Fundings

PRIN PNRR 2022

MOTUS - Automated Analysis and Prediction of Human Movement Qualities

The project will develop new methods aimed at achieving radical improvements in technologies for the analysis and prediction of human movement qualities. Particular emphasis is placed on automated detection and tracking of movement qualities fundamental to nonverbal communication between humans. Imagine that we must pass a heavy object to a partner. In this case, anticipatory adjustments to maintain postural equilibrium will involve torso and lower limb adaptations that will dramatically differ if the object is light (i.e. adaptations limited to the arm). Most importantly, our partner will read that information early during the interaction and prepare to expect a heavy or light object. These subtle but fundamental communicative signals are summarized in the concept of Origin of Movement (OoM), i.e. the part of the body perceived by an external observer as the joint in which the movement originates. This project will investigate theoretical developments in the automatic detection and tracking of OoM and related novel applications of machine learning methods and architectures (e.g. hierarchical clustering, matrix completion, and graph neural networks) to human movement analysis during social interactions at different spatio-temporal scales.

 Giorgio Gnecco

PRIN PNRR 2022

G-local Value Chains, Covid, Conflict, and the Labor Market

Recent global shocks have highlighted how little we know about Global Value Chains, their configurations, their determinants and their effects on different categories of stakeholders. On the one hand, the return of geopolitical risks has shown hitherto underestimated costs from an overreliance on overstretched supply chains. On the other hand, an excessive fragmentation of global production has coincided with significant changes in labour markets and income inequality, with a rising tension between equity and efficiency. How are GVCs reshaping to reduce the impact of shocks? What are the consequences for firms and workers participating in different GVC types? Do we expect reshoring in the short or the long term? Are there strategic segments of GVCs that need special provisions and regulations? These questions have clearly important implications for policymakers and the broader social welfare. We plan to investigate GVCs from various angles, specifically focusing on their impact on the labour market. We aim to assemble a unique firm- and worker-level database to study the determinants and consequences of GVC configurations at a granular microeconomic level. Multidimensional indicators of competitiveness, safeness, and vulnerability of network configurations and contractual environments will be produced. We also intend to understand the determinants of specific sourcing modes (outsourcing, offshoring, reshoring, friend-shoring) and identify the geographic and technological characteristics that are conducive to efficient and well-diversified supply chains. A distinctive feature of this proposal is a novel employer-employee perspective that only could provide us with a clearer picture of the consequences for both firms and workers of integration in different organizations of supply chains..

 Armando Rungi

PRIN PNRR 2022

Strategic thinking development in an ever-changing world

In the last 30 years, technological advancements have transformed the world, drastically changing the way people communicate and interact. In global societies, through the internet, people can access a potentially infinite amount of information and communicate with people from all over the world, in real time. Furthermore, globalization transformed strategic interactions. Exchanges that until a few years ago occurred among people that shared culture, education, and language, are now among people that share none of those (e.g., international political settings and multinational industries). Success in strategic interaction now hinges on the capacity to extrapolate the correct information from a complex context, incorporate it into a system of beliefs, and use it to develop an

optimal strategy. People that master the complex process of managing and exploiting information, have a great advantage on those who do not.

The goal of this project is to unpack the process of collection, elaboration, and exploitation of information, in interactive contexts and with different partners. The results will be used to design a machine-learning based model able to forecast people's behavior based on the process of information collection. The project comprises 4 working packages. WP1 investigates how strategic behavior is affected by the characteristics of feedback. WP2 studies whether experienced-based strategic learning is transferred to new situations and persists in time. WP3 tests how repeated interaction with different counterparts affects strategic thinking (where counterparts differ both in type - i.e., human VS machine - and strategic thinking - i.e., apply different strategies). WP4 uses the previous results to develop a machine-learning base model that exploits information about data collection to forecast future behavior.

 Sibilla Di Guida

PRIN PNRR 2022

GAMEDON - Game-Based Policies for Blood and Plasma Donation 

Employing experimental evidence rooted in behavioral economics, we want to measure the effectiveness of programs aimed at promoting blood and plasma donation (BD) with techniques inspired by game-based policy (GP). Promoting BD is crucial for the sustainability of the Italian Health System. In particular, for the long-term sustainability it is of great importance to guarantee donations routinely by young and adult generations (age 18-50). In fact, data collected during the recent COVID-19 suggest that new donors aged less than 50 are less likely to keep on donating in the following months (Bilancini et. al 2022).

