Abstract
Project Management Maturity Models (PMMM) are considered practical tools to deal with poor Project Management (PM) performance, an issue that concerns academics and practitioners. However, the models that exist are something close to one size fits all. This means that those models might not be suitable for activity sectors with specific requirements, such as construction, in particular, the renovation and remodelling construction firms. The present research proposes a PMMM to assess the PM capabilities of Portuguese renovation and remodelling Project-Based Firms (PBF). To achieve this goal, the authors developed documental research, followed by exploratory research through qualitative analysis. The researchers conducted semi-structured interviews and performed a content analysis of the fully transcribed interviews. Compared with the literature review's findings, qualitative analysis results made it possible to find closure on previous research that indicated two models to have the best fit for an assessment project on construction PBFs: the OPM3 from the PMI and the MMGP-Prado. Based on those findings, the latter has the best fit for an assessment project on construction PBF. However, the model needs adjustments to fit the Portuguese context of renovation and remodelling organisations. This article presents a new PMMM for Portuguese renovation and remodelling construction firms based on obtained results. Furthermore, regarding construction PM, this article is among the few that studied PMMM on renovation and remodelling construction companies. Unlike large construction companies, these are small organisations that academics do not target for research.

Abstract
Traditional identity management (IdM) systems rely on third-party identity providers (IdPs) and are centralised, which can make them vulnerable to data breaches and other security risks. Self-sovereign identity (SSI) is a newer IdM model that allows users to control their own identities by using decentralised technologies like blockchain to store and verify them. However, SSI systems have their own security concerns, such as digital wallet vulnerabilities, blockchain threats and conflicts with general data protection regulation (GDPR). Additionally, the lack of incentives for issuers, verifiers and data owners could limit its acceptance. This paper proposes SPIDVerify, which is a decentralised identity verification framework that utilises an SSI-based architecture to address these issues. The framework uses a mixed method for acquiring a W3C standard verified credentials and to ensure that only a thoroughly verified entity acquires verified credential, and employs secure key cryptographic protocols; Diffie-Hellman (DH) and Extended Triple Diffie-Hellman (X3DH) for forward secrecy secure communication, single-use challenge-response for authentication, and Swarm network for decentralised storage of data. These methods enhance the security of the proposed framework with better resilience against impersonation and credential stealing. To evaluate the proposal, we have outlined the limitations in related works and demonstrated two scenarios to showcase the strength and effectiveness of SPIDVerify in dealing with the threats identified. We have also tested the methods used in SPIDVerify by measuring the time taken to execute certain processes. © 2023 IEEE.

Abstract
It is well known that conditional heteroscedasticity is exhibited by many economic and financial time series such as stock prices or returns. Empirical analysis is often based on a subseries obtained through systematically sampling from an underlying time series and we analyze how that can affect testing for heteroscedasticity. The results show the distribution of the test statistics is changed by systematic sampling, causing a serious power loss that increases with the sampling interval. Consequently, the tests often fail to reject the hypothesis of no conditional heteroscedasticity, leading to the wrong decision and missing the true nature of the data-generating process.

Abstract
In this chapter, we describe the design and implementation of a Pedagogical Innovation Center (PIC) at the Polytechnic of Porto. The COVID-19 pandemic disrupted our day to day lives, our businesses, the world trade and movements. Education was not spared. In fact, it was one of the sectors most heavily affected by COVID-19 pandemic. Teachers were forced, from night to day, to adjust a purely face-to-face teaching style, to a 100\% online set. This is known as emergency remote teaching. Several difficulties have arisen both for teachers and students. The first had to structure all their teaching materials from scratch, had to design and apply new assessment methods, and struggled to get their students' motivation. On their side, the students lacked engagement, social interaction with peers and teachers, the ability to have a more autonomous learning style. © 2023, IGI Global. All rights reserved.

Abstract
Cities worldwide have agreed on ambitious goals regarding carbon neutrality; thus, smart cities face challenges regarding active and shared mobility due to public transportation's low attractiveness and lack of real-time multimodal information. These issues have led to a lack of data on the community's mobility choices, traffic commuters' carbon footprint and corresponding low motivation to change habits. Besides, many consumers are reluctant to use some software tools due to the lack of data privacy guarantee. This paper presents a methodology developed in the FranchetAI project that addrebes these issues by providing distributed privacy-preserving machine learning models that identify travel behaviour patterns and respective GHG emissions to recommend alternative options. Also, the paper presents the developed FranchetAI mobile prototype. © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)

Abstract
Deep learning-based automatic segmentation methods have become state-of-the-art. However, they are often not robust enough for direct clinical application, as domain shifts between training and testing data affect their performance. Failure in automatic segmentation can cause sub-optimal results that require correction. To address these problems, we propose a novel 3D extension of an interactive segmentation framework that represents a segmentation from a convolutional neural network (CNN) as a B-spline explicit active surface (BEAS). BEAS ensures segmentations are smooth in 3D space, increasing anatomical plausibility, while allowing the user to precisely edit the 3D surface. We apply this framework to the task of 3D segmentation of the anal sphincter complex (AS) from transperineal ultrasound (TPUS) images, and compare it to the clinical tool used in the pelvic floor disorder clinic (4D View VOCAL, GE Healthcare; Zipf, Austria). Experimental results show that: 1) the proposed framework gives the user explicit control of the surface contour; 2) the perceived workload calculated via the NASA-TLX index was reduced by 30% compared to VOCAL; and 3) it required 70% (170 seconds) less user time than VOCAL (p< 0.00001).