Abstract
This paper proposes a methodology to detect early signs of Parkinson's disease (PD) through free-speech in uncontrolled background conditions. The early detection mechanism uses signal and speech processing techniques integrated with machine learning algorithms. Three distinct speech databases containing patients' recordings at different stages of the PD are used for estimation of the parameters during the training and evaluation stages. The results reveal the potential in using Random Forest (RF) or Support Vector Machine (SVM) techniques. Once tuned, these algorithms provide a reliable computational method for estimating the presence of PD with a very high accuracy.

Abstract
Goal: The main goal of this research is to analyse the behaviour of a set of ten lot-sizing methods applied to different application scenarios, within the context of more traditional MRP-based manufacturing environments and on JIT/ Kanbans oriented ones. Design/Methodology/Approach: After an extended literature review, a quantitative research method is used to provide a comparative analysis on the performance of the lot-sizing methods under different simulated application scenarios, with variations in demand and peaks of seasonality. Moreover, a final summary provides the error deviations for lot-sizing methods regarding increases in demand variations and seasonality indexes. Results: The study analyses lot-sizing methods and discusses benefits and risks associated to its use in application scenarios marked by a considerable variation in demand or peaks in seasonality. Limitations of the investigation: As the application scenarios did not explore variations in the ordering and stock holding costs, further analysis including these kinds of variations is encouraged. Practical implications: The findings of this research enable the enhancement of the conscience of industrial practitioners, regarding the selection of best suited lot-sizing methods for being applied on each kind of manufacturing scenario, regarding MRP or JIT/Kanban environments. Originality/Value: Given the diversity of the existing lot-sizing methods, for instance, the heuristic ones, authors can find it quite difficult to select appropriate methods for solving their problems for each kind of application scenario. Therefore, the present study can provide useful knowledge to better support decision making in the lot-sizing domain.

Abstract
The domain of Production Planning and Control, or in a broader sence Production Management has been deserving a special and increasing attention by the companies, which intend to continuously achieve better results through continuous improvement, which also fits in the context of Industry 4.0. Companies tend to implement management systems with the purpose of achieving greater competitiveness and, consequently, greater sustainability in their sector. The selection of the appropriate production management system is a serious problem for the companies. The main objective of this study is to support companies in the correct choice of a Decision Support System. The method used to achieve the proposed objective consists on formulating a model for comparing functionalities and specifications, where selection of criteria were also defined and analyzed. Based on a large Company scenario, the model is applied to three production execution systems: SAP PP (Systems Applications and Products - Production Planning), Prodsmart and GenSYS. © Springer Nature Switzerland AG 2019.

Abstract
The domain of Production Planning and Control, or in a broader sence Production Management has been deserving a special and increasing attention by the companies, which intend to continuously achieve better results through continuous improvement, which also fits in the context of Industry 4.0. Companies tend to implement management systems with the purpose of achieving greater competitiveness and, consequently, greater sustainability in their sector. The selection of the appropriate production management system is a serious problem for the companies. The main objective of this study is to support companies in the correct choice of a Decision Support System. The method used to achieve the proposed objective consists on formulating a model for comparing functionalities and specifications, where selection of criteria were also defined and analyzed. Based on a large Company scenario, the model is applied to three production execution systems: SAP PP (Systems Applications and Products - Production Planning), Prodsmart and GenSYS.

Abstract

Abstract
The increasing use of renewable energy sources and distributed generation brought deep changes in power systems, namely with the operation of competitive electricity markets. With the eminent implementation of micro grids and smart grids, new business models able to cope with the new opportunities are being developed. Virtual Power Players are a new type of player, which allows aggregating a diversity of entities, e.g. generation, storage, electric vehicles, and consumers, to facilitate their participation in the electricity markets and to provide a set of new services promoting generation and consumption efficiency, while improving players' benefits. In order to achieve this objective, it is necessary to define tariff structures that benefit or penalize agents according to their behavior. In this paper a method for determining the tariff structures has been proposed, optimized for different load regimes. Daily dynamic tariff structures were defined and proposed, on an hourly basis, 24 hours day-Ahead from the characterization of the typical load profile, the value of the electricity market price and considering the renewable energy production. © 2018 IEEE.