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Deniz Bayrakdar

In this essay, I explore the land-, sea-, and cityscapes in six films (five Turkish and one Turkish German)—Bliss, The Wound, Rıza, Broken Mussels, The Guest, and Seaburners—and their use of place and non-place. Hamid Naficy’s concept of transitional space and Marc Augé’s notion of non-place, based on Foucault’s concept of heterotopia, will be the basis of the theoretical discussion. I focus on what I see as a major shift in the representation of the migrant experience in the Turkish cinema of the early and late 2000s, a shift from the land- and cityscapes to films whose setting is the seascape. This shift, I argue, corresponds to changes in the phases of migration that flow within and through Turkey, and both government policies and the public perception.

2022 ◽  
Vol 34 (5) ◽  
pp. 1-19
Xiaohui Wu

In this paper, Artificial Intelligence assisted rule-based confidence metric (AI-CRBM) framework has been introduced for analyzing environmental governance expense prediction reform. A metric method is to assess a level of collective environmental governance representing general, government, and corporate aspects. The equilibrium approach is used to calculate improvements in the source of environmental management based on cost, and it is tailored to test the public sector-corporation for environmental shared governance. The overall concept of cost prediction or estimation of environmental governance is achieved by the rule-based confidence method. The framework compares the expected cost to the environment of governance to determine the efficiency of the cost prediction process.

2022 ◽  
Vol 22 (3) ◽  
pp. 1-24
Yizhang Jiang ◽  
Xiaoqing Gu ◽  
Lei Hua ◽  
Kang Li ◽  
Yuwen Tao ◽  

Artificial intelligence– (AI) based fog/edge computing has become a promising paradigm for infectious disease. Various AI algorithms are embedded in cooperative fog/edge devices to construct medical Internet of Things environments, infectious disease forecast systems, smart health, and so on. However, these systems are usually done in isolation, which is called single-task learning. They do not consider the correlation and relationship between multiple/different tasks, so some common information in the model parameters or data characteristics is lost. In this study, each data center in fog/edge computing is considered as a task in the multi-task learning framework. In such a learning framework, a multi-task weighted Takagi-Sugeno-Kang (TSK) fuzzy system, called MW-TSKFS, is developed to forecast the trend of Coronavirus disease 2019 (COVID-19). MW-TSKFS provides a multi-task learning strategy for both antecedent and consequent parameters of fuzzy rules. First, a multi-task weighted fuzzy c-means clustering algorithm is developed for antecedent parameter learning, which extracts the public information among all tasks and the private information of each task. By sharing the public cluster centroid and public membership matrix, the differences of commonality and individuality can be further exploited. For consequent parameter learning of MW-TSKFS, a multi-task collaborative learning mechanism is developed based on ε-insensitive criterion and L2 norm penalty term, which can enhance the generalization and forecasting ability of the proposed fuzzy system. The experimental results on the real COVID-19 time series show that the forecasting tend model based on multi-task the weighted TSK fuzzy system has a high application value.

2022 ◽  
Vol 1 (3) ◽  
pp. 1-4
Farha Yashmin Rohman ◽  

Pandemic like COVID-19 has triggered disruptions in personal and collective lives globally. It is not only a pandemic, but also an Infodemic of misinformation about the virus which raises demand for reliable and trustworthy information. With the advent of social media creation and consumption of news have been changing among the young generation. Student leaders have taken on additional work and assumed new responsibilities by volunteering in their communities and creating awareness among the public about the accuracy of information and measures to be taken against the deadly virus. This study explores the use of Facebook handles by the student leaders of two universities in Guwahati in creating awareness about the health-related messages regarding Covid-19 and its vaccination. The researcher will use critical discourse analysis to evaluate the use of social networking sites by the students’ leaders. To understand the usage by the leaders, Facebook pages of the leaders would be followed and studied backed with unstructured interviews with the leaders to understand the purpose of and pattern of using the social media handles.

2022 ◽  
Vol 147 ◽  
pp. 105597
Philip John Beards ◽  
Gillian Frost ◽  
Nicola Healey ◽  
Liz Yeomans ◽  
Robert Shaw ◽  

Nagendra Nath Mondal ◽  

Objectives: There is a lot of speculation, debate, and hypothesis about the new coronavirus disease 2019 (COVID-19) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Also the world famous media British Broadcasting Corporation (BBC), National Center for Biotechnology Information (NCBI), and World Health Organization (WHO) are informing their emergency concern for the public attention. But we are far behind yet to say the origin of COVID-19 and its outbreak. The main purpose of this study is to put an end to all speculations, fantasies, theories and debates.

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