ABSTRACT
Lungs being the earliest organ to be affected carry more risk of long-term morbidity. [...]pulmonary function tests (PFTs) on post-COVID young health care workers provide a better view on the impact, recovery and residual effects. [2] Till date, no pharmacological intervention has found to be completely effective in such cases. [...]it is important to follow-up patients with pulmonary function tests (PFT) even after the infective period. The improvement or deterioration in PFT along with sequelae can be adequately tested only with a routine follow-up. [...]the novelty of the project lies in focusing on health care workers with regards to COVID exposure, assessing the lung function tests. MATERIALS AND METHODS This observational and cross-sectional study was done in the department of pulmonary medicine of a tertiary care teaching hospital in Madurai for a period of 4 months between January and April 2021 after obtaining Institutional Ethical committee clearance.
ABSTRACT
The aim of the study is to assess the impact of various nationalities, cultures, and religions on the spread of the coronavirus in the human environment. Particular attention was paid to compliance with legal and ethical standards during a pandemic. Different cultures, nationalities, and religions have a significant influence on the development and spread of the coronavirus in the world. During the COVID-19 pandemic, due to the tightening of legal standards, it is necessary to ensure: protection of patient confidentiality;of freedom of the expression;accesses to critical information;the opportunities to belong to social organizations and civil society;the accesses to professionals healthcare;ensure equal rights for women and guarantee the right to water and sanitation;continuity of humanitarian aid and targeted economic aid. Travel locks and bans should comply with legal standards;the right to education should be strictly respected. Artificial intelligence can be used in the fight against the crown. © 2022 Elsevier Inc. All rights reserved.
ABSTRACT
The pandemic Coronovirus disease 2019 (COVID-19) gave rise to a huge impact on everyone's lives. Millions of people have been infected worldwide. Now the vaccination has been found, WHO has still insisted on wearing mask and maintaining social distance is necessary as it is the most feasible way to prevent ourselves from the virus. This notion motivated me to contribute an efficient computer vision based detection system to public safety.The detection system detects if there is any violations through the camera and or on any video or image input. In this system, we have used deep learning algorithm. This system I have proposed here can be applied in places where people gather in huge amount. This automated detection technique makes it easier to inspect the public gathering in malls, streets, any firms, apartments, etc. © 2021 IEEE.
ABSTRACT
This paper studies emotion detection using deep learning on the prevalent usage of face masks in the Covid-19 pandemic. Internet repository data Karolinska Directed Emotional Faces (KDEF) [1] was used as a base database, in which it was segmented into different portions of the face, such as forehead patch, eye patch, and skin patch to be representing segments of the face covered or exposed by the mask were transfer learned to an Inception v3 model. Results show that the full-face model had the highest accuracy 74.68% followed by the skin patch (area occluded by the mask) 65.09%. The models trained on full-face were then used to inference the different face segments/patches that showed poor inferencing results. However, certain emotions are more distinct around the eye region. Therefore, this paper concludes that upper segmented faces result in higher accuracy for training models over full faces, yet future research needs to be done on additional occlusion near the eye section. © 2021, Springer Nature Switzerland AG.
ABSTRACT
Crowdfunding platforms like Kickstarter provide new product developers with a novel and readily accessible platform to interact with potential customers, seek funding, and gain valuable feedback from target users. Successful startup design processes are often praised due to startups’ agility and ability to adapt to changing markets under extreme resource constraints. The COVID-19 pandemic changed the market in a number of significant ways, and publicly available data on Kickstarter provides valuable insight into the fundamental ways that product developers adapt to volatiles markets created by a global crisis. Leveraging linguistic analysis, topic modelling, k-means clustering, and principal component analysis, startup data was analyzed to identify overarching trends in startup launch campaigns across years. Specifically, this work identifies emerging trends in the Kickstarter market in 2020 to understand how external factors affect launch practices. Subtle yet significant changes in Kickstarter campaigns show that new clusters of campaigns emerged during 2020, not present in prior years, indicating that product developers adapted language to meet the needs of a changing market. Findings motivate a more thorough investigation of the relationships between startup market trends and external events to build a deeper understanding of the fundamental ways developers adapt design processes due to external stimuli. Copyright © 2021 by ASME.
ABSTRACT
Crude oil price becoming negative is one of the rare events that happened after World War II in the energy sector. The real reason for oil prices to move into sub-zero level is not attributable to supply-demand mismatch, rather it owes to the timing of this movement and specifi cally what is popularly known as the "day-of-the-week effect." Though India is importing almost 82% of its oil, falling global crude oil prices are not going to benefi t the end users much. But, the interaction effect of the lower crude oil price with COVID-19 is defi nitely going to have an impact on the country and investors. © 2020 Economic and Political Weekly. All rights reserved.