Youth Unemployment, Education and Job Training: An Analysis of PLFS Data in India

This paper highlights the changes in the unemployment rate across different age cohorts among young people in India due to the COVID-19 pandemic. The study observes that the youth unemployment rate increased at a much higher rate in urban areas both for male and female youth during the initial phase of pandemic. To understand the severity of the effects of pandemic on unemployment across different age groups among the youth, this study enquires into the nature and causes of unemployment among them. The probit estimates of unemployment function for the youth reveals that the likelihood of youth being unemployed is less in upper age cohorts within the young age range. An inverse relationship is observed between general education and employment among the youth in the Indian youth labour market. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

MLCNN-COV: A multilabel convolutional neural network-based framework to identify negative COVID medicine responses from the chemical three-dimensional conformer

To treat the novel COronaVIrus Disease (COVID), comparatively fewer medicines have been approved. Due to the global pandemic status of COVID, several medicines are being developed to treat patients. The modern COVID medicines development process has various challenges, including predicting and detecting hazardous COVID medicine responses. Moreover, correctly predicting harmful COVID medicine reactions is essential for health safety. Significant developments in computational models in medicine development can make it possible to identify adverse COVID medicine reactions. Since the beginning of the COVID pandemic, there has been significant demand for developing COVID medicines. Therefore, this paper presents the transfer-learning methodology and a multilabel convolutional neural network for COVID (MLCNN-COV) medicines development model to identify negative responses of COVID medicines. For analysis, a framework is proposed with five multilabel transfer-learning models, namely, MobileNetv2, ResNet50, VGG19, DenseNet201, and Inceptionv3, and an MLCNN-COV model is designed with an image augmentation (IA) technique and validated through experiments on the image of three-dimensional chemical conformer of 17 number of COVID medicines. The RGB color channel is utilized to represent the feature of the image, and image features are extracted by employing the Convolution2D and MaxPooling2D layer. The findings of the current MLCNN-COV are promising, and it can identify individual adverse reactions of medicines, with the accuracy ranging from 88.24% to 100%, which outperformed the transfer-learning model's performance. It shows that three-dimensional conformers adequately identify negative COVID medicine responses. 1225-6463/$ © 2023 ETRI.

Street food nostalgia and COVID-19 perceptions on street food desire

With COVID-19 paralyzing street food businesses, street food vendors are trying to sustain their operations. The current study helps them by identifying the importance of five prominent stimuli viz. authenticity, quality, staff-service, ambience, and value for money in developing desire for street food in individuals in India. Furthermore, the study contributes by identifying the role of street food nostalgia (as a mediator) and perceived risk of COVID-19, age, and gender (as moderators) on the direct impact of each stimulus on the desire for street food. The study uses partial least squares path modelling to validate the hypotheses using SmartPLS. The findings are comparable to other developing Asian countries, as the proposed associations are validated with perceptual responses from three prominent cities and well-known street food destinations in India. The study showed the relative importance of the five-stimuli based on the stimulus-organism-response framework in developing a desire for street food. The findings suggest partial to complete mediation of street food nostalgia across the three samples. Lastly, the perceived risk of COVID-19 along with age and gender emerged as prominent moderators for many of the direct effects of stimuli on desires for street food. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Association between stress depression anxiety and coping styles among Indian university students during COVID-19 outbreak

Introduction: Psychiatric illnesses such as Depression and Anxiety can have a substantial impact on one's mental health. Depression is the most common psychiatric condition diagnosed among students. Objectives: To find out the association of factors which are linked to Stress, Depression, Anxiety and coping styles among Indian University students during COVID-19 outbreak. Methods: A self-administered questionnaire consisting of socio-demographic, DASS-21 and Brief-COPE inventory were used. Results: The study comprised a total of 201 University students ranging in age from 17 to 36 years old. Female students (n=150) were the most common responses, followed by male students (n=51). Depression, anxiety and stress correlated with active coping, planning, positive reframing, acceptance, humor, emotional support, instrumental support, self-distraction, denial, venting, substance use, behavioral disengagement, selfblame and religion. Conclusion: Venting, behavioural disengagement and self-blame are all examples of maladaptive coping techniques that have a significant impact on University students' Stress, Anxiety and Depression levels. This research will provide a better understanding of the underlying influence of coping methods on Stress, Depression, and Anxiety among university students during the COVID-19 outbreak, enabling for early intervention and improved outcomes.

IoT Based Intelligent System for Covid - 19 hotspot detection by CNN Crowd Density Algorithm

The worldwide health crisis is caused by the widespread of the Covid-19 virus. The virus is transmitted through droplet infection and it causes the common cold, coughing, sneezing, and also respiratory distress in the infected person and sometimes becomes fatal causing death. As the world battles against covid-19, the proposed approach can help to contain the clustering of covid hotspot areas for the treatment of over a million affected patients. Drones/ Unmanned Aerial Vehicles (UAVs) offer a great deal of support in this pandemic. As suggested in this research, they can also be used to get to remote places more quickly and efficiently than with conventional means. In the hospital's control room, there would be a person in command of the ambulance drone. For hotspot area detection, the drone would be equipped with FLIR camera and for detection and recognition of face the video transmission is used by raspberry pi camera. The detection of face is done by Haar cascade Classifier and recognition of the face with LBPH algorithm. This is used for identify the each individual's medical history or can be verified by Aadhar Card. Face recognition between still and video photos was compared, and the average accuracy of still and video images was 99.8 percent and 99.57 percent, respectively. To find the hotspot area is to use the CNN Crowd counting algorithm. If the threshold value is less than equal to 0.5 than it is hotspot area , if it is greater than 0.5 and less than equal to 0.75 than it is semi-normal area , if it is greater than 0.75 and less than equal to 1 than it is normal area. © 2022 IEEE.

(RE)THINKING SUFFERING AND MOURNING THROUGH COVID-19 PHOTOGRAPHS IN INDIA

This article offers a close textual and visual analysis of some of the most iconic photographs of the COVID pandemic's second wave in India, to examine how they evoke violence both within and outside the photographic frame, and become sites of critical reflection. The pandemic's second wave witnessed a proliferation of images in social media and online news portals representing mass deaths, corpses floating in rivers, and mass funerals executed in makeshift crematoria. Pointing out how the widely circulated photographs deployed an excess of violence to represent death as a dreaded event external to life, this article contends that the violence in these images not only evoked shock and horror but also interrupted mourning both in the private and the collective realm, furthered through violence in the images and the interruption of familiar mourning rituals for those dead. Deriving from Ariella Azoulay's idea of the ‘civil gaze' to ‘watch' photographs, the article explores how these photographs configure a space of critical reflection and responsibility towards those photographed, which uncovers the crucial biopolitical interfaces between disease, death, the state, and the precarity of citizens. It simultaneously deliberates on the role of the visual and the photographic gaze in foregrounding these intersections. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

Medical Diagnosis and Identification of Covid -19 by Intelligent IoT System and Resnet 18 Bilinear Deep Greedy Network

An international health crisis has been caused by the widespread COVID-19 epidemic. COVID-19 patient diagnoses are made using deep learning, although this necessitates a massive radiography data collection in order to efficiently deliver an optimum result. This paper presents a novel Intelligent System with IoT sensors for covid 19 and "Bilinear Resnet 18 Deep Greedy Network,"which is effective with a limited amount of datasets. Despite peculiarities brought on by a small dataset, the suggested approach could successfully combat the anomalies of over fitting and under fitting. The suggested architecture ensures a successful conclusion when the trained model is correctly evaluated using the provided X-ray datasets of COVID-19 cases. The recommended model offers accuracy of 97%, which is superior to existing methodologies. Better precision, recall, and F1 score are provided;which are 98%, 96%, and 96.94% respectively, which is better than other existing methodology. © 2022 IEEE.

