The study aims to predict Total Dissolved Solids (TDS) as a water quality indicator parameter at spatial and temporal distribution of the Tigris River, Iraq by using Artificial Neural Network (ANN) model. This study was conducted on this river between Mosul and Amarah in Iraq on five positions stretching along the river for the period from 2001to 2011. In the ANNs model calibration, a computer program of multiple linear regressions is used to obtain a set of coefficient for a linear model. The input parameters of the ANNs model were the discharge of the Tigris River, the year, the month and the distance of the sampling stations from upstream of the river. The sensitivity analysis indicated that the distance and discharge have the most significant affect on the predicted TDS concentrations. The results showed that a network with (8) hidden neurons was highly accurate in predicting TDS concentration. The correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE) between measured data and model outputs were calculated as 0.975, 113.9 and 11.51%, respectively for testing data sets. Comparisons between final results of ANNs and multiple linear regressions (MLR) showed that the ANNs model could be successfully applied and provides high accuracy to predict TDS concentrations as a water quality parameter.
The current research aims at finding out how to properly and correctly manage waste and solid waste and reduce the difficulties faced by all countries. However, it is becoming increasingly acute in developed cities because their economies are growing rapidly. It is necessary to identify the modern methods used in developed countries in managing wastes. The use of modern waste management techniques is a coordinated effort by international agencies within the borders responsible for them. The problem of the study can be identified in the lack of clarity of environmental management procedures in place. The importance of the research contributes to providing greater capacity to the administrative and technical leadership in the municipality
... Show MoreNominal ellipsis is a linguistic phenomenon found in English and Arabic .It is
based on leaving out a part of a nominal construction or more for the sake of good
style , compactness and connectedness .This phenomenon is found in the language of
the Glorious Qur’an .The study in hand is concerned with how translators handle
translating Qur’anic verses which contain ellipted nouns , i ,e. , to what extent the
translated Qur’anic verses are close to the original ones , and to what extent their
translations serve understanding the meanings of the glorious verses while at the
same time maintaining their beauty in style. The study aims at shedding light on
nominal ellipsis in English and Arabic .The study undertak
Abstract Infinitives and gerunds are non-finite verb forms which cannot be indicated by tense, number, or person. The construction of non-finite verbs is intricate because of their nature of meanings, forms, and functions. The major problem is that both infinitival and gerundial complements have identical functions and occupy identical positions in the sentences. Thus, there is a confusion in using an infinitival and gerundial forms after aspectual verbs. The selection of either one of these two forms as complements is controlled syntactically or semantically. Moreover, both forms can be used usually with similar predicate but with neat difference in meaning. In addition, there are problems with controlling the use of aspect, since aspectua
... Show MoreOccupies total quality management applications play a key role in the development of institutions of higher education performance and achieve its strategic objectives through the commitment of senior management and their employees to continuous improvement of the quality of performance in the various areas of work, and can be integrated knowledge management processes, which means identifying information of value and how to take advantage. The data were collected using the style of the questionnaire for the purpose of analyzing their results on a sample composed of 83 member of the administrative leadership in colleges as representing the decision-making centers in those colleges .
 
... Show MoreThere are many animal models for polycystic ovary (PCO); using exogenous testosterone enanthate is one of the methods of induction of these models. However, induction of insulin resistance should also be studied in the modeling technics. Therefore, the present study aims to investigate the expression of insulin receptor substrate (Irs)-2 mRNA in the liver tissue of rat PCO model. Nineteen Wistar rats were divided into three groups; (1) PCO modeling group (N =7) received daily 1.0 mg/100g testosterone enanthate solved in olive oil along with free access dextrose water 5%, (2) vehicle group (N =6), which handled like the PCO group, but did not receive testosterone enanthate, (3) control group (N =6) with standard care. Al
... Show MoreAuthors in this work design efficient neural networks, which are based on the modified Levenberg - Marquardt (LM) training algorithms to solve non-linear fourth - order three -dimensional partial differential equations in the two kinds in the periodic and in the non-periodic - Periodic. Software reliability growth models are essential tools for monitoring and evaluating the evolution of software reliability. Software defect detection events that occur during testing and operation are often treated as counting processes in many current models. However, when working with large software systems, the error detection process should be viewed as a random process with a continuous state space, since the number of faults found during testin
... Show MoreIn this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database
The economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The s
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