We present an all-around study of the visitors flow in crowded museums: a combination of Lagrangian field measurements and statistical analyses enable us to create stochastic digital-twins of the guest dynamics, unlocking comfort- and safety-driven optimizations. Our case study is the Galleria Borghese museum in Rome (Italy), in which we performed a real-life data acquisition campaign. We specifically employ a Lagrangian IoT-based visitor tracking system based on Raspberry Pi receivers, displaced in fixed positions throughout the museum rooms, and on portable Bluetooth Low Energy beacons handed over to the visitors. Thanks to two algorithms: a sliding window-based statistical analysis and an MLP neural network, we filter the beacons RSSI and accurately reconstruct visitor trajectories at room-scale. Via a clustering analysis, hinged on an original Wasserstein-like trajectory-space metric, we analyze the visitors paths to get behavioral insights, including the most common flow patterns. On these bases, we build the transition matrix describing, in probability, the room-scale visitor flows. Such a matrix is the cornerstone of a stochastic model capable of generating visitor trajectories in silico. We conclude by employing the simulator to enhance the museum fruition while respecting numerous logistic and safety constraints. This is possible thanks to optimized ticketing and new entrance/exit management.

This book offers an informal, easy-to-understand account of topics in modern physics and mathematics. The focus is, in particular, on statistical mechanics, soft matter, probability, chaos, complexity, and models, as well as their interplay. The book features 28 key entries and it is carefully structured so as to allow readers to pursue different paths that reflect their interests and priorities, thereby avoiding an excessively systematic presentation that might stifle interest. While the majority of the entries concern specific topics and arguments, some relate to important protagonists of science, highlighting and explaining their contributions. Advanced mathematics is avoided, and formulas are introduced in only a few cases. The book is a user-friendly tool that nevertheless avoids scientific compromise. It is of interest to all who seek a better grasp of the world that surrounds us and of the ideas that have changed our perceptions. Il libro prova a colmare almeno in parte quel vuoto lasciato dalla divulgazione scientifica mainstream, interessata principalmente a dare risalto agli aspetti più sensazionalistici e bizzarri delle scoperte scientifiche, svelando il fascino presente in argomenti che non vengono solitamente discussi nei libri di divulgazione e nelle vite di scienziati poco conosciuti al grande pubblico, ma che hanno posto le basi per la scienza come la conosciamo ora.

This work aims at studying the crystallization process of Hydroxyapatite samples in three different chemical environments (Cit, Glr, CitOH), as a function of time and temperature (25°C, 37°C or biomimetic temperature, 60°C and 80°C) . In particular non-crystalline and/or precursor states (SAXS) are expected to play a key-role in this analysis. Due to the huge amount of data collected at Synchrotron facilities, a preliminary correlation evaluation is needed in order to extract the most representative curves showing significant modification in shape and/or in the regions of interest. An algorithm based on Hierarchical Non-Negative Matrix Factorization (intensity SAXS profiles are positive) has been developed and applied in order to select 2^n profiles (n==number of bisections of the original data set). The comparison of the algorithm findings to the known particle morphologies (SAXS fitting) has spotted the HA crystallization dynamics (time resolved) beneath, both at different temperatures and chemical environments.

