The present study investigates the effect of the de-sanding (recycling system) on the bearing capacity of the bored piles. Full-scale models were conducted on two groups of piles, the first group was implemented without using this system, and the second group was implemented using the recycling system. All piles were tested by static load test, considering the time factor for which the piles were implemented. The test results indicated a significant and clear difference in the bearing capacity of the piles when using this system. The use of the recycling system led to a significant increase in the bearing capacity of the piles by 50% or more. Thus it was possible to reduce the pile length by (15 % or more) thus, and implementation costs

The present study investigates the effect of the de-sanding (recycling system) on the bearing capacity of the bored piles. Full-scale models were conducted on two groups of piles, the first group was implemented without using this system, and the second group was implemented using the recycling system. All piles were tested by static load test, considering the time factor for which the piles were implemented. The test results indicated a significant and clear difference in the bearing capacity of the piles when using this system. The use of the recycling system led to a significant increase in the bearing capacity of the piles by 50% or more. Thus it was possible to reduce the pile length by (15 % or more) thus, and implementation c

Olfactory impairment and abnormal frontal EEG oscillations are recognized as early markers of Alzheimer’s disease (AD). Using a publicly available olfactory EEG dataset of 35 subjects spanning normal cognition, amnestic mild cognitive impairment (aMCI), and AD, each with MMSE scores and demographics, stimulus-locked epochs from four electrodes (Fp1, Fz, Cz, Pz) were processed with wavelet-based time–frequency analysis. Band-limited power ratios (delta, theta, alpha, beta) were computed as log-transformed post-odor/baseline values and aggregated to subject-level features. Statistical analyses revealed graded attenuation of odor-evoked frontal (Fp1) band-power ratios across groups, with significant differences in several band–od

         The duration of sunshine is one of the important indicators and one of the variables for measuring the amount of solar radiation collected in a particular area. Duration of solar brightness has been used to study atmospheric energy balance, sustainable development, ecosystem evolution and climate change. Predicting the average values of sunshine duration (SD) for Duhok city, Iraq on a daily basis using the approach of artificial neural network (ANN) is the focus of this paper. Many different ANN models with different input variables were used in the prediction processes. The daily average of the month, average temperature, maximum temperature, minimum temperature, relative humidity, wind direction, cloud level and atmosp

The importance of Baghdad city as the capital of Iraq and the center of the attention of delegations because of its long history is essential to preserve its environment. This is achieved through the integrated management of municipal solid waste since this is only possible by knowing the quantities produced by the population on a daily basis. This study focused to predicate the amount of municipal solid waste generated in Karkh and Rusafa separately, in addition to the quantity produced in Baghdad, using IBM SPSS 23 software. Results that showed the average generation rates of domestic solid waste in Rusafa side was higher than that of Al-Karkh side because Rusafa side has higher population density than Al-Karkh side. T

Abstract

The current research aims to construct a scale for the nine types of students’ personality according to Rob Fitzel model. To do this, (162) items were formed that present the nine types of personality with (18) items for each type. To test the validity of the scale, a sample of (584) students of Al-Mustansrya University were chosen. The data of their responses was analyzed by using factor analysis. The findings explored (9) factors as one factor for each type of personality with (12) items for each one. Then, the reliability of the scale was found by using the test-retest method and Alfa Cronbach method.

This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it

Support vector machine (SVM) is a popular supervised learning algorithm based on margin maximization. It has a high training cost and does not scale well to a large number of data points. We propose a multiresolution algorithm MRH-SVM that trains SVM on a hierarchical data aggregation structure, which also serves as a common data input to other learning algorithms. The proposed algorithm learns SVM models using high-level data aggregates and only visits data aggregates at more detailed levels where support vectors reside. In addition to performance improvements, the algorithm has advantages such as the ability to handle data streams and datasets with imbalanced classes. Experimental results show significant performance improvements in compa

Abstract: Data mining is become very important at the present time, especially with the increase in the area of information it's became huge, so it was necessary to use data mining to contain them and using them, one of the data mining techniques are association rules here using the Pattern Growth method kind enhancer for the apriori. The pattern growth method depends on fp-tree structure, this paper presents modify of fp-tree algorithm called HFMFFP-Growth by divided dataset and for each part take most frequent item in fp-tree so final nodes for conditional tree less than the original fp-tree. And less memory space and time.