Internet of Things (IoT) contributes to improve the quality of life as it supports many applications, especially healthcare systems. Data generated from IoT devices is sent to the Cloud Computing (CC) for processing and storage, despite the latency caused by the distance. Because of the revolution in IoT devices, data sent to CC has been increasing. As a result, another problem added to the latency was increasing congestion on the cloud network. Fog Computing (FC) was used to solve these problems because of its proximity to IoT devices, while filtering data is sent to the CC. FC is a middle layer located between IoT devices and the CC layer. Due to the massive data generated by IoT devices on FC, Dynamic Weighted Round Robin (DWRR)

Background: Sex variations in coronary artery disease (CAD) are well documented. However, sex differences in coronary artery calcium (CAC) and its role in the detection of coronary artery stenosis remain controversial. Objective: To assess the impact of sex variation on coronary artery calcification and its efficacy in predicting coronary artery stenosis. Methods: This is a cross-sectional observational study including 230 consecutive patients with suspected CAD (120 men and 110 women) referred for coronary computed tomography angiography (CCTA). The study analyzed sex-based differences in the sensitivity and specificity of coronary artery calcification (CAC) for detecting moderate to severe stenosis across various coronary arteries

The research aims at integrating the disclosure of the business models with the qualitative characteristics of accounting information. To achieve this, the elements of the business model should be identified and disclosed, and then study the possibility of integrating the disclosure of the business model with the qualitative characteristics of accounting information.

To achieve this objective, the research was based on the indicators of disclosure of the business model of the International Accounting Standards Board to measure the disclosure of the business model.

The research reached a number of conclusions, the most important of which were as follows:

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The isolates were screened according to their capability for pectinase production, screening process identified the best pectinolytic isolate and it was characterized by cultural and biochemical, as Pesudomonas sp. Pectinolytic enzyme producing bacterium Pesudomonas sp. was isolated from the Iraqi soil on nutrient agar plate. Optimiztion of process parameters were carried out by altering some of environmental conditions of chemo-physical environment for the production medium. The highest pectinase production was observed at 48 hrs of incubation at 35 °C with the initial pH of 6.0. Different nutrients and environmental conditions were investigated in terms of their effect on the production of extracellular pectinase using citrus pectin a

Bioavailability is the objective for an optimum formulation. The target of the analysis is to maximize both the fluidity and disintegration profile of class II weakly compounds that are water-soluble. Anti-dyslipidemia drug rosuvastatin calcium (RC) (bioavailability 20%) through formulating as nanofibers (NFs) using electrospinning (ES) technology. Twenty formulas were prepared, and different polymers and polymer combinations with various concentrations were used such as polyethylene oxide (PEO) polyvinyl pyrrolidine (PVPK-30), and hydroxypropyl methylcellulose (HPMC). Three distinct groups of maximum parameters, including polymeric solution, electrospinning method, and ambient parameter, are capable of influencing the creation alon

Drilling deviated wells is a frequently used approach in the oil and gas industry to increase the productivity of wells in reservoirs with a small thickness. Drilling these wells has been a challenge due to the low rate of penetration (ROP) and severe wellbore instability issues. The objective of this research is to reach a better drilling performance by reducing drilling time and increasing wellbore stability.

In this work, the first step was to develop a model that predicts the ROP for deviated wells by applying Artificial Neural Networks (ANNs). In the modeling, azimuth (AZI) and inclination (INC) of the wellbore trajectory, controllable drilling parameters, unconfined compressive strength (UCS), formation

Drilling deviated wells is a frequently used approach in the oil and gas industry to increase the productivity of wells in reservoirs with a small thickness. Drilling these wells has been a challenge due to the low rate of penetration (ROP) and severe wellbore instability issues. The objective of this research is to reach a better drilling performance by reducing drilling time and increasing wellbore stability.

In this work, the first step was to develop a model that predicts the ROP for deviated wells by applying Artificial Neural Networks (ANNs). In the modeling, azimuth (AZI) and inclination (INC) of the wellbore trajectory, controllable drilling parameters, unconfined compressive strength (UCS), formation