Sludge from stone-cutting (SSC) factories and stone mines cannot be used as decorative stones, stone powder, etc. These substances are left in the environment and cause environmental problems. This study aim is to produce artificial stone composite (ASC) using sludge from stone cutting factories, cement, unsaturated resin, water, silicon carbide nanoparticles (SiC-NPs), and nano-graphene oxide (NGO) as fillers. Nano graphene oxide has a hydrophobic plate structure that water is not absorbed due to the lack of surface tension on these plates. NGO has a significant effect on the properties of artificial stone due to its high specific surface area and low density in the composite. Its uniform distribution in ASC is very low due to its hydropho

A method has been demonstrated to synthesise effective zeolite membranes from existing crystals without a hydrothermal synthesis step.

Introduction: The introduction of analytics tools in sports indicates that artificial neural networks can be one of the intelligent approaches to process complex data and identify patterns that help players move according to their most suitable positions. Objective: The purpose of this research is to investigate the possibility of using artificial neural networks to determine the physical and motor abilities of football players and determine their suitable playing positions based on exact quantitative indicators. Method: The study sample consists of 45 youth players aged (15–16) years from the Espanyol Football Academy in Baghdad. The results are analyzed using a multilayer perceptron (MLP) artificial neural network model to ident

Finger vein recognition and user identification is a relatively recent biometric recognition technology with a broad variety of applications, and biometric authentication is extensively employed in the information age. As one of the most essential authentication technologies available today, finger vein recognition captures our attention owing to its high level of security, dependability, and track record of performance. Embedded convolutional neural networks are based on the early or intermediate fusing of input. In early fusion, pictures are categorized according to their location in the input space. In this study, we employ a highly optimized network and late fusion rather than early fusion to create a Fusion convolutional neural network

The biosorption of lead (II) and chromium (III) onto dead anaerobic biomass (DAB) in single and binary systems has been studied using fixed bed adsorber. A general rate multi- component model (GRM) has been utilized to predict the fixed bed breakthrough curves for single and dual- component system. This model considers both external and internal mass transfer resistances as well as axial dispersion with non-liner multi-component isotherm (Langmuir model). The effects of important parameters, such as flow rate, initial concentration and bed height on the behavior of breakthrough curves have been studied. The equilibrium isotherm model parameters such as maximum uptake capacities for lead (II) and chromium (III) were found to be 35.12 and

The adsorption of Pb(II) ions onto bentonite and activated carbon was investigated. The effects of pH, initial adsorbent dosage, contact time and temperature were studied in batch experiments. The maximum adsorption capacities for bentonite and activated carbon were 0.0364 and 0.015 mg/mg, respectively. Thermodynamic parameters such as Gibbs free energy change, Enthalpy change and Entropy change have been calculated. These thermodynamic parameters indicated that the adsorption process was thermodynamically spontaneous under natural conditions and the adsorption was endothermic in nature. Experimental data were also tested in terms of adsorption kinetics, the results showed that the adsorption processes followed well pseudo second- order