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

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

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

Knowledge and awareness towards cryptosporidiosis were assessed in this study during the period from April 2009 till June 2011, among 188 person of both gender which were divided in different groups and sub groups as following Group 1: include 48 physician from different hospitals and primary health care centers in Baghdad this group were sub divided into 30 Specialist doctors and 18 General practitioners doctors. Group 2 : include 45 teaching staff member from Biology department , Biotechnology department in University of Baghdad and AL-Nahrain University as well as teaching staff member from the college of medicine – University of Baghdad and University of Mustansiryah, this group were sub divided into 9 Ph.D and 36 M.Sc. + B.Sc. me

تم تقييم المعرفة والوعي تجاه داء الكريبتوسبوريديوس في هذه الدراسة خلال الفترة من أبريل 2009 حتى يونيو 2011 ، من بين 188 شخصًا من كلا الجنسين تم تقسيمهم إلى مجموعات ومجموعات فرعية مختلفة على النحو التالي المجموعة 1: تشمل 48 طبيبًا من مختلف المستشفيات والرعاية الصحية الأولية مراكز في بغداد تم تقسيم هذه المجموعة إلى 30 طبيبًا متخصصًا و 18 طبيبًا ممارسًا عامًا. المجموعة الثانية: تضم 45 عضو هيئة تدريس من قسم الأحياء ، قس

As performers in a social world, we communicate with other people by sharing information on many different levels. Each utterance includes linguistic information and conveys much information about the speaker’s identity. Variation in voice quality indexes information about the speaker and marks the speaker’s identity as a unique individual. The present study aims to validate the belief that each individual has an inalienable voice print that can’t be imitated. The study verifies that, even the more similar personality between two individuals, or the close position in society the variance is voice quality. The acoustic analysis is performed via analysing the acoustic parameters namely: the fundamental frequency, amplitude, inten

Unconfined Compressive Strength is considered the most important parameter of rock strength properties affecting the rock failure criteria.  Various research have developed rock strength for specific lithology to estimate high-accuracy value without a core.  Previous analyses did not account for the formation's numerous lithologies and interbedded layers. The main aim of the present study is to select the suitable correlation to predict the UCS for hole depth of formation without separating the lithology. Furthermore, the second aim is to detect an adequate input parameter among set wireline to determine the UCS by using data of three wells along ten formations (Tanuma, Khasib, Mishrif, Rumaila, Ahmady, Maudud, Nahr Um