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

The lowest layer of the atmosphere is called the atmospheric mixed layer, characterized by small-scale, irregular air motions defined by winds that change in speed and direction. Aerosol radiative effects impact the atmospheric boundary layer (ABL), which holds most aerosols in the lower atmosphere. Aerosol absorption and scattering both lower the quantity of solar energy that reaches the ground, which has an impact on the spectral signature of the land coverings. In this study, 51 locations in downtown Baghdad were chosen for four different types of land cover (water bodies, farms, open areas, and residential areas) for Sentinel 2 satellite imagery, and the time the pictures were taken was 8:00 am ( 22 March, 22 June, 20 September,

In this paper, a construction microwave induced plasma jet(MIPJ) system was used to produce a non-thermal plasma jet at atmospheric pressure, at standard frequency of 2.45 GHz and microwave power of 800 W. The working gas Argon (Ar) was supplied to flow through the torch with adjustable flow rate using flow meter regulator. The influence of the MIPJ parameters such as applied voltage and argon gas flow rate on macroscopic microwave plasma parameters were studied. The macroscopic parameters results show increasing of microwave plasma jet length with increasing of applied voltage, argon gas flow rate where the plasma jet length exceed 12 cm as maximum value. While the increasing of argon gas flow rate will cause increasing into the ar

The current study aimed to assess the biological efficacy of the triple arbuscular mycorrhiza (AM) mixture of fungi Glomus etunicatum, G. leptotichum and Rhizophagus intraradices, and mix it with organic matter (O) and pathogenic fungi Fusarium oxysporum f.sp.lycopersici by using voyeurism in the plastic house in the growth of the tomato plant after four and eight weeks of cultivation. The results were shown after the treatment of the tomato plant in agriculture with the mixture of mycorrhiza and the pathogenic fungi and organic matter were treated with the mixture of mycorrhiza, organic matter and pathogenic fungi together. The effect of mycorrhiza and organic matter interference on the increase in the percentage of the lignin afte

            The increase in population resulted in an increase in the consumption of water. The present work investigates the performance of a recycling solar- powered greywater treatment system for the purposes of irrigation, used to reduce the amount of waste grey water and reduce electricity consumption and reduce the costs of constructing large scale water treatment plants. The system consumes about 3814W per hour and provides water treatment about 1.4 m³ per day. The proposed system is designed to    residential, office and governmental buildings application. Tests are conducted in an office building at the Ministry of Science and Technology site in Baghdad. Laboratorial water samples testing analyses are co

Yeasts are distributed in all environments and have been reported as potential biocontrol agents against various phytopathogenic fungi. To investigate their enzymatic and biological activities, 32 yeasts were isolated from 15 date vinegar samples. Evaluation of the antagonistic activities of isolated yeasts against the plant pathogens Fusarium oxysporium, Sclerotinia sclerotiorum, and Macrophomina phaseolina indicated that there are two yeasts had the highest inhibitory effect against plant pathogens, these yeasts identified as Kluyveromyces marxianus and Torulaspora delbrueckii using traditional and molecular methods. These yeast isolates were tested for fungal cell wall degrading enzymes (in vitro), and results indicated that the

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Background: Hyperthyroidism refers to overactive of thyroid gland leading to excessive synthesis of thyroid hormones and accelerated metabolism in the peripheral tissue. Objective: The aim of this study is to evaluate a new member of the IL-1 super family of cytokines interleukin-33(IL-33) levels in serum .in order to evaluate its utility as clinical bio marker of autoimmune disease (i.e. hyperthyroidism) Methods: The present study was conducted on 30 patients from the Iraqi female patients with hyperthyroidism attending Baghdad teaching hospital, in addition to 30 healthy controls. All subjects were (35-65) years old. Parameters measured in the sera of patients and healthy groups, were interleukin -33 (IL-33), Thyroxin (T4), Thyroxin (T3)

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class