Inefficient wastewater disposal and wastewater discharge problems in water bodies have led to increasing pollution in water bodies.  Pollutants in the river contribute to increasing the biological oxygen demand (BOD), total suspended solids (SS), total dissolved solids (TDS), chemical oxygen demand (COD), and toxic metals render this water unsuitable for consumption and even pose a significant risk to human health. Over the last few years, water conservation has been the subject of growing awareness and concern throughout the world, so this research focused on review studies of researches that studied the importance of water quality of wastewater treated disposal in water bodies and modern technology to management w

        Artificial Neural networks (ANN) are powerful and effective tools in time-series applications. The first aim of this paper is to diagnose better and more efficient ANN models (Back Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and

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

In the last decades, using mineral admixture in concrete became very necessary to improve concrete properties and reduce CO₂ emissions associated with the cement production process. Subsequently, more sustainable concrete can be obtained.  Ternary blended cement containing two different types of mineral admixture can achieve ambitious steps in this trend. In this research, the synergic effects of mineral admixtures in ternary blended cement and its effects on concrete fresh properties, strength, durability, and efficiency factors of mineral admixture in ternary blended cement, were reviewed. The main conclusion reached after reviewing many literature pieces is that the concrete with ternary blended cement

In order to implement the concept of sustainability in the field of construction, it is necessary to find an alternative to the materials that cause pollution by manufacturing, the most important of which is cement. Because factory wastes provide siliceous and aluminous materials and contain calcium such as fly ash and slag that are used in the production of high-strength geopolymer concrete with specifications similar to ordinary concrete, it was necessary for developing this type of concrete that is helping to reduce CO₂ (dioxide carbon) in the atmosphere. Therefore, the aim of this study was to study the influence of incorporating various percentages of slag as a replacement for fly ash and the effect of sl

In order to reduce hydrostatic pressure in oil wells and produce oil from dead oil wells, laboratory rig was constructed, by injecting LPG through pipe containing mixture of two to one part of East Baghdad crude oil and water. The used pressure of injection was 2.0 bar, which results the hydrostatic pressure reduction around 246 to 222 mbar and flow rate of 34.5 liter/hr fluid (oil-water), at 220 cm injection depth. Effects of other operating parameters were also studied on the behavior of two phase flow and on the production of oil from dead oil wells.

Magnetosphere is a region of space surrounding Earth magnetic field, the formation of magnetosphere depends on many parameters such as; surface magnetic field of the planet, an ionized plasma stream (solar wind) and the ionization of the planetary upper atmosphere (ionosphere). The main objective of this research is to find the behavior of Earth's magnetosphere radius (R_mp) with respect to the effect of solar wind kinetic energy density (Usw), Earth surface magnetic field (B_o), and the electron density (N_e) of Earth's ionosphere for three years 2016, 2017 and 2018. Also the study provides the effect of solar activity for the same period during strong geomagnetic storms on the behavior of R_mp. F

Abstract

The environmental conditions are important factors, because they affect both the efficiency of a photovoltaic module and the energy load. This research was carried out experimentally and modeling was done in MATLAB –Simulink by monitoring the variation in power output of the system with environmental conditions such as solar radiation, ambient temperature, wind speed, and humidity of Baghdad city. From the results, the ambient temperatures are inversely proportional to humidity and the output power performance of the system, while the wind speed is directly proportional with the output power performance of the system.  

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Three complexes of copper(II) and iron(II) with mixed ligands acetylacetonebis(thio-semicarbazone)- ABTSH2 and benzaldazine- BA have been prepared and characterized using different physico-chemical techniques including the determination of metal contents, mole-cular weight, measurement of molar conductivity, magnetic moment, molar refraction, infrared and electronic spectra. Accordingly, octahedral complexes having general formulaes [Cu2(ABTSH2)2(BA)2Cl2]Cl2 and [M2(ABTSH2)2(BA)2(SO4)2] {M= Cu(II) or (Fe(II)} have been proposed. The resulted complexes screened for antifungal activity in vitro against the citrus pathogen Aspergillus niger and Fusarium sp. which caused root rot of sugar and the beans pathogen Alternaria sp. All the complex

Background/Objectives: The purpose of this study was to classify Alzheimer’s disease (AD) patients from Normal Control (NC) patients using Magnetic Resonance Imaging (MRI). Methods/Statistical analysis: The performance evolution is carried out for 346 MR images from Alzheimer's Neuroimaging Initiative (ADNI) dataset. The classifier Deep Belief Network (DBN) is used for the function of classification. The network is trained using a sample training set, and the weights produced are then used to check the system's recognition capability. Findings: As a result, this paper presented a novel method of automated classification system for AD determination. The suggested method offers good performance of the experiments carried out show that the