Progetti

The training network NEWFRAC is a leading European research consortium composed by eight top university research teams and five high-tech companies. The high-level doctoral training offered by NEWFRAC to thirteen creative, entrepreneurial and innovative early-stage researchers is focused on new strategies for prediction and analysis of multi-field fracture phenomena in heterogeneous engineering systems at different scales.

These researchers are developing new failure-predictive computational tools and apply them to relevant problems in strategic industrial sectors like Energy, Health and Transport. Their individual research projects address most relevant questions of current interest in failure prediction in heterogeneous systems, that will generate high-impact research outputs which go beyond the current state of the art in fracture modelling. To this aim, NEWFRAC integrates two new strategies for computational fracture modelling: Finite Fracture Mechanics and a variational approach to fracture referred to as Phase Field, and solves wide-ranging interconnected fundamental issues of fracture modelling like: fragmentation and dynamic crack propagation, toughening composites by micro- and meso-structural optimization, simultaneous crack initiation and propagation interacting with interfaces in: ultra-thin ply composites, curved laminates, injection molded fibre reinforced plastic, thermoplastics, layered ceramics, photovoltaics, solid oxide fuel cells and human bones, among others. NEWFRAC guarantees the attendance of early-stage researchers to all network-wide training activities, and favours their training through interdisciplinary and intersectoral research, mobility and exposure to industry, aiming at their transfer to industry after the network. These early stage researchers will become experts in fracture prediction in heterogeneous systems and will contribute to reduce the innovation gap by enhancing two ways academia-industry transfer of knowledge.

SoBigData RI, with its tools and services, empowers researchers and innovators through a platform for the design and execution of large-scale data science and social mining experiments, open to users with diverse backgrounds, accessible on cloud (aligned with EOSC guidelines), and also exploiting supercomputing facilities. SoBigData RI will render social mining experiments more efficiently designed, adjusted, and repeatable by non-data scientists' domain experts by pushing the FAIR (Findable, Accessible, Interoperable) and FACT (Fair, Accountable, Confidential and Transparent) principles. SoBigData RI will orient resources from multiple perspectives: infrastructures and online services developers; big data analytics and AI; complex systems focussed on modelling social phenomena; ELSEC (Ethical, Legal, SocioEconomic and Cultural) aspects of data protection (as defined by the HLEG-AI); privacy-preserving techniques. SoBigData RI PPP will move our RI forward from the simple awareness of ethical and legal challenges in social mining to the development of concrete tools that operationalize ethics with value-sensitive design, incorporating values and norms for privacy protection, fairness, transparency, and pluralism.

Cyber ​​attacks alarm the population, damage the economy and endanger the very safety of citizens when they affect the distribution networks of essential services such as health, energy, transport, and critical infrastructures of modern society. The SERICS project aims to identify the key security and privacy issues that cause societal harm, economic losses and hinder the adoption of smart city technologies, taking into account technical issues but also legal and societal aspects related to security and privacy.

The project is structured in ten Thematic Areas that correspond to the ten Spokes of the proposal:

