Kinetic Monte Carlo (MC) is the main stochastic strategy used to simulate polymerization systems, as it gives good results with simple formulation. Normally, the algorithm used in this method presents high computational times, being necessary to choose suitable control volume sizes, which gives reliable results in moderate simulation times. The use of high-level languages (Python, MATLAB) over low-level languages (C, Fortran) usually aggravates this scenario, as it is slower despite being easier to use. The current study presents a simple method for speeding up the MC simulation of polymerization reactions. First, the code itself is optimized to reduce by half the computational time required compared with the original code, and then a benchmark of pure Python and Python with Numba is made. The results show a drop in the computational times above 99% when using Numba instead of pure Python codes.

Emotion, importantly displayed by facial expressions, is one of the most significant memory modulators. The interaction between memory and the different emotional valences change across lifespan, while young adults (YA) are expected to better recall negative events (Negativity Bias Hypothesis), older adults (OA) tend to focus on positive stimuli (Positivity Effect Hypothesis). This research work aims at verifying whether cortical electrical activity of these two age groups would also be differently influenced by emotional valences in a visuo-spatial working memory task. 27 YA (13 males) and 25 OA (14 males), all healthy volunteers, underwent electroencephalographic recordings (21 scalp electrodes montage), while performing the Spatial Delayed Recognition Span Task using a touch screen with different stimuli categories: neutral, positive and negative faces and geometric pictures. YA obtained higher scores than OA, and showed higher activation of theta and alpha bands in the frontal and midline regions, besides a more evident right-hemispheric asymmetry on alpha band when compared to OA. For both age groups, performance in the task was worse for positive faces than to negative and to neutral faces. Facial stimuli induced a better performance and higher alpha activation on the pre-frontal region for YA, and on the midline, occipital and left temporal regions for OA when compared to geometric figures. The superior performance of YA was expected due to the natural cognitive deficits connected to ageing, as was a better performance with facial stimuli due to the evolutionary importance of faces. These results were related to cortical activity on areas of importance for action-planning, decision making and sustained attention. Taken together, they are in accordance with the Negativity Bias but do not support the Positivity Effect. The methodology used was able to identify age-related differences in cortical activity during emotional mnemonic processing and may be interesting to future investigations.

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Controlled polymerization causes the final product to have predictable properties, allowing it to have a more diverse application, in turn, increasing its added value. In the present work, a dynamic Monte Carlo (MC) model is used to describe the copolymerization of styrene and n-butyl acrylate through atom transfer radical polymerization (ATRP). A further investigation was performed to estimate the values of kinetic constants with no explicit value reported in the literature using particle swarm optimization (PSO). The model had excellent performance when compared with the experimental data from the literature. The model also provided access to the molecular weight and chemical composition distributions and the polymer microstructures. The results show that the propagation and termination constants' difference affected the polymer structure directly and significantly impacted the distribution curves' broadening. The developed model might be used in the future for monitoring and controlling the average molecular weights and the molecular weight distributions in the chemical process industry.

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