The low-power sensing platform proposed by the Convergence project is foreseen as a wireless, low-power and multifunctional wearable system empowered by energy-efficient technologies. This will allow meeting the strict demands of life-style and healthcare applications in terms of autonomy for quasi-continuous collection of data for early-detection strategies. The system is compatible with different kinds of sensors, able to monitor not only health indicators of individual person (physical activity, core body temperature and biomarkers) but also the environment with chemical composition of the ambient air (NO_x, CO_x, NH_x particles) returning meaningful information on his/her exposure to dangerous (safety) or pollutant agents. In this article, we introduce the specifications and the design of the low-power sensing platform and the different sensors developed in the project, with a particular focus on pollutant sensing capabilities and specifically on NO₂ sensor based on graphene and CO sensor based on polyaniline ink.

The paper discusses the outcomes of the conference organized by the InDeal project. The conference took place on 12 December 2018 in Montpellier as part of the EnerGaia energy forum 2018. A holistic interdisciplinary approach for district heating and cooling (DHC) networks is presented that integrates heterogeneous innovative technologies from various scientific sectors. The solution is based on a multi-layer control and modelling framework that has been designed to minimize the total plant production costs and optimize heating/cooling distribution. Artificial intelligence tools are employed to model uncertainties associated with weather and energy demand forecasts, as well as quantify the energy storage capacity. Smart metering devices are utilized to collect information about all the crucial heat substations’ parameters, whereas a web-based platform offers a unique user environment for network operators. Three new technologies have been further developed to improve the efficiency of pipe design of DHC systems: (i) A new sustainable insulation material for reducing heat losses, (ii) a new quick-fit joint for an easy installation, and (iii) a new coating for reducing pressure head losses. The results of a study on the development and optimization of two energy harvesting systems are also provided. The assessment of the environmental, economic and social impact of the proposed holistic approach is performed through a life cycle analysis. The validation methodology of the integrated solution is also described, whereas conclusions and future work are finally given.

This paper presents a refined framework for the development of digital twins in subsystem-level applications, leveraging a virtual sensor approach. To validate the proposed framework, a case study was conducted focusing on damage localization within a mechanical structure. By integrating physics-based models with data-driven techniques, the digital twin enables real-time monitoring and accurate damage localization using a reduced number of strain sensors. The digital twin developed operates in two phases: an offline phase for modeling and simulating virtual sensors, and an online phase that processes real-time strain data from the physical system. The results demonstrate that the digital twin accurately localizes damage in real-world mechanical structures, highlighting its potential for broad application in structural health monitoring.

The EU project FAMOSOS (Farm monitoring via real-time soil sensing) aims to develop a real-time monitoring system for soil data, including nitrogen (ammonium, nitrate, nitrite), moisture, pH, and dissolved oxygen with increased sensitivity, reduced response times and sample volumes. This IoT system will be composed of a wireless underground sensor network, composed of porous ceramic probe, combined with a micro pump, a sensor, and an antenna for wireless data transmission. The system will be tested in Germany, Portugal, and Finland, with data used in the biophysical Agricultural Production Systems Simulator (APSIM) model to identify sustainable, climate-resilient agricultural practices.

Component alignment in total knee arthroplasty (TKA) is critical for postoperative success, as malalignment can lead to aseptic loosening. This study explores the potential for detecting knee prosthesis malalignment through strain analysis of the tibial tray membrane, leveraging a digital twin approach, which combines physics-based simulation with data-driven modeling. A finite element model (FEM) of the complete femur–insert–tibia system, equipped with a strain-based sensing tray, was used to analyze the mechanical response of the assembly under various loading conditions. Malalignment was simulated by varying the angle between the femur and insert in the coronal plane. The metric of interest is strain at the surface of the membrane. The resulting strain values were converted into synthetic voltage signals, representing the expected measurement of the real strain gauge output. These signals were used to train a neural network model to predict the prosthesis malalignment angle. The model demonstrated a strong correlation between malalignment and strain signal with a root mean square error (RMSE) of 0.02° on the simulated data. These results support the creation of a virtual alignment sensor, enhancing the capabilities of smart orthopedic implants for intraoperative and postoperative assessment.