Abstract
Manufacturing scheduling is a complex combinotorial problem, particularly in distributed and dynamic environments. This paper presents a holonic approach to manufacturing scheduling, which in opposite to traditional approaches, distributes the scheduling functions over several entities, combining their calculation power and local optimization. In this scheduling and control approach, the scheduling mechanism evolves dynamically to combine optimized scheduling, achieved by central entities, and distributed scheduling, improving its responsiveness and robustness.

Abstract
The bipolar Watson distribution is frequently used for modeling axial data. We extend the one-way analysis of variance based on this distribution to a two-way layout. We illustrate the method with directional data in three dimensions.

Abstract
The Watson distribution defined on the hypersphere is one of the most used distributions for modelling axial data. In this paper, we consider the discriminant analysis for axial data assumed to come from a mixture of Watson distributions defined on the hypersphere. We develop an optimal classification rule, which enables us to assign a new observation into one of several Watson subpopulations defined on the hypersphere. As the probabilities of misclassification cannot be calculated in closed form, we report on a simulation study to estimate, in some cases, the probabilities of misclassification and a distance between the two Watson subpopulations defined on the hypersphere. An illustration of our approach is provided using data defined on the sphere given in the literature.

Abstract
The ability to describe business processes as executable models has always been one of the fundamental premises of workflow management. Yet, the tacit nature of human knowledge is often an obstacle to eliciting accurate process models. On the other hand, the result of process modeling is a static plan of action, which is difficult to adapt to changing procedures or to different business goals. In this article, we attempt to address these problems by approaching workflow management with a combination of learning and planning techniques. Assuming that processes cannot be fully described at buildtime, we make use of learning techniques, namely Inductive Logic Programming (ILP), in order to discover workflow activities and to describe them as planning operators. These operators will be subsequently fed to a partial-order planner in order to find the process model as a planning solution. The continuous interplay between learning, planning and execution aims at arriving at a feasible plan by successive refinement of the operators. The approach is illustrated in two simple scenarios. Following a discussion of related work, the paper concludes by presenting the main challenges that remain to be solved.

Abstract
In the last decades significant changes in the manufacturing environment have been noticed: moving from a local economy towards a global economy, with markets asking for products with higher quality at lower costs, highly customised and with short life cycle. In these circumstances, the challenge is to develop manufacturing control systems with intelligence capabilities, fast adaptation to the environment changes and more robustness against the occurrence of disturbances. This paper presents an agile and adaptive manufacturing control architecture that addresses the need for the fast reaction to disturbances at the shop floor level, increasing the agility and flexibility of the enterprise, when it works in volatile environments. The proposed architecture introduces an adaptive control that balances dynamically between a more centralised structure and a more decentralised one, allowing combining the global production optimisation with agile reaction to unexpected disturbances.

Abstract
A map can be a major advantage when exploring unfamiliar environments. With the proliferation of mobile devices, such as PDAs and even mobile phones, the tourist industry is currently exploring the potential of new presentation strategies that will maximize the promotional appeal of tourism in their region. Mobile devices are capable of guiding a tourist when he/she is exploring a city. These mobile devices offer the potential for providing relevant 3D information to enable tourists to locate themselves within the city, rapidly navigate around the unfamiliar environment and explore it interactively. However, the computational resources of current mobile technology prevents the display of full complex 3D content in real-time, and thus selective rendering techniques must be adopted to ensure the viewer is provided with the perceptually most important information at interactive rates. This paper presents a series of experiments which help to identify key features of a scene for users to orientate themselves in that environment. Knowledge of these salient key features enable them to be provided to a user at a high quality while the remainder of the scene can be rendered in a much lower quality, saving significant bandwidth and computing power, without the user being perceptually aware of this difference in quality within the image.