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Artificial intelligence, particularly the growth of neural network research and development, has become an invaluable tool for data analysis, offering unrivalled solutions for image generation, natural language processing, and personalised suggestions. In the meantime, biomedicine has been presented as one of the pressing challenges of the 21st century. The inversion of the age pyramid, the increase in longevity, and the negative environment due to pollution and bad habits of the population have led to a necessity of research in the methodologies that can help to mitigate and fight against these changes.The combination of both fields has already achieved remarkable results in drug discovery, cancer prediction or gene activation. However, challenges such as data labelling, architecture improvements, interpretability of the models and translational implementation of the proposals still remain. In haematology, conventional protocols follow a stepwise approach that includes several tests and doctor-patient interactions to make a diagnosis. This procedure results in significant costs and workload for hospitals.In this paper, we present an artificial intelligence model based on neural networks to support practitioners in the identification of different haematological diseases using only rutinary and inexpensive blood count tests. In particular, we present both binary and multiclass classification of haematological diseases using a specialised neural network architecture where data is studied and combined along it, taking into account the clinical knowledge of the problem, obtaining results up to 96% accuracy for the binary classification experiment. Furthermore, we compare this method against traditional machine learning algorithms such as gradient boosting decision trees and transformers for tabular data. The use of these machine learning techniques could reduce the cost and decision time and improve the quality of life for both specialists and patients while producing more precise diagnoses.

La cuantificación del desarrollo morfológico de una pastura resulta de interés a los fines de la aplicación de prácticas de manejo. El objetivo fue cuantificar el estado medio de desarrollo morfológico, analizar su variación a través del tiempo térmico y establecer la relación existente entre el estado medio de desarrollo y la proporción de lámina viva en pasturas de digitaria (Digitaria eriantha Steud.) y mijo perenne (Panicum coloratum L.). A principios de octubre, sobre pasturas de digitaria y mijo perenne se marcaron tres parcelas de 3,0 x 6,0 m en las que se cosecharon a ras del suelo 30 macollos por parcela, tomados al azar, cada siete días y durante 16 semanas. Se cuantificó el estado de desarrollo de cada macollo cortado a través de índices numéricos que relacionan el peso o la cantidad de macollos en cada estado con el peso (EMP) o la cantidad (EMC) total de macollos cortados, y se determinó la proporción de lámina viva. Los datos se sometieron a análisis de regresión lineal y modelos lineales mixtos para mediciones repetidas. Los índices EMP y EMC, en digitaria y mijo perenne, se relacionaron en forma estrecha (p

This paper addresses the optimal operation of a petrochemical complex under economic and environmental criteria. The site mathematical model includes linear and nonlinear simplified models for single plants to calculate site production taking into account main operating variables, intermittent deliveries and inventory variable profiles. The objective function considers the maximization of total profit and the minimization of environmental impact, subject to constraints on mass balances, bounds on product demands, equipment capacities and intermediate and final product storage tanks limitations. The environmental objective is measured with the global warming potential (GWP) and Eco-indicator 99 metrics according to the life cycle assessment procedures. The resulting mixed integer nonlinear programming (MINLP) models have been implemented in GAMS. The bi-criteria MINLP model is solved with the e-constraint method. The resulting pareto-optimal curve shows the tradeoff between the economic and environmental aspects to pursuit a sustainable operation of an existing integrated petrochemical complex. The optimal solution shows that polyethylene plants have the best environmental and economic performances in the entire complex. As compared to current situation, optimization results show that the petrochemical complex can satisfy product demands while improving the environmental behavior by decreasing greenhouse gases emissions in almost 26 %, from 1,018 to 750.42 kt CO2-eq/y.