Classical cryptography systems exhibit major vulnerabilities because of the rapid development of quan tum computing algorithms and devices. These vulnerabilities were mitigated utilizing quantum key distribution (QKD), which is based on a quantum no-cloning algorithm that assures the safe generation and transmission of the encryption keys. A quantum computing platform, named Qiskit, was utilized by many recent researchers to analyze the security of several QKD protocols, such as BB84 and B92. In this paper, we demonstrate the simulation and implementation of a modified multistage QKD protocol by Qiskit. The simulation and implementation studies were based on the “local_qasm” simulator and the “FakeVigo” backend, respectively. T

Metasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction rat

An extensive program of laboratory testing was conducted on ring footing rested on gypseous soil brought from the north of Iraq (Salah El-Deen governorate) with a gypsum content of 59%. There are limited researches available, and even fewer have been done experimentally to understand how to ring footings behave; almost all the previous works only concern the behavior of ring footing under vertical loads, Moreover, relatively few studies have examined the impact of eccentric load and inclined load on such footing. In this study, a series of tests, including dry and wet tests, were carried out using a steel container (600×600×600) mm, metal ring footing (100 mm outer diameter and 40 mm inner diameter) was placed in the m

The current research aims to diagnose the type and level of influence of social media, whose dimensions are represented by (attitudes towards social media marketing, knowledge and use of social media, influence on the Internet and social media, following up on social media) on customer behavior in its dimensions (attracting attention, raising Interest, desire creation, customer response), and the research sought to answer questions related to the research problem by testing the main hypotheses to explore correlations and influence between the two variables, and to achieve research hypothesis tests, the field study was used, as the research sample reached (135) individuals from the director and the assistant director And marketing personnel

The current research aims to identify the level of compulsive buying behavior and Histrionic Personality among a sample of primary school teachers for the academic year (2021-2022) and in the light of some variables

(sex, marital status). To measure the Histrionic Personality, the researcher applied two scales to a random stratified sample of (200) male and female teachers. The results showed statistically significant differences in the level of compulsive buying behavior according to the gender variable and in favor of female teachers. There are no statistically significant differences in terms of marital status. There are statistically significant differences in the Histrionic Personality based on gender variables in favor of f

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod