Performance analysis of a multimode waveguide-based optical disk readout system

We propose a method to improve the optical resolution to read out optical disks, without making the spot size on the disk smaller than the diffraction limit. The idea is to reconstruct the bit pattern from the complete field profile (including amplitude and phase) of the light reflected from the disk. We measure the phase and amplitude information by picking up the wave front into different modes of a bimodal waveguide. Once picked up, these modes can be split by a photonic integrated circuit to be measured by separate detectors. By combining the information from the responses from the different modes, we can improve the bit error rate substantially.     

Dedicated model for the efficient assessment of wireless power transfer in the radiative near‐field

In this paper, a novel method to efficiently calculate the power transfer efficiency of a wireless power transfer system in the radiative near‐field is proposed. The technique allows repositioning of the antennas without large additional cost. It relies on a single simulation (or measurement) of the radiation pattern of the antennas used. Thanks to its high computational efficiency, it can be used in multi‐transmitter and multi‐receiver scenarios when there is no coupling between devices via the reactive near‐field. Our method is applied to the latter to demonstrate its accuracy and computational efficiency. Copyright © 2015 John Wiley & Sons, Ltd.     

Assessing Stability and Dynamics in Flood Risk Governance

European urban agglomerations face increasing flood risks due to urbanization and the effects of climate change. These risks are addressed at European, national and regional policy levels. A diversification and alignment of Flood Risk Management Strategies (FRMSs) can make vulnerable urban agglomerations more resilient to flooding, but this may require new Flood Risk Governance Arrangements (FRGAs) or changes in existing ones. While much technical knowledge on Flood Risk Management is available, scientific insights into the actual and/or necessary FRGAs so far are rather limited and fragmented. This article addresses this knowledge gap by presenting a research approach for assessing FRGAs. This approach allows for the integration of insights from policy scientists and legal scholars into one coherent framework that can be used to identify Flood Risk Management Strategies and analyse Flood Risk Governance Arrangements. In addition, approaches for explaining and evaluating (shifts in) FRGAs are introduced. The research approach is illustrated by referring to the rise of the Dutch risk-based approach called ‘multi-layered safety’ and more specifically its application in the city of Dordrecht. The article is concluded with an overview of potential next steps, including comparative analyses of FRGAs in different regions. Insights in these FRGAS are crucial to enable the identification of action perspectives for flood risk governance for actors at the level of the EU, its member states, regional authorities, and public-private partnerships.     

An indicator approach to sustainable urban freight transport

Several indicators have been established to monitor and evaluate the sustainability of cities. Logistics and related transportation activities are underrepresented in these established frameworks despite the substantial negative impact of urban freight transport (UFT) on the environment, society and economy. The result is the lack of an understanding of freight flows’ impact on the liveability of cities. This paper fills this gap by introducing a comprehensive set of freight transport related indicators with an operational target in policy support and urban planning. It provides a discussion of its hierarchical design and 45 indicators. Using this indicator set, local authorities can assess and enhance UFT sustainability.     

Ambient contracts: verifying and enforcing ambient object compositions à la carte

Current programming languages do not offer adequate abstractions to discover and compose heterogenous objects over unreliable networks. This forces programmers to discover objects one by one, compose them manually, and keep track of their individual connectivity state at all times. In this paper we propose Ambient Contracts, a novel programming abstraction to deal with the difficulties of composing objects connected over unreliable networks. Ambient Contracts provide declarative heterogenous group discovery and composition while dealing with the unreliability of the network. An ambient contract allows runtime verification and enforcement of the messages sent between the participants in the contract. The use of our abstraction significantly reduces the code base and allows programmers to focus on the core functionality of their application. Our claims are reinforced by comparing the implementation of an example scenario in our contracts with a Java implementation using M2MI.     

Book Review

Larisa V. Shavinina (2003) The International Handbook on Innovation. Pergamon, Amsterdam. Hback, 1208 pp, ISBN 008044198X     

Optimal experiment design under process noise using Riccati differential equations

In this paper, we present a numerical method for optimal experiment design of nonlinear dynamic processes. Here, we suggest to optimize an approximation of the predicted variance–covariance matrix of the parameter estimates, which can be computed as the solution of a Riccati differential equation. In contrast to existing approaches, the proposed method allows us to take process noise into account and requires less derivative states to be computed compared to the traditional Fisher information matrix based approach. This process noise is assumed to be a time-varying random disturbance which is not known at the time when the experiment is designed. We illustrate the technique by solving an optimal experiment design problem for a fed-batch bioreactor benchmark case study. Here, we concentrate on how the optimal input design and associated accuracy of the parameter identification is influenced when process noise is present.     

Improving customer retention in financial services using kinship network information

This study investigates the advantage of social network mining in a customer retention context. A company that is able to identify likely churners in an early stage can take appropriate steps to prevent these potential churners from actually churning and subsequently increase profit. Academics and practitioners are constantly trying to optimize their predictive-analytics models by searching for better predictors. The aim of this study is to investigate if, in addition to the conventional sets of variables (socio-demographics, purchase history, etc.), kinship network based variables improve the predictive power of customer retention models. Results show that the predictive power of the churn model can indeed be improved by adding the social network (SNA-) based variables. Including network structure measures (i.e. degree, betweenness centrality and density) increase predictive accuracy, but contextual network based variables turn out to have the highest impact on discriminating churners from non-churners. For the majority of the latter type of network variables, the importance in the model is even higher than the individual level counterpart variable.