The aim of this study was to Identifying The Effect of using Linear programming and Branching programming by computer in Learning and Retention of movement concatenation(Linkwork) in parallel bars in Artistic Gymnastics. The searchers have used the experimental method. The search subject of this article has been taken (30) male - students in the second class from the College of Physical Education/University of Baghdad divided into three groups; the first group applied linear programming by computer, and the second group has been applicated branching programming by computer, while precision group used traditional method in the college. The researchers concluded the results by using the statistical bag for social sciences (spss) such as both

In this paper, an algorithm is suggested to train a single layer feedforward neural network to function as a heteroassociative memory. This algorithm enhances the ability of the memory to recall the stored patterns when partially described noisy inputs patterns are presented. The algorithm relies on adapting the standard delta rule by introducing new terms, first order term and second order term to it. Results show that the heteroassociative neural network trained with this algorithm perfectly recalls the desired stored pattern when 1.6% and 3.2% special partially described noisy inputs patterns are presented.

Personalized Medicine represents a recent revolution in healthcare practice, focusing on tailoring different therapies to be precise for a specific individual; this is aided by exploring the number of genetic predispositions and lifestyle choices that fit each individual. In this article, the authors utilize and gather recent literature and opinions to discuss the impact of personalized medicine on chronic disease management and patient quality of life. Additional attention is paid to limits and possible ethical issues. Chronic diseases such as Hypertension, Diabetes, and chronic kidney diseases adversely affect multiple health indicators, including Quality of Life (QoL) and well-being. This will have additional impacts on physical

Abstract
The current research aims to identify mental health and its role in promoting self-confidence and positive behavior of female university students. The researcher adopted the descriptive analytical approach in this research. The researcher depended on the availability of sources and references, literature, and previous field studies to analyze and study all aspects related to mental health and its role in promoting self-confidence and positive behavior of university students and then expand its importance and identify the areas of mental health, self-confidence, positive behavior, and university. The second chapter included the concept of mental health, the importance of the study, the most important factors of health and psyc

Abstract: Despite the distinct features of the continuous wave (CW) Terahertz (THz) emitter using photomixing technique, it suffers from the relatively low radiation output power. Therefore, one of effective ways to improve the photomixer emitter performance was using nanodimensions electrodes inside the optical active region of the device. Due to the nanodimension sizes and good electrical conductivity of silver nanowires (Ag-NWs), they have been exploited as THz emitter electrodes. The excited surface plasmon polariton waves (SPPs) on the surface of nanowire enhances the incident excitation signal. Therefore, the photomixer based Ag-NW compared to conventional one significantly exhibits higher THz output signal. In thi

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par