This study aims to investigate the degree of practicing the motivated classroom evaluation environment for learning and its relationship to different feedback patterns. To achieve the objectives of the study, the correlational descriptive research design was employed. A questionnaire was constructed consisting of two parts: the classroom evaluation environment (13) items, and feedback patterns (24) items on a five-point scale. The psychometric properties of the questionnaire were verified in terms of validity and reliability. The questionnaire was applied to a sample of (265) male and female teachers who work in the second cycle schools for grades (5-10) of basic education in all academic majors in the Governorate of Muscat in the Sultan

The recent advancements in security approaches have significantly increased the ability to identify and mitigate any type of threat or attack in any network infrastructure, such as a software-defined network (SDN), and protect the internet security architecture against a variety of threats or attacks. Machine learning (ML) and deep learning (DL) are among the most popular techniques for preventing distributed denial-of-service (DDoS) attacks on any kind of network. The objective of this systematic review is to identify, evaluate, and discuss new efforts on ML/DL-based DDoS attack detection strategies in SDN networks. To reach our objective, we conducted a systematic review in which we looked for publications that used ML/DL approach

Praise be to God who created the soul, perfected it, and inspired it with its immorality and piety. He says in His Noble Book: ﭤ Fajr: 27-30 Glory be to You, O God! O Lord, on whom I have chosen for You as a beloved from myself and the soul of the two worlds, may our master Muhammad be sacrificed for him, who enlightened the horizons with his introduction to enlightenment. It soon ends with the end of pleasure, which is the opposite of the pleasure of thought and meaning, as it is permanent and continuous, and from here we see that the Messenger of God, may God’s prayers and peace be upon him, urges us in various places to seek knowledge and fortify the soul and thought in a way that raises one’s status and protects him from the st

Background: H.pylori colonized gastric mucosal
epithelium will virtually develop gastritis and had the
capacity to persist for decades. Pathogenesis is
dependent upon strain, virulence host genetic
susceptibility, and environmental cofactors. Leptin is
a member of the class 1 cytokine family so altered
leptin production during ifnect and inflammation that
leptin part of the cytokine cascade ,which
orchestrates the defense mechanism.
Objective: Examin the effect of H.pylori infection
on serum leptin level.
Methods: One hundred and thirty(130) Patients
attending the Endoscopic Unit at "Gastroenterology
and Hepatology Teaching Hospital/ Baghdad Medical
City"were included in this study with ages rang

A batch adsorption system was applied to study the adsorption of methylene blue from aqueous solution by Iraqi bentonite and treated bentonite with different amount of zinc oxide (ZnO). The adsorption capacities of methylene blue onto bentonite were evaluated. The equilibrium between liquid and solid phase was described by Langmuir model better than the Freundlich model. Langmuir and Freundlich constants have been determined. The separation factor or equilibrium parameter, RL which is used to predict if an adsorption system is favourable or unfavourable was calculated for all cases.

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

Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall