El modelo EVM-ANN utiliza parámetros que influyen en la finalización de la implementación del proyecto, lo que facilita la evaluación del desempeño del tiempo del proyecto en función de diversas condiciones en el campo para que el proyecto pueda obtener la mejor estrategia para garantizar su finalización a tiempo.

Abstract: During construction, uncontrolled resources impact project performance. Earned Value Management (EVM) is a widespread method used for project management based on time and cost control. Advances in Information Technology (IT) provide options to improve the EVM method. The EVM is a project-level method that excludes detailing the behavior of project parameters at the level of construction operations, they are handled in aggregated economic terms over time. Thus, this work studies the improvement of EVM using IT to express the handling of operational variables. This article uses a road construction project as a case study, to evaluate three approaches (i.e., Bayesian Network (BN), Artificial Neural Network (ANN), and Hybrid EVM-ANN) as improvement options for the EVM method. It was found that the ANN provides the best improvement of EVM results. The use of ANN and project parameters improves the handling of EVM. By mayor forecast effectiveness, is expected to improve the quality and availability of data for decision making, a condition which in turn may improve agility and adaptability of the project as-built outcomes. The model EVM-ANN uses parameters that influence project implementation completion, making it easier to assess project time performance based on various conditions in the field so that the project can obtain the best strategy to ensure project completion on time.

Abstract In the present study, a prediction of hardness deterioration and abrasive wear was performed through a neural network using artificial intelligence on a material printed in SLA. This article aims to predict the mechanical properties, wear resistance and surface hardness of parts manufactured by SLA stereolithography printing. A full factorial DOE was used to associate the peculiar parameters (print orientation, cure time, layer height) to perform experiments. The mechanical properties were evaluated according to ASTM regulations, with the objective of obtaining feeding data and validation of the predictions of the Taber Wear Index and hardness using an artificial neural network. The experimental results are in good agreement with the measured data with satisfactory prediction errors with a mean square error (MSE) of 0.01 corresponding to abrasive wear using the clear resin and a mean absolute error (MSE) of 0.09 with an R2 of 0.756, the prediction with the neural network with a mean square error (MSE) of 2.47 corresponding to abrasive wear using the tough resin and a mean absolute error (MSE) of 14.3 with an R2 of 0.97. It was shown that the accuracy of the prediction is reasonable, and the network has the potential to be improved if the experimental database for training the network could be expanded. Therefore, wear and hardness mechanical properties can be predicted appropriately with an ANN.

Resumen En el presente estudio se realizó una predicción del deterioro de la dureza y el desgaste abrasivo a través de una red neuronal utilizando inteligencia artificial sobre un material impreso en SLA. Esta investigación tiene como objetivo predecir las propiedades mecánicas de resistencia al desgaste y dureza superficial de piezas fabricadas mediante impresión por estereolitografía (SLA). Para realizar los experimentos se utilizó un diseño factorial de dos niveles o DOE factorial completo y así asociar los parámetros peculiares (orientación de impresión, tiempo de curado, altura de la capa). Las propiedades mecánicas fueron evaluadas según normativas ASTM, con el objetivo de obtener datos de alimentación y validación de las predicciones del índice de desgaste Taber y la dureza empleando una red neuronal artificial. Los resultados experimentales concuerdan con los datos medidos con errores de predicción satisfactorios con un error cuadrático medio (MSE) de 0,01 correspondiente al desgaste abrasivo utilizando la resina transparente y un error absoluto medio (MSE) de 0,09 con un R2 de 0,76. La predicción con la red neuronal tiene un error cuadrático medio (MSE) de 2.47 perteneciente al desgaste abrasivo utilizando la resina resistente y un error absoluto medio (MSE) de 14,3 con un R2 de 0,97. Se demostró que la precisión de la predicción es razonable, y que la red tiene potencial para mejorar si se pudiera ampliar la base de datos experimental para entrenar la red. Por lo tanto, las propiedades mecánicas de desgaste y dureza se pueden predecir, adecuadamente, con una RNA.

