Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables

The impact of undergraduate research experiences on students' academic development and retention in STEM fields is significant. Students' success in STEM fields is based on developing strong research and critical thinking skills that make it essential for students to engage in research activities throughout their academic programs. This work evaluates the effectiveness of undergraduate research experiences with respect to its influence on student retention and academic development. The cases presented are based on years of experience implementing undergraduate research programs in various STEM fields at Colorado State University Pueblo (CSU Pueblo) funded by HSI STEM Grants. The study seeks to establish a correlation between students' reten

The rise of edge-cloud continuum computing is a result of the growing significance of edge computing, which has become a complementary or substitute option for traditional cloud services. The convergence of networking and computers presents a notable challenge due to their distinct historical development. Task scheduling is a major challenge in the context of edge-cloud continuum computing. The selection of the execution location of tasks, is crucial in meeting the quality-of-service (QoS) requirements of applications. An efficient scheduling strategy for distributing workloads among virtual machines in the edge-cloud continuum data center is mandatory to ensure the fulfilment of QoS requirements for both customer and service provider. E

Reliability analysis methods are used to evaluate the safety of reinforced concrete structures by evaluating the limit state function 𝑔(𝑋𝑖). For implicit limit state function and nonlinear analysis , an advanced reliability analysis methods are needed. Monte Carlo simulation (MCS) can be used in this case however, as the number of input variables increases, the time required for MCS also increases, making it a time consuming method especially for complex problems with implicit performance functions. In such cases, MCS-based FORM (First Order Reliability Method) and Artificial Neural Network-based FORM (ANN FORM) have been proposed as alternatives. However, it is important to note that both MCS-FORM and ANN-FORM can also be time-con

The research aimed to identify and build two specialized scales for cognitive load and mental stress and to identify the level of each of them among 110-meter steeplechase runners among youth, and to prepare a psychological counseling approach to reduce the level of cognitive load and mental stress among 110-meter steeplechase runners among youth, so that the two research hypotheses are that there are differences. There are statistically significant differences between the results of the pre- and post-tests of the experimental group in measuring cognitive load. There are statistically significant differences between the results of the pre- and post-tests of the experimental group in measuring mental stress. The experimental method w

In the recent decade, injection of nanoparticles (NPs) into underground formation as liquid nanodispersions has been suggested as a smart alternative for conventional methods in tertiary oil recovery projects from mature oil reservoirs. Such reservoirs, however, are strong candidates for carbon geo-sequestration (CGS) projects, and the presence of nanoparticles (NPs) after nanofluid-flooding can add more complexity to carbon geo-storage projects. Despite studies investigating CO2 injection and nanofluid-flooding for EOR projects, no information was reported about the potential synergistic effects of CO2 and NPs on enhanced oil recovery (EOR) and CGS concerning the interfacial tension (γ) of CO2-oil system. This study thus extensively inves

One of the main parts in hydraulic system is directional control valve, which is needed in order to operate hydraulic actuator. Practically, a conventional directional control valve has complex construction and moving parts, such as spool. Alternatively, a proposed Magneto-rheological (MR) directional control valve can offer a better solution without any moving parts by means of MR fluid. MR fluid consists of stable suspension of micro-sized magnetic particles dispersed in carrier medium like hydrocarbon oil. The main objectives of this present research are to design a MR directional control valve using MR fluid, to analyse its magnetic circuit using FEMM software, and to study and simulate the performance of this valve. In this research, a