Archivio della ricerca - Fondazione Bruno Kessler
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Depolarization in Religion and Ethics: A Perspective from the Social Sciences
No full textThis book offers a critical exploration of how digital technologies are reshaping religious belief, ethical frameworks, and social cohesion. As faith and ideology increasingly migrate to online platforms, the book examines how algorithm-driven content, echo chambers, and decentralized authority structures influence discourse and deepen societal divides. Drawing from interdisciplinary perspectives in religious studies, sociology, philosophy, and digital humanities, this volume interrogates how sacred rituals, spaces, and moral narratives are being transformed in the digital age. Amid rising concerns over misinformation and polarization, this timely work challenges the assumption that technology must inevitably fragment communities. Instead, it explores how digital spaces might also foster new forms of dialogue, connection, and meaning. From online religious practices to surveillance ethics and the digital reshaping of spiritual authority, Digitally Divided maps the risks and possibilities at the intersection of belief and technology. Essential reading for scholars, students, and practitioners across multiple disciplines, it is a vital contribution to understanding how we live, believe, and connect in an increasingly digitized world
Digital Health and Artificial Intelligence: a research approach to enable sustainable and personalised local healthcare
No full textBackground
The integration of Artificial Intelligence (AI) into healthcare services and technologies offers substantial potential for personalised medicine. The Autonomous Province of Trento (Italy) provides a unique setting for AI-driven healthcare research, due to its unified healthcare system, advanced IT infrastructure, and strong public-private collaborations. This paper explores an initiative aimed at improving healthcare accessibility and promoting innovation through AI in three clinical domains: Cardiology, Diabetic Retinopathy, and Paediatric Ophthalmology.
Methods
The project employs a structured approach, involving specialised working groups addressing clinical needs, AI techniques, legal and ethical compliance and data management. The initiative aims to develop predictive models aligned with European and national data protection regulations.
Results
Three primary clinical objectives were defined: estimating individual risk profiles in heart failure patients, personalising screening intervals for diabetic retinopathy, and supporting early diagnosis of anterior segment opacities in infants. Data relevant for the selected outcomes were identified. A dedicated platform for compliant, secure and structured access to data was developed. A data analysis plan was designed, including data processing, models selection, optimization and evaluation. All research protocols were approved by the local Ethics Committee.
Discussion
The initiative investigates the AI potential to improve clinical outcomes and establish a sustainable, personalised healthcare system. Key challenges include data accessibility, regulatory compliance, and adherence to ethical standards. The project's comprehensive framework offers a model for broader applications. Future research will focus on model validation and expanding the initiative to other clinical domains.
Public Interest Summary
This article presents the "Digital Health and Artificial Intelligence" project, an initiative funded by The Autonomous Province of Trento (Italy) to enhance healthcare accessibility and foster innovative healthcare models using technology and Artificial Intelligence (AI). The current work presents the design and preparatory work for the implementation of three AI-based solutions for research purposes, encompassing three areas: i) Cardiology, ii) Diabetic Retinopathy, and iii) Paediatric Ophthalmology. The paper outlines the legal and organizational frameworks, mathematical modelling and data management emphasising the necessity of cross-disciplinary endeavour and collaboration. Overall, this project represents a forward-looking initiative promoting research conducted on citizen data to address healthcare needs through innovative AI-driven approaches in line with legal and ethical standards
Deep learning-based control optimization for glass bottle forming
No full textIn glass bottle manufacturing, precise control of forming machines is critical for ensuring quality and minimizing defects. This study presents a deep learning-based control algorithm designed to optimize the forming process in real production environments. Using real operational data from active manufacturing plants, our neural network predicts the effects of parameter changes based on the current production setup. Through a specifically designed inversion mechanism, the algorithm identifies the optimal machine settings required to achieve the desired glass gob characteristics. Experimental results on historical datasets from multiple production lines show that the proposed method yields promising outcomes, suggesting potential for enhanced process stability, reduced waste, and improved product consistency. These results highlight the potential of deep learning to process control in glass manufacturing
The Modeling of a Single-Electron Bipolar Avalanche Transistor in 150 nm CMOS
No full textThis paper addresses the complex behavior of Single-Electron Bipolar Avalanche Transistors (SEBATs) through a comprehensive modeling approach. TCAD simulations were used to analyze the behavior of the device during avalanche pulses triggered by electron injection. The simulations consider the avalanche process and charge flow and include the parasitic capacitances and resistances. A SPICE model is proposed using parameters extracted from the TCAD simulations. Both TCAD and SPICE simulations are validated against experimental results obtained on 150 nm CMOS devices and are employed to provide a clear understanding of the phenomena observed experimentally during SEBAT operation. The impact of parasitic elements on device operation is studied using simulations. This work enables the optimization of SEBAT devices and their integration in circuits for better signal-to-noise ratios, efficiency, and potential applications in sensing and digitizing low-level signals
A precise measurement of the jet energy scale derived from single-particle measurements and in situ techniques in proton–proton collisions at √s= 13 TeV with the ATLAS detector
