Il rapporto tecnico fornisce una panoramica dei risultati raggiunti dalla pagina facebook Comunicazione.Cnr per il periodo di gestione da parte dell'autrice in coordinamento con l'Unità Comunicazione e relazioni con il pubblico del CNR. Il periodo di analisi dati insight di facebook è 9 marzo 2020 al 26 marzo 2020 (pagina ed eventi facebook). Il report descrive anche le rubriche attivate a partire dal 9 marzo 2020 e gestite dall'autrice in coordinamento con la Responsabile dell'Unità: #lezionidiscienza, nel cui ambito è stato definito anche #lezionidiscienzaraddoppia e #lezionidiscienzayong; Comunicazione.cnr Live, nel cui ambito è stato sviluppato il contenitore Comics&Science on air assieme ad altre dirette; #scienzasulbalcone. Il rapporto è composto da un'analisi delle attività svolte e dei risultati conseguiti e dai dati social di riferimento allegati.

Estrazione dati della campagna promozionale.

La comunicazione pubblica di scienza e tecnologia migliora l'alfabetizzazione scientifica dei cittadini I suoi modelli di classificazione forniscono un quadro interpretativo a partecipazione crescente, arrivando con la citizen science a definire pratiche dei cittadini di eseguire la scienza e degli scienziati di lavorare con essi. La scienza digitale, inoltre, amplia le possibilità di apertura di scienza e innovazione nella società grazie alla trasformazione attivata dai servizi e dagli strumenti delle tecnologie dell'informazione e della comunicazione e supporta alfabetizzazione digitale e ai media. Il contributo seleziona e analizza azioni specifiche del recente studio "Work/Technology 2050. Scenarios and Actions" del Millennium Project che possono migliorare comunicazione pubblica di scienza e tecnologia e scienza dei cittadini e digitale, rielaborando i suggerimenti forniti dalle centinaia di esperti che vi hanno partecipato e proponendo in conclusione di integrare queste attività per un'alfabetizzazione partecipativa. La comunicazione pubblica di scienza e tecnologia migliora l'alfabetizzazione scientifica dei cittadini I suoi modelli di classificazione forniscono un quadro interpretativo a partecipazione crescente, arrivando con la citizen science a definire pratiche dei cittadini di eseguire la scienza e degli scienziati di lavorare con essi. La scienza digitale, inoltre, amplia le possibilità di apertura di scienza e innovazione nella società grazie alla trasformazione attivata dai servizi e dagli strumenti delle tecnologie dell'informazione e della comunicazione e supporta alfabetizzazione digitale e ai media. Il contributo seleziona e analizza azioni specifiche del recente studio "Work/Technology 2050. Scenarios and Actions" del Millennium Project che possono migliorare comunicazione pubblica di scienza e tecnologia e scienza dei cittadini e digitale, rielaborando i suggerimenti forniti dalle centinaia di esperti che vi hanno partecipato e proponendo in conclusione di integrare queste attività per un'alfabetizzazione partecipativa.

Relazione fine secondo anno assegno di ricerca CNR-IAC di tipologia A) "assegni professionalizzanti" nell'ambito del progetto europeo ERC Advanced Grant "COPMAT" (GA _N. 739964), con elenco attività svolte e analisi dei risultati raggiunti e dei prossimi passi. Periodo 18/06/2019 - 17/06/2020.

Il report contiene l'impostazione del rendiconto del primo periodo del progetto iPC, individualized Pediatric Cure: Cloud-based virtual patient models for precision pediatric oncology" (GA n 826121; Call H2020-SC1-DTH-2018-1, per il periodo di rendiconto gennaio 2019 - giugno 2020), comprensivo anche del dettaglio relativo ai costi di personale strutturato e non strutturato (costi diretti), viaggio ed equipment (altri costi diretti) e il complessivo dei costi indiretti, e del complessivo dei costi del periodo 1. Per ognuno dei costi, inoltre, è presente l'elenco dei materiali raccolti e le cose da fare per completare il rendiconto entro agosto 2020.

Relazione primo mese del terzo anno assegno di ricerca CNR-IAC di tipologia A) "assegni professionalizzanti" nell'ambito del progetto europeo ERC Advanced Grant "COPMAT" (GA _N. 739964), con elenco attività svolte e analisi dei risultati raggiunti. Periodo 18/06/2020 - 14/07/2020.

