Prediction of optical properties of rare-earth doped phosphate glasses using gene expression programming.

The progression of optical materials and their associated applications necessitates a profound comprehension of their optical characteristics, with the Judd-Ofelt (JO) theory commonly employed for this purpose. However, the computation of JO parameters (Ω2, Ω4, Ω6) entails wide experimental and theoretical endeavors, rendering traditional calculations often impractical. To address these challenges, the correlations between JO parameters and the bulk matrix composition within a series of Rare-Earth ions doped sulfophosphate glass systems were explored in this research. In this regard, a novel soft computing technique named genetic expression programming (GEP) was employed to derive formulations for JO parameters and bulk matrix composition. The predictor variables integrated into the formulations consist of JO parameters. This investigation demonstrates the potential of GEP as a practical tool for defining functions and classifying important factors to predict JO parameters. Thus, precise characterization of such materials becomes crucial with minimal or no reliance on experimental work.

Crystal Plasticity Parameter Optimization in Cyclically Deformed Electrodeposited Copper-A Machine Learning Approach.

This paper describes an application of a machine learning approach for parameter optimization. The method is demonstrated for the elasto-viscoplastic model with both isotropic and kinematic hardening. It is shown that the proposed method based on long short-term memory networks allowed a reasonable agreement of stress-strain curves to be obtained for cyclic deformation in a low-cycle fatigue regime. The main advantage of the proposed approach over traditional optimization schemes lies in the possibility of obtaining parameters for a new material without the necessity of conducting any further optimizations. As the power and robustness of the developed method was demonstrated for very challenging problems (cyclic deformation, crystal plasticity, self-consistent model and isotropic and kinematic hardening), it is directly applicable to other experiments and models.

Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm.

It is important to determine the risk for admission to the intensive care unit (ICU) in patients with COVID-19 presenting at the emergency department. Using artificial neural networks, we propose a new Data Ensemble Refinement Greedy Algorithm (DERGA) based on 15 easily accessible hematological indices. A database of 1596 patients with COVID-19 was used; it was divided into 1257 training datasets (80 % of the database) for training the algorithms and 339 testing datasets (20 % of the database) to check the reliability of the algorithms. The optimal combination of hematological indicators that gives the best prediction consists of only four hematological indicators as follows: neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase, ferritin, and albumin. The best prediction corresponds to a particularly high accuracy of 97.12 %. In conclusion, our novel approach provides a robust model based only on basic hematological parameters for predicting the risk for ICU admission and optimize COVID-19 patient management in the clinical practice.