The development of wireless sensor networks (WSNs) in the underwater environment leads to underwater WSN (UWSN). It has severe impact over the research field due to its extensive and real-time applications. However effective execution of underwater WSNs undergoes several problems. The main concern in the UWSN is sensor nodes’ energy depletion issue. Energy saving and maintaining quality of service (QoS) becomes highly essential for UWASN because of necessity of QoS application and confined sensor nodes (SNs). To overcome this problem, numerous prevailing methods like adaptive data forwarding techniques, QoS-based congestion control approaches, and various methods have been devised with maximum throughput and minimum network lifesp

المقال منشور على موقع مجلة الفورين بوليسي الأميركية (Foreign Policy) على الانترنت في 27 أيلول/سبتمبر 2019. يُشير مصطلح العزل (Impeachment) في الثقافة السياسية الأميركية إلى مجموعة الإجراءات التي يتم بموجبها عزل الرئيس من منصبه، وهذه الإجراءات هي بمثابة عملية طويلة تجري داخل الكونجرس، وتتم وفقاً لخطوات يؤدي فيها كل من مجلسيّ النواب والشيوخ دوراَ. ولا يعني القيام بهذه الإجراءات أن يتم عزل الرئيس، فقد تتم إ

HM Al-Dabbas, RA Azeez, AE Ali, Iraqi Journal of Science, 2023

Background: Symptoms related to the upper gastro-intestinal tract are very common. Attribution of these symptoms to upper G. I. T.diseases are usually done on clinical bases, which could be confirmed by Esophago Gastro Duodenoscopy (EGD). The use of such tools might increase the diagnosis accuracy for such complaints. The indications for upper G I endoscopy might decrease the negative results of endoscopies.Objective: To follow strict indications for Esophago Gastro Duodenoscopy in order to decrease the negative endoscopy results. Methods: One thousand eight hundred and ninety cases were subjected to EGD from Feb. 1999 to Feb 2009 at Alkindy Teaching Hospital and Abd-Al-Majeed private hospital in Baghdad, Iraq. A special endoscopy unit f

 In this paper, we propose a method using continuous wavelets to study the multivariate fractional Brownian motion through the deviations of the transformed random process to find an efficient estimate of Hurst exponent using eigenvalue regression of the covariance matrix. The results of simulations experiments shown that the performance of the proposed estimator was efficient in bias but the variance get increase as signal change from short to long memory the MASE increase relatively. The estimation process was made by calculating the eigenvalues for the variance-covariance matrix of Meyer’s continuous wavelet details coefficients.

Background: Acute cholecystitis is common surgical
problem, which was treated previously by conservative
treatment .Later early open has been introduced as an
alternative to interval for treatment of acute cholecystitis.
Early open was found to be a safe, successful with
comparable postoperative complication rate. With the
advent of laparoscopy laparoscopic have been used for
chronic cholecystitis and became the first line of
treatment. New reports have shown that laparoscopic can
be used as an alternative to open for surgical treatment of
acute cholecystitis.
Objectives: to compare the success, safety of early
laparoscopic versus early open as a primary treatment of
acute cholecystitis.
Methods:

In this paper, we characterize the percolation condition for a continuum secondary cognitive radio network under the SINR model. We show that the well-established condition for continuum percolation does not hold true in the SINR regime. Thus, we find the condition under which a cognitive radio network percolates. We argue that due to the SINR requirements of the secondaries along with the interference tolerance of the primaries, not all the deployed secondary nodes necessarily contribute towards the percolation process- even though they might participate in the communication process. We model the invisibility of such nodes using the concept of Poisson thinning, both in the presence and absence of primaries. Invisibility occurs due to nodes

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a

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