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"Simone, Andrea"
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A first top partner hunter’s guide
A
bstract
We provide a systematic effective lagrangian description of the phenomenology of the lightest top-partners in composite Higgs models. Our construction is based on symmetry, on selection rules and on plausible dynamical assumptions. The structure of the resulting simplified models depends on the quantum numbers of the lightest top partner and of the operators involved in the generation of the top Yukawa. In all cases the phenomenology is conveniently described by a small number of parameters, and the results of experimental searches are readily interpreted as a test of naturalness. We recast presently available experimental bounds on heavy fermions into bounds on top partners: LHC has already stepped well inside the natural region of parameter space.
Journal Article
Tomb Raider library edition
\"Lara Croft embarks on a new globetrotting adventure, discovering a dangerous organization that's threatening her friends\"-- Provided by publisher.
Book
Benchmarks for dark matter searches at the LHC
A
bstract
We propose some scenarios to pursue dark matter searches at the LHC in a fairly model-independent way. The first benchmark case is dark matter co-annihilations with coloured particles (gluinos or squarks being special examples). We determine the masses that lead to the correct thermal relic density including, for the first time, strong Sommerfeld corrections taking into account colour decomposition. In the second benchmark case we consider dark matter that couples to SM particles via the
Z
or the Higgs. We determine the couplings allowed by present experiments and discuss future prospects. Finally we present the case of dark matter that freezes out via decays and apply our results to invisible
Z
and Higgs decays.
Journal Article
Adelaide Ristori sublime declamatrice dantesca sulla scena italiana e internazionale del secondo Ottocento
This essay examines the presence of Alighieri’s poetry in the repertory and the self-promotion of Adelaide Ristori. During her career the exceptional tragic performer showed a clear interest in recitals where she used to combine excerpts from famous tragedies and Italian classics. Among others the Divina Commedia’s episodes were accurately selected to please the European public. The format conception was influenced by the collaboration with the young actor Ernesto Rossi, her scene partner in Paris with the Compagnia Reale Sarda between the months of June and August 1855, who was an excellent Dantean reciter. Reviews, reports, and playbills testify that over the years Ristori assimilated and re-elaborated the practice by way of an original dramaturgical operation. Her ability to recite the poem, particularly the Inferno’s fifth canto, emerged several times between 1856 and 1865, when she acted in London and in Paris. According to a consolidated bibliography, the Ristori’s international triumphs launched the season of the Italian Grande Attore. The recitation of Dante’s popular plots contributed to the success of the actress outside and inside Italian borders, and it represented both a typical brand of ‘Italianness’ and a masterpiece of her theatrical creations.
Journal Article
Guiding new physics searches with unsupervised learning
We propose a new scientific application of unsupervised learning techniques to boost our ability to search for new phenomena in data, by detecting discrepancies between two datasets. These could be, for example, a simulated standard-model background, and an observed dataset containing a potential hidden signal of New Physics. We build a statistical test upon a test statistic which measures deviations between two samples, using a Nearest Neighbors approach to estimate the local ratio of the density of points. The test is model-independent and non-parametric, requiring no knowledge of the shape of the underlying distributions, and it does not bin the data, thus retaining full information from the multidimensional feature space. As a proof-of-concept, we apply our method to synthetic Gaussian data, and to a simulated dark matter signal at the Large Hadron Collider. Even in the case where the background can not be simulated accurately enough to claim discovery, the technique is a powerful tool to identify regions of interest for further study.
Journal Article
EEG-Based Mental Workload Neurometric to Evaluate the Impact of Different Traffic and Road Conditions in Real Driving Settings
Car driving is considered a very complex activity, consisting of different concomitant tasks and subtasks, thus it is crucial to understand the impact of different factors, such as road complexity, traffic, dashboard devices, and external events on the driver's behavior and performance. For this reason, in particular situations the cognitive demand experienced by the driver could be very high, inducing an excessive experienced mental workload and consequently an increasing of error commission probability. In this regard, it has been demonstrated that human error is the main cause of the 57% of road accidents and a contributing factor in most of them. In this study, 20 young subjects have been involved in a real driving experiment, performed under different traffic conditions (rush hour and not) and along different road types (main and secondary streets). Moreover, during the driving tasks different specific events, in particular a pedestrian crossing the road and a car entering the traffic flow just ahead of the experimental subject, have been acted. A Workload Index based on the Electroencephalographic (EEG), i.e., brain activity, of the drivers has been employed to investigate the impact of the different factors on the driver's workload. Eye-Tracking (ET) technology and subjective measures have also been employed in order to have a comprehensive overview of the driver's perceived workload and to investigate the different insights obtainable from the employed methodologies. The employment of such EEG-based Workload index confirmed the significant impact of both traffic and road types on the drivers' behavior (increasing their workload), with the advantage of being under real settings. Also, it allowed to highlight the increased workload related to external events while driving, in particular with a significant effect during those situations when the traffic was low. Finally, the comparison between methodologies revealed the higher sensitivity of neurophysiological measures with respect to ET and subjective ones. In conclusion, such an EEG-based Workload index would allow to assess objectively the mental workload experienced by the driver, standing out as a powerful tool for research aimed to investigate drivers' behavior and providing additional and complementary insights with respect to traditional methodologies employed within road safety research.