Conformational variability and heterogeneity are crucial determinants of the function of biological macromolecules. The possibility of accessing this information experimentally suffers from severe under-determination of the problem, since there are a few experimental observables to be accounted for by a (potentially) infinite number of available conformational states. Several computational methods have been proposed over the years in order to circumvent this theoretically insurmountable obstacle. A large share of these strategies is based on reweighting an initial conformational ensemble which arises from, for example, molecular simulations of different qualities and levels of theory. In this work, we compare the outcome of three reweighting approaches based on radically different views of the conformational heterogeneity problem, namely Maximum Entropy, Maximum Parsimony and Maximum Occurrence, and we do so using the same experimental data. In this comparison we find both expected as well as unexpected similarities.

Convergence of new quadrature rules for approximating the Hilbert transform are given. Numerical tests show the goodness of such approximations

This paper describes a signal processing application an embedded hardware platform, based on Hilbert transform method for demodulation of digital signals.

In this survey we consider mathematical models and methods recently developed to control crowd dynamics, with particular emphasis on egressing pedestrians. We focus on two control strategies: the first one consists in using special agents, called leaders, to steer the crowd towards the desired direction. Leaders can be either hidden in the crowd or recognizable as such. This strategy heavily relies on the power of the social influence (herding effect), namely the natural tendency of people to follow group mates in situations of emergency or doubt. The second one consists in modify the surrounding environment by adding in the walking area multiple obstacles optimally placed and shaped. The aim of the obstacles is to naturally force people to behave as desired. Both control strategies discussed in this paper aim at reducing as much as possible the intervention on the crowd. Ideally the natural behavior of people is kept, and people do not even realize they are being led by an external intelligence. Mathematical models are discussed at different scales of observation, showing how macroscopic (fluid-dynamic) models can be derived by mesoscopic (kinetic) models which, in turn, can be derived by microscopic (agent-based) models.

Background: Systems Medicine is a novel approach to medicine, i.e. an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modelling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review we collect and explain over one hundred terms related to Systems Medicine. These include both modelling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for keep digging into the topic.

Drug research, therapy development, and other areas of pharmacology and medicine can benefit from simula- tions and optimization of mathematical models that contain a mathematical description of interactions between systems elements at the cellular, tissue, organ, body, and population level. This approach is the foundation of systems medicine and precision medicine. Here, simulated experiments are performed with computers (in silico) first, and they are then replicated through lab experiments (in vivo or in vitro) or clinical studies. In turn, these experiments and studies can be used to validate or improve the models. This iterative loop of dry and wet lab work is successful when biomedical researchers tightly collaborate with data scientists and modelers. From an educational point of view, the interdisciplinary research in systems biology can be sustained most ef- fectively when specialists have been trained to have both a strong background in the disciplines of biology or modeling and strong communication skills, which make them able to communicate with other specialists. This overview addresses possible interdisciplinary communication gaps. Focusing our attention on biomedical re- searchers, we describe the reasons for using modeling and ways to collaborate with modelers, including their needs for specific biological expertise and data. This review includes an introduction to the principles of several widely used mechanistic modeling methods, focusing on their areas of applicability as well as their limitations. A potential complementary role of machine-learning methods in the development of mechanistic models is also discussed. The descriptions of the methods also include links to corresponding modeling software tools as well as practical examples of their application. Finally, we also explicitly address different aspects of multiscale mod- eling approaches that allow a more complete and holistic perspective of the human body.

Background: The aim of a recent research project was the investigation of the mechanisms involved in the onset of type 2 diabetes in the absence of familiarity. This has led to the development of a computational model that recapitulates the aetiology of the disease and simulates the immunological and metabolic alterations linked to type-2 diabetes subjected to clinical, physiological, and behavioural features of prototypical human individuals. Results: We analysed the time course of 46,170 virtual subjects, experiencing different lifestyle conditions. We then set up a statistical model able to recapitulate the simulated outcomes. Conclusions: The resulting machine learning model adequately predicts the synthetic dataset and can, therefore, be used as a computationally-cheaper version of the detailed mathematical model, ready to be implemented on mobile devices to allow self-assessment by informed and aware individuals. The computational model used to generate the dataset of this work is available as a web-service at the following address: http://kraken.iac.rm.cnr.it/T2DM.

