Video streaming is widely available nowadays. Moreover, since the pandemic hit all across the globe, many people stayed home and used streaming services for news, education,  and entertainment. However,   when streaming in session, user Quality of Experience (QoE) is unsatisfied with the video content selection while streaming on smartphone devices. Users are often irritated by unpredictable video quality format displays on their smartphone devices. In this paper, we proposed a framework video selection scheme that targets to increase QoE user satisfaction. We used a video content selection algorithm to map the video selection that satisfies the user the most regarding streaming quality. Video Content Selection (VCS) are classified in

Find cares studying ways in the development of industrial products and designs: the way the progressive development (how typical) and root development (jump design), was the aim of the research: to determine the effectiveness of the pattern and the jump in the development of designs and industrial products. After a process of analysis of a sample of research and two models of contemporary household electrical appliances, it was reached a set of findings and conclusions including:1-leaping designs changed a lot of entrenched perceptions of the user on how the product works and its use and the size and shape of the product, revealing him about the possibilities of sophisticated relationships with the product, while keeping the typical desi

Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amo

            Human behavior is one of the topics that has captured the attention of researchers throughout the ages, and motivation is one of the manifestations of this behavior, which indicates the extent of interest in a particular topic and their unwillingness to rush towards a particular topic. The topic of motivation is one of the important topics of interest to the teacher and coach in the field of sports. The aim of this research was identifying the level of motivation for junior students, and the differences in the dimensions of motivation for junior students in squash lessons. We used the descriptive survey method, and the research sample was chosen randomly. Only male of the junior students in the College of Phys

While traditional energy sources such as oil, coal, and natural gas drive economic growth, they also seriously affect people’s health and the environment. Renewable energies (RE) are presently seen as an efficient choice for attaining long-term sustainability in development. They provide an adequate response to climate change and supply sufficient electricity. The current situation in Iraq results from a decades-long scarcity of reliable electricity, which has impacted various industries, including agriculture. There are diverse prospects for using renewable energy sources to address the present power crisis. The economic and environmental impacts of renewable energy systems were investigated in this study by using the solar pumpi

This study proposes a hybrid predictive maintenance framework that integrates the Kolmogorov-Arnold Network (KAN) with Short-Time Fourier Transform (STFT) for intelligent fault diagnosis in industrial rotating machinery. The method is designed to address challenges posed by non-linear and non-stationary vibration signals under varying operational conditions. Experimental validation using the FALEX multispecimen test bench demonstrated a high classification accuracy of 97.5%, outperforming traditional models such as SVM, Random Forest, and XGBoost. The approach maintained robust performance across dynamic load scenarios and noisy environments, with precision and recall exceeding 95%. Key contributions include a hardware-accelerated K