Archivio della ricerca - Fondazione Bruno Kessler
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Clinical characteristics of COVID-19 in children and adolescents: insights from an Italian paediatric cohort using a machine-learning approach
No full textIntroduction The epidemiology and clinical characteristics of COVID-19 evolved due to new SARS-CoV-2 variants of concern (VOCs). The Omicron VOC’s higher transmissibility increased paediatric COVID-19 cases and hospital admissions. Most research during the Omicron period has focused on hospitalised cases, leaving a gap in understanding the disease’s evolution in community settings. This study targets children with mild to moderate COVID-19 during pre-Omicron and Omicron periods. It aims to identify patterns in COVID-19 morbidity by clustering individuals based on symptom similarities and duration of symptoms and develop a machine-learning tool to classify new cases into risk groups.
Methods We propose a data-driven approach to explore changes in COVID-19 characteristics by analysing data from 581 children and adolescents collected within a paediatric cohort at the University Hospital of Padua. First, we apply an unsupervised machine-learning algorithm to cluster individuals into groups. Second, we classify new patient risk groups using a random forest classifier model based on sociodemographic information, pre-existing medical conditions, vaccination status and the VOC as predictive variables. Third, we explore the key features influencing the classification through the SHapley Additive exPlanations.
Results The unsupervised clustering identified three severity risk profile groups. Cluster 0 (mildest) had an average of 1.2 symptoms (95% CI 0.0 to 5.0) and mean symptom duration of 1.26 days (95%CI 0.0 to 9.0), cluster 1 had 2.27 symptoms (95% CI 1.0 to 6.0) lasting 3.47 days (95% CI 1.0 to 12.0), while cluster 2 (strongest symptom expression) exhibited 3.41 symptoms (95% CI 2.0 to 7.0) over 5.52 days (95% CI 0.0 to 16.0). Feature importance analysis showed that age was the most important predictor, followed by the variant of infection, influenza vaccination and the presence of comorbidities. The analysis revealed that younger children, unvaccinated individuals, those infected with Omicron and those with comorbidities were at higher risk of experiencing a greater number and longer duration of symptoms.
Conclusions Our classification model has the potential to provide clinicians with insights into the children’s risk profile of COVID-19 using readily available data. This approach can support public health by clarifying disease burden and improving patient care strategies. Furthermore, it underscores the importance of integrating risk classification models to monitor and manage infectious diseases
Switchable CPW-Based Band-Pass Filter into Band-Stop Filter for Broadband RF Applications
No full textIn this paper, a coplanar waveguide (CPW)-based BPF and a BSF is designed for RF applications. The BPF is designed utilizing series open-ended stubs within CPW structure. A short stub is inserted between the gap of the open-ended stubs to transform the BPF into a BSF. The CPW structure is integrated with ECE-shaped DGS resonators. Simulations and parametric analysis are carried out using the HFSS tool. The BPF demonstrates an insertion loss of −4 dB, with the filters achieving a center frequency of 21 GHz and a return loss of just −0.04 dB. The resulting bandwidth of the filter is 19 GHz. Additionally, the lower passband insertion loss remains below −4 dB. This design is compatible with K-band (13–28 GHz) applications
Investigation on Effect of Thickness Variation of Active Layer in Organic Solar Cell and Analysis of Fill Factor and Power Conversion Efficiency
No full textIn recent years, research into low-cost Organic Solar Cell (OSC) technology has gained popularity. The usage of OSC and its numerous advantages has helped to accelerate the shift from crystalline to organic solar cells. The purpose of this research work is to estimate the highest power conversion efficiency of the PM6_D18_L8-BO organic solar cell by modifying its active layer thickness using GPVDM software. The impact of various active layer thicknesses on the efficiency of organic photovoltaics has been studied. The electrical and optical characteristics of OSCs based on ITO/PEDOT:PSS/PM6_D18_L8-BO/PNDIT-F3N/Ag were simulated using the General-Purpose Photovoltaic Device Model. The optimum power conversion efficiency is 17.876%
Orientation of ambiguous image sequences with similar and repeated structures