GP identifies techniques and approaches that employ game-based activities designed to promote specific beliefs and behaviors. GP appears to be particularly effective in the presence of prior gaming experience or familiarity, of intrinsic motivation towards the topics involved and when the entire household is engaged (Bilancini et al 2021). These features make GP well suited for promoting BD among future young adults (now students) and current adults (students’ parents). This conjecture has been supported by the results of a pilot study that we have run in the spring of 2022 involving around 300

students who participated in a randomized controlled trial (RCT). The RCT was implemented within a program focusing on BD which

was run by the Italian Volunteering Association of Blood Donors (AVIS) in the province of Lucca. Collected data suggest that GP may

be effective in promoting awareness and understanding of BD-related issues, including the social relevance of donations.

 Ennio Bilancini

PRIN PNRR 2022

LISTEN -  "Listen to me, I will respond”: A randomized communication trial on health decisions

Through experimental evidence and a supporting theoretical framework, we evaluate and formalize the effectiveness of framing health communication campaigns with techniques inspired by motivational interviewing (MI). In the context of health, MI consists of communication techniques used by health professionals to reduce distrust towards preventative healthcare practices. MI uses four core skills (Breckenridge et al., 2021): (1) Open-ended questions: patients freely describe their worries and their experiences; (2) Affirmation: acknowledging the patient’s concerns; (3) Reflective listening: expressing empathy and understanding for the feelings verbalized by the patient; (4) Summary: summarizing the conversation to allow clarification of possible misinformation received by patients. A motivational interviewer avoids discrepancy and conflict with patients and recognizes their feelings in a non-judgmental way.

We provide a theoretical framework based on dual-process theory and reference cues aimed at investigating the mechanisms of action of MI in informational campaigns and producing testable implications (e.g., Bilancini and Boncinelli, 2018).

To test them and to provide implementable and cost-effective policy recommendations that can be readily included in existing communication campaigns, we conduct a series of Randomized Controlled Trials where the treatment consists of a framed video and survey bundle. Primary outcomes are vaccination uptake and screenings aimed at different target populations. 

 Ennio Bilancini

PRIN PNRR 2022

Towards Circular Economy: A Business Model Innovation Perspective 

The circular economy is a paradigmatic change for economic activity and people's lifestyles. It responds to a clearly recognized need by civil society to substantially change current practices to ensure continuity and development for the environment and future generations.

This research enters the field of the transition to the circular economy and aims to advance our theoretical understanding of the firms’ business model innovation oriented to the circular economy and its drivers, with a particular focus on digitization and managerial practices.

The research also has significant implications for firms and policymakers, as it will help highlight which drivers affect the capacity of business model innovation, combining the needs of competitiveness and performance with the growing expectations of sustainability and environmental protection expressed by civil society. In this view, during 2020 Pandemic environmental problems and climate change became pressure trend that firms must consider as one of the most important objectives to pursue. ESG performance became very important to investors because data showed that the flow of funds privileged lower environmental

and governance risks. So, in the debate about harmonizing ESG standards, there is an open space about how to develop the passage from a traditional Business Model to a new sustainable paradigm more suitable for a Circular Economy

 Nicola Lattanzi

PRIN PNRR 2022

SAFEJOBS  - Job Safety, Globalization and Labor Market Institutions 

This project aims to investigate the effects of import competition and labour market institutions on non-pecuniary labour market outcomes, with a particular focus on workplace safety. The empirical analyses use longitudinal data from the WHIP SALUTE dataset containing workers' histories with information on pecuniary labour outcomes and workplace injuries 

Francesco Serti

PRIN PNRR 2022

SIGNUM - Study of mobile phone siGNals for the evalUation of the interconnections between Mobility and the environment in Lombardia

