The COVID-19 is a new disease, the infection can cause respiratory illness with symptoms such as cough, fever, and, in severe cases, pneumonia. Early diagnosis is crucial for the correct treatment to reduce as much as possible the stress in the healthcare system. The need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available. Application of advanced artificial intelligence techniques coupled with radiological imaging can be helpful for the accurate detection of this disease. In this study, we have applied learning transfer to a convolutional neural network known as AlexNet for chest X-ray recognition between COVID-19 and Healthy. We have fine tunned AlexNet for our specific problem. The first layer, which works with color images, is replaced for images in a single intensity. 11,312 chest X-ray images from six public databases were used to train the network. Among them are samples of healthy people and samples that present the effect of pneumonia and COVID-19 diseases. The results prove that deep learning with chest X-ray images can extract significant biomarkers related to COVID-19, since the obtained accuracy, sensitivity, and specificity were 96.5%, 98.0%, and 91.7%, respectively. Receiver operating characteristic analysis and confusion matrices are used to validate the results of the fine-tunned AlexNet network.

An innovative reconfiguration application is proposed to re-calculate the parameters of the Ferragina and Manzini exact search algorithm (or FM indexes), using a modular and efficient hardware implementation to accelerate alignment programs of short DNA sequence reads. Although these programs use multi-core execution strategies or multiple computers, they have become slow considering the very high speed at which the new massively parallel sequencing machines produce the reads to be aligned. Consequently, a search for different ways to accelerate the alignment is crucial. The proposed design runs with software functions in a hybrid system, and has the ability to align millions of reads to reference as large as the human genome. Tests on the M505k325t card show that a single alignment core can accelerate the computation by a factor close to [Formula: see text] in relation to BWA. Due to the minor consumption of area and power, multiple alignment cores can fill the Field Programmable Gate Array (FPGA) by multiplying the computation speed. With a multiple-core implementation, the processing speed of the design outperforms applications that are accelerated by GPUs and competes with similar FPGA proposals whose cost is much higher.

Esta investigación analiza las experiencias de estudiantes de educación superior en la migración de la educación presencial a la educación virtual en el contexto de la Covid-19. Para llevar a cabo el análisis se recopilaron experiencias de alumnos del programa educativo de Ingeniería en Computación de la Universidad del Istmo, campus Tehuantepec, Oaxaca (México), durante los meses de marzo-julio de 2021. El análisis se realizó empleando herramientas de minería de texto, se inició con la exploración de los datos a través de nubes de palabras, bi-gramas hasta penta-gramas, redes de co-ocurrencia e ítems de control. Los resultados del análisis indicaron que el profesor mantuvo el rol central, que el contenido pedagógico que los estudiantes experimentaron estaba basado en entornos no estructurados con tendencia al ensayo y error, que se necesita conocer el perfil cognitivo y afectivo del estudiante para definir un contenido pedagógico adecuado en los planes y programas de estudio, e incluso, recurrir a tecnologías como la inteligencia artificial para mejorar las experiencias educativas docentes y estudiantiles.

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Los avances en materia de robótica y el auge de los dispositivos móviles inteligentes son dos líneas de desarrollo que indican el creciente avance de la tecnología. En este artículo se presenta una propuesta de arquitectura para sistemas basados en una plataforma electrónica Arduino y un dispositivo Android. La utilidad de la arquitectura es mostrada a través del desarrollo de una aplicación que hace uso de las lecturas del acelerómetro del dispositivo, para enviar órdenes por medio de una conexión bluetooth a un robot móvil basado en una tarjeta Arduino Due. La aplicación permite el control de la dirección y velocidad del movimiento del robot a través de una interfaz que muestra facilidades de uso.