The current study focuses on utilizing machine learning (ML) models in optimizing and predicting laser-induced surface cold hardening in aluminum alloy 6061-O using a pulsed fiber laser. Three input features were utilized: power density (Pd), frequency (f), and pulse overlap (OV), with surface hardness as the matching aim. A set of ML models, including XGBoost and Adaptive Ensemble, was employed. The dataset was preprocessed for normalization and outlier handling, hyperparameter tuning, and assessment through the grid search and cross-validation, and model performance was evaluated using the coefficient of determination (R²) and mean square error (MSE) metrics. The Linear regression and Random Forest regression models were excluded

This study includes adescription of Human serum Albumin by amodified using ion- exchange chromatography with manipulated comparison with cold ethanol precipitation method , It has been nticed that this procedure is superior orer the classical method . The Final yield by the new method 69.32% with purity of 83.42% compared with cohn which yield 60.30 % with purity of 80.7 % . The new method prored that it suitable for the pusi Fication of such material because it yield no precipitation material and it increases the Final yield of albumin solutions . • Human serum Albumin . • Albumin purification . • Ion – exchange chromatography . • Human plasma . • Albumin extraction .

     The nuclear shell model was used to investigate the bulk properties of lithium isotopes (6,7,8,9,11Li), i.e., the ground state density distributions and C0 and C2 components of charge form factors. The theoretical treatment was based on supposing that the Harmonic-oscillator (HO) potential governs the core nucleons while the valence nucleon(s) move through Hulthen potential. Such assumptions were applied for both stable and exotic lithium isotopes. The HO size parameters (  and ), the core radii ( ) and the attenuation parameters (  and ) were fixed to recreate the available empirical size radii for lithium isotopes under study.

The removal of SO2 from simulated gas stream (SO2 + air) in a fixed bed reactor using Modified Activated Carbon (MAC) catalysts was investigated. All the experiments were conducted at atmospheric pressure, initial SO2 concentration of 2500 ppm and bed temperature of 90oC. MAC was prepared by loading a series of nickel and copper oxides 1, 3, 5, 7, and 10 w

Silver nanoparticles synthesized by different species

     The open hole well log data (Resistivity, Sonic, and Gamma Ray) of well X in Euphrates subzone within the Mesopotamian basin are applied to detect the total organic carbon (TOC) of Zubair Formation in the south part of Iraq. The mathematical interpretation of the logs parameters helped in detecting the TOC and source rock productivity. As well, the quantitative interpretation of the logs data leads to assigning to the organic content and source rock intervals identification. The reactions of logs in relation to the increasing of TOC can be detected through logs parameters. By this way, the TOC can be predicted with an increase in gamma-ray, sonic, neutron, and resistivity, as well as a decrease in the density log