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Fondazione Bruno Kessler
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232 Projects, page 1 of 47
  • Funder: European Commission Project Code: 840752
    Overall Budget: 171,473 EURFunder Contribution: 171,473 EUR

    NanoEnHanCeMent (Nanoparticle Enhanced Hadron-therapy: a comprehensive Mechanistic description) is an action aimed to apply basic Physics and Chemistry methods to uncover the microscopic mechanisms behind nanoparticle enhancement of hadron-therapy for cancer treatment (or ion beam cancer therapy). Hadron-therapy (radiotherapy using accelerated ion beams) is one of the most advanced radiotherapies available, with superior dose delivery and biological effectiveness as compared to conventional radiotherapy. The increased effectiveness of hadron-therapy relies on physico-chemical phenomena occurring on the nanoscale. There is experimental evidence pointing out to nanoparticles enhancing the biological effects of ion beams. Since nanoparticles can be tuned to target cancer cells, they might be used to further improve hadron-therapy. However, it is still unknown how nanoparticles produce this effect. A proper exploitation of the nanoparticle radioenhancement in hadron-therapy depends on improving the understanding of the physico-chemical mechanisms responsible for it. In this project, a theory and modelling approach is proposed, in which a series of semiempirical and ab initio methods will be extended and interfaced with Monte Carlo track-structure simulation tools, in order to advance the basic understanding of the nanoparticle enhanced hadron-therapy physical and chemical mechanisms. The action also encompasses an integral training program for the Experienced Researcher, with a deepening in already mastered methods and learning of new methodologies, together with the acquisition of complementary and transferable skills, all this in an environment where theory, experiment and clinical applications meet. A complete communication and outreach program is also envisaged, to disseminate the results to the scientific community and also to show to the citizenship how the investment in basic science and European cooperation pave the way for addressing societal challenges.

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  • Funder: European Commission Project Code: 101115870
    Overall Budget: 1,493,750 EURFunder Contribution: 1,493,750 EUR

    Planning - devising a strategy to achieve a desired objective - is one of the basic forms of intelligence. Temporal planning studies the automated synthesis of strategies when time and temporal constraints matter. Temporal planning is one of the most strategic fields of Artificial Intelligence, with applications in autonomous robotics, logistics, flexible production, and many other fields. Historically, the research on temporal planning follows a general-purpose framework: a generic engine searches for the strategy by reasoning on the problem statement (i.e. the starting condition and the desired objective), as well as on a formal model of the domain (i.e. the possible actions). Despite substantial progress in the recent years, domain-independent temporal planning still suffers from scalability issues, and fails to deal with real-word problems. The alternative is to devise ad-hoc, domain-specific solutions that, although efficient, are costly to develop, rigid to maintain, and often inapplicable in non-nominal situations. STEP-RL will study the foundations of a new approach to Temporal Planning, that is domain-independent and efficient at the same time. The idea is to adopt a framework based on Reinforcement Learning, where a domain-independent temporal planner is specialized with respect to the domain at hand. STEP-RL continuously improves its ability to solve temporal planning problems by learning from experience, thus becoming increasingly efficient by means of self-adaptation. STEP-RL will advance the state of the art in temporal planning beyond the "efficiency vs flexibility" dilemma, that I had to personally face in the many industrial projects I worked on.

  • Funder: European Commission Project Code: 101111036
    Funder Contribution: 288,859 EUR

    Electronic skin (e-skin) is a fast-emerging soft system to provide tactile sensations like our own skin. However, most of the prototypes today focus on the integration of various sensors on flexible substrates, which can hardly be integrated neurologically onto biological systems nor used over a large area as sensing components for robots: this is mainly because of their mismatch in various aspects including mechanical softness, computing/encoding capability, power consumption. This proposal aims to bring a step-change by developing an e-skin truly rooted in biological systems: the proposed e-Skin will respond to external stimuli (e.g., force) and encode the sensory information in the form of action potentials, just as the biological systems (i.e., sensory neurons) do. This will be achieved by innovative fabrication of neuron circuit arrays over a large area using nanomaterials, and further interfaced with tactile sensors, all on the soft substrate. Such “bio-like” localised processing, offered by the soft system, greatly decreases the latency of the sensory data, necessary for the upscaling of the sensing pixels to achieve human-level tactile sensation. Furthermore, this paves the way for the interfacing between soft electronics and biology, triggering transformations in the next generation of neurorobotics, neuroprosthesis and interactive systems.

  • Funder: European Commission Project Code: 267224
  • Funder: European Commission Project Code: 245000

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