The adoption of decentralized architectures for health data management offers benefits including patient data sovereignty and elimination of single points of failure, but introduces questions about network overhead compared to traditional centralized systems. This paper presents a network overhead analysis comparing Firebase Real-Time Database with IPFS-based storage via Pinata for mobile health data transmission. We implemented an Android application that collects physiological data from wearable devices and transmits this information to both backends using REST APIs. Our experimental evaluation across eight transmission scales reveals that Firebase demonstrates lower fixed overhead and latency for small payloads, while Pinata exhibits superior scaling characteristics for larger data volumes. A crossover point occurs around 50 records per payload, beyond which the decentralized architecture transmits less total data than the centralized alternative. The results indicate that neither architecture maintains uniform efficiency across all operational scales, with architectural choice depending on expected transaction patterns in the deployment context.

Digital Twins (DTs) are increasingly adopted as a technical solution for the digital representation of complex physical entities through models that process near-real-time data streams and provide feedback on the state and behavior of the Physical Twin (PT). When DT models are trained using Machine Learning (ML) techniques, their performance may degrade over time as the DT acquires new operational data that may differ from the data observed during initial training. In this paper, following Machine Learning Operations (MLOps) principles, we investigate how Digital Twin Aggregates (DTAs) can be integrated into DT-based systems to enable continuous monitoring of model performance and to support adaptive retraining strategies for the continuous delivery and maintenance of ML models ensuring that the DT remains a reliable representation of the PT throughout its lifecycle. We evaluate the approach in a healthcare case study involving DTs for diabetic patients and compare adaptive with periodic retraining showing that performance-based retraining mantains stable accuracy while reducing model updates. These results suggest that DTA-level monitoring enables more efficient adaptive MLOps lifecycles by triggering retraining in response to actual performance degradation rather than fixed schedules.

Current health data management relies on centralized architectures that create a single point of failure, limit patient autonomy, and increase vulnerability to data breaches and vendor lock-in. This paper presents a decentralized approach to continuous health monitoring through the integration of wearable devices and distributed file systems. We implemented an Android application that collects physiological data and contextual information from a wearable device, storing it on the IPFS via Pinata API. Additionally, we propose a blockchain architecture for role-based access control. Performance evaluation comparing our IPFS-based implementation against Firebase Real-Time database reveals that the resource requirements remain negligible for modern smartphones while achieving significant benefits, including no single point of failure, enhanced data portability, and patient data sovereignty. The results demonstrate that decentralized health data management is technically feasible on mobile devices, offering an alternative approach to traditional centralized health data architectures.

Digital Twin technology in healthcare offers personalized care through advanced analytics, real-time data, and virtual models. In this paper we propose the adoption of the Digital Twin approach as a means for modeling hands, both injured and healthy, in a Double Digital Twin (DDT) using the Virtual Glove. The VG acts as a supportive rehabilitation device, capable of collecting data and reconstructing models for both the impaired and healthy hands. This study emphasizes the calibration process, which adjusts the model of the healthy hand to match the injured hand. Additionally, the transformed healthy hand model is used to guide the task with the injured system, ensuring synchronization and repeatability of exercises. Furthermore, the framework associated with DDT facilitates the analysis and quantification of the mobility of the impaired hand in comparison to the healthy one. Finally, a preliminary experiment is presented. Index Terms—Virtual Glove, Tele-Medicine, Double Digital Twin, Hand Rehabiliation, Virtual Reality

Digital Twins have been adopted in different healthcare contexts and several work suggests, beyond reporting specific applications, their exploitation to track and monitor resources and assets for clinical and managerial purposes. In this paper we present a pervasive ecosystem of digital twins, designed for mirroring the operating room and for supporting the whole operating suite management. A real-world architecture, devised for the operating suite of the Local Health Authority of Romagna, is then described, together with the resulting prototype implementation in which we particularly focus on the integration with the legacy systems and the standardisation of information according to the reference standard FHIR.

Chatbot-based systems are recognised in literature as an effective tool to support chronic diseases self-management. However, the core of most of this work is on the description of the application domain and on the motivations behind the adoption of recommendation systems that exploit chatbot to mediate the interaction with users, but they fail in providing sufficient details on the system architecture and on the technology adopted. Moreover, they are usually designed with a strong focus on the specific pathology, and a reference architectural solution that can be adopted in different contexts is missing, thus making the work useful only in the domain it is devised for. In this paper we provide a framework for developing recommendation systems based on chatbots that is meant to be applied in different scenarios. The framework is composed by a back-end recommendation engine that autonomously computes the user's adherence profile to prescription, and proactively provides motivational feedback to the user through the application front-end based on a chatbot. The chatbot is also meant to collect and aggregate data for profiling the individual health and habits. To demonstrate the feasibility of our framework, we present a recommendation system, based on a Telegram chatbot, that has been developed and trained for managing hypertensive patients.

