Abstract

The study aims to reveal the level of inclusion of citizenship values in the English language textbooks (We Can) series for elementary education in the Kingdom of Saudi Arabia. To achieve this objective, the "content analysis" methodology was used. A content analysis card was designed to include (6) main areas of citizenship values, it consisted of (28) sub-indicators of citizenship values that were supposed to be included in the English language textbook series (We Can) for elementary education. The study sample consisted of all the English language textbooks (We Can) series for elementary education, which consisted of (6) textbooks, two for each class. The results of the study indicated that there is a

The status of women in any society is one of the basic criteria for measuring the degree of progress that society and follow renaissance march side by sidewith men , and is no doubt that women are now of interest to the state , even if limited , which promotes public ideologies on the need for womens participation in economic infrastructure operations and social and the right to gain information and knowledge, entertainment and exercise its role in development through its president and actor in the family and raising the next generation configuration.

And affect the basic tributaries that draws them women information and ideas have a direct impact on the composition of cultural and cognitive entity woman comes satellite channels

Big data analysis is essential for modern applications in areas such as healthcare, assistive technology, intelligent transportation, environment and climate monitoring. Traditional algorithms in data mining and machine learning do not scale well with data size. Mining and learning from big data need time and memory efficient techniques, albeit the cost of possible loss in accuracy. We have developed a data aggregation structure to summarize data with large number of instances and data generated from multiple data sources. Data are aggregated at multiple resolutions and resolution provides a trade-off between efficiency and accuracy. The structure is built once, updated incrementally, and serves as a common data input for multiple mining an

Different ANN architectures of MLP have been trained by BP and used to analyze Landsat TM images. Two different approaches have been applied for training: an ordinary approach (for one hidden layer M-H1-L & two hidden layers M-H1-H2-L) and one-against-all strategy (for one hidden layer (M-H1-1)xL, & two hidden layers (M-H1-H2-1)xL). Classification accuracy up to 90% has been achieved using one-against-all strategy with two hidden layers architecture. The performance of one-against-all approach is slightly better than the ordinary approach

  The research aims to measure  the efficiency of health services Quality  in the province of Karbala, using the Data Envelopment analysis Models in ( 2006). According to these models the degree of efficiency ranging between zero and unity. We estimate Scale efficiency  for two types of orientation direction, which are input and output oriented direction.

  The results showed, according Input-oriented efficiency that the levels of Scale efficiency on average is ( 0.975), in the province of Karbala. While the index of Output-oriented efficiency on average is (o.946).

The research aims to find the impact of a proposed strategy according to the Luria model on realistic thinking among fifth-class scientific students and their achievement in mathematics. To achieve it, the experimental research method and the quasi-experimental design were used for two equal groups, one of them is a control group taught in traditional way and the other is an experimental one taught according to strategy based on Luria model. The research community represents the students of the fifth scientific class from the General Directorate of Education of Karkh First. The research sample (40) students were deliberately chosen and distributed equally between the two groups after making sure that they were equals in their previo

Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d’A