Abstract
The Industry 4.0 paradigm (I4.0) supports the improvement of industrial processes through Information and Communication Technologies (ICT), with information systems providing real-time information to humans and machines, in order to make the production process more flexible and efficient. In this context, Virtual Assistants (VA) collect and process production data and provide contextualized and real-time information to the workers in the production environment. This paper presents a prototype of a VA developed to collect production data from heterogeneous sources in the factory, process them based on contextual information, and provide workers with useful information to assist them in taking informed decisions. In that context, VA can represent a valuable aid to improve overall productivity and efficiency in the I4.0 factories. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

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People around the globe struggle with health issues requiring regular medication. Children, in particular, suffer more and more from chronic illnesses. In 2024, a team of six EPS@ISEP students chose to design a solution for this problem, followed by the assembly and testing of the corresponding proof-of-concept prototype. The aim was to design a solution for children to take the right medication, at the right time and in the right dose, in a pleasant and engaging way, based on technical, ethical, sustainability and market analyses. Focusing on children between the ages of 8 and 12, the team decided to incorporate a motivational system based on rewards to ensure that they take their medication correctly. The outcome is billy, a pill dispenser controlled via an app which allows carers to plan doses and release rewards, and children to autonomously take their medication. The system dispenses up to 21 doses of medication to the child through fingerprint authentication, and photographs the child taking the medication to reassure carers.

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Lock-freedom offers significant advantages in terms of algorithm design, performance and scalability. A fundamental building block in software development is the usage of hash map data structures. This work extends a previous lock-free hash map to support a new simplified design that is able to take advantage of most state-of-the-art safe memory reclamation methods, thus outperforming the previous design.

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Our team previously introduced an innovative concept for an "invisible" Electrocardiography (ECG) system, incorporating electrodes and sensors into a toilet seat design to enable signal acquisition from the thighs. Building upon that work, we now present a novel dataset featuring real-world, single-lead ECG signals captured at the thighs, offering a valuable resource for advancing research on thigh-based ECG for cardiovascular disease assessment. To our knowledge, this is the first dataset of its kind. The tOLIet dataset comprises 149 ECG recordings collected from 86 individuals (50 females, 36 males) with an average age of 31.73 ± 13.11 years, a mean weight of 66.89 ± 10.70 kg, and an average height of 166.82 ± 6.07 cm. Participants were recruited through direct contact with the Principal Investigator at Centro Hospitalar Universitario de Lisboa Central (CHULC) and via clinical consultations conducted at the same institution. Each recording includes four differential signals acquired from electrode pairs embedded in the toilet seat, with reference signals obtained from a standard 12-lead hospital ECG system.