RESUMEN
The global pandemic has forced academics to engage in remote doctoral supervision, and the need to understand this activity is greater than ever before. This contribution involved a cross-field review on remote supervision pertinent in the context of a global pandemic. We have utilised the results of an earlier study bringing a supervision model into a pandemic-perspective integrating studies published about and during the pandemic. We identified themes central to remote supervision along five theory-informed dimensions, namely intellectual/cognitive, instrumental, professional/technical, personal/emotional and ontological dimensions, and elaborate these in the light of the new reality of remote supervision.
RESUMEN
COVID-19 is a large-scale contagious respiratory disease that has spread across the world in 2020. Therefore, a low-cost, fast, and easily available solution is needed to provide a COVID-19 diagnosis to curb the outbreak. According to recent studies, one of the main symptoms of COVID-19 is coughing. The goal of this research effort is to develop a method for the automatic diagnosis of COVID-19 by detecting cough during recorded conversations. The method is composed of five main modules: sound extraction, sound feature extraction, cough detection, cough classification, and COVID-19 diagnosis. The method extracts relevant features from the audio signal and then uses machine learning and deep learning models, like SVM, KNN, and RNN, to classify them, which can successfully diagnose COVID-19 from audio recordings. Our method has relatively high accuracy when dealing with completely unfamiliar cough samples. When the training set and the test set are from two different databases, it still achieves an accuracy of 81.25% (AUC of 0.79). As more data sets are collected, the model can be further developed and improved to create a machine learning solution based on cough analysis for COVID-19 detection, which may be promoted as a non-clinical self-inspection solution.
RESUMEN
At present, the 2019-novel coronavirus (2019-nCoV) is rampant all over the country, and controlling the spread of its epidemic has become a top priority. It is very difficult to control the epidemic spreading because there is no specific drug to fight against it. Therefore, it is urgent task for us to develop a specific drug as soon as possible, and attracting worldwide attention. The studies on anti-2019-nCoV drugs were generally carried out in three modes, as vaccine, chemical drugs and traditional Chinese medicine (TCM). Because the developing on vaccines and chemicals takes too long, it is hard to solve the urgent problem. Moreover, the RNA of coronavirus has been recombined so quickly that the vaccines and drugs studied with great efforts not only may miss the epidemic time, but also cannot be used in the next generation of new coronavirus treatment, as a result that the work of epidemic prevention is formidable. However, the prescriptions from traditional Chinese medicine theory can cure the epidemic disease, but how to develop it into a specific drug with clear chemical composition and clear pharmacologic mechanism, is also an urgent problem to be solved. In this paper, the new coronavirus and its drug development are reviewed, while the advantages and disadvantages are analyzed, and the drug dilemma research phenomenon is clarified by using the supramolecular “qi chromatography” theory. It is suggested that the strategy of anti-coronavirus drug developing should be put forward based on the impact of “qi chromatography” of biological supramolecular “imprinting template”, in order to develop new drugs against 2019-nCoV to control the spread of the epidemic as quick as we can, and establish a new research and development model of anti-coronavirus drug on integration of traditional Chinese and Western medicine, turning passive into active.