Corrosion inhibition of Tubing steel during acidization of oil and gas wells

Acidization is an oil reservoir stimulation technique for increasing well productivity. Hydrochloric acid, HCl, is used in oil and gas production to stimulate the formation. The acid treatment occurs through steel tubes. The process requires a high degree of corrosion inhibition. The present study deals with the evaluation of the effectiveness of the new synthesized compound namely; 6-methyl-5-[m-nitro styryl]-3-mercapto-1,2,4-triazine as corrosion inhibitor for mild steel in 12% HCl solution at 50 °C using various chemical and electrochemical techniques. The results showed that anodic dissolution of mild steel was remarkably decreased. The strong adsorption ability of this new synthesized compound can be attributed to the presence of multiple adsorption centers of nitrogen as well as π-donor moieties.     

Fatigue Crack Tip Plasticity for Inclined Cracks

The evaluation of the crack tip deformation is essential to the estimation of crack growth under either static or cyclic loading. A 3-D elastic–plastic finite element analysis was developed to simulate the crack tip deformation along mixed mode inclined edge cracks in a steel plate subjected to either monotonic or cyclic loading at selected R-ratios. In this paper, two types of crack configurations were investigated: inclined cracks with equal inclined lengths (EICL) and inclined cracks with equal horizontal projection length (ECHP). The development of the monotonic (Δm) and cyclic (Δc) crack tip plastically zones and the monotonic (CTOD) and cyclic (ΔCTOD) crack tip opening displacements were traced to find the effect of the crack inclination angle, which significantly affected the size and shape of the crack tip plastic zone. The finite element results compared well with the analytical results based on modified Dugdale’s model. It was observed that Mode II has a significant effect on the plastic zone in the case of equal inclined crack length (EICL), i.e., Mode II increases as the crack angle decreases. Also, it is interesting to note that for the EICL case, the magnitude of Δc is delayed to appear with decreasing the inclination angle, for example, for θ?=?90° the cyclic plastic zone appeared at Δσ?=?103.32 MPa, while for θ?=?45° the cyclic plastic zone appeared at Δσ?=?132.84 MPa. Whereas, the variation of monotonic and cyclic plastic zone size in the equal crack horizontal projection (ECHP) case is not affected by the crack inclination angle. Furthermore, it was observed that the static crack tip opening displacement (CTOD) and the cyclic (ΔCTOD) are independent of the crack inclination angle in case of ECHP, due to such cracks take into consideration the effect of inclination angle through its length.     

Reliable Mark-Embedded Algorithm for Verifying Archived/Encrypted Image Contents in Presence Different Attacks with FEC Utilizing Consideration

Wireless Personal Communications - Due to the widely spreading of fake news utilizing image manipulation and its bad effects, this paper investigates efficient image contents verification and...     

Double S-Shaped Polarization – Voltage Curve and Negative Capacitance from Al2O3-Hf0.5Zr0.5O2-Al2O3 Triple-Layer Structure

The negative capacitance (NC) effect, recently discovered in a fluorite-based ferroelectric thin film, has attracted great attention as a rescue to overcome the scaling limitations of the conventional memory and logic devices of highly integrated circuits. The NC effect manifesting an S-shaped polarization–voltage (P–V) curve is initially interpreted by a 1-dimensional Landau Ginzburg Devonshire (LGD) model. However, a series of recent studies have found that this effect can also be explained by the inhomogeneous stray field energy (ISE) model. In this study, by extending the ISE model in the ferroelectric (FE)-dielectric (DE) layered structure, an analytical model that considers the influence of the interfacial screening charge distribution is presented. This model showed that the NC effect in the FE-DE heterostructure can be manifested in various forms other than a single S-shaped P–V curve. In particular, a double S-shaped P–V curve is expected from the fully compensated anti-parallel domain structure, confirmed experimentally in the actual Al₂O₃/(Hf_0.5Zr_0.5)O₂/Al₂O₃ triple-layer structure. Furthermore, to reveal the origin of the double S-shaped P–V curve, a multidomain LGD model is presented. It is confirmed that this phenomenon is attributed to the evolution of inhomogeneous stray field energy.     

IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19

The prediction of human diseases, particularly COVID-19, is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment. To deal with the prediction and diagnosis of COVID-19, we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System (IoMTSM-HMFIS). The proposed system determines the various factors like fever, cough, complete blood count, respiratory rate, Ct-chest, Erythrocyte sedimentation rate and C-reactive protein, family history, and antibody detection (lgG) that are directly involved in COVID-19. The expert system has two input variables in layer 1, and seven input variables in layer 2. In layer 1, the initial identification for COVID-19 is considered, whereas in layer 2, the different factors involved are studied. Finally, advanced lab tests are conducted to identify the actual current status of the disease. The major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19; the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for quarantining. MATLAB-2019a tool is used to conduct the simulation. The COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%. Finally, to achieve improved performance, the analysis results of the system were shared with experts of the Lahore General Hospital, Lahore, Pakistan.     

Prediction of Cloud Ranking in a Hyperconverged Cloud Ecosystem Using Machine Learning

Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet. The design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter metric. In a hyperconverged cloud ecosystem environment, building high-reliability cloud applications is a challenging job. The selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud rankings. The emergence of cloud computing is significantly reshaping the digital ecosystem, and the numerous services offered by cloud service providers are playing a vital role in this transformation. Hyperconverged software-based unified utilities combine storage virtualization, compute virtualization, and network virtualization. The availability of the latter has also raised the demand for QoS. Due to the diversity of services, the respective quality parameters are also in abundance and need a carefully designed mechanism to compare and identify the critical, common, and impactful parameters. It is also necessary to reconsider the market needs in terms of service requirements and the QoS provided by various CSPs. This research provides a machine learning-based mechanism to monitor the QoS in a hyperconverged environment with three core service parameters: service quality, downtime of servers, and outage of cloud services.