Abstract: The increased interest in developing new photonic devices that can support high data rates, high sensitivity and fast processing capabilities for all optical communications, motivates a pre stage pulse compressor research. The pre-stage research was based on cascading single mode fiber and polarization maintaining fiber to get pulse compression with compression factor of 1.105. The demand for obtaining more précised photonic devices; this work experimentally studied the behavior of Polarization maintaining fiber PMF that is sandwiched between two cascaded singe mode fiber SMF and fiber Bragg gratings FBG. Therefore; the introduced interferometer performed hybrid interference of both Mach-Zehnder

This study aimed to measure the alterations in serum zinc (Zn) and acute phase
proteins ( C-reactive protein and Ceruloplasmin) concentrations in patients with
visceral leishmaniasis (VL). A total of 62 individuals were enrolled in this study :
52 individuals were infected with visceral leishmaniasis and 10 individuals as
healthy control. Serum zinc levels were significantly (p<0.05) decreased in patient
group(76.25 ± 4.59 μg/dl ) when compared with healthy control (103.75 ± 3.77
μg/dl ) . C-reactive protein , as a mediator of innate immunity, removed damaged
cells by activating the classical complement pathway revealed elevated levels in
patients (4.36± 0.23mg/l ) when compared with the healthy control (2

The aim of this study is to assess the prevalence of lung infections among a group of hospitalized cancer patients who received chemotherapy as well as to describe a population of these patients. The clinical data and demographic information were collected from the archived files of  in-patients  referred to  hematology center  / Baghdad Teaching Hospital / Medical City , ministry of health, Iraq  during the period  of  2018.

    This study was carried out on 250 patients with different types of cancer ,they were mostly of age group (40 - 49)  59 / 250 (23.6)% , (14-19) 49 /250 (19.6%) and (60-69) 41/ 250(16.4%) . The patients had two major types of hematological malignancies

To date, comprehensive reviews and discussions of the strengths and limitations of Remote Sensing (RS) standalone and combination approaches, and Deep Learning (DL)-based RS datasets in archaeology have been limited. The objective of this paper is, therefore, to review and critically discuss existing studies that have applied these advanced approaches in archaeology, with a specific focus on digital preservation and object detection. RS standalone approaches including range-based and image-based modelling (e.g., laser scanning and SfM photogrammetry) have several disadvantages in terms of spatial resolution, penetrations, textures, colours, and accuracy. These limitations have led some archaeological studies to fuse/integrate multip

Sorting and grading agricultural crops using manual sorting is a cumbersome and arduous process, in addition to the high costs and increased labor, as well as the low quality of sorting and grading compared to automatic sorting. the importance of deep learning, which includes the artificial neural network in prediction, also shows the importance of automated sorting in terms of efficiency, quality, and accuracy of sorting and grading. artificial neural network in predicting values and choosing what is good and suitable for agricultural crops, especially local lemons.

Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

     Deep learning techniques allow us to achieve image segmentation with excellent accuracy and speed. However, challenges in several image classification areas, including medical imaging and materials science, are usually complicated as these complex models may have difficulty learning significant image features that would allow extension to newer datasets. In this study, an enhancing technique for object detection is proposed based on deep conventional neural networks by combining levelset and standard shape mask. First, a standard shape mask is created through the "probability" shape using the global transformation technique, then the image, the mask, and the probability map are used as the levelset input to apply the image segme

Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr