ABSTRACT
Traditionally, the term Moonlighting refers to having a second job in addition to one's regular employment which does not necessarily relate to their main job. The COVID-19 pandemic adversely affected income source of people. There was reduction in income of all the sector of the economy. Due to this there was salary reduction of many employees. To cope up the situation, many employees tried to earn additional Income apart from their salary. Due to advancement in Information technology many tech professionals took the advantage of doing additional work apart from their main job. Availability of 365 x 24 x 7, Information Technology and digital Gadgets has created many remote job opportunities to the employees. Now they can work from any place, at any time according to their talents and skills. Unskilled employees also started working on side income source in order to balance family budget. Employees supplement their income moonlighting. Due to growth of IT sector, many jobs opportunities are available across different geographies and timelines. This has made moonlighting easier. This paperfocuses on various aspects of moonlighting and its effects on employment. It also discusses various motives behind moonlighting trends among employees.
ABSTRACT
Using agent based simulator (ABS), we attempt to explain the infectiousness of the delta variant through scenario analysis to best match the observed fatality data in Mumbai, where the variant initially spread. Our somewhat prescient conclusion, based on analysis conducted in March-April 2021 was that the new variant was 2-2.5 times more infectious than the original Wuhan variant. We also observed then that certain performance measures such as timings of peaks and troughs were quite robust to the variations in model parameters and hence can be reliably projected even in presence of model uncertainties. Furthermore, we introduce enhancements to help model variants, vaccinations, basic and effective reproduction number in ABS. Our analysis suggests an interesting observation - although slums have around half of Mumbai population and are much more dense and have higher prevalence, the effective reproduction number between slums and non-slums equalises early on and largely moves together thereafter. © 2022 IEEE.
ABSTRACT
Background: Covid-19 infection time and again has been causing major morbidities and mortalities. Increased vulnerability of Covid-19 recovered patients was seen towards mucormycosis infection. Mucormycosisis is an aggressive, angioinvasive fungal disease caued by fungi of order Mucorales. This increase in cases may be attributed to a weakened immune system, pre-existing comorbidities such as diabetes, overzealous use of steroids. We conducted a study on 25 cases admitted in mucor ward in a tertiary care setting to highlight this association and focusing on possible causes so that we can be prepared to handle any such catastrophe in future in a better way. Methods and Results: We did a retrospective study on 25 cases admitted in a tertiary care center catering to large population of Covid -19 patients with varying severity.Covid-19 associated mucormycosis(CAM) was found to be more common in males(76%).Diabetes mellitus was the most common underlying condition(72%).68% patients had received steroids and antibiotics, 28% patients had history of receiving Oxygen. In CAM predominant presentation was rhino-orbital mucormycosis. Unilateral orbit involvement was seen in (88%) cases. Conclusion(s): As severe acute respiratory syndrome coronavirus-2 is highly susceptible to mutations and is causingseries of waves, its association with opportunistic fungal infection is a serious concern. Incidences of mucormycosis were increased in Covid-19 patients due to immune modulation and coexistence of immunosuppressive conditions such as diabetes. Concurrent glucocorticoid therapy further heightens the risk. Early diagnosis and prompt intervention can help improve outcome. Copyright © 2023 Ubiquity Press. All rights reserved.
ABSTRACT
Background: Covid-19 infection time and again has been causing major morbidities and mortalities. Increased vulnerability of Covid-19 recovered patients was seen towards mucormycosis infection. Mucormycosisis is an aggressive, angioinvasive fungal disease caued by fungi of order Mucorales. This increase in cases may be attributed to a weakened immune system, pre-existing comorbidities such as diabetes, overzealous use of steroids. We conducted a study on 25 cases admitted in mucor ward in a tertiary care setting to highlight this association and focusing on possible causes so that we can be prepared to handle any such catastrophe in future in a better way. Methods and Results: We did a retrospective study on 25 cases admitted in a tertiary care center catering to large population of Covid -19 patients with varying severity.Covid-19 associated mucormycosis(CAM) was found to be more common in males(76%).Diabetes mellitus was the most common underlying condition(72%).68% patients had received steroids and antibiotics, 28% patients had history of receiving Oxygen. In CAM predominant presentation was rhino-orbital mucormycosis. Unilateral orbit involvement was seen in (88%) cases. Conclusion(s): As severe acute respiratory syndrome coronavirus-2 is highly susceptible to mutations and is causingseries of waves, its association with opportunistic fungal infection is a serious concern. Incidences of mucormycosis were increased in Covid-19 patients due to immune modulation and coexistence of immunosuppressive conditions such as diabetes. Concurrent glucocorticoid therapy further heightens the risk. Early diagnosis and prompt intervention can help improve outcome. Copyright © 2023 Ubiquity Press. All rights reserved.
ABSTRACT
In this paper, we describe an approach for representation learning of audio signals for the task of COVID-19 detection. The raw audio samples are processed with a bank of 1-D convolutional filters that are parameterized as cosine modulated Gaussian functions. The choice of these kernels allows the interpretation of the filterbanks as smooth band-pass filters. The filtered outputs are pooled, log-compressed and used in a self-attention based relevance weighting mechanism. The relevance weighting emphasizes the key regions of the time-frequency decomposition that are important for the downstream task. The subsequent layers of the model consist of a recurrent architecture and the models are trained for a COVID-19 detection task. In our experiments on the Coswara data set, we show that the proposed model achieves significant performance improvements over the baseline system as well as other representation learning approaches. Further, the approach proposed is shown to be uniformly applicable for speech and breathing signals and for transfer learning from a larger data set. Copyright © 2022 ISCA.
ABSTRACT
In this paper, we propose an approach to automatically classify COVID-19 and non-COVID-19 cough samples based on the combination of both feature engineering and deep learning models. In the feature engineering approach, we develop a support vector machine classifier over high dimensional (6373D) space of acoustic features. In the deep learning-based approach, on the other hand, we apply a convolutional neural network trained on the log-mel spectrograms. These two methodologically diverse models are then combined by fusing the probability scores of the models. The proposed system, which ranked 9th on the 2021 Diagnosing COVID-19 using Acoustics (Di- COVA) challenge leaderboard, obtained an area under the receiver operating characteristic curve (AUC) of 0:81 on the blind test data set, which is a 10:9% absolute improvement compared to the baseline. Moreover, we analyze the explainability of the deep learning-based model when detecting COVID-19 from cough signals. Copyright © 2021 ISCA.