Sentiment Analysis on COVID-19 Tweets

The global proliferation of COVID-19, as well as the growth of platforms as a primary source of information for many individuals, has elicited a wide spectrum of reactions. However, keeping up with this mass scenario is difficult. A large number of people offer their opinions and perspectives on current events on social media, making it difficult for a human to read and comprehend everything. Tweets disseminate a great deal of information. Using public Twitter comments, our study attempted to conduct a sentiment analysis of the entire discourse about COVID-19 in a paper. We improved on earlier methodologies and methods for sentiment analysis. Our primary goal is to examine tweets concerning COVID-19 from the previous year using natural language processing and neural network methods. We used a multiclass dataset and applied it to BOW, TFIDF, and One Hot Encoding. Furthermore, after training four distinct classifiers with these different pre-processing algorithms in each classifier, we performed a competitive analysis and found RoBERTa as the best performing with 90% accuracy. © 2022 IEEE.

A study on the morbid histopathological changes in COVID-19 patients with or without comorbidities using minimally invasive tissue sampling

Introduction: COVID-19 causes morbid pathological changes in different organs including lungs, kidney, liver,etc especially in those who succumb. Though clinical outcomes in those with comorbidities are known to be different from those without - not much is known about the differences at histopathological level. Aim(s): It was to compare the morbid histopathological changes in COVID-19 patients between those who were immunocompromised(Gr 1), malignancy(Gr 2) or had cardiometabolic conditions (hypertension, diabetes or coronary artery disease)(Gr 3). Method(s): Post-mortem tissue sampling (MITS) was done from the lungs, kidney, heart, and liver using biopsy gun within two hours of death. Routine (H & E stain) and special stains (AFB, SM, PAS) were done besides immunohistochemistry. Result(s): A total of 100 patients underwent MITS and data of 92 were included (immunocompromised: 27, maligancy:18, cardiometabolic conditions:71). Within lung histopathology, capillary congestion was more in those with malignancy while others like diffuse alveolar damage, microthrombi, pneumocyte hyperplasia etc was equally distributed. Within liver, architecture distortion was significantly different in immunocompromised while steatosis, portal inflammation, Kupffer cell hypertrophy, confluent necrosis were equally distributed. There was a trend towards higher acute tubular injury in those with cardiometabolic conditions as compared to the other groups. No significant histopathological differences in heart was discerned. Conclusion(s): Certain histopathological features are markedly different in different groups (Gr 1,2 and3)of COVID-19 patients with fatal outcome.

Forecasting the Growth in Covid-19 Infection Rates

The recent times have seen the global rise in infection rates from the virus Covid-19, leading to a pandemic. The exponential rise in infections and deaths lead to panic and nation-wide lockdowns across the globe. Advancements in biotechnical and medical research have paved the way for the development and mass distribution of vaccines. To build an understanding of the current situation we did a comparative analysis of the rise in infection rates among citizens across the countries and also the growth in vaccinations in the pre-vaccination phase and the post-vaccination phase of the on-going pandemic to determine whether the rate of vaccination is more than the rate of infection or otherwise. Then, a comparison is done among two prediction models we built, one using polynomial regression and other using SVM to determine which model provides better prediction results of infection rates in a country. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A survey and classification of publicly available COVID-19 datasets

The current study curates a list of authentic and open-access sources of alphanumeric COVID-19 pandemic data. We have gathered 74 datasets from 42 sources, including sources from 18 countries. The datasets are searched through the Kaggle and GitHub repositories besides Google, providing a representation of varieties of pandemic-related datasets. The datasets are categorized according to their sources- primary and secondary, and according to their geographical distribution. While analyzing the dataset, we came across some classes in which the datasets can be categorized. We present the categorization in the form of taxonomy and highlight the present COVID-19 data collection and use challenges. The study will help researchers and data curators in the identification and classification of pandemic data.

