Výsledky vyhledávání

Report

The State of FaaS: An analysis of public Functions-as-a-Service providers

Serverless computing is a growing and maturing field that is the focus of much research, industry interest and adoption. Previous works exploring Functions-as-a-Service providers have focused primarily on the most well known providers AWS Lambda, Goo

Externí odkaz: http://arxiv.org/abs/2408.03021

Zobrazit plný text záznamu

Akademický článek

Serum Lipid and Glycemic Profile in HARRT-Naïve and HARRT-Exposed HIV Patients in Enugu State, Nigeria: A Case–Control Study

Autor: Esther A Ekwe

Publikováno v: International Journal of Medicine and Health Development, Vol 29, Iss 3, Pp 205-211 (2024)

Background: Although highly active antiretroviral therapy (HAART) has improved immunologic and virologic outcomes, as well as the quality of life of human immunodeficiency virus (HIV)-positive patients, it has worsened a wide range of metabolic deran

Externí odkaz: https://doaj.org/article/34ea2cd643454026b590ba5973c56775

Zobrazit plný text záznamu

Akademický článek

Explicit and explainable artificial intelligent model for prediction of CO2 molecular diffusion coefficient in heavy crude oils and bitumen

Autor: Saad Alatefi, Okorie Ekwe Agwu, Ahmad Alkouh

Publikováno v: Results in Engineering, Vol 24, Iss , Pp 103328- (2024)

Optimizing CO2 injection for enhanced oil recovery (EOR) requires a precise estimation of the CO2-diffusivity coefficient in porous media. This study developed a predictive model for the molecular diffusivity coefficient of CO2 in bitumen and heavy c

Externí odkaz: https://doaj.org/article/decb4addb9ed4d42aecaaeee91cbf2c3

Zobrazit plný text záznamu

Akademický článek

Utilization of machine learning for the estimation of production rates in wells operated by electrical submersible pumps

Autor: Okorie Ekwe Agwu, Ahmad Alkouh, Saad Alatefi, Reda Abdel Azim, Razaq Ferhadi

Publikováno v: Journal of Petroleum Exploration and Production Technology, Vol 14, Iss 5, Pp 1205-1233 (2024)

Abstract In this study, a neural network model is developed for the prediction of oil flow rates in wells lifted by electrical submersible pumps (ESPs). Three attributes of the model in this work make this study unique. First, the knowledge on the co

Externí odkaz: https://doaj.org/article/f531787f3d3f4b538bc6ae03dfdcfb57

Zobrazit plný text záznamu