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This paper has touched one of the most critical area affected due to pandemic situation created by the COVID-19 and its powerful spillover effect on education sector by customizing education pedagogy. Earlier researchers have studied online education separately, whereas this paper discussed the natural transition and systematic review of upsurge of e-learning. The objective of this paper is to make the systematic review of COVID spillover and transition towards e-learning education pedagogy through theoretical framework. The study makes systematic review of switchover towards e-learning and spillover effect of COVID-19 and customization of the education pedagogy. In this study, past literature has been utilized to make critical analysis of spillover effect of COVID-19 and impact on education pedagogy by creation of prepositions. The findings of the study reveal that in the exceptional circumstances of COVID-19, e-learning transition has taken place from conventional to e-learning modules. All over the world, countries have shifted towards online education by schooling out but classes in campaign ignited by Chinese government. The same has also been replicated in other countries of the world during COVID-19.

ABSTRACTSelf‐inflicted injury to oral mucosa is a rare entity. These injuries can be premeditated, accidental, or can result from an abnormal habit. These uncommon gingival injuries can sometimes test the clinician's diagnostic abilities as well as treatment planning skills. In conventional treatment, removal of etiology and application of topical drugs is usually sufficient for healing. However, some cases require alternative or adjunctive wound healing methods. Low‐level laser therapy (LLLT) has been reported to be effective in pain management and improvement in wound healing through promotion, fortification, and commissioning of cellular cycle to generate productive and substitute cells. This report presents a case of 25‐year‐old female with complaints of a painful, nonhealing wound on the palate for last 6 months. She had an unusual habit of keeping burning matchsticks in her mouth. Although she had quit the habit 2 months ago after psychiatric counseling, the wound on her palate did not show any improvement in symptoms. Based on the history and clinical findings, diagnosis of chronic wound by self‐inflicted thermal injury was made. LLLT was administered on the wound every 48 hours for next 10 days. The burn wound healed completely after five applications of LLLT.

Plastic pollution is one of the most significant environmental issues in the world. The rapid increase of the cumulative amount of plastic waste has caused alarm, and the public have called for actions to mitigate its impacts on the environment. Numerous governments and social activists from various non-profit organisations have set up policies and actively promoted awareness and have engaged the public in discussions on this issue. Nevertheless, social responsibility is the key to a sustainable environment, and individuals are accountable for performing their civic duty and commit to behavioural changes that can reduce the use of plastics. This paper explores a set of topic modelling techniques to assist policymakers and environment communities in understanding public opinions about the issues related to plastic pollution by analysing social media data. We report on an experiment in which a total of 274,404 tweets were collected from Twitter that are related to plastic pollution, and five topic modelling techniques, including (a) Latent Dirichlet Allocation (LDA), (b) Hierarchical Dirichlet Process (HDP), (c) Latent Semantic Indexing (LSI), (d) Non-Negative Matrix Factorisation (NMF), and (e) extension of LDA—Structural Topic Model (STM), were applied to the data to identify popular topics of online conversations, considering topic coherence, topic prevalence, and topic correlation. Our experimental results show that some of these topic modelling techniques are effective in detecting and identifying important topics surrounding plastic pollution, and potentially different techniques can be combined to develop an efficient system for mining important environment-related topics from social media data on a large scale.