Resumo O Brasil enfrenta altas taxas de inadimplência, devido em parte à pandemia, levando à busca de novas estratégias de cobranças de dívidas. O Machine Learning (ML), empregado com sucesso em inúmeras áreas, é aliado para elevar a eficácia dessas operações. Este artigo busca apresentar um panorama atual das pesquisas sobre aplicações de ML nas operações de cobrança de dívidas, por meio de uma Revisão Sistemática da Literatura. Foi utilizada a metodologia PICO, identificando inicialmente 41 documentos, dos quais 11 passaram por revisão sistemática. Os resultados mostraram quatro objetivos buscados pelos estudos: previsão de inadimplência, personalização das estratégias de cobrança, otimização das ações de recuperação de dívidas e previsão de recuperação de crédito. E os principais algoritmos utilizados foram Decision Tree, Logistic Regression, Random Forest, Naive Bayes, Artificial Neural Network e Deep Learning. Os resultados revelaram que ML é ainda pouco explorado nessa área, oferecendo potencial para avanços substanciais das pesquisas.

Abstract Brazil is facing high default rates, due in part to the pandemic, leading to the search for new debt collection strategies. Machine Learning (ML), successfully used in numerous areas, is an ally to increase the effectiveness of these operations. This article seeks to present a current overview of research on ML applications in debt collection operations, through a Systematic Literature Review. The PICO methodology was used, initially identifying 41 documents, of which 11 underwent systematic review. The results showed four objectives pursued by the studies: default prediction, personalization of collection strategies, optimization of debt recovery actions and credit recovery prediction. And the main algorithms used were Decision Tree, Logistic Regression, Random Forest, Naive Bayes, Artificial Neural Network and Deep Learning. The results revealed that ML is still little explored in this area, offering potential for substantial research advances.

Abstract Introduction: Upper endoscopy is the standard method for diagnosing early-stage gastric cancer. However, according to estimates, up to 20% of tumors are not detected, and their accuracy may be affected by the variability in their performance. In Colombia, most diagnoses take place in advanced stages, which aggravates the problem. Protocols have been proposed to ensure the complete observation of areas prone to premalignant lesions to address variability. Objective: To build and validate an automatic audit system for endoscopies using artificial intelligence techniques. Methodology: In this study, 96 patients from a teaching hospital underwent video-documented endoscopies, spanning 22 stations rearranged to minimize overlaps and improve the identification of 13 key gastric regions. An advanced convolutional network was used to process the images, extracting visual characteristics, which facilitated the training of artificial intelligence in the classification of these areas. Results: the model, called Gastro UNAL, was trained and validated with images of 67 patients (70% of cases) and tested with 29 different patients (30% of cases), which reached an average sensitivity of 85,5% and a specificity of 98,8% in detecting the 13 gastric regions. Conclusions: The effectiveness of the model suggests its potential to ensure the quality and accuracy of endoscopies. This approach could confirm the regions evaluated, alerting less experienced or trained endoscopists about blind spots in the examinations, thus, increasing the quality of these procedures.

Resumen Introducción: La endoscopia digestiva alta es el método estándar para diagnosticar el cáncer gástrico en etapas tempranas. Sin embargo, su precisión puede verse afectada por la variabilidad en su realización, y se estiman hasta 20% de tumores no detectados. En Colombia, la mayoría de los diagnósticos se realizan en etapas avanzadas, lo que agrava el problema. Para abordar la variabilidad, se han propuesto protocolos con el fin de asegurar la observación completa de áreas propensas a lesiones premalignas. Objetivo: Construir y validar un sistema de auditoría automática para endoscopias usando técnicas de inteligencia artificial. Metodología: En este estudio, 96 pacientes de un hospital universitario se sometieron a endoscopias documentadas en video, abarcando 22 estaciones reorganizadas para minimizar solapamientos y mejorar la identificación de 13 regiones gástricas clave. Se utilizó una red convolucional avanzada para procesar las imágenes, extrayendo características visuales, lo que facilitó el entrenamiento de la inteligencia artificial en la clasificación de estas áreas. Resultados: El modelo, llamado Gastro UNAL, fue entrenado y validado con imágenes de 67 pacientes (70% de los casos) y probado con 29 pacientes distintos (30% de los casos), con lo que alcanzó una sensibilidad promedio del 85,5% y una especificidad del 98,8% en la detección de las 13 regiones gástricas. Conclusiones: La eficacia del modelo sugiere su potencial para asegurar la calidad y precisión de las endoscopias. Este enfoque podría confirmar las regiones evaluadas, alertando puntos ciegos en la exploración a los endoscopistas con menos experiencia o en entrenamiento, de tal forma que se aumente la calidad de estos procedimientos.