The impact of protein-coding genes on cancer onset and progression is a well-establishedparadigm in molecular oncology. Nevertheless, unveiling the contribution of the noncoding genes--including long noncoding RNAs (lncRNAs)--to tumorigenesis represents a great challenge forpersonalized medicine, since they (i) constitute the majority of the human genome, (ii) are essentialand flexible regulators of gene expression and (iii) present all types of genomic alterations describedfor protein-coding genes. LncRNAs have been increasingly associated with cancer, their highlytissue- and cancer type-specific expression making them attractive candidates as both biomarkersand therapeutic targets. Medulloblastoma is one of the most common malignant pediatric braintumors. Group 3 is the most aggressive subgroup, showing the highest rate of metastasis at diagnosis.Transcriptomics and reverse genetics approaches were combined to identify lncRNAs implicatedin Group 3 Medulloblastoma biology. Here we present the first collection of lncRNAs dependenton the activity of the MYC oncogene, the major driver gene of Group 3 Medulloblastoma. Weassessed the expression profile of selected lncRNAs in Group 3 primary tumors and functionallycharacterized these species. Overall, our data demonstrate the direct involvement of three lncRNAsin Medulloblastoma cancer cell phenotypes The impact of protein-coding genes on cancer onset and progression is a well-established paradigm in molecular oncology. Nevertheless, unveiling the contribution of the noncoding genes--including long noncoding RNAs (lncRNAs)--to tumorigenesis represents a great challenge for personalized medicine, since they (i) constitute the majority of the human genome, (ii) are essential and flexible regulators of gene expression and (iii) present all types of genomic alterations described for protein-coding genes. LncRNAs have been increasingly associated with cancer, their highly tissue- and cancer type-specific expression making them attractive candidates as both bi-omarkers and therapeutic targets. Medulloblastoma is one of the most common malignant pediatric brain tumors. Group 3 is the most aggressive subgroup, showing the highest rate of metastasis at diagnosis. Transcriptomics and reverse genetics approaches were combined to identify lncRNAs implicated in Group 3 Medulloblastoma biology. Here we present the first collection of lncRNAs dependent on the activity of the MYC oncogene, the major driver gene of Group 3 Medulloblastoma. We assessed the expression profile of selected lncRNAs in Group 3 primary tumors and functionally characterized these species. Overall, our data demonstrate the direct involvement of three lncRNAs in Medulloblastoma cancer cell phenotypes.

The ionospheric D-region affects propagation of electromagnetic waves including ground-based signals and satellite signals during its intensive disturbances. Consequently, the modeling of electromagnetic propagation in the D-region is important in many technological domains. One of sources of uncertainty in the modeling of the disturbed D-region is the poor knowledge of its parameters in the quiet state at the considered location and time period. We present the Quiet Ionospheric D-Region (QIonDR) model based on data collected in the ionospheric D-region remote sensing by very low/low frequency (VLF/LF) signals and the Long-Wave Propagation Capability (LWPC) numerical model. The QIonDR model provides both Wait's parameters and the electron density in the D-region area of interest at a given daytime interval. The proposed model consists of two steps. In the first step, Wait's parameters are modeled during the quiet midday periods as a function of the daily sunspot number, related to the long-term variations during solar cycle, and the seasonal parameter, providing the seasonal variations. In the second step, the output of the first step is used to model Wait's parameters during the whole daytime. The proposed model is applied to VLF data acquired in Serbia and related to the DHO and ICV signals emitted in Germany and Italy, respectively. As a result, the proposed methodology provides a numerical tool to model the daytime Wait's parameters over the middle and low latitudes and an analytical expression valid over a part of Europe for midday parameters.

In this work, we study the impact of high-energy radiation induced by solar X-ray flares on the determination of the temporal change in precipitable water vapor (?PWV) as estimated using the synthetic aperture radar (SAR) meteorology technique. As recent research shows, this radiation can significantly affect the ionospheric D-region and induces errors in the estimation of the total electron content (TEC) by the applied models. Consequently, these errors are reflected in the determination of the phase delay and in many different types of measurements and models, including calculations of meteorological parameters based on SAR observations. The goal of this study is to quantify the impact of solar X-ray flares on the estimation of ?PWV and provide an estimate of errors induced if the vertical total electron content (VTEC) is obtained by single layer models (SLM) or multiple layer models (MLM) (these models do not include ionosphere properties below the altitude of 90 km as input parameters and cannot provide information about local disturbances in the D-region). The performed analysis is based on a known procedure for the determination of the D-region electron density (and, consequently, the vertical total electron content in the D-region (VTEC)) using ionospheric observations of very low frequency (VLF) radio waves. The main result indicates that if the D-region, perturbed by medium-sized and intense X-ray flares, is not modeled, errors occur in the determination of ?PWV. This study emphasizes the need for improved MLMs for the estimation of the TEC, including observational data at D-region altitudes during medium-sized and intense X-ray flare events.

