Abstract
In this paper it is presented an educational experiment, that consists of a mechatronic system applied to demonstrate concepts such as prototyping and control. The described mechatronic system is based on a conveyor belt, that was integrated with a manipulator, being physical devices commonly used in the industry. The conveyor Belt was prototyped from scratch, using 3d print technology. Its movement is based on the closed loop control of a DC Motor, based on a PID. The Conveyor Belt was integrated with a Braccio Manipulator from Arduino, using the ZMQ communication library, which is a high-performance asynchronous messaging library. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Abstract
Industry 4.0 confronts companies, in particular SMEs, with various technological, organizational and cultural challenges with great impact on traditional business models. This paradigmatic socio-technical shift, implies the redefinition of the role of people in the organisation, the integration of all organisational decision layers (from the factory floor to the decision support structures) and the digital connection of the entire value chain, including processes, people and machines. However, the lack of qualified resources and the lack of an holistic understanding of industry 4.0 derail SMES’ digital transformation journey. This research work discusses the need for industry 4.0 re-conceptualisation, tailored to SMES’ needs. A lightweight ontology is presented and discussed how it contributes to the organisation and structuring a Community Of Practice, to share knowledge in the context of SMES’ industry 4.0 initiatives. Despite of the discussed use case, the developed artefact might be used to assess SME’s digital readiness. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Computing subgraph frequencies is a fundamental task that lies at the core of several network analysis methodologies, such as network motifs and graphlet-based metrics, which have been widely used to categorize and compare networks from multiple domains. Counting subgraphs is, however, computationally very expensive, and there has been a large body of work on efficient algorithms and strategies to make subgraph counting feasible for larger subgraphs and networks. This survey aims precisely to provide a comprehensive overview of the existing methods for subgraph counting. Our main contribution is a general and structured review of existing algorithms, classifying them on a set of key characteristics, highlighting their main similarities and differences. We identify and describe the main conceptual approaches, giving insight on their advantages and limitations, and we provide pointers to existing implementations. We initially focus on exact sequential algorithms, but we also do a thorough survey on approximate methodologies (with a trade-off between accuracy and execution time) and parallel strategies (that need to deal with an unbalanced search space).

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The negative impact of false information on social networks is rapidly growing. Current research on the topic focused on the detection of fake news in a particular context or event (such as elections) or using data from a short period of time. Therefore, an evaluation of the current proposals in a long-term scenario where the topics discussed may change is lacking. In this work, we deviate from current approaches to the problem and instead focus on a longitudinal evaluation using social network publications spanning an 18-month period. We evaluate different combinations of features and supervised models in a long-term scenario where the training and testing data are ordered chronologically, and thus the robustness and stability of the models can be evaluated through time. We experimented with 3 different scenarios where the models are trained with 15-, 30-, and 60-day data periods. The results show that detection models trained with word-embedding features are the ones that perform better and are less likely to be affected by the change of topics (for example, the rise of COVID-19 conspiracy theories). Furthermore, the additional days of training data also increase the performance of the best feature/model combinations, although not very significantly (around 2%). The results presented in this paper build the foundations towards a more pragmatic approach to the evaluation of fake news detection models in social networks.

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