Abstract: In actuality, the financial investment in Enterprises of the world is common. This investment is performed using internet platforms and value markets. This can generate a loss for many investors due to the uncertainty of the future financial health of the Enterprise. A comparison for the prediction of financial health based on algorithms of machine learning, particularly Support Vector Machine (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Adaptive Neuro-Fuzzy Networks (ANFIS) is presented. The database of the Taiwan Economic Journal from 1999 to 2009 is used, with 95 financial ratios of enterprises financially healthiest and with bankruptcy problems. In the ANN, the epoch number, numbers of neurons, activation functions in each layer, loss function, and learning rate are tested; also, an architecture of a Convolutional Neural Network (CNN) is implemented. In SVM, the experiments are performed using different kernels, polynomial, RBF, linear. Besides, the size of C, size of gamma, and size of the polynomial are varied. In KNN, experiments with different numbers of neighbors, types of weight, and values of P are realized. In ANFIS, experiments with variants of the numbers of fuzzy rules, quantity and type of membership functions, number of epochs, and input dimensions are performed. Optimization using Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) of the three models with the best results are performed; the optimization is based on the search for the best hyperparameters that would provide a higher accuracy. The neural network models presented the best average for all the proposed tests.

Hierdie studie ondersoek die faktore wat dikwels oor die hoof gesien word wat industríele produktiwiteit beïnvloed, met die fokus op arbeid en masjienonderhoud as sleuteldrywers. Omvattende navorsing is onderneem in 'n werkswinkel wat motorkomponente vervaardig om hierdie faktore te identifiseer en te evalueer. Deur gebruik te maak van multi-kriteria besluitnemingsmodelle soos analitiese hiërargie proses, vae analitiese hiërargie proses, tegniek vir orde van voorkeur volgens ooreenkoms met ideale oplossing (TOPSIS), viekriterijumsko kompromisno rangiranje metode, onderneming verspreide toepassingsdiens, en entropie TOPSIS, het die studie verskeie produktiwiteitskriteria gerangskik. Kunsmatige neurale netwerke is toe aangewend om die rangorde te valideer. Die navorsing het die belangrikheid van vervaardigingstoerusting en rou materiaal beklemtoon, en ook die prioritisering van die arbeidsmag. Die implementering van materiaalhanteringstelsels wat daarop gemik is om foute te verminder en produktiwiteit te verbeter, was deurslaggewend. As gevolg van hierdie strategieë het nie-waardetoegevoegde aktiwiteite met 65,56% afgeneem, prosestyd het met 61,03% verbeter, wagtyd het beduidend met 66.66% verminder, mannekrag het met 35% afgeneem, en koste het met 45% afdaal. Hierdie uitkomste het gelei tot 'n noemenswaardige 23% toename in produksievlakke in die kernwinkel. Die studie beklemtoon die doeltreffendheid van innoverende werkmetodes en gestandaardiseerde bedryfsprosedures om produktiwiteit te maksimeer.

This study delves into the often-overlooked factors influencing industrial productivity, focusing on labour and machine maintenance as key drivers. Extensive research was undertaken in a core shop producing automotive components to identify and assess these factors. Using multi-criteria decision models (MCDM) such as analytic hierarchy process (AHP), fuzzy analytic hierarchy process (FAHP), technique for order of preference by similarity to ideal solution (TOPSIS), viekriterijumsko kompromisno rangiranje (VIKOR) method, enterprise distributed application service (EDAs), and Entropy TOPSIS, the study ranked various productivity criteria. Artificial neural networks were then employed to validate these rankings. The research emphasised the significance of manufacturing equipment and raw materials, following the prioritisation of the workforce. Implementing material handling systems aimed at reducing errors and enhancing productivity proved pivotal. As a result of these strategies, non-value-added activities (NVA) decreased by 65.56%, process time improved by 61.03%, waiting time reduced significantly by 66.66%, manpower decreased by 35%, and costs decreased by 45%. These outcomes translated into a notable 23% increase in production levels in the core shop. The study underscores the efficacy of innovative work methods and standardised operating procedures in maximising productivity.

