Archivio della ricerca della Scuola Superiore Sant'Anna
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Il diritto di asilo tra sostanza e procedura. Fondamenti costituzionali, sviluppi interpretativi e sfide applicative
No full textGreen nudges, metacognition and rational agency. Do energy defaults undermine individual autonomy?
No full textWe define climate-change-responsive behaviors (CRBs) as
explicit individual behaviors that directly result in
a reduction of emissions. At present, the election of greener
energy tariffs is a CRB well worth encouraging, and energy
defaults have proved remarkably effective in achieving this
goal. According to an apparently compelling objection, however,
defaults would seriously undermine our rational
agency, and their use in policy should therefore be opposed
on moral grounds. We argue that this objection, while motivated
by sharable concerns, appears to rest on an overly
demanding view of intellectual autonomy – one that, we
suggest, is ultimately implausible – coupled with an incomplete
understanding of defaults’ psychology. In fact, once the
metacognitive component of default-induced decisional processes
is taken into due account, energy defaults can be seen
as not just fully compatible with our rational agency, but also
as an extremely valuable opportunity to more fully exercise
it
La trasformazione digitale a supporto della sostenibilità del business: rischi e opportunità
No full textRicombinazione di conoscenza e esternalità spaziali nel contesto dell'Industria 4.0 europea
No full textProcess Mining for legal Courts: Visualising, analysing and comparing Italian divorce proceedings
No full textProcess Mining (PM) is a family of data-driven techniques that use data to study the underlying processes generating the data, i.e., the data-generating process. Despite being initially tailored for the engineering and industrial domain, it is becoming popular also in more human-centric domains like the legal and healthcare ones. We present a PM methodology using the fuzzy miner technique aimed at analysing and optimising the complex processes underlying decision making by legal Courts. We consider specifically the domain of civil proceedings, with a focus on divorces. In PM terms, we see a legal proceeding as a process instance, and the different internal phases in which a legal proceeding transits as activities. The studied process is, therefore, the internal process followed by a Court, possibly varying over the years, to handle specific types of proceedings. By leveraging PM techniques, this article compares consensual divorce proceedings within a Court across time, and across Courts. As a case study we take two Courts in Northern Italy. Our PM analysis identifies key performance indicators and uncovers hidden process efficiencies and inefficiencies. The findings highlight the ability of PM to reveal critical process patterns, enabling organisations to make data-driven decisions and implement targeted process improvements
La protezione dei dati sanitari nelle pubblicazioni giurisdizionali online e gli obblighi positivi dello Stato
No full textWhat matters most to the population in case of chronic conditions? Results from a discrete choice experiment in Italy
No full textRelational continuity, care coordination, and teamwork are widely recognized as key components of quality in primary care. This study investigates population preferences regarding organizational models of primary care, with a particular focus on the roles of general practitioners, specialists, and nurses. A Discrete Choice Experiment (DCE) was conducted through a nationwide online cross-sectional survey, employing a full factorial experimental design with 20 randomly selected choice sets to minimize cognitive burden. The attributes examined included coordination, relational continuity, and teamwork. Data were collected from a representative sample of 2,553 respondents across Italy in early 2021. Results underscore the centrality of teamwork (OR=1.85 in mild and 2.31 in severe chronic conditions), followed by relational continuity (OR=1.60 in mild and 1.55 in severe conditions). Coordination ranks third (OR=1.31) for mild conditions but reaches parity with relational continuity in the context of severe chronic conditions. These findings offer robust evidence of differentiated preferences based on chronic disease severity and support the design of tailored primary care models. In conclusion, this analysis highlights the importance of incorporating coordination, relational continuity, and teamwork in the configuration of primary care services, offering policy-relevant insights for adapting delivery models to the needs of patients with varying levels of chronicity
Real‐Time Behavior Recognition Using a Legged Robot for Animal–Robot Interaction
No full textAnimal–robot interaction is an emerging interdisciplinary field that explores the dynamics between animals and robotic systems, as well as the design principles for effective engagement. While previous approaches have investigated animal responses to robotic stimuli, they have yet to integrate artificial intelligence (AI) for real-time behavioral analysis during the interaction. This paper addresses this gap by introducing an AI-driven framework that enables a robotic dog to autonomously monitor and analyze livestock behavior, specifically in cows and chickens. Our system processes real-time camera observations using deep-learning models to detect animal presence and recognize actions. It integrates three neural networks: YOLO-Chicken and YOLO-Cows, for accurate detection of chickens and cows, respectively, and DARTEMIS, a novel, distilled unimodal variant of a state-of-the-art Animal Action Recognition model. The networks communicate efficiently via Redis in a lightweight manner, with all processing conducted onboard the robot. We trained YOLO-Cow and YOLO-Chicken on a subset of the COCO data set for cows and a public data set for chickens, achieving mAP@50-95 scores of 0.67 and 0.56, respectively. DARTEMIS, trained on the Animal Kingdom data set like ARTEMIS, reached an mAP of 77.3. With these models, we tested our system in real-world conditions through field trials, evaluating its ability to accurately detect animals and classify their behaviors. This study presents the first successful integration of efficient deep- learning models into a robotic platform for real-time animal behavior analysis. The proposed framework paves the way for continuous automated livestock monitoring, with potential applications in improving animal welfare and farm management. The full implementation is publicly available and designed to be adaptable to various robotic platforms and related challenges