10 pages., via online journal., Findings indicate that analysis of news reports of agricultural injuries provide more current data than traditional surveillance databases.
35 pages, Despite the significance and growth of wind energy as a major source of renewable energy, research on the risks of wind turbines in the form of accidents and failures has attracted limited attention. Research that applies data analytics methodologically in this context is scarce. The research presented here, upon construction of a text corpus of 721 selected wind turbine accident and failure news reports, develops and applies a custom-developed data analytics framework that integrates tabular analysis, visualization, text mining, and machine learning. Topic modeling was applied for the first time to identify and classify recurring themes in wind turbine accident news, and association mining was applied to identify contextual terms associated with death and injury. The tabular and visual analyses relate accidents to location (offshore vs. onshore), wind turbine life cycle phases (transportation, construction, operation, and maintenance), and the incidence of death and injury. As one of the insights, more incidents were found to occur during operation and transportation. Through topic modeling, topics associated most with deaths and injuries were revealed. The results could benefit wind turbine manufacturers, service providers, energy companies, insurance companies, government bodies, non-profit organizations, researchers, and other stakeholders in the wind energy sector.
3 pages, Big data represent a new productive factor (the "new oil" for advocates) that generates new realities in agriculture. By adding an extra "cyber" dimension to current farming systems, big data lead to the emergence of new, complex cyber-physical-social systems. However, our understanding of the sustainability of such systems is still at a rudimental stage. In this critical review we attempt to shed some light on this topic, by identifying and presenting some issues that put in doubt the sustainability of big data agriculture. By using a punctuated equilibria lens, we argue that despite their contribution to the economic and environmental performance of farming, big data act as a speciation mechanism. Hence, they lead to new forms of intraspecific, interspecific and intergeneric competition, thus putting at risk the most vulnerable players of the game. We conclude by pointing out that to holistically address the interrelation between big data and agricultural sustainability we need a hybrid research line, which will combine the qualities of both technology-oriented research and critical social science.
