Most Internet-tomography problems such as shared congestion detection depend on network measurements. Usually, such measurements are carried out in multiple locations inside the network and relied on local clocks. These clocks usually skewed with time making these measurements unsynchronized and thereby degrading the performance of most techniques. Recently, shared congestion detection has become an important issue in many computer networked applications such as multimedia streaming and
peer-to-peer file sharing. One of the most powerful techniques that employed in literature is based on Discrete Wavelet Transform (DWT) with cross-correlation operation to determine the state of the congestion. Wavelet transform is used as a de-noisin

The rapid increase in the number of older people with Alzheimer's disease (AD) and other forms of dementia represents one of the major challenges to the health and social care systems. Early detection of AD makes it possible for patients to access appropriate services and to benefit from new treatments and therapies, as and when they become available. The onset of AD starts many years before the clinical symptoms become clear. A biomarker that can measure the brain changes in this period would be useful for early diagnosis of AD. Potentially, the electroencephalogram (EEG) can play a valuable role in early detection of AD. Damage in the brain due to AD leads to changes in the information processing activity of the brain and the EEG which ca

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%.

Notes are given regarding new record species of the braconid parasite short note in Iraq. That is Apanteles angalcti it is found to parasitize early larval instaxs of Ectomyslois ceratontae, one of the most serious pests of pomegra- nate fruit.

Background: The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor historically recognized for its role in the regulation of toxicity mediated by environmental chemicals. Recent research points to AhR's critical participation in male reproductive physiology, particularly in spermatogenesis, hormone signaling, and the maintenance of sperm quality. Both endogenous ligands (e.g., dietary and gut microbiota-derived metabolites) and exogenous pollutants (e.g., dioxins and benzo-α-pyrene) influence AhR-mediated pathways, making it a key link between environmental exposures and male fertility. Results: This review highlights AhR's influence on the male reproductive system, emphasizing the role of endogenous AhR ligands an

One of the significant stages in computer vision is image segmentation which is fundamental for different applications, for example, robot control and military target recognition, as well as image analysis of remote sensing applications. Studies have dealt with the process of improving the classification of all types of data, whether text or audio or images, one of the latest studies in which researchers have worked to build a simple, effective, and high-accuracy model capable of classifying emotions from speech data, while several studies dealt with improving textual grouping. In this study, we seek to improve the classification of image division using a novel approach depending on two methods used to segment the images. The first