Higher Himalayan catchments are often poorly monitored for hydrological activities involving flood flow prediction for the safety of riverside communities and the successful operation of hydropower projects. This study aimed to estimate the comparative performance of artificial neural network (ANN) based flow prediction models using 10 years of daily river flow data of Kaligandaki catchment at Kotagaun, Nepal, which is a snow-fed catchment in the Himalayan region. The flow prediction models were trained and tested at a hydrological station using the previous 3 days' river flow data to predict the 1-day ahead flow data. Eight different training functions were employed in an ANN model for comprehensive statistical assessment of accuracy and precision of each training function. The most significant and validated result obtained in this study is the comprehensive comparison of various training functions' performance, and identification )f the most efficient training function for the study case. Among the training functions investigated, the Levenberg-Marquardt backpropagation function exhibits the best performance for the model having Nash-Sutcliffe efficiency, root mean square error and mean absolute error values of 0.866, 209.578 and 75.422, respectively. This study provides a fundamental basis for accurate flow prediction of topographically challenged catchments where hydrological monitoring and data collection may be limited. In particular, r his model will help to improve early warning system, hydrological planning, and the safety of riverside communities in the Himalayan region.

Resumen El ámbito de la atención médica, impulsado por el crecimiento constante de datos relacionados con la salud humana y el curso en desarrollo de la transformación digital, está experimentando una notable evolución. En este estudio de carácter experimental, se llevó a cabo una comparativa de técnicas de Inteligencia Artificial, específicamente redes neuronales, Random Forest y árbol de decisión, con el propósito de evaluar su eficacia en el diagnóstico de enfermedades cardiovasculares. Esto se logró aprovechando datos clínicos disponibles en bases de datos de acceso abierto. La metodología se enfocó en la identificación de las variables más influyentes en el diagnóstico de enfermedades cardiovasculares mediante una revisión exhaustiva de la literatura. Luego, se determinaron las técnicas de Aprendizaje automático a emplear y se adquirió el conjunto de datos más apropiado para estas variables. Los resultados revelaron que las tres técnicas de Inteligencia Artificial demostraron un buen desempeño en el diagnóstico de enfermedades cardiovasculares. Es importante resaltar que el modelo basado en redes neuronales destacó con una precisión del 89%, consolidándose como una herramienta de gran relevancia para respaldar el diagnóstico oportuno de estas enfermedades. Estos hallazgos sugieren un posible impacto positivo en la práctica clínica y la atención médica futura al proporcionar a los profesionales de la salud un recurso valioso para tomar decisiones informadas en el diagnóstico y tratamiento de enfermedades cardiovasculares. En última instancia, esto podría mejorar la calidad de la atención y la vida de los pacientes. Este estudio refuerza la noción de que las técnicas de Aprendizaje automático desempeñan un rol fundamental en la transformación de la atención médica y la toma de decisiones clínicas en el ámbito de la salud, ofreciendo nuevas perspectivas para la prevención y el tratamiento de enfermedades cardiovasculares y otros trastornos médicos.

Abstract The field of healthcare, driven by the continuous growth of data related to human health and the ongoing course of digital transformation, is undergoing a significant evolution. In this experimental study, a comparison of Artificial Intelligence techniques, specifically neural networks, Random Forest, and decision tree, was conducted to evaluate their effectiveness in diagnosing cardiovascular diseases. This was achieved by leveraging clinical data available in open-access databases. The methodology focused on identifying the most influential variables in cardiovascular disease diagnosis through a comprehensive literature review. Subsequently, the Machine Learning techniques to be employed were determined, and the most suitable dataset for these variables was acquired. The results revealed that all three Artificial Intelligence techniques demonstrated good performance in diagnosing cardiovascular diseases. It is worth highlighting that the neural network-based model excelled with an accuracy of 89%, establishing itself as a highly relevant tool for supporting timely disease diagnosis. These findings suggest a potential positive impact on clinical practice and future healthcare by providing healthcare professionals with a valuable resource for making informed decisions in the diagnosis and treatment of cardiovascular diseases. Ultimately, this could enhance the quality of patient care and their overall well-being. This study reinforces the notion that Machine Learning techniques play a crucial role in transforming healthcare and clinical decision-making in the field of health, offering new perspectives for the prevention and treatment of cardiovascular diseases and other medical disorders.

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 Climate change poses a significant threat to the agricultural sector around the world, and it could impact the rice crop yields of the Philippines's major rice-producing provinces in Central Luzon; thus, there is a need to investigate the possible influence, both in the near and long term, of climate change on rice crop yields in these major rice-producing provinces. This paper presents the use of random forest, gradient boosting, regression analysis, and artificial neural network (ANN) methods for modeling climate factors (pressure, temperature, relative humidity, rainfall, wind, cloudiness, and sunshine) and rice crop yield (RCY) data from Central Luzon, Philippines, covering the years 2009 to 2018. The projected temperature and rainfall for Representative Concentration Pathways 4.5 and 8.5 (RCP4.5 and RCP8.5, respectively) scenarios were used as input to the models to estimate the possible influence of climate change on the rice crop yields in the rice-producing provinces in Central Luzon. The results suggest an increase in temperature of 1.14 °C in RCP4.5, which surges to 1.62 °C in RCP8.5, and an increase in rainfall by a mean of 3.70 mm in RCP4.5 and 12.28 mm in RCP8.5. Furthermore, three of the models: ANN, random forest (RF), and gradient boosting (GB) showed an increase in RCY, while linear regression (LR) showed a decrease in RCY for both the RCP4.5 and RCP8.5 scenarios compared to the current RCY. Therefore, it is uncertain whether climate change, as characterized by rainfall and temperature, will have a positive or negative impact on rice crop yields in Central Luzon, Philippines.

Resumen El cambio climático representa una amenaza significativa para el sector agrícola en todo el mundo, y podría afectar los rendimientos del cultivo de arroz en las principals provincias productoras de arroz de Filipinas en Luzón Central, por lo que es necesario investigar la posible influencia, tanto a corto como a largo plazo, del cambio climático en los rendimientos del cultivo de arroz en estas principales provincias productoras de arroz. Este artículo presenta el uso de Random Forest, Gradient Boosting, Análisis de Regresión y Redes Neuronales Artificiales (ANN) en la modelización de factores climáticos (presión, temperatura, humedad relativa, lluvia, viento, nubosidad y luz solar) y datos de rendimiento del cultivo de arroz de Luzón Central, Filipinas, que cubren los años 2009 a 2018. Se utilizaron las proyecciones de temperatura y lluvia para los escenarios de Ruta de Concentración Representativa 8.5 y 4.5 (RCP4.5 y 8.5) en los modelos para estimar la posible influencia del cambio climático en los rendimientos del cultivo de arroz en las provincias productoras de arroz de Luzón Central. Los resultados sugieren un aumento de la temperatura de 1,14 °C en RCP4.5, y aumenta a 1,62 °C en RCP8.5; y un aumento en la lluvia en una media de 3,70 mm en RCP4.5 y 12,28 mm en RCP8.5. Además, tres de los modelos (ANN, RF y GB) mostraron un aumento en los rendimientos del cultivo de arroz (RCY), mientras que LR mostró una disminución en RCY; tanto para los escenarios RCP4.5 y 8.5 en comparación con los RCY actuales. El estudio concluye que es incierto si el cambio climático, caracterizado por la lluvia y la temperatura en este estudio, ha tenido un impacto positivo o negativo en los rendimientos del cultivo de arroz semilla.

