Abstract
An early diagnosis of cancer is essential for a good prognosis, and the identification of differentially expressed genes can enable a better personalization of the treatment plan that can target those genes in therapy. This work proposes a pipeline that predicts the presence of lung cancer and the subtype allowing the identification of differentially expressed genes for lung cancer adenocarcinoma and squamous cell carcinoma subtypes. A gradient boosted tree model is used for the classification tasks based on RNA-seq data. The analysis of gene expressions that better differentiate cancerous from normal tissue, and features that distinguish between lung subtypes is the main focus of the present work. Differential expressed genes are analyzed by performing hierarchical clustering in order to identify gene signatures that are commonly regulated and biological signatures associated with a specific subtype. This analysis highlighted patterns of commonly regulated genes already known in the literature as cancer or subtype-specific genes, and others that are not yet documented in the literature.

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.