Abstract
É sabido que a digitalização será o grande desígnio das empresas e a condição essencial para a sua competitividade nos próximos anos. Sabe-se também que, embora a atual pandemia COVID-19 tenha acelerado a necessidade de digitalização em diferentes áreas do negócio, sendo o seu maior impacto notado ao nível das plataformas de e-commerce, a falta de uma visão digital, para as diferentes áreas, poderá deixar as empresas sem competências para atuar de forma competitiva num mercado já global. Assim, neste artigo, adotando uma metodologia Design Science, é proposta uma solução para a obtenção de uma radiografia do nível de Maturidade Digital da indústria transformadora na região do Tâmega e Sousa - Digital Industry Survey. Procura-se desta forma criar conhecimento sobre a realidade desta indústria, que permita apoiar as empresas nos desafios colocados pelo paradigma da Indústria 4.0.

Abstract
Lung cancer is the leading cause of cancer death worldwide. Early low-dose computed tomography (CT) screening can decrease its mortality rate and computer-aided diagnoses systems may make these screenings more accessible. Radiomic features and supervised machine learning have traditionally been employed in these systems. Contrary to supervised methods, unsupervised learning techniques do not require large amounts of annotated data which are labor-intensive to gather and long training times. Therefore, recent approaches have used unsupervised methods, such as clustering, to improve the performance of supervised models. However, an analysis of purely unsupervised methods for malignancy prediction of lung nodules from CT images has not been performed. This work studies nodule malignancy in the LIDC-IDRI image collection of chest CT scans using established radiomic features and unsupervised learning methods based on k-Means, Spectral Clustering, and Gaussian Mixture clustering. All tested methods resulted in clusters of high homogeneity malignancy. Results suggest convex feature distributions and well-separated feature subspaces associated with different diagnoses. Furthermore, diagnosis uncertainty may be explained by common characteristics captured by radiomic features. The k-Means and Gaussian Mixture models are able to generalize to unseen data, achieving a balanced accuracy of 87.23% and 86.96% when inference was tested. These results motivate the usage of unsupervised approaches for malignancy prediction of lung nodules, such as cluster-then-label models. Clinical Relevance - Unsupervised clustering of radiomic features of lung nodules in chest CT scans can differentiate between malignant and benign cases and reflects experts' diagnosis uncertainty

Abstract
This study aims to examine if the pricing strategy "Pay What You Want" can be the best option for the industry of digital products' distribution, when compared with other fixed prices policies. To verify the adequacy of Pay What You Want Pricing strategy, we conducted an online survey using a sample of online consumers, to evaluate their buying intention and the willingness to pay regarding a set of digital products. Results show that, in some instances, the Pay What You Want Pricing strategy yields a greater sales revenue through the reduction of the individual amounts paid, which is counter-balanced by the increasing number of transactions. We conclude that this pricing strategy is as much suitable for companies, as they may potentially increase their sales revenue.

Abstract
Likewise to code, clone-and-own is a common way to create variants of a model, to explore the impact of different features while exploring the design of a software system. Previously, we have introduced Colorful Alloy, an extension of the popular Alloy language and toolkit to support feature-oriented design, where model elements can be annotated with feature expressions and further highlighted with different colors to ease understanding. In this paper we propose a catalog of refactoring laws for Colorful Alloy models, and show how they can be used to iteratively merge cloned Alloy models into a single featureannotated colorful model, where the commonalities and differences between the different clones are easily perceived, and more efficient aggregated analyses can be performed. We then show how these refactorings can be composed in an automated merging strategy that can be used to migrate Alloy clones into a Colorful Alloy SPL in a single step. The paper extends a conference version [1] by formalizing the semantics and type system of the improved Colorful Alloy language, allowing the simplification of some rules and the evaluation of their soundness. Additional rules were added to the catalog, and the evaluation extended. The automated merging strategy is also novel.

Abstract
The development of emerging smart grid technologies has led to more and more penetration of renewable energy resources and electric energy storage in the residential sectors. Besides, owing to the significant evolution of power electronic devices, there is a rapid growth in penetration of DC loads and generations, such as PV and electric vehicles (EVs), into the buildings and homes as a building block of the future smart cities. This is despite the fact that the electricity infrastructure of the conventional buildings is designed based on AC electricity and as a result, there would be a lot of losses due to the frequent power conversion from AC to DC and vice versa. Besides, according to a significant amount of energy consumption in the residential sector, buildings have a prominent role to confront environmental problems and obtain sustainability. In such circumstances, and considering the energy outlook, rethinking the electrification structure of the built environment is necessary. This work is an effort in this regard and looks for a sustainable energy infrastructure for the cyber-physical homes of the future. Three disparate electrification architectures are analyzed. The proposed framework, which is formulated as a mixed-integer linear programming (MILP) problem, not only considers costs associated with investment and operation but also evaluates the reliability of each structure by considering the different ratios of DC loads. Moreover, the optimal size of renewable energy resources and the effect of EV demand response, and different prices of PV and battery are precisely investigated. The efficacy of the proposed approach is evaluated via numerical simulation.

Abstract
This article presents a restoration approach for improving the resilience of electric distribution systems (EDSs) by taking advantage of several operational resources. In the proposed approach, the restoration process combines dynamic network reconfiguration, islanding operation of dispatchable distributed generation units, and the prepositioning and displacement of mobile emergency generation (MEG) units. The benefit of exploring a demand response (DR) program to improve the recoverability of the system is also taken into account. The proposed approach aims to separate the in-service and out-of-service parts of the system while maintaining the radiality of the grid. To assist the distribution system planner, the problem is formulated as a stochastic-scenario-based mixed-integer linear programming model, where uncertainties associated with PV-based generation and demand are captured. The objective function of the problem minimizes the amount of energy load shedding after a fault event as well as PV-based generating curtailment. To validate the proposed approach, adapted 33-bus and 83-bus EDSs are analyzed under different test conditions. Numerical results demonstrate the benefits of coordinating the dynamic network reconfiguration, the prepositioning and displacement of MEG units, and a DR program to improve the restoration process.