Archivio della ricerca della Scuola Superiore Sant'Anna
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Clathrin to the rescue: Clathrin-mediated trafficking fine-tunes Arabidopsis copper tolerance
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Understanding the householder solar panel consumer: A Markovian model and its societal implications
No full textHousehold adoption of rooftop photovoltaic (PV) systems is central to the green energy transition, yet diffusion
depends on social influence and behavioral biases, as well as payback economics. This study develops a
parsimonious Markovian model in which households move sequentially from being unengaged (‘‘Carbon")
to informed, to planning, and finally to adoption (‘‘Green"). Transition rates are micro-founded by two
mechanisms: (i) social contagion/communication, proxied by the current share of adopters, and (ii) economic
profitability, proxied by payback time computed from a Net Present Value framework. Novel to this diffusion
setting, bounded rationality is introduced via hyperbolic discounting, creating a procrastination loop that
delays adoption even when PV is economically attractive in a long-run perspective. Calibrated on the Italian
residential PV diffusion path (2006–2020) and assessed in national and regional applications, the model
reproduces observed trajectories and enables forward-looking scenario analysis (2020–2026). Results show
that policies yielding similar payback improvements can produce different outcomes once present bias is
accounted for and that behaviorally informed intervention are stronger. The findings contribute a micro-tomacro
bridge between behavioral economics and technology diffusion modeling and imply that effective policy
portfolios (and PV business models) should complement incentives with commitment devices and social-norm
peer strategies to accelerate PV uptake and its spillover emissions benefits
How plants adapt to combined and sequential abiotic stresses: A transcriptomics approach
No full textA Holistic Approach Towards Evaluating Upper Limb Function in Children with Unilateral Cerebral Palsy: A Narrative Review of Clinical Tools and Promising Technologies for Comprehensive Assessment
No full textOptimal upper limb (UpL) function is essential for performing daily activities; however, children with unilateral spastic cerebral palsy (USCP) often experience impairments in UpL function, which can impact their quality of life or independence. While UpL motor impairments are a primary concern, non-motor functions, such as cognition, attention, and visual function, commonly impaired in USCP, may also play a role in UpL performance. Nevertheless, these non-motor functions are often not considered in evaluation protocols that focus on the UpL. Moreover, clinical evaluation is typically conducted in structured and controlled settings and may not accurately reflect the child’s abilities in daily life. Non-invasive, novel technologies are a promising solution for filling this gap, by providing additional quantitative and ecologically valid information to clinicians. In this context, this overview aims (i) to present the most frequently used tools for a holistic evaluation in children with USCP, ensuring a thorough understanding of the UpL function, and (ii) to report the evidence of how novel, non-invasive technologies can enhance clinical evaluation in daily life, enabling a more comprehensive evaluation. This work could set a basis for multidimensional evaluation protocols for UpL function in USCP, providing a different approach to the current standards
Transformer-based long-term predictor of subthalamic beta activity in Parkinson’s disease
No full textDeep brain stimulation (DBS) of the subthalamic nucleus (STN) is a mainstay treatment for patients with Parkinson’s disease (PD). The adaptive DBS approach (aDBS) modulates stimulation, based on the power in the beta range ([12–30] Hz) of STN local field potentials, aiming to follow the patient’s clinical state. Control of aDBS relies on identifying the correct thresholds of pathological beta power. Currently, in-person reprogramming sessions, due to changes in beta power distribution over time, are needed to ensure clinical efficacy. Here we present LAURA, a Transformer-based framework predicting the nonlinear evolution of subthalamic beta power up to 6 days in advance, based on the analysis of chronic recordings. High prediction accuracy (>90%) was achieved in four PD patients with chronic DBS over months of recordings, independently of stimulation parameters. Our study paves the way for remote monitoring strategies and the implementation of new algorithms for personalized auto-tuning aDBS devices
The AI system Definition under the AI Act, a New Nomen Rosae?
No full textThis short paper aims to establish how to apply the ’AI system’ definition provided in the AI Act’s Article 3(1) and further clarified in the dedicated European Commission’s Guidelines in practice. Thanks to an interdisciplinary collaboration between legal scholars and bioengineers, we identify alignments and discrepancies between the legal definitions and the actual use of the same terms in the engineering community. We hence define a commented comparative interdisciplinary thesaurus of key terms. We further discuss the implications that terminological and interpretative issues may have on legal certainty and the compliance activities of developers of AI systems
