scholarly journals Hybrid Policy Learning for Energy-Latency Tradeoff in MEC-Assisted VR Video Service

Author(s):  
Chong Zheng ◽  
Shengheng Liu ◽  
Yongming Huang ◽  
Luxi Yang
2019 ◽  
Author(s):  
M. Evren Tok ◽  
Duygu Sever

This study investigates the case of Qatar Singapore Regional Training Center for Public Administration.As a tool for this process of policy transfer, the article further evaluates the case of Singapore- Qatar Asia-Middle East Dialogue (AMED) Regional Training Centre for Public Administration (RTCPA) in Doha, Qatar, as a mechanism to foster this policy transferThe study suggests that this evaluation would be a fruitful example in revealing the strengths and weakness of such initiatives and can offer a scheme for insights regarding effective tools of policy learning.


Author(s):  
Sunita Gupta ◽  
Sakar Gupta ◽  
Dinesh Goyal

: A serious problem in Wireless Sensor Networks (WSNs) is to attain high-energy efficiency as battery is used to power and have limited stored energy. They can’t be suitably replaced or recharged. Appearance of renewable energy harvesting techniques and their combination with sensor devices gives Energy Harvesting Wireless Sensor Networks (EHWSNs). IoT is now becoming part of our lives, comforting simplifying our routines and work life. IoT is very popular . It connects together, computes, communicates and performs the required task. IoT is actually a network of physical devices or things that can interact with each other to share information. This paper gives an overview of WSN and IoT, related work, different ways of connecting WSN with internet, development of smart home, challenges for WSN etc. Next a Framework for performance optimization in IoT is given and QC-PC-MCSC heuristic is analyzed in terms of Energy Efficiency and Life Time of a sensor on Energy Latency Density Design Space, a topology management application that is power efficient. QC-PC-MCSC and QC-MCSC are compared for Energy Efficiency and Life Time of a sensor over energy latency density design space, a topology management application.


Author(s):  
Alastair Stark

This chapter examines the logics for action that inquiry actors bring into a lesson-learning episode. Logics for action is a term that describes the knowledge-related preferences that actors use in inquiries to make decisions. Analysis of the logics in these cases leads to three specific arguments. First, that political logics for action do not compromise inquiries in the ways which inquiry research currently suggests. Second, that public-managerial logics are essential to inquiry success in terms of policy learning. Finally, that legal-judicial logics need not necessarily lead to blaming and adversarial proceedings, which derail the lesson-learning function. These three arguments once again suggest that we need to rethink much of the conventional wisdom surrounding inquiries.


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