Abstract
Motivated by the need to reason about hybrid systems, we study limits in categories of coalgebras whose underlying functor is a Vietoris polynomial one - intuitively, the topological analogue of a Kripke polynomial functor. Among other results, we prove that every Vietoris polynomial functor admits a final coalgebra if it respects certain conditions concerning separation axioms and compactness. When the functor is restricted to some of the categories induced by these conditions, the resulting categories of coalgebras are even complete. As a practical application, we use these developments in the specification and analysis of non-deterministic hybrid systems, in particular to obtain suitable notions of stability and behaviour.

Abstract
In face of the continuously growing process of the fourth industrial revolution, the industries are forced to innovate their industrial manufacturing process to remain competitive and active in the market, perfecting the manufacturing process through new interconnected and autonomous technologies. Since time reduction, quality enhancement and cost reduction for manufacturing of industrial products are the major catalysts of a successful company for the current Era called Maintenance 4.0, this paper proposes to illustrate an Information Systems Architecture where, using the Viable Systems Model, it is possible to perform automatic adjustments in the subsystems related to Cyber-Physical Systems and Manufacturing Execution Systems (MES) within the Digital Manufacturing model and, in addition, to mitigate machine failures through predictive analyzes of massive volumes of data using algorithms with intelligent functions and Data Mining (DM) in order to automatically stabilize the entire system chain quickly and efficiently. © 2019 IEEE.

Abstract

Abstract
This paper presents the application of collaborative reinforcement learning models to enable the distributed learning of energy contracts negotiation strategies. The learning model combines the learning process on the best negotiation strategies to apply against each opponent, in each context, from multiple learning sources. The diverse learning sources are the learning processes of several agents, which learn the same problem under different perspectives. By combining the different independent learning processes, it is possible to gather the diverse knowledge and reach a final decision on the most suitable negotiation strategy to be applied. The reinforcement learning process is based on the application of the Q-Learning algorithm; and the continuous combination of the different learning results applies and compares several collaborative learning algorithms, namely BEST-Q, Average (AVE)-Q; Particle Swarm Optimization (PSO)-Q, and Weighted Strategy Sharing (WSS)-Q. Results show that the collaborative learning process enables players’ to correctly identify the negotiation strategy to apply in each moment, context and against each opponent. © Springer Nature Switzerland AG 2019.

Abstract
Nowadays, companies are facing a constant need to develop and increase coordination between operational functions to respond rapidly and accurately to customer requests. Linked with this need, an increasing number of practitioners are resorting to an established and integrated business management methodology, the Sales and Operations Planning (S&OP). The concept of S&OP has gained increased recognition over the years by several authors and companies. This project describes the S&OP implementation in Sogrape Vinhos (wines) S.A., a Portuguese wine producer and distributer. The company was confronted with low accuracy in the establishing the forecast demand plans, especially on a long-term horizon. In order to increase the demand plans accuracy, the company started a S&OP implementation program. This paper describes the company's current planning process, explains the S&OP's implementation model presenting the selected parameters adequate to the company's context, and finally, evaluate the expected outcomes of this project. Preliminary results from the S&OP implementation project at Sogrape indicate significant savings at the operational level and greater effectiveness in developing the company's demand plans. (C) 2019 The Authors. Published by Elsevier Ltd.

Abstract
Accounting for the increasing uncertainties related to forecast of renewables is becoming an essential requirement while assessing the security of future power system scenarios. Project iTesla in the Seventh Framework Program (FP7) of the European Union (EU) tackles these needs and reaches several major objectives, including the development of a security platform architecture. In particular, the platform implements a stochastic dependence model to simulate a reasonable cloud of plausible 'future' states - due to renewable forecast - around the expected state, and evaluates the security on relevant states after sampling the cloud of uncertainty. The paper focuses on the proposed model for the uncertainty and its exploitation in power system security assessment process and it reports the relevant validation results. Copyright © 2019 Inderscience Enterprises Ltd.