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

     Chronic renal disease (CRD) is a pathophysiologic process with multiple etiologies, resulting in the inexorable attrition of Nephron number and function and frequently leading to end-stage renal disease (ESRD). In turn, ESRD represents a clinical state or condition in which there has been an irreversible loss of endogenous renal function, of a degree sufficient to render the patient permanently dependent upon renal replacement therapy (dialysis of transplantation) in order to avoid life threatening uremia, reflecting a dysfunction of all organ systems as a result of untreated or under treated acute or chronic renal failure. The current study was involved 80 patients, the age range within 25-70 ye

This study was designed to know the effect of Diclofenac sodium (voltaren) drug on the histological composition of kidney in domestic rabbits . Twelve adult males with (1.4) kg weight . The first group of animals administrated orally by 1 ml of Voltaren with 6 mg/kg , while the second group with (1) ml of distal water (as control group ) . the administrated continue for (60) days sequentially. The treatment with voltaren showed pathological cases in tissues and cells of kidney including necrosis, infilteration ,congestion in blood vessels edema.Also epithelial separation in kidney tubules in comparison with control group Conclusion from the above results, revealed that voltaren had negative effects on the kidn

Knowledge of the distribution of the rock mechanical properties along the depth of the wells is an important task for many applications related to reservoir geomechanics. Such these applications are wellbore stability analysis, hydraulic fracturing, reservoir compaction and subsidence, sand production, and fault reactivation. A major challenge with determining the rock mechanical properties is that they are not directly measured at the wellbore. They can be only sampled at well location using rock testing. Furthermore, the core analysis provides discrete data measurements for specific depth as well as it is often available only for a few wells in a field of interest. This study presents a methodology to generate synthetic-geomechani

Nano-silver oxide thin films with high sensitivity for NH3 gas were deposited on glass substrates by the chemical bath deposition technique. The preparations were made under different values of pH and deposition time at 70áµ’ C, using silver nitrate AgNO3 and triethanolamine. XRD analysis showed that all thin films were
polycrystalline with several peaks of silver oxides such as Ag2O, AgO and Ag3O4, with an average crystallite size that ranged between 31.7 nm and 45.8 nm, depending on the deposition parameters. Atomic force microscope (AFM) technique illustrated that the films were homogenous with different surface roughness and the
grain size ranged between 55.69 nm and 86.23 nm. The UV-Vis spectrophotometer showed that the op

in this paper fourth order kutta method has been used to find the numerical solution for different types of first liner

    Predicting the network traffic of web pages is one of the areas that has increased focus in recent years. Modeling traffic helps find strategies for distributing network loads, identifying user behaviors and malicious traffic, and predicting future trends. Many statistical and intelligent methods have been studied to predict web traffic using time series of network traffic. In this paper, the use of machine learning algorithms to model Wikipedia traffic using Google's time series dataset is studied. Two data sets were used for time series, data generalization, building a set of machine learning models (XGboost, Logistic Regression, Linear Regression, and Random Forest), and comparing the performance of the models using (SMAPE) and