Geotechnical characterization of the sites has been investigated with the collection of borehole data from different sources. Using the data, grain size distribution curves have been developed to understand the particle size distribution of the alluvium present. These curves were further used for preliminary assessment of liquefiable areas. From geotechnical characterization, it has been observed that the soil profile in the two sites is dominated by sand and silty sand.Seed and Idriss (1971) approachhas been usedevaluatethe liquefaction potentialbydeterminationof the relation between the maximum ground acceleration (a max/g) valuesdue to an earthquake and the relative density of a sand deposit in the field. The results reveal that

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

Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges in reservoir characterization. Permeability is one of the most difficult petrophysical parameters to predict using conventional logging techniques. Clarifications of the work flow methodology are presented alongside comprehensive models in this study. The purpose of this study is to provide a more robust technique for predicting permeability; previous studies on the Bazirgan field have attempted to do so, but their estimates have been vague, and the methods they give are obsolete and do not make any concessions to the real or rigid in order to solve the permeability computation. To

In this research the performance of 5G mobile system is evaluated through the Ergodic capacity metric. Today, in an­­y wireless communication system, many parameters have a significant role on system performance. Three main parameters are of concern here; the source power, number of antennas, and transmitter-receiver distance. User equipment’s (UEs) with equal and non-equal powers are used to evaluate the system performance in addition to using different antenna techniques to demonstrate the differences between SISO, MIMO, and massive MIMO. Using two mobile stations (MS) with different distances from the base station (BS), resulted in showing how using massive MIMO system will improve the performance than the standar

Simulation experiments are a means of solving in many fields, and it is the process of designing a model of the real system in order to follow it and identify its behavior through certain models and formulas written according to a repeating software style with a number of iterations. The aim of this study is to build a model  that deals with the behavior suffering from the state of (heteroskedasticity) by studying the models (APGARCH & NAGARCH) using (Gaussian) and (Non-Gaussian) distributions for different sample sizes (500,1000,1500,2000) through the stage of time series analysis (identification , estimation, diagnostic checking and prediction). The data was generated using the estimations of the parameters resulting f

This work aims to analyze a three-dimensional discrete-time biological system, a prey-predator model with a constant harvesting amount. The stage structure lies in the predator species. This analysis is done by finding all possible equilibria and investigating their stability. In order to get an optimal harvesting strategy, we suppose that harvesting is to be a non-constant rate. Finally, numerical simulations are given to confirm the outcome of mathematical analysis.

In this article we study the variance estimator for the normal distribution when the mean is un known depend of the cumulative function between unbiased estimator and Bays estimator for the variance of normal distribution which is used include Double Stage Shrunken estimator to obtain higher efficiency for the variance estimator of normal distribution when the mean is unknown by using small volume equal volume of two sample .

Objective: the aim of this study is to invest age and determine the effect of using (2) packing
technique (conventional and new tension technique) on hardness of (2) types of heat cure acrylic
resin which are (Ivoclar and Qual dental type).
Methodology : this study was intended the using of two types of heat cure acrylic (IVoclar and
Qual dental type) which are used in construction of complete denture which packed in two different
packing technique (conventional and new tension technique) and accomplished by using a total of
(40) specimens in diameter of ( 2mm thickness, 2 cm length and 1 cm width) . This specimens were
sectioned and subdivide into (4) group each (10) specimens for one group , then signed as (A, Al B