Static Synchronous Series Compensator (SSSC) is a well known device for effectively regulating the active power flow in a power system. In this paper, the SSSC linearized power flow equations are incorporated into Newton-Raphson algorithm in a MATLAB written program to investigate the control of active poweer flow and the transient stability of a five bus and a thirty bus IEEE test systems, during abnormal conduction (three phase fault near buses). A comparison of the results obtained for the base case without SSSC and with it to investigate the effectiveness of the device on both of the active power flow and the transient stability.

Nonlinear differential equation stability is a very important feature of applied mathematics, as it has a wide variety of applications in both practical and physical life problems. The major object of the manuscript is to discuss and apply several techniques using modify the Krasovskii's method and the modify variable gradient method which are used to check the stability for some kinds of linear or nonlinear differential equations. Lyapunov function is constructed using the variable gradient method and Krasovskii’s method to estimate the stability of nonlinear systems. If the function of Lyapunov is positive, it implies that the nonlinear system is asymptotically stable. For the nonlinear systems, stability is still difficult even though

In the image processing’s field and computer vision it’s important to represent the image by its information. Image information comes from the image’s features that extracted from it using feature detection/extraction techniques and features description. Features in computer vision define informative data. For human eye its perfect to extract information from raw image, but computer cannot recognize image information. This is why various feature extraction techniques have been presented and progressed rapidly. This paper presents a general overview of the feature extraction categories for image.

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

The sorption of Cu²⁺ ions from synthetic wastewater using crushed concrete demolition waste (CCDW) which collected from a demolition site was investigated in a batch sorption system. Factors influencing on sorption process such as shaking time (0-300min), the initial concentration of contaminant (100-750mg/L), shaking speed (0-250 rpm), and adsorbent dosage (0.05-3 g/ml) have been studied. Batch experiments confirmed that the best values of these parameters were (180 min, 100 mg/l, 250 rpm, 0.7 g CCDW/100 ml) respectively where the achieved removal efficiency is equal to 100%. Sorption data were described using four isotherm models (Langmuir, Freundlich, Redlich-Peterson, and Radke-Prausnitz). Results proved that the pure ads

This research was designed to study the effect of water and alcoholic crude extracts of Calvatia craniiformis in vitro and in vivo On the other hand this study tested the toxic effect of both extracts in normal laboratory mice. The results showed that water and alcoholic extracts relatively have an acute toxic effect in mice in respect to LD50 (85 mg/kg, and 177mg/kg respectively). However the chronic toxicity of water extract at three different concentration (50, 75, 100 mg/kg) and alcoholic extract at concentrations of (100, 150, 200 mg/kg) was investigated in normal mice by (I.P) administration for 30 days alternatively and one drag in 48 hours . The results indicated significant effect (P ? 0.01) increasing in (MI) and (BI) of bone mar