Archivio della ricerca - Fondazione Bruno Kessler
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Radiation Damage on SiPM for High Energy Physics Experiments in space missions
No full textSilicon photomultipliers (SiPMs) are a popular choice for various
applications, especially in astroparticle physics. These devices are coupled with organic or inorganic scintillators, allowing them to detect scintillation light and Cherenkov light. They are particularly promising for space missions because of their compact size, low operating bias, and non sensitivity to magnetic fields. We studied the effects of proton irradiation at fluences up to 1 × 1011p/cm2 on FBK’s NUV-HD-lowCT SiPMs with 40 μm and 15 μm cell pitches. Protoninduced bulk damage increased the dark count rate (DCR) and dark current, with no significant changes in the breakdown voltage. These results align with
previous studies on radiation effects in SiPMs and provide insights to mitigate performance degradation in space applications
Silicon selective etching by gold implantation: Feasibility and nanofabrication capabilities
No full textSilicon nanofabrication plays a crucial role in the development of advanced electronic, photonic, and quantum devices. Focused ion beam (FIB) milling is widely used for direct patterning at the nanoscale, but it requires high ion fluences, leading to long processing times, material redeposition, and increased contamination. In this work, we demonstrate an alternative FIB-based approach that relies on gold ion implantation at significantly lower fluences, enabling selective silicon etching while minimizing these drawbacks.
Gold ions (Au+) were implanted into silicon substrates with a kinetic energy of 35 keV, followed by wet etching in tetramethylammonium hydroxide (TMAH). We identified the process window of Au fluences between 1 × 1015 and 1 × 1017 ions/cm2, with secondary ion mass spectrometry (SIMS) confirming an Au concentration threshold of 3.5 × 1020 atoms/cm3 necessary to sustain etching resistance, value predicted also by Monte Carlo simulations (TRIDYN). This approach enables the fabrication of suspended silicon nanowires with a minimum width of 36 nm, a thickness of 20 nm, and lengths up to 8 μm, achieving aspect ratios exceeding 400, as well as more complex suspended structures likes nets which can be targeted for applications in nanoelectromechanical systems (NEMS) reaching nanowire width over pitch down to 2 %.
The proposed method presents a promising alternative to conventional silicon patterning, significantly reducing processing complexity while enhancing nanostructure resolution. The results provide new insights into ion-implantation-assisted etching mechanisms and expand the possibilities for silicon nanostructure fabrication
Il sacro obliquo. La montagna come santuario delle antinomie moderne
Get PDFIl saggio indaga la dimensione epifanica e ierofanica della scoperta moderna dei valori estetici, culturali, filosofici della montagna. Lo fa ponendo al centro della discussione la scissione tipicamente moderna tra spirito e natura e il non meno tipico bisogno moderno di riconnessione tra i due. Quando i disagi della modernità vengono vissuti soggettivamente come una vera e propria crisi della presenza, il sacro obliquo dei paesaggi montani si offre agli amanti delle terre alte come una via diagonale lunga abbastanza per permettere di immaginare modi non distruttivi di abitare la modernità
Religious Actors and European Union Policies on Artificial Intelligence
No full textThe chapter examines the interactions between religious actors and European Union (EU) policymaking on artificial intelligence (AI), highlighting how ethical, legal, and theological perspectives intersect with EU regulatory frameworks. It traces the evolution of EU AI governance from the General Data Protection Regulation to the 2024 Artificial Intelligence Act, situating religious engagement within the broader discourse on fundamental rights and human-centric technological development. It lays out how religious groups and organisations have participated in institutional consultations and ethical debates on AI in Europe, focusing on human dignity, the common good, and the moral responsibility inherent in AI design and deployment. The implications of the AI Act for freedom of religion or belief are explored, particularly regarding biometric data, manipulation risks, and the regulation of AI in media and public spaces. Based on an analysis of the the EU’s “big democracy” model of AI governance, the chapter argues that, even though religion enjoys strong protective recognition in EU regulation, several
critical issues remain open. It concludes by identifying areas for further interdisciplinary research on AI, religion, and European democracy
Interconnect Simulation Using Padé Approximation for Image Sensors
No full textA lightweight simulator for on-chip interconnects is presented. The simulator is meant to aid in the optimization of interconnect circuits in pixel arrays where the timing of signals is crucial. The simulator implements an implicit integration scheme and takes advantage of the Padé approximation of the interconnect impedance. Moreover, transistors are treated according to the n-th power model, which is compact and efficient. The performance of the proposed simulator is compared with SPICE in a 110 nm standard CMOS technology, achieving a relative error smaller than 11.3 % in propagation delay estimates
Mitigating norovirus spread on cruise ships: a model-based assessment of diagnostic timing and isolation
