Abstract
The Cyber-Physical System (CPS) paradigm promotes the decentralization and distribution of the logic control as well as the integration of cyber and physical counterparts. In parallel, self-organization allows the dynamic and automatic system re-configuration responding to condition and environment changes. Modeling and simulation assume a crucial importance in the design of such complex, distributed, and self-organized systems, in the way that the detected and debugged errors may he corrected before the deployment into the real system, as well different strategies can be tested and evaluated. Agent-based modeling tools are computational frameworks able to analyze, experiment and compare systems populated by cooperative agents, supporting the fast prototyping of agent-based solutions exhibiting self-* properties. In this paper, the NetLogo tool was used to model and simulate the agent-based control layer of a small scale CPS, which control uses self-organization principles.

Abstract
The world is moving towards to the fourth industrial revolution, usually linked with the Industrie 4.0 initiative, enables the digitization of manufacturing factories by using Cyber-Physical Systems and emergent technologies like Internet of Things and Internet of Services. The seamless reconfiguration of these complex industrial cyber-physical systems is an important challenge for the complete implementation of this revolution, being necessary to re-think the way such mechanisms can be designed and engineered. This paper presents an agent based reconfiguration system for the dynamic and seamless reconfiguration of a physically-reconfigurable modular micro-flow production system in the area of manufacturing of aerospace engine components.

Abstract
Industrial agent technologies have been integrated in key elements coupling industrial systems and software logic, which is an important issue in the design of cyber-physical systems. Although several efforts have been tried out over the last decades to integrate software agents with physical hardware devices, and some commonalities can be observed among the existing practices, there is no uniform way overall. This work presents an empirical survey of existing practices in three application area, namely factory automation, power & energy systems and building automation. It identifies pertaining common issues and discusses how they integrate low level automation functions by utilizing industrial agents. The surveyed practices reveal high diversity, customized traditional integration focusing mostly on I/O functions, without security, and an overall approach that is mostly coupled rather than embedded.

Abstract
The digitalization advent is characterized by the increasing availability of huge amounts of data that allows to generate new knowledge to support decision-making, problem solving and process optimization. The successful implementation of this digital transformation strongly depends on the skills and competences that professionals can have in the different dimensions of the multidisciplinary vision associated to the data scientist profile. This paper describes a study, conducted under the scope of the Da.Re Erasmus+ project, that surveys the existing trainings paths dedicated to the academic education of data scientists and the required hard and soft skills of the job offers for these professionals. The survey covers three countries of the project consortium, namely Portugal, Italy and UK.

Abstract
Smart devices and Internet of Things (IoT) technologies are becoming each day more common. At the same time, besides the exponentially increasing demand to analyze the produced data, there is an evolving trend to perform the data analysis closer to the data sources, particularly at the Fog and Edge levels. In this sense, the development of testbeds that can, e.g., simulate smart devices in IoT environments, are important to explore and develop the technologies to enable the complete realization of such IoT concepts. This paper describes the digitization of an electric motor, through the incorporation of sensing and an analytical computational environment, towards the development of a testbed for IoT and Big Data technologies. The smart electric motor testbed provides real-time data streams, enabling a continuous monitoring of its operation along all the device life-cycle through advanced data analytics. Furthermore, the paper discusses how specific data analytics features fit the different IoT layers, while preliminary experiments demonstrate the testbed potentials.

Abstract
Manufacturing companies generate huge amounts of relevant data that, potentially, can help the decision makers to monitor and supervise the health status of the shop-floor. Many problems are present that prevent this monitoring, namely the identification of which data should be collected, how to execute this collection or how this data should be analysed and displayed to the user. This paper deals with the last issue, where a dynamic and responsive User Interface is developed enabling the decision maker, having an in-the-mesh intervention, to constantly monitor relevant Key-Performance Indicators (KPI). The paper describes the development of such tool, considering a web based approach under a platform agnostic perspective. The user interface presents an dynamic view over the aggregated KPI, where data is annotated with relevant warning information.