Abstract
In recent years, digital technologies have been used to support discussions about the city and also to involve citizens in participatory public processes. However, despite the widespread use of social media platforms, old issues related to engagement and participation still persist in digital initiatives. The main goal of this study is to carry out an empirical evaluation of a collective intelligence model that combines crowdsourcing and social storytelling to support discussions about the city from a bottom-up perspective. Within a design science research approach we designed a participatory action study that was carried out through a workshop with students and professionals from different areas, such as architecture, urban design and information technology. As a result, we were able to assess whether the collective intelligence model was acceptable to the participants by investigating whether the behavioral assumptions were valid and thus outlining some contributions to the field of urban informatics.

Abstract
In hospital and after ICU discharge deaths are usual, given the severity of the condition under which many of them are admitted to these wings. Because of this, there is an urge to identify and follow these cases closely. Furthermore, as ICU data is usually composed of variables measured in varying time intervals, there is a need for a method that can capture causal relationships in this type of data. To solve this problem, we propose ItsPC, a causal Bayesian network that can model irregular multivariate time-series data. The preliminary results show that ItsPC creates smaller and more concise networks while maintaining the temporal properties. Moreover, its irregular approach to time-series can capture more relationships with the target than the Dynamic Bayesian Networks.

Abstract
This paper explores the relationships among variables and determines the influences of dimensions (i.e., municipal satisfaction, organizational performance, perceived quality, contestations and complaints of the municipal executive) on the notoriety, image, and reputation (NIR) of municipal executives. We attempted to understand if citizens' opinions influenced the evaluations, recommendations, and contestations based on NIR. Parishes in the municipality of Valongo were selected and analysed, namely Alfena, Campo e Sobrado, Valongo, and Ermesinde; a total of 998 questionnaires were collected. It was concluded that all of the studied dimensions were statistically significant in the final structural estimated model. The structural results point to municipal satisfaction and contestations and complaints of municipal executives as having directly positive and statistically significant influences on NIR. Organizational performance and perceived quality have directly positive but not statistically significant influences on NIR. The results of this research suggest that obtaining the personal opinions of citizens (e.g., regarding the work performances of their mayors) allows citizens to feel heard and active in their municipalities. From the point of view of public executives, the results of this type of study could provide valid information that allows stakeholders to make political decisions that are appropriate for the interests of their communities (e.g., by listening to their citizens).

Abstract
The current world context emphasizes the need and urgency of the sustainable cities theme. The construction industry is considered an essential role in satisfying the needs of society and contributing to the economy. However, it is heavily criticized for being a significant contributor to environmental degradation. Moreover, the building sector accounts for considerable energy and water consumption, waste formation, and extensive greenhouse gas emissions. Consequently, sustainable urban development becomes increasingly challenging due to the building sector's high potential for reducing its environmental impacts. Therefore, the research methodology used within this article was the literature review and document analysis to present to the construction industry stakeholders the contribution of the Building's Sustainable Assessment Systems towards the Sustainable Development Goals and essential information that arouses interest in applying them. It was concluded that the Building's Sustainable Assessment Systems contribute to creating sustainable cities, contributing to some of the Sustainable Development Goals.

Abstract
Industrial Cyber-Physical Systems (ICPS) deploy a network of connected and heterogeneous systems, integrating computational and physical components, improving production and quality. However, a fault-free system is still utopian, but methodologies related to fault detection and diagnosis are still being treated in isolation or a centralized approach, overlooking the technological advances related to ICPS such as IoT, AI and edge computing. With this in mind, the present work proposes a collaborative architecture for fault detection and diagnosis, regarding the exchange of information for collaborative detection and diagnosis adopting disruptive technologies. Laboratory-scale ICPS experiments were carried out to compare the proposed approach with the approach where each component separately intends to identify and diagnose faults. The results present a faster response generating a system more flexible and robust. © 2022 IEEE.

Abstract
Accurate predictions of time series are increasingly required to support judgments in a variety of decisions. Several predictive models are available to support these predictions, depending on how each field offers a data variety with varied behavior. The use of artificial neural networks (ANN) at the beginning of the COVID-19 pandemic was significant since the tool may offer forecasting data for various conditions and hence assist in governing critical choices. In this context, this paper describes a system for predicting the daily number of cases, fatalities, and Intensive Care Unit (ICU) patients for the next 28 days in five European countries: Portugal, the United Kingdom, France, Italy, and Germany. The database selection is based on comparable mitigation processes to analyze the impact of safety procedure flexibilization with the most recent numbers of COVID-19. Additionally, it is intended to check the algorithm's adaptability to different variants throughout time. The network's input data has been normalized to account for the size of the countries in the study and smoothed by seven days. The mean absolute error (MAE) was employed as a comparing criterion of two datasets, one with data from the beginning of the pandemic and another with data from the last year, since all variables (cases, deaths, and ICU patients) may be tendentious in percentage analysis. The best architecture produced a general MAE prediction for the 28 days ahead of 256,53 daily cases, 0,59 daily deaths, and 1,63 ICU patients, all numbers normalized by million people. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.