The availability of big data drawn from mobile phone data is becoming increasingly essential in many fields of study that pertain to the analysis of social, economic and environmental aspects, since they allow a dynamic and fine-grained representation of human activities. In this context, recently developed technology has been able to capture signals produced by many kinds of devices and transmit them from/to terminal devices through global positioning systems (GPS). Depending on the purpose, one can then retrieve useful insights related to smart and sustainable mobility. The idea of this project is based on the availability (for Lombardia) of different types of mobile phone data. The aim of SIGNUM is twofold. First, we aim to develop statistical methods to estimate and predict the amount of traffic during specific time intervals in specific areas of interest using mobile phone data. Furthermore, a successive goal is to investigate the relation between mobility, pollution and natural disasters by matching the existing indices on air quality and pollutant emissions with the estimated traffic data obtained from the application of the developed methods to Lombardia. In pursuing the above targets, the primary results that this project intends to offer are as follows: 1. Developing robust spatiotemporal statistical methods to accurately estimate and predict traffic at the “small” area level by combining all sources of mobile phone data. 2. Matching predicted traffic data obtained from the application of the developed methods to Lombardia with existing indices on air quality and pollutant emissions by using specific spatial and geo-statistical aggregation and interpolation methods. An ancillary result is the production of a spatiotemporal dataset for traffic in Lombardia. 3. Studying the relation between air quality, pollutant emissions and traffic by conducting a counterfactual analysis aimed at investigating the role of pollution policies. 4. Studying the relation between traffic and natural disasters from a prevention perspective. The importance of these goals is evident due to the increasing interest in the nexus between mobility and the environment. Moreover, the application of robust methods to innovative data is another valuable contribution of this project.

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 Francesco Biancalani

Assegni di Ricerca 2023
Regione Toscana 

ROBOFARM - Development of edge computing tools for robotic platforms in precision agriculture

The project aims to develop edge computing methods optimized for processing data from various types of sensors on board robotic platforms in the context of precision agriculture (project in collaboration with Sigma Engineering).

R.U.: Giorgio Gnecco

PRIN 2022

MAHATMA - Multiscale Analysis of Human and Artificial Trajectories: Models and Applications

The project will develop new mathematical and computational models of expressive trajectories for under-investigated senses (touch) and scales (public-address sound). Supramodal models of trajectories will be derived through cross-sensory translation by means of experimental phenomenology, and validated through controlled experiments. Explainable, feature-based models will be developed as extensions of existing models, and will inform the development of featureless data-driven models. Applications will be developed, as proofs-of-concept for the trajectory models, in the areas of sport and performing arts, with implications in a variety of fields, including culture- and art-enabled motor reactivation and rehabilitation, and navigation help for sensory-impaired people.

U.R.: Giorgio Gnecco

THE_Spoke 3

THE Tuscany Health Ecosystem - Spoke 3 dedicated to Advanced Technologies, Methods and Materials for Human Health and Wellbeing

"THE - Tuscany Health Ecosystem" is one of the 11 innovation ecosystems financed at the national level for the PNRR and is the result of a project proposal submitted by the University of Florence, as lead partner, in line with one of the strategic lines of the Region of Tuscany. THE is the only ecosystem dedicated to Life Sciences, which focuses on the needs of the population. The project has received funding totaling EUR 110 million from the Ministry of University and Research. THE's challenge is to make Tuscany the 'health region'; by pushing research towards applications and companies to grow technologies dedicated to health and wellbeing. The ecosystem will devote part of the funding to provide funds for applied research to companies and spin-offs on the basis of special public calls for tenders, as well as the temporary hiring of about 300 new young researchers.

Spoke 3 is dedicated to Advanced technologies, methods, and materials for human health and well-being.

P.I.: Massimo Riccaboni

MammoScreen

Innovative and safe microwave-based imaging technology to make breast cancer screening more accurate, inclusive and female-friendly

Breast cancer (BC) is the most common cancer in women worldwide, affecting 1 in 8 women. Mammography is the gold standard technology for breast screening, which has been demonstrated through different randomized controlled trials to reduce breast cancer mortality. However, it has limitations and potential harms, such as the use of ionizing radiation, breast compression and performance restrictions due to the intrinsic nature of X-rays. In particular, breast density is a restrictive property that can prevent breast cancer detection in mammograms of women with radiographically dense breasts. Other existing techniques (MRI, Ultrasound, biopsy) also suffer from drawbacks. The overall aim of the MammoScreen proposal is to generate evidence about the use of MammoWave (a technology developed by UBT) as screening technique in population-based programs promoted by National or Regional Health Systems, to reach a revolution in breast screening. To do so, the consortium aims to confirm that MammoWave reaches sensitivity >90% and specificity >95% in BC detection on 10000 study participants undergoing regular screening programs. MammoWave uses safe non-invasive and non-ionizing microwave signals, does not apply any compression to the breast and is very effective with dense breasts. A comprehensive health economic assessment will be undertaken in this project and innovative way to implement patient engagement approaches is sought. An effective policy makers’ engagement plan will be carried out to ensure that MammoWave is recommended as a screening approach due to the benefits that it brings to women and healthcare systems. This action is part of the Cancer Mission cluster of projects on ‘Prevention, including Screening.

