The present study aims to remove nickel ions from solution of the simulated wastewater using (Laminaria saccharina) algae as a biosorbent material. Effects of experimental parameters such as temperature at (20 - 40) C⁰, pH at (3 - 7) at time (10 - 120) min on the removal efficiency were studied.
Box-Wilson method was adopted to obtain a relationship between the above three experimental parameters and removal percentage of the nickel ions. The experimental data were fitted to second order polynomial model, and the optimum conditions for the removal process of nickel ions were obtained.
The highest removal percentage of nickel ions obtained was 98.8 %, at best operating conditions (Temperature 35 C⁰, pH 5 and Time 10 min).

Abstract

Robust controller design requires a proper definition of uncertainty bounds. These uncertainty bounds are commonly selected randomly and conservatively for certain stability, without regard for controller performance.  This issue becomes critically important for multivariable systems with high nonlinearities, as in Active Magnetic Bearings (AMB) System. Flexibility and advanced learning abilities of intelligent techniques make them appealing for uncertainty estimation. The aim of this paper is to describe the development of robust H₂/H_∞ controller for AMB based on intelligent estimation of uncertainty bounds using Adaptive Neuro Fuzzy Inference System (ANFIS).  Simulatio

The central marshes are one of the most important wetlands/ecosystems in the southern area of Iraq. This study evaluates the bed soil's mechanical, physical, and chemical properties at certain southern Iraqi central marshes sites. This was conducted to investigate their types and suitability for enhancing the agricultural reality of most field crops and for construction purposes. Soil samples were collected from 15 sites at 10-100 cm depth. Hence, numerous parameters were determined: index properties, unconfined compressive strength, direct shear strength, consolidation, texture, and sieve analysis, water content, specific gravity, dry density, permeability, pH, total soluble salts (TSS), organic materials (OM) and total

The current study aimed to identify the difficulties faced by the student in mathematics and possible proposals to address these difficulties. The study used a descriptive method also used the questionnaire to collect data and information were applied to a sample of (163) male and female teachers. The results of the study found that the degree of difficulties in learning mathematics for the fifth and sixth grades is high for some paragraphs and intermediate for other paragraphs, included the student's field. The results also revealed that there were no statistically significant differences at the level of significance (α = 0.05) between the responses of the members of the study sample from male and female teachers to the degree of diffi

A reduced-order extended state observer (RESO) based a continuous sliding mode control (SMC) is proposed in this paper for the tracking problem of high order Brunovsky systems with the existence of external perturbations and system uncertainties. For this purpose, a composite control is constituted by two consecutive steps. First, the reduced-order ESO (RESO) technique is designed to estimate unknown system states and total disturbance without estimating an available state. Second, the continuous SMC law is designed based on the estimations supplied by the RESO estimator in order to govern the nominal system part. More importantly, the robustness performance is well achieved by compensating not only the lumped disturbance, but also its esti

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

Abstract                          

 Should be the goal of Government controls, including environmental controls and safety laws to protect citizens from the harmful effects of negative secreted by human's additions and changes in the environment. and perhaps the protection aspects of the protection of citizens from the adverse effects of communications towers, including those produced by towers of health effects. The people the right to choose the nature of the physical environment, which should not be imposed on them by others. Communications towers are one of the main problems that have been