The rapid depletion of fossil fuels and ever-growing energy demand have led to a search for renewable clean energy sources. The storage of renewable energy calls for immediate attention to the fabrication of efficient energy storage devices like supercapacitors (SCs). As an electrode material for SCs, MnO2 has gained wide research interest because of its high theoretical capacitance, variable oxidation state, vast abundance, and low cost. However, the low electric conductivity of MnO2 limits its practical application. The conductivity of MnO2 can be enhanced by tuning the electronic states through substitution doping with cobalt. In the present work, first principles analysis based on density functional theory (DFT) has been used to examine the quantum capacitance (CQC) and surface charge (Q) of Co-doped MnO2. Doping enhanced the structural stability, electrical conductivity, potential window, and quantum capacitance of α-MnO2. The shortened band gap and localized states near the Fermi level improve the CQC of α-MnO2. For the narrow potential range (-0.4 to 0.4 V), the CQC is observed to increase with doping concentration. The highest CQC value at +0.4 V is observed to be 2412.59 µF cm-2 for Mn6Co2O16 (25% doping), five times higher than that of pristine MnO2 (471.18 µF cm-2). Mn6Co2O16 also exhibits better CQC and 'Q' at higher positive bias. Hence, it can be used as an anode material for asymmetric supercapacitors. All these results suggest better capacitive performance of Co-doped α-MnO2 for aqueous SCs and as an anode material for asymmetric supercapacitors.
Examination of pathological images is the golden standard for diagnosing and screening many kinds of cancers. Multiple datasets, benchmarks, and challenges have been released in recent years, resulting in significant improvements in computer-aided diagnosis (CAD) of related diseases. However, few existing works focus on the digestive system. We released two well-annotated benchmark datasets and organized challenges for the digestive-system pathological cell detection and tissue segmentation, in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). This paper first introduces the two released datasets, i.e., signet ring cell detection and colonoscopy tissue segmentation, with the descriptions of data collection, annotation, and potential uses. We also report the set-up, evaluation metrics, and top-performing methods and results of two challenge tasks for cell detection and tissue segmentation. In particular, the challenge received 234 effective submissions from 32 participating teams, where top-performing teams developed advancing approaches and tools for the CAD of digestive pathology. To the best of our knowledge, these are the first released publicly available datasets with corresponding challenges for the digestive-system pathological detection and segmentation. The related datasets and results provide new opportunities for the research and application of digestive pathology.
Benchmarking , Diagnosis, Computer-Assisted , Colonoscopy , Humans , Image Processing, Computer-Assisted/methods
Routine radiographs made in the orthodontic office can often give way to detection and diagnosis of unusual syndromes, signs or foreign bodies. A case report of one such accidental detection of a very unusual foreign body within the nasal cavity of an orthodontic patient has been presented here to emphasize the importance of routine radiographic views, which over and above aiding in Orthodontic diagnosis, can also often detect potentially life threatening problems in the Oro-facial region.
