Left bundle branch block (LBBB) is a common finding in electrocardiography, there are many causes of LBBB.
The aim of this study is to discuss the true prevalence of coronary artery disease (CAD) in patients with LBBB and associated risk factors in the form of hypertension and diabetes mellitus.
Patients with LBBB were admitted to the Iraqi heart center for cardiac disea
Background: Cluster of differentiation 14 (CD14) is a serum/cell surface glycoprotein; and it is a pattern recognition receptor. CD14 expressed on the surface of various cells, or it found soluble in saliva and other body fluids. It has been proposed that soluble CD14 (sCD14) may play a protective role by controlling Gram negative bacterial infections through its capacity to bind lipopolysaccharide. This study was conducted to assess the level of soluble CD14 in saliva of patients with different periodontal diseases and healthy subjects and determine its correlation with clinical periodontal parameters. Materials & Methods: A total of 80 subjects, age ranged (25-50) years old, divided into three main groups, group ? consisted of 45 chronic
... Show MoreThe electrocardiogram (ECG) is the recording of the electrical potential of the heart versus time. The analysis of ECG signals has been widely used in cardiac pathology to detect heart disease. The ECGs are non-stationary signals which are often contaminated by different types of noises from different sources. In this study, simulated noise models were proposed for the power-line interference (PLI), electromyogram (EMG) noise, base line wander (BW), white Gaussian noise (WGN) and composite noise. For suppressing noises and extracting the efficient morphology of an ECG signal, various processing techniques have been recently proposed. In this paper, wavelet transform (WT) is performed for noisy ECG signals. The graphical user interface (GUI)
... Show MoreTraffic‐induced ground vibrations cause significant problems for residents and nearby structures. Reducing the effect of these vibrations on the neighboring environment is a key challenge, particularly in urban areas. This study presents both numerical and experimental investigations of the performance of mass scatters for screening ground vibrations. A three‐dimensional numerical model is validated and extended to conduct a comparative study on the efficiency of three geotechnical methods of isolation. These methods include trench barriers, wave‐impeding blocks (WIBs), and mass scatters. The results showed that mass scatters represent an efficient way of scattering ground vi
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for
It is important that real time stability in smart grids is ensured as the integration of renewables and the complexity of the systems grows. In this paper, we provide a solid architecture, which combines a Residual CNNLSTM deep neural network predictor, FPGA-accelerated Model Predictive Control (MPC), and SHAP-based explainability. The proposed method predicted with 99.8% accuracy using the Electrical grid Stability Simulated Dataset (UCI) and minimized the instability rates surpassing 85 percent in all operating conditions. Meeting real-time operating needs, FPGA deployment on a Xilinx Zynq UltraScale+ provided 3.1 ms latency and 5 times reduced energy consumption against CPU processing. By emphasizing bus voltage and frequency as major in
... Show MoreThis study aimed at isolating uropathogenic Escherichia coli from urinary tract infections (UTIs) of human and cattle to examine the molecular diversity and phylogenetic relationship of the isolates. A total of 100 urine samples were collected from UTIs of human and cattle. The isolates identification was done using routine diagnostic methods and confirmed by Vitek2. Antimicrobial susceptibility was tested against 10 antimicrobials. Random amplified polymorphic DNA (RAPD)-polymerase chain reaction (PCR) was applied to identify the genetic diversity among E. coli isolates from human and animal origin by using five different octamer primers. The gelJ software for the phylogenetic analysis created Dendrograms. Out of 50 human urine samples, E.
... Show MoreThis research dealt with the analysis of murder crime data in Iraq in its temporal and spatial dimensions, then it focused on building a new model with an algorithm that combines the characteristics associated with time and spatial series so that this model can predict more accurately than other models by comparing them with this model, which we called the Combined Regression model (CR), which consists of merging two models, the time series regression model with the spatial regression model, and making them one model that can analyze data in its temporal and spatial dimensions. Several models were used for comparison with the integrated model, namely Multiple Linear Regression (MLR), Decision Tree Regression (DTR), Random Forest Reg
... Show MoreThis study analyses six political cartoons selected based on their relevance to current Iraqi political issues, specifically the period between 2005 and 2015, from American online newspapers (calgecartooms.com). The selection criteria included the cartoons' satirical elements, visual rhetoric, and their ability to engage with themes such as power dynamics, social issues, and public opinion. It sheds light on how these cartoons can function as mediators of meanings between the cartoonists and the readers. The data is examined using multimodal discourse analysis (MDA), which combines language study with the analysis of other visual elements, like colors, gestures, and images, to understand meaning (O’Halloran et al., 2011). The Visual Socia
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