Accompanied by false information, mass media content is hindering efforts to cope with the current outbreak. Although the World Health Organization and other concerned bodies are notified regarding misinformation, myths and rumors are highly prevalent. This paper aims to highlight the misinformation and its potential impacts during the Covid-19 by using the Systematic Review Approach. The researcher randomly selected n = 35 research articles published from 2015 to 2020, witnessing the misinformation as a major concern during previous endemics and the current Covid-19 pandemic. Myths and rumors through traditional and new media platforms cause Xenophobia, LGBT Rights violations, and psychological disorders among the masses. Despite the efforts made by the World Health Organization, much more is required to nullify the impacts of misinformation and Covid-19. Therefore, the researcher recommended improved global healthcare policies and strategies to counteract against misinformation to mitigate the impacts of Covid-19.
The rise of SNS facilitated politicians with new opportunities to communicate directly with voters. Especially during election campaigns. Twitter provides female politicians with a space to exercise their political tasks beyond traditional media, especially in some Arab countries. Based on the framing theory, this study aims to identify how the female politicians in Bahrain utilised Twitter to present themselves for Parliamentary election campaigns in 2018. The researchers scrutinised the phenomenon using a thematic analysis of n = 263 tweets posted by two Bahraini female candidates. Results revealed that although politicians largely preferred Twitter in election campaigns to reinforce support and mobilisation for political engagement, two selected candidates lacked interaction with their supporters. Thus, the researchers concluded that the Bahraini female politicians have a long way to represent themselves in digital media politics as men widely benefit from personalisation more than females.
Despite the immense educational challenges during the Covid-19 outbreak, the role of ICT in education proved highly beneficial. The use of web-based Learning and Digital Library dependency remarkably increased, resulting in sustaining academic activities. The current research examined Digital Library usage and its impacts on students' educational activities and reading habits due to the recent healthcare crisis and closure of standard libraries in Pakistan. The researchers adopted a cross-sectional study design and distributed n= 230 closed-ended questionnaires among the public sector universities' students in the Twin Cities. The data is manipulated, coded, and analyzed through Structural Equation Modelling using IBM AMOS Version 23. Results revealed a robust and significant relationship between Digital Library Acceptance, Dependency, Sustaining Educational Activities, and Improved Reading Habits during the Covid-19 pandemic. However, the results explicitly rejected the intervening role of Prior Experience on Digital Library Dependency and Improved Reading Habits. Findings also highlighted the role of Digital Libraries as a fundamental part of the crisis management system. Thus, this investigation recommends more investigations to discuss the other positive aspects of Digital Libraries, especially during emergencies, to cope with the potential educational challenges and barriers.
Wireless sensor networks have become incredibly popular due to the Internet of Things' (IoT) rapid development. IoT routing is the basis for the efficient operation of the perception-layer network. As a popular type of machine learning, reinforcement learning techniques have gained significant attention due to their successful application in the field of network communication. In the traditional Routing Protocol for lowpower and Lossy Networks (RPL) protocol, to solve the fairness of control message transmission between IoT terminals, a fair broadcast suppression mechanism, or Drizzle algorithm, is usually used, but the Drizzle algorithm cannot allocate priority. Moreover, the Drizzle algorithm keeps changing its redundant constant k value but never converges to the optimal value of k. To address this problem, this paper uses a combination based on reinforcement learning (RL) and trickle timer. This paper proposes an RL Intelligent Adaptive Trickle-Timer Algorithm (RLATT) for routing optimization of the IoT awareness layer. RLATT has triple-optimized the trickle timer algorithm. To verify the algorithm's effectiveness, the simulation is carried out on Contiki operating system and compared with the standard trickling timer and Drizzle algorithm. Experiments show that the proposed algorithm performs better in terms of packet delivery ratio (PDR), power consumption, network convergence time, and total control cost ratio.
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