Linear Modeling of the Glass Transition Temperature of the system SiO2-Na2O-CaO

This work aimed to mathematically model the glass transition temperature (Tg), one of the most important parameters regarding the behavior of slag, responsible for the sudden change in thermomechanical properties of non-crystalline materials, by the chemical composition of the SiO2-Na2O-CaO system, widely applicable in the production of glasses and constituent of iron, magnesium and aluminum metallurgy slags. The SciGlass database was used to provide data for mathematical modeling through the Python programming language, using the method of least squares. A new equation was established, called P Model, and it presented a lower mean absolute error and lower standard deviation of absolute errors in relation to 3 equations in the literature. The raised equation provides significant results in the mathematical modeling of Tg by the chemical system SiO2-Na2O-CaO, valid for the limits of the data used in the mathematical modeling.Comment: 5 pages, 3 figures, 2 table

Development of Non-Linear Equations for Predicting Electrical Conductivity in Silicates

Electrical conductivity is of fundamental importance in electric arc furnaces (EAF) and the interaction of this phenomenon with the process slag results in energy losses and low optimization. As mathematical modeling helps in understanding the behavior of phenomena and it was used to predict the electrical conductivity of EAF slags through artificial neural networks. The best artificial neural network had 100 neurons in the hidden layer, with 6 predictor variables and the predicted variable, electrical conductivity. Mean absolute error and standard deviation of absolute error were calculated, and sensitivity analysis was performed to correlate the effect of each predictor variable with the predicted variable.Comment: 8 pages, 6 figures, 1 table (AISTech 2023 - Presented and Accepted

Liquidus temperature nonlinear modeling of silicates SiO2−R2O−ROSiO_2-R_2O-RO

The liquidus temperature is an important parameter in understanding the crystalline behavior of materials and in the operation of blast furnaces. Its modeling can be carried out by linear and nonlinear methods through data, considering the artificial neural network a modeling method with high efficiency because it presents the theorem of universal approximation and with that better performances and possibility of greater oscillations. The best linear model and the best nonlinear model were modeled by structural parameters and presented a good numerical approximation, thus demonstrating that mathematical modeling can be performed using structural arguments and also showing a dimensionality reduction method for modeling a thermophysical property of the materials.Comment: 11 pages, 8 figures, 3 table

EEG-Based Epileptic Seizure Prediction Using Temporal Multi-Channel Transformers

Epilepsy is one of the most common neurological diseases, characterized by transient and unprovoked events called epileptic seizures. Electroencephalogram (EEG) is an auxiliary method used to perform both the diagnosis and the monitoring of epilepsy. Given the unexpected nature of an epileptic seizure, its prediction would improve patient care, optimizing the quality of life and the treatment of epilepsy. Predicting an epileptic seizure implies the identification of two distinct states of EEG in a patient with epilepsy: the preictal and the interictal. In this paper, we developed two deep learning models called Temporal Multi-Channel Transformer (TMC-T) and Vision Transformer (TMC-ViT), adaptations of Transformer-based architectures for multi-channel temporal signals. Moreover, we accessed the impact of choosing different preictal duration, since its length is not a consensus among experts, and also evaluated how the sample size benefits each model. Our models are compared with fully connected, convolutional, and recurrent networks. The algorithms were patient-specific trained and evaluated on raw EEG signals from the CHB-MIT database. Experimental results and statistical validation demonstrated that our TMC-ViT model surpassed the CNN architecture, state-of-the-art in seizure prediction.Comment: 15 pages, 10 figure