Abstract
The spectral behavior in the C-band of fiber Bragg gratings (FBGs) was analyzed as a function of temperature and strain. The FBGs were fabricated in pure silica four-leaf-clover- shaped suspended-core fibers by (DUV) femtosecond laser exposure (3.6 W at 800 nm, 130 fs, 1 kHz frequency tripled to 350 fs, 650 mW at 267 nm). A defect fiber (with a hollow hole in the core) and nondefect fiber were compared both yielding approximate to 1 pm/mu epsilon sensitivity to strain but different sensitivity to temperature (from 3.0 pm/degrees C to 8.4 pm/degrees C for the defect fiber and 10 pm/degrees C for the nondefect fiber). The 16% to 70% relative difference between the thermal coefficients of the two fibers, together with their similar strain sensitivity enables the simultaneous measurement of strain and temperature.

Abstract
News can contain information which may provide an indication of the future direction of a share or stock market index. The possibility of predicting future stock market prices has attracted an increasing numbers of industry practitioners and academic researchers to this area of investigation. Popular approaches have relied upon either: models constructed from manually selected training or manually constructed dictionaries. A potential flaw of manually selecting data is that the effectiveness of the trained model is dependent upon the ability of the human annotator. An alternative approach is to manually align news stories with trends in a specific market. A negative story is inferred if it co-occurs with a market losing value where as positive story is associated with a rise. This approach may have its flaws because news stories may co-occur with market movements by chance and consequently may inhibit the construction of a robust classifier with data gathered by this method. This paper presents a strategy which combines a: rule classifier, alignment strategy and self-training to induce a robust model for classifying news stories. The proposed method is compared with several competing methodologies and is evaluated with: estimated F-Measure and estimated trading returns. In addition the paper provides an evaluation of classifying a news story with its: headline, description or story text with: Language Models and Naive Bayes. The results demonstrate a clear advantage for the proposed methodology when evaluated by estimated F-Measure. The proposed strategy also produces the highest trading returns. In addition the paper clearly demonstrates that a news story's headline provides the greatest assistance for classification. The models induced from headlines gained the highest estimated F-Measure and trading returns for each strategy with the exception of the alignment method which performed uniformly poorly. © Technomathematics Research Foundation.

Abstract
The vision towards the Network of the Future cannot be separated from the fact that today's networks, and networking services are subject to sophisticated and very effective attacks. When these attacks first appeared, spoofing and distributed denial-of-service attacks were treated as apocalypse for networking. Now, they are considered moderate damage, whereas more sophisticated and inconspicuous attacks, such as botnets activities, might have greater and far reaching impact. As the Internet is expanding to mobile phones and smart dust and as its social coverage is liberalized towards the realization of ubiquitous computing (with communication), the concerns on security and privacy have become deeper and the problems more challenging than ever. Re-designing the Internet as the Network of the Future is self-motivating for researchers, and security and privacy cannot be provided again as separate, external, add-on, solutions. In this paper, we discuss the security and privacy challenges of the Network of the Future and try to delimit the solutions space on the basis of emerging techniques. We also review methods that help the quantification of security and privacy in an effort to provide a more systematic and quantitative treatment of the area in the future. Copyright (c) 2011 John Wiley & Sons, Ltd.

Abstract
In this paper, we present MDSHEET, a framework for the embedding, evolution and inference of spreadsheet models. This framework offers a model-driven software development mechanism for spreadsheet users.

Abstract
In this research programme methodologies to improve the accuracy in the results measured with embedded fibre Bragg gratings (FBG) sensors were studied and implemented in order to produce a composite overwrapped pressure vessel (COPV) prototype that incorporate a non-destructive sensing technologies. Using a carbon/epoxy prepreg system, test specimens were manufactured with longitudinally embedded FBG sensors. The combined behaviour of the sensors and the host material was characterized and a calibration rule (correction factor) was determined for the chosen material. The consistency of the results with both theoretical and empirical assumptions suggests that the proposed method is applicable to a wide range of FBG sensors and host materials. In this paper, the experimental setup and procedure used to assess to the calibration rule is addressed and further detailed.

Abstract
In this extended abstract we present a bidirectional model-driven framework to develop spreadsheets. By being model driven, our approach allows to evolve a spreadsheet model and automatically have the data co-evolved. The bidirectional component achieves precisely the inverse, that is, to evolve the data and automatically obtain a new model to which the data conforms.