This research aims to identify the effectiveness of modern media technologies represented by satellite TV in the development of good citizenship values when students. To achieve this purpose were selected a sample of 44 students in the Department of Electrical technologies in Technical Institute in Nasiriyah in southern Technical University during the school year (2015-2016).
Was determined the general objectives of the use of modern media techniques represented satellite TV is to promote the values of good citizenship among the sample of students in order to make them good citizens in the community could contribute to the development and upgrading it. And put the general plan of research. Promising researchers measure of good citizen

In this work a hybrid composite materials were prepared containing matrix of polymer (polyethylene PE) reinforced by different reinforcing materials (Alumina powder + Carbon black powder CB + Silica powder). The hybrid composite materials prepared are: • H1 = PE + Al2O3 + CB • H2 = PE + CB + SiO2 • H3 = PE + Al2O3 + CB + SiO2 All samples related to electrical tests were prepared by injection molding process. Mechanical tests include compression with different temperatures and different chemical solutions at different immersion times The mechanical experimentations results were in favour of the samples (H3) with an obvious weakness of the samples (H1) and a decrease of these properties with a rise in temperature and the increasing

In this research a proposed technique is used to enhance the frame difference technique performance for extracting moving objects in video file. One of the most effective factors in performance dropping is noise existence, which may cause incorrect moving objects identification. Therefore it was necessary to find a way to diminish this noise effect. Traditional Average and Median spatial filters can be used to handle such situations. But here in this work the focus is on utilizing spectral domain through using Fourier and Wavelet transformations in order to decrease this noise effect. Experiments and statistical features (Entropy, Standard deviation) proved that these transformations can stand to overcome such problems in an elegant way.

The impact of COVID-19 pandemic on education models was mainly through the expansion of technology use in the different educational programs. Earlier impact of COVID-19 was manifested in the complete and sudden transition to distance education regardless of institution preparedness status. Gradually, many institutions are moving back to on-campus face-to-face education. However, others including all higher education institutions in Iraq are adopting the hybrid education model. This report presents part of the end of semester evaluation survey conducted at the University of Baghdad College of Pharmacy for the Spring 2021 semester. The survey aims to address points of strength and weakness associated with the hybrid education model and spe

This study presents an investigation about the effect of fire flame on the punching shear strength of hybrid fiber reinforced concrete flat plates. The main considered parameters are the fiber type (steel or glass) and the burning steady-state temperatures (500 and 600°C). A total of 9 half-scale flat plate specimens of dimensions 1500mm×1500mm×100mm and 1.5% fiber volume fraction were cast and divided into 3 groups. Each group consisted of 3 specimens that were identical to those in the other groups. The specimens of the second and the third groups were subjected to fire flame influence for 1 hour and steady-state temperature of 500 and 600°C respectively. Regarding the cooling process, water sprinkling was applied directly aft

Nowadays, people's expression on the Internet is no longer limited to text, especially with the rise of the short video boom, leading to the emergence of a large number of modal data such as text, pictures, audio, and video. Compared to single mode data ,the multi-modal data always contains massive information. The mining process of multi-modal information can help computers to better understand human emotional characteristics. However, because the multi-modal data show obvious dynamic time series features, it is necessary to solve the dynamic correlation problem within a single mode and between different modes in the same application scene during the fusion process. To solve this problem, in this paper, a feature extraction framework of

This study explores the challenges in Artificial Intelligence (AI) systems in generating image captions, a task that requires effective integration of computer vision and natural language processing techniques. A comparative analysis between traditional approaches such as retrieval- based methods and linguistic templates) and modern approaches based on deep learning such as encoder-decoder models, attention mechanisms, and transformers). Theoretical results show that modern models perform better for the accuracy and the ability to generate more complex descriptions, while traditional methods outperform speed and simplicity. The paper proposes a hybrid framework that combines the advantages of both approaches, where conventional methods prod

A substantial percentage of the world’s energy consumption (almost 40%) and carbon dioxide (CO2) emissions (around 37%) come from the construction industry, especially schools. This work presents a new hybrid artificial intelligence (AI) engineering model that aims to maximize energy performance on campuses in a holistic way. Modules for data-driven forecasting, metaheuristic optimization, and real-time adaptive control are all part of the concept. A thorough energy simulation of a university campus building is used in conjunction with the AI model to assess its performance through a co-simulation framework. Findings show that yearly peak electricity demand may be reduced by 18.7% and total site energy consumption by 22.4% when co

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

Tourism plays an important role in Malaysia’s economic development as it can boost business opportunity in its surrounding economic. By apply data mining on tourism data for predicting the area of business opportunity is a good choice. Data mining is the process that takes data as input and produces outputs knowledge. Due to the population of travelling in Asia country has increased in these few years. Many entrepreneurs start their owns business but there are some problems such as wrongly invest in the business fields and bad services quality which affected their business income. The objective of this paper is to use data mining technology to meet the business needs and customer needs of tourism enterprises and find the most effective