Abstract

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
Dissipative quartic solitons have gained interest in the field of mode-locked lasers for their energy-width scaling which allows the generation of ultrashort pulses with high energies. Pursuing the characterization of such pulses, here we found soliton solutions of a distributed model for mode locked lasers in the presence of either positive or negative fourth-order dispersion (4OD). We studied the impact the laser parameters may have on the profiles, range of existence, and energy-width relation of the output pulses. The most energetic and narrowest solutions occur for negative 4OD, with the energy having an inverse cubic dependence with the width in most cases. Our simulations showed that the spectral filtering has the biggest contribution in the generation of short (widths as low as 39 fs) and very energetic (391 nJ) optical pulses.(c) 2023 Optica Publishing Group

Abstract
In recent years, it has been increasingly necessary for citizens to understand real life statistical data—an ability that is rarely taught in schools, where the majority of tasks in statistics classes contain fictional data without context and make no demands on students to explore or explain. Since most real-world phenomena are multivariate (See Chap. 2), there is a need to develop students’ abilities dealing with complex data and stories they encounter in the media, in order to help prepare them for informed citizenship. The ProCivicStat project has developed materials to support teaching and learning, in the form of detailed lesson plans; a large repository of resources (http://iase-web.org/islp/pcs/) (in several languages) is freely available. This chapter describes our approach to the development of teaching resources. It introduces our storytelling approach in lesson plans, where we use real data in context to encourage students to explore and understand complex data, produce narrative accounts, and often make recommendations about appropriate social actions. The structure of this chapter is as follows: we start with a brief introduction on problems in most tasks commonly encountered in statistics education, and the need for real data in statistics teaching (Sect. 7.1), followed by the presentation of the milestones that are important for creation of lesson plans (Sect. 7.2), and after that we address the use of real data and our storytelling approach (Sect. 7.3). In Sect. 7.4 we talk briefly about empowering teachers (Sect. 7.4) and describe the teachers’ version of the lesson plan (Sect. 7.5). In Sect. 7.6 we present the guidelines for designing student activities, then proceed with an excerpt of a lesson plan to exemplify products of the proposed guidelines (Sect. 7.7). We then highlight the visualization tools that help promote the data exploration step (Sect. 7.8), and finish with a conclusion (Sect. 7.9). © Springer Nature Switzerl and AG 2022.

Abstract
Forests worldwide have been devastated by fires. Forest fires cause incalculable damage to fauna and flora. In addition, a forest fire can lead to the death of people and financial damage in general, among other problems. To avoid wildfire catastrophes is fundamental to detect fire ignitions in the early stages, which can be achieved by monitoring ignitions through sensors. This work presents an integer programming approach to decide where to locate such sensors to maximize the coverage provided by them, taking into account different types of sensors, fire hazards, and technological and budget constraints. We tested the proposed approach in a real-world forest with around 7500 locations to be covered and about 1500 potential locations for sensors, showing that it allows obtaining optimal solutions in less than 20 min. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Agriculture work is physically demanding and the sector workers have a high incidence of musculoskeletal disorders. The shift to Agriculture 5.0 and the advancement of precision agriculture have involved the digitalization of this industry, but tend to marginalise the workers, though they are still essential to more thorough tasks that cannot be automated. In order to tackle the necessity to support the monitoring of agriculture workers, we developed quantification algorithms, incorporated in a mobile application, which calculate metrics based on the signals gathered by wearable sensors. Our proximity to the Douro region lead us to focus on metrics that could be more meaningful for viniculture, namely the quantification of trunk inclinations and shear cuts, very common in this production. The developed algorithms showed an error of 1.36 degrees for the calculus of inclination and 2.43 cuts for the prediction of cuts when tested with on-field data. These results suggest that the created system has the viability to be used by agricultures and give reliable feedback on their workers.