Unsupervised learning approaches are frequently employed to stratify patients into clinically relevant subgroups and to identify biomarkers such as disease-associated genes. However, clustering and biclustering techniques are oblivious to the functional relationship of genes and are thus not ideally suited to pinpoint molecular mechanisms along with patient subgroups.We developed the network-constrained biclustering approach BiCoN (Biclustering Constrained by Networks) which (i) restricts biclusters to functionally related genes connected in molecular interaction networks and (ii) maximizes the difference in gene expression between two subgroups of patients. This allows BiCoN to simultaneously pinpoint molecular mechanisms responsible for the patient grouping. Network-constrained clustering of genes makes BiCoN more robust to noise and batch effects than typical clustering and biclustering methods. BiCoN can faithfully reproduce known disease subtypes as well as novel, clinically relevant patient subgroups, as we could demonstrate using breast and lung cancer datasets. In summary, BiCoN is a novel systems medicine tool that combines several heuristic optimization strategies for robust disease mechanism extraction. BiCoN is well-documented and freely available as a python package or a web interface.PyPI package: https://pypi.org/project/biconhttps://exbio.wzw.tum.de/biconSupplementary data are available at Bioinformatics online.

Invasive species cause huge amounts of environmental, economic, social and cultural damage in Europe and worldwide. Improving measures to control them is an ongoing challenge, and mathematical modeling and optimization are becoming increasingly popular as a tool to assist management (1; 2; 4). We analyse an optimal control model for the control of invasive species which aims to find the best temporal resource allocation strategy for the population reduction, under a budget constraint (3). We derive the optimality system in the state and control variables and we use the phase-space analysis to provide qualitative insights about the behaviour of the optimal solution. We pay special attention to the nature of the optimal trajectories in long time intervals and the explore the Turnpike property of the problem (5). Finally, we introduce a numerical scheme for the solution of the state-costate nearly-Hamiltonian system, based on exponential-Lawson symplectic Runge-Kutta schemes applied in a forward-backward procedure.

We analyse the lower ionosphere disturbances in the time period around the Mw 5.4 Kraljevo earthquake (EQ), which occurred on 3 November 2010 in Serbia. The results presented herein are based on analysis of the amplitudes of three VLF signals emitted in Italy, UK, and Germany and recorded in Serbia whose variations primarily result from changes in the electrical properties of the lower ionosphere at a distance more than 120 km from the epicentre of the EQ. The primary goals of this study are to reveal specific variations as possible EQ precursors as well as disturbances following the EQ event recorded by the observational equipment, and to investigate whether better time resolution data can affect the analysis of the lower ionosphere disturbances possibly connected to the EQ. We process two sets of data with sampling periods of 1 min and 0.1 s. As the first analysis indicates the absence of long-term disturbances, which can clearly be connected to the Kraljevo EQ, our attention is focused on the study of short-period noise amplitude and the excitation and attenuation of acoustic and gravity waves in the lower ionosphere. Processing of the amplitudes of three VLF signals during the nights of the four EQs with magnitude greater than 4 that occurred in Serbia, as well as EQs of all magnitudes during the three days of 3, 4, and 9 November, indicates that the detected ICV radio signal noise amplitude reduction starting before the Kaljevo EQ is also observed for 13 additional EQ events near the signal propagation path, and occurred during all three days (for all EQs with magnitude greater than 4 and several less intensive events). Excitation and attenuation of acoustic waves are also found for all these EQ events with a magnitude greater than 4.

This study describes a semi-empirical model developed to estimate volumetric soil moisture (theta v) in bare soils during the dry season (March-May) using C-band (5.42 GHz) synthetic aperture radar (SAR) imagery acquired from the Sentinel-1 European satellite platform at a 20 m spatial resolution. The semi-empirical model was developed using backscatter coefficient (sigma degrees dB) and in situ soil moisture collected from Siruguppa taluk (sub-district) in the Karnataka state of India. The backscatter coefficients sigma VV0 and sigma VH0 were extracted from SAR images at 62 geo-referenced locations where ground sampling and volumetric soil moisture were measured at a 10 cm (0-10 cm) depth using a soil core sampler and a standard gravimetric method during the dry months (March-May) of 2017 and 2018. A linear equation was proposed by combining sigma VV0 and sigma VH0 to estimate soil moisture. Both localized and generalized linear models were derived. Thirty-nine localized linear models were obtained using the 13 Sentinel-1 images used in this study, considering each polarimetric channel Co-Polarization (VV) and Cross-Polarization (VH) separately, and also their linear combination of VV + VH. Furthermore, nine generalized linear models were derived using all the Sentinel-1 images acquired in 2017 and 2018; three generalized models were derived by combining the two years (2017 and 2018) for each polarimetric channel; and three more models were derived for the linear combination of sigma VV0 and sigma VH0. The above set of equations were validated and the Root Mean Square Error (RMSE) was 0.030 and 0.030 for 2017 and 2018, respectively, and 0.02 for the combined years of 2017 and 2018. Both localized and generalized models were compared with in situ data. Both kind of models revealed that the linear combination of sigma VV0 + sigma VH0 showed a significantly higher R-2 than the individual polarimetric channels.