• Thematic Area 1: Human, Social, and Legal Aspects, which main objective is to investigate how to create a compelling and secure Cyberspace by combining sound technological systems with strong and robust regulation of human behavior;
• Thematic Area 2: Misinformation and Fakes, which main objective is to design and develop innovative solutions to identify and manage information disorder threats that come alive through fake news and deep fake spreading;
• Thematic Area 3: Attacks and Defenses, which aims to analyze emerging attack methodologies and develop advanced methods for detecting attacks and identifying guidelines for the design of computer systems guaranteeing reduced vulnerabilities for new attack categories;
• Thematic Area 4: Operating Systems and Virtualization Security, which is concerned with developing high-level automated security services and innovative security assessment and assurance methodologies to support the secure-by-design development and verification of cloud, edge, and 5G applications;
• Thematic Area 5: Cryptography and Distributed Systems Security mainly concerned with research activities in cryptography and distributed system security domains;
• Thematic Area 6: Software and Platform Security, which first scientific goal is to provide an ecosystem where software developers can easily reason about software security to develop new formal techniques based on secure compilation and secure composition, to reduce the gap between formal models, essential to provide full guarantees of correctness, and actual implementations. The second scientific goal is to provide innovative solutions to protect the software supply chain, including the software management and development process;
• Thematic Area 7: Infrastructure Security, which general objective is the advancement of infrastructure security technologies;
• Thematic Area 8: Risk Management and Governance, which aims to contribute to the cyber resilience of future systems and services characterized by increasingly interconnected digital components that are intrinsically vulnerable, as required by the EU through NIS and NIS2, and by the Italian National Agency for Cybersecurity (ACN);
• Thematic Area 9: Securing Digital Transformation, which has the main objective of studying new approaches, methodologies, solutions, and tools that can provide adequate security guarantees for new application scenarios that are emerging today as a consequence of strong acceleration towards pervasive digital transformation;
• Thematic Area 10: Data Governance and Protection, which responds to the need for trust in the security and privacy of data, and solutions that guarantee correct protection and use of data by empowering the various actors involved in data sharing and using scenarios with control over their data, supporting shared data in a selective and secure way, at the same time guaranteeing functionality, efficiency, and scalability.

Scuola IMT is affiliated to:

• Spoke 2: Misinformation and Fakes;
• Spoke 4: Operating Systems and Virtualization Security; 
• Spoke 6: Software and Platform Security;
• Spoke 7: Infrastructure Security;

Tuscany x.0 è uno dei 13 progetti vincitori italiani del bando Europeo per la creazione di un European Digital Innovation Hub con lo scopo di favorire il trasferimento tecnologico attraverso l’adozione di tecnologie digitali avanzate, Intelligenza Artificiale, Calcolo ad Alte Prestazioni, Sicurezza Informatica. Pubblica amministrazione e piccole e medie imprese avranno, così, l’opportunità di usufruire di servizi e tecnologie: facili, accessibili e a Km0. In particolare, Tuscany X.0 è il Polo dedicato alle aziende toscane, monitorerà il livello di maturità digitale delle aziende tramite lo strumento Digital Maturity Assessment (DMA) e contribuirà a creare e a consolidare una rete di EDIH e di altri stakeholder interessati.

Il coordinamento tecnico è stato affidato a GATE 4.0, Distretto Tecnologico della Manifattura Avanzata della Regione Toscana insieme al Polo Tecnologico di Navacchio che si occuperà del coordinamento amministrativo.  Insieme ad un network allargato che raccoglie il know-how e le competenze trasversali di 13 partner provenienti dal mondo industriale, accademico e della ricerca, supporta la pubblica amministrazione e le piccole e medie imprese della Toscana nella trasformazione digitale e green, attraverso un catalogo di  servizi specifici e mirati.

Il progetto è attivo dal 1° ottobre 2022 e sarà finanziato per circa 5 milioni di euro in tre anni, da fondi in parte europei e in parte dal MIMIT, che serviranno a coprire i servizi a catalogo soprattutto (ma non solo) in materia di Intelligenza artificiale, supercalcolo e cybersecurity.

L’industria conciaria italiana, la cui produzione vale il 65% del totale prodotto nell’Unione Europea e circa il 20% della produzione mondiale, ricopre un ruolo di assoluto rilievo nel panorama internazionale.

Il contesto è caratterizzato da (a) contrazione del fatturato con conseguente riduzione di imprese e posti di lavoro e (b) richiesta da parte del mercato di articoli in pelle prodotti in modo eco-sostenibile, questo progetto BASILISCO si pone l’obiettivo di:

• Applicare prodotti naturali per sviluppare una concia a basso impatto ambientale
• Monitorare e ottimizzare il processo produttivo mediante lo sviluppo di un nuovo bottale e di algoritmi di apprendimento automatico in linea con la strategia 4.0

Per il raggiungimento di tali obiettivi saranno coinvolti i rappresentanti della filiera del settore conciario:

