Abstract
This paper describes an approach towards workflow management based on the combination of learning and planning. Assuming that processes cannot be fully described at build-time, the approach makes use of learning techniques, namely Inductive Logic Programming (ILP), in order to discover workflow activities 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. The paper concludes by relating the proposed approach with previous developments in this area.

Abstract
This paper proposes a new method to manage the use of ontologies in the context of Virtual Organisations Breeding Environments (VBEs). The research focuses on the dissolution phase of a Virtual Enterprise (VE), where ontology decomposition techniques are used to enrich the ontology library. First, an overview of the process used and the Ontology Library System (OLS) adopted are described. Then, the ontologies' ranking and classification method are described, explaining a set of metrics inspired by social network approaches. Finally, the composition method used to construct a global ontology and the decomposition algorithm implemented to segment an ontology are presented. Copyright © 2010 Inderscience Enterprises Ltd.

Abstract
This paper proposes a new method for managing the use of ontologies in the context of a Virtual Breeding Environment. This research work focus is on the dissolution phase of a Virtual Enterprise or Collaborative Network, where ontology segmentation technique are user to enrich the VBE's ontology libray. Firstly, an overview of the process of ontology composition and decomposition is given and the ontology library system adopted described. Then, the ontologies' ranking and classification method is described, explaining a set of metrics inspired in social network approaches. Finally, the results of preliminary tests are discussed.

Abstract
Quality inspection is a critical step in ensuring the quality and efficiency of textile production processes. With the increasing complexity and scale of modern textile manufacturing systems, the need for accurate and efficient quality inspection and defect detection techniques has become paramount. This paper compares supervised and unsupervised Machine Learning techniques for defect detection in the context of industrial textile production, in terms of their respective advantages and disadvantages, and their implementation and computational costs. We explore the use of an autoencoder for the detection of defects in textiles. The goal of this preliminary work is to find out if unsupervised methods can successfully train models with good performance without the need for defect labelled data. © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)