Abstract

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
Federated learning (FL) demonstrates its advantages in integrating distributed infrastructure, communication, computing and learning in a privacy-preserving manner. However, the robustness and capabilities of existing FL methods are challenged by limited and dynamic data and conditions, complexities including heterogeneities and uncertainties, and analytical explainability. Bayesian federated learning (BFL) has emerged as a promising approach to address these issues. This survey presents a critical overview of BFL, including its basic concepts, its relations to Bayesian learning in the context of FL, and a taxonomy of BFL from both Bayesian and federated perspectives. We categorize and discuss client- and server-side and FLbased BFL methods and their pros and cons. The limitations of the existing BFL methods and the future directions of BFL research further address the intricate requirements of real-life FL applications.

Abstract
This paper introduces a tomography-like method for assessing grape maturation. It analyses inner tissue spectra through point-of-measurement (POM) sensing. A multi-block hierarchical principal component analysis (MHPCA) algorithm was used for the spectral reconstruction of total grapes (skin, pulp, and seed). Two grape cultivars, Loureiro (white; n = 216) and Vinhao (red; n = 205) were measured at 12 dates after veraison (DAV). The reconstructed spectra showed no significant differences (p < 0.001) from the originals for both grapes. Loureiro had better statistical metrics (Person's correlation coefficient (r) values for: total grape: 0.99, skin: 1; pulp: 1, seed: 0.94) than Vinhao (r values for: total grape: 0.92, skin: 0.92; pulp: 0.95, seed: 0.95). Using self learning artificial intelligence (SL-AI), the following parameters were predicted for both grapes: soluble solids content (%; MAPE <13%), puncture force (N; MAPE <29%), chlorophyll content (a.u.; MAPE <29%), and anthocyanin content (a.u.; MAPE <17%, Vinhao only). When comparing observed values with predicted skin, pulp, and seed spectra, Vinhao showed no statistical differences for most parameters, except pulp chlorophyll on one DAV in the final maturation stage. The same was done with the Loureiro cultivar. Although Loureiro mostly showed no statistical differences in assessed parameters across tissues and dates, variations were found in pulp and skin chlorophyll content and puncture force. This tomography-like approach based on tissue maturation can help viticulturists to access instant data on grape maturation, supporting informed decision-making and promoting more sustainable agricultural practices.

Abstract
How might you use data visualisation in your teaching? Here, we offer some ideas, and some provocations to review your teaching. We begin with an invitation to examine some of the historical landmarks in data visualisation (DV), to classify the data presented, and to describe the benefits of a sample of the DV to users. Early uses of DV by Nightingale and Neurath are shown, to provide examples of DV which communicated the need for action, and provoked social change. A number of modern DVs are presented, categorised as: tools to display individual data sets and tools for the exploration of specific rich data sets. We argue that students introduced to the core features of Civic Statistics can acquire skills in all of the facets of Civic Statistics set out in Chap. 3. We conclude by revisiting Herschel, to provoke thoughts about the balance of activities appropriate to statistics courses. © Springer Nature Switzerl and AG 2022.

Abstract
Wearable Health Devices (WHDs) are increasingly becoming an integral part of daily life and significantly contributing to self-monitoring in healthcare. WHDs have a wide range of applications, ranging from sports to clinical settings, where the monitoring of cardiovascular health, particularly through ECG, plays a crucial role. This study introduces a unique WHD called VitalSticker, which exhibits distinctive features such as having a comfortable tiny patch form-factor to be attached to the chest, collecting multiple vital signs with medical-grade quality (ECG, respiration, temperature and actigraphy) and seamlessly sending data to a companion app. This paper encompasses a detailed description of the hardware, firmware, and case design of the WHD. A study was conducted to assess the quality of the ECG signal acquired by VitalSticker, comparing it with the signal obtained from a CE medical-grade certified ambulatory device. The results demonstrate that our VitalSticker achieves similar medicalgrade quality when compared to the reference device, surpassing its counterpart in several specifications. Furthermore, this study presents the successful implementation of an ECG baseline wander correction filter that runs on the tiny on-board wearable microcontroller without introducing any artifacts into the ECG signal, reducing the need for further processing for this outside the wearable patch.