• Leather Kem che svilupperà il processo di concia con elementi naturali
• S.C. Costruzioni Meccaniche che progetterà un nuovo bottale con integrato sistema di dosaggio e monitoraggio dei parametri di interesse
• Scuola IMT Alti Studi Lucca che svilupperà gli algoritmi di apprendimento automatico
• Italpel che effettuerà le prove sperimentali

Many decisions in modern societies have a very complex scientific basis. Clinicians have to choose between different drugs for treating a patient. Central bankers have to forecast the evolution of financial markets, and to control the amount of money that circulates in a society. Physicists have to evaluate the impact of continued CO2 emissions for life on the planet. All these decisions are based on the forecasts of scientific models, and sometimes, their predictions reach a great degree of exactness (e.g., in identifying high-risk hospital patients and allocating resources efficiently).

In short, how does science based on uncertain models contribute to good decisions?

Our project investigates the interface between modeling and decision-making. We develop an understanding of how scientific models function, how they advance our knowledge despite their intrinsic uncertainty, and how they are interpreted in a decision context. More specifically, we focus on the following three questions, which correspond to our main targets:

1. How can highly idealized and intrinsically uncertain scientific models be successful in prediction?

2. Why can we trust and accept scientific models in spite of their intrinsic uncertainty and how should we factor in this uncertainty?

3. How should we synthesize actuarial, model-based judgment with human expertise in making practical decisions?

In answering these three questions, our projects integrates foundational philosophical analysis (e.g., rational criteria for theory acceptance), formal and conceptual analysis, and case studies about construction and use of models in a number of relevant scientific disciplines like financial economics and evidence-based medicine.

This project brings together a variety of competences and methods to address an issue that has recently gained attention by scholars working at the intersection of different disciplines: economics, psychology, sociology, cognitive sciences. The issue under debate concerns the interaction between cognitive modes and prosocial behavior, i.e., the extent to which individual behavior is beneficial to the society as a whole. The basic question is: which cognitive mode is more likely to foster prosocial behavior? Up to now, such a debate is lively and far from being set. Indeed, the literature provides both conflicting theoretical arguments and inconclusive empirical evidence (see the state of the art in B.1.2).

Our hypothesis is that the different definitions of intuition used in the literature can, to some extent, explain opposing results in the analysis of intuitive behavior and prosociality.

The project investigates four research lines:

(1) from a theoretical perspective, we aim at providing an evolutionary explanation of the linkage between social motives and prosocial behavior in each cognitive mode;

(2) from a methodological perspective, we aim at identifying effective methods to manipulate the cognitive mode of subjects in an experimental setting (the aim is to be able to promote reliance on automatic intuition, conscious intuition and deliberation);

(3) from a behavioral perspective, we aim at understanding how cognitive modes and social motives interact to determine actual prosocial behavior;

(4) again from a behavioral perspective, we aim at understanding how cognitive modes affect the degree of discrimination of prosocial behavior (i.e., to what extent prosocial behavior is limited to interactions with individuals similar to the decision-maker).

By developing these research lines, we can obtain a rationalization of apparently conflicting evidence provided by the literature and, more in general, we can foster the understanding of the relationship between cognitive modes and prosocial behavior, as mediated by social motives.

Both deforestation and pollution are key factors in global climate change. This is because forests are natural pollution sinks able to capture carbon dioxide emissions from the atmosphere and convert them into oxygen, which both humans and animals can breathe safely. Unless proper countermeasures are taken, deforestation represents a big problem for climate: by cutting down forests without replacing the trees that are removed, the absorption efficiency of such carbon sinks is reduced, with possible catastrophic consequences. To counteract these effects, suitable policies are needed, such as reductions in the emission levels, and reforestation.

This project complements the few available attempts in the literature to unify deforestation and pollution issues.

These systems are, and will be more and more, pervasive and ubiquitous, also in safety-critical situations. Examples range from robots and drones delivering goods to self-driving vehicles and smart buildings. These systems must satisfy safety requirements, and meet performance goals. It is fundamental to keep all such requirements into account from the design phase, to reduce the cost of development and avoid dangerous situations coming from unexpected requirement violation.

