A comparative study of two state-of-the-art stochastic model predictive controllers for linear systems with parametric and additive uncertainties is presented. On the one hand, Stochastic Model Predictive Control (SMPC) is based on analytical methods and solves an optimal control problem (OCP) similar to a classic Model Predictive Control (MPC) with constraints. SMPC defines probabilistic constraints on the states, which are transformed into equivalent deterministic ones. On the other hand, Scenario-based Model Predictive Control (SCMPC) solves an OCP for a specified number of random realizations of uncertainties, also called scenarios. In this paper, Classic MPC, SMPC and SCMPC are compared through two numerical examples. Thanks to several Monte-Carlo simulations, performances of classic MPC, SMPC and SCMPC are compared using several criteria, such as number of successful runs, number of times the constraints are violated, integral absolute error and computational cost. Moreover, a Stochastic Model Predictive Control Toolbox was developed by the authors, available on MATLAB Central, in which it is possible to simulate a SMPC or a SCMPC to control multivariable linear systems with additive disturbances. This software was used to carry out part of the simulations of the numerical examples in this article and it can be used for results reproduction.

Convolutional Neural Networks (CNNs) are becoming increasingly popular in deep learning applications, e.g. image classification, speech recognition, medicine, to name a few. However, the CNN inference is computationally intensive and demanding a large among of memory resources. In this work is proposed a CNN inference hardware accelerator, which was implemented in a co-processing scheme. The aim is to reduce the hardware resources and achieve the better possible throughput. The design was implemented in the Digilent Arty Z7-20 development board, which is based on System on Chip (SoC) Zynq-7000 of Xilinx. Our implementation achieved a  of accuracy for the MNIST database using only 12-bits fixed-point format. The results show that the co-processing scheme operating at a conservative speed of 100 MHz can identify around 441 images per second, which is about 17% times faster than a 650 MHz - software implementation. It is difficult to compare our results against other implementations based on Field-Programmable Gate Array (FPGA), because the others implementations are not exactly like ours. However, some comparisons, regarding the logical resources used and accuracy, suggest that our work could be better than previous works.

Dipyrone tablet was characterized by x-ray photoelectron spectroscopy. Sample was fixed to a stainless-steel sample holder with copper double-sided adhesive tape. Survey spectra, C 1s, O 1s, N 1s, S 2p, and Na 1s core levels spectra were acquired. The results showed the presence of carbon, oxygen, nitrogen, and sulfur, elements that constitute the Dipyrone/Metamizole molecule; however, carbon and oxygen are also found in the excipients (other ingredients). In addition, sodium was detected, which is associated with excipients.

El artículo presenta dos miradas sobre el tema de contabilidad y mujer, una antropológica y la otra sociológica. En relación con la primera, se estudiará la contabilidad de las “hijas del agua” como se autodenominaron las culturas indígenas de Colombia. Con respecto a la segunda, se analizará la problemática del mercado laboral femenino en nuestro país. La metodología empleada es cualitativa, utilizando un método histórico-hermenéutico para comprender fenómenos sociales, mediante un estudio comparado de dos situaciones problemáticas. Se concluye que lo que vincula estas dos miradas es el concepto de ecocontabilidad como un saber comprometido con el ecosistema y con el patrimonio cultural en el contexto de la equidad de género.

The production of nanoparticles (Np) is an important sector in current technological development. Processes such as high‐energy grinding are among the most promising because of their high efficiency and low energy cost. In this sense, the filling ratio of vial (FRV) is one of the most interesting and poorly studied process variables that can optimize the production of Np on a large scale. In the present work, dry ZnO Np are produced, with different FRV values (40%, 38%, 23%, and 10%), as well as wet samples. The results show that the variation of the FRV allows to generate an energetic increase of the grinding, modifying the morphology, the particle size, and the surface area of the ZnO powders, without generating structural or chemical changes. The X‐ray diffraction (XRD) results show the presence of the zincite phase (hexagonal) with a variation in crystallinity depending on the FRV, which is due to the deformation of the Np during the process. The XPS results showed a slight chemical shift (variation in binding energy) as a function of grinding because of the activation shown by deformation. UV–VIS analyses showed a marked increase in the magnitude of absorbed intensity for all analyzed samples compared with the commercial powder. These results confirm that FRV is a sensitive parameter in the process of obtaining ZnO Np by the planetary mill technique, which allows efficient control of its characteristics for a given application.