Abstract

Abstract
The VR2Care project aims to create age-friendly virtual environments fostering interactive technologies for promoting physical activity, rehabilitation, and social interaction. Smart living environments have intelligent interfaces in a single or multi-user context. The VR2Care ecosystem combines virtual reality technologies for supervised exercise with natural interaction techniques, enhancing social virtualization with gamification aspects in the practice of physical activity in a metaverse context. This paper presents the infrastructure and the high-level functional architecture of the VR2Care ecosystem which provides the necessary tools to construct the communication bridges to bring together people sharing common interests, objectives, or ambitions.

Abstract
In today's competing business market, companies are constantly challenged to dynamically adapt to customer expectations by diminishing the time response that goes from the beginning of the business opportunity to the satisfaction of the customer need. Simultaneously, there is increased recognition of the advantages that companies obtain in focusing on their core business and seeking other competencies through partnerships with other partners by forming collaborative networks. These new collaborative organizational structures require a new set of methods and tools to support the management of manufacturing processes across the entire supply chain. The present paper addresses the collaborative production planning problem in networks of non-hierarchical, decentralized, and independent companies. By proposing a collaborative planning intelligent framework composed of a web-based set of methods, tools, and technologies, the present study intends to provide network stakeholders with the necessary means to responsively and efficiently address each one of the market business opportunities. Through this new holistic framework, the managers of the networked companies can address the challenges posed during collaborative network formation and supply chain production planning.

Abstract
Human motion analysis has seen drastic improvements recently, however, due to the lack of representative datasets, for clinical in-bed scenarios it is still lagging behind. To address this issue, we implemented BlanketGen, a pipeline that augments videos with synthetic blanket occlusions. With this pipeline, we generated an augmented version of the pose estimation dataset 3DPW called BlanketGen3DPW. We then used this new dataset to fine-tune a Deep Learning model to improve its performance in these scenarios with promising results. Code and further information are available at https://gitlab.inesctec.pt/brain-lab/brainlab-public/blanket-gen-releases.

Abstract
The present study aimed to evaluate whether a 14-day period of balneotherapy influences the inflammatory status, health-related quality of life (QoL) and quality of sleep, underlying overall health state, and clinically relevant benefits of patients with musculoskeletal diseases (MD). The health-related QoL was evaluated using the following instruments: 5Q-5D-5L, EQ-VAS, EUROHIS-QOL, B-IPQ, and HAQ-DI. The quality of sleep was evaluated by a BaSIQS instrument. Circulating levels of IL-6 and C-reactive protein (CRP) were measured by ELISA and chemiluminescent microparticle immunoassay, respectively. The smartband, Xiaomi MI Band 4, was used for real-time sensing of physical activity and sleep quality. MD patients improved the health-related QoL measured by 5Q-5D-5L (p < 0.001), EQ-VAS (p < 0.001), EUROHIS-QOL (p = 0.017), B-IPQ (p < 0.001), and HAQ-DI (p = 0.019) after balneotherapy; the sleep quality was also improved (BaSIQS, p = 0.019). Serum concentrations of IL-6 were markedly decreased after the 14-day balneotherapy (p < 0.001). No statistically significant differences were observed regarding the physical activity and sleep quality data recorded by the smartband. Balneotherapy may be an effective alternative treatment in managing the health status of MD patients, with a decrease in inflammatory states, along with positive effects on pain reduction, patient's functionality, QoL, quality of sleep, and disability perception status.

Abstract
Smart cities, leveraging IoT technologies, are revolutionizing the quality of life for citizens. However, the massive data generated in these cities also poses significant privacy risks, particularly in de-anonymization and re-identification. This survey focuses on the privacy concerns and commonly used techniques for data protection in smart cities, specifically addressing geolocation data and video surveillance. We categorize the attacks into linking, predictive and inference, and side-channel attacks. Furthermore, we examine the most widely employed de-identification and anonymization techniques, highlighting privacy-preserving techniques and anonymization tools; while these methods can reduce the privacy risks, they are not enough to address all the challenges. In addition, we argue that de-identification must involve properties such as unlikability, selective disclosure and self-sovereignty. This paper concludes by outlining future research challenges in achieving complete de-identification in smart cities.