Abstract
A pandemia Covid-19 teve um profundo impacto nos processos pedagógicos das universidades de ensino presencial. O confinamento obrigatório da população universitária implicou a transposição do ensino presencial em sala para as aulas realizadas online, levando a um aumento considerável do uso de sistemas de videoconferência, estabelecendo novas (ou reforçando as existentes) comunidades de aprendizagem online. Estas práticas de aprendizagem online tendem a se manter após o fim da pandemia na medida em que proporcionem alternativas complementares de contacto, partilha e colaboração em rede, determinando formas não estruturadas de modelos pedagógicos híbridos de ensino a distância. Neste artigo descrevemos práticas concretas de ensino online implementadas durante a pandemia em duas universidades de ensino presencial, a Universidade de São José em Macau; e a Universidade de São Paulo, Brasil, procedendo à sua análise crítica à luz da práxis de ensino a distância online em voga na Universidade Aberta, Portugal, uma universidade virtual de ensino a distância, tendo em vista a definição de um modelo pedagógico virtual de cariz geral que possa proporcionar princípios e linhas mestras para o planeamento, organização e implementação de oferta educativa de nível universitário administrável online para os tempos pós-pandémicos suportada nas evidências da pesquisa e tendências emergentes. 

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Abstract
This paper aims to undertake a closer examination of the design ecosystem model, considering it has recently emerged to justify and support the implementation of design policies within the systems failure theory. It does so by identifying and analyzing diverse perspectives and some of the gaps in the literature, and to propose adaptations in the model by looking at design capabilities as its substance, and as well to identify avenues for further research. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Abstract
This paper reviewed machine learning algorit hms, particularly deep learning architectures applied to end-of-line testing systems in industrial environment. In industry, data is also produced when any product is being manufactured. All this information registered when manufacturing a specific product can be manipulated and interpreted using Machine Learning algorithms. Therefore, it is possible to draw conclusions from data and infer valuable results that can positively impact the future of the production line. The reviewed papers showed that machine learning algorithms play a crucial role in detecting, isolating, and preventing anomalies, helping operators make decisions, and allowing industries to save resources. © International Journal of Emerging Technology and Advanced Engineering.All right reserved.

Abstract
New challenges arise with the upsurge of a Big Data era. Huge volumes of data, from the most varied natures, gathered from different sources, collected in different timings, often with high associated uncertainty, make the decision-making process a harsher task. Current methods are not ready to deal with characteristics of the new problems. This paper proposes a novel data selection methodology that filters big volumes of data, so that only the most correlated information is used in the decision-making process in each given context. The proposed methodology uses a clustering algorithm, which creates sub-groups of data according to their correlation. These groups are then used to feed a forecasting process that uses the relevant data for each situation, while discarding data that is not expected to contribute to improving the forecasting results. In this way, a faster, less computationally demanding, and effective forecasting is enabled. A case study is presented, considering the application of the proposed methodology to the filtering of electricity market data used by forecasting approaches. Results show that the data selection increases the forecasting effectiveness of forecasting methods, as well as the computational efficiency of the forecasts, by using less yet more adequate data. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.