This article deals with the approximate algorithm for two dimensional multi-space fractional bioheat equations (M-SFBHE). The application of the collection method will be expanding for presenting a numerical technique for solving M-SFBHE based on “shifted Jacobi-Gauss-Labatto polynomials” (SJ-GL-Ps) in the matrix form. The Caputo formula has been utilized to approximate the fractional derivative and to demonstrate its usefulness and accuracy, the proposed methodology was applied in two examples. The numerical results revealed that the used approach is very effective and gives high accuracy and good convergence.

The primary aim of the present study was to prepare a set of exercises on the multi-resistor VertiMax device and to identify the effect of these exercises on the development of the endurance of discus throwers under 16 years old. The design of the present study was experimental. Participants were selected using purposive sampling method. A total of 5 discuss players constituted the sample of the study. The authors found a significant improvement in the levels of endurance and performance as a result of the training on the VertiMax device. Therefore, it is recommendable to use exercises on the VertiMax device to improve the endurance and performance of under 16-years of age discus throwers.

A field experiment was carried out in the College of Agricultural Engineering Sciences - University of Baghdad, during the fall season of 2021 to find out which cultivated cultivars of maize are efficient under nitrogen fertilization. The experiment was applied according to an RCBD (split-plot design with three replications). The cultivars of the experiment (Baghdad, 5018, Sarah) supply three levels of nitrogen fertilizer, which are N1 (100 kg.N/ha), N2 (200 kg.N/ha) and N3 (300 kg.N/ha). The statistical analysis results showed the superiority of the Sarah genotype, which gave the highest value of SOD and CAT enzymes, reaching 11.59 units mg-1 and 10.76 units mg-1 . Protein sequentially, while cultivar5018 outperformed as it gave th

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