P.I.: Massimo Riccaboni

Contributi Liberali 2022

Applicazione di tecniche di Matrix completion per la definizione di economic and financial recommender systems

Il progetto mira a velocizzare l'impiego di una specifica tecnica di machine learning supervisionato (matrix completion) per l'analisi della complessità economica di Paesi e città, mediante l'applicazione di opportune tecniche di parallelizzazione.

P.I.: Giorgio Gnecco

GNAMPA-INdAM 2023

Sviluppo di metodi di machine learning per la stima del valore Shapley e di sue generalizzazioni

In questo progetto si intendono investigare metodi di machine learning per la stima del valore Shapley e di sue estensioni, con particolare riferimento a giochi su grafo, modellanti ad esempio reti di trasporto o interazioni tra giocatori all’interno di una squadra.

P.I.: Giorgio Gnecco

GAME IN LAB 2022

Increasing accessibility of online board games to blind and visually impaired people via machine learning

The project aims at surveying accessibility of board games in the case of blind and visually impaired people, and proposing developments based on machine learning and sonification techniques. As a proof of concept, a specific board game (Quantik) is considered.

P.I.: Giorgio Gnecco

CIPENSO

Economic Shocks on the Tuscan Economy: 

international competition, energy crisis, and the COVID-19

The project aims to identify and study the phenomena and conditions that can bring out vulnerability and risks of poverty and marginalization of people and territories following recent pandemic and geopolitical events, with particular attention to international competition and rising prices of raw materials and energy. To achieve this goal it combines insights from industrial, labor, energy, and international economics.

P.I.: Francesco Serti

ARTES 4.0

Advanced Robotics and enabling digital Technologies & Systems 4.0

ARTES 4.0 è uno degli 8 Centri di Competenza selezionati dal Ministero dello Sviluppo Economico nell’ambito del Piano Nazione Impresa 4.0 ed è per questo soggetto abilitato alla presentazione di bandi di finanziamento. Grazie ad ARTES 4.0 le imprese possono proporre progetti di innovazione ed essere selezionate per ricevere il contributo da parte del MiSE. Il Competence Centre ARTES 4.0 nasce per associare partner universitari, Enti di ricerca ed istituti di formazione ad elevata qualificazione, fondazioni e aziende partner a carattere innovativo al fine di fornire alle imprese (in particolare alle MPMI) tecnologie e servizi dedicati a rispondere ai loro bisogni mediante attività di orientamento, formazione, progetti di innovazione, ricerca industriale e sviluppo sperimentale.

P.I.: Marco Paggi/Nicola Lattanzi/Massimo Riccaboni

Programma "Galileo" 2021

Automatic movement analysis techniques for applications
in cognitive/motor rehabilitation

The project applies artificial intelligence techniques to medicine by contributing to the design of systems for the automated analysis of full-body human movements for cognitive/motor rehabilitation. The proposed approach has an interdisciplinary nature, being based on motion-capture techniques and advanced computational methods grounded on graph-theoretic and game-theoretic approaches. The project is focused on the automatic analysis of the origin of movement. The research has applications, among others, in the fields of physiotherapy and sports science.

P.I.: Giorgio Gnecco

GNAMPA-INdAM 2020

Trade-off tra numero di esempi e
precisione in varianti del fixed-effects panel data model

In varie applicazioni economiche, ingegneristiche, fisiche, e mediche, si richiede di approssimare una funzione a partire da un insieme finito di esempi supervisionati e rumorosi. Tale problema è studiato, tra gli altri, dal machine learning supervisionato. In alcuni casi, la varianza del rumore sull’etichetta assegnata a ciascun esempio può essere ridotta, aumentando  il costo/tempo di acquisizione di ciascuna supervisione. Questo motiva lo studio del trade-off ottimale tra precisione e numero di esempi. Lo scopo del progetto consiste nell'estendere al fixed-effects generalized-least-squares (FEGLS) model l’analisi di tale trade-off, già studiato in precedenza per modelli più semplici.

P.I.: Giorgio Gnecco

Programma "Galileo" 2019

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.

P.I.: Giorgio Gnecco

PAI 2019: Pro.co.pe.

