The aim of research is to show the effect of Ferric Oxide (Fe2O3) on the electricity production and wastewater treatment, since 2.5% of Ferric Oxide (Fe2O3) (heated and non heated) nanoparticles has been used. Characterization of nanoparticles was done using X-ray Diffraction (XRD) and Scan Electron Microscopy (SEM). The influence of acidity was also studied on both wastewater treatmenton the Chemical Oxygen demand (COD) and Biological Oxygen Demand (BOD) and voltage output was studied. From the results, it was infused that the dosage of 0.025 g/l and an initial pH 7 were founded to be optimum for the effective degradation of effluents. The results concluded that the treatment of anaerobic sludge wastewater using Ferric Oxide (Fe2O3) in

Sustainable crop production in a coarse soil texture is challenging due to high water permeability and low soil water holding capacity. In this paper, subsurface water retention technology (SWRT) through impermeable polyethylene membranes was placed at depth 35 cm below ground surface and within the root zone to evaluate and compare the impact of these membranes and control treatment (without using the membranes) on yield and water use efficiency of eggplant inside the greenhouse. The study was conducted in Al-Fahamah Township, Baghdad, Iraq during spring growing season 2017. Results demonstrated the yield and water use efficiencies were 3.483 kg/m² and 5.653 kg/m³, respectively for SWRT treatment p

Roughness length is one of the key variables in micrometeorological studies and environmental studies in regards to describing development of cities and urban environments. By utilizing the three dimensions ultrasonic anemometer installed at Mustansiriyah university, we determined the rate of the height of the rough elements (trees, buildings and bridges) to the surrounding area of the university for a radius of 1 km. After this, we calculated the zero-plane displacement length of eight sections and calculated the length of surface roughness. The results proved that the ranges of the variables above are Z_H (9.2-13.8) m, Z_d (4.3-8.1) m and Z_o (0.24-0.48) m.

This paper proposes a self organizing fuzzy controller as an enhancement level of the fuzzy controller. The adjustment mechanism provides explicit adaptation to tune and update the position of the output membership functions of the fuzzy controller. Simulation results show that this controller is capable of controlling a non-linear time varying system so that the performance of the system improves so as to reach the desired state in a less number of samples.

Abstract

Zigbee is considered to be one of the wireless sensor networks (WSNs) designed for short-range communications applications. It follows IEEE 802.15.4 specifications that aim to design networks with lowest cost and power consuming in addition to the minimum possible data rate. In this paper, a transmitter Zigbee system is designed based on PHY layer specifications of this standard. The modulation technique applied in this design is the offset quadrature phase shift keying (OQPSK) with half sine pulse-shaping for achieving a minimum possible amount of phase transitions. In addition, the applied spreading technique is direct sequence spread spectrum (DSSS) technique, which has

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