Model-based engineering (MBE) of CPS is challenging due to some specific features of these systems: they act in an open physical space environment, which is subject to unpredictable changes. Hence, approaches to MBE of CPS have to explicitly take into account the uncertainty and the spatial structure of the environment in which they act. In this project, we will propose a framework in this direction, dealing with spatially distributed CPS in an uncertain environment. The framework will allow engineers to describe the system and the requirements with a high-level, UML like specification language, which will be automatically converted in a domain specific language from which a formal executable model of the system and a formalization of requirements will be extracted

co -finanziamento di n. 8 Assegni di ricerca di durata biennale per la realizzazione dei progetti

“Management and Sustainable Innovation for the Community Involvement” - MANSIFORTHCOMIN (n. 3 Assegni di ricerca);

“Villa Guinigi Organization for the Community Involvement” -VIGORFORTHCOMIN (n. 3 Assegni di ricerca);

“Piano Strategico della Cultura di Pistoia” – PSCPT (n. 2 Assegni di ricerca).

(approvazione della Regione Toscana con decreto dirigenziale del 26 novembre 2019, n. 20180)

Gli Assegni di Ricerca in ambito culturale sono finanziati con le risorse del POR FSE TOSCANA 2014-2020 e rientrano nell’ambito di Giovanisì (www.giovanisi.it), il progetto della Regione Toscana per l’autonomia dei giovani

The present project pursues three main objectives.

(1) A complete inventory of the manuscripts preserving Avicenna’s Ilāhiyyāt, through the systematic inspection of the catalogues of Arabic collections in libraries worldwide; the obtainment of copies of the censed manuscripts; and their description.

(2) To prepare an on-line critical edition of the Arabic text of the Ilāhiyyāt, divided in ten parts corresponding to the ten treatises of the work, with apparatus of variants, apparatus of sources, and apparatus of loci paralleli; to connect the edition with an English translation, based on the wording of the new critical edition, and with the Latin Medieval translation, according to its already existing critical edition; to provide a general introduction to the work and specific doctrinal introductions to the single treatises, with detailed overviews of their main doctrines and their function in the overall work, and a running commentary, with all the relevant explanatory information; to append complete indexes and lexica. Techniques of digital humanities will consent the creation and interplay of the different elements envisaged.

(3) A web platform, consisting of four main elements: i) a codicological database, containing all the relevant information and the key images of the manuscripts brought to light and used in the project; ii) the edition of the text and its complements (translations, commentary, lexica etc.); iii) a bibliographical archive of editions, translations, and studies of Avicenna’s philosophy, with particular regard to his metaphysics; iv) a newsletter for the sharing of information among scholars of Avicenna’s thought worldwide. Technology will provide instruments to store information on and images of manuscripts into the database; to record the collation of the testimonia of the work, and to convert the results of the collation into the critical apparatus; to link the Arabic text with its various complements (apparatuses, translations, commentary, indices, lexica); and to compile indices and lexica.

The BRIGHT project aims at enhancing the visibility and perception of researchers among the general population in the Tuscany Region, simultaneously with all the European Researchers’ Night (ERN) initiatives. BRIGHT acronym means “Brilliant Researchers Impact on Growth Health and Trust in research”, wishing to underline the positive aspect of the research activity in order to convey a positive message to the general public. BRIGHT focuses on creating awareness on the important role played by researchers in addressing the great challenges ahead, such as health and wellbeing, new technologies, sustainable development, physical and biological challenges, cultural heritage. BRIGHT will be successful if it will be able to convince the population of the IMPACT of the researchers and the research's products for the development of the society, and to make people TRUST the researchers instead of being driven away by hoaxes with no scientific basis.

The OpenMaker project aims to create a transformational and collaborative ecosystem that fosters collective innovations within the European manufacturing sector and drives it towards more sustainable business models, production processes, products, and governance systems.

Building on the paradigm of Open Manufacturing, the project will achieve this goal by bringing together traditional manufacturers and digital-savvy makers and engaging in the process also universities, local authorities, civil society organisations and policy-makers.

