Archivio della ricerca - Fondazione Bruno Kessler
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Search for a heavy charged Higgs boson decaying into a W boson and a Higgs boson in final states with leptons and b-jets in √s = 13 TeV pp collisions with the ATLAS detector
No full textThis article presents a search for a heavy charged Higgs boson produced in association with a top quark and a bottom quark, and decaying into a W boson and a 125 GeV Higgs boson h. The search is performed in final states with one charged lepton, missing transverse momentum, and jets using proton-proton collision data at √s = 13 TeV recorded with the ATLAS detector during Run 2 of the LHC at CERN. This data set corresponds to a total integrated luminosity of 140 fb−1. The search is conducted by examining the reconstructed invariant mass distribution of the Wh candidates for evidence of a localised excess in the charged Higgs boson mass range from 250 GeV to 3 TeV. No significant excess of data over the expected background is observed and 95% confidence-level upper limits between 2.8 pb and 1.2 fb are placed on the production cross-section times branching ratio for charged Higgs bosons decaying into Wh
Socioeconomic disparities in mobility behavior during the COVID-19 pandemic in developing countries
No full textMobile phone data have played a key role in quantifying human mobility during the COVID-19 pandemic. Existing studies on mobility patterns have primarily focused on regional aggregates in high-income countries, obfuscating the accentuated impact of the pandemic on the most vulnerable populations. Leveraging geolocation data from mobile-phone users and population census for 6 middle-income countries across 3 continents between March and December 2020, we uncovered common disparities in the behavioral response to the pandemic across socioeconomic groups. Users living in low-wealth neighborhoods were less likely to respond by self-isolating, relocating to rural areas, or refraining from commuting to work. The gap in the behavioral responses between socioeconomic groups persisted during the entire observation period. Among users living in low-wealth neighborhoods, those who commute to work in high-wealth neighborhoods pre-pandemic were particularly at risk of experiencing economic stress, facing both the reduction in economic activity in the high-wealth neighborhood and being more likely to be affected by public transport closures due to their longer commute distances. While confinement policies were predominantly country-wide, these results suggest that, when data to identify vulnerable individuals are not readily available, GPS-based analytics could help design targeted place-based policies to aid the most vulnerable
Sustainable Production of Microcrystalline and Nanocrystalline Cellulose from Textile Waste Using HCl and NaOH/Urea Treatment
No full textBio-nanomaterials are gaining increasing attention due to their renewable and eco-friendly characteristics. Among these, nanocrystalline cellulose (NCC) stands out as one of the most advanced materials for applications in food, healthcare, composite production, and beyond. In this study, NCC was successfully extracted from cotton-based textile waste using a combination of chemical and mechanical methods. The cellulose fibers were first hydrolyzed using a dilute HCl solution, neutralized, and then dried, resulting in microcrystalline cellulose (MCC) with diameters ranging from 7 to 15 μm and lengths up to 300 μm (as observed via optical microscopy and scanning electron microscopy, SEM). To achieve nanoscale dimensions, NaOH/urea solution with mechanical treatment was applied, resulting in the successful extraction of NCC in the supernatant, particularly under room-temperature conditions. Dynamic light scattering (DLS) analysis confirmed the presence of nanostructures (average sizes ranging from 120 nm to 750 nm), and atomic force microscopy (AFM) analysis verified the nanoscale range (diameters between 2 and 4 nm and lengths from 200 nm to 1 μm). Fourier transform infrared (FTIR) spectroscopy revealed the conversion of cellulose I to cellulose II, confirming the successful transformation into NCC. For the first time, NCC was obtained from undyed cotton textile wastes using NaOH/urea treatment after HCl hydrolysis, eliminating the need for pre-treatment and intermediate steps
Measurement of tt production in association with additional b-jets in the eμ final state in proton–proton collisions at √s= 13 TeV with the ATLAS detector
No full textThis paper presents measurements of top-antitop quark pair (
) production in association with additional b-jets. The analysis utilises 140 fb−1 of proton–proton collision data collected with the ATLAS detector at the Large Hadron Collider at a centre-of-mass energy of 13 TeV. Fiducial cross-sections are extracted in a final state featuring one electron and one muon, with at least three or four b-jets. Results are presented at the particle level for both integrated cross-sections and normalised differential cross-sections, as functions of global event properties, jet kinematics, and b-jet pair properties. Observable quantities characterising b-jets originating from the top quark decay and additional b-jets are also measured at the particle level, after correcting for detector effects. The measured integrated fiducial cross-sections are consistent with