The need for effective simulation techniques, when studying the performance of portfolio investments in financial applications, was recognized since it was observed that backtesting typically introduces significant bias. However, while Monte Carlo simulations are commonly used in this application scenario, up to now no general frameworks have been proposed. This paper describes a general modeling and simulation framework that is used to study how allocation schemes perform when different synthetic time series generation models are employed. Moreover, we devised a novel portfolio allocation scheme where assets are nodes of a complex network and communities of correlated assets are detected and measured by means of modularity. Allocation is than obtained by equally distributing weights among different communities. We compare this novel scheme against state-of-the-art approaches in various scenarios, under Gaussian, Geometric Brownian motion and ARFIMA generation models. Results show that the proposed scheme outperforms the others in many scenarios.

In recent years, digital twins have been pervading different application domains—from manufacturing to healthcare—as an approach for virtualising different kinds of physical entities (things, products, machines). The dominant view developed in the literature so far is about the virtualisation of individual physical assets in a closed-system perspective. In this article, we introduce and explore a broader perspective that we call Web of Digital Twins (WoDT), in which the digital twin paradigm is exploited for the pervasive softwarisation of possibly large-scale interrelated physical realities. A WoDT can be conceived as an open, distributed and dynamic ecosystem of connected digital twins, functioning as an interoperable service-oriented layer for applications running on top, especially smart applications and multiagent systems. The article introduces an abstract model and architecture aimed to capture key aspects of the idea not bound to any specific application domains or implementing technologies and discusses their adoption in engineering real-world systems. To this purpose, two concrete case studies are considered, in the context of healthcare and smart mobility. Finally, the article includes a discussion of a selected set of research directions.

Boolean networks are a notable model of gene regulatory networks and, particularly, prominent theories discuss how they can capture cellular differentiation processes. One frequent motif in gene regulatory networks, especially in those circuits involved in cell differentiation, is autoregulation. In spite of this, the impact of autoregulation on Boolean network attractor landscape has not yet been extensively discussed in literature. In this paper we propose to model autoregulation as self-loops, and analyse how the number of attractors and their robustness may change once they are introduced in a well-known and widely used Boolean networks model, namely random Boolean networks. Results show that self-loops provide an evolutionary advantage in dynamic mechanisms of cells, by increasing both number and maximal robustness of attractors. These results provide evidence to the hypothesis that autoregulation is a straightforward functional component to consolidate cell dynamics, mainly in differentiation processes.

The problem of tracking has gained a central role in healthcare research since it enables the acquisition of the information needed for improving healthcare management and efficiency, alongside patient safety. In literature, it is mainly discussed as an allocation problem that must deal with limited resources (rooms, physicians, equipment) to optimise workflows, and Real-Time Location Systems have been introduced with the main goal of locating and identifying assets and personnel in a healthcare facility. In this paper, we propose a novel perspective of pervasive tracking into Hospital 4.0, devised explicitly for time-dependent acute patient flow. The goal is to develop a tracking system that acquires not only the time and location of entities, exploiting state-of-the-art techniques, but also the main clinical events occurred. As an example application we describe TraumaTracker, a system developed to support the accurate and complete documentation of trauma resuscitation processes from pre-hospital care.

To support and engineer the spatial coordination of distributed pervasive services, we propose a chemical-inspired model, which extends tuple spaces with the ability of evolving tuples mimicking chemical systems, that is, in terms of reaction and diffusion rules that apply to tuples modulo semantic match. The suitability of this model is studied by considering a self-adaptive display infrastructure providing people nearby with several visualization services (advertisements, news, personal and social content). The key result of this article is that general-purpose chemical reactions inspired by population dynamics can be used in pervasive applications to enact spatial computing patterns of competition and gradient-based interaction.

A state-of-the-art problem in Computational Systems Biology is to provide suitable tools to model and predict the behaviour of multicellular systems (tissues, embryos) where biological interactions occur both inside and between cells (or compartments in general). Starting from existing computational models and languages such as stochastic pi-calculus, Petri Nets, mobile ambients, and membrane computing, we developed a new computational framework based on (i) a compositional model for biological compartments, and (ii) an enhanced model of chemical rules addressing also biomechanical actions such as substances diffusion across membranes or compartments splitting. We tested a fragment of the framework using a case study based on spatial pattern formation in embryogenesis, where the interplay between cells' internal dynamics and cell-to-cell interactions has a central role.