Abstract
Over the past fewdecades, the amount of information generated turned the Web into the largest knowledge infrastructure existing to date. Web archives have been at the forefront of data preservation, preventing the losses of significant data to humankind. Different snapshots of the web are saved everyday enabling users to surf the past web and to travel through this overtime. Despite these efforts, many people are not aware that the web is being preserved, often finding these infrastructures to be unattractive or difficult to use, when compared to common search engines. In this paper, we give a step towards making use of this preserved information to develop Public Archive an intuitive interface that enables end-users to search and analyze a large-scale of 67,242 past preserved news articles belonging to a Portuguese reference newspaper (Jornal Publico). The referred collection was obtained by scraping 10,976 versions of the homepage of the Jornal Publico preserved by the Portuguese web archive infrastructure (Arquivo.pt) during the time-period of 2010 to 2021. By doing this, we aim, not only to mark a stand in what respects to make use of this preserved information, but also to come up with an easy-to-follow solution, the Public Archive python package, which creates the roots to be used (with minor adaptations) by other news source providers interested in offering their readers access to past news articles.

Abstract
Physiological signals are often corrupted by noisy sources. Usually, artificial intelligence algorithms analyze the whole signal, regardless of its varying quality. Instead, experienced cardiologists search for a high-quality signal segment, where more accurate conclusions can be draw. We propose a methodology that simultaneously selects the optimal processing region of a physiological signal and determines its decoding into a state sequence of physiologically meaningful events. Our approach comprises two phases. First, the training of a neural network that then enables the estimation of the state probability distribution of a signal sample. Second, the use of the neural network output within an integer program. The latter models the problem of finding a time window by maximizing a likelihood function defined by the user. Our method was tested and validated in two types of signals, the phonocardiogram and the electrocardiogram. In phonocardiogram and electrocardiogram segmentation tasks, the system's sensitivity increased on average from 95.1% to 97.5% and from 78.9% to 83.8%, respectively, when compared to standard approaches found in the literature.

Abstract
In an industrial setting, predicting the remaining useful life-time of equipment and systems is crucial for ensuring efficient operation, reducing downtime, and prolonging the life of costly assets. There are state-of-the-art machine learning methods supporting this task. However, in this paper, we argue, that both efficiency and understandability can be improved by the use of explainable AI methods that analyze the importance of features used by the machine learning model. In the paper, we analyze the feature importance before a failure occurs to identify events in which an increase in importance can be observed and based on that indicate attributes with the most influence on the failure. We demonstrate how the analyses of Shap values near the occurrence of failures can help identify the specific features that led to the failure. This in turn can help in identifying the root cause of the problem and developing strategies to prevent future failures. Additionally, it can be used to identify areas where maintenance or replacement is needed to prevent failure and prolong the useful life of a system.

Abstract
There is a recognized need to study sustainability in fashion. Several studies have documented the determinants that influence fashion purchase intention. However, the determinants that influence the purchase of sustainable fashion still need to be understood, particularly those factors associated with influencers and electronic word-of-mouth. This study aimed to examine the constructs influencer credibility, influencer expertise, influencer similarity, influencer'sparasocial relationship, E-WOM homophily, E-WOM expertise, trust in the influencer, trust in social media, performance expectation, consumer knowledge, environmental beliefs, brand awareness and willingness to pay more, and their effect on purchase intention. The research methodology consisted of consumer interviews that were conducted using an online platform, and structural equation modeling was used to test the research hypotheses. The results obtained indicate that consumer knowledge and willingness to pay more are the only constructs that positively affect the purchase intention of sustainable fashion. © 2023, IGI Global. All rights reserved.

Abstract
This study examined the determinants of purchase intention of green cosmetics, and eight semi-structured interviews were performed to identify them. The determinants identified were environmental awareness, lifestyle, willingness to pay, ethical issues and social and economic justice, cosmetic quality, concern with health, certification labels, trust in the brand, and advertising. Environmental awareness, lifestyle, willingness to pay, quality issues, ethics, and social and economic justice, as well as quality expectations, health concerns, and product knowledge, are the most significant determinants in the intention to purchase green cosmetics. Determinants such as certification labels, brand trust, and advertising are less significant. The research is relevant for the cosmetics industry and its brands to adapt their strategy and product offering to meet consumers’ needs and increase the consumption of green cosmetics and can also serve as a basis for the development of new quantitative studies on the purchase intention of green cosmetics. © 2023 by IGI Global.

Abstract
The use of social media in health is emerging as a means of bringing the various actors together with several benefits. In the specific case of cancer disease, these tools can help patients to improve their psychological well-being and their outcomes. As cancer is the cause of a quarter of deaths in Portugal, it is a pressing issue to understand which tools and information both patients and health professionals find most useful to build effective health social media. It was observed that there is a latent need for an oncology social environment, allowing greater well-being for patients and strengthening their relationship with health professionals and institutions, constituting an asset to the services provided. This chapter fills a gap in the bibliography by bringing together the views of both patients and health professionals from several areas, in close collaboration with the Francisco Gentil Portuguese Oncology Institute of Porto, E.P.E. © 2024, IGI Global. All rights reserved.