predictions from various next-to-leading-order matrix element calculations matched to a parton shower within the uncertainties of the predictions. State-of-the-art theoretical predictions are compared with the differential measurements; none of them simultaneously describes all observables. Differences between any two predictions are smaller than the measurement uncertainties for most observables
Modelling the seasonal dynamics of Aedes albopictus populations using a spatio-temporal stacked machine learning model
No full textVarious modelling techniques are available to understand the temporal and spatial variations of the phenology of species. Scientists often rely on correlative models, which establish a statistical relationship between a response variable (such as species abundance or presence-absence) and a set of predominantly abiotic covariates. The choice of the modeling approach, i.e., the algorithm, is itself a significant source of variability, as different algorithms applied to the same dataset can yield disparate outcomes. This inter-model variability has led to the adoption of ensemble modelling techniques, among which stacked generalisation, which has recently demonstrated its capacity to produce robust results. Stacked ensemble modelling incorporates predictions from multiple base learners or models as inputs for a meta-learner. The meta-learner, in turn, assimilates these predictions and generates a final prediction by combining the information from all the base learners. In our study, we utilized a recently published dataset documenting egg abundance observations of Aedes albopictus collected using ovitraps. and a set of environmental predictors to forecast the weekly median number of mosquito eggs using a stacked machine learning model. This approach enabled us to (i) unearth the seasonal egg-laying dynamics of Ae. albopictus for 12 years; (ii) generate spatio-temporal explicit forecasts of mosquito egg abundance in regions not covered by conventional monitoring initiatives. Our work establishes a robust methodological foundation for forecasting the spatio-temporal abundance of Ae. albopictus, offering a flexible framework that can be tailored to meet specific public health needs related to this species
Comparison of UV-enhanced SiPM technologies for estimating 511 keV photon arrival time using BGO Cherenkov Luminescence
No full textTime-of-flight (TOF) is a method to boost reconstructed image signal-to-noise ratio in positron emission tomography (PET); the SNR gain increases as detector coincidence time resolution (CTR) improves. It is also desirable to control detector costs, especially when developing long axial field of view PET systems. With these two goals in mind, it has been proposed to exploit the prompt luminescence, predominantly Cherenkov light, in the scintillation crystal BGO, to estimate the annihilation photon arrival time for PET detectors. However, properly identifying the relatively low number of Cherenkov photons generated per interaction event in BGO is not easy, requires low noise photodetectors, fast readout electronics, and accurate signal processing techniques. Since Cherenkov photons are produced in the ultra-violet (UV) realm, photosensor technology with enhanced performance in the UV region is essential for best performance. In this work we have evaluated and compared the performance of three different UV-enhanced silicon photomultiplier (SiPMs) coupled to 3 × 3 mm2 BGO scintillators ranging from 3 mm to 15 mm length, and two different readout circuits. State-of-the-art data postprocessing methods have been employed for CTR estimation, consistently yielding CTR values < 300 ps full-width-at-half-maximum (FWHM) for all crystal lengths studied. To our knowledge, this is the first work comparing these UV-SiPM models under the same conditions
Machine Learning Clustering of Water-Water Interactions in the Cambridge Structural Database
No full textThis study investigated the application of clustering machine learning techniques to analyze water-water contacts in crystal structures deposited in the Cambridge Structural Database. The initial dataset was divided into three groups regarding interaction energies between water molecules, and were separately analyzed. The application of machine learning methods enabled finding similar groups of contacts and defining their geometrical parameters. By carefully scrutinizing geometric parameters and visually examining clustering results, we demonstrated how valuable insights into the diverse spectrum of interactions between water molecules can be gained. Expanding the applicability of clustering methods can be achieved by integrating them into existing software for visualizing crystal structures. This approach has the potential to discover new types of interactions and enhance our understanding of molecular behavior
Demo: Automated Multiparty Conversations Analysis in VR Massive Multiplayer Online Games Environments
No full textThe study of social interactions in Massively Multiplayer Online Games (MMOGs) has largely focused on chat communication and speech content analysis. This paper introduces a novel tool designed to analyze social interactions among MMOG players by leveraging Conversation Analysis methodologies to systematically assess multi-party interactions during 6DOF Virtual Reality sessions. The Multiparty Conversation Analysis (MCA) tool can be applied in several MMOG contexts where social analysis is needed. In this demo paper, we present a specific application of the tool focusing on VR MMOG-based therapy for autistic teens
