In this paper, we investigate the connection between the hierarchical models and the power prior distribution in quantile regression (QReg). Under specific quantile, we develop an expression for the power parameter ( ) to calibrate the power prior distribution for quantile regression to a corresponding hierarchical model. In addition, we estimate the relation between the  and the quantile level via hierarchical model. Our proposed methodology is illustrated with real data example.

This paper presents a study of the application of gas lift (GL) to improve oil production in a Middle East field. The field has been experiencing a rapid decline in production due to a drop in reservoir pressure. GL is a widely used artificial lift technique that can be used to increase oil production by reducing the hydrostatic pressure in the wellbore. The study used a full field model to simulate the effects of GL on production. The model was run under different production scenarios, including different water cut and reservoir pressure values. The results showed that GL can significantly increase oil production under all scenarios. The study also found that most wells in the field will soon be closed due to high water cuts. Howev

   Deficiencies in revenue-related accounting standards, including American accounting standards as well as international accounting standards, prompted the issuance of the International Financial Reporting Standard IFRS 15 "Revenue from contracts with customers" as part of the convergence plan between the FASB and the International Accounting Standards Board (IASB) according to the requirements of The joint venture between the two councils, whereby the standard aims to define the basis for reporting useful information to the users of the financial statements about the nature, amount, timing and uncertainty about the revenues and cash flows arising from a contract with the customer, The standard is base

The rapid increase in the number of older people with Alzheimer's disease (AD) and other forms of dementia represents one of the major challenges to the health and social care systems. Early detection of AD makes it possible for patients to access appropriate services and to benefit from new treatments and therapies, as and when they become available. The onset of AD starts many years before the clinical symptoms become clear. A biomarker that can measure the brain changes in this period would be useful for early diagnosis of AD. Potentially, the electroencephalogram (EEG) can play a valuable role in early detection of AD. Damage in the brain due to AD leads to changes in the information processing activity of the brain and the EEG which ca

The problem of the research is focused on importance limited of Iraq industrial companies in application of scientific measurements of supply chains performance,  The research sought to achieve a group of goals, the most important are , identifying the strengths and weaknesses in the reality of supply chain in General Company for Cotton Industries, The data and information required are gathered from the dependence company, records through the field observations and personal interviews, the research used some quantitative indicators to measure of supply chain performance, The research reached to many conclusions , the most outstanding among them is the existence of a strong inverse correlatio

Extracting, studying and interpreting the morphological database of a basin is a basic building block for building a correct geomorphological understanding of this basin. In this work, Arc GIS 10.8 software and SRTM DEM satellite images were used. The principle of data integration was adopted by extracting the quantitative values of the morphometric characteristics that are affected by the geomorphological condition of the studied basin, then eliciting an optimal conception of the geomorphological condition of the basin from the meanings and connotations of these combined transactions. Hypsometric integration was extracted for each region in the basin separately with the value of integration of the plot curve for the relative heights of

<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

ZnO organic hybrid junction (electroluminescence EL device) was fabricated using phase segregation method. ZnO-nanoparticle (NPs) was prepared as a colloidal by self–assembly method of Zinc acetate solution with KOH solution. Nanoparticle is employed to form organic-inorganic hybrid film and generate white light emission, while N,N’–diphenyl-N,N’ –bis(3-methylphenyl)-1,1’-biphenyl 4,4’-diamine (TPD) and polymethyl methacrylate (PMMA) are adopted as the organic matrices. ZnO NPs was used to fabricate TPD: PMMA: ZnO NPs hybrid junction device. The photoluminescence (PL) and electroluminescence (EL) spectra of the TPD: PMMA: ZnO NPs hybrid device provided a broad emission band covering entirely the visible spectrum (∼350-∼700

This paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number