Abstract
Theoretical and experimental results of three different high-birefringent fiber loop mirrors with output ports are analyzed. For theoretical model, the Jones matrix analysis is used. The theoretical studies present similar results for all experimental configurations. The last configuration is tested as an interrogation system where the spectral response arises from the combination of the reference signal modulated by the sensor signal. The configuration is characterized in strain with the phase changes recovered from two quadrature phase signals, providing a sensitivity of 16 mrad/mu epsilon with a resolution of 1.9 mu epsilon.

Abstract
Purpose: The purpose of this paper is to provide practical justification for performance measurement and management within a collaborative business network. The basic performance measurement indicators are elaborated within the scope of this research. Design/methodology/approach: Performance measurement techniques are highlighted through the application of an ICT-based solution approach, with special focus on business collaboration among small and medium sized enterprises (SMEs). Findings: From the basic need to measure the performance of individual partners within a business network, this research proposes a generic framework and process flow with the objective of evaluating the individual partners in terms of various performance indicators such as key success factors (KSF), key performance factors (KPF) and key performance indicators (KPI). The outcomes from this framework or process flow will help partners in the network to build valuable trust, cooperation and coordination. Originality/value: The focus of this paper is to demonstrate the methodological approach of measuring the performance through identifying and prioritizing the performance indicators (KSF, KPF, KPI) among collaborative partners and to highlight their importance for successful business operations. © Emerald Group Publishing Limited.

Abstract
Pervasive environments offer an increasing number of services to a large number of people moving within these environments, including timely information about where to go and when, and contextual information about the surrounding environment. This information may be conveyed to people through public displays or direct to a person's mobile phone. People using these services interact with the system but they are also meeting other people and performing other activities as relevant opportunities arise. The design of such systems and the analysis of collective dynamic behaviour of people within them is a challenging problem.We present results on a novel usage of a scalable analysis technique in this context.We show the validity of an approach based on stochastic process-algebraic models by focussing on a representative example, i.e. emergency egress.The chosen case study has the advantage that detailed data is available from studies employing alternative analysis methods, making cross-methodology comparison possible. We also illustrate how realistic, context-dependent human behaviour, often observed in emergency egress, can naturally be embedded in the models, and how the effect of such behaviour on evacuation can be analysed in an efficient and scalable way. The proposed approach encompasses both the agent modelling viewpoint, as system behaviour emerges from specific (discrete) agent interaction, and the population viewpoint,when classes of homogeneous individuals are considered for a (continuous) approximation of overall system behaviour. © 2011 BCS.

Abstract
Decision rules are one of the most interpretable and flexible models for data mining prediction tasks. Till now, few works presented online, any-time and one-pass algorithms for learning decision rules in the stream mining scenario. A quite recent algorithm, the Very Fast Decision Rules (VFDR), learns set of rules, where each rule discriminates one class from all the other. In this work we extend the VFDR algorithm by decomposing a multi-class problem into a set of two-class problems and inducing a set of discriminative rules for each binary problem. The proposed algorithm maintains all properties required when learning from stationary data streams: online and any-time classifiers, processing each example once. Moreover, it is able to learn ordered and unordered rule sets. The new approach is evaluated on various real and artificial datasets. The new algorithm improves the performance of the previous version and is competitive with the state-of-the-art decision tree learning method for data streams. © 2012 ACM.

Abstract
As wind power generation undergoes rapid growth, lightning damages involving wind turbines have come to be regarded with more attention. Electric and magnetic fields generated by lightning represent a serious hazard to wind turbines. A new case study is presented with two interconnected wind turbines, considering that lightning strikes directly the blade of one wind turbine. Computer simulations obtained by using EMTP-RV are presented and conclusions are duly drawn.

Abstract
This paper describes the formal modelling and validation of the behaviour of a multi-agent system that integrates the production and quality control processes in a washing machine production line. The modelling, analysis and validation process uses the Petri nets formalism that provides a rigorous and formal language based on its powerful mathematical foundation, supporting the complete verification of the system correctness during the design phase and before to proceed to the deployment phase. The behaviour models of each agent belonging to the system architecture are edited, analysed and simulated in the PnDK framework.