No full textImage orientation, nowadays called Structure from Motion (SfM), is still an open research topic in particular in case of scenes featuring visual aliasing, or doppelgangers. Indeed, visually similar but distinct elements of the scene can cause incorrect matches, not detected by geometric or learning-based outliers removal methods, leading to misplaced camera poses and wrong 3D reconstructions. The paper reviews various state-of-the-art approaches to orient ambiguous image sequences and determination correct camera orientation parameters. We also present an in-house graph-based approach to reliably and precisely orient sets of images with doppelgangers. Different experiments on common ambiguous datasets are reported and commented
High-Resolution Fabrication Procedure for an Optically-Transparent Modular Smart Electromagnetic Skin
No full textA Smart ElectroMagnetic Environment (SEME) is enabled by introducing smart electromagnetic (EM) skins, or metasurfaces, with the final aim of manipulating the reflection and/or transmission properties to improve the wireless interconnectivity in non-line-of-sight areas. We focus on manufacturing of passive and static smart EM skins for wavefront manipulation, to enable optically transparent and conformable solutions. The most significant technological innovation of this contribution is the creation of a metasurface by means of micro-manufactured modules on a transparent medium, which allows us to reach high-resolution standards and to work on unconventional substrates. Indeed, micro-fabrication of smart skins offers many advantages, e.g. choice of materials as in this case (i.e. transparent), miniaturization and, in turn, operation at higher frequencies. This constitutes a valuable counterpart to most literature works, which exploit “standard” technologies and substrates, such as classic PCB, severely limiting the manufacturing degrees of freedom (DoF) and material variety
Effects of Thermal Oxidation and Proton Irradiation on Optically Detected Magnetic Resonance Sensitivity in Sub-100 nm Nanodiamonds
No full textIn recent decades, nanodiamonds (NDs) haveemerged as innovative nanotools for weak magnetic fields andsmall temperature variation sensing, especially in biologicalsystems. At the basis of the use of NDs as quantum sensors arenitrogen-vacancy center lattice defects, whose electronic structuresare influenced by the surrounding environment and can be probedby the optically detected magnetic resonance technique. Ideally,limiting the NDs’ size as much as possible is important to ensurehigher biocompatibility and provide higher spatial resolution.However, size reduction typically worsens the NDs’ sensingproperties. This study endeavors to obtain sub-100 nm NDssuitable to be used as quantum sensors. Thermal processing andsurface oxidations were performed to purify NDs and control theirsurface chemistry and size. Ion irradiation techniques were also employed to increase the concentration of the nitrogen-vacancycenters. The impact of these processes was explored in terms of surface chemistry (diffuse reflectance infrared Fourier transformspectroscopy), structural and optical properties (Raman and photoluminescence spectroscopy), dimension variation (atomic forcemicroscopy measurements), and optically detected magnetic resonance temperature sensitivity. Our results demonstrate how surfaceoptimization and defect density enhancement can reduce the detrimental impact of size reduction, opening to the possibility ofminimally invasive high-performance sensing of physical quantities in biological environments with nanoscale spatial resolution
3D Robotics and LMM for Vineyard Inspection
Get PDFAutonomous mobile robotic solutions are increasingly being explored in precision agriculture to aid human workers in labour-intensive or repetitive tasks. Moreover, the emergence of foundation models in vision-based AI domain presents an opportunity to perform automated interpretation of in-field collected data. This study presents a cost-effective mobile robotic research platform designed for autonomous vineyard inspection: it integrates mission planning, real-world navigation and a post-processing pipeline of multimodal data. The system, based on the Leo rover, is equipped with LiDAR, RGB cameras and GNSS-visual-inertial positioning, ensuring reliable operation in GNSS-degraded vineyard environments. We propose a novel methodology for automating several stages of the workflow using various open and in-situ collected data. The robotic platform and processing pipeline were validated through simulation and field experiments, demonstrating its capability for autonomous navigation, 3D reconstruction, AI-based fruit detection and an initial plant health assessment through Large Multimodal Models (LMM). Results show that while 3D mapping provides highresolution spatial data, AI-driven object detection and vision models require further domain adaptation for reaching reliable and trustable operation. The study highlights the feasibility of cost-effective mobile robotic solutions in vineyard monitoring and the potential of integrating AI to enhance agricultural automation
