Consens interdisciplinari sobre l’abordatge de la persona amb malaltia renal crònica avançada: pla operatiu de la malaltia renal crònica

Malalts crònics; Malaltia renal crònica; AbordatgeEnfermos crónicos; Enfermedad renal crónica; AbordajeChronically ill; Chronic kidney disease; ApproachEl present consens té per voluntat millorar l’atenció en aquesta fase de l’MRC, donar eines als professionals de cara a la valoració preventiva prèvia a la decisió del tractament que cal seguir en la fase d’MRCA i l’homogeneïtzació de l’atenció específica a partir de la decisió d’instaurar un tractament conservador aprofitant les eines establertes al Departament de Salut per a l’atenció a les persones amb malalties cròniques avançades (MACA)

Retos actuales de la farmacia

Retos actuales de la farmacia es un proyecto que está coordinado por Leobargo Manuel Gómez Oliván y un equipo de investigadores que forman parte del claustro de la Facultad de Química en el área de posgrado, ellos han incentivado el espíritu investigador y científico de los estudiantes adscritos al programa para adentrarse en el ámbito farmacéutico. Los capítulos que conforman esta edición son el reflejo de la actividad académica desarrollada en este posgrado en las diferentes áreas de acentuación que lo conforman: farmacia molecular, farmacia social y tecnología farmacéutica

A Brief Panorama of Artificial Intelligence in Mexico

Artificial Intelligence (AI) allows that computer-based systems learn from experience and perform a task similar to how humans would. Currently, Mexico is considered one of the Latin American countries with significant progress in adopting technologies related to AI. In this paper, we present a summary of what is happening in Mexico regarding AI. First, we introduce the concept of AI. Second, we present a timeline and speak about the Mexican society of AI and the related conferences and journals. Then, we present the academic programs and the challenges of AI. Finally, we present the conclusions

Study of the Effect of Combining Activation Functions in a Convolutional Neural Network

Convolutional Neural Networks (CNN’s) have proven to be an effective approach for solving image classification problems. The output, the accuracy and the computational efficiency of a CNN are determined mainly by the architecture, the convolutional filters, and the activation functions. Based on the importance of an activation function, in this paper, nine new activation functions based on combinations of classical functions such as ReLU and sigmoid are presented. Also, a study about the effects caused by the activation functions in the performance of a CNN is presented. First, every new function is described, also, their graphs, analytic forms and derivatives are presented. Then, a traditional CNN model with each new activation function is used to classify three 10-class databases: MNIST, Fashion MNIST and a handwritten digit database created by us. Experimental results illustrate that some of the proposed activation functions lead to better performances on classifying than classical activation functions. Moreover, our study demonstrated that the accuracy of a CNN could be increased by 1.18% with the new proposed activation functions

A Convolutional Neural Network for Handwritten Digit Recognition

Technological development in recent years has generated the constant need to digitalize and analyze data, where handwritten digit recognition is a popular problem. This paper focuses on the creation of two handwritten digit datasets and their use to train a Convolutional Neural Network (CNN) to classify them, also, a proposed extra preprocessing technique is applied to the images of one of the data sets. Experiments show that the proposed preprocessing technique lead to obtain accuracies above 98%, which were higher than the values obtained with the dataset without the additional preprocessing