Prosociality, Cognition and Peer Effects

PRO.CO.P.E. is a multidisciplinary research project funded by the IMT School for Advanced Studies Lucca that studies how the likelihood of prosocial behavior is affected by the mode of cognition, the structure of social interactions and their interplay. PRO.CO.P.E. contributes to the research-based design of policies fostering prosociality.

P.I.: Ennio Bilancini

PAI 2018: VeriOSS

VeriOSS: a Blockchain-based Bug Bounty Platform

VeriOSS is an internal project, funded by IMT School For Advanced Studies under the PAI junior program. Its mission is to imagine a novel solution for a fair, reliable and efficient market where ethical hackers can disclose OSS vulnerabilities. VeriOSS will develop a smart contract-based platform for bug disclosure and reward payment. It offers a fair trade support to boost bug bounty programs and their effectiveness.

P.I.: Gabriele Costa

PAI 2018: Ecopoly

Regional business clusters as economic polymers: mathematical modelling, forecasting, and optimal policy design

The project aims at characterizing the inter-sectoral and intra-sectoral dynamics and business performance of regional economic clusters with the purpose of forecasting possible future evolutions and of suggesting optimal investment policies to public decision makers. In the spirit of a truly multidisciplinary approach, we will exploit computational methodologies drawn from systems engineering (mathematical modeling of discrete systems used in computational materials science to characterize complex polymer chains, machine learning algorithms, numerical optimization, and feedback control design techniques) and methods of strategy and management science (business modeling, balance sheet analysis, competitiveness analysis of companies and clusters). The project will consider the yachting industrial cluster located in Region Tuscany as a specific case study.

P.I.: Nicola Lattanzi

PAI 2018: Future job

Technological change, soft skills and future high skilled jobs

In this project, we put soft and cognitive skills at work to develop a truly interdisciplinary approach to better forecast future occupations and job market dynamics. First, we aim at creating metrics that are theoretically sound and suitable for the task of measuring soft skills. Second, we combine expert consensus forecast and machine learning to better predict future job trends. Mixed methods which complement data-driven forecasting techniques with the insight of teams of experts are increasingly utilized to better predict macroeconomic trends. We contribute to this literature by developing and implementing a new strategy to integrate qualitative (human) and quantitative (machine) forecasting capabilities. Finally, we derive a series of policy implications for R&D, education, productivity and employment. This project contributes to address the socioeconomic effects of globalization and technological transformations by harnessing the combined power of human and artificial intelligence.

P.I.: Massimo Riccaboni

PRIN 2017

Cognitive Modes, Social Motives and Prosocial Behavior

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 

P.I.: Ennio Bilancini

ENI Spa

Studio avanzato delle reti sociali di collaborazione e di innovazione tramite lo strumento di Social Network

Il progetto prevede lo studio avanzato delle reti sociali, di collaborazione e di innovazione emergenti in contesti sociali e organizzativi tramite lo strumento di Social Network Analysis. L'attività di ricerca è volta alla finalizzazione di una metodologia avanzata di Network Analysis nonché alla valorizzazione della qualità all'interno del Knowledge Management System (#KMS).

P.I.: Massimo Riccaboni

Made in viareggio

Il Made in Viareggio nell'industria nautica: pattern recognition, tecnologie abilitanti e competenze per la digital innovation

La ricerca focalizza e inquadra il modello di business e il sistema valoriale di riferimento, e al suo interno il ruolo delle competenze e delle risorse umane, nelle imprese manifatturiere operanti nel distretto della nautica e della portualità toscana avendo riguardo all’introduzione delle tecnologie abilitanti note come “Industria 4.0” e al processo di digital innovation.

L’obiettivo del progetto è la stesura di una policy design strategy che (a) raccolga e racchiuda lo stato dell’arte della situazione mediante la somministrazione di un survey e che (b) indichi come le tecnologie abilitanti possano contribuire al processo di digital innovation avendo il focus sul ruolo delle competenze e delle risorse umane e che (c) disegni percorsi specifici di formazione e business education e attivi insieme al partner selezionato (Consorzio Na.Vi.Go. s.c. a r.l., da ora in avanti Na.Vi.Go., la più estesa rete di aziende di nautica da diporto della Toscana, con finalità di promozione e coordinamento delle attività imprenditoriali del distretto nautico di Viareggio) le iniziative più idonee al trasferimento tecnologico e alla strutturazione delle competenze qualificanti e abilitanti.

P.I.: Mirco Tribastone