Diverse Presentation of Mucormycosis: A Post COVID Catastrophe - A Case Series

The corona virus disease-19 (COVID-19) produced by SARS-CoV-2 has resulted in a wide spectrum of illness ranging from mild to life-threatening conditions thus enhancing the incidence of opportunistic infections among individuals harbouring comorbidities. Mucormycosis is a dreadful angio-invasive opportunistic infection produced by zygomycetes fungus in an immunocompromised host. The clinical manifestations of mucormycosis include rhino-orbital-cerebral (ROC), pulmonary, cutaneous, gastrointestinal, with ROC accounting for around 40% of cases. Diabetes, neutropenia, iron overload, cancer, and organ transplant are all major culprits. Both Aspergillus and Candida have been identified as the primary fungal pathogens causing co-infection in COVID-19 preyed individuals. The most predominant variety, Rhizopus Oryzae, is responsible for roughly 60% of mucormycosis infections in humans, as well as 90% of the Rhino-orbital-cerebral (ROCM) variant. Mucormycosis is the most widespread ailment in India. Until lately, India was declared to be the world's diabetes capital, with the second-largest number of people suffering from diabetes mellitus (DM). Diabetes has been recognised as the most common predictive marker for mucormycosis which explains the dramatic rise in Mucor cases in India lately particularly during second wave of COVID-19. The inflammatory onslaught caused by COVID 19 has debilitated patients' immune systems, making individuals vulnerable to mucormycosis outbreaks. The possible explanation that Mucorales spores appear to be expediting germination in people with COVID-19 is due to the perfect scenario of oxygen deprivation (hypoxia), hyperglycemia (steroid-induced), acidic form of media (metabolic acidosis, diabetic ketoacidosis), increased iron levels (elevated ferritin), and significantly reduced phagocytic activity of white blood cells (WBC) due to immunosuppression (SARS-CoV-2 or steroid mediated or associated comorbidities). Copyright © 2022 International Medical Sciences Academy. All rights reserved.

Towards assisting visually impaired individuals: A review on current status and future prospects

Visually impaired people require support with regular tasks including navigating, detecting obstacles, and maintaining safety, especially in both indoor and outdoor environments. As a result of the advancement of assistive technology, their lives have become substantially more convenient. Here, cutting-edge assistive devices and technologies for the visually impaired are reviewed, along with a chronology of their evolution. These methodologies are classified according to their intended applications. The taxonomy is combined with a description of the tests and experiments that can be used to examine the characteristics and assessments of assistive technology. In addition, the algorithms used in assistive devices are examined. This paper looks at solar industry innovations and promotes using renewable energy sources to create assistive devices, as well as, addresses the sudden advent of COVID-19 and the shift in the development of assistive devices. This review can serve as a stepping stone for further research on the topic. Copyright © 2022 The Author(s)

Virtual Education, Training and Internships in Hospitality and Tourism during Covid-19 Situation

The study model structure so as to takes into explanation a changes to the hospitality as well as tourism trade due to COVID-19, consequent changes to trade learning mechanisms, along with the prospective implications in favour of work-integrated training internship placements on behalf of the upcoming generation of students, former students as well as trade experts, is offered. The configuration for future studies so as to evaluate COVID19 hospitality in addition to tourism business internship would-be approaches is projected in favour of conversation. The paper is an educational discussion which outlines showing analysis of virtual training, also online training, and internships during COVID19 in hospitality and tourism. Copyright © 2023 Authors. All rights reserved.

A Brief Report Of The Upcoming & Present Economic Impact To Hospitality Industry In COVID19 Situations

The focal point of this investigation is the appraisal of the effect of the Covid-19 pandemic in the travel industry and accommodation area which has prompted worldwide frenzy because of the current circumstance. The extent of a task is in the direction of examine the impacts of Covid-19, current occasions, and evaluation from end to end understanding while it is fundamental to research how a business determination recuperate subsequent to Corona virus and how it tends to be supportable. The present task is to break down the upcoming through a small amount of way in addition to quick recuperation what's more, recapture of the travel industry and friendliness area in favour of the Indian financial system, and work along with industry. The examination has a few proposals of the neighbourhood effect of the episode, impacts as well as is fundamentally assessed in this survey. This is the need of great importance to set aside gauges before effort to manage the belongings. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Impact Of Different Antimicrobial Stewardship Strategies For Reducing Antimicrobial Resistance And Antimicrobial Uses - A Systematic Literature Review

