Abstract
In recent years, cognitive robots have started to find their way into manufacturing halls. However, the full potential of these robots can only be exploited through (a) an integration of the robots with the Manufacturing Execution System (MES), (b) a new and simpler way of programming based on robot skills, automated task planning, and knowledge modeling, and (c) enabling the robots to function in a shared human/robot workspace with the ability to handle unexpected situations. The STAMINA project has built a robotic system that meets these objectives for an automotive kitting application, which has also been tested, validated, and demonstrated in a relevant environment (TRL6). This paper describes the STAMINA robot system and the evaluation of this system on a series of realistic kitting tasks. The structure of the system, evaluation methodology, and experimental results, are presented along with the insights and experiences gained from this work.

Abstract
In this paper, we study the concept of Evolutionarily Stable Strategies (ESSs) for symmetric games with n >= 3 players. The main properties of these games and strategies are analyzed and several examples are provided. We relate the concept of ESS with previous literature and provide a proof of finiteness of ESS in the context of symmetric games with n >= 3 players. We show that unlike the case of n = 2, when there are more than two populations an ESS does not have a uniform invasion barrier, or equivalently, it is not equivalent to the strategy performing better against all strategies in a neighborhood. We also construct the extended replicator dynamics for these games and we study an application to a model of strategic planning of investment.

Abstract
Promotional tools such as cross-market discounts have been increasingly used as a means to increase customer satisfaction and sales. This paper aims to assess whether the implementation of a cross-market discount campaign by a retailing company encouraged customers to increase their purchases level. It contributes to the literature by using neural networks to detect novelties in a real context involving cross-market discounts. Besides the computation of point predictions, the methodology proposed involves the estimation of neural networks prediction intervals. Sales predictions are compared with the observed values in order to detect significant changes in customers' spending. The use of neural networks is validated through the comparison with the forecasting estimates of support vector regression, regression trees, and linear regression. The results reveal that the promotional campaign under analysis did not significantly impact the sales of the rewarded customers.

Abstract
Disruptive requirements that currently drive the so-called Industry 4.0 (I4.0) are increasingly present in today's industries, where factories are forced to innovate in search of improvement in the quality of manufacturing of products aligned with the reductions of: manufacturing time, environmental and cost impacts with the manufacturing process. For this, an Information Systems (IS) architecture is proposed to reduce the negative impacts on an industrial operation caused by manual configuration failures in manufacturing systems, machines that are worn out in the production process and unstable integrations between industrial subsystems. The suggested SI model uses the Viable Systems Model adapted to Maintenance 4.0 technologies (Cyber-physical Systems (CPS), Manufacturing Execution Systems (MES), Data Mining and Digital Manufacturing concepts/technologies) with the goal to create an automatic purchase flow to replace parts by mitigating impending failures in industrial equipment through data mining and predictive analysis.

Abstract
Surveillance has been defined as the continual scrutiny of all aspects in emerging and the spread of a disease that is pertinent to effective control, involving a systematic collection, analysis, interpretation, and dissemination of health data. Given their fragmentation several problems inherent to data must be recognized. This paper aims to provide an overview of European Public Health Surveillance Systems emphasizing their structure and main challenges. The HIV-AIDS surveillance is overview as a particular case. The most common issues are unrepresentativeness, changes in the implementation through time, inconsistent use of case definitions, miss diagnoses, miss or fail to report a case, reporting delay, and errors during completion of the form or data entry. The HIV - AIDS surveillance is one of the most complex mainly due to the special epidemiology of the disease surrounding the transmission modes and the lack of treatment and all the socio-ecological framework involved. © Springer Nature Switzerland AG 2019.

Abstract
Preferential sampling in time occurs when there is stochastic dependence between the process being modeled and the times of the observations. Examples occur in fisheries if the data are observed when the resource is available, in sensoring when sensors keep only some records in order to save memory and in clinical studies, when a worse clinical condition leads to more frequent observations of the patient. In all such situations the observation times are informative on the underlying process. To make inference in time series observed under Preferential Sampling we propose, in this work, a numerical method based on a Laplace approach to optimize the likelihood and to approximate the underlying process we adopt a technique based on stochastic partial differential equation. Numerical studies with simulated and real data sets are performed to illustrate the benefits of the proposed approach. © 2019 SEIO