No full textThe Female Body and Leontion. Why were Epicurean women capable of philosophy?, in J. Muller (ed.), Women and their Body, Berlin-Boston, De Gruyter, pp. 287-301
No full text:Epicurus believed in women’s philosophical capabilities, as shown by his inclusion of female philosophers in his Kepos. However, existing scholarship has not clarified why he held this view. This paper aims to fill this gap. Despite the fragmentary evidence, preserved by hostile sources, I will contend that what clearly emerges from the ancient sources is that Epicurus grounded women’s philosophical aptitude in their possession of a human body. Moreover, I will argue that the Epicurean analogical argument in favor of divine anthropomorphism supports this perspective, for it suggests that anthropomorphic beings inherently possess intellectual faculties. Finally, the essay explores the hypothesis that the Epicurean courtesan Leontion may have used this argument in her critique of Theophrastus’ theology
The role and scope of gamification in education: A scientometric literature review
No full textGamification – the use of game elements in non-game contexts – represents a promising solution to enhance motivation and engagement in education. Traditional lecture-based teaching has been increasingly viewed as insufficient for effective learning, promoting interest in gamified education to sustain student engagement and foster a positive learning environment. This study presents a comprehensive scientometric literature review on the use of gamification in education, analyzing 9163 manuscripts and over 300,000 references from the Scopus database. Through document co-citation analysis, author co-citation analysis, and keyword co-occurrence analysis, the review identifies the most influential publications, authors, and research trends shaping the field. The findings reveal a research trend that initially focused on game design and best practices but has shifted towards systematic literature reviews and evaluations of gamification's educational effectiveness. Six key research clusters emerged: gamified learning experience, student learning, K-12 education, science education, gamification effectiveness, and gaming elements. The study highlights the growing application of gamification in STEM and formal K-12 education, as well as the increasing relevance of online and personalized learning environments. The review also emphasizes significant research gaps, particularly concerning the long-term impact of gamification, the isolated effects of individual game elements, and the need for improved research methodologies. This review offers a research agenda for future studies, calling for more rigorous, context-sensitive research that better addresses the complexity of gamified learning environments
Test of lepton flavour universality in W-boson decays into electrons and τ-leptons using pp collisions at √s = 13 TeV with the ATLAS detector
No full textEngaging Local Stakeholders to Enhance Climate Change Mitigation and Adaptation Strategies and Policies: A Case Study from the NEVERMORE Project
No full textThe Autonomous Province of Trento (Italy) participates as partner of the NEVERMORE project (Horizon EU 2022-2026), contributing to the development of innovative models and interactive digital tools that aim to assist policymakers and citizens in envisioning and assessing future scenarios and shaping more effective adaptation and mitigation policies. The Province is one of the Case Study regions together with four other European locations. The active involvement of these territories helps deliver and localize the project and its solutions to better understand and tackle climate change across different backgrounds and sectors. In Trentino, the chosen focus is on tourism and its interlinked sectors, water and energy management. One of the pillars of the project is to ensure the participation of local public and private stakeholders in the collaborative processes of co-design, co-creation and co-assessment of the activities planned.
Therefore, a Local Council has been established to engage with and consult local key players. These stakeholders represent the diverse needs and the multiple perspectives of the tourism sector. The Local Council involves stakeholders from diverse fields, including representatives from academia, policymakers, natural parks, tourism boards, media, mountain professionals, private businesses and civil society. Consultations of the Local Council aim at i) understanding and collecting perceptions on the challenges posed by climate change, ii) examining policy measures of both mitigation and adaptation amidst measures collected in NEVERMORE activities for a catalogue of adaptation and mitigation measures, iii) determining and prioritizing immaterial and material assets vulnerable to climate change from an economic, social and environmental perspective iv) involving stakeholders in a questionnaire on drivers and barriers on the policies v) presenting the initial user experience for a digital tool of a catalogue of policies and in a prioritization exercise for possible measures
to model. A set of consultations engaged around 40 stakeholders. Trentino's experience highlights an approach that harnesses local knowledge and climate change insights to enhance the relevance of scientific findings for regional needs. By integrating diverse local perspectives, this process supports the implementation of tailored adaptation and mitigation measures, as well as the development of
innovative digital tools for multi-sectoral climate impact assessment
On the cospectrality between graphs and pseudographs
No full textIn this paper we introduce a spectrum-preserving relation between graphs with loops and graphs without loops. Our approach generalizes the spectral results obtained on
stars to a wider class of graphs, namely
stars with or without loops. The proposed equivalence of the two classes of graphs allows to study pseudographs as simple graphs, by extending the techniques developed for simple graphs to pseudographs, without losing information, and it could be relevant for applications of graph theory to complex systems physics and neural networks. Finally, in order to make the demonstrated results easily applicable, we have provided a public Github repository where Python code that allows straightforward implementations of the outcomes is made available