The present study assesses the viability of including water vapor data from Interferometry Synthetic Aperture Radar (InSAR) in the initialization of numerical weather prediction (NWP) models, using already available Sentinel-1 A and B products. Despite the limitations resulting from the 6-day return period of images produced by the 2-satellite system, it is found that for a sufficiently large domain designed to contain a set of images every 12 h (at varying locations), the impact on model performance is beneficial or at least neutral. The proposed methodology is tested in 24 consecutive 12 h forecasts, covering two cycles of the Sentinel-1 system and 214 images, for a domain containing Iberia. A statistical analysis of the forecast precipitable water vapor (PWV) against independent GNSS observations concluded for relevant improvements in the different scores, especially during a consecutive 3-day period where the standard initial data were less accurate. An analysis of the rain forecasts against gridded remote sensing observations further indicates an overall improvement in the grid-point distribution of different precipitation classes throughout the simulation, even when the mean impact of PWV assimilation was not significant. It is suggested that current InSAR data are already a useful source of NWP data and will only become more relevant as new systems are put into operation.

In this paper we analyse temporal variations of the phase of a very low frequency (VLF) signal, used for the lower ionosphere monitoring, in periods around four earthquakes (EQs) with magnitude greater than 4. We provide two analyses in time and frequency domains. First, we analyse time evolution of the phase noise. And second, we examine variations of the frequency spectrum using Fast Fourier Transform (FFT) in order to detect hydrodynamic wave excitations and attenuations. This study follows a previous investigation which indicated the noise amplitude reduction, and excitations and attenuations of the hydrodynamic waves less than one hour before the considered EQ events as a new potential ionospheric precursors of earthquakes. We analyse the phase of the ICV VLF transmitter signal emitted in Italy recorded in Serbia in time periods around four earthquakes occurred on 3, 4 and 9 November 2010 which are the most intensive earthquakes analysed in the previous study. The obtained results indicate very similar changes in the noise of phase and amplitude, and show an agreement in recorded acoustic wave excitations. However, properties in the obtained wave attenuation characteristics are different for these two signal parameters.

In multitemporal interferometric synthetic aperture radar (InSAR) applications, propagation delay in the troposphere introduces a major source of disturbance known as atmospheric phase screen (APS). This study proposes a novel framework to compensate for the APS from multitemporal ground-based InSAR data. The proposed framework first performs time-series clustering in accordance with the temporal APS behavior realized by the k-means clustering approach. In the second step, joint estimation of the APS and displacement velocity is performed. For this purpose, a novel interferometric signal model, including the APS modeled by the median profiles defined in each cluster, is proposed. The proposed framework is validated with the Ku-band ground-based synthetic aperture radar data sets measured over a mountainous area in Kumamoto, Japan. Tests on these data sets reveal that compared with the conventional approach, the presented approach improves displacement estimation accuracy under severe atmospheric conditions.

In this paper, we study linear parabolic equations on a finite oriented star-shaped network; the equations are coupled by transmission conditions set at the inner node, which do not impose continuity on the unknown. We consider this problem as a parabolic approximation of a set of the first-order linear transport equations on the network, and we prove that when the diffusion coefficient vanishes, the family of solutions converges to the unique solution to the first-order equations satisfying suitable transmission conditions at the inner node, which are determined by the parameters appearing in the parabolic transmission conditions.

The degradation of monumental stones resulting from the mutual interaction between mechanical actions and environment/pollution conditions is investigated here. In particular, the stone degradation is estimated as a function of the environmental conditions and the prediction of damaging phenomena, which can compromise permanently the fruition of monuments. This is done through a macroscopic phenomenological model which accounts for the main aspects of the problem: the chemical reaction and the mechanical behavior of stones. The sulphation reaction and the diffusion of the pollutant agents are described by suitable differential equations coupled with a variational formulation of fracture mechanics. The proposed model permits to evaluate how much aggressive atmospheric agents contribute to the decay of the mechanical properties of the stones as well as to establish the impact of the synergic chemical aggression and stress state. The latter is also influenced by the chemical reaction and by the evolving mechanical properties of the material. The main features of this approach are illustrated by specific numerical simulations.