Lucca, con il suo patrimonio culturale, è sede di una grande varietà di eventi culturali che offrono un ampio potenziale per lo sviluppo locale della città in termini economici e sociali. Questi festival non solo attraggono turisti italiani e stranieri, e quindi flussi economici, ma contribuiscono anche a dar forma ad un’immagine specifica della città e dell’identità lucchese, in aggiunta ad altri benefici di natura non economica. Esplorare quindi gli impatti di questi grandi eventi culturali rappresenta un’operazione di cruciale importanza, in modo particolare per quanto riguarda il city-branding, lo sviluppo locale e il turismo.

Il progetto andrà ad affinare la conoscenza della scena culturale lucchese e internazionale e al tempo stesso a individuare priorità e interessi dei vari attori coinvolti, attraverso un approccio interdisciplinare che mobilita metodi e approcci tipici delle scienze umani, delle scienze sociali e dell'informatica. Nel suo ruolo di intermediario fra la politica e gli organizzatori da un lato, i residenti e il pubblico dall’altro, l’iniziativa mira ad analizzare la situazione attuale e proporre suggerimenti per concorrere alla crescita culturale ed economica della città.

The BRIGHT -“Brilliant Researchers Impact on Growth Health and Trust in research“ project aims at enhancing the visibility and perception of researchers among the general population in Tuscany, simultaneously with all the European Researchers’ Night (ERN) initiatives.

BRIGHT will bring the researchers in the squares and streets of the historical centres of the many cities invloved in the project, and will open the doors of the laboratories to the citizens. They will have a chance to talk to the researchers, appreciate their preparation and passion, and listen from their own words about the positive impact of the results of their research on the quality of our lives. “Researchers make your life better!” is the guiding principle stemming from the passion, the enthusiasm and hard intellectual work of researchers’ daily work and how this contributes to the wellbeing of the general population.

BRIGHT focuses on creating awareness on the important role played by researchers in addressing the great challenges ahead, such as health and wellbeing, new technologies, sustainable development, the physical and biological world, cultural heritage.

Le principali finalità del programma di intervento sono: favorire lo sviluppo del capitale umano e la sua occupabilità attraverso il finanziamento di percorsi di alta formazione tramite la ricerca; sostenere l’occupabilità degli assegnisti promuovendo lo sviluppo di percorsi di esperienza che ne assicurino la crescita professionale; creazione di un laboratorio sperimentale condiviso tra la Scuola IMT e l’azienda CROMOLOGY ITALIA SpA, a partire dal laboratorio MUSAM-Lab precedentemente finanziato dall’European Research Council e dal laboratorio di R&D di CROMOLOGY, per supportare e incrementare le attività di trasferimento tecnologico della Scuola IMT sul terrirorio nell’ambito dei materiali polimerici; accrescimento delle attività di ricerca e trasferimento tecnologico della Scuola IMT in ambito informatico in cooperazione con le aziende del territorio interessate a sviluppare metodi di cyber-security e con Fondazioni e associazioni interessate all’applicazione di metodi per l’analisi di big data scaturiti da eventi culturali e sociali, archivi museali, o applicazioni mediche.

Research Unit:

• LYNX
• SYSMA
• MUSAM

The capacity to have an aesthetic experience is a characteristic human trait and it is the result of a complex interplay between sensation, emotions and cognition. Although the concept of “beauty” has been the object of centuries of philosophical and psychological debate, only in recent decades has it been the object of an organized cognitive neuroscience research program. Although recent developments are very promising, more work is needed to uncover the functional and neural mechanisms mediating the aesthetic experience.

Our project aims to use neuroimaging (IMT Unit) and brain stimulation (Milano-Bicocca Unit) techniques to shed light on the neural network underpinning aesthetic experience for different categories of visual stimuli. Furthermore, consistent research points to the existence of several multisensory brain regions that process information regardless the sensory modality in which the input is acquired. Focusing here on touch, whether aesthetic appreciation of the same artworks in the visual and haptic modality lead to similar neural activations is not known. We will investigate this aspect, also considering a related fascinating question, i.e., whether the mechanisms mediating aesthetic experience in the blind brain are the same as in case of a normally sighted brain. What is “beauty” for a blind person’s brain? (Milano-Bicocca and Pisa Unit have a long experience in studying blindness).

