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We extend the generic framework of reproducibility for reuse of randomness in multi-recipient encryption schemes as proposed by Bellare et al. (PKC 2003). A new notion of weak reproducibility captures not only encryption schemes which are (fully) reproducible under the criteria given in the previous work, but also a class of efficient schemes which can only be used in the single message setting. In particular, we are able to capture the single message schemes suggested by Kurosawa (PKC 2002), which are more efficient than the direct adaptation of the multiple message schemes studied by Bellare et al. Our study of randomness reuse in key encapsulation mechanisms provides an additional argument for the relevance of these results: by taking advantage of our weak reproducibility notion, we are able to generalise and improve multi-recipient KEM constructions found in literature. We also propose an efficient multi-recipient KEM provably secure in the standard model and conclude the paper by proposing a notion of direct reproducibility which enables tighter security reductions.

Abstract
Differential group delay (DGD) generation by inducing birefringence in uniform fiber Bragg gratings (FBG) is studied. Applying a transverse load, a birefringence is induced on a uniform FBG creating a tunable differential group delay. A theoretical behavior of this phenomenon is presented being in good agreement with the experimental results obtained. A maximum experimental tunable DGD of similar to 60 ps was obtained with this induced birefringent process in the uniform FBG. (C) 2007 Wiley Periodicals. Inc.

Abstract
This paper presents an artificial neural network approach for short-term electricity prices forecasting. In the new deregulated framework, producers and consumers require short-term price forecasting to derive their bidding strategies to the electricity market. Accurate forecasting tools are required for producers to maximize their profits and for consumers to maximize their utilities. A three-layered feedforward artificial neural network, trained by the Levenberg-Marquardt algorithm, is used for forecasting the next 168 hour electricity prices. We evaluate the accuracy of the price forecasting attained with the proposed approach, reporting the numerical results from a real-world case study based on an electricity market.

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