Abstract

Abstract
When compared with traditional local shops where the customer has a personalised service, in large retail departments, the client has to make his purchase decisions independently, mostly supported by the information available in the package. Additionally, people are becoming more aware of the importance of the food ingredients and demanding about the type of products they buy and the information provided in the package, despite it often being hard to interpret. Big shops such as supermarkets have also introduced important challenges for the retailer due to the large number of different products in the store, heterogeneous affluence and the daily needs of item repositioning. In this scenario, the automatic detection and recognition of products on the shelves or off the shelves has gained increased interest as the application of these technologies may improve the shopping experience through self-assisted shopping apps and autonomous shopping, or even benefit stock management with real-time inventory, automatic shelf monitoring and product tracking. These solutions can also have an important impact on customers with visual impairments. Despite recent developments in computer vision, automatic grocery product recognition is still very challenging, with most works focusing on the detection or recognition of a small number of products, often under controlled conditions. This paper discusses the challenges related to this problem and presents a review of proposed methods for retail product label processing, with a special focus on assisted analysis for customer support, including for the visually impaired. Moreover, it details the public datasets used in this topic and identifies their limitations, and discusses future research directions of related fields.

Abstract
Storytelling is a powerful marketing tool that can be used to attract the audience's attention and influence behavior. However, this investigation seeks to understand more deeply the importance of written narratives in the marketing field. In this sense, the objective of this article is to explore similarities and identify patterns, phases or stages, variables, indicators, and respective relationships (models) to help marketers to create storytelling. In the analogy of the visual methodology analytical process, this literature review created a selection of the sample and developed content to structure storytelling within the marketing framework. Through the analysis of 78 papers published in the Web of Science main collection, this research finds out three clusters: marketing cluster, content cluster, and context cluster. Our results indicate that, in marketing, storytelling elicits brand identification, awareness, and customer engagement, as well as it helps with self-expression, a sense of belonging, and a perception of the value of the product or brand. We find some variables, indicators, and respective relationships (models) that can be used in storytelling in the marketing field to improve value awareness. This review also allowed us to identify factors related to the structuring of marketing storytelling. And we found three key elements of the story construct that are transversal: content, classification, and character. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
When designing a custom-made product it is important to provide the customer with a budget that resembles the final price. In this work it will be developed a simple application in Python to perform automatic data extraction from computer aided design (CAD) files to estimate multiple linear regression models with the intent of obtaining a more accurate cost estimate. The application will provide an estimate of the amount of raw material needed and time taken to produce a simple inflatable and related products. © 2023 ITMA.

Abstract
Radon concentration was continuously monitored in a horizontal dead-end gallery near Vyhne (Central Slovakia) from October 2005 to April 2008. Hourly average of radon varied from 2800 to 10 500 Bq/m(3). Temporal variation of radon, which contains periodic and non-periodic signals, spans variation of annual to diurnal scale. Time series of radon were analyzed together with meteorological parameters. The annual variation of radon seems to be connected with the annual variation of atmospheric pressure. The amplitude and shape of diurnal variation of radon changed during the year and is correlated with corresponding changes in the daily amplitude of atmospheric pressure.

Abstract
Ever since there has been an organized collection and use of data for informing decision making, there has been a debate about the extent to which these data have been put to the best use for improving social welfare in terms of general well-being of a community or an entire society. This chapter offers a contribution to that debate, showing how different facets of civic statistics can be translated into action that delivers social impact. We first introduce data movements and how they emerged as a response to the unmet need for data science services to scale social impact of nonprofit and governmental organizations. These movements focused on feasible hands-on projects which are simultaneously educational, impactful, and scalable. Their success is notable, and their operational model applicable in the context of formal educational organizations, as we show using two exemplary cases. The cases offer insights about how organizations can engage with society through civic action and applied data science to create new academic and training programs. Our intention is to share the lessons learned from the data movements and their interactions with educational institutions, also in the context of service-learning, to inspire others to create exciting, engaging educational programs with lasting social impact. © Springer Nature Switzerl and AG 2022.