No full textBackground: Acute gastroenteritis outbreaks caused by noroviruses are a common public health issue on cruise ships. Understanding the main drivers of sustained outbreaks is critical for evaluating the effectiveness of preventive interventions such as the isolation of infected individuals. Methods: We analysed a line-list of 121 cases from a norovirus outbreak on a cruise visiting Mediterranean ports (cumulative incidence among passengers 9.7%). We used a Bayesian inference model to reconstruct likely transmission chains, taking into account different transmission settings and the isolation of cases after diagnosis. We then calibrated a branching process model to simulate alternative isolation scenarios and estimate their effectiveness in reducing transmission. Results: Reconstructed transmission chains revealed a high heterogeneity in individual transmission, with 57% (95% CrI: 48%-65%) of secondary cases caused by 10% of infected individuals (here termed 'superspreaders'). Superspreaders exhibited longer diagnostic delays (mean 83 hours, 95% CrI: 70-96 hours) compared to other infectors (mean 47 hours, 95% CrI: 44-50 hours) and a halved frequency of vomiting and diarrhoea episodes. The 72-hour isolation protocol implemented during the outbreak averted 71% of potential cases compared to a no-intervention scenario, halving the effective reproduction number from 9.8 (95%CrI of the mean: 7.1-12.7) to 4.9 (95%CrI: 3.0-7.1). Reducing diagnostic delays further reduced the effective reproduction number, resulting in lower case numbers and probability of sustained outbreaks. Conclusions: Timely diagnosis and isolation have a remarkable impact on norovirus containment on cruise ship outbreaks. Targeted information campaigns encouraging passengers to seek immediate medical assistance upon gastrointestinal symptoms can significantly improve outbreak management
Machine Learning-Enhanced Flexible IL-6 Sensor for Rapid Threshold Detection
No full textCytokine detection—particularly interleukin-6 (IL-6)—is of growing interest due to its role in chronic inflammation and acute conditions like sepsis, where elevated concentrations above physiological levels require urgent clinical attention. Given the strong correlation between sweat and blood IL-6 levels, wearable sweat-based detection offers a promising non-invasive monitoring approach. Electrochemical sensors are especially well-suited for such applications due to their high sensitivity and accuracy. Among these, electrochemical impedance spectroscopy (EIS) provides excellent sensitivity but is challenging to implement in embedded, low-power systems. Conversely, cyclic voltammetry (CV) is more compatible with low-power, wearable devices but generally lacks the sensitivity needed for ultra-low concentration detection. To advance CV-based IL-6 sensing, this study establishes the core electrochemical and analytical performance of a novel CV-based sensor under controlled laboratory conditions. We developed a flexible electrochemical biosensor using a screen-printed carbon three-electrode system functionalized with gold nanoparticles and IL-6-specific aptamers. Electrochemical characterization was conducted using both EIS and CV with a benchtop potentiostat in 1x phosphate-buffered saline (PBS). To enhance threshold-based classification, machine learning (ML) was integrated with CV data analysis. Using a k-Nearest Neighbors (KNN) algorithm, we classified CV datasets with 96.7% accuracy, distinguishing IL-6 concentrations in 1x PBS as physiological (<20 pg/mL) or pathological (>20 pg/mL). These findings demonstrate the potential of combining electrochemical sensing with ML for sensitive, threshold-based detection of ultra-low analyte concentrations, supporting future translation toward wearable health monitoring platforms
DivNoise: A Data Collection for Source Identification on Diverse Camera Sensors
No full textIdentifying the acquisition source of media data is one of the most widely studied problems in multimedia forensics. A crucial aspect in this field is the availability of representative, diverse and up-to-date data corpora, so that the potential of existing and newly proposed techniques can be assessed in a reliable and reproducible manner. In this light, we present a novel dataset, named DivNoise, which encompasses both image and video data from a wide range of device cameras and collected in different environmental conditions. In particular, differently from existing databases, the dataset also includes data acquired from frontal cameras of mobile devices (smartphones and tablets) and from webcams, which are increasingly used tools to enable remote video communications in many application scenarios. The dataset is made publicly available to the research community, with the goal of supporting the development of novel source identification techniques. We perform an experimental evaluation on the DivNoise dataset through state-of-the-art algorithms, thus exposing preliminary yet intriguing empirical insights
Enhancing door-to-door waste collection forecasting through ML
No full textWe explore the application of machine learning (ML) techniques to forecast door-to-door waste collection, addressing the challenges in municipal solid waste (MSW) management. ML models offer a promising solution to optimize waste collection operations, especially amid growing urban populations and evolving waste generation rates. Leveraging comprehensive data from a northeastern Italian municipality, including various waste types, our study investigates ML algorithms' efficacy in predicting household waste collection requirements. We examine two key tasks: predicting daily waste exposure likelihood and forecasting fulfilled pickups over monthly and weekly periods. Both tasks are developed at the user level, forecasting user behavior based on features that describe the user. We split the data based on its temporal distribution and evaluated the models by forecasting user behavior in a future period, using the data from earlier periods to train the models. This study addresses a novel and challenging scenario, as, to the best of our knowledge, no prior work has specifically focused on door-to-door waste management using machine learning techniques. Results highlight ML models' potential in enhancing waste collection efficiency, aiding route planning, resource allocation, and environmental sustainability in urban areas. Additionally, our findings underscore the importance of tailoring strategies to waste categories and pickup frequencies for optimal performance