ABSTRACT Background: The growing number of echocardiographic tests and the need for strict adherence to international quantification guidelines have forced cardiologists to perform highly extended and repetitive tasks when interpreting and analyzing increasingly overwhelming amounts of data. Novel machine learning (ML) techniques, designed to identify images and perform measurements at relevant visits, are becoming more common to meet this obvious need for process automation. Objectives: Our objective was to evaluate an alternative model for the interpretation and analysis of echocardiographic tests mostly based on the use of ML software in order to identify and classify views and perform standardized measurements automatically. Methods: Images came from 2000 healthy subjects, 1800 of whom were used to develop ML algorithms and 200 for subsequent validation. First, a convolutional neural network was developed in order to identify 18 standard echocardiographic views and classify them based on 8 thematic groups (stacks). The results of automatic identification were compared to classification by experts. Later, ML algorithms were developed to automatically measure 16 Doppler scan parameters for regular clinical evaluation, which were compared to measurements by an expert reader. Finally, we compared the time required to complete the analysis of an echocardiographic test using conventional manual methods with the time needed when using the ML model to classify images and perform initial echocardiographic measurements. Inter- and intra-observer variability was also analyzed. Results: Automatic view classification was possible in less than 1 second per test, with a 90% accuracy for 2D images and a 94% accuracy for Doppler scan images. Stacking images had a 91% accuracy, and it was possible to complete the groups with any necessary images in 99% of cases. Expert agreement was outstanding, with discrepancies similar to those found between two human readers. Applying ML to echocardiographic imaging classification and measurement reduced time of analysis by 41% and showed lower variability than conventional reading methods. Conclusion: Application of ML techniques may significantly improve reproducibility and efficiency of echocardiographic interpretations and measurements. Using this type of technologies in clinical practice may lead to reduced costs and increased medical staff satisfaction.

RESUMEN Introducción: El número creciente de estudios ecocardiográficos y la necesidad de cumplir rigurosamente con las recomendaciones de guías internacionales de cuantificación, ha llevado a que los cardiólogos deban realizar tareas sumamente extensas y repetitivas, como parte de la interpretación y análisis de cantidades de información cada vez más abrumadoras. Novedosas técnicas de machine learning (ML), diseñadas para reconocer imágenes y realizar mediciones en las vistas adecuadas, están siendo cada vez más utilizadas para responder a esta necesidad evidente de automatización de procesos. Objetivos: Nuestro objetivo fue evaluar un modelo alternativo de interpretación y análisis de estudios ecocardiográficos, basado fundamentalmente en la utilización de software de ML, capaz de identificar y clasificar vistas y realizar mediciones estandarizadas de forma automática. Material y métodos: Se utilizaron imágenes obtenidas en 2000 sujetos normales, libres de enfermedad, de los cuales 1800 fueron utilizados para desarrollar los algoritmos de ML y 200 para su validación posterior. Primero, una red neuronal convolucional fue desarrollada para reconocer 18 vistas ecocardiográficas estándar y clasificarlas de acuerdo con 8 grupos (stacks) temáticos. Los resultados de la identificación automática fueron comparados con la clasificación realizada por expertos. Luego, algoritmos de ML fueron desarrollados para medir automáticamente 16 parámetros de eco Doppler de evaluación clínica habitual, los cuales fueron comparados con las mediciones realizadas por un lector experto. Finalmente, comparamos el tiempo necesario para completar el análisis de un estudio ecocardiográfico con la utilización de métodos manuales convencionales, con el tiempo necesario con el empleo del modelo que incorpora ML en la clasificación de imágenes y mediciones ecocardiográficas iniciales. La variabilidad inter e intraobservador también fue analizada. Resultados: La clasificación automática de vistas fue posible en menos de 1 segundo por estudio, con una precisión de 90 % en imágenes 2D y de 94 % en imágenes Doppler. La agrupación de imágenes en stacks tuvo una precisión de 91 %, y fue posible completar dichos grupos con las imágenes necesarias en 99% de los casos. La concordancia con expertos fue excelente, con diferencias similares a las observadas entre dos lectores humanos. La incorporación de ML en la clasificación y medición de imágenes ecocardiográficas redujo un 41 % el tiempo de análisis y demostró menor variabilidad que la metodología de interpretación convencional. Conclusión: La incorporación de técnicas de ML puede mejorar significativamente la reproducibilidad y eficiencia de las interpretaciones y mediciones ecocardiográficas. La implementación de este tipo de tecnologías en la práctica clínica podría resultar en reducción de costos y aumento en la satisfacción del personal médico.