Antimicrobial resistance (AMR) is a serious global warning to social wellbeing. Antimicrobial stewardship Intervention is evident to provide a good effect on AMR. Antimicrobial Stewardship Programs (ASPs) have been established to minimize the usage of antibiotics, antibiotic resistance, and healthcare expenditure. Prospective audits with feedback, formulary restriction/preauthorization along with many other supportive elements are found effective techniques of antimicrobial stewardship programme. However, the proportional effect of different techniques is unclear. This ASP strategies utilization is compared in hospital setting and adult hospital units implementing ASP have provided much evidence demonstrating their value. This study includes the effectiveness of various explicit AMS strategies, measuring the outcome in various clinical setups such as reducing antibiotic resistance and antibiotic use.The death toll from COVID-19 is the highest of any other respiratory virus outbreak but Multiple Drug Resistant (MDR) and deadly resistant pathogens outbreaks could be at any point of time.Even in this present antibiotic era required primarily require combat it andstrong vigilance is also desired. The primary objective of this study is to enhance patient care and to lower medical expenses, but the ultimate objective is to safeguard already available and newly developed antibiotics against the threat of AMR. It has been noted that Prospective Audit and Feedback (PAF) intervention is one of the most often employed strategies among all other interventions of AMS. Although this is time-taking, it is accepted by physicians for more than formulary restrictions and pre-authorization procedures. There have been several digital initiatives to support antimicrobial stewardship (AMS), while they have been concentrated on individual interventions.Pre-authorization or recommended formulary limitation was found in 31% of all trials. 20% of these studies implemented this intervention independently, and many trials were also undertaken in conjunction with other interventions mostly in addition to training and education. Using the available pool information, we observed in various strategies a significant reduction in antimicrobial resistance and a reduced Days of Therapy (DOT)/Length of Treatment (LOT) ratio. Formulary restriction limited the use of broad-spectrum antibiotics without any major causality. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Forecasting the Growth in Covid-19 Infection Rates

The recent times have seen the global rise in infection rates from the virus Covid-19, leading to a pandemic. The exponential rise in infections and deaths lead to panic and nation-wide lockdowns across the globe. Advancements in biotechnical and medical research have paved the way for the development and mass distribution of vaccines. To build an understanding of the current situation we did a comparative analysis of the rise in infection rates among citizens across the countries and also the growth in vaccinations in the pre-vaccination phase and the post-vaccination phase of the on-going pandemic to determine whether the rate of vaccination is more than the rate of infection or otherwise. Then, a comparison is done among two prediction models we built, one using polynomial regression and other using SVM to determine which model provides better prediction results of infection rates in a country. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Cross Sectional Study on Perception of Medical Students on Online Medical Teaching in Present Scenario of COVID 19 in a Tertiary Care Medical Institute

Introduction: The present pandemic caused by COVID 19 has affected the world as a whole and social distancing became a part of our lives. Medical education like any other form of education was affected and online classes came to the rescue. Methods: This study was conducted with the rationale to find out how effective are online classes and what measures can be taken to further to simulate online classes as near as possible to physical classes. Google forms were given to undergraduate medical students. 183 students participated in this study. Likert’s scale was used to grade the scores from strongly agree to strongly disagree. Results: The observations revealed that only 2.73% students strongly agreed being comfortable with online classes and 6.55% participants strongly agreed that their internet connectivity was good. 1.64% strongly agreed and that they were able to follow the class and 14.98% strongly disagreed that there were no distracting agent. 16.94% strongly agreed that they are accustomed with the smart devices being used. About 25.13% disagreed that voice quality was good. Majority strongly agreed that recorded videos and hard copies of the class material would be helpful. On being asked if online classes are as good as the physical classes for theoretical knowledge and practical knowledge, most of them disagreed. Conclusion: Online classes is the important resource in hand for continuing medical teaching during pandemics, there is need to take more efforts for making it student-friendly by incorporating audio, video and texts which are interesting for the students. Technological support should be provided by institution and appropriate Faculty training is needed. © 2022, Medicolegal Association of Maharashtra. All rights reserved.