Abstract

Abstract
Considering the beauty industry's potential for further expansion and the mismatch between the attitudes of consumers and their buying behavior, brands should comprehend the factors that influence consumers' intention to purchase environmentally friendly cosmetics. As such, the present study examined what encourages consumers of environmentally friendly cosmetics to choose these products. To answer the main objective of the work, the elaborated literature review aimed at identifying the factors that influence the buying of environmentally friendly cosmetics. Thus, the following were found: environmental consciousness, certification labels, brand trust, quality expectation, lifestyle, advertising, willingness to pay the price, ethical concerns and social and financial equity, physical health considerations, and knowledge of the product. The study was conducted using exploratory research with a qualitative approach. Data was collected from eight interviews, and it was identified that factors such as environmental consciousness, lifestyle, willingness to pay the price, quality expectations, ethical concerns and social and financial equity, as well as physical health considerations and knowledge of the product are the most significant determinants in the intention to buying environmentally friendly cosmetics. One of the aims of the investigation was to distinguish between the notions of green, traditional, organic, and natural cosmetics. As a result, it was found that there is a lack of clarification of the green cosmetic concept in literature, as well as a lack of standardization of criteria used by multiple systems to define different cosmetics.

Abstract
With the massive data collection from different devices, spanning from mobile devices to all sorts of IoT devices, protecting the privacy of users is a fundamental concern. In order to prevent unwanted disclosures, several Privacy-Preserving Mechanisms (PPMs) have been proposed. Nevertheless, due to the lack of a standardized and universal privacy definition, configuring and evaluating PPMs is quite challenging, requiring knowledge that the average user does not have. In this paper, we propose a privacy toolkit - Privkit - to systematize this process and facilitate automated configuration of PPMs. Privkit enables the assessment of privacy-preserving mechanisms with different configurations, while allowing the quantification of the achieved privacy and utility level of various types of data. Privkit is open source and can be extended with new data types, corresponding PPMs, as well as privacy and utility assessment metrics and privacy attacks over such data. This toolkit is available through a Python Package with several state-of-the-art PPMs already implemented, and also accessible through a Web application. Privkit constitutes a unified toolkit that makes the dissemination of new privacy-preserving methods easier and also facilitates reproducibility of research results, through a repository of Jupyter Notebooks that enable reproduction of research results.

Abstract
Organizations assume a key role in the goal of achieving sustainable development and are influential elements on the path to sustainability. Allied with competitiveness, today, there is also a strategy based on sustainability, anchored in the concept of responsibility, minimizing the potential negative effects of our actions through innovative products, services, processes, and models. Measuring and monitoring these efforts is currently a challenge for organizations. This study adopts a mixed methods approach to address this challenge and identifies 13 tools and 16 dimensions that are central elements in the process of measuring and monitoring sustainable innovation. The findings indicate that the dimensions related to social and governance components are the most relevant in sustainable innovation, while inclusion and entrepreneurship are dimensions that are not highly valued by these tools. © 2024 The Authors

Abstract
Data-to-text systems offer a transformative approach to generating textual content in data-rich environments. This paper describes the architecture and deployment of Prosebot, a community-driven data-to-text platform tailored for generating textual summaries of football matches derived from match statistics. The system enhances the visibility of lower-tier matches, traditionally accessible only through data tables. Prosebot uses a template-based Natural Language Generation (NLG) module to generate initial drafts, which are subsequently refined by the reading community. Comprehensive evaluations, encompassing both human-mediated and automated assessments, were conducted to assess the system's efficacy. Analysis of the community-edited texts reveals that significant segments of the initial automated drafts are retained, suggesting their high quality and acceptance by the collaborators. Preliminary surveys conducted among platform users highlight a predominantly positive reception within the community.

Abstract
The textile industry, a vital sector in global production, relies heavily on dyeing processes to meet stringent quality and consistency standards. This study addresses the challenge of identifying and mitigating non-conformities in dyeing patterns, such as stains, fading and coloration issues, through advanced data analysis and machine learning techniques. The authors applied Random Forest and Gradient Boosted Trees algorithms to a dataset provided by a Portuguese textile company, identifying key factors influencing dyeing non-conformities. Our models highlight critical features impacting non-conformities, offering predictive capabilities that allow for preemptive adjustments to the dyeing process. The results demonstrate significant potential for reducing non-conformities, improving efficiency and enhancing overall product quality.