Abstract: Explainable artificial intelligence (XAI) is a field of research that has attracted the interest of researchers in recent years. These algorithms seek to provide transparency to artificial intelligence (AI) models. One application of these algorithms is in the medical area, created as an auxiliary tool for corroborating predictions obtained by an AI when classifying pathologies, for example, Acute Lymphoblastic Leukemia (ALL). The present work evaluates the amount of information heat maps provide and how they relate to the blood components' morphological characteristics. For the assessment, four Convolutional Neural Network (CNN) models were retrained and fine-tuned to classify unsegmented images (ALL_IDB2 database). Subsequently, their respective heat maps were generated with the LRP (Layer-wise Relevance Propagation), Deep Taylor, Input*Gradient, and Grad-Cam methods. The best results were obtained with the GoogleNet model and the Grad-Cam heat map generation method, having a percentage of 43.61% of relevant pixels within at least one cell morphological feature present. Moreover, the most significant pixels are within the nucleus, with 73.97% of important pixels inside. According to the results, the Grad-Cam method best relates the relevant pixels generated in the heat map to the morphology of the cell of interest to classify a healthy or diseased cell.

Abstract This paper analyzes the antecedent factors that lead to cyberloafing and deviant behavior in response to the announcement of formal controls. The research is descriptive, with a quantitative approach. It was developed through a survey of 517 employees from different organizations using a hybrid model in two stages (SEM-ANN). While a multi-analytical approach using exploratory and confirmatory factor analysis with covariance-based structural equation modeling (CB-SEM) validated the model, the SEM results were used as input to an artificial neural network (ANN) model to predict the factors for cyberloafing intention. As a result, we observed that ‘peer cyberloafing’ results from the influence of co-workers’ use of technology. This justifies the behavior as acceptable and harmless and brings a collective view of this practice and helps promote the organization’s social capital. The ‘self-efficacy’ in the use of electronic devices that leads to cyberloafing should not only be seen as a negative phenomenon to be banned, but as something that can stimulate creativity and the generation of ideas. Finally, this research provides clues on how to deal with the practice of cyberloafing in a balanced way, considering the management style, whether more controlling or flexible, as well as the feelings of employees concerning the topic, as it is valuable for the organization and society to reflect on the limits of this practice. The introduction of this new methodology and the theoretical contribution of the proposed hybrid model open new horizons for the existing knowledge in the literature related to the understanding cyberloafing. controls descriptive 51 SEMANN. SEMANN ANN . (SEM-ANN) multianalytical multi analytical covariancebased covariance based CBSEM CB (CB-SEM (ANN intention result peer coworkers co workers co-workers technology s capital ‘selfefficacy selfefficacy ‘self efficacy self ‘self-efficacy banned ideas Finally way style flexible topic 5 (SEM-ANN

Resumo Este artigo analisa os fatores antecedentes que levam ao cyberloafing e desvio de comportamento, a partir do anúncio de controles formais. A pesquisa é descritiva, com uma abordagem quantitativa. Foi desenvolvida por meio de uma survey com 517 funcionários de diferentes organizações, utilizando um modelo híbrido em duas etapas (MEE-RNA). Enquanto uma abordagem multi-analítica usando análise fatorial exploratória e confirmatória com modelagem de equações estruturais (MEE) baseada em covariância validou o modelo, os resultados da MEE foram utilizados como entrada para um modelo de rede neural artificial (RNA) para prever a intenção em relação aos fatores de cyberloafing . Como resultado, observou-se que o ‘ cyberloafing entre pares’ resulta da influência do uso de tecnologias pelos colegas. Isso justifica o comportamento como aceitável e inofensivo, proporcionando uma visão coletiva dessa prática e contribuindo para o capital social da organização. A ‘autoeficácia’ no uso de dispositivos eletrônicos, que leva ao cyberloafing, não deve ser vista apenas como um fenômeno negativo a ser proibido, mas como algo que pode estimular a criatividade e a geração de ideias. Por fim, esta pesquisa fornece pistas sobre como lidar, de maneira equilibrada, com a prática do cyberloafing , considerando o estilo de gestão, mais controlador ou flexível, bem como os sentimentos dos funcionários em relação ao tema, pois é valioso para a organização e a sociedade refletir sobre os limites dessa prática. A introdução desta nova metodologia e a contribuição teórica do modelo híbrido proposto abrem perspectivas para o conhecimento existente na literatura relacionada à compreensão do cyberloafing . formais descritiva quantitativa 51 organizações MEERNA. MEERNA RNA (MEE-RNA) multianalítica multi analítica (MEE (RNA resultado observouse observou se pares colegas inofensivo ‘autoeficácia autoeficácia eletrônicos proibido ideias fim lidar equilibrada gestão flexível tema 5 (MEE-RNA

