This paper aims to present an updated review of parallel algorithms for solving square and rectangular single and double precision matrix linear systems using multi-core central processing units and graphic processing units. A brief description of the methods for the solution of linear systems based on operations, factorization and iterations was made. The methodology implemented, in this article, is a documentary and it was based on the review of about 17 papers reported in the literature during the last five years (2016-2020). The disclosed findings demonstrate the potential of parallelism to significantly decrease extreme learning machines training times for problems with large amounts of data given the calculation of the Moore Penrose pseudo inverse. The implementation of parallel algorithms in the calculation of the pseudo-inverse will allow to contribute significantly in the applications of diversifying areas, since it can accelerate the training time of the extreme learning machines with optimal results.
Severe acute respiratory syndrome coronavirus is a type 2 highly contagious, and transmissible among humans; the natural human immune response to severe acute respiratory syndrome-coronavirus-2 combines cell-mediated immunity (lymphocyte) and antibody production. In the present study, we analyzed the dynamic effects of adaptive immune system cell activation in the human host. The methodology consisted of modeling using a system of ordinary differential equations; for this model, the equilibrium free of viral infection was obtained, and its local stability was determined. Analysis of the model revealed that lymphocyte activation leads to total pathogen elimination by specific recognition of viral antigens; the model dynamics are driven by the interaction between respiratory epithelial cells, viral infection, and activation of helper T, cytotoxic T, and B lymphocytes. Numerical simulations showed that the model solutions match the dynamics involved in the role of lymphocytes in preventing new infections and stopping the viral spread; these results reinforce the understanding of the cellular immune mechanisms and processes of the organism against severe acute respiratory syndrome-coronavirus-2 infection, allowing the understanding of biophysical processes that occur in living systems, dealing with the exchange of information at the cellular level.
The environment suffers constant damage due to the excessive manufacture of non-degradable materials. Since petroleum-derived plastic polymers degrade slowly, it is necessary to promote the use of biodegradable plastics, such as polylactic acid. Polylactic acid is produced from natural and renewable resources, it has suitable physical and chemical properties, and biodegrades under appropriate temperature and humidity conditions, which are achieved in the composting process. Currently, there are models that are based on the measurement of carbon dioxide, and mass loss, among others. However, there are no models that describe the biodegradation of polylactic acid considering the effect of the influencing factors of the composting process. The objective of this work is to model the dynamics of polylactic acid biodegradation under controlled composting conditions, considering the main influencing factors, such as temperature, moisture content, and oxygen. Using mathematical modeling from ordinary differential equations as a methodology, simulations were carried out based on the degradation of dry matter from different substrates. The results aim to predict the dynamics of polylactic acid biodegradation, through a model that integrates the influencing factors of the composting process.
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