The goal of this work is to check the presence of PNS (photon number splitting) attack in quantum cryptography system based on BB84 protocol, and to get a maximum secure key length as possible. This was achieved by randomly interleaving decoy states with mean photon numbers of 5.38, 1.588 and 0.48 between the signal states with mean photon numbers of 2.69, 0.794 and 0.24. The average length for a secure key obtained from our system discarding the cases with Eavesdropping was equal to 125 with 20 % decoy states and 82 with 50% decoy states for mean photon number of 0.794 for signal states and 1.588 for decoy states.

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

Mammography is at present one of the available method for early detection of masses or abnormalities which is related to breast cancer. The most common abnormalities that may indicate breast cancer are masses and calcifications. The challenge lies in early and accurate detection to overcome the development of breast cancer that affects more and more women throughout the world. Breast cancer is diagnosed at advanced stages with the help of the digital mammogram images. Masses appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. The incidence of breast cancer in women has increased significantly in recent years.
This paper proposes a computer aided diagnostic system for the extracti

This presented study is to make comparison of cross sections to produce 71As, 72As, 73As and 74As via different reactions with particle incident energy up to 60 MeV of alpha 100 MeV of proton as a part of systematic studies on particle-induced activations on enriched Ge, Ga, Rb and Nb targets and neutron capture. Theoretical calculation of production yield, and suggestion of optimum reaction to produce 71As, 72As, 73As and 74As, based on the main published and approved experimental results of excitation functions were calculated.

Hydrogen   productions  were  achieved    by  irradiating  ethanol ic aqueous solutions (20%. v/v) containing  mixtures of  the  ligand  2,4- dimethoxybcnzylidene-2-hydroxy    aniline     (HL)     or    one    of     i ts complexes  (ML2)   wi th  the  following  divalent  ions:  fVbl  (II),  Fc(IT), Co(II). Ni( rt ), Cu(H) and  Zn (11), as photosensi1izers, methyl  viol ogen (MY.:-)    as   electron   acceptor.  ethylene    diamine &nbsp

In order to scrutinize the impact of the decoration of Sc upon the sensing performance of an XN nanotube (X = Al or Ga, and XNNT) in detecting sarin (SN), the density functionals M06-2X, τ-HCTHhyb, and B3LYP were utilized. The interaction of the pristine XNNT with SN was a physical adsorption with the sensing response (SR) of approximately 5.4. Decoration of the Sc metal into the surface of the AlN and GaN led to an increase in the adsorption energy of SN from −3.4 to −18.9, and −3.8 to −20.1 kcal/mol, respectively. Also, there was a significant increase in the corresponding SR to 38.0 and 100.5, the sensitivity of metal decorated XNNT (metal@XNNT) is increased. So, we found that Sc-decorating more increases the sensitivity of GaNN

This research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%.

Organophosphorus insecticide and growth regulator namely Ethephon (2-chloroethylphosphonic acid) are widely used as a ripening process accelerator and a cultivation duration inhibitor. Pomegranate extract (PPE) has recently been taken into consideration due to its pharmacological effects especially those associated with renal diseases. Thus, this study aims to investigate the possible protective effect of PPE against ethephon-induced nephrotoxicity in rats. In this study four groups of adult male rats were divided into control group, PPE 400 mg/kg group, Ethephon 250 mg/kg group, and finally, PPE + Ethephon group (treated with the same dose of PPE group and Ethephon group). In the current study, kidney function parameters (KIM-1, creatin

     In this work, the detection of zinc (Zn) ions that cause water pollution is studied using the CSNPs- Linker-alkaloids compound that was prepared by linking extracted alkaloids from Iraqi Catharanthus roseus plant with Chitosan nanoparticles (CSNPs) using maleic anhydride. This compound is characterized by an X-ray diffractometer (XRD) which shows that it has an orthorhombic structure with crystallite size in the nano dimension. Zeta Potential results show that the CSNPs-Linker-alkaloids carried a positive charge of 54.4 mV, which means it possesses high stability.  The Fourier transform infrared spectroscopy (FTIR) shows a new distinct band at 1708.93 cm-1 due to C=O esterification. Scanning electron microscope (SEM) image