Smart-Pomodoro is an application aimed at improving efficiency, focus and time management in study sessions for children with Attention-Deficit Hyperactivity Disorder (ADHD). By using Smart-Pomodoro, caregivers can design customized study sessions, thus assisting children to mitigate their ADHD symptoms and influence their motivation. Therefore, the caregiver can set goal-oriented objectives and gamification strategies to make studying more engaging, enhancing self-efficacy, focus, and behaviour. The system also leverages the smartwatch’s features to facilitate clear caregiver–child communication. Accordingly, Smart-Pomodoro employs a real-time, smartwatch-based and rule-driven approach to prompt children to plan and manage their study sessions and breaks. This unique feature delivers real-time feedback and gamification for children with ADHD, while increasing the caregiver’s awareness of their condition and behaviour throughout the session. In the performed evaluation, parents of children with ADHD (from both primary and secondary) rated the system positively, praising its ease of use, usefulness, and effectiveness.

The Digital Twin (DT) concept has evolved into its current definition since its creation in 2003. It now comprises a physical entity and its virtual counterpart, plus their interrelated data connections and uses the virtual counterpart to monitor, simulate, control, and predict the physical entitys behaviour. Most of the proposals in this field have focused on specific use cases rather than on describing the proper guidelines for designing DTs. This paper addresses a significant research challenge by means of a domain-agnostic proposal for DT design. Our new proposal, MAS4DT, is a method for guiding stakeholders in their DT designs following the Multi-Agent Systems (MAS) paradigm, so that the MAS provides the support for the development of the virtual counterpart of the DT. In this work, the application of MAS4DT is illustrated by a DT wind turbine design. We also describe MAS4DTs evaluation in a controlled experiment of its understandability with regard to the 5 dimensions model, another well-known proposal for this type of design. The evaluation was designed considering the properties that a DT should support. The results show that MAS4DT outperforms the 5 dimensions model and indicates its potential use for DT design. Finally, this controlled experiment represents the first empirical validation of proposals for developing D

: Digital Twins (DTs) are used to augment physical entities by exploiting assorted computational approaches applied to the virtual twin counterpart. A DT is generally described as a physical entity, its virtual counterpart, and the data connections between them. Multi-Agent Systems (MAS) paradigm is alike DTs in many ways. Agents of MAS are entities operating and interacting in a specific environment, while exploring and collecting data to solve some tasks. : This paper presents the results of a systematic literature review (SLR) focused on the analysis of current proposals exploiting the synergies of DTs and MAS. This research aims to synthesize studies that focus on the use of MAS to support DTs development and MAS that exploit DTs, paving the way for future research. : A SLR methodology was used to conduct a detailed study analysis of 64 primary studies out of a total of 220 studies that were initially identified. This SLR analyses three research questions related to the synergies between MAS and DT. : The most relevant findings of this SLR and their implications for further research are the following: i) most of the analyzed proposals design digital shadows rather than DT; ii) they do not fully support the properties expected from a DT; iii) most of the MAS properties have not fully exploited for the development of DT; iv) ontologies are frequently used for specifying semantic models of the physical twin. : Based on the results of this SLR, our conclusions for the community are presented in a research agenda that highlights the need of innovative theoretical proposals and design frameworks that guide the development of DT. They should be defined exploiting the properties of MAS to unleash the full potential of DT. Finally, ontologies for machine learning models should be designed for its use in DT.