To examine the association between HIV infection and psychiatric disorders among prisoners, where mental illness, substance abuse, and HIV are disproportionately represented.
The effectiveness of CO2-enhanced oil recovery (EOR) is strongly dependent on the CO2-oil minimum miscible pressure (MMP) value, which can be estimated using various methods. In this study, interfacial tension (IFT) and slim-tube tests were used to estimate the MMP value. Experimental results indicated that the IFT test had a higher MMP value than the slim-tube test. Particularly, the outcomes of IFT and the slim-tube tests differed slightly, i.e., 0.7% and 4.3% at 60 and 66 °C, respectively. Furthermore, the current work also compares MMP data gathered using visual observation and equation of state (EOS) simulation. The MMP estimated by EOS is higher but close to the IFT and slim-tube recovery factor method, where all results are within the 1650-1700 psi and 1700-1800 psi visual observation ranges at 60 and 66 °C, respectively. However, MMP deviations concerning the slim-tube test and EOS were consistent at different temperatures. This study offers an alternative to estimate and evaluate CO2-oil MMP for EOR applications accurately and efficiently.
Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Screening for DR, coupled with timely consultation and treatment, is a globally trusted policy to avoid vision loss. However, implementation of DR screening programs is challenging due to the scarcity of medical professionals able to screen a growing global diabetic population at risk for DR. Computer-aided disease diagnosis in retinal image analysis could provide a sustainable approach for such large-scale screening effort. The recent scientific advances in computing capacity and machine learning approaches provide an avenue for biomedical scientists to reach this goal. Aiming to advance the state-of-the-art in automatic DR diagnosis, a grand challenge on "Diabetic Retinopathy - Segmentation and Grading" was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI - 2018). In this paper, we report the set-up and results of this challenge that is primarily based on Indian Diabetic Retinopathy Image Dataset (IDRiD). There were three principal sub-challenges: lesion segmentation, disease severity grading, and localization of retinal landmarks and segmentation. These multiple tasks in this challenge allow to test the generalizability of algorithms, and this is what makes it different from existing ones. It received a positive response from the scientific community with 148 submissions from 495 registrations effectively entered in this challenge. This paper outlines the challenge, its organization, the dataset used, evaluation methods and results of top-performing participating solutions. The top-performing approaches utilized a blend of clinical information, data augmentation, and an ensemble of models. These findings have the potential to enable new developments in retinal image analysis and image-based DR screening in particular.