Spatial data observed on a group of areal units is common in scientific applications. The usual hierarchical approach for modeling this kind of dataset is to introduce a spatial random effect with an autoregressive prior. However, the usual Markov chain Monte Carlo scheme for this hierarchical framework requires the spatial effects to be sampled from their full conditional posteriors one-by-one resulting in poor mixing. More importantly, it makes the model computationally inefficient for datasets with large number of units. In this article, we propose a Bayesian approach that uses the spectral structure of the adjacency to construct a low-rank expansion for modeling spatial dependence. We propose a pair of computationally efficient estimati

With the proliferation of both Internet access and data traffic, recent breaches have brought into sharp focus the need for Network Intrusion Detection Systems (NIDS) to protect networks from more complex cyberattacks. To differentiate between normal network processes and possible attacks, Intrusion Detection Systems (IDS) often employ pattern recognition and data mining techniques. Network and host system intrusions, assaults, and policy violations can be automatically detected and classified by an Intrusion Detection System (IDS). Using Python Scikit-Learn the results of this study show that Machine Learning (ML) techniques like Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) can enhance the effectiveness of an Intrusi

BACKGROUND: The number of coronavirus disease-19 (COVID-19) positive patients and fatalities keeps rising. It is important to recognize risk factors for severe outcomes. Evidence linking vitamin D deficiency and the severity of COVID-19 is tangential but substantial – relating to race, obesity, and institutionalization. OBJECTIVE: This study aims to examine the function of vitamin D and nutritional defense against infections such as COVID-19, which is the goal of this research. METHODS: This study includes observational cohort, cross-sectional, and case-control studies that estimated variances in serum levels of vitamin D among patients with mild or severe forms of COVID-19, and in patients who died or were discharged from hospit

COVID-19 is a unique viral infectious illness that causes a variety of symptoms and health hazards, particularly to the respiratory system and has been declared a worldwide pandemic. The disease is characterized by a cytokine release in severe conditions. Interleukin-6 (IL-6), a proinflammatory cytokine, mediates an important immunomodulatory process. Also, vitamin D was identified to have a role in the innate immunity of individuals. Our study was designed to find the role of IL-6 and vitamin D in COVID-19 patients, as well as, to see whether there is a link between vitamin D deficiency and cytokine syndrome development. The study included 90 COVID-19 patients and 30 control people from Baghdad, Iraq. The age of the participants was non-s

Increasing hydrocarbon recovery from tight reservoirs is an essential goal of oil industry in the recent years. Building real dynamic simulation models and selecting and designing suitable development strategies for such reservoirs need basically to construct accurate structural static model construction. The uncertainties in building 3-D reservoir models are a real challenge for such micro to nano pore scale structure. Based on data from 24 wells distributed throughout the Sadi tight formation. An application of building a 3-D static model for a tight limestone oil reservoir in Iraq is presented in this study. The most common uncertainties confronted while building the model were illustrated. Such as accurate estimations of cut-off permeab

Increasing hydrocarbon recovery from tight reservoirs is an essential goal of oil industry in the recent years. Building real dynamic simulation models and selecting and designing suitable development strategies for such reservoirs need basically to construct accurate structural static model construction. The uncertainties in building 3-D reservoir models are a real challenge for such micro to nano pore scale structure. Based on data from 24 wells distributed throughout the Sadi tight formation. An application of building a 3-D static model for a tight limestone oil reservoir in Iraq is presented in this study. The most common uncertainties confronted while building the model were illustrated. Such as accurate estimations of cut-off

Molecularly imprinted polymers (MIPs) are an effective method for separating enantiomeric compounds. The main objective of this research is to synthesize D-arabinitol MIPs, which can selectively separate D-arabinitol and its potential application to differentiate it from its enantiomer compound through a non-covalent approach. A macroporous polymer was synthesized using D-arabinitol as a template, acrylamide as a functional monomer, ethylene glycol dimethacrylate (EGDMA) being a cross-linker, dimethylsulfoxide (DMSO) being a porogen, as well as benzoyl peroxide being an initiator. After polymer synthesis, D-arabinitol was removed by a mixture of methanol and acetic acid (4:1, v/v). Fourier-Transform Infrared spectroscopy (FT-IR) and Scan

Background: Deep vein thrombosis is a multi causal disease and its one of most common venous disorder, but only one quarter of the patients who have signs and symptoms of a clot in the vein actually have thrombosis and need treatment .The disease can be difficult to diagnose. Venous ultrasound in combination with clinical finding is accurate for venous thromboembolism, its costly because a large number of patients with suspicious signs and symptoms. Venography still the gold standard for venous thromboembolism but it is invasive. The D-dimer increasingly is being seen as valuable tool rolling out venous thromboembolism and sparing low risk patients for further workup.Objectives: this study has designed the role of D-dimer to confirm diag

The organization and development of the irrigation system and agrarian reform in Iraq had an impact on the diversity of its methods and uses.