Abstract
The rapid evolution of information and communication technologies, the changing client's demands, and market conditions impelled enterprises to adapt their way of undertaking business, from traditional practices to e-business, and to participate in new forms of collaboration, such as networked organizations. In this context, standards, frameworks, technologies, and infrastructures supporting collaborative business, in a networked environment, become key factors in achieving environments with a desired high level of collaboration and inter- and intra-organization business processes alignment. The aim of this chapter is to underline the main issues, trends, and opportunities related to business integration from a technological perspective, analyzing and discussing the most relevant (existing and still under development) business integration reference models, frameworks, standards, technologies, and supporting infrastructures, and to briefly present relevant research projects in the area of business networking. A special emphasis is made on frameworks such as ebXML and RosettaNet, and the importance of papiNet, BPLE4WS, and freebXML is underlined. Challenges regarding self-forming networked organizations are also advanced. © 2007, Idea Group Inc.

Abstract
A fiber sensing system is presented that allows simultaneous measurement of both refractive index and temperature while providing passive referencing to intensity fluctuations. © 2006 OSA/OFS 2006.

Abstract
Applications designed for event driven monitoring represent a challenging class of applications for wireless sensor networks. They are a special kind of monitoring applications, since they usually need law data rates, but also require mechanisms for low latency and asynchronous communication. In this paper we will focus on optimizations at the MAC layer that enable low energy consumption when contention-based protocols are adopted. We present B-MAC+, an enhanced version of a widely adopted MAC protocol, and we show that substantial improvements, in terms of network lifetime, can be reached over the original protocol.

Abstract
All over the world we have been assisting to a significant increase of the telecommunication systems usage. People are faced day after day with strong marketing campaigns seeking their attention to new telecommunication products and services. Telecommunication companies struggle in a high competitive business arena. It seems that their efforts were well done, because customers are strongly adopting the new trends and use (and abuse) systematically communication services in their quotidian. Although fraud situations are rare, they are increasing and they correspond to a large amount of money that telecommunication companies lose every year. In this work, we studied the problem of fraud detection in telecommunication systems, especially the cases of superimposed fraud, providing an anomaly detection technique, supported by a signature schema. Our main goal is to detect deviate behaviors in useful time, giving better basis to fraud analysts to be more accurate in their decisions in the establishment of potential fraud situations.

Abstract
This paper presents the implementation of a real-time adaptive controller to regulate the air temperature, humidity and C02 concentration for a greenhouse located in the north of Portugal. The controller outputs are computed in real-time with the aim of achieving1, adequate set-point tracking, minimization of the actuators efforts and minimization of the energy inputs for heating, cooling and CO2injection. This is done by computing a sequence of future control actions to minimize a cost function over a prediction horizon of one hour. The cost function is proportional to the sum of the squared errors between the predicted and desired greenhouse climates plus the square of the energy inputs computed over the prediction horizon. The prediction climate models employ data from the air temperature, relative humidity and carbon dioxide concentration, inside and outside the greenhouse, solar radiation, wind speed and control actuating signals. In this way the performance of the adaptive controller depends largely on the accuracy of the prediction models. Since the greenhouse-crop system is time-variant, recursive identification techniques were applied to adapt in real-time the models parameters. Several simulations and experiments were realized with the proposed real-time adaptive controller and the results achieved proved to be suitable for this application. Also, the controller performance was compared with the one achieved using common PID, Proportional-Integrative-Derivative, controllers. With the proposed algorithm the set-point tracking and the minimization of actuator effort and energy consumption are significantly improved.

Abstract
Agricultural Engineers and Biologists often need to develop and use functions in order to explain observed events, perform predictions under different scenarios, find relevant characteristics of the data, such as growth rates, etc.. The purpose of curve fitting is to specify an appropriate function and adjust its parameters in a way that it matches, as close as possible, an experimental or historical data set. Fitting functions can be derived by applying regression techniques, interpolation, and spline curves. This paper deals with the development and application of linear and nonlinear regression techniques for fitting functions to agricultural and biological data sets. The application and utility of these fitting techniques are illustrated using experimental sets of data. The examples cover from simple linear curve fitting techniques, which use least squares algorithms, to complex real world problems, which must be described by functions that are nonlinear in the parameters and so needs the use of nonlinear regression techniques. In these illustrative examples, a particular attention is paid to the validation and adequacy of the fitting functions, using graphical inspection and numerical performance criteria's, such as: statistical analysis of the residuals, the R-square, adjusted R-square, the RMSE-Root mean squared errors, confidence bounds for the estimated function parameters and prediction bounds for the fitted functions.