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During the Covid-19 pandemic, most schools had to adopt online learning, which is a special kind of e-learning that provides a virtual classroom via a live session for both teachers and learners. However, studies on education in the Covid-19 pandemic shows that there should be more efforts from researchers as well as governments to effectively support learners. In this paper, we focus on the problem of Quality of Experience in online learning. We discuss the enabling technologies of online learning. Also, we make an extensive review of QoE in video streaming, the key enabling technology of online leaning. Finally, the key challenges and potential solutions of QoE management for future online learning will be discussed. © 2022 IEEE.
ABSTRACT
In general, a psychological disorder is a syndrome with significant rapid variability in control of emotions, cognitive control, and behaviors that reflect dysfunction in the biological, psychological, or development process underlying the function of cognitive behavior. Psychological disorders are occurring more prominently in post-COVID-19 patients. This work aims to investigate a detailed literature review on psychological disorders and mathematical models of the system to predict and forecast the psychological and mental illnesses and the spread of COVID-19 during the pandemic. A systematic statistical analysis was more appreciable to determine the significant level to suggest an appropriate medical direction to overcome the psychological disorders. The mathematical models, such as the system dynamics model used to predict the growth rate and causes of the various disorders such as depression, bipolar disorder, eating disorders, personality disorders, stress disorders, psychotic disorders, etc., propose the medical precautions and treatment methods for the dynamic combination of psychological disorder patients effectively with a systematic approach. Further, the extended work on machine learning approaches enhances its accuracy. It matches the scenario in real-time to predict the effects of disorders in post-COVID-19 patients. A set of massive data from the open-source will substantiate the model's effectiveness in predicting disorders of various levels and simulate the data using the system dynamics model using VenSim to foresee its future growth rate to design and develop the methodology to minimize the psychological disorders. The planned contents are: stating the various psychological disorders, mapping the psychological disorders of post-COVID patients with the standard, and work performed in the multimodal analysis of psychological analysis in the literature to date with the mapped psychological disorders. A questionnaire survey from doctors on psychological disorders was quantified and analyzed using statistical methods. Various mathematical models, such as Markov chain, Monte Carlo simulation, SIR model, and simulation models of multimodal analysis of psychological disorders, substantiate the model using the system dynamics using the VenSim package to predict the effects. Using open-source data, the researchers proved the accuracy of the computational modelling and stochastic mathematical modelling results in building the models to predict the disorders and viable proposed solutions for patients' healthy well-being by minimizing the effect of the disorder at the earliest. © 2022 Nova Science Publishers, Inc. All rights reserved.
ABSTRACT
Recent scholarship has established that conspiracist narratives proliferated in mainstream online discourse during the coronavirus pandemic. This proliferation has been provocatively characterized as a 'conspiracy singularity' in which previously divergent conspiracy narratives converged into a single, overarching narrative. Yet while the idea of narrative convergence has long figured in conspiracy theory research, empirical evidence has been scarce. The present article aims to address this gap by means of an investigation of an archive containing over 470,000 conspiracy-related Instagram posts from 2020. Given the size and conceptual complexity of the dataset, the paper introduces a 'digital hermeneutics' approach, which combines data science methods with qualitative interpretation and theorization. Operating across three levels of observation (hashtag analysis, text analysis, and image analysis) we identify patterns of convergence among different conspiracy narratives (including anti-vax, QAnon, anti-5G, and 'The Great Reset') over the year 2020 as well as the apparent role of protagonists and antagonists (notably Donald Trump and Bill Gates) in creating connections. In interpreting these findings we focus on the concept of 'the Deep State' as a bridge between various conspiracist narratives, which seems to cut diagonally across political ideologies.
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The medical tourism industry, which was seriously affected by the coronavirus disease of 2019 (COVID19), needs to give attention to its online promotional message strategy to boost the industry. Cultural variability is also crucial since the market for the medical tourism industry is global. However, studies involving cultural variability have only focused on examining single discourse mode, mainly the linguistic mode and overlooked the multimodal perspective. This study, therefore, examined the way in which the Prince Court Medical Centre (PCMC), a private hospital in Malaysia is presented and how the various modes in the hospital's website are combined to deliver promotional messages to international medical tourists. A total of three web pages from the website of PCMC were analysed using the Systemic Functional Theory framework. This study employed Halliday's metafunction theory (for language analysis and Kress and van Leeuwen's model for image analysis. The ways in which the multimodal features of the website reflect communicative style from the cultural perspective were also explored. Hall's (2000) cultural dimension of context dependency which classifies cultures into high-context and low-context cultures was used to present the analysis. The findings revealed that PCMC's hospital website has elements that are mainly encountered in low-context cultures such as elaborated code systems as well as direct, explicit, and highly structured messages. The findings help create awareness of communicative strategies in designing medical tourism websites that involve meaning making through texts and images and the possible cultural interpretation especially among copywriters, website designers or medical tourism stakeholders.
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OBJECTIVE: Investigating how the spatial and audiovisual conditions in video remote interpreting (VRI) shape communicative interaction in a language-discordant clinical consultation. METHODS: We conducted a multimodal analysis of an authentic VRI-mediated consultation with special reference to spatial arrangements, audiovisual conditions, and the healthcare professional's use of embodied communicative resources (body orientation, eye gaze, gestures). RESULTS: The physician is found to pursue his communicative goals for the consultation by first creating an appropriate spatial and technical environment and then supporting his information-giving and relationship-building actions through the use of nonverbal (embodied) resources like body orientation, gaze and gestures as well as specific turn-management behaviour. CONCLUSION: VRI allows healthcare professionals to access professional interpreters for language-discordant consultations but requires appropriate technical and spatial arrangements as well as users capable of adapting their communicative behaviour to spatial and audiovisual constraints. PRACTICE IMPLICATIONS: Alongside telephone interpreting, VRI is the solution of choice for language-discordant clinical encounters in times of the Covid-19 pandemic. Its use requires appropriate technical and spatial arrangements as well as specific skills on the part of healthcare professionals to cope with inherent audiovisual constraints.
Subject(s)
COVID-19 , Remote Consultation , Gestures , Humans , Pandemics , SARS-CoV-2 , TranslatingABSTRACT
The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce "weighted-nearest neighbor" analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.