In aired and semiarid areas like Iraq, saline soil may be considered one of the major concerns. In addition to environmental effects, they may produce significant geotechnical hazards that could interrupt the structure stability depending on the salt type and its concentration. So, it is crucial to identify the degree of the soil salinity with a proper tool for getting a qualified assessment and consequently offering a suitable treatment. In this paper, the electrical resistivity technique has been employed to detect the degree of soil salinity by considering a new electronic system. The system used a single-phase Direct Current (DC) to Alternating Current (AC) inverter accompanied by a transformer. Natural soils became artificially saline

The aim of this work is to enhance the mechanical properties of the glass ionomer cement GIC (dental materials) by adding Zirconium Oxide ZrO₂ in both micro and nano particles. GIC were mixed with (3, 5 and 7) wt% of both ZrO₂ micro and nanoparticles separately. Compressive strength (CS), biaxial flexural strength (BFS), Vickers Microhardness (VH) and wear rate losses (WR) were investigated. The maximum compression strength was 122.31 MPa with 5 wt. % ZrO₂ micro particle, while 3wt% nanoparticles give highest Microhardness and biaxial flexural strength of 88.8 VHN and 35.79 MPa respectively. The minimum wear rate losses were 3.776µg/m with 7 wt. % ZrO₂ nanoparticle. GIC-contai

In the early 90s military operations and United Nations Special Commission “UNSCOM” teams have been destroyed the past Iraqi chemical program. Both operations led an extensive number of scattered remnants of contaminated areas. The quantities of hazardous materials, incomplete destructed materials, and toxic chemicals were sealed in two bunkers. Deficiency of appropriate destruction technology led to spreading the contamination around the storage site. This paper aims to introduce the environmental detection of the contamination in the storage site area using geospatial analysis technique. The environmental contamination level of nutrients and major ions such as sulphate (SO₄), potassium (K), sodium (Na), magnesi

Electrical Discharge Machining (EDM) is a non-traditional cutting technique for metals removing which is relied upon the basic fact that negligible tool force is produced during the machining process. Also, electrical discharge machining is used in manufacturing very hard materials that are electrically conductive. Regarding the electrical discharge machining procedure, the most significant factor of the cutting parameter is the surface roughness (Ra). Conventional try and error method is time consuming as well as high cost. The purpose of the present research is to develop a mathematical model using response graph modeling (RGM). The impact of various parameters such as (current, pulsation on time and pulsation off time) are studied on

The new azo dye was prepared from the reaction of the diazonium salt derived from 3-aminophenol with 2- hydroxyquinoline, then it was used to prepare a series of complexes with the chlorides of cobalt, chromium, copper, nickel, platinum, palladium and ammonium molybdate. The ligand was identified by a proton and carbon nuclear magnetic resonance spectroscopy, and the compounds were collected. The prepared materials were subjected to infrared, ultraviolet-visible, and mass spectrometry, as well as thermogravimetric analysis, differential calorimetry, and elemental analysis. Conductivity, magnetic susceptibility, metal content, and chlorine content of the complexes were also measured. The results showed that the ligand behaves in a trigonal b

In this research the results of applying Artificial Neural Networks with modified activation function to
perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance
Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of
identification strategy consists of a feed-forward neural network with a modified activation function that
operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have
been trained online and offline have been used, without requiring any previous knowledge about the
system to be identified. The activation function that is used in the hidden layer in FFNN is a modified
version of the wavelet func

In this research the results of applying Artificial Neural Networks with modified activation function to perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of identification strategy consists of a feed-forward neural network with a modified activation function that operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have been trained online and offline have been used, without requiring any previous knowledge about the system to be identified. The activation function that is used in the hidden layer in FFNN is a modified version of the wavelet function. This approach ha