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

Abstract

The aim of the research is to identify the level of awareness and emotional experience among university students and to identify the effect of the educational program based on (Guttmann) model for developing awareness and emotional experience among university students by verifying the validity of the following zero hypotheses: 1) There are no statistically significant differences in the development of awareness and emotional experience among university students at the level of (0.05) between the mean scores of the experimental group in the pre and post-tests. 2) There are no statistically significant differences in the development of awareness and emotional experience among university students at the lev

The aim of the current research is to identify the effect of the active thinking model in the achievement of students of the fifth grade applied science of physics, and their pivotal thinking by verifying the two zero hypotheses, where there is no significant difference at the level of significance (0.05) between the average scores of the experimental group who studied physics using the active thinking model and the average scores of the control group students who studied the same material in the usual way in the achievement test, as well as in the pivotal thinking test. The research sample consisted of (77) students of the applied fifth grade students in two divisions (a) and (b), randomly selected (a) to be the experimental group, and (b)

The research aims to determine the mix of production optimization in the case of several conflicting objectives to be achieved at the same time, therefore, discussions dealt with the concept of programming goals and entrances to be resolved and dealt with the general formula for the programming model the goals and finally determine the mix of production optimization using a programming model targets to the default case.

The logistic regression model is one of the oldest and most common of the regression models, and it is known as one of the statistical methods used to describe and estimate the relationship between a dependent random variable and explanatory random variables. Several methods are used to estimate this model, including the bootstrap method, which is one of the estimation methods that depend on the principle of sampling with return, and is represented by a sample reshaping that includes (n) of the elements drawn by randomly returning from (N) from the original data, It is a computational method used to determine the measure of accuracy to estimate the statistics, and for this reason, this method was used to find more accurate estimates. The ma

Studies on the flexural behavior of post-tensioned beams subjected to strand damage and strengthened with near-surface mounted (NSM) technique using carbon fiber-reinforced polymer (CFRP) are limited and fail to examine the effect of CFRP laminates on strand strain and strengthening efficiency systematically. Furthermore, a design approach for UPC structures in existing design guidelines for FRP strengthening techniques is lacking. Hence, the behavior of post-tensioned beams strengthened with NSM-CFRP laminates after partial strand damage is investigated in this study. The testing program consists of seven post-tensioned beams strengthened by NSM-CFRP laminates with three partial strand damage ratios (14.3% symmetrical damage, 14.3%

Expanded use of antibiotics may increase the ability of pathogenic bacteria to develop antimicrobial resistance. Greater attention must be paid to applying more sustainable techniques for treating wastewater contaminated with antibiotics. Semiconductor photocatalytic processes have proven to be the most effective methods for the degradation of antibiotics. Thus, constructing durable and highly active photocatalytic hybrid materials for the photodegradation of antibiotic pollutants is challenging. Herein, FeTiO3/Fe-doped g-C3N4 (FTO/FCN) heterojunctions were designed with different FTO to FCN ratios by matching the energy level of semiconductors, thereby developing effective direct Z-type heterojunctions. The photodegradation behaviors of th