Abstract
Even while the scientific community has shown great interest in the analysis of meta-heuristics, the analysis of their parameterization has received little attention. It is the parameterization that will adapt a meta-heuristic to a problem, but it is still performed, mostly, empirically. There are multiple parameterization techniques; however, they are time-consuming, requiring considerable computational effort and they do not take advantage of the meta-heuristics that they parameterize. In order to approach the parameterization of meta-heuristics, in this paper, a self-parameterization framework is proposed. It will automatize the parameterization as an optimization problem, precluding the user from spending too much time on parameterization. The model will automate the parameterization through two meta-heuristics: A meta-heuristic of the solution space and one of the parameter space. To analyze the performance of the framework, a self-parameterization prototype was implemented. The prototype was compared and analyzed in a SP (scheduling problem) and in the TSP (traveling salesman problem). In the SP, the prototype found better solutions than those of the manually parameterized meta-heuristics, although the differences were not statistically significant. In the TSP, the self-parameterization prototype was more effective than the manually parameterized meta-heuristics, this time with statistically significant differences.

Abstract
Disasters, both natural and man-made, are extreme and complex events with consequences that translate into a loss of life and/or destruction of properties. The advances in IT and Big Data analysis represent an opportunity for the development of resilient environments once the application of analytical methods allows extracting information from a significant amount of data, optimizing the decision-making processes. This research aims to apply the CRISP-DM methodology to extract information about incidents that occurred in the city of Lisbon with emphasis on occurrences that affected buildings, constituting a tool to assist in the management of the city. Through this research, it was verified that there are temporal and spatial patterns of occurrences that affected the city of Lisbon, with some types of occurrences having a higher incidence in certain periods of the year, such as floods and collapses that occur when there are high levels of precipitation. On the other hand, it was verified that the downtown area of the city is the area most affected by occurrences. Finally, machine learning models were applied to the data and the predictive model Random Forest obtained the best result with an accuracy of 58%.

Abstract
This reference distills information about emerging technologies and applications for smart city design and sustainable urban planning. Chapters present technology use-cases that have radical novelty and high scalability with a prominent impact on community living standards. These technologies prepare urban and rural dwellings for the transformation to the smart world.Applications and techniques highlighted in the book use a combination of artificial intelligence and IoT technologies in areas like transportation, energy, healthcare, education, governance, and manufacturing, to name a few.The book serves as a learning resource for smart city design and sustainable infrastructure planning. Scholars and professionals who are interested in understanding ways for transforming communities into smart communities can also benefit from the cases presented in the book.

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Abstract
This paper proposes a decision support model to define electricity consumers' remuneration structures when providing consumption flexibility, optimized for different load regimes. The proposed model addresses the remuneration of consumers when participating in demand response programs, benefiting or penalizing those who adjust their consumption when needed. The model defines dynamic remuneration values with different natures for the aggregator (e.g. flexibility aggregator or curtailment service provider) and for the consumer. The preferences and perspective of both are considered, by incorporating variables that represent the energy price, the energy production and the flexibility of consumers. The validation is performed using real data from the Iberian market, and results enable to conclude that the proposed model adapts the remuneration values in a way that it is increased according to the consumers' elastic, while being reduced when the generation is higher. Consequently, the model boosts the active consumer participation when flexibility is required, while reaching a solution that represents an acceptable g tradeoff between the aggregators and the consumers. (C) 2022 The Authors. Published by Elsevier Ltd.