This paper provides an overview of the main manufacturing technologies of displays, focusing on those with low and ultra-low levels of power consumption, which make them suitable for current societal needs. Considering the typified value obtained from the manufacturer's specifications, four technologies-Liquid Crystal Displays, electronic paper, Organic Light-Emitting Display and Electroluminescent Displays-were selected in a first iteration. For each of them, several features, including size and brightness, were assessed in order to ascertain possible proportional relationships with the rate of consumption. To normalize the comparison between different display types, relative units such as the surface power density and the display frontal intensity efficiency were proposed. Organic light-emitting display had the best results in terms of power density for small display sizes. For larger sizes, it performs less satisfactorily than Liquid Crystal Displays in terms of energy efficiency.
Collaborative management of natural resources requires that multiple stakeholders come together to negotiate agreements. Who is a stakeholder, what is the boundary and the definition of an issue, and who has legitimacy to convene are interrelated, systemic questions. To address this challenge, this paper provides a conceptual framework with nine Contextual Dimensions. A workshop on collaborative and conflict management of the fishery in Chile, designed and facilitated by the authors, serves as a case study where multiple stakeholders came together to vision desirable futures and explored organizational linkages that could accommodate the different agendas. The workshop is analyzed through a conceptual framework and some conclusions are drawn on the value of experiential learning and on the importance of the iterative consultations that must precede such collaborative management events.
A Smart Home is able to generate energyrelated values such as electricity consumption, temperature, or luminosity without higher infrastructure requirements. The main aim of this research is to extract information from that raw data that could contribute to improving the energy efficiency management. This paper presents a system which, using different Machine Learning approaches to learn about the users' consumption habits, is able to generate collaborative recommendations and consumption predictions that help the user to consume better, which will in turn improve the demand curve. Moreover, from consumption values, the system learns to identify devices, enabling the demand to be anticipated. Taking into account the fact that the amount of energy data is increasing in real-time, the use of Big Data techniques will be the key to handling all the operations and one of the more innovative features of the system.
In this contribution we present an overview of some recent works carried out in our group to develop systematic and efficient methods for model reduction and its application to simulation, dynamic optimization and robust control of complex distributed process systems. The numerical projection methods we developed exploit the underlying finite element structure of the numerical PDE system to efficiently evaluate and to integrate the spatial differential terms, and thus to systematically project the original PDE set into a low dimensional subspace. This results into a reduced order description which is able to capture the relevant dynamics of the original system. Details on computational aspects of the methodology as well as applications in the context of dynamic optimization and robust control will be discussed on a number of representative case studies involving nonlinear diffusion-reaction and fluid dynamic systems.
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