The estimation of recharge to ground water is the important basics to improve the use of ground water with other available resources, and to save ground water resource from depletion, especially when using large quantity of ground water during a long time such as for agricultural purposes. Al-Wand River Basin in Iraq suffers from water shortage of its requirement of Blajo–Al-Wand Project, and to cover this shortage, the ground water plays a good role to overcome this problem. In this study, three methods were used to estimate the recharge and ground water storage for Al-Wand Basin, these methods are: Water Table Fluctuation (WTF), Water Balance of Climatic for Basin, and Water Table Balance for Basin. The results showe

Abstract:

Robust statistics Known as, resistance to errors caused by deviation from the stability hypotheses of the statistical operations (Reasonable, Approximately Met, Asymptotically Unbiased, Reasonably Small Bias, Efficient ) in the data selected in a wide range of probability distributions whether they follow a normal distribution or a mixture of other distributions deviations different standard .

power spectrum function lead to, President role in the analysis of Stationary random processes, form stable random variables organized according to time, may be discrete random variables or continuous. It can be described by measuring its total capacity as function in frequency.

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The research aims to identify the impact of using the electronic participatory learning strategy according to internet programs in learning some basic basketball skills for middle first graders according to the curricular course, and the sample of research was selected in the deliberate way of students The first stage of intermediate school.As for the problem of research, the researchers said that there is a weakness in the levels of school students in terms of teaching basketball skills, which prompted the researchers to create appropriate solutions by using a participatory learning strategy.The researchers imposed statistically significant differences between pre and post-test tests, in favor of the post tests individually and in favor of

Metasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction rat

The research aims to verify the role of the Human Resources Strategic Management (HRSM) in enhancing the strategic success factors for talent (SSFT) in the General Tourism Authority by distributing a questionnaire consisting of (36) paragraphs on an intentional sample represented by the higher departments as it reached (50) and the sample valid for testing was (44) Person and to test the relationships between the two research variables, the researchers used statistical methods represented by (Bartlett test / mean / simple regression coefficient / difference coefficient, alpha- cronbachAch, confirmatory factor Analysis ) through the statistical program (SPSS v.23 & AMOS v.23). In enhancing the factors of success for talent management in the

<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, c

The growth of developments in machine learning, the image processing methods along with availability of the medical imaging data are taking a big increase in the utilization of machine learning strategies in the medical area. The utilization of neural networks, mainly, in recent days, the convolutional neural networks (CNN), have powerful descriptors for computer added diagnosis systems. Even so, there are several issues when work with medical images in which many of medical images possess a low-quality noise-to-signal (NSR) ratio compared to scenes obtained with a digital camera, that generally qualified a confusingly low spatial resolution and tends to make the contrast between different tissues of body are very low and it difficult to co

The study aims to design an electronic puppet educational theater by Camtasia studio and identify the effectiveness in learning some of the artistic gymnastics skills for first grade, the research curriculum is experimental by designing two equal groups, and the research sample first grade students are distributed among 4 grade, and by the pumpkin determines two divisions (15 from each) representing the experimental group and control group, the main experiment conducted for 8 weeks by two educational units per week after which the post-tests were conducted, SPSS was used to process the results, and it was found that the electronic puppet educational theater contributed by making the learning process enjoyable and interesting and meeting the

Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems.  Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic.  Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance.  In this study, two different sets of select