Abstract
PurposeThis study analyzes the impact of packaging-free practices in food retail stores, particularly supermarkets, on customer loyalty.Design/methodology/approachBased on the literature on the impacts of sustainable practices and corporate social responsibility (CSR) policies on consumer behavior, this study defined a set of seven hypotheses that were tested using data collected from 447 consumers that regularly buy food products at supermarkets. The data were subjected to structural equation modeling using SmartPLS.FindingsThis study confirmed that packaging-free practices positively influence brand image, brand trust, satisfaction and customer loyalty. The expected positive impacts of brand image and satisfaction on customer loyalty were also confirmed. However, the expected impact of brand trust on customer loyalty was not confirmed.Practical implicationsThis article demonstrates how a competitive sector can reap benefits from implementing sustainable practices in the operational domain, particularly by offering packaging-free products at the point of purchase. Thus, as recommended, general retail stores (e.g. supermarkets) gradually increase the stores' offering of packaging-free food products, as this practice has been shown to have positive impacts not only on brand image, but also on customer satisfaction and loyalty.Originality/valueThis study extends the literature on the effects of sustainable practices on customer loyalty, by focusing on a specific practice. Furthermore, this study contributes to the advancement of research on packaging-free practices in retail by developing a research framework and providing evidence on the direct and indirect effects of this specific practice on customer loyalty.

Abstract
Laser-induced breakdown spectroscopy is a spectroscopic technique that allows for fast elemental mapping of heterogeneous samples. Yet, detailed maps need high-resolution sampling grids, which can turn the task into a time-consuming process and can increase sample damage. In this work, we present the implementation of an imaged-based intelligent mesh algorithm that makes use of superpixel segmentation to optimize elemental mapping processes. Our results show that the approach can increase the elemental mapping resolution and decrease acquisition times, fostering opportunities for applications that benefit from minimal sample damage such as heritage analysis, or timely analysis such as industrial applications. © 2022 The Author(s)

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
Citizens are more and more encouraged to participate in public policy decision processes and, therefore, critical questions regarding our lives are asked every day. Informed citizens need access to data, and knowledge in order to explore, understand, and reason about information of a multivariate nature; it is not obvious how to access such data, or how to work with them. Educators face the challenge of adopting new approaches, and grasping new opportunities in order to support the development of students into informed citizens as adults. Educators often do not have time to locate information sources; moreover, it is a challenge to exploit the possibilities of open data wisely. This chapter points to data sets we have found valuable in teaching Civic Statistics; data must be authentic, and reflect the complexities of data used to inform decision making about social issues (whose features are explained in Chap. 2). Topics include refugees, malnutrition, and climate change. We provide enough details so teachers can locate and employ these data sets, or similar ones, as part of regular instruction. Information is made accessible using the innovative tool CivicStatMap, developed to provide access to teaching materials, along with data and analysis tools, including tools to support data visualisation.

Abstract

Abstract
We present MONET, a new multimodal dataset captured using a thermal camera mounted on a drone that flew over rural areas, and recorded human and vehicle activities. We captured MONET to study the problem of object localisation and behaviour understanding of targets undergoing large-scale variations and being recorded from different and moving viewpoints. Target activities occur in two different land sites, each with unique scene structures and cluttered backgrounds. MONET consists of approximately 53K images featuring 162K manually annotated bounding boxes. Each image is timestamp-aligned with drone metadata that includes information about attitudes, speed, altitude, and GPS coordinates. MONET is different from previous thermal drone datasets because it features multimodal data, including rural scenes captured with thermal cameras containing both person and vehicle targets, along with trajectory information and metadata. We assessed the difficulty of the dataset in terms of transfer learning between the two sites and evaluated nine object detection algorithms to identify the open challenges associated with this type of data. Project page: https://github.com/fabiopoiesi/monet-dataset.