Journal Article
How to Assess the Carbon Footprint of a Large University? The Case Study of University of Bologna’s Multicampus Organization
University campuses represent a heterogeneous ecosystem as to social, economic, energetic, and personal travel planning with a huge impact on hosting cities and territories. Sustainable policies are thus fundamental to reduce this impact and to adopt ecological behaviors. The measures for any University Sustainability Plan should be evaluated in terms of GHG emissions, as well as the overall impact of the university itself. Carbon footprint (CF) calculation is a relevant Decision Support tool that allows university organizations to measure and communicate the environmental effects of their activities. The aim of this paper is to present a carbon footprint methodology specifically designed to calculate the carbon footprint of large universities. The methodology was applied to calculate the CF of the University of Bologna by following international standards—i.e., the GHG protocol, the ISO 14064, and the ISO/TR 14069 guide—to understand the environmental impact caused by greenhouse gas emissions from direct and indirect university activities. The study was conducted upon the data available in 2020 and then was compared to the 2018 data, with the aim to recognize if the effect of the pandemic could have altered the results. In 2020, the University of Bologna emitted 16,467 tCO2e which became 15,753 tCO2e considering the offset and avoided emission provided by the internal production of energy from renewable sources. Comparison between 2020 and 2018 shows how, in 2018, most of the emissions came from transportation, representing 74% of the total emissions, while in 2020 almost 50% of total emissions derived by IT procurements. The case application demonstrates the way with which the methodology may be applied to assess environmental impact for complex university campuses.
Journal Article
Laser Scanning on Road Pavements: A New Approach for Characterizing Surface Texture
The surface layer of road pavement has a particular importance in relation to the satisfaction of the primary demands of locomotion, such as security and eco-compatibility. Among those pavement surface characteristics, the “texture” appears to be one of the most interesting with regard to the attainment of skid resistance. Specifications and regulations, providing a wide range of functional indicators, act as guidelines to satisfy the performance requirements. This paper describes an experiment on the use of laser scanner techniques on various types of asphalt for texture characterization. The use of high precision laser scanners, such as the triangulation types, is proposed to expand the analysis of road pavement from the commonly and currently used two-dimensional method to a three-dimensional one, with the aim of extending the range of the most important parameters for these kinds of applications. Laser scanners can be used in an innovative way to obtain information on areal surface layer through a single measurement, with data homogeneity and representativeness. The described experience highlights how the laser scanner is used for both laboratory experiments and tests in situ, with a particular attention paid to factors that could potentially affect the survey.
Journal Article
Integrating Environmental Sensing into Cargo Bikes for Pollution-Aware Logistics in Last-Mile Deliveries
Cycling represents a significant share of urban transportation, especially in terms of last-mile delivery. It has clear benefits for delivery times, as well as for environmental issues related to freight distribution. Furthermore, the increasing accessibility of low-cost environmental sensors (LCSs) provides an opportunity for urban monitoring in any situation. Moving in this direction, this research aims to investigate the use of LCSs to monitor particulate PM2.5 and PM10 levels and map them over delivery ride paths. The calibration process took 49 days of measurements into account, spanning different seasonal conditions (from May 2024 to November 2024). The employment of multiple linear regression and robust regression revealed a strong correlation between pollutant levels from two sources and other factors such as temperature and humidity. Subsequently, a one-month trial was carried out in the city of Faenza (Italy). In this study, a commercially available LCS was mounted on a cargo bike for measurement during delivery processes. This approach was adopted to reveal biker exposure to air pollutants. In this way, the operator’s route would be designed to select the best route in terms of delivery timing and polluting factors in the future. Furthermore, the integration of environmental monitoring to map urban environments has the potential to enhance the accuracy of local pollution mapping, thereby supporting municipal efforts to inform citizens and develop targeted air quality strategies.
Journal Article