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This paper investigates a possibility of sustainable growth in a multi-output endogenous growth framework where the capital accumulation takes place mainly through the production of the dirty manufactured goods. It is shown that in a closed economy, economic growth is not environmentally sustainable, even under an optimal pollution tax unless the consumption elasticity of substitution between clean and dirty goods approaches infinity as in a small open economy which exports dirty goods. There exists a minimal threshold level of the ratio of clean to dirty capital that ensures sustainable growth in a closed economy.
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Resonance Raman spectroscopy using 532nm excitation was used to distinguish normal brain tissue from different grades of glioma tissues. Principal component analysis was used to analyze the spectral data and achieved high accuracy. © 2018 The Author(s).
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This chapter provides an overview of public engagement, its importance and effective approaches to meaningfully involve the public with coastal sustainability transitions. It presents two case studies, Guernsey, British Channel Islands and Cape Wind, Massachusetts. These illustrate how concepts of identity, marginality and engagement are essential to the development of coastal sustainability transitions. As the case study of Guernsey outlines, coastal sustainability transition-related public engagement should seek to enhance the local distinctiveness of coastal areas and 'fit within the wider narrative of place identity and how individuals feel attached to places that are ascribed with specific meanings'. Furthermore, as the case study of Cape Wind illustrates, coastal sustainability transitions need to connect with local communities in terms of addressing immediate social and economic concerns. Therefore, as a research agenda, the human geographies of coastal sustainability transitions can be of substantial value to the practical application of sustainability. © 2019 selection and editorial matter, C. Patrick Heidkamp and John Morrissey.
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Twitter users often crave more followers to increase their social popularity. While a variety of factors have been shown to attract the followers, very little work has been done to analyze the mechanism how Twitter users follow or unfollow each other. In this paper, we apply game theory to modeling the follow-unfollow mechanism on Twitter. We first present a two-player game which is based on the Prisoner’s Dilemma, and subsequently evaluate the payoffs when the two players adopt different strategies. To allow two players to play multiple rounds of the game, we propose a multi-stage game model. We design a Twitter bot analyzer which follows or unfollows other Twitter users by adopting the strategies from the multi-stage game. We develop an algorithm which enables the Twitter bot analyzer to automatically collect and analyze the data. The results from analyzing the data collected in our experiment show that the follow-back ratios for both of the Twitter bots are very low, which are 0.76%0.76%0.76\% and 0.86%0.86%0.86\%. This means that most of the Twitter users do not cooperate and only want to be followed instead of following others. Our results also exhibit the effect of different strategies on the follow-back followers and on the non-following followers as well.
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