The long-term monitoring of land movements represents the most successful application of the Global Navigation Satellite System (GNSS), particularly the Global Positioning System. However, the application of long term monitoring of land movements depends on the availability of homogenous and consistent daily position time series of stations over a period of time. Such time series can be produced very efficiently by using Precise Point Positioning and Double Difference techniques based on particular sophisticated GNSS processing softwares. Nonetheless, these rely on the availability of GNSS products which are precise satellite orbit and clock, and Earth orientation parameters. Unfortunately, several changes and modifications have been mad

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

High temperature superconductors with a nominal composition HgBa2Ca2Cu3O8+δ
for different values of pressure (0.2,0.3, 0.5, 0.6, 0.9, 1.0 & 1.1)GPa were prepared by
a solid state reaction method. It has been found that the samples were semiconductor
P=0.2GPa.while the behavior of the other samples are superconductor in the rang
(80-300) K. Also the transition temperature Tc=143K is the maximum at P is equal to
0.5GPa. X-ray diffraction showed a tetragonal structure with the decreasing of the
lattice constant c with the increasing of the pressure. Also we found an increasing of
the density with the pressure.

Background: Acute appendicitis is the most common abdominal surgical emergency. The diagnosis of this condition is still essentially clinical and there is difficulty in the clinical diagnosis, especially among elderly, children and patients with a typical presentation, so early and accurate diagnosis of acute appendicitis is important to avoid its complications.Objectives: To evaluate the degree of accuracy of Alvarado scoring system in the diagnosis of acute appendicitis.Method: Two hundred patients were admitted to the Alkindy Teaching Hospital from January 2011 to april 2014- presented with symptoms and signs suggestive of acute appendicitis. After examination and investigations all patients were given a score according to Alvarado sc

Herein, an efficient inorganic/organic hybrid photocatalyst composed of zeolitic imidazolate framework (ZIF-67) decorated with Cd0.5Zn0.5S solid solution semiconductor was constructed. The properties of prepared ZIF- [email protected] nanocomposite and its components (ZIF-67 and Cd0.5Zn0.5S) were investigated using XRD, FESEM, EDX, TEM, DRS and BET methods. The photocatalytic activity of fabricated [email protected] nanocomposite were measured toward removal of methyl violet (MV) dye as a simulated organic contaminant. Under visible-light and specific conditions (photocatalyst dose 1 g/l, MV dye 10 mg/l, unmodified solution pH 6.7 and reaction time 60 min.), the acquired [email protected] photocatalyst showed advanced photocatalytic activity

The mixed-spin ferrimagnetic Ising system consists of two-dimensional sublattices A and B with spin values and respectively .By used the mean-field approximation MFA of Ising model to find magnetism( ).In order to determined the best stabile magnetism , Gibbs free energy employ a variational method based on the Bogoliubov inequality .The ground-state (Phase diagram) structure of our system can easily be determined at , we find six phases with different spins values depend on the effect of a single-ion anisotropies .these lead to determined the second , first orders transition ,and the tricritical points as well as the compensation phenomenon .