This research introduced the derivation of mathematical equations to calculate the Cartesian and geographical coordinates of a site situated at a far distance from the observer position by using GPS data. The geographical coordinates (Ï•obs., λ obs., hobs.) for observer position were transformed to Cartesian coordinates (X obs., Y obs., Z obs.) of observer position itself. Then the Cartesian coordinates of unknown position mathematically were calculated from these calculated equations, and its transformed to geographical coordinates of (Ï•unk., λunk.) position.
 

 The solar radiation plays an important role on the energy balance of the earthatmosphere, which is the main source of energy.  Also the solar radiation is a main factor of all applications which use a solar energy as renewable energy source.  The purpose of this research is to study the monthly average changes for solar radiation for the period from 1985 to 1989 by using satellite Antenna Alignment from (NASA). The result shows that the monthly average radiation changes from one year to another because of the changing of it component of atmosphere, (gases, clouds and Aerosols) and as an enhancement for this conclusion, we compared the results with the monthly average radiation at clear atmosphere where the change was slig

With the increasing demands to use remote sensing approaches, such as aerial photography, satellite imagery, and LiDAR in archaeological applications, there is still a limited number of studies assessing the differences between remote sensing methods in extracting new archaeological finds. Therefore, this work aims to critically compare two types of fine-scale remotely sensed data: LiDAR and an Unmanned Aerial Vehicle (UAV) derived Structure from Motion (SfM) photogrammetry. To achieve this, aerial imagery and airborne LiDAR datasets of Chun Castle were acquired, processed, analyzed, and interpreted. Chun Castle is one of the most remarkable ancient sites in Cornwall County (Southwest England) that had not been surveyed and explored

Although a great deal of works has been done on the area of capital structure and dividend policy, there is still insufficient knowledge of how these policies affect stock prices. This shortcoming may have been originated from the separation between both policies when investigating their effect on stock prices. Based on this point, this research adopts a new technique (completely randomized design), to combine the effect of capital structure and dividend policy on stock prices rather than separating between them. The study used panel based regression analysis depending on the sample of 30 service and industrial Jordanian firms for the period of 2001-2010. The result of test hypotheses found the following; 1) dividend payout has a

In this study, we focused on the random coefficient estimation of the general regression and Swamy models of panel data. By using this type of data, the data give a better chance of obtaining a better method and better indicators. Entropy's methods have been used to estimate random coefficients for the general regression and Swamy of the panel data which were presented in two ways: the first represents the maximum dual Entropy and the second is general maximum Entropy in which a comparison between them have been done by using simulation to choose the optimal methods.

The results have been compared by using mean squares error and mean absolute percentage error to different cases in term of correlation valu

There is an assumption implicit but fundamental theory behind the decline by the time series used in the estimate, namely that the time series has a sleep feature Stationary or the language of Engle Gernger chains are integrated level zero, which indicated by I (0). It is well known, for example, tables of t-statistic is designed primarily to deal with the results of the regression that uses static strings. This assumption has been previously treated as an axiom the mid-seventies, where researchers are conducting studies of applied without taking into account the properties of time series used prior to the assessment, was to accept the results of these tests Bmanueh and delivery capabilities based on the applicability of the theo

Survival analysis is widely applied in data describing for the life time of item until the occurrence of an event of interest such as death or another event of understudy . The purpose of this paper is to use the dynamic approach in the deep learning neural network method, where in this method a dynamic neural network that suits the nature of discrete survival data and time varying effect. This neural network is based on the Levenberg-Marquardt (L-M) algorithm in training, and the method is called Proposed Dynamic Artificial Neural Network (PDANN). Then a comparison was made with another method that depends entirely on the Bayes methodology is called Maximum A Posterior (MAP) method. This method was carried out using numerical algorithms re

Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amo