Predicting vertical stress was indeed useful for controlling geomechanical issues since it allowed for the computation of pore pressure for the formation and the classification of fault regimes. This study provides an in-depth observation of vertical stress prediction utilizing numerous approaches using the Techlog 2015 software. Gardner's method results in incorrect vertical stress values with a problem that this method doesn't start from the surface and instead relies only on sound log data. Whereas the Amoco, Wendt non-acoustic, Traugott, average technique simply needed density log as input and used a straight line as the observed density, this was incorrect for vertical computing stress. The results of these methods

Wireless channels are typically much more noisy than wired links and subjected to fading due to multipath  propagation which result in ISI and hence high error rate. Adaptive modulation is a powerful technique to improve the tradeoff between spectral efficiency and Bit Error Rate (BER). In order to adjust the transmission rate, channel state information (CSI) is required at the transmitter side.

In this paper the performance enhancement of using linear prediction along with channel estimation to track the channel variations and adaptive modulation were examined. The simulation results shows that the channel estimation is sufficient for low Doppler frequency shifts (<30 Hz), while channel prediction is much more suited at

In this study a combination of two basics known methods used to daily prediction of solar insolation in Baghdad city, Iraq, for the first time, the harmonic and the classical linear regression analyses, thus it is called HARLIN model. The resulted prediction data compared with basics data for Baghdad city for two years (2010-2011), where the model showed a great success application in the accurate results, compared with the linear famous and well known model which is used the classical linear Angstrom equations with various formulations in many previous studies.

One of the most severe problems with flexible asphalt pavements is permanent deformation in the form of rutting. Accordingly, the practice of adding fiber elements to asphalt mix to improve performance under dynamic loading has grown significantly in order to prevent rutting distress and ensure a safe and long-lasting road surface. This paper explores the effects of a combination of ceramic fiber (CF), a low-cost, easily available mineral fiber, and thermal insulator fiber reinforced to enhance the Marshall properties and increase the rutting resistance of asphalt mixes at high temperatures. Asphalt mixtures with 0%, 0.75%, 1.5%, and 2.25% CF content were prepared, and Marshall stability and wheel tracking tests were employed to stu

Pandemic COVID-19 is a contagious disease affecting more than 200 countries, territories, and regions. Recently, Iraq is one of the countries that have immensely suffered from this outbreak. The Kurdistan Region of Iraq (KRI) is also prone to the disease. Until now, more than 23,000 confirmed cases have been recorded in the region. Since the onset of the COVID-19 in Wuhan, based on epidemiological modelling, researchers have used various models to predict the future of the epidemic and the time of peak, yielding diverse numbers in different countries. This study aims to estimate the basic reproductive number [R₀] for COVID-19 in KRI, using the standard SIR (Susceptible-Infected-Removed) epidemic model. A system of non

The most common cause of upper respiratory tract infection is coronavirus, which has a crown appearance due to the existence of spikes on its envelope. D-dimer levels in the plasma have been considered a prognostic factor for COVID-19 patients.

This study uses an Artificial Neural Network (ANN) to examine the constitutive relationships of the Glass Fiber Reinforced Polymer (GFRP) residual tensile strength at elevated temperatures. The objective is to develop an effective model and establish fire performance criteria for concrete structures in fire scenarios. Multilayer networks that employ reactive error distribution approaches can determine the residual tensile strength of GFRP using six input parameters, in contrast to previous mathematical models that utilized one or two inputs while disregarding the others. Multilayered networks employing reactive error distribution technology assign weights to each variable influencing the residual tensile strength of GFRP. Temperatur

In this research, the stopping power and range of protons in biological human soft and hard tissues (blood, brain, skeleton-cortical bone, and skin) of both child and adult are calculated at the energies ranging from 1MeV to 350 MeV. The data is collected from ICRU Report 46 and calculated the stopping power employing the Bethe formula. Moreover, the simple integration (continuous slowing down approximation) method is employed for calculating protons range at the target. Then, the stopping power and range of protons value in human tissues have been compared with the program called SRIM. Moreover, the results of the stopping power vs energy and the range vs energy have been presented graphically. Proper agreement is found between the gain

Background: Periodontitis is a chronic inflammatory disease which is initiated by an infection of the oral microorganisms and it involves the humoral and cellular characteristics of the host response. The periodontal disease is found to develop due to a series of interactions among the periodontotrophic herpes viruses, the periodontopathic bacteria and the host immune reactions.Recent studies have demonstrated that various human viruses, especially human cytomegalovirus seems to play a part in the pathogenesis of periodontitis. Periodontitis is an infectious disease involving specific bacteria and viruses.
Objectives: The present study was initiated to evaluate the percentage of human cytomegalovirus in periodontitis patients and dete

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c