Social media is a transformative digital technology, collapsing the “six degrees ofseparation” which have previously characterized many social networks, and breaking down many of the barriers to individuals communicating with each other. Some commentators suggest that this is having profound effects across society, that social media have opened up new channels for public debates and have revolutionized the communication of prominent public issues such as climate change. In this article we provide the first systematic and critical review of the literature on social media and climate change. We highlight three key findings from the literature: a substantial bias toward Twitter studies, the prevalent approaches to researching climate change on social media (publics, themes, and professional communication), and important empirical findings (the use of mainstream information sources, discussions of “settled science,” polarization, and responses to temperature anomalies).Following this, we identify gaps in the existing literature that should beaddressed by future research: namely, researchers should consider qualitativestudies, visual communication and alternative social media platforms to Twitter.We conclude by arguing for further research that goes beyond a focus on sciencecommunication to a deeper examination of how publics imagine climate changeand its future role in social life.
This paper analyzes the institutional context of maintenance purchasing in higher education. It aims to provide insights into the institutional complexities of smart maintenance purchasing in higher education institutes. In a case study, six external institutional fields and two internal institutional logics are identified. They create two types of institutional complexities that impede innovation if not treated correctly. Three ways are discussed to deal with those institutional complexities, 1) negotiating institutional field boundaries, 2) creating new institutional logics and practices, and 3) implementing institutional changes.
MULTIFILE
Light scattering is a fundamental property that can be exploited to create essential devices such as particle analysers. The most common particle size analyser relies on measuring the angle-dependent diffracted light from a sample illuminated by a laser beam. Compared to other non-light-based counterparts, such a laser diffraction scheme offers precision, but it does so at the expense of size, complexity and cost. In this paper, we introduce the concept of a new particle size analyser in a collimated beam configuration using a consumer electronic camera and machine learning. The key novelty is a small form factor angular spatial filter that allows for the collection of light scattered by the particles up to predefined discrete angles. The filter is combined with a light-emitting diode and a complementary metal-oxide-semiconductor image sensor array to acquire angularly resolved scattering images. From these images, a machine learning model predicts the volume median diameter of the particles. To validate the proposed device, glass beads with diameters ranging from 13 to 125 µm were measured in suspension at several concentrations. We were able to correct for multiple scattering effects and predict the particle size with mean absolute percentage errors of 5.09% and 2.5% for the cases without and with concentration as an input parameter, respectively. When only spherical particles were analysed, the former error was significantly reduced (0.72%). Given that it is compact (on the order of ten cm) and built with low-cost consumer electronics, the newly designed particle size analyser has significant potential for use outside a standard laboratory, for example, in online and in-line industrial process monitoring.
MULTIFILE
