In this article, an inverse problem of finding timewise-dependent thermal conductivity has been investigated numerically. Numerical solution of forward (direct) problem has been solved by finite-difference method (FDM). Whilst, the inverse (indirect) problem solved iteratively using Lsqnonlin   routine  from MATLAB. Initial guess for unknown coefficient expressed by explicit relation   based on nonlocal overdetermination conditions and intial input data .The obtained numrical results are presented and discussed in several figures and tables. These results are accurate and stable even in the presense of noisy data.

A load flow program is developed using MATLAB and based on the Newton–Raphson method,which shows very fast and efficient rate of convergence as well as computationally the proposed method is very efficient and it requires less computer memory through the use of sparsing method and other methods in programming to accelerate the run speed to be near the real time.
The designed program computes the voltage magnitudes and phase angles at each bus of the network under steady–state operating conditions. It also computes the power flow and power losses for all equipment, including transformers and transmission lines taking into consideration the effects of off–nominal, tap and phase shift transformers, generators, shunt capacitors, sh

   In this research the effect of grain size and effect of La2O3 doping on densification  rate for the  initial and intermediate  stages of sintering were studied .The experimental results for α – cristobilite powder are modeled using ( L2-Regression ) technique in studying  the effect of grain size and La2O3 doping using  three particles size (6.12, 8.92, 13.6 ) µm, with undoped  initial powder and with La2O3 doping . The mathematical simulation showes that the densification rates increase as the initial particles sizes decrease and vice versa. This shows that the densification depends directly on the initial compact density which reflects the contacts area between the particles . How

Developing smart city planning requires integrating various techniques, including geospatial techniques, building information models (BIM), information and communication technology (ICT), and artificial intelligence, for instance, three-dimensional (3D) building models, in enabling smart city applications. This study aims to comprehensively analyze the role and significance of geospatial techniques in smart city planning and implementation. The literature review encompasses (74) studies from diverse databases, examining relevant solutions and prototypes related to smart city planning. The focus highlights the requirements and preparation of geospatial techniques to support the transition to a smart city. The paper explores various aspects,

Long-term use of non-steroidal anti-inflammatory drugs (NSAIDs) mostly associated with renal and hepatic adverse effects, and the adjunct use of compounds with potent protective effects, like silymarin, may be one of the choices to avoid these effects. This project was designed to evaluate the protective effect of silymarin against the suspected renal and hepatic injury induced with long term use of NSAIDs; 220 patients with osteoarthritis were randomized into 5 groups and treated with either silymarin 300mg/day alone, piroxicam 20mg/day alone, meloxicam 15mg/day alone or the combination of each of them with silymarin for 8 weeks. The renal and hepatic functions were evaluated before starting treatment and after 8 weeks including assessm

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

In the present work, different remote sensing techniques have been used to analyze remote sensing data spectrally using ENVI software. The majority of algorithms used in the Spectral Processing can be organized as target detection, change detection and classification. In this paper several methods of target detection have been studied such as matched filter and constrained energy minimization.
The water body mapping have been obtained and the results showed changes on the study area through the period 1995-2000. Also the results that obtained from applying constrained energy minimization were more accurate than other method comparing with the real situation.

In this paper, integrated quantum neural network (QNN), which is a class of feedforward

neural networks (FFNN’s), is performed through emerging quantum computing (QC) with artificial neural network(ANN) classifier. It is used in data classification technique, and here iris flower data is used as a classification signals. For this purpose independent component analysis (ICA) is used as a feature extraction technique after normalization of these signals, the architecture of (QNN’s) has inherently built in fuzzy, hidden units of these networks (QNN’s) to develop quantized representations of sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that