Amplitude variation with offset (AVO) analysis is an 1 efficient tool for hydrocarbon detection and identification of elastic rock properties and fluid types. It has been applied in the present study using reprocessed pre-stack 2D seismic data (1992, Caulerpa) from north-west of the Bonaparte Basin, Australia. The AVO response along the 2D pre-stack seismic data in the Laminaria High NW shelf of Australia was also investigated. Three hypotheses were suggested to investigate the AVO behaviour of the amplitude anomalies in which three different factors; fluid substitution, porosity and thickness (Wedge model) were tested. The AVO models with the synthetic gathers were analysed using log information to find which of these is the

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob

<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121

In this study, gold nanoparticles were synthesized in a single step biosynthetic method using aqueous leaves extract of thymus vulgaris L. It acts as a reducing and capping agent. The characterizations of nanoparticles were carried out using UV-Visible spectra, X-ray diffraction (XRD) and FTIR. The surface plasmon resonance of the as-prepared gold nanoparticles (GNPs) showed the surface plasmon resonance centered at 550[Formula: see text]nm. The XRD pattern showed that the strong four intense peaks indicated the crystalline nature and the face centered cubic structure of the gold nanoparticles. The average crystallite size of the AuNPs was 14.93[Formula: see text]nm. Field emission scanning electron microscope (FESEM) was used to s

We have studied Bayesian method in this paper by using the modified exponential growth model, where this model is more using to represent the growth phenomena. We focus on three of prior functions (Informative, Natural Conjugate, and the function that depends on previous experiments) to use it in the Bayesian method. Where almost of observations for the growth phenomena are depended on one another, which in turn leads to a correlation between those observations, which calls to treat such this problem, called Autocorrelation, and to verified this has been used Bayesian method.

The goal of this study is to knowledge the effect of Autocorrelation on the estimation by using Bayesian method. F

Coupling reaction of 2-amino benzoic acid with the 8-hydroxy quinoline gave the azo ligand (H2L): 5-(2-benzoic acid azo )-8-hydroxy quinoline.Treatment of this ligand with some metal ions (CoII, NiII and CuII ) in ethanolic medium with a (1:2) (M:L) ratio yielded a series of neutral complexes with general Formula[M(HL)2],where: M=Co(II), Ni(II) and Cu(II), HL=anion azo ligand (-1).The prepared complexes were characterized using flame atomic absorption,FT-IR and UV-Vis spectroscopic methods as well as magnetic susceptibility and conductivity measurements.

  The formation of  Co(II), Ni(II), Cu(II), Zn(II), and Cd(II)-complexes (C1-C5) respectively was studied with new Schiff base ligand [benzyl(2-hydroxy-1-naphthalidene) hydrazine carbodithioate  derived from reaction of  2-hydroxy-1-naphthaldehyde and benzyl hydrazine carbodithioate. The suggested structures of the ligand and its complexes have been determined   by using C.H.N.S analyzer, thermal analysis, FT-IR, U.V-Visible, 1HNMR, 13CNMR , conductivity measurement , magnetic susceptibility and atomic absorption. According to these studies,  the ligand coordinates as  a tridentate with metal ions through nitrogen atom of azomethane , oxygen atom of  hydroxyl, and  sulfur atom of thione  

Four complexes of Co(II),Ni(II),Cu(II) and Zn(II) with the azo ligand (4-chloro-N-(2-(dimethylamino)ethyl)-5-((2-hydroxy- 4,6-dimethylphenol)diazenyl)-2-methoxybenzamide) L. The structure of ligand and complexes were confirmed on the basis of their analytical and spectral data, these dyes were tested as dyeing in cotton fabric, and also testing in light and cleaner firmness. Also, antimicrobial and antifungal activities of ligand and their complexes were evaluated and the results showed that the ZnL compound showed the higher antibacterial activity with inhibition zone of 13mm against Staphyloco-ccus epidermidis, Steptococcus sp. and Escherichia coli compared with ligand and other metal complexes .In case of ZnL compound the antifungal acti

Four complexes of Co(II),Ni(II),Cu(II) and Zn(II) with the azo ligand (4-chloro-N-(2-(dimethylamino)ethyl)-5-((2-hydroxy-4,6-dimethylphenol)diazenyl)-2-methoxybenzamide) L. The structure of ligand and complexes were confirmed on the basis of their analytical and spectral data, these dyes were tested as dyeing in cotton fabric, and also testing in light and cleaner firmness. Also, antimicrobial and antifungal activities of ligand and their complexes were evaluated and the results showed that the ZnL compound showed the higher antibacterial activity with inhibition zone of 13mm against Staphyloco-ccus epidermidis, Steptococcus sp. and Escherichia coli compared with ligand and other metal complexes .In case of ZnL compound the antifungal activ