Adsorption of silica oligomers on biomolecules: Structural and dynamical insights for atom probe tomography via classic molecular dynamics simulations
No full text: Atom Probe Tomography (APT) is a spatially-resolved, mass-spectrometric technique, mostly employed in the characterization of metals and alloys. Recently, a novel APT-based protocol has been proposed to resolve the three-dimensional structures of biomolecules, involving the encapsulation of the substrate within an amorphous silica matrix followed by its ablation employing short laser pulses. A critical aspect of this technique lies in the interaction between the silica matrix and the biomolecular substrate, which must keep the native framework of the biomolecule while minimizing the mechanical stresses. Building on earlier works, here we characterize the adsorption of silica monomers and short oligomers onto biomolecular surfaces via classical Molecular Dynamics (MD) simulations. We observe significant differences in the behavior of the diverse silica species, with the dimers and trimers showing a higher affinity for the substrates. Additionally, unfolded protein domains exhibit an enhanced adsorption efficacy, likely on account of their inherent flexibility and availability of hydrogen-bonding moieties: This apparent affinity dampens their local fluctuations upon interaction with silica, significantly affecting their ensemble dynamics. These findings suggest APT as a suitable technique for the structural characterization of intrinsically disordered regions and the metastable conformational landscapes thereof
Generative AI mitigates representation bias and improves model fairness through synthetic health data
No full text: Representation bias in health data can lead to unfair decisions and compromise the generalisability of research findings. As a consequence, underrepresented subpopulations, such as those from specific ethnic backgrounds or genders, do not benefit equally from clinical discoveries. Several approaches have been developed to mitigate representation bias, ranging from simple resampling methods, such as SMOTE, to recent approaches based on generative adversarial networks (GAN). However, generating high-dimensional time-series synthetic health data remains a significant challenge. In response, we devised a novel architecture (CA-GAN) that synthesises authentic, high-dimensional time series data. CA-GAN outperforms state-of-the-art methods in a qualitative and a quantitative evaluation while avoiding mode collapse, a serious GAN failure. We perform evaluation using 7535 patients with hypotension and sepsis from two diverse, real-world clinical datasets. We show that synthetic data generated by our CA-GAN improves model fairness in Black patients as well as female patients when evaluated separately for each subpopulation. Furthermore, CA-GAN generates authentic data of the minority class while faithfully maintaining the original distribution of data, resulting in improved performance in a downstream predictive task
XANES Absorption Spectra of Penta-Graphene and Penta-SiC2 with Different Terminations: A Computational Study
No full textPentagonal two-dimensional allotropes—penta-graphene (PG) and penta-SiC2—are promising but experimentally elusive materials whose identification requires spectroscopic fingerprints that extend beyond ground-state descriptors. Using density functional theory within a core-hole formalism and polarisation-resolved cross sections, we compute element- and site-resolved K-edge spectra for pristine H- and OH-terminated PG, Si-substituted PG, and pristine/H-passivated penta-SiC2. In PG, the C K-edge shows a π* onset at 285 eV from three-coordinated C and σ* bands at 293–303 eV, yielding three plateaus and a strong low-energy z-polarised response. The H/OH functionalisation suppresses the 283–288 eV plateau and weakens the polarisation anisotropy, which can be rationalised by PDOS changes at the two non-equivalent C sites. Si substitution generates a polarisation-dependent Si K-edge doublet (∼1844/1857 eV). In penta-SiC2, the high-energy Si feature broadens (1850–1860 eV) and the C K-edge becomes strongly anisotropic; H-passivation yields a sharp, almost polarisation-independent C K-edge at 290 eV. The presence of clearly resolved, system-dependent spectral features enables unambiguous experimental discrimination between phases and terminations, facilitating spectroscopic discovery and supporting device development in 2D pentagonal materials