"There is a Fabulous Play in the House of Cards:” Gender and Memory-Mapping in the Film Tasher Ghawr

The pandemic's collective memory features large-scale destruction in the public and private realms. This paper studies the latter by speculating on the complex interrelationship between gender, media, and collective memory. By foregrounding the potential of fictional experientialities to engage with real-life phenomena, the paper analyzes the movie Tasher Ghawr as an epitome of women's experience of the COVID-19 lockdown. This movie was selected pertaining to its current relevance. The paper undertakes a qualitative investigation through a textual analysis of the movie's narrative. The researchers use theories such as collective memory, gender performativity, affect, and counter-memory to illustrate how the protagonist Sujatha's individual gendered memory constantly constructs and deconstructs the collective memory of women as it pertains to the pandemic. The notion of collective memory is highlighted as complexly entangled and dialogically engaged with the memories of the individuals. This paper demonstrates this by constructing Sujatha as a subject defined by the norms embedded in the female collective memory and then shedding light on her subversive brilliance in questioning the stronghold of these discourses. This act of subversion produces a new strand of collective memory where women are no longer simply victims. The results of this study indicate that while women are constructed as subjects through collective memory processes, they also demonstrate a potential to subvert and question the stronghold of this collective memory that presupposes their submissiveness and servility. For future researchers, this movie provides ample critical space to discuss the notion of traumatic memory. © The Author(s) 2023.

The Problem of Gendered Emojis in Online Communication Platforms: A Study to Understand Digital Dependence on Using Emotions During Pandemic

Digitalization, affordable smart gadgets, and social distancing have turned virtual communication into a lived phenomenon. However, we should be aware of the fact that the virtual communication process is entangled with positive and negative consequences. On the one hand, it has enabled people to develop a feeling of togetherness and belonging, and on the other, it is steeped in conflict and dispute due to the extensive use of emojis that are context-sensitive and are subjected to multiple interpretations. The problem of emojis connected with sexual connotations has not been studied in an online conversation parameter. Hence, the current study examines the sexual connotations that are embedded in the usage of non-facial emojis such as eggplant, cherry, etc., in virtual communication and analyses sexual connotations that are generated in closed group interactions. The methodology undertaken in this study is a quantitative experimental research method to collect data. Participants (N=64) will determine how certain context-sensitive emojis are perceived by them in closed group online conversations. Results suggest that non-facial emojis possess sexual connotations which are highly context-specific and used extensively in interpersonal conversations. In this way, this paper will prepare the ground to study more hidden sexual connotations in emojis. © 2023 ACADEMY PUBLICATION.

COVID‐19 Detection from Chest X‐Ray (CXR) Images Using Deep Learning Models

Due to the tremendous rise in COVID cases around the world, early detection of Covid-19 has become critical. Deep learning technology has recently sparked a lot of attention as a means of detecting and classifying diseases quickly, automatically, and accurately. The goal of this study is to develop a deep learning based automatic COVID‐19 detection system for better, faster, and more accurate COVID‐19 detection from chest X‐Ray (CXR) images. In our work, we have used pre-trained deep learning models such as VGG16, ResNet50, DenseNet201, InceptionV3 and Xception utilizing openly accessible dataset. Experimental results show that the DenseNet201 model performs the best with more than 97% accuracy. Moreover, in terms of size, DenseNet121 is beating the rest of the models. As a results, DenseNet201 is most suitable Deep Convolutional neural networks (CNN) architecture for developing an automatic covid-19 detection tool. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COMPARISON OF THE ASSOCIATION OF THE AREA DEPRIVATION INDEX, COVID-19 COMMUNITY VULNERABILITY INDEX, MINORITY HEALTH-SOCIAL VULNERABILITY INDEX, AND SOCIAL VULNERABILITY INDEX WITH US COVID-19 INCIDENCE AND MORTALITY

BACKGROUND: To promote equitable allocation of scarce COVID-19 resources, most US states added place-based social disadvantage indices in allocation plans. Here we compare how 4 common indices of social disadvantage (differing on social variables including race, and geographic levels)-the Social Vulnerability Index (SVI), Area Deprivation Index (ADI), COVID-19 Community Vulnerability Index (CCVI), and Minority Health-Social Vulnerability Index (MH-SVI)-are associated with COVID-19 incidence/mortality. METHODS: This cross-sectional study uses aggregated COVID-19 cases/ deaths in 3,135 US counties/county-equivalents reported by health departments (New York Times repository), merged with social disadvantage indices from CDC (SVI and MH-SVI), Surgo Ventures® (CCVI), and University of Wisconsin Neighborhood Atlas® (ADI). The SVI, MH-SVI, and CCVI are available at the US county level. ADI was transformed from census block group level to county-level using a population-weighted average. All indices/subindices are national percentile rankings. SVI, MH-SVI, and CCVI range from 0-1 and ADI range from 0-100;higher scores indicating greater disadvantage. For analysis, we converted all indices/subindices into deciles. Mixed effects negative binomial regression models adjusted for population density, urbanicity, and including an offset for county population, were used to estimate associations of each index/subindex with COVID-19 incidence/ mortality, as of July 31, 2021. RESULTS: All 4 disadvantage indices had similar positive associations with COVID-19 incidence (incidence rate ratios [IRR] ranging from 1.03-1.04). Each index was also significantly associated with COVID-19 mortality, but ADI had a stronger association (IRR 1.17, 95%CI 1.16-1.18) than CCVI (IRR 1.07, 95%CI 1.06-1.08), SVI (IRR 1.06, 95%CI 1.05-1.07), and MH-SVI (IRR 1.04, 95%CI 1.03-1.04). Each SVI, MH-SVI, and CCVI subindex was significantly associated with COVID-19 incidence, and most were significantly associated with mortality. CONCLUSIONS: With Omicron and other emerging COVID-19 variants, the need may again arise for allocation of scarce resources like testing, vaccines, and treatments. Despite differences in component measures and weighting, all 4 indices demonstrated an association between greater disadvantage and increased COVID-19 incidence/mortality, suggesting that any index can be used to assist public health leaders in targeting COVID-19 resources to regions most vulnerable to negative COVID-19 outcomes. Of note, unlike SVI, MH-SVI, and CCVI, the ADI does not include race, which can matter for legal/political issues associated with prioritization. Targeting underserved populations with indices that include race as a variable has been challenged by some state policymakers with allegations of reverse discrimination. Policymakers may weigh potential tradeoffs in the political/ practical acceptability when considering use of these indices to target equitable allocation of COVID-19 resources.