Abstract
The impact of climate on wine production (WP) temporal cycles in Douro (DR) and Vinhos Verdes (VVR) wine regions for a period of about 80 years, characterized by strong technological trend and climate variability, was modelled. The cyclical properties of WP, and which cycles are determined by spring temperature (ST) and soil water during summer (SW), were identified. It was achieved by applying a time-frequency approach, which is based on Kalman filter in the time domain. The time-varying autoregressive model can explain more than 67% (DR) and 95% (VVR) of the WP' variability and the integration of the ST and mainly SW increase the models' reliability. The results were then transferred into the frequency domain, and can show that WP in both regions is characterized by two cycles close to 5-6 and 2.5 years around the long run trend. The ST and SW showed great capacity to explain the cyclicality of WP in the studied regions being the coherence temporarily much more stable in VVR than in the DR, where a shift of the relative importance away from ST to SW can be recognized. This could be an indicator of lower impact of the foreseen hot and dry climate scenarios on WP in the regions with a maritime climate, such as the VVR, compared with hot and dry wine regions. Despite the marked differences in the two studied regions on ecological, viticulture practices and technological trend, the modelling approach based on time-frequency proved to be an efficient tool to infer the impact of climate on the dynamics of cyclical properties of regional WP, foreseeing its generalized use in other regions. This modelling approach can be an important tool for planning in the wine industry as well as for mitigation strategies facing the scenarios that combine technological progress and climate change.

Abstract
The practice is the crux of learning to program. Automated assessment plays a key role in enabling timely feedback without access to teachers but alone is insufficient to engage students and maximize the outcome of their practice. Graphical feedback and game-thinking promote positive effects on students' motivation as shown by some serious programming games, but those games are complex to create and adapt. This paper presents Asura, an environment for assessment of game-based coding challenges, built on a specialized framework, in which students are invited to develop a software agent (SA) to play it. During the coding phase, students can take advantage of the graphical feedback to complete the proposed task. Some challenges also encourage students to think of a SA that plays in a setting with interaction among SAs. In such a case, the environment supports the creation and visualization of tournaments among submitted agents. Furthermore, the validation of this environment from the learners' perspective is also described. 2012 ACM Subject Classification Applied computing ! Interactive learning environments; Applied computing ! E-learning.

Abstract
The expansion and development of the electricity distribution grid is a complex multicriteria decision problem. The planning definition should take into consideration the investment benefits on the security of supply, quality of service, losses, as well as in other network features. Given the variety of assets and their context-dependent effects, estimating their global impact is very challenging. An additional difficulty is the combination of different types of benefits into a simple and clear portrayal of the planning alternatives. This paper proposes a methodology to estimate the benefits of distribution investments, in terms of five features: security of supply, quality of service, network losses, operational efficiency and new services. The approach is based on the adoption of objective and measurable indicators for each feature. The approach was tested with real data of Portuguese distribution grids and the results support the adopted approach and are being used as a decision-aid tool for grid planning. © 2020 IEEE.

Abstract

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.