Abstract
This article presents an analysis of conventional single-phase cascaded H-bridge multilevel inverters composed of two and three cells. The dc-link can be a dc source or a floating capacitor. Two methods are given to regulate the floating capacitor voltage. In this way, according to the capacitor voltage balancing method used, operating regions and power distribution on the floating capacitor are presented for different dc-link voltage ratios and several ranges of modulation index and load power factor. A mathematical analysis is done in order to demonstrate the influence of the modulation index and load power factor over the power distribution. Finally, simulation and experimental results are provided to validate the theoretical considerations.

Abstract
This paper aims to contribute to the lack of design knowledge on digital platforms (DPs), by studying the new and specific context of DPs managed by industrial business associations (IBAs) to improve the inter- nationalisation of small and medium enterprises (SMEs). A specific objective is to elicit detailed digital plat- form ?s requirements and features for this particular organisational context. A design science research (DSR) approach is adopted to develop design propositions (the artifact), following the context -intervention -me- chanism -outcome logic (CIMO-logic). The design propositions are derived for DPs that can support different types of generative mechanisms of social interaction: information sharing, collaboration, and collective action. The design propositions are obtained by balancing empirical knowledge based on interviews performed with IBAs and SMEs in Portugal and in the UK, with theoretical knowledge from the literature of information systems, DPs and collaborative networks (CNs). The utility of the design propositions is further evaluated by experts and IBAs. The findings are proved to be relevant for practice, mainly for IBAs, SMEs, and digital platform designers to develop more effective collaborative DPs and sociotechnical systems, supporting CNs and the internationalisa- tion needs of SMEs. The knowledge generated in this study brings new design knowledge on DPs, contributing with design propositions translated into tangible and concrete requirements and capabilities, situated in a specific context and empirical setting.

Abstract
The paper introduces Gamified Education Interoperability Language (GEdIL), designed as a means to represent the set of gamification concepts and rules applied to courses and exercises separately from their actual educational content. This way, GEdIL allows not only for an easy yet effective specification of gamification schemes for educational purposes, but also sharing them among instructors and reusing in various courses. GEdIL is published as an open format, independent from any commercial vendor, and supported with dedicated open-source software.

Abstract
Agricultural robotics is nowadays a complex, challenging, and relevant research topic for the sustainability of our society. Some agricultural environments present harsh conditions to robotics operability. In the case of steep-slope vineyards, there are several robotic challenges: terrain irregularities, characteristics of illumination, and inaccuracy/unavailability of the Global Navigation Satellite System. Under these conditions, robotics navigation, mapping, and localization become a challenging task. Performing these tasks with safety and accuracy, a reliable and advanced Navigation stack for robots working in a steep slope vineyard is required. This paper presents the integration of several robotic components, path planning aware of robot centre of gravity and terrain slope, occupation grid map extraction from satellite images, a localization and mapping procedure based on high-level visual features reliable under GNSS signals blockage/missing, for steep-slope robots. © 2021, Springer Nature Switzerland AG.

Abstract
Collaborative Filtering (CF) has become the standard approach to solve recommendation systems problems. Collaborative Filtering algorithms try to make predictions about interests of a user by collecting the personal interests from multiple users. There are multiple CF algorithms, each one of them with its own biases. It is the Machine Learning practitioner that has to choose the best algorithm for each task beforehand. In Recommender Systems, different algorithms have different performance for different users within the same dataset. Meta Learning has been used to choose the best algorithm for a given problem. Meta Learning is usually applied to select algorithms for a whole dataset. Adapting it to select the to the algorithm for a single user in a RS involves several challenges. The most important is the design of the metafeatures which, in typical meta learning, characterize datasets while here, they must characterize a single user. This work presents a new meta-learning based framework named µ-cf2vec to select the best algorithm for each user. We propose using Representation Learning techniques to extract the metafeatures. Representation Learning tries to extract representations that can be reused in other learning tasks. In this work we also implement the framework using different RL techniques to evaluate which one can be more useful to solve this task. In the meta level, the meta learning model will use the metafeatures to extract knowledge that will be used to predict the best algorithm for each user. We evaluated an implementation of this framework using MovieLens 20M dataset. Our implementation achieved consistent gains in the meta level, however, in the base level we only achieved marginal gains. © 2020 IEEE.

Abstract
The growing digitalisation, grid complexity and the number of digitally connected devices that communicate with systems in the distribution grid are enabling the continuous development of automation and intelligence, acquisition of data from sensors, meters and devices for monitoring and managing the distribution network, to achieve an enhanced, preventive, resilient and flexible network operation philosophy. This study presents a set of use cases towards the demonstration of the benefits of implementing a platform that collects, aggregates and facilitates horizontal integration and data correlation from various sources, enabling these use cases across the distribution grid. The adequacy analysis of current distribution network architecture considered derived requirements on the characterisation of its evolution taking advantage of key digital technologies, towards the implementation of distributed control and management strategies. It is also presented a benefit analysis of implementing a centralised common data and service platform, i.e. an internet of things (IoT) platform, regarding new functionalities and applications.