This work presents a methodology to monitor the dynamic behaviour of tall metallic towers based on ground-based radar interferometry, and apply it to the case of telecommunication towers. Ground-based radar displacement measurements of metallic towers are acquired without installing any Corner Reflector (CR) on the structure. Each structural element of the tower is identified based on its range distance with respect to the radar. The interferometric processing of a time series of radar profiles is used to measure the vibration frequencies of each structural element and estimate the amplitude of its oscillation. A methodology is described to visualize the results and provide a useful tool for the real-time analysis of the dynamic behaviour of metallic towers.

The Global Navigation Satellite System (GNSS) meteorology contribution to the comprehension of the Earth's atmosphere's global and regional variations is essential. In GNSS processing, the zenith wet delay is obtained using the difference between the zenith total delay and the zenith hydrostatic delay. The zenith wet delay can also be converted into precipitable water vapor by knowing the atmospheric weighted mean temperature profiles. Improving the accuracy of the zenith hydrostatic delay and the weighted mean temperature, normally obtained using modeled surface meteorological parameters at coarse scales, leads to a more accurate and precise zenith wet delay estimation, and consequently, to a better precipitable water vapor estimation. In this study, we developed an hourly global pressure and temperature (HGPT) model based on the full spatial and temporal resolution of the new ERA5 reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The HGPT model provides information regarding the surface pressure, surface air temperature, zenith hydrostatic delay, and weighted mean temperature. It is based on the time-segmentation concept and uses the annual and semi-annual periodicities for surface pressure, and annual, semi-annual, and quarterly periodicities for surface air temperature. The amplitudes and initial phase variations are estimated as a periodic function. The weighted mean temperature is determined using a 20-year time series of monthly data to understand its seasonality and geographic variability. We also introduced a linear trend to account for a global climate change scenario. Data from the year 2018 acquired from 510 radiosonde stations downloaded from the National Oceanic and Atmospheric Administration (NOAA) Integrated Global Radiosonde Archive were used to assess the model coefficients. Results show that the GNSS meteorology, hydrological models, Interferometric Synthetic Aperture Radar (InSAR) meteorology, climate studies, and other topics can significantly benefit from an ERA5 full-resolution model.

This article presents a methodology for the monitoring of tall structures based on the joint use of a terrestrial laser scanner (TLS), configured in line scanner mode, and a ground-based real aperture radar (GB-RAR) interferometer. The methodology provides both natural frequencies and oscillation amplitudes of tall structures. Acquisitions of the surface of the tall structure are performed by the TLS with a high sampling rate: each line scan provides an instantaneous longitudinal section. By interpolating the points of each line, oscillation profiles are estimated with a much better precision than each single point. The amplitude and frequency of the main oscillation mode of the whole structure are derived from the TLS profiles. GB-RAR measurements are used to measure the vibration frequencies of higher oscillation modes which are not caught by the TLS due its lower precision in the measurement of displacements. In contrast, the high spatial resolution of TLS measurements provides an accurate description of oscillation amplitude along the tower, which cannot be caught by the GB-RAR, due to its poorer spatial resolution. TLS and GB-RAR acquisitions are simultaneous. The comparison with the analytical solution for oscillation modes demonstrates that the proposed methodology can provide useful information for structural health monitoring (SHM). The methodology does not require the use of targets on the structure and it can be applied during its normal use, even in presence of dynamic loads (wind, traffic vibrations, etc.). A test was carried out on a wind tower where the synergistic use of TLS and GB-RAR made it possible to fully describe the spectral properties of the tower and at the same time measure the amplitude of the first oscillation mode along the tower with a high spatial resolution.