A new copolymer (MFA) was prepared from condensation of melamine (M) with p- methyl – anisole (A) in the presence of condensation agent like 37% (w/v) of formaldehyde. The new copolymer was characterized by elemental, IR and HNMR spectra. The chelating ion-exchange property of this polymer was studied for methylene blue dye in aqueous solution in 100-200ppm concentrations. The adsorption study was carried out over a wide range of pH, shaking time and in media of various kinetic parameters models. Thermal parameters like enthalpy, entropy and Gibbs free energy of adsorption process of methylene blue on surface of MFA resin were determined on the basis of kinetic parameters at different temperatures. To describe the equilibrium of adsorp

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

Coaches and analysts face a significant challenge of inaccurate estimation when analyzing Men's 100 Meter Sprint Performance, particularly when there is limited data available. This necessitates the use of modern technologies to address the problem of inaccurate estimation. Unfortunately, current methods used to estimate Men's 100 Meter Sprint Performance indexes in Iraq are ineffective, highlighting the need to adopt new and advanced technologies that are fast, accurate, and flexible. Therefore, the objective of this study was to utilize an advanced method known as artificial neural networks to estimate four key indexes: Accelerate First of 10 meters, Speed Rate, Time First of 10 meters, and Reaction Time. The application of artifi

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

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 dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of

The investigation of signature validation is crucial to the field of personal authenticity. The biometrics-based system has been developed to support some information security features.Aperson’s signature, an essential biometric trait of a human being, can be used to verify their identification. In this study, a mechanism for automatically verifying signatures has been suggested. The offline properties of handwritten signatures are highlighted in this study which aims to verify the authenticity of handwritten signatures whether they are real or forged using computer-based machine learning techniques. The main goal of developing such systems is to verify people through the validity of their signatures. In this research, images of a group o