Quantum key distribution (QKD) provides unconditional security in theory. However, practical QKD systems face challenges in maximizing the secure key rate and extending transmission distances. In this paper, we introduce a comparative study of the BB84 protocol using coincidence detection with two different quantum channels: a free space and underwater quantum channels. A simulated seawater was used as an example for underwater quantum channel. Different single photon detection modules were used on Bob’s side to capture the coincidence counts. Results showed that increasing the mean photon number generally leads to a higher rate of coincidence detection and therefore higher possibility of increasing the secure key rate. The secure key rat

Blend solid polymer electrolytes (BSPEs) comprising PVA/HPMC/CuSO4 were prepared using a solution casting approach. BSPEs were synthesized with varying weight percentages of CuSO4 (0, 10, 20, and 30 wt.%). A variety of experimental methods, including X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and linear sweep voltammetry (LSV), were used to characterize these BSPE systems. The polymer blend matrix underwent structural alterations, according to the XRD data. The standard data from JCPDS card numbers for copper sulfate matches well with the observed strong peaks of PVA/HPMC+20 wt.% of CuSO4 BSPE. The complex

In this article we  study a single stochastic process model for the evaluate the assets pricing and stock.,On of the models le'vy . depending on the so –called Brownian subordinate as it has been depending on the so-called Normal Inverse Gaussian (NIG). this article aims as the estimate that the parameters of his model using my way (MME,MLE) and then employ those  estimate of the parameters is the study of stock returns and evaluate asset pricing for both the united Bank and Bank of North which their data were taken from the Iraq stock Exchange.

which showed the results to a preference MLE on MME based on the standard of comparison the average square e

The aim of this paper is to approximate multidimensional functions by using the type of Feedforward neural networks (FFNNs) which is called Greedy radial basis function neural networks (GRBFNNs). Also, we introduce a modification to the greedy algorithm which is used to train the greedy radial basis function neural networks. An error bound are introduced in Sobolev space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result is published in [16]).

The current research seeks to identify the most important humanitarian issues of a sacred and very important group in all the heavenly religions and human societies, namely the elderly, to identify their significant problems and health problems, and What are the effects of these problems on their mental health and which is the ultimate goal of human resources in All parts of the world? The study relied on what is available from the sources in the literature starting from the messages of heaven and the Islamic religion followed with humanitarian, social, legal and psychological postulates. The research included four systematic chapters included the definition research and identification of the problem, importance, objectives and terminolo

Abstract:

                    This research aims to apply the Performance Focused Activity Based Costing System in the consultant office of Al-Khwarizmi College of Engineering at the University of Technology for the purpose of measuring the cost of consulting services provided by these offices in order to reduce costs and their reflection in achieving profits. For the purpose of calculating costs accurately, and to test the hypothesis of the research, the research was applied in the office of the consultant of the College of Engineering Al-Khwarizm - University of Baghdad through the financial statement

Background/Objectives: Early and accurate discrimination of neurological conditions, dementia, stroke and healthy aging, remains a critical clinical challenge. Electroencephalography (EEG) is a non-invasive measure of brain dynamics and entropy-based features obtained from multichannel EEG have shown strong discriminative ability. However, existing deep learning approaches do not sufficiently address the combined challenges of small clinical cohorts and high-dimensional entropy feature spaces. In this study, a novel architecture is proposed for multi-class neurological EEG classification under extreme small-sample conditions. Methods: A novel dual-branch Channel-wise Transformer and Attention-Branch Network (EEG-ChTABNet) are pr to