This study shows how the structural and dielectric properties of (SnO2)1-x(Mn2O3)x, (where x=0.00, 0.03, 0.05,0.07, and 0.09) prepared using the solid state reaction technique, are affected by the doping of semiconducting metal oxide Mn2O3. Structural analysis of the composites was carried out using information from the composite samples obtained from X-ray diffraction (XRD). The diffraction peak shifting in XRD patterns indicated that Mn ions were successfully incorporated into the SnO2 crystal lattice. Mn ions were successfully doped in the Tin oxide matrix lattice with the subsequent increase of doping levels. The average crystal size evaluated using Scherrer's equation was found to vary from 33 to 37 nm, and the lattice constant

Structural and optical properties of CdO and CdO0.99Cu0.01 thin
films were prepared in this work. Cadmium Oxide (CdO) and
CdO0.99Cu0.01semiconducting films are deposited on glass substrates
by using pulsed laser deposition method (PLD) using SHG with Qswitched
Nd:YAG pulsed laser operation at 1064nm in 6x10-2 mbar
vacuum condition and frequency 6 Hz. CdO and CdO0.99Cu0.01 thin
films annealed at 550 C̊ for 12 min. The crystalline structure was
studied by X-ray diffraction (XRD) method and atomic force
microscope (AFM). It shows that the films are polycrystalline.
Optical properties of thin films were analyzed. The direct band gap
energy of CdO and CdO0.99Cu0.01 thin films were determined from
(αhυ)1/2 v

In the present work, the nuclear shell model with Hartree–Fock (HF) calculations have been used to investigate the nuclear structure of ²⁴Mg nucleus. Particularly, elastic and inelastic electron scattering form factors and transition probabilities have been calculated for low-lying positive and negative states. The sd and sdpf shell model spaces have been used to calculate the one-body density matrix elements (OBDM) for positive and negative parity states respectively. Skyrme-Hartree-Fock (SHF) with different parameterizations has been tested with shell model calculation as a single particle potential for reproducing the experimental data along with a harmonic oscillator (HO) and Woods-Saxo

This study was conducted to determine the activity of plant Sesbania rostrata and two isolate from arbuscular mycorrhizae fungi (A,B) as a bioremediation of soil polluted by cadmium and lead elements in north and south of Baghdad city. The results showed that the average of soil pollution by cadmium and lead elements in north of Baghdad was less than the average of soil pollution in the south of Baghdad which recorded 10.0, 9.0 mg/kg and 27.0, 25.0 mg/kg respectively. The plant Sesbania recorded ability to accumulate the lead element in shoot system 19.65 mg/kg and in root system 27.2 mg/kg and for cadmium element 19.6, 24.6 mg/kg in shoot and root respectively. The results showed that the isolate A from soil pollution is more effected

Information about soil consolidation is essential in geotechnical design. Because of the time and expense involved in performing consolidation tests, equations are required to estimate compression index from soil index properties. Although many empirical equations concerning soil properties have been proposed, such equations may not be appropriate for local situations. The aim of this study is to investigate the consolidation and physical properties of the cohesive soil. Artificial Neural Network (ANN) has been adapted in this investigation to predict the compression index and compression ratio using basic index properties. One hundred and ninety five consolidation results for soils tested at different construction sites

Abstract

   The method binery logistic regression and linear discrimint function of the most important statistical methods used in the classification and prediction when the data of the kind of binery (0,1) you can not use the normal regression therefore resort to binary logistic regression and linear discriminant function in the case of two group in the case of a Multicollinearity problem between the data (the data containing high correlation) It became not possible to use binary logistic regression and linear discriminant function, to solve this problem, we resort to Partial least square regression.

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