The physical and elastic characteristics of rocks determine rock strengths in general. Rock strength is frequently assessed using porosity well logs such as neutron and sonic logs. The essential criteria for estimating rock mechanic parameters in petroleum engineering research are uniaxial compressive strength and elastic modulus. Indirect estimation using well-log data is necessary to measure these variables. This study attempts to create a single regression model that can accurately forecast rock mechanic characteristics for the Harth Carbonate Formation in the Fauqi oil field. According to the findings of this study, petrophysical parameters are reliable indexes for determining rock mechanical properties having good performance p

ASTRACT

The current study aimed to identify the quality of health services provided by the Omani health sector through the comparison between public and private hospitals in Dhofar Governorate, Oman. A questionnaire has been developed to collect data from 360 patients who received health services in one public Hospital (Sultan Qaboos) and three private Hospitals (Badr Al-Sama, Lifeline, and Al-Hakeeim). The data were analyzed using independent samples T-Test and One Way ANOVA. The results of the study showed that the quality levels of health services offered in private hospitals were better than public hospitals. The study results also reveled that there are significant differences between public hospitals and private hos

Glass Ionomer Cement (GIC) is one of the important dental temporary filing materials. The aim of this study is to evaluate the effect of adding 3, 5 and 7 wt. % of TiO₂ microparticles to conventional GIC powder (Riva Self Cure) on mechanical properties and its effect on absorption and solubility processes. TiO₂ particles additives improved compressive strength and biaxial flexural strength, where the compressive strength increased with increasing in the added ratio, while the highest value of the biaxial flexural strength was at 3 wt.%. The addition of TiO₂ particles improved the surface Vickers microhardness values, with highest value at 5 wt. %. On other hand TiO₂ addition im

ABSTRACTObjective: The objective of this study is to develop a controlled release matrix tablet of candesartan cilexetil to reduce the frequency of administration,enhance bioavailability and improve patient compliance; a once daily sustained release formulation of candesartan cilexetil is desirable.Methods: The prepared tablets from F1 to F24 were evaluated with different evaluation parameters like weight variation, drug content, friability,hardness, thickness and swelling ability. In vitro release for all formulas were studied depends on the type and amount of each polymer, i.e. (16 mg,32 mg and 48 mg) respectively beside to the combination effect of polymers on the release of the drug from the tablet.Results: In vitro release show

This work studied the electrical and thermal surface conductivity enhancement of polymethylmethacrylate (PMMA) clouded by double-walled carbon nanotubes (DWCNTs) and multi-walled carbon nanotube (MWCNTs) by using pulsed Nd:YAG laser. Variable input factors are considered as the laser energy (or the relevant power), pulse duration and pulse repetition rate. Results indicated that the DWCNTs increased the PMMA’s surface electrical conductivity from 10-15 S/m to 0.813×103 S/m while the MWCNTs raised it to 0.14×103 S/m. Hence, the DWCNTs achieved an increase of almost 6 times than that for the MWCNTs. Moreover, the former increased the thermal conductivity of the surface by 8 times and the later by 5 times.

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