Moreover, experience of and sensitivity to aesthetics has received little attention in the clinical setting. We know that aesthetical appreciation of paintings is spared in dementia and schizophrenia affects art perception, but very little is known for instance about major depression disorder, of which a core symptom is anhedonia. Can exposure to beautiful artistic images reduce non-physical pain? This hypothesis is grounded in neuroimaging and brain stimulation evidence showing that aesthetic viewing increases activity in the left dorsolateral prefrontal cortex that is usually hypo-activated in depressed patients.

Finally, although a certain simplification is inherent to experimental neuroscience (that necessarily tend to look at “average” experiences), an interdisciplinary approach in which knowledge from art history and experimental methods converge is needed (against reductionism). The project strongly aims to open a constructive dialogue between disciplines, such as art history and neuroscience, that have been often considered as having far (or even incompatible) approaches. The IMT-Lucca unit will have a fundamental role in assisting the other units in the selection of stimuli and paradigms and in critically contextualizing the findings about neural correlates of aesthetic appreciation in a broader perspective in which art history and philosophy are considered.

Research Unit:

• LYNX
• MOMILAB

Long-living software systems are typically available in a rich set of variants to deal with differing customer requirements and application contexts. Furthermore, users are often given the possibility to change to a different configuration online to dynamically adapt to varying environmental conditions. In addition to satisfying functional requirements, such changes are to preserve existing service-level agreements. The focus of this project is to define a methodology for expressing system variability and its impact on performance.

 The project proposes imaging techniques, acquisition protocols, and image analysis and pattern recognition algorithms that enable the assessment of cardiovascular disease without a single discomfort to the patient (no needles, no stress). We are involved with developing all the registration, segmentation, and pattern recognition methods that enable the visualization and characterization of any underlying disease.

Increased global competition and an urgent need to address sustainability and resource-efficiency of operations force European industries and large-scale infrastructure operators to look for efficient real-time decision-making systems that allow them to react rapidly, consistently and effectively to a continually changing economical and environmental landscape. Advances in optimization tools and the emergence of new highly pervasive, cheap, and reconfigurable wireless sensing technologies now motivate the research for novel distributed layers of plant management that are highly coordinated through the plant-wide circulation of information. The WIDE project aims at developing a rigorous framework for advanced control and real-time optimization of truly large-scale and spatially distributed processes, based on the integrated use of distributed model predictive control and wireless sensor feedback.
While the technologies and methodologies investigated in WIDE are general enough for many contexts, the project employs its unique access to an operational European city water distribution network to prove the effectiveness of the new concept in real practice.

Project PHIDIAS has the objective of providing an affordable, high-throughput, distributed, knowledge-sharing platform for phenotype collection and analysis. PHIDIAS will incorporate lessons learned from prior phenotyping efforts and will be a simple to use platform for acquiring (non-destructively and continuously) and analyzing phenotypes from plant experiments.

Il progetto si propone di studiare i flussi turistici all’interno di un centro storico: il caso studio scelto è la città di Lucca. La ricerca presenta due obiettivi: dal punto di vista tecnologico, l’obiettivo è quello di sviluppare un sistema semi-automatico basato su tecniche di machine learning in grado di analizzare grandi quantità di immagini (fotografie), per determinare con precisione i punti di interesse di un centro storico, misurare tempi di attesa e percorsi compiuti dai visitatori al suo interno; dal punto di vista umanistico, invece, lo scopo è quello di valutare come i flussi turistici siano cambiati nel corso dei secoli sino al presente, e utilizzare questa indagine nel passato per ricavare dati utili alla conoscenza, gestione e programmazione del turismo oggi.

