Double hydrothermal method was used to prepare nano gamma alumina   using aluminum nitrate nano hydrate and sodium aluminate as an aluminum source, CTAB (cetyltrimethylammonium bromide) as surfactant, and variable acids: weak acids like; citric, and acitic acids, and strong acids like; hydrochloric and nitric acids as a bridge between aluminum salts and surfactant. Different crystallization times  12, 24, 48, and 72 hrs were applied. All the batches were prepared at pH equals to 9. XRD diffraction technique was used to investigate the crystalline nano gamma alumina pure from surfactant. N₂ adsorption-desorption (BET) was used to measure the surface area and pore volume of the prepared nano alumina, the average p

         In the present work, the magnetic dipole and electric quadrupole moments for some sodium isotopes have been calculated using the shell model, considering the effect of the two-body effective interactions and the single-particle potentials. These isotopes are; ²¹Na (3/2+), ²³Na (3/2+), ²⁵Na (5/2+), ²⁶Na (3+), ²⁷Na (5/2+), ²⁸Na (1+) and, ²⁹Na (3/2+). The one-body transition density matrix elements (OBDM) have been calculated using the (USDA, USDB, HBUMSD and W) two-body effective interactions carried out in the sd-shell model space. The sd shell model space consists of the active 2s_1/2, 1d_5/2,

Stereolithography (SLA) has become an essential photocuring 3D printing process for producing parts of complex shapes from photosensitive resin exposed to UV light. The selection of the best printing parameters for good accuracy and surface quality can be further complicated by the geometric complexity of the models. This work introduces multiobjective optimization of SLA printing of 3D dental bridges based on simple CAD objects. The effect of the best combination of a low-cost resin 3D printer’s machine parameter settings, namely normal exposure time, bottom exposure time and bottom layers for less dimensional deviation and surface roughness, was studied. A multiobjective optimization method was utilized, combining the Taguchi me

      In this work, a test room was built in Baghdad city, with (2*1.5*1.5) m³  in dimensions, while the solar chimneys (SC) were designed with aspect ratio (ar) bigger than 12. Test room was supplied by many solar collectors; vertical single side of air pass with ar equals 25, and tilted 45^o double side of air passes with ar equals 50 for each pass, both collectors consist of flat thermal energy storage box collector (TESB) that covered by transparent clear acrylic sheet, third type of collector is array of evacuated tubular collectors with thermosyphon in 45^o instelled  in the bottom of TESB of vertical SC. The TESB was

This study synthesized zeolite 4A, and hierarchical composite structure consisting of zeolite 4A- carbon were successfully prepared. Hydrothermal method was used to grow a layer of zeolite 4A over porous carbon surfaces to enhance mass transfer and increase surface area of zeolite. The products then were used to remove radioactive cesium¹³⁷Cs from liquid wastewater. Iraqi dates leaves midribs (DM) were used as locally available agricultural waste to prepare low- cost porous carbon, using carbonization method in tubular furnace at 900C for two hours. Hierarchical porous structures including zeolite are prepared by mechanically activating the carbon surface via Ultrasonicating nanoparticles suspension of ground zeolite type 4A.F

A new method for construction ion-selective electrode (ISE) by heating reaction of methyl orange with ammonium reineckate using PVC as plasticizer for determination methyl orange and determination Amitriptyline Hydrochloried drug by formation ion-pair on electrode surface . The characteristics of the electrode and it response as following : internal solution 10-4M , pH (2.5-5) ,temperature (20-30) and response time 2 sec. Calibration response for methyl orange over the concentrationrange 10-3 -10-9 M with R=0.9989 , RSD%=0.1052, D.O.L=0.315X10-9 MEre%=(-0.877- -2.76) , Rec%.=(97.230 -101.711) .

The use of real-time machine learning to optimize passport control procedures at airports can greatly improve both the efficiency and security of the processes. To automate and optimize these procedures, AI algorithms such as character recognition, facial recognition, predictive algorithms and automatic data processing can be implemented. The proposed method is to use the R-CNN object detection model to detect passport objects in real-time images collected by passport control cameras. This paper describes the step-by-step process of the proposed approach, which includes pre-processing, training and testing the R-CNN model, integrating it into the passport control system, and evaluating its accuracy and speed for efficient passenger flow