Diabetic nephropathy (DN) is a principle cause of microangiopathy and the main reason for kidney disease at the end stage in patients with type 2 diabetes mellitus (T2DM). This work aimed to study the relation of pentosidine with kidney injury in the case of diabetic nephropathy. This study included 75 patients suffering from T2DM and 75 apparently healthy subjects. The patients group was divided into three groups ((normoalbumin, microalbuminuria, and macroalbuminuria; 25 patients for each) on the basis of albumin-creatinine ratio (ACR) . The level of serum pentosidine was determined using an ELISA kit. The level of pentosidine was found to be significantly higher in DN patients than in the healthy group. Also, the results revealed a str

Convolutional Neural Networks (CNN) have high performance in the fields of object recognition and classification. The strength of CNNs comes from the fact that they are able to extract information from raw-pixel content and learn features automatically. Feature extraction and classification algorithms can be either hand-crafted or Deep Learning (DL) based. DL detection approaches can be either two stages (region proposal approaches) detector or a single stage (non-region proposal approach) detector. Region proposal-based techniques include R-CNN, Fast RCNN, and Faster RCNN. Non-region proposal-based techniques include Single Shot Detector (SSD) and You Only Look Once (YOLO). We are going to compare the speed and accuracy of Faster RCNN,

This study aims to analyze the spectral properties of plasma produced from rice husk(Rh) using the laser breakdown spectroscopy (LIBS) method. The plasma generation process used the fundamental harmonic (1064 nm) of a Q-switched Nd:YAG laser. Yttrium aluminum garnet (YAG) is a man-made crystalline material. The laser fired pulses with a duration of 10 ns and a repetition rate of 6 Hz. Thus, the energy outputs achieved were 50–200 mJ at the wavelength of 1064 (nm). The silica content in the rice hulls was verified using an XRF measurement, which revealed the presence of silica in the rice hulls in a high percentage. Precise beam focusing was achieved by focusing the laser on the target material. This target material is placed with

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

The study area is located within the Hit area, western Iraq. The measurements of Graphical Bristow’s method were carried out by using Pole-dipole array, to delineate the anomaly of apparent resistivity caused by a known cavity target. The survey was applied along two traverses: traverse in W-E direction and traverse in S-N direction above Um El-Githoaa cavity. Data interpretation of the traverse trending W-E, with a-spacing equal to(2m)identified the anomaly of the cavity at a depth of (2.6m), (1.6m) height, and( 9.5m) width, while the actual dimensions of depth, height, and width were (3.80m),( 2.2m), and (12.30m) respectively, with variations of depth equal to (1.2m), high (0.8m), and width( 2.8m). The data interpretation with a-spac

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.

This study included the isolation and identification of Aspergillus flavus isolates associated with imported American rice grains and local corn grains which collected from local markets, using UV light with 365 nm wave length and different media (PDA, YEA, COA, and CDA ). One hundred and seven fungal isolates were identified in rice and 147 isolates in corn.4 genera and 7 species were associated with grains, the genera were Aspergillus ,Fusarium ,Neurospora ,Penicillium . Aspergillus was dominant with occurrence of 0.47% and frequency of 11.75% in rice grains whereas in corn grains the genus Neurospora was dominant with occurrence of 1.09% and frequency 27.25% ,results revealed that 20 isolates out of 50 A. flavus isolates were able