 Si intende dotare la Biblioteca del materiale bibliografico necessario al potenziamento della ricerca e della didattica in tutti gli ambiti disciplinari della Scuola, mettere a disposizione dei cittadini e degli studenti lucchesi servizi e materiale non posseduti da altre biblioteche lucchesi (banche dati e riviste elettroniche specializzate), potenziando in tal modo il ruolo di punto di riferimento culturale per Lucca. Si prevede inoltre di rafforzare i rapporti di interscambio culturale con gli altri enti che operano nel campo della cultura e dell’educazione presenti sul territorio locale.

FOC is a Scientific Project Financed by FET OPEN Scheme in the field of Information and Communication Technology by the European Commission. The research topic is to understand and possibly forecast systemic risk and global financial instabilities We want to provide a novel integrated and network-oriented approach to the issue. On one hand, we will offer a theoretical framework to measure systemic risk in global financial market and financial networks. On the other hand, we will deliver an ICT collaborative platform for monitoring systemic fragility and the propagation of financial distress across institutions and markets around the world. Experts will be able to evaluate algorithms and models to forecast financial crises as well as visualise interactively possible future scenarios.

Future software-intensive systems, such as sensor networks, power grids, satellite and robot swarms, will generally exhibit a number of characteristic features: - Massive numbers of nodes, nodes with complex behavior, or complex interactions between nodes. - Operation in open and non-deterministic environments with variable network topology. - Need for adaptation, e.g., to changing environments and requirements.We call this future generation of software-intensive systems ensembles. The potentially huge impact – both positive and negative - of ensembles means that we need to understand ways to reliably and predictably model, design, and program them. Although there is a lot of research in this area, so far no theoretically well-founded technique for building ensembles exists. The goal of the ASCENS project is to develop such a method and to demonstrate its feasibility in three important application domains: robot swarms, cloud computing and e-mobility.

L’Obiettivo principale del programma è il miglioramento della fruizione dei servizi pubblici e della qualità della vita nei contesti urbani attraverso la virtualizzazione e la digitalizzazione del cose. Il perseguimento di tale obiettivo deve passare attraverso il raggiungimento dei seguenti due grandi sotto-obiettivi: 1. sviluppo di una piattaforma Web aperta per la virtualizzazione degli oggetti e la loro gestione. 2. sviluppo di una soluzione wireless mobile basata sull’innovativa tecnologia multi-antenna che vada a superare il trade-off banda/costi/copertura attualmente presente nel mercato dei sistemi di comunicazione wireless.

Future advancements in ICT domain are closely linked to the understanding about how multi-level complex systems function. Indeed, multi-level dependencies may amplify cascade failures or make more sudden the collapse of the entire system. Recent large-scale blackouts resulting from cascades in the power-grid coupled to the control communication system witness this point very clearly. A better understanding of multi-level systems is essential for future ICT’s and for improving life quality and security in an increasingly interconnected and interdependent world. In this respect, complex networks science is particularly suitable for the many challenges that we face today, from critical infrastructures and communication systems, to techno-social and socio-economic networks. MULTIPLEX proposes a substantial paradigm shift for the development of a mathematical, computational and algorithmic framework for multi-level complex networks. Firstly, this will lead to a significant progress in the understanding and the prediction of complex multi-level systems. Secondly, it will enable a better control, and optimization of their dynamics. By combining mathematical analyses, modelling approaches and the use of massive heterogeneous data sets, we shall address several prominent aspects of multi-level complex networks, i.e. their topology, dynamical organization and evolution.

 Sviluppo su Lucca di una piattaforma di competenze e di tecnologie in tre ambiti ad alta rilevanza per la crescita del territorio: a) risparmio energetico e urbano b) sviluppo di tecnologie per la valorizzazione del patrimonio culturale locale c) sviluppo di un polo di competenze per analisi e servizi ad alto valore aggiunto per il sistema delle imprese; concorrendo a sviluppare Lucca come polo di rilevanza nazionale a tre linee di ricerca ad alta rilevanza applicativa per lo sviluppo del territorio, contribuendo a qualificare Lucca come filiera della Ricerca e dell’Innovazione.

