Exploitation of Transparent Conductive Oxides in the Implementation of a Window-Integrated Wireless Sensor Node

Exploitation of transparent conductive oxides (TCO) to implement an energy-autonomous sensor node for a wireless sensor network (WSN) is studied and a practical solution presented. In the practical implementations, flexible and rigid substrates that is polyimide and glass, are coated with TCO, namely aluminum doped zinc oxide (AZO). AZO-coated flexible substrates are used to form thermoelectric generators (TEG) that produce electricity for the sensor electronics of the node from thermal gradients on a window. As the second solution to utilize AZO, its conductive properties are exploited to implement transparent antennas for the sensor node. Antennas for a UHF RFID transponder and the Bluetooth radio of the node are implemented. A prototype of a flexible transparent TEG, with the area of 67 cm2 when folded, was measured to produce power of 1.6 uW with a temperature difference of 43 K. A radiation efficiency of -9.1 dB was measured for the transparent RFID antenna prototype with the center frequency of 900 MHz. Radiation efficiencies between -3.8 dB and -0.4 dB, depending on the substrate, were obtained for the 2.45 GHz Bluetooth antenna.Comment: 10 pages, 14 figures, last author version accepted for publication in IEEE Sensors Journa

Exploring value co-creation within networks : actor-to-actor service provision within a public transport service system

Purpose: This study explores how value co-creation occurs at a network level in a service system comprising representatives of business, consumer, and community actors. The research centres on the following questions: 1) what kind of operand and operant resources are contributed and integrated in the value co-creation process? 2) What value-in-use is experienced by actors? 3) What factors facilitate service-system functionality and value cocreation? Drawing on service-dominant logic, IMP literature and a qualitative case study the paper provides new insights into value co-creation at a network/system-level. Methodology: A case study approach is employed to examine a unique partnership between a public transport provider and community groups who are invited to ‘adopt’ railway stations in Scotland. The ‘adopt a station’ scheme allows community users to utilize unused space within the station free of charge in order to provide services or facility improvements to benefit the community. The case represents a service-system where value co-creation occurs within Actor to Actor interactions in the interplay of C-to-C, B-to-C and B-to-B context, involving consumers, members of the community, rail staff and governmental organisations. Findings: The study describes resource contribution and integration involving a range of actors. In the Adopt a Station case, organizational actors contributed principally operand (financial and physical) resources, and the community actors and rail operator become in themselves the operant resources that integrate resources, promote the network and build relationships through their drive and passion to make the adopt project a success. The provision of resources was motivated by the value-in-use each actor anticipates gaining from involvement in the service-system. Four critical prerequisites for value co-creation within the service-system were identified: the provision of access and nature of that access; the level of ownership taken by adopters; user empowerment, and an increased level of support from other actors in the service-system. Contribution: The study of value creation within service systems comprising of relationships between a range of actors (both business and consumer) represents an interesting research gap in both S-D logic and IMP literature. This paper addresses calls for research to increase understanding of value co-creation at the service system and network level. The paper contributes by illustrating a) resources contributed and integrated at network-level and b) the value-in-use experienced by multiple actors c) the prerequisites for successful value co-creation. We suggest that firms should explore the potential for engaging versatile stakeholders and their networks of relationships around a common cause and make use of organically emerging service systems

Exploring co-creation networks : creating balanced centricity within a public transport service

This presentation looks at exploring co-creation networks and creating balanced centricity within a public transport servic

Thirty years of growing cereal without P and K fertilization

Over thirty years a significant depletion of P and K in soil occured when the were not given in fertilizers. This caused a reduction in crop yield. An abundant P application exceeding the crop uptake very clearly prevented the yield reduction but did not raise the extractable P concentration in the soil. Severe K deficiency did not start to appear until 20 years of growing cereal without fertilizer K. K application compensating for the uptake by the crop did not prevent the decrease of its extractable concentration in this soil, but this decrease did not affect crop yield

Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models

We propose maximum likelihood estimation for learning Gaussian graphical models with a Gaussian (ell_2^2) prior on the parameters. This is in contrast to the commonly used Laplace (ell_1) prior for encouraging sparseness. We show that our optimization problem leads to a Riccati matrix equation, which has a closed form solution. We propose an efficient algorithm that performs a singular value decomposition of the training data. Our algorithm is O(NT^2)-time and O(NT)-space for N variables and T samples. Our method is tailored to high-dimensional problems (N gg T), in which sparseness promoting methods become intractable. Furthermore, instead of obtaining a single solution for a specific regularization parameter, our algorithm finds the whole solution path. We show that the method has logarithmic sample complexity under the spiked covariance model. We also propose sparsification of the dense solution with provable performance guarantees. We provide techniques for using our learnt models, such as removing unimportant variables, computing likelihoods and conditional distributions. Finally, we show promising results in several gene expressions datasets.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013