Innovative approach: it is one of the first real scientific contacts between the mathematical community and the experts in cultural heritage Effective multidisciplinary interaction: the results of concrete collaboration projects between research groups dealing with the degradation of the historical and artistic heritage are presented Development potential: the possibility of finding suitable mathematical models to provide an effective and non-invasive predictive analysis tool can be a fundamental task in the cultural heritage conservation field

(1) Background: A better understanding of COVID-19 dynamics in terms of interactions among individuals would be of paramount importance to increase the effectiveness of containment measures. Despite this, the research lacks spatiotemporal statistical and mathematical analysis based on large datasets. We describe a novel methodology to extract useful spatiotemporal information from COVID-19 pandemic data. (2) Methods: We perform specific analyses based on mathematical and statistical tools, like mathematical morphology, hierarchical clustering, parametric data modeling and non-parametric statistics. These analyses are here applied to the large dataset consisting of about 19,000 COVID-19 patients in the Veneto region (Italy) during the entire Italian national lockdown. (3) Results: We estimate the COVID-19 cumulative incidence spatial distribution, significantly reducing image noise. We identify four clusters of connected provinces based on the temporal evolution of the incidence. Surprisingly, while one cluster consists of three neighboring provinces, another one contains two provinces more than 210 km apart by highway. The survival function of the local spatial incidence values is modeled here by a tapered Pareto model, also used in other applied fields like seismology and economy in connection to networks. Model's parameters could be relevant to describe quantitatively the epidemic. (4) Conclusion: The proposed methodology can be applied to a general situation, potentially helping to adopt strategic decisions such as the restriction of mobility and gatherings.

For the finite weighted Hilbert transform we consider two different product integration rules, the VP rule and the L-rule, based on the same nodes and obtained by approximating the density function with filtered de la Vallée Poussin and classical Lagrange interpolation polynomials, respectively. The L-rule is well known and widely studied. The VP rule is here introduced and we will prove the convergence in suitable weighted uniform spaces. Hence we will examine the performance of both the product rules, showing that in case of density functions that have some pathologies (peaks, cusps, etc.) localized in isolated points, VP rules inherit the good properties of the filtered de la Vallée Poussin type approximation, providing better performances than L-rules.

Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange is often restricted by privacy legislation, meta-analyses are frequently employed to pool local results. However, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. Flimma (https://exbio.wzw.tum.de/flimma/) addresses this issue by implementing the state-of-the-art workflow limma voom in a federated manner, i.e., patient data never leaves its source site. Flimma results are identical to those generated by limma voom on aggregated datasets even in imbalanced scenarios where meta-analysis approaches fail.

FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) will flight as the 9th ESA's Earth Explorer mission, and an End-to-End Simulator (E2ES) has been developed as a support tool for the mission selection process and the subsequent development phases. The current status of the FORUM E2ES project is presented, together with the characterization of the capabilities of a full physics retrieval code applied to FORUM data. We show how the instrument characteristics and5the observed scene conditions impact on the spectrum measured by the instrument, accounting for the main sources of error related to the entire acquisition process, and the consequences on the retrieval algorithm. Both homogeneous and heterogeneous case studies are simulated in clear and cloudy conditions, validating the E2ES against two independent codes: KLIMA (clear sky) and SACR (cloudy sky). The performed tests show that the performance of the retrieval algorithm is compliant with the project requirements both in clear and cloudy conditions. The far infrared (FIR) part of the FORUM spectrum is shown to be10sensitive to surface emissivity, in dry atmospheric conditions, and to cirrus clouds, resulting in improved performance of the retrieval algorithm in these conditions. The retrieval errors increase with increasing the scene heterogeneity, both in terms of surface characteristics and in terms of fractional cloud cover of the scene.

