The nonlinear refractive (NLR) index and third order susceptibility (X3) of carbon quantum dots (CQDs) have been studied using two laser wavelengths (473 and 532 nm). The z-scan technique was used to examine the nonlinearity. Results showed that all concentrations have negative NLR indices in the order of 10−10 cm2/W at two laser wavelengths. Moreover, the nonlinearity of CQDs was improved by increasing the concentration of CQDs. The highest value of third order susceptibility was found to be 3.32*10−8 (esu) for CQDs with a concentration of 70 mA at 473 nm wavelength.

The proposed method is sensitive, simple , fast for the determination of mebeverine hydrochloride in pure form or in pharmaceutical dosage . Using Homemade instrument fluorimeter continuous flow injection analyser with solid state laser (405 nm) as a source. Where it is based upon the fluorescence of fluorescein sodium salt and quenching effect of fluorescence by mebeverine in aqueous medium. The calibration graph was linear in the concentration range 0.05 to10 mMol.L-1 (r= 0.9629) with relative standard deviation (RSD%) for 1 mMol.L-1mebeverine solution was lower than 3% (n=6). Three pharmaceutical drugs were used as an application for the determination of mebeverine. A comparison was made between the newly developed method of analysis wit

In recent years, the field of research around the congestion problem of 4G and 5G networks has grown, especially those based on artificial intelligence (AI). Although 4G with LTE is seen as a mature technology, there is a continuous improvement in the infrastructure that led to the emergence of 5G networks. As a result of the large services provided in industries, Internet of Things (IoT) applications and smart cities, which have a large amount of exchanged data, a large number of connected devices per area, and high data rates, have brought their own problems and challenges, especially the problem of congestion. In this context, artificial intelligence (AI) models can be considered as one of the main techniques that can be used to solve ne

Sewer sediment deposition is an important aspect as it relates to several operational and environmental problems. It concerns municipalities as it affects the sewer system and contributes to sewer failure which has a catastrophic effect if happened in trunks or interceptors. Sewer rehabilitation is a costly process and complex in terms of choosing the method of rehabilitation and individual sewers to be rehabilitated.  For such a complex process, inspection techniques assist in the decision-making process; though, it may add to the total expenditure of the project as it requires special tools and trained personnel. For developing countries, Inspection could prohibit the rehabilitation proceeds. In this study, the researchers propos

In this paper new methods were presented based on technique of differences which is the difference- based modified jackknifed generalized ridge regression estimator(DMJGR) and difference-based generalized  jackknifed ridge regression estimator(DGJR), in estimating the parameters of linear part of the partially linear model. As for the nonlinear part represented by the nonparametric function, it was estimated using Nadaraya Watson smoother. The partially linear model was compared using these proposed methods with other estimators based on differencing technique through the MSE comparison criterion in simulation study.

The study aims to study the geographical distribution of electricpower plants in Iraq, except the governorates of Kurdistan Region (Dohuk, Erbil, Sulaymaniyah) due to lack of data.

In order to reach the goal of the research was based on some mathematical equations and statistical methods to determine how the geographical distribution of these stations (gas, hydropower, steam, diesel) within the provinces and the concentration of them as well as the possibility of the classification of power plants in Iraq to facilitate understanding of distribution in a scientific manner is characterized by objectively.

The most important results of the research are that there are a number of factors that led to the irregular distribution

Abstract

The Classical Normal Linear Regression Model Based on Several hypotheses, one of them is Heteroscedasticity as it is known that the wing of least squares method (OLS), under the existence of these two problems make the estimators, lose their desirable properties, in addition the statistical inference becomes unaccepted table. According that we put tow alternative,  the first one is  (Generalized Least Square) Which is denoted by (GLS), and the second alternative is to (Robust covariance matrix estimation) the estimated parameters method(OLS), and that the way (GLS) method neat and certified, if the capabilities (Efficient) and the statistical inference Thread on the basis of an acceptable

This paper presents a modified training method for Recurrent Neural Networks. This method depends on the Non linear Auto Regressive (NARX) model with Modified Wavelet Function as activation function (MSLOG) in the hidden layer. The modified model is known as Modified Recurrent Neural (MRN). It is used for identification Forward dynamics of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot. This model is also used in the design of Direct Inverse Control (DIC). This method is compared with Recurrent Neural Networks that used Sigmoid activation function (RS) in the hidden layer and Recurrent Neural Networks with Wavelet activation function (RW). Simulation results shows that the MRN model is bett

An adaptive nonlinear neural controller to reduce the nonlinear flutter in 2-D wing is proposed in the paper. The nonlinearities in the system come from the quasi steady aerodynamic model and torsional spring in pitch direction. Time domain simulations are used to examine the dynamic aero elastic instabilities of the system (e.g. the onset of flutter and limit cycle oscillation, LCO). The structure of the controller consists of two models :the modified Elman neural network (MENN) and the feed forward multi-layer Perceptron (MLP). The MENN model is trained with off-line and on-line stages to guarantee that the outputs of the model accurately represent the plunge and pitch motion of the wing and this neural model acts as the identifier. Th