Optimization of sugarcane bagasse pretreatment using alkaline hydrogen peroxide through ANN and ANFIS modelling.

The present study compares the optimization using Artificial Neural Networks (ANN) and Adaptive Network-based Fuzzy Inference System (ANFIS) in the sugarcane bagasse delignification process using Alkaline Hydrogen Peroxide (AHP). Two variables were assessed experimentally: temperature (25-45 °C) and hydrogen peroxide concentration (1.5-7.5%(w/v)). The Klason Method was used to measure the amount of insoluble lignin, the High Performance Liquid Chromatography (HPLC) was used to determine the glucose and xylose concentrations and the Fourier Transform Infrared Spectroscopy (FT-IR) was applied to identify oxidized lignin structure in the samples. The analytical results were used for training and testing of ANN and ANFIS models. The statistical quality of the models was significant due to the low values of the errors indices (RMSE) and determination coefficient R2 between experimental and calculated values.

Neural network modeling to support an experimental study of the delignification process of sugarcane bagasse after alkaline hydrogen peroxide pre-treatment.

The present study examines the use of Artificial Neural Networks (ANN) as prediction and fault detection tools for the delignification process of sugarcane bagasse via hydrogen peroxide (H2O2). Experimental conditions varied from 25 to 45°C for temperature and from 1.5% to 7.5% (v/v) for H2O2 concentrations. Analytical results for the delignification were obtained by Fourier Transform Infrared (FT-IR) analysis and used for the ANN training and testing steps, allowing for the development of ANN models. The condition experimentally identified as the most suitable for the delignification process was of 25°C with 4.5% (v/v) H2O2, oxidizing 54% of total lignin. An ANN topology was selected for each proposed model, whose performance was evaluated by the correlation coefficient (R2) and error indices (MSE and SSE). The values obtained for R2 and the error indices indicated good agreements of the theoretical and actual data, of close to 1 and close to 0, respectively.

Age-related differences in cortical activity during a visuo-spatial working memory task with facial stimuli.

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.