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

One of the important units in Sharq Dijla Water Treatment Plant (WTP) first and second extensions are the alum solution preparation and dosing unit. The existing operation of this unit accomplished manually starting from unloading the powder alum in the preparation basin and ending by controlling the alum dosage addition through the dosing pumps to the flash mix chambers. Because of the modern trend of monitoring and control the automatic operation of WTPs due to the great benefits that could be gain from optimum equipment operation, reducing the operating costs and human errors. This study deals with how to transform the conventional operation to an automatic monitoring and controlling system depending on a Programmable

The map of permeability distribution in the reservoirs is considered one of the most essential steps of the geologic model building due to its governing the fluid flow through the reservoir which makes it the most influential parameter on the history matching than other parameters. For that, it is the most petrophysical properties that are tuned during the history matching. Unfortunately, the prediction of the relationship between static petrophysics (porosity) and dynamic petrophysics (permeability) from conventional wells logs has a sophisticated problem to solve by conventional statistical methods for heterogeneous formations. For that, this paper examines the ability and performance of the artificial intelligence method in perme

Copper oxide thin films were deposited on glass substrate using Successive Ionic Layer Adsorption and Reaction (SILAR) method at room temperature. The thickness of the thin films was around 0.43?m.Copper oxide thin films were annealed in air at (200, 300 and 400°C for 45min.The film structure properties were characterized by x-ray diffraction (XRD). XRD patterns indicated the presence of polycrystalline CuO. The average grain size is calculated from the X-rays pattern, it is found that the grain size increased with increasing annealing temperature. Optical transmitter microscope (OTM) and atomic force microscope (AFM) was also used. Direct band gap values of 2.2 eV for an annealed sample and (2, 1.5, 1.4) eV at 200, 300,400oC respect

Hydrogen and nanocarbon were produced by the catalytic decomposition of electrocracking gas obtained by the pyrolysis of liquid organic waste via electric arc discharge. The GIAP-16 (NiO-Al2O3) industrial catalyst was used to reduce the maximum decomposition temperature to 700 °C. In a fixed-bed reactor, under atmospheric pressure, reasonable amounts of high-purity hydrogen were produced, accompanied by deposits of nanocarbon by-product. The NiO-Al2O3 catalyst showed excellent catalytic activity. X-ray powder diffraction analysis of the NiO-Al2O3 composite revealed the presence of cubic NiO and rhombohedral Al2O3, which were chemically stable. However, above 500 °C, NiAl2O4 began to appear. The specific surface area of the catalyst was de

Background: Breast cancer has become one of the most predominant health risks among women and its mass death rate has continued to escalate world over. New data indicate that breast cancer may be modification by lifestyle factors, especially diet. Lactobacillus and Lactococcus type of probiotics have been identified to cure or promote health by regulation of the immune mechanism. Such bacteria are frequently found in healthy breast tissue and they could possibly help prevent breast cancer. Combining with a well established probiotic, Bifidobacterium longum, Lactobacillus acidophilus has proved to have immunomodulatory and anti-inflammatory effects, therefore, it should be a prospective probiotic strain to maintain breast health. Aim

In this paper, turbidimetric and reversed-phase ultra-fast liquid chromatography (UFLC) methods were described for the quantitative determination of ephedrine hydrochloride in pharmaceutical injections form. The first method is based on measuring the turbidimetric values for the formed yellowish white precipitate in suspension status in order to determine the ephedrine hydrochloride concentration. The suspended substance is formed as a result of the reaction of ephedrine hydrochloride with phosphomolybdic acid which was used as a reagent. The physical and chemical characteristics of the complex were investigated. The calibration graphs of ephedrine were established by turbidity method. While the second method (UFLC) was conducted using the

Pseudomonas aeruginosa is a common and major opportunistic human pathogen, its causes many and dangersinfectious diseases due to death in some timesex: cystic fibrosis , wounds inflammation , burns inflammation , urinary tract infection , other many infections otitis external , Endocarditis , nosocomial infection and also causes other blood infections (Bacteremia). thereforebecomes founding fast and exact identification of P. aeruginosafrom samples culture very important.However, identification of this species may be problematic due to the marked phenotypic variabilitydemonstrated by samples isolates and the presence of other closely related species. To facilitate species identification, we used 16S ribosomal DNA(rRNA) sequence data