Abstract

Abstract
Evidence of the asymmetric wealth effect has important implications for investors and continues to merit research attention, not least because much of the evidence based on linear models has been refuted. Indeed, stock and house prices are influenced by economic activity and react non-linearly to positive/negative shocks. This problem justifies our research. The objective of this study is to examine evidence of cointegrations between the US housing and stock markets and between the US and European stock markets, given the international relevance of these exchanges. Using data from 1989:Q1 to 2020:Q2, the Threshold Autoregression model as well as the Momentum Threshold Autoregression model were calculated by combining the US Freddie, DJIA, and SPX indices and the European STOXX and FTSE indices. The results suggest a long-term equilibrium relationship with asymmetric adjustments between the housing market and the US stock markets, as well as between the DJIA, SPX, and FTSE indices. Moreover, the wealth effect is stronger when stock prices outperform house prices above an estimated threshold. This empirical evidence is useful to portfolio managers in their search for non-perfectly related markets that allow investment diversification and control risk exposure across different assets.

Abstract
The authors present the Learning Based on Effective Solutions that derives from Project-Based Learning, but applied to real problems in order to build effective solutions. The emphasis is placed on effectiveness in the assumption that encourages greater involvement and commitment on the part of students, ensuring a context that is intended to be more attractive and closer to what will be the professional reality of students. Effectiveness is measured by the functionalities considered essential for the full resolution of the problem, but also by the feasibility of the application being effectively used, without the need for continued student involvement. Empirical evidence points to a clear increase in the acquisition of skills, in the number of students approved and in the improvement of the grades. It was also possible to find a strategic positioning of cooperation with the local community, in which everyone wins (students, teachers, institution, local and regional entities and, employers).

Abstract

Abstract
The growing use of data-driven decision systems based on Artificial Intelligence (AI) by governments, companies and social organizations has given more attention to the challenges they pose to society. Over the last few years, news about discrimination appeared on social media, and privacy, among others, highlighted their vulnerabilities. Despite all the research around these issues, the definition of concepts inherent to the risks and/or vulnerabilities of data-driven decision systems is not consensual. Categorizing the dangers and vulnerabilities of data-driven decision systems will facilitate ethics by design, ethics in design and ethics for designers to contribute to responsibleAI. Themain goal of thiswork is to understand which types of AI risks/ vulnerabilities are Ethical and/or Technological and the differences between human vs machine classification. We analyze two types of problems: (i) the risks/ vulnerabilities classification task by humans; and (ii) the risks/vulnerabilities classification task by machines. To carry out the analysis, we applied a survey to perform human classification and the BERT algorithm in machine classification. The results show that even with different levels of detail, the classification of vulnerabilities is in agreement in most cases.

Abstract
The Digital Model concept of factory floor equipment allows simulation, visualization and processing, and the ability to communicate between the various workstations. The Digital Twin is the concept used for the digital representation of equipment on the factory floor, capable of collecting a set of data about the equipment and production, using physical sensors installed in the equipment. Within the scope of data visualization and processing, there is a need to manage information about parameters/conditions that the assembly line equipments must present to start a production order, or in a shift handover. This study proposes a paperless checklist to manage equipment information and monitor production ramp-up. The proposed solution is validated in a real-world industrial scenario, by comparing its suitability against the current paper-based approach to log information. Results show that the paperless checklist presents advantages over the current approach since it enables multi-access viewing and logging while maintaining a digital history of log changes for further analysis. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)