It will be a radically new approach for long-term continuous monitoring of insulin-treated persons with diabetes (ITD) moving from a glucose-only to a multi-metabolite monitoring - glucose, lactate and 3 ß OH-butyrate– paradigm. Multi-metabolite monitoring will also lead to a diabetes therapy breakthrough by algorithmically driving a continuous physiologic insulin delivery by a maintenance-free implanted MEMS pump using the peritoneal route enabling an optimal therapy for subjects with ITD. Our vision is a fully implantable artificial organ to replace insulin secretion loss by targeting metabolic health instead of mere glucose control and mimicking physiological insulin action. While offering a really burden-free life for insulin-treated children and adults, it is expected to allow a dramatic reduction of metabolic variations, hence a minimization of acute and long-term complications and an abating of the still high mortality of T1D patients. Unobtrusive living with diabetes will be reached by the calibration-free, implantable, long-term multi-metabolite monitoring solution connected wirelessly to a novel highly miniaturized silicon MEMS micropump, with down to 50 nl stroke volume, able to operate a newly developed U1000 insulin and enabling reservoir refill cycles of 180 days up to 365 days. Both devices hold durable battery operating life of more than 8 years without recharge and are suitable for children. Newly designed control algorithms based upon multiple signal inputs will drive automated insulin delivery without the need of obtrusive meal and physical exercise announcements.

The policies driving the green and digital transitions, or twin transitions, are intended to level the field to achieve the European Growth Model and attain the EU Green Deal and the UN’s SDGs. However, these policies have had unintended and unforeseen effects, creating new inequalities and/or aggravating existing ones. Those primarily affected are social groups already at risk and EU’s most vulnerable regions. Public authorities and policy-makers at local, national and European levels therefore need evidence-based understanding of these inequalities and concrete ways to prevent and/or mitigate these. The READJUST project aims to suggest policy options for overcoming these (potential) trade-offs between efficiency and equality in twin transitions, in the key sectors of mobility and agri-food. The green and digital policies are intended to level the field for attaining SDGs; however, they may return uneven distribution of access to the transitions and their benefits. READJUST aims to suggest options for overcoming the perceived trade-off between efficiency and equality in policy and to make inclusive growth a reality. Policymakers portray a future that is green and digital for the EU, and they aim to continuously contain the unintended consequences of the green and digital transitions in terms of inequalities. Generating zero negative effects on the climate can be efficiently achievable by twining green and digital transitions. Nonetheless, individually and jointly, the transitions might widen the existing inequality gaps. This project aims to contribute to policies for fair and just twin transitions to mitigate existing inequalities driven by the twin transitions and minimize the transitions’ unintended consequences for equality. In this project, we strive to address the inequalities created or exacerbated by the twin transitions policies in certain domains. Policies of green and digital transitions which are aimed at the growth of the entirE, or its subsections.

In the EU, most of the nuclear power plants (NPPs) are currently in the second half of their designed lifetime, making lifetime extension an important aspect for the EU countries. One of the most limiting safety assessments for long term operation (LTO) is the reactor pressure vessel (RPV) integrity assessment for pressurized thermal shock (PTS). The goal is to demonstrate the safety margin against fast fracture initiation or RPV failure. To verify safe operation of existing NPPs going through LTO upgrades, advanced methods and improvements are necessary. In the EU, currently used PTS analyses are based on deterministic assessment and conservative boundary conditions. This type of PTS analyses is reaching its limits in demonstrating the safety for NPPs facing LTO and need to be enhanced. However, inherent safety margins exist and several LTO improvements and advanced methods are intended to increase the safety margins of PTS analysis. Additionally, the quantification of safety margins in terms of risk of RPV failure by advanced probabilistic assessments becomes more important. The main objectives of this project are establishing of state-of-the-art for LTO improvements having an impact on PTS analysis: NPP improvements (hardware, software, procedures), development of advanced deterministic and probabilistic PTS assessment method including thermal hydraulic (TH) uncertainty analyses, quantification of safety margins for LTO improvements and development of best-practice guidance. After establishing the LTO improvements, TH calculations will be performed including also uncertainty quantification relevant to PTS assessment. Benchmark calculations for both deterministic and probabilistic RPV integrity assessment will be performed with the goal to establish the impact of LTO improvements and TH uncertainties on the overall RPV integrity margins.

The MELISSA project aims to provide a clinically validated, effective, trustworthy, and cost-efficient artificial intelligence (AI)-based digital diabetes management solution to support both health care providers (HCPs) and insulin-treated patients with diabetes (PwD) in their daily routine with personalised treatment and care recommendations. The solution is independent of the used glucose monitoring devices and is based on the combined use of already prototyped advanced AI-approaches and innovative tools for quantification of lifestyle and behavioural factors, taking into consideration sex/gender aspects, age, and socio-economic parameters related to the development of diabetes. More specifically, core element of the project is the daily insulin treatment adjustment to ensure glucose control using an already introduced self-learning approach based on reinforcement learning. The approach is data-driven, real-time and of low computational cost and allows daily adjustment of the insulin infusion profile, on the basis of the fluctuations in the patient?s glucose. The approach takes into consideration patients treatment-related (glucose, insulin, and carbohydrate intake) and conceptual information and during the project will be further extended and optimized to include additional lifestyle and behavioural parameters. Furthermore, tools for assessing the risk of short- and long-term complications will assist HCPs in reaching better decisions on adjustments to the treatment schemes. To meet the objectives the consortium brings together partners active in the fields of diabetes (PwD and HCPs), diabetes technology, AI, behavioral sciences, ethics in AI, regulatory affairs, healthcare economics and clinical trials to further co-create, clinically develop, optimize, and clinically validate an AI-based solution for more effective and cost-efficient diabetes management through personalised treatment and care.