Error control schemes became a necessity in network-on-chip (NoC) to improve reliability as the on-chip interconnect errors increase with the continuous shrinking of geometry. Accordingly, many researchers are trying to present multi-bit error correction coding schemes that perform a high error correction capability with the simplest design possible to minimize area and power consumption. A recent work, Multi-bit Error Correcting Coding with Reduced Link Bandwidth (MECCRLB), showed a huge reduction in area and power consumption compared to a well-known scheme, namely, Hamming product code (HPC) with Type-II HARQ. Moreover, the authors showed that the proposed scheme can correct 11 random errors which is considered a high

A new simple and sensitive spectrophotometric method is described for quantification of Nifedipine (NIF) and their pharmaceutical formulation. The selective method was performed by the reduction of NIF nitro group to yield primary amino group using zinc powder with hydrochloric acid. The produced aromatic amine was submitted to oxidative coupling reaction with pyrocatechol and ammonium ceric nitrate to form orange color product measured spectrophotometrically with maximum absorption at 467nm. The product was determined through flow injection analysis (FIA) system and all the chemical and physical parameters were optimized. The concentration range from 5.0 to 140.0 μg.mL-1 was obeyed Beer’s law with a limit of detection and quantitatio

Characterizing dementia is a global challenge in supporting personalized health care. The electroencephalogram (EEG) is a promising tool to support the diagnosis and evaluation of abnormalities in the human brain. The EEG sensors record the brain activity directly with excellent time resolution. In this study, EEG sensor with 19 electrodes were used to test the background activities of the brains of five vascular dementia (VaD), 15 stroke-related patients with mild cognitive impairment (MCI), and 15 healthy subjects during a working memory (WM) task. The objective of this study is twofold. First, it aims to enhance the recorded EEG signals using a novel technique that combines automatic independent component analysis (AICA) and wave

Background: Prolactin is a hormone, as well as a cytokine which is synthesized and secreted from the anterior pituitary gland and various extra pituitary sites including immune cells under control of a superdistal promoter that contains a single nucleotide polymorphism -1149 G/T. Rheumatoid Arthritis has been associated with increased serum prolactin levels.Objectives: To investigate the association of the extra pituitary -1149 G/T promoter polymorphism among Iraqi rheumatoid arthritis patients and prolactin levels.Methods: We tested 73 patients with rheumatoid arthritis and 40 healthy individuals. The DNA samples were genotyped using the Polymerase Chain Reaction-Restriction fragment Length Polymorphism method and the levels of prolacti

MM ABDUL-WAHHAB, SA AHMED, International Journal of Pharmaceutical Research, 2020 - Cited by 2

Comparative literature is one of the important research topics in finding new relations and results that other types of studies do not allow.

The present research is a comparative study between two contemporary poets : Al-Sayyab and Prévert. The reason for accomplishing this research is Al-Sayyab’s reading for the western literature. Moreover, the study sheds a light on translational criticism.

It tackles the lives of the two writers and their points of similarities and differences. Prévert and Al-Sayyab’s are two modern poets. The first employed his daily routines to express reality, specially the events of the two world wars. The second’s pain, on the other hand, was the starting point to express others’ suffe

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod