The separation of fluid-fluid mixtures is a major issue in various sectors. The liquid-liquid hydro-cyclone has a wide range of applications in various sectors due to its great efficiency in separating fluid mixtures, ease of installation, and low cost. In crude oil production, high water is consumed following the degrading of production quality, high processing costs, costs, and environmental impacts. Axial hydro cyclone is a tool for Downhole Oil/Water Separation (DOWS) in the petroleum production industry, but it has limitations. The main purpose of this work is to simulate the effect of compacting the conventional inline hydro cyclone with a converging-diverging nozzle on the oil/water dynamic flow and the separation process to

This investigation was undertaken to evaluate the effectiveness of using Hydrated lime as a (partial substitute) by weight of filler (lime stone powder) with five consecutive percentage namely (1.0, 1.5, 2.0, 2.5, 3.0) % by means of aggregate treatment, by introducing dry lime on dry and 2–3% Saturated surface aggregate on both wearing and binder coarse. Marshall design method, indirect tensile test and permanent deformation under repeated loading of Pneumatic repeated load system at full range of temperature (20, 40, 60) C0 were examined The study revealed that the use of 2.0% and 1.5 % of dry and wet replacement extend the pavement characteristics by improving the Marshall properties and increasing the TSR%. Finally, increase permanent

With the vast usage of network services, Security became an important issue for all network types. Various techniques emerged to grant network security; among them is Network Intrusion Detection System (NIDS). Many extant NIDSs actively work against various intrusions, but there are still a number of performance issues including high false alarm rates, and numerous undetected attacks. To keep up with these attacks, some of the academic researchers turned towards machine learning (ML) techniques to create software that automatically predict intrusive and abnormal traffic, another approach is to utilize ML algorithms in enhancing Traditional NIDSs which is a more feasible solution since they are widely spread. To upgrade t

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

BACKGROUND: Acute coronary syndrome (ACS) is the clinical manifestation of acutely diminished coronary arterial blood supply. The rate of increase of intraventricular pressure during isovolumetric contraction (left ventricular dP/dt) represents the rate of change of pressure during ejection. OBJECTIVE: The aim of this study is to evaluate the usefulness of the rate of increase of intraventricular pressure during isovolumetric contraction (dP/dt) in assessment of left ventricular function in patients with acute coronary syndrome and its relation to certain clinical and echocardiographic features. PATIENTS AND METHODS: The study is a cross sectional study including 50 patients with an established diagnosis of acute coronary syndrome. The

This study aims to find the chemosensitive dysfunction incidence in COVID-19-positive patients and its recovery.

We collected the data from sixty-five patients, all COVID-19 positive, quarantined in-hospital between 5 April 2020 and 17 May 2020, by a questionnaire distributed in the quarantine ward.