Abstract
With the advent of cloud platforms and the IoT paradigm, the concept of micro-services has gained even more strength, making crucial the process of selection, manipulation, and deployment. However, this whole process is time-consuming and error pruning. In this paper, we present the design of a framework that allows the chaining of several microservices as a composite service in order to solve a single problem. The framework includes a client that will allow the orchestration f the composite service based on a straightforward API. The framework also includes a gamification engine to engage users not only to use the framework, by contributing with new microservices. We expect to have briefly a functional prototype of the framework so we can prove this concept. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The rapid growth of wind generation in northeast Brazil has led to multiple benefits to many different stakeholders of energy industry, especially because the wind is a renewable resource - an abundant and ubiquitous power source present in almost every state in the northeast region of Brazil. Despite the several benefits of wind power, forecasting the wind speed becomes a challenging task in practice, as it is highly volatile over time, especially when one has to deal with long-term predictions. Therefore, this paper focuses on applying different Machine Learning strategies such as Random Forest, Neural Networks and Gradient Boosting to perform regression on wind data for long periods of time. Three wind farms in the northeast Brazil have been investigated, whose data sets were constructed from the wind farms data collections and the National Institute of Meteorology (INMET). Statistical analyses of the wind data and the optimization of the trained predictors were conducted, as well as several quantitative assessments of the obtained forecast results.

Abstract
This chapter provides implementation of various optimization algorithms to various power system problems that utilize power flow calculations. Determination of the schedule (ON/OFF status and amount of power generated) of generating units within a power system results in great saving for electric utilities. The unit commitment problem can be formulated in order to minimize the total operating cost, satisfying the system, the unit, and several operational constraints. The power transfer limit of overhead transmission lines (OTLs) is an important constraint for power systems’ planning and operation. This constraint plays an essential role in the secure and economic management of power systems. The chapter presents economic dispatch problem by considering GAs and particle swarm optimization (PSO) in complex power system analysis. It uses a hybrid PSO to solve load flow problem while uses artificial bee colony optimization for solving the optimal power flow problem. © 2020 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.

Abstract
This paper focuses on the drivers, curriculum and Project-Based Learning (PBL) learning strategies applied to the Business Process Modelling course, part of the Master in Services Engineering and Management (MESG), while presenting critical reflections on said course. The curriculum unit aims to develop skills that we consider essential in the analysis, design, management and improvement of processes that support the services provided by an organisation to its customers. Since the creation of the course, the main objective has been to motivate students to look into exploratory approaches to address specific challenges. In this sense, the PBL approaches explored have proved to be quite successful. Students are organised into larger teams and asked to come up with an innovative business idea. Then, they ought to carry out a project focused on the analysis and design of the business processes of the organisation/company, as well as specifying the respective supporting technological elements. The project, carried out as a team, is of medium/high complexity and long duration (throughout the semester). Each team is encouraged to use appropriate digital tools to support the collaborative work, namely, to facilitate information sharing, activity coordination, documentation management and communication. In this paper, we focus on the implementation and evaluation of the PBL practice, as well as on the analysis and consideration of the lecturers and students’ experience. We’ve adopted a cooperative and student-centred teaching and learning strategy since the beginning, in order to provide the right conditions to put into effect the skills of "doing" and "learning", without neglecting “knowledge”. Accordingly, we point out the main challenges, the lessons learned and the future views regarding the PBL practice.

Abstract
Background:Amyotrophic lateral sclerosis (ALS) is a fatal progressive motor neuron disease. People with ALS demonstrate various speech problems.Summary:We aim to provide an overview of studies concerning the diagnosis of ALS based on the analysis of voice samples. The main focus is on the feasibility of the use of voice and speech assessment as an effective method to diagnose the disease, either in clinical or pre-clinical conditions, and to monitor the disease progression. Specifically, we aim to examine current knowledge on: (a) voice parameters and the data models that can, most effectively, provide robust results; (b) the feasibility of a semi-automatic or automatic diagnosis and outcomes; and (c) the factors that can improve or restrict the use of such systems in a real-world context.Key Messages:The studies already carried out on the possibility of diagnosis of ALS using the voice signal are still sparse but all point to the importance, feasibility and simplicity of this approach. Most cohorts are small which limits the statistical relevance and makes it difficult to infer broader conclusions. The set of features used, although diverse, is quite circumscribed. ALS is difficult to diagnose early because it may mimic several other neurological diseases. Promising results were found for the automatic detection of ALS from speech samples and this can be a feasible process even in pre-symptomatic stages. Improved guidelines must be set in order to establish a robust decision model.

Abstract