Abstract
The power and energy sector transition into decentralized and distributed models brings a set of challenges that need to be overcome. Among them is consumer empowerment, which requires secure economic transactions and reliable system operation. Thus, new technologies and procedures such as blockchain have gained prominence, due to their reliability and privacy-preserving capabilities. These characteristics are essential for the proliferation of energy community markets. Therefore, this paper provides an overview of blockchain technology applicability to consumer-centric electricity markets, highlighting existing projects and initiatives. Additionally, key enablers and barriers to blockchain deployment in local electricity markets are discussed, followed by a roadmap for the comprehensive adoption of such technology.

Abstract
The transport sector plays a fundamental role in the European Union economy and its efficiency is fundamental to strengthen the region's environmental and economic performance. Unfortunately, the sector still remains heavily dependent on oil resources and is responsible for a large part of the air pollution. The European Union has been promoting various initiatives towards sustainable transport development by setting targets in the sector such as the ones proposed in the 2011 White Paper on transport. Under this context, this study aims at evaluating the environmental performance of the transport sector in 28 European Union countries, from 2015 to 2018, towards the policy agenda established in the strategic documents. The assessment of the transport environmental performance is made through the aggregation of seven sub-indicators into a composite indicator using a Data Envelopment Analysis technique. A variant of the Benefit of the Doubt model is used to determine the weights to aggregate the sub-indicators. The results obtained indicate that the European Union countries have been improving their transport environmental performance in the last two years of the time span under analysis, i.e., 2017 and 2018. Regarding the inefficient countries, results suggest they should improve the transport sustainability mainly by drastically reducing the greenhouse gas emissions from fossil fuel-based propulsion, increasing the share of freight transport using rail and inland waterways and also the share of transport energy from renewable sources.

Abstract
COVID-19 has brought new challenges to the achievement of the Sustainable Development Goals as proposed by the United Nations in the 2030 Agenda. However, innovative projects developed by governments, private sector, and civil society present themselves as an opportunity to mitigate the effects of COVID-19 on sustainable development. This study uses the Observatory of Public Sector Innovation promoted by the Organization for Economic Co-Operation and Development to explore how innovative projects address the 17 Sustainable Development Goals. The Portuguese context is used to qualitatively characterize this phenomenon. The findings reveal that these projects also offer relevant contributions in areas such as public infrastructure support, health promotion, quality of education, and reduced inequalities. Copyright © 2022, IGI Global.

Abstract
This work introduces a new fuzzy epistemic logic with public announcement with fuzzyness on both transitions and propositions. The interpretation of the connectives is done over the Godel algebra and the interpretation of public announcements in this logic generalises the traditional update one. The core idea is that, the effect of a public announcement is reflected on the transitions degrees of the models. The update takes in account not only the truth degree of the announcement, at a target state, but also the degree of the transitions reaching that state. We prove the soundness of all axioms of the multi-agent epistemic logic with public announcements with respect to this graded semantics. Finally, we introduce the notion of bisimulation and prove the modal invariance property for our logic.

Abstract
[No abstract available]

Abstract
Analyzing the way individuals move is fundamental to understand the dynamics of humanity. Transportation mode plays a significant role in human behavior as it changes how individuals travel, how far, and how often they can move. The identification of transportation modes can be used in many applications and it is a key component of the internet of things (IoT) and the Smart Cities concept as it helps to organize traffic control and transport management. In this paper, we propose the use of ensemble methods to infer the transportation modes using raw GPS data. From latitude, longitude, and timestamp we perform feature engineering in order to obtain more discriminative fields for the classification. We test our features in several machine learning algorithms and among those with the best results we perform feature selection using the Boruta method in order to boost our accuracy results and decrease the amount of data, processing time, and noise in the model. We assess the validity of our approach on a real-world dataset with several different transportation modes and the results show the efficacy of our approach.