Background: The world is in front of two emerging problems being scarceness of virgin re-sources for bioactive materials and the gathering of waste production. Employment of the surplus waste in the mainstream production can resolve these problems. The current study aimed to prepare and characterize a natural composite CaO-SiO2 based bioactive material derived from naturally sustained raw materials. Then deposit this innovative novel bioactive coating composite materials overlying Yttria-stabilized tetragonal zirconia substrate. Mate-rials and method; Hen eggshell-derived calcium carbonate and rice husk-derived silica were extracted from natural resources to prepare the composite coating material. The manufac-tured powder was characterized

Ag-doped NiO thin films were fabricated by dip coating and subsequently annealed at 373, 473, 573, and 673 K to elucidate the influence of annealing temperature on their structural, morphological, optical, and NO2-sensing characteristics. X-ray diffraction confirmed the formation of polycrystalline Ag/NiO films, with progressively improved crystallinity and larger crystallite size at elevated annealing temperatures. Atomic force microscopy revealed a homogeneous surface morphology with grain growth, with the average grain diameter increasing from 26.40 to 34.2 nm. Optical analysis showed high visible-region transmittance (65–70%) and a gradual reduction in the optical band gap from 2.75 to 2.07 eV with increasing annealing temperature. Ga

The results of research to reach the conditions that prevents the emergence of primary or secondary voids and achieve worker benefit from molded by almost 100%, which was the situation that cast poured in a mold heated and insulated from all sides to achieve freezing directional full starting from the region remote from the casting and ending then. Has also been compared to the microscopic structure of the resulting castings of various molding conditions, as these conditions have achieved the best sound microscopic structures.

In this research a new compounds were synthesized started from compound 1 which was synthesized from two moll of piperidine (secondary cyclic amine) with dichloro acetic acid, compound 1 reacted by condensation reaction with methanol and H 2 SO 4 as a catalyst to give the ester compound 2. Compound 2 was reacted with hydrazine hydrate 80 % to give compound 3 , then the compounds 4-13 were synthesized from refluxing of compound 3 with the selected aldehydes and ketones via using few drops of glacial acetic acid, finely step the compounds 4-13 were reacted with phtalic anhydride to give compounds 14-23.. All these compounds were characterized by using of melting point, FTIR, 1 HNMR and mass spectroscopy. Scheme 1 and Scheme 2 shown the all re

Multiple eliminations (de-multiple) are one of seismic processing steps to remove their effects and delineate the correct primary refractors. Using normal move out to flatten primaries is the way to eliminate multiples through transforming these data to frequency-wavenumber domain. The flatten primaries are aligned with zero axis of the frequency-wavenumber domain and any other reflection types (multiples and random noise) are distributed elsewhere. Dip-filter is applied to pass the aligned data and reject others will separate primaries from multiple after transforming the data back from frequency-wavenumber domain to time-distance domain. For that, a suggested name for this technique as normal move out- frequency-wavenumber domain

     The objective of the study is to demonstrate the predictive ability is better between the logistic regression model and Linear Discriminant function using the original data first and then the Home vehicles to reduce the dimensions of the variables for data and socio-economic survey of the family to the province of Baghdad in 2012 and included a sample of 615 observation with 13 variable, 12 of them is an explanatory variable and the depended variable is number of workers and the unemployed.

     Was conducted to compare the two methods above and it became clear by comparing the  logistic regression model best of a Linear Discriminant  function written