Abstract Unmanned Aerial Vehicles (UAVs), often called drones, have gained progressive prevalence for their swift operational ability as well as their extensive applicability in diverse real-world situations. Of late, UAV usage in precision agriculture has attracted much interest from scientific community. This study will look at drone aid in precise farming. Big data has the ability to analyze enormous amounts of data. Due to this, it is one of the diverse crucial technologies of Information and Communication Technology (ICT) which had applied in precision agriculture for the abstraction of critical information as well as for assisting agricultural practitioners in the comprehension of the most feasible farming practices, and also for better decision-making. This work analyses communication protocols, as well as their application toward the challenge of commanding a drone fleet for protecting crops from infestations of parasites. For computer-vision tasks as well as data-intensive applications, the method of deep learning has shown much potential. Due to its vast potential, it can also be used in the field of agriculture. This research will employ several schemes to assess the efficacy of models includes Visual Geometry Group (VGG-16), the Convolutional Neural Network (CNN) as well as the Fully-Convolutional Network (FCN) in plant disease detection. The methods of Artificial Immune Systems (AIS) can be used in order to adapt deep neural networks to the immediate situation. Simulated outcomes demonstrate that the proposed method is providing superior performance over various other technologically-advanced methods. UAVs, UAVs , (UAVs) drones realworld real world situations late community this ICT (ICT practices decisionmaking. decisionmaking decision making. making decision-making protocols parasites computervision computer vision dataintensive intensive applications potential VGG16, VGG16 VGG 16 (VGG-16) CNN (CNN FullyConvolutional Fully FCN (FCN detection AIS (AIS situation technologicallyadvanced technologically advanced (UAVs VGG1 1 (VGG-16 (VGG-1 (VGG- (VGG

Resumo Os veículos aéreos não tripulados (UAVs), muitas vezes chamados de drones, ganharam prevalência progressiva por sua rápida capacidade operacional, bem como por sua ampla aplicabilidade em diversas situações do mundo real. Ultimamente, o uso de UAV na agricultura de precisão tem atraído muito interesse da comunidade científica. Este estudo analisará a ajuda de drones na agricultura de precisão. O big data tem a capacidade de analisar enormes quantidades de dados. Por isso é uma das diversas tecnologias cruciais de tecnologia da informação e comunicação (TIC) que foram aplicadas na agricultura de precisão para a abstração de informações críticas, bem como para auxiliar os praticantes agrícolas na compreensão das práticas agrícolas mais viáveis, e também para uma melhor tomada de decisão. Este trabalho analisa protocolos de comunicação, bem como sua aplicação no desafio de comandar uma frota de drones para proteção de lavouras contra infestações de parasitas. Para tarefas de visão computacional, bem como para aplicações com uso intensivo de dados, o método de aprendizagem profunda mostrou muito potencial. Devido ao seu vasto potencial, também pode ser utilizado na área agrícola. Esta pesquisa empregará vários esquemas para avaliar a eficácia de modelos, incluindo o Grupo de Geometria Visual (VGG-16), a Rede Neural Convolucional (CNN), bem como a Rede Totalmente Convolucional (FCN) na detecção de doenças de plantas. Os métodos de sistemas imunológicos artificiais (AIS) podem ser utilizados para adaptar redes neurais profundas à situação imediata. Os resultados simulados demonstram que o método proposto oferece desempenho superior em relação a vários outros métodos tecnologicamente avançados. UAVs, UAVs , (UAVs) operacional real Ultimamente científica dados TIC (TIC críticas viáveis decisão parasitas computacional potencial agrícola modelos VGG16, VGG16 VGG 16 (VGG-16) CNN, CNN (CNN) FCN (FCN plantas AIS (AIS imediata avançados (UAVs VGG1 1 (VGG-16 (CNN (VGG-1 (VGG- (VGG