Suchergebnisse

Abstract
To what degree can we determine people's connections with groups through the language they use? In recent years, large archives of behavioral data from social media communities have become available to social scientists, opening the possibility of tracking naturally occurring group identity processes. A feature of most digital groups is that they rely exclusively on the written word. Across 3 studies, we developed and validated a language-based metric of group identity strength and demonstrated its potential in tracking identity processes in online communities. In Studies 1a–1c, 873 people wrote about their connections to various groups (country, college, or religion). A total of 2 language markers of group identity strength were found: high affiliation (more words like we, togetherness) and low cognitive processing or questioning (fewer words like think, unsure). Using these markers, a language-based unquestioning affiliation index was developed and applied to in-class stream-of-consciousness essays of 2,161 college students (Study 2). Greater levels of unquestioning affiliation expressed in language predicted not only self-reported university identity but also students' likelihood of remaining enrolled in college a year later. In Study 3, the index was applied to naturalistic Reddit conversations of 270,784 people in 2 online communities of supporters of the 2016 presidential candidates—Hillary Clinton and Donald Trump. The index predicted how long people would remain in the group (3a) and revealed temporal shifts mirroring members' joining and leaving of groups (3b). Together, the studies highlight the promise of a language-based approach for tracking and studying group identity processes in online groups.

This paper gives a wide-ranging overview of hybrid electric vehic1es (REVs). Essential costs and development within the area of-HEV s are analyzed to show the starring role that this technology plays in the current automotive market. The paper will discuss the technologies used in the making of Hybrid Cars such as "Hybrid Solar Vehicle", "Hybrid Electric Vehicle" and also "Plug-In hybrid electric vehicles". The advantages and disadvantages of this vehic1e technology are also deliberated in detail. The paper will also focus on the current and future market projections of HEVs; particularly on the legislative movements that are assisting to raise the market share for ecologically friendly vehic1es. Opinions of researchers and automotive companies will be taken into account to expect which will be the prominent technology developments in the future.

AbstractThe recent surge in political authoritarianism has triggered interest in the factors that regulate its rise and fall. We explored these phenomena in the time around the January 6, 2021, insurrection in the United States. Identity fusion (synergistic union) with Trump predicted the perception that Democrats represented an existential threat to the American way of life; higher perceived threat, in turn, predicted endorsement of authoritarian actions against Democrats. Biden supporters did not display analogous effects. Among Trump supporters and, to a lesser extent, Biden supporters, fusion with the United States negatively predicted both the perception that out‐party members represented an existential threat and endorsement of authoritarian actions against them. These findings provide unique insight into the role of identity in the nation's closest brush with authoritarian takeover in over a century.

Vaccinations are an important and effective cornerstone of preventive medical care with significant health benefits. Vaccination is crucial to limit the pandemic spread of SARS-CoV-2/COVID-19. The government has started vaccination to prevent the continuous spread of corona infection in India. Therefore, besides the development and supply of vaccines, it is essential that sufficient individuals are willing to get vaccinated, but concerning proportions of populations worldwide show vaccine hesitancy. However, it soon became clear that to end the pandemic, we would have to address another ubiquitous problem: the widespread hesitancy toward or downright rejection of vaccination. To achieve population immunity first we have to find the non-vaccinated population to this end, this project proposed an Aadhaar-based facial recognition system is used to find non-vaccination citizen and alert them using Artificial Intelligence. Deep learningin the form of Convolutional Neural Networks (CNNs) to perform the face recognition and seems to be an adequate method to carry outface recognition due to its high accuracy.A CNN is a type of Deep Neural Network (DNN) that is optimized for complex tasks such as image processing, which is required for facial recognition. CNNs consist of multiple layers of connected neurons: there is an input layer, an output layer, and multiple layers between these two.In the context of the coronavirus disease (COVID-19) pandemic, A face recognition-based person's current vaccination status to protect against COVID-19 can then be used for continuity of care or as proof of vaccination for purposes other than health care. Facial recognition technology (FRT) along with the Aadhaar to authenticate people before entering into any kinds of service.This project provides COVID-19 vaccination status using their face and attest that an individual has received a vaccine or not and alert them to get vaccinated.