Abstract
Increasingly, consumer demand of fashionable products is arising as significant challenge for company managers. In order to respond to this demand, companies are asked to supply small series of innovative and fashionable goods of high quality, affordable price and eco-compatibility in short periods of time and with high service levels. As a result of these rapidly evolving challenges, companies are forming collaborative networks in order to design, develop, produce and distribute such products and services in a collaboratively way. These highly integrated and dynamic supply networks depend intensively in new set of tools, methods and related services in which rely the collaborative networking operation. This paper presents a specific case study implementation of these collaborative supporting services, through the realization of a collaborative portal.

Abstract
This paper addresses the problem of joint estimation of the state and parameters for a deterministic continuous time system, with discrete time observations, in which the parameter vector is constant but its value is not known, being a random variable with a known distribution. Along time, the uncertainty in the parameter induces uncertainty in the plant state. The joint probability density function (pdf) satisfies the Liouville partial differential equation that is a limit case of the Fokker-Planck equation for vanishing diffusion. The continuous-discrete filter proposed operates as follows: Between two consecutive output sampling time instants, the pdf is propagated by solving the Liouville equation for an augmented state and is then corrected by using the last observation and Bayes law. An application to state estimation of the neuromuscular blockade of patients subject to general anesthesia, where parameter uncertainty is due to inter-patient variability, is described.

Abstract
This paper presents numerical analysis of two alternative optimization approaches intended to support an EV aggregation agent in optimizing buying bids for the day-ahead electricity market. A study with market data from the Iberian electricity market is used for comparison and validation of the forecasting and optimization performance of the global and divided optimization approaches. The results show that evaluating the forecast quality separately from its impact in the optimization results is misleading, because a forecast with a low error might result in a higher cost than a forecast with higher error. Both bidding approaches were also compared with an inflexible EV load approach where the EV are not controlled by an aggregator and start charging when they plug-in. Results show that optimized bids allow a considerable cost reduction when compared to an inflexible load approach, and the computational performance of the algorithms satisfies the requirements for operational use by a future real EV aggregation agent.

Abstract
This paper is concerned with the use of simulation as a decision support tool in maintenance systems, specifically in MFS (Maintenance Float Systems). For this purpose and due to its high complexity, in this paper the authors explore and present a way to develop a flexible MFS model, for any number of machines in the workstation, spare machines and maintenance crews, using Arena simulation language. Also in this paper, some of the most common performance measures are identified, calculated and analysed. Nevertheless this paper would concentrate on the two most important performance measures in maintenance systems: system availability and maintenance total cost. As far as these two indicators are concerned, it was then quite clear that they assumed different behaviour patterns, especially when using extreme values for periodic overhauls rates. In this respect, system availability proved to be a more sensitive parameter.

Abstract
This paper addresses the developing of Learning-Assisted Intelligent Scheduling Systems that uses active learning by accumulation and interpretation of scheduling experience or even by observation of expert's decisions. The design of intelligent systems (IS) that learn with experts is a very hard and challenging domain because current systems are becoming more and more complex and subject to rapid changes. The model for the proposed system will be presented.

Abstract
The Arterial Pressure Waveform (APW) can provide essential information about arterial wall integrity and arterial stiffness. Most of APW analysis frameworks individually process each hemodynamic parameter and do not evaluate inter-dependencies in the overall pulse morphology. The key contribution of this work is the use of machine learning algorithms to deal with vectorized features extracted from APW. With this purpose, we follow a five-step evaluation methodology: (1) a custom-designed, non-invasive, electromechanical device was used in the data collection from 50 subjects; (2) the acquired position and amplitude of onset, Systolic Peak (SP), Point of Inflection (Pi) and Dicrotic Wave (DW) were used for the computation of some morphological attributes; (3) pre-processing work on the datasets was performed in order to reduce the number of input features and increase the model accuracy by selecting the most relevant ones; (4) classification of the dataset was carried out using four different machine learning algorithms: Random Forest, BayesNet (probabilistic), J48 (decision tree) and RIPPER (rule-based induction); and (5) we evaluate the trained models, using the majority-voting system, comparatively to the respective calculated Augmentation Index (AIx). Classification algorithms have been proved to be efficient, in particular Random Forest has shown good accuracy (96.95%) and high area under the curve (AUC) of a Receiver Operating Characteristic (ROC) curve (0.961). Finally, during validation tests, a correlation between high risk labels, retrieved from the multi-parametric approach, and positive AIx values was verified. This approach gives allowance for designing new hemodynamic morphology vectors and techniques for multiple APW analysis, thus improving the arterial pulse understanding, especially when compared to traditional single-parameter analysis, where the failure in one parameter measurement component, such as Pi, can jeopardize the whole evaluation. © 2013 by the authors; licensee MDPI, Basel, Switzerland.