The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) measured the middle-infrared limb emission spectrum of the atmosphere from 2002 to 2012 on board ENVISAT, a polar-orbiting satellite. Recently, the European Space Agency (ESA) completed the final reprocessing of MIPAS measurements, using version 8 of the level 1 and level 2 processors, which include more accurate models, processing strategies, and auxiliary data. The list of retrieved gases has been extended, and it now includes a number of new species with weak emission features in the MIPAS spectral range. The new retrieved trace species include carbonyl chloride (COCl2), also called phosgene. Due to its toxicity, its use has been reduced over the years; however, it is still used by chemical industries for several applications. Besides its direct injection in the troposphere, stratospheric phosgene is mainly produced from the photolysis of CCl4, a molecule present in the atmosphere because of human activity. Since phosgene has a long stratospheric lifetime, it must be carefully monitored as it is involved in the ozone destruction cycles, especially over the winter polar regions.In this paper we exploit the ESA MIPAS version 8 data in order to discuss the phosgene distribution, variability, and trends in the middle and lower stratosphere and in the upper troposphere. The zonal averages show that phosgene volume mixing ratio is larger in the stratosphere, with a peak of 40 pptv (parts per trillion by volume) between 50 and 30 hPa at equatorial latitudes, while at middle and polar latitudes it varies from 10 to 25 pptv. A moderate seasonal variability is observed in polar regions, mostly between 80 and 50 hPa. The comparison of MIPAS-ENVISAT COCl2 v8 profiles with the ones retrieved from MIPAS balloon and ACE-FTS (Atmospheric Chemistry Experiment - Fourier Transform Spectrometer) measurements highlights a negative bias of about 2 pptv, mainly in polar and mid-latitude regions. Part of this bias is attributed to the fact that the ESA level 2 v8 processor uses an updated spectroscopic database. For the trend computation, a fixed pressure grid is used to interpolate the phosgene profiles, and, for each pressure level, VMR (volume mixing ratio) monthly averages are computed in pre-defined 10? wide latitude bins. Then, for each latitudinal bin and pressure level, a regression model has been fitted to the resulting time series in order to derive the atmospheric trends. We find that the phosgene trends are different in the two hemispheres. The analysis shows that the stratosphere of the Northern Hemisphere is characterized by a negative trend of about -7 pptv per decade, while in the Southern Hemisphere phosgene mixing ratios increase with a rate of the order of +4 pptv per decade. This behavior resembles the stratospheric trend of CCl4, which is the main stratospheric source of COCl2. In the upper troposphere a positive trend is found in both hemispheres.

Motivated by the recent outbreak of coronavirus (COVID-19), we propose a stochastic model of epidemic temporal growth and mitigation based on a time-modulated Hawkes process. The model is sufficiently rich to incorporate specific characteristics of the novel coronavirus, to capture the impact of undetected, asymptomatic and super-diffusive individuals, and especially to take into account time-varying counter-measures and detection efforts. Yet, it is simple enough to allow scalable and efficient computation of the temporal evolution of the epidemic, and exploration of what-if scenarios. Compared to traditional compartmental models, our approach allows a more faithful description of virus specific features, such as distributions for the time spent in stages, which is crucial when the time-scale of control (e.g., mobility restrictions) is comparable to the lifetime of a single infection. We apply the model to the first and second wave of COVID-19 in Italy, shedding light onto several effects related to mobility restrictions introduced by the government, and to the effectiveness of contact tracing and mass testing performed by the national health service.

Two-dimensional dissipative and isotropic kinetic models, like the ones used in neutron transport theory, are considered. Especially, steady-states are expressed for constant opacity and damping, allowing to derive a scattering S-matrix and corresponding "truly 2D well-balanced" numerical schemes. A first scheme is obtained by directly implementing truncated Fourier-Bessel series, whereas another proceeds by applying an exponential modulation to a former, conservative, one. Consistency with the asymptotic damped parabolic approximation is checked for both algorithms. A striking property of some of these schemes is that they can be proved to be both 2D well-balanced and asymptotic-preserving in the parabolic limit, even when setting up IMEX time-integrators: see Corollaries 3.4 and A.1. These findings are further confirmed by means of practical benchmarks carried out on coarse Cartesian computational grids.

The phase separation of a two-dimensional active binary mixture is studied under the action of an applied shear through numerical simulations. It is highlighted how the strength of the external flow modifies the initial shape of growing domains. The activity is responsible for the formation of isolated droplets which affect both the coarsening dynamics and the morphology of the system. The characteristic dimensions of domains along the flow and the shear direction are modulated in time by oscillations whose amplitudes are reduced when the activity increases. This induces a broadening of the distribution functions of domain lengths with respect to the passive case due to the presence of dispersed droplets of different sizes.