Toxic dyes are commonly discharged into waste waters and dyes are extensively used in the textile industry so it is necessary to find out efficient and eco-friendly method for treating waste waters resulting from industrial effluences. To achieve this aim the fungus Trichoderma sp. is employed into two lines: first line was self – immobilized fungal pellets in (Czapek – Dox medium) to adsorbs two dyes crystal violet, congo red by concentrations 0.01, 0.02, 0.03, 0.04, 0.05, 0.06 mg/L to both dyes, PH 2, room temperature with shaker in ( hrs.²,hrs.⁴,hrs.²⁴) , by Uv- Visible spectrum . the removal efficiency of 0.05 mg/L crystal violet by Trichoderma sp was 96%. but there was no remova

Face Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility to d

This work was conducted to study the oxidation of phenol in aqueous solution using copper based catalyst with zinc as promoter and different carrier, i.e. γ-Alumina and silica. These catalysts were prepared by impregnation method.
The effect of catalyst composition, pH (5.6-9), phenol to catalyst concentration ratio (2-0.5), air feed rate (30-50) ml/s, stirring speed (400-800) rpm, and temperature (80-100) °C were examined in order to find the best conditions for phenol conversion.
The best operating conditions which lead to maximum phenol conversion (73.1%) are : 7.5 pH, 4/6 phenol to catalyst concentration, 40 ml/s air feed rate, 600 rpm stirring speed, and 100 °C reaction temperature. The reaction involved an induction period

This paper studies the combination of fluid viscous dampers in the outrigger system to add supplementary damping into the structure, which purpose to remove the dependability of the structure to lower variable intrinsic damping. This optimizes the accuracy of the dynamic response and by providing higher level of damping, basically minimizes the wanted stiffness of the structure while at the same time optimizing the achievement.

     The modal considered is a 36 storey square high rise reinforced concrete building. By constructing a discrete lumped mass model and using frequency-based response function, two systems of dampers, parallel and series systems are studied. The maximu

It is well known that petroleum refineries are considered the largest generator of oily sludge which may cause serious threats to the environment if disposed of without treatment. Throughout the present research, it can be said that a hybrid process including ultrasonic treatment coupled with froth floatation has been shown as a green efficient treatment of oily sludge waste from the bottom of crude oil tanks in Al-Daura refinery and able to get high yield of base oil recovery which is 65% at the optimum operating conditions (treatment time = 30 min, ultrasonic wave amplitude = 60 micron, and (solvent: oily sludge) ratio = 4). Experimental results showed that 83% of the solvent used was recovered meanwhile the main water

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D