Abstract
Fake news spreads rapidly, creating issues and making detection harder. The purpose of this study is to determine if fake news contains sentiment polarity (positive or negative), identify the polarity of sentiment present in their textual content and determine whether sentiment polarity is a reliable indication of fake news. For this, we use a deep learning model called BERT (Bidirectional Encoder Representations from Transformers), trained on a sentiment polarity dataset to classify the polarity of sentiments from a dataset of true and fake news. The findings show that sentiment polarity is not a reliable single feature for recognizing false news correctly and must be combined with other parameters to improve classification accuracy. © 2024 The Author(s). Published by Elsevier B.V.

Abstract
In the original version of this article, "Data availability" statement was mistakenly inserted. The following data availability statement should be removed. As a final point, while the traditional independent demand model involves comparing unconstrained bookings with unconstrained demand forecasts to assess prediction accuracy, handling dependent demand is more complex, since the availability of a class affects the demand for other classes. Therefore, it is essential to have forecast data for all control policies, as advocated by Fiig et al. (2014), to establish a standardized method for computing forecast errors. This ensures the accurate functionality of the predictive model for optimal margin correction. The original article has been corrected. © The Author(s), under exclusive licence to Springer Nature Limited 2024.

Abstract
Quality inspection is crucial in the textile industry as it ensures that the final products meet the required standards. It helps detect and address defects, such as fabric flaws and stitching irregularities, enhancing customer satisfaction, and optimizing production efficiency by identifying areas of improvement, reducing waste, and minimizing rework. In the competitive textile market, it is vital for maintaining customer loyalty, brand reputation, and sustained success. Nonetheless, and despite the importance of quality inspection, it is becoming increasingly harder to hire and train people for such tedious and repetitive tasks. In this context, there is an increased interest in automated quality control techniques that can be used in the industrial domain. In this paper we describe a computer vision model for localizing and classifying different types of defects in textiles. The model developed achieved an mAP@0.5 of 0.96 on the validation dataset. While this model was trained with a publicly available dataset, we will soon use the same architecture with images collected from Jacquard looms in the context of a funded research project. This paper thus represents an initial validation of the model for the purposes of fabric defect detection.

Abstract
In wireless communications, the need to cover operation areas, such as seaports, is at the forefront of discussion, especially regarding network capacity provisioning. Radio network planning typically involves determining the number of fixed cells, considering link budgets and deploying them geometrically centered across targeted areas. This paper proposes a solution to determine the optimal position for a mobile cell, considering 3GPP pathloss models. The obtained position for the mobile cell maximises the aggregate network capacity offered to a set of User Equipments (UEs), with gains up to 187% compared to the positioning of the mobile cell at the UEs’ geometrical center. The proposed solution can be used by network planners and integrated into network optimisation tools. This has the potential to reduce costs associated with the radio access network planning by enhancing flexibility for on-demand deployments.

Abstract
The footwear industry is energy-intensive and, consequently, a source of large amounts of greenhouse gas emissions every year. Issues related to climate change and growing conflicts on a global scale that impact the prices of raw materials and energy prices have led companies in the footwear industry to take actions to mitigate these impacts. Among these actions is the growing focus on producing its energy from energy systems based on renewable sources and battery energy storage units. This paper addresses the energy-efficient manufacturing scheduling in footwear industries with onsite energy production from a photovoltaic system with batteries. The problem is formulated as a mixed integer linear programming problem. Different objectives are presented, depending on the priorities of the entity that owns the footwear factory, namely, minimizing operation costs, minimizing CO2 emissions, or both. The case study is footwear factory located in Portugal that uses a manufacturing process based on injection molding. The results show the effectiveness of the proposed approach, with active demand side management playing a fundamental role in shifting periods of higher energy consumption to periods of lower prices or lower CO2 emissions. Also, Pareto fronts are depicted to make the trade-off between CO2 emissions and operation costs. As expected, the reduction of CO2 emissions promotes an increase on operation costs. Furthermore, a sensitivity analysis is carried out on the increase in photovoltaic capacity and battery capacity. The results show that increasing photovoltaic and battery capacity promotes reductions in costs up to 30% and in the emissions up to 37%.

Abstract
This book results from an initiative by the European university consortium EUGLOH - European University Alliance for Global Health - between the University of Porto, Portugal (U.P.), and the University of the Arctic, Norway (UiT). To support the activity consisting of a three-day masterclass and workshop at UiT, designated as “Tromsø Idea Camp 2024 - Developing ideas through co-creation using human-centred design,” which had as its motto: . “Identify the needs and expectations of Vardesenteret’s patients before, during, and after treatment, considering their and volunteers’ points of view and creating projectual answers that foster empathy and communication”, a handbook was created, which later formed the basis of this book. It has three sections dedicated to Person-Centred Design in the Context of Health and Well-being. The first section— ”Reported Experience”, — Relates to the doctoral project “Ludic Activities in Health Context: An Anorexia Nervosa Case in the Hospital Environment,” which systematizes Ludic methods and tools created in and for a clinical context to support the treatment and monitoring of Anorexia Nervosa. Part of the project stems from collaborative work with São João Central Hospital, Portugal, in a group dedicated to treating eating disorders. The second section - “Documented Experience” — Explores the concept of Positive Play, coined by Giesteira, B., Cardoso, P., and Peçaibes, V. in various papers and book chapters from 2021 onwards. It presents a group of methods and tools created by the authors in recent years, such as the SEED Toolkit - “SomaEsthetics for Enabling Designers” (Giesteira, B. 2023) and the ANgame Collab (Cardoso, P., Peçaibes, V., Giesteira, B. 2022). The first tool aims to work on the concept of Somaesthetics, promoting empathy and contributing to identifying feelings and sensations interdependent on body activities and movements. The second tool aims to mediate playful sessions of Participatory Design, especially in the ‘user research’ phase. Other tools and methods were selected because, based on the aforementioned authors’ experience, they are most suitable for applied research in Health Technology Design. The third section - “Lived Experience” — Records the three days of the workshop dedicated to the themes: “Emotional Design and Somaesthetics”; “Human-Centered and Participatory Design”; “Ideation, Lateral Thinking, and Prototype.” In addition to the aforementioned themes, the different phases of development of the participants' projects from the various universities of the EUGLOH university consortium are recorded, which, in a Participatory Design approach, involved the collaboration of volunteers and oncology patients from Vardesenteret. This section shares the tools used to support each phase of product or service design and the final results of the low-fidelity prototypes.