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A tracheal bronchus is a congenital anomaly that is often asymptomatic but can be associated with a respiratory infection and chest pain. It is more common in men and the right lung and is seen between the carina and the cricoid cartilage. If it is asymptomatic, it does not need treatment and may be associated with other disorders such as Down syndrome. It is often diagnosed accidentally in bronchoscopy and bronchography. The left tracheal bronchus is often associated with brain abnormalities. In this study, we report a 37-year-old man who was referred to a hospital with coronavirus disease, and CT scans revealed RT tracheal bronchus abnormalities.

Enhanced oil recovery (EOR) has long proven to be a good method to mobilize the residual oil that is by passed and capillary trapped by secondary recovery methods. Chemical EOR methods enhance the microscopic and macroscopic efficiency, and ultimately the overall oil recovery is improved. However, the adsorption rate of the surfactant, low resistance to high temperature and salinity are some of the factors that would turn chemical flooding impractical and uneconomic in many cases. Lately, the application of nanotechnology in enhanced oil recovery has showcased some good and prolific results in terms of incremental oil recovery. In this study, the applicability of Nanoparticle flooding in carbonate rocks of Pilaspi formation was probed through a series of tests such as thin section analysis, x-ray diffraction, x-ray fluorescence, interfacial tension and contact angle measurements. The results showed that the composition of the carbonate rocks is predominantly calcite (CaCO3) with minor traces of quartz and dolomite. From the interfacial tension (IFT) measurements, it was figured out that the silica and alumina Nanofluids lowered the IFT by 27% and 42% with the light oil, and 43% and 49% with the heavy oil, respectively. The contact angle measurements revealed that the Alumina Nano-fluid at 0.25 wt. % reduced the contact angle on the surface of the light and heavy oil aged thin sections from 169◦ and 115◦ to nearly 119◦ and 78◦. On the other hand, the silica nanoparticle at 0.25 wt. % reduced the contact angles on both thin section types to around 129◦ and 80◦, respectively.

Mobile health monitoring plays a central role in the future of cyber physical systems (CPS) for healthcare applications. Such monitoring systems need to process user data accurately. Unlike in other human-centered CPS, in healthcare CPS, the user functions in multiple roles all at the same time: as an operator, an actuator, the physical environment and, most importantly, the target that needs to be monitored in the process. Therefore, mobile health CPS devices face highly dynamic settings generally, and accuracy of the machine learning models the devices employ may drop dramatically every time a change in setting happens. Novel learning architecture that specifically address challenges associated with dynamic environments are therefore needed. Using active learning and transfer learning as organizing principles, we propose a collaborative multiple-expert architecture and accompanying algorithms for the design of machine learning models that autonomously adapt to a new configuration, context, or user need. Specifically, our architecture and its constituent algorithms are designed to manage heterogeneous knowledge sources or experts with varying levels of confidence and type while minimizing adaptation cost. Additionally, our framework incorporates a mechanism for collaboration among experts to enrich their knowledge, which in turn decreases both cost and uncertainty of data labeling in future steps. We evaluate the efficacy of the architecture using two publicly available human activity datasets. We attain activity recognition accuracy of over 85 % (for the first dataset) and 92 % (for the second dataset) by labeling only 15 % of unlabeled data.