The DOLFINS project addresses the global challenge of making the financial system better serve society by placing scientific evidence and citizens participation at the centre of the policy process in finance. The project strives to give scientific evidence and citizens participation central roles in the policy process concerning finance. DOLFINS will focus on two crucial and interconnected policy areas that will shape the public debate in the coming 5 years: How to achieve financial stability and how to facilitate the long-term investments required by the transition to a more sustainable, more innovative, less unequal and greener EU economy. The expected impact is to achieve crucial advances in reshaping the policy process to overcome the financial and political crisis faced by the EU. We will deliver quantitative tools to evaluate policies aiming to tame systemic risk and to foster sustainable investing. The tools will be based on fundamental research combining network models and algorithmic game theory with broader economic insights. 

 Crisis Lab, is a novel strategic research project financed by Italian Government (Progetti di Interesse CNR). The aim is of creating an Observatory of risks in domains ranging from finance, energy, markets, transport and urban systems, with interdisciplinary methodology based on the new science of Complex Networks. The focus of the Crisis lab is to develop models using empirical data to describe the vulnerabilities of diverse socio-technical systems which have a complex underlying dependency structure. The globalization of our planet requires state-of-the-art methodologies for better understanding the dependency structure of multi-level systems which can be vulnerable to catastrophic failure. The Crisis Lab project is an international movement for the development of a new economic thinking, which has the strategic aim to develop new ideas, which lead our economic future and the recovery of the financial sector of its supporting role to society and to the real economy.

Immagini dinamiche generate da tecniche avanzate di imaging biomedico possono essere rappresentate come grafi, o reti, i cui vertici sono costituiti dai “pixel” dell’immagine, e i link“ da una misura di correlazione tra coppie di pixel. Il progetto si propone di applicare sviluppi recenti nell’analisi della struttura e della topologia delle reti complesse all’analisi di immagini dinamiche di sistemi biologici.
 

One of the most pressing and fascinating challenges scientists face today, is understanding the complexity of our globally interconnected society. The big data arising from the digital breadcrumbs of human activities promise to let us scrutinize the ground truth of individual and collective behaviour at an unprecedented detail and scale. There is an urgent need to harness these opportunities for scientific advancement and for the social good. The main obstacle to this accomplishment, besides the scarcity of data scientists, is the lack of a large-scale open infrastructure, where big data and social mining research canbe carried out.  To this end, SoBigData proposes to create the Social Mining & Big Data Ecosystem: a research infrastructure (RI) providing an integrated ecosystem for ethic-sensitive scientific discoveries and advanced applications of social data mining on the various dimensions of social life, as recorded by “big data”. Building on several established national infrastructures, SoBigData will open up new research avenues in multiple research fields, including mathematics, ICT, and human, social and economic sciences, by enabling easy comparison, re-use and integration of state-of-the-art big social data, methods, and services, into new research. It will not only strengthen the existing clusters of excellence in social data mining research, but also create a pan-European, inter-disciplinary community of social data scientists, fostered by extensive training, networking, and innovation activities. In addition, as an open research infrastucture, SoBigData will promote repeatable and open science. Although SoBigData is primarily aimed at serving the needs of researchers, the openly available datasets and open source methods and services provided by the new research infrastructure will also impact industrial and other stakeholders (e.g. government bodies, non-profit organisations, funders, policy makers).

L’obiettivo è sviluppare un algoritmo per il calcolo dell’età vascolare, basato sul database europeo SCORE. In particolare verrà sviluppata una App rivolta ai medici, che sarà validata mediante misurazioni dirette vascolari con diagnostica strumentale non invasiva. A tale scopo saranno sottoposti alla valutazione dei fattori di rischio cardiovascolare e a valutazioni strumentali soggetti di età superiore ai 40 anni, i cui risultati saranno successivamente confrontati con la valutazione dell’età vascolare calcolata mediante algoritmo. Il progetto potrebbe avere una concreta ricaduta operativa sulle Aziende Sanitarie del territorio della Provincia di Lucca, permettendo di migliorare il servizio di prevenzione cardiovascolare ai pazienti.