The Accounting Disclosure for non-current intangible assets is necessary to rely on accounting information by decision makers in the economic unity, two international accounting standards issued (IAS16,36), which aims to provide the foundations of the recognition, measurement and disclosure of appropriate assets Non-current tangible. (IAS16) allowed to use re-evaluation approach to measure assets entrance due to the inadequacy of the accounting information resulting from the application of the historical cost of the entrance under increasing technical developments and continuing that leave clear their effects on non-current intangible assets, As well as the requirements of what came (IAS36) the importance of accounting for the impairment

Solar activity monitoring is important in our life because of its direct or indirect influence on our life, not only on ionospheric communications. To study solar activity, researchers need measuring and monitoring instruments, these instruments are mostly expensive and are not available in all universities. In this paper, a very low frequency radio receiver had been designed and implemented with components available in most markets to support the researchers, college students, and radio astronomy amateurs with a minimum input voltage less than 100µV, an output voltage less than 135 m V with no distortion and an overall gain of 34dB. A comparison had been done between two circuit structures using a workbench software program and experim

This research examines the use of vibratory treatments to reduce residual stresses in small welded parts. In this experimental investigation, a post weld vibration treatment was applied to T- A106 steel pipe fitting specimens to study the effect of the treatment on the residual stress and the hardness of the material. The vibratory stress relief treatment was carried out at different vibration frequency. The results have demonstrated that post-weld vibratory stress relief of small size fittings is possible and residual stress may be relieved, and the treatment may be an alternative method for heat treatment especially when unchange in dimensions and material stability are required.

Abstract

  Bivariate time series modeling and forecasting have become a promising field of applied studies in recent times. For this purpose, the Linear Autoregressive Moving Average with exogenous variable ARMAX model is the most widely used technique over the past few years in modeling and forecasting this type of data. The most important assumptions of this model are linearity and homogenous for random error variance of the appropriate model. In practice, these two assumptions are often violated, so the Generalized Autoregressive Conditional Heteroscedasticity (ARCH) and (GARCH) with exogenous varia

Shear wave velocity is an important feature in the seismic exploration that could be utilized in reservoir development strategy and characterization. Its vital applications in petrophysics, seismic, and geomechanics to predict rock elastic and inelastic properties are essential elements of good stability and fracturing orientation, identification of matrix mineral and gas-bearing formations. However, the shear wave velocity that is usually obtained from core analysis which is an expensive and time-consuming process and dipole sonic imager tool is not commonly available in all wells. In this study, a statistical method is presented to predict shear wave velocity from wireline log data. The model concentrated to predict shear wave velocity fr

       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial