The Particle Swarm Optimization (PSO) algorithm is a well known alternative for global optimization based on a bio‐inspired heuristic. PSO has good performance, low computational complexity and few parameters. Heuristic techniques have been widely studied in the last twenty years and the scientific community is still interested in technological alternatives that accelerate these algorithms in order to apply them to bigger and more complex problems. This article presents an empirical study of some parallel variants for a PSO algorithm, implemented on a Graphic Process Unit (GPU) device with multi‐thread support and using the most recent model of parallel programming for these cases. The main idea is to show that, with the help of a multithreading GPU, it is possible to significantly improve the PSO algorithm performance by means of a simple and almost straightforward parallel programming, getting the computing power of cluster in a conventional personal computer.

This paper presents a proposal for the characterization of the set and reset thresholds for Knowms SDC memristors. The purpose is to incorporate the variability of the hysteresis cycles within the Generalized Mean Metastable Switch (GMMS) memristor model and, in this way, be able to perform simulations that reproduce these phenomena in a meaningful and computationally efficient way. We depart from the assumption that their probabilistic behavior can be well represented by using -stable random variables. The main advantage of using -stable variables is that they can capture both skewness and high variability (i.e., heavy tails), which can be exhibited by the observed phenomenon. At the same time, they also include the Gaussian random variable as a particular case, thus increasing the modeling flexibility.

A basic pattern in the body plan architecture of many animals, plants and some molecular and cellular systems is five-part units. This pattern has been understood as a result of genetic blueprints in development and as a widely conserved evolutionary character. Despite some efforts, a definitive explanation of the abundance of pentagonal symmetry at so many levels of complexity is still missing. Based on both, a computational platform and a statistical spatial organization argument, we show that five-fold morphology is substantially different from other abundant symmetries like three-fold, four-fold and six-fold symmetries in terms of spatial interacting elements. We develop a measuring system to determine levels of spatial organization in 2D polygons (homogeneous or heterogeneous partition of defined areas) based on principles of regularity in a morphospace. We found that spatial organization of five-fold symmetry is statistically higher than all other symmetries studied here (3 to 10-fold symmetries) in terms of spatial homogeneity. The significance of our findings is based on the statistical constancy of geometrical constraints derived from spatial organization of shapes, beyond the material or complexity level of the many different systems where pentagonal symmetry occurs.

An updated specification and logical design for a minimal processing system, inspired by Emil L. Post's machine, is presented. The proposed system comprises a central processing unit with a set of eight instructions, basic memory for both code and data spaces, and a minimal I/O interface, primarily supported by an SPI communication block. The obtained design is fully functional and self-contained, and, although it operates with limited computational resources, it functions as a universal computer. The design was tested using an FPGA development board, and the necessary adjustments were made to submit the design to the Tiny Tapeout initiative to manufacture it as an ASIC, using SkyWater's 130 nm technology, under the Multi-Wafer Project concept.

Based on a measuring system to determine the statistical heterogeneity of individual polygons we propose a method to use polygonal shape patterns as a source of data in order to determine the Shannon entropy of biological organizations. In this research, the term entropy is a particular amount of data related with levels of spatial heterogeneity in a series of different geometrical meshes and sets of random polygons. We propose that this notion of entropy is important to measure levels of information in units of bits, measuring quantities of heterogeneity in geometrical systems. In fact, one important result is that binarization of heterogeneity frequencies yields a supported metric to determine geometrical information from complex configurations. Thirty-five geometric aggregates are tested; biological and non-biological, in order to obtain experimental results of their spatial heterogeneity which is verified with the Shannon entropy parameter defining low particular levels of geometrical information in biological samples. Geometrical aggregates (meshes) include a spectrum of organizations ranging from cell meshes to ecological patterns. Experimental results show that a particular range (0.08 and 0.27) of information is intrinsically associated with low rates of heterogeneity. We conclude it as an intrinsic feature of geometrical organizations in multi-scaling biological systems.