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
Crowdsourced data streams are popular and extremely valuable in several domains, namely in tourism. Tourism crowdsourcing platforms rely on past tourist and business inputs to provide tailored recommendations to current users in real time. The continuous, open, dynamic and non-curated nature of the crowd-originated data demands specific stream mining techniques to support online profiling, recommendation, change detection and adaptation, explanation and evaluation. The sought techniques must, not only, continuously improve and adapt profiles and models; but must also be transparent, overcome biases, prioritize preferences, master huge data volumes and all in real time. This article surveys the state-of-art of adaptive and explainable stream recommendation, extends the taxonomy of explainable recommendations from the offline to the stream-based scenario, and identifies future research opportunities.

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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Abstract
Email marketing plays a key role in business communications and is one of the most widely used applications by consumers. The literature review points to several determinants that, when applied, increase the open rate of newsletters. This research evaluates the impact of six determinants of persuasion on the opening rate of a newsletter in the hotel industry. The determinants are the day of sending, the time of sending, subject line personalization, scarcity appeal, curiosity appeal, and authority figure. The chosen methodology focused on real experiments, using a high-end luxury hotel, and the respective customer database. The newsletter was sent to the subscriber list, where one part received the control and the other part received a variant with the test version. Ten A/B tests were conducted for each determinant. The results obtained were not in line with what is indicated in the literature review. Although the literature review yielded results that showed that the application of determinants increased the open rate of newsletters, this study obtained findings to the opposite and did not confirm what was prescribed by the reviewed literature. The results of the A/B tests were conclusive and revealed that the determinants did not increase the open rate of newsletters.

Abstract
The aim of this study is to examine the impacts of board size, gender diversity and independence on ESG performance whilst also examining the impact of country-level social trust on such performance. We perform a panel data analysis and the least squares method for a sample of 75 European banks and a time span of 4 years from 2016 to 2019. We find that ESG performance is positively associated with board gender diversity and independence, and negatively associated with board size. Surprisingly, we find a negative relationship between country-level social trust and ESG performance. This is an important finding that we interpret as being related to the loss of confidence in the banking sector in the wake of the 2008 financial crisis. To regain such trust, the banking sector is likely to have suffered higher social pressure to engage in ESG activities in countries where social trust is lower. © 2023 by the authors.

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.