Mapping erosion-corrosion of carbon steel in oil-water solutions : Effect of velocity and applied potential

In this study, the combined effects of erosion and corrosion on carbon steel were investigated in three environments containing crude oil, reservoir water, and a mixture of both solutions at a range of applied potentials, velocities and impact angle. The results indicate that the corrosion contribution was augmented with an increase in the percentage of reservoir water. Both the erosion and corrosion contributions increased with impact velocity for all three environments. Following exposure of the carbon steel in the crude oil, the extent of the erosion was greater than that of corrosion, whilst in the reservoir water, the erosion and corrosion contributions were similar. Mechanisms of erosion-corrosion were proposed based on the change in erosion behaviour at various impact angles and applied potentials in the various environments. Erosion-corrosion maps were constructed based on the results, showing the change in mechanisms and wastage rates as a function of impact velocity and applied potential at various impact angles

Cardiac Myopathy and Metabolic Disorders among Elderly Patients in Saudi Arabia: A Systematic Review

Cardiac myopathy and metabolic disorders are increasingly common health issues among the elderly population in Saudi Arabia. These conditions can have significant impacts on the health and well-being of older individuals, and it is important to understand their prevalence and risk factors in this population

Susceptible exposed infectious recovered-machine learning for COVID-19 prediction in Saudi Arabia

Susceptible exposed infectious recovered (SEIR) is among the epidemiological models used in forecasting the spread of disease in large populations. SEIR is a fitting model for coronavirus disease (COVID-19) spread prediction. Somehow, in its original form, SEIR could not measure the impact of lockdowns. So, in the SEIR equations system utilized in this study, a variable was included to evaluate the impact of varying levels of social distance on the transmission of COVID-19. Additionally, we applied artificial intelligence utilizing the deep neural network machine learning (ML) technique. On the initial spread data for Saudi Arabia that were available up to June 25th, 2021, this improved SEIR model was used. The study shows possible infection to around 3.1 million persons without lockdown in Saudi Arabia at the peak of spread, which lasts for about 3 months beginning from the lockdown date (March 21st). On the other hand, the Kingdom's current partial lockdown policy was estimated to cut the estimated number of infections to 0.5 million over nine months. The data shows that stricter lockdowns may successfully flatten the COVID-19 graph curve in Saudi Arabia. We successfully predicted the COVID-19 epidemic's peaks and sizes using our modified deep neural network (DNN) and SEIR model

Erosion-corrosion of carbon steel in crude oil : Velocity effects on the erosion-corrosion map

In this work, the effects of erosion-corrosion on carbon steel in two environments, crude oil and crude oil/ 20% water environments containing Al2O3 particles were investigated using an impinging erosion test rig, at two velocities and at a range of impact angles and applied potentials. The results indicated that the corrosion contribution increased with an increase in the percentage of reservoir water. It was observed that the erosion rate changed as a function of impact angle and applied potential and depended on the nature of the corrosion product formed on the surface and the presence of the oil film. Furthermore, increases in velocity altered the erosion-corrosion mechanism significantly. Mechanisms of erosion-corrosion were proposed based on the laboratory studies. The results were used to construct erosion–corrosion maps showing mechanistic interactions, as a function of impact angle and electrochemical potential, at the various exposure velocities

External validation of yonsei nomogram predicting chronic kidney disease development after partial nephrectomy: An international, multicenter study

Objective: To externally validate Yonsei nomogram. Methods: From 2000 through 2018, 3526 consecutive patients underwent on-clamp PN for cT1 renal masses at 23 centers were included. All patients had two kidneys, preoperative eGFR ≥60 ml/min/1.73 m2, and a minimum follow-up of 12 months. New-onset CKD was defined as upgrading from CKD stage I or II into CKD stage ≥III. We obtained the CKD-free progression probabilities at 1, 3, 5, and 10 years for all patients by applying the nomogram found at https://eservices.ksmc.med.sa/ckd/. Thereafter, external validation of Yonsei nomogram for estimating new-onset CKD stage ≥III was assessed by calibration and discrimination analysis. Results and limitation: Median values of patients' age, tumor size, eGFR and follow-up period were 47 years (IQR: 47-62), 3.3 cm (IQR: 2.5-4.2), 90.5 ml/min/1.73 m2 (IQR: 82.8-98), and 47 months (IQR: 27-65), respectively. A total of 683 patients (19.4%) developed new-onset CKD. The 5-year CKD-free progression rate was 77.9%. Yonsei nomogram demonstrated an AUC of 0.69, 0.72, 0.77, and 0.78 for the prediction of CKD stage ≥III at 1, 3, 5, and 10 years, respectively. The calibration plots at 1, 3, 5, and 10 years showed that the model was well calibrated with calibration slope values of 0.77, 0.83, 0.76, and 0.75, respectively. Retrospective database collection is a limitation of our study. Conclusions: The largest external validation of Yonsei nomogram showed good calibration properties. The nomogram can provide an accurate estimate of the individual risk of CKD-free progression on long-term follow-up

Coronal Heating as Determined by the Solar Flare Frequency Distribution Obtained by Aggregating Case Studies

Flare frequency distributions represent a key approach to addressing one of the largest problems in solar and stellar physics: determining the mechanism that counter-intuitively heats coronae to temperatures that are orders of magnitude hotter than the corresponding photospheres. It is widely accepted that the magnetic field is responsible for the heating, but there are two competing mechanisms that could explain it: nanoflares or Alfv\'en waves. To date, neither can be directly observed. Nanoflares are, by definition, extremely small, but their aggregate energy release could represent a substantial heating mechanism, presuming they are sufficiently abundant. One way to test this presumption is via the flare frequency distribution, which describes how often flares of various energies occur. If the slope of the power law fitting the flare frequency distribution is above a critical threshold, $\alpha=2$ as established in prior literature, then there should be a sufficient abundance of nanoflares to explain coronal heating. We performed $>$600 case studies of solar flares, made possible by an unprecedented number of data analysts via three semesters of an undergraduate physics laboratory course. This allowed us to include two crucial, but nontrivial, analysis methods: pre-flare baseline subtraction and computation of the flare energy, which requires determining flare start and stop times. We aggregated the results of these analyses into a statistical study to determine that $\alpha = 1.63 \pm 0.03$. This is below the critical threshold, suggesting that Alfv\'en waves are an important driver of coronal heating.Comment: 1,002 authors, 14 pages, 4 figures, 3 tables, published by The Astrophysical Journal on 2023-05-09, volume 948, page 7