Carbon dioxide fluxes in Alpine grasslands at the Nivolet Plain, Gran Paradiso National Park, Italy 2017-2023.

We introduce a georeferenced dataset of Net Ecosystem Exchange (NEE), Ecosystem Respiration (ER) and meteo-climatic variables (air and soil temperature, air relative humidity, soil volumetric water content, pressure, and solar irradiance) collected at the Nivolet Plain in Gran Paradiso National Park (GPNP), western Italian Alps, from 2017 to 2023. NEE and ER are derived by measuring the temporal variation of CO2 concentration obtained by the enclosed chamber method. We used a customised portable non-steady-state dynamic flux chamber, paired with an InfraRed Gas Analyser (IRGA) and a portable weather station, measuring CO2 fluxes at a number of points (around 20 per site and per day) within five different sites during the snow-free season (June to October). Sites are located within the same hydrological basin and have different geological substrates: carbonate rocks (site CARB), gneiss (GNE), glacial deposits (GLA, EC), alluvial sediments (AL). This dataset provides relevant and often missing information on high-altitude mountain ecosystems and enables new comparisons with other similar sites, modelling developments and validation of remote sensing data.

Spatial and temporal variability of carbon dioxide fluxes in the Alpine Critical Zone: The case of the Nivolet Plain, Gran Paradiso National Park, Italy.

The dynamics of carbon dioxide fluxes in the high-altitude Alpine Critical Zone is only partially understood. The complex geomorphology induces significant spatial heterogeneity, and a strong interannual variability is present in the often-extreme climatic and environmental conditions of Alpine ecosystems. To explore the relative importance of the spatial and temporal variability of CO2 fluxes, we analysed a set of in-situ measurements obtained during the summers from 2018 to 2021 in four sampling plots, characterised by soils with different underlying bedrock within the same watershed in the Nivolet plain, in the Gran Paradiso National Park, western Italian Alps. Multi-regression models of CO2 emission and uptake were built using measured meteo-climatic and environmental variables considering either individual years (aggregating over plots) or individual plots (aggregating over years). We observed a significant variability of the model parameters across the different years, while such variability was much smaller across different plots. Significant changes between the different years mainly concerned the temperature dependence of respiration (CO2 emission) and the light dependence of photosynthesis (CO2 uptake). These results suggest that spatial upscaling can be obtained from site measurements, but long-term flux monitoring is required to properly capture the temporal variability at interannual scales.

The network of plants volatile organic compounds.

Plants emission of Volatile Organic Compounds (VOCs) is involved in a wide class of ecological functions, as VOCs play a crucial role in plants interactions with biotic and abiotic factors. Accordingly, they vary widely across species and underpin differences in ecological strategy. In this paper, VOCs spontaneously emitted by 109 plant species (belonging to 56 different families) have been qualitatively and quantitatively analysed in order to provide an alternative classification of plants species. In particular, by using bipartite networks methodology from Complex Network Theory, and through the application of community detection algorithms, we show that is possible to classify species according to chemical classes such as terpenes and sulfur compounds. Such complex network analysis allows to uncover hidden plants relationships related to their evolutionary and adaptation to the environment story.

Echo Chambers: Emotional Contagion and Group Polarization on Facebook.

Recent findings showed that users on Facebook tend to select information that adhere to their system of beliefs and to form polarized groups - i.e., echo chambers. Such a tendency dominates information cascades and might affect public debates on social relevant issues. In this work we explore the structural evolution of communities of interest by accounting for users emotions and engagement. Focusing on the Facebook pages reporting on scientific and conspiracy content, we characterize the evolution of the size of the two communities by fitting daily resolution data with three growth models - i.e. the Gompertz model, the Logistic model, and the Log-logistic model. Although all the models appropriately describe the data structure, the Logistic one shows the best fit. Then, we explore the interplay between emotional state and engagement of users in the group dynamics. Our findings show that communities' emotional behavior is affected by the users' involvement inside the echo chamber. Indeed, to an higher involvement corresponds a more negative approach. Moreover, we observe that, on average, more active users show a faster shift towards the negativity than less active ones.

Distress Propagation in Complex Networks: The Case of Non-Linear DebtRank.

We consider a dynamical model of distress propagation on complex networks, which we apply to the study of financial contagion in networks of banks connected to each other by direct exposures. The model that we consider is an extension of the DebtRank algorithm, recently introduced in the literature. The mechanics of distress propagation is very simple: When a bank suffers a loss, distress propagates to its creditors, who in turn suffer losses, and so on. The original DebtRank assumes that losses are propagated linearly between connected banks. Here we relax this assumption and introduce a one-parameter family of non-linear propagation functions. As a case study, we apply this algorithm to a data-set of 183 European banks, and we study how the stability of the system depends on the non-linearity parameter under different stress-test scenarios. We find that the system is characterized by a transition between a regime where small shocks can be amplified and a regime where shocks do not propagate, and that the overall stability of the system increases between 2008 and 2013.

Networks of plants: how to measure similarity in vegetable species.

Despite the common misconception of nearly static organisms, plants do interact continuously with the environment and with each other. It is fair to assume that during their evolution they developed particular features to overcome similar problems and to exploit possibilities from environment. In this paper we introduce various quantitative measures based on recent advancements in complex network theory that allow to measure the effective similarities of various species. By using this approach on the similarity in fruit-typology ecological traits we obtain a clear plant classification in a way similar to traditional taxonomic classification. This result is not trivial, since a similar analysis done on the basis of diaspore morphological properties do not provide any clear parameter to classify plants species. Complex network theory can then be used in order to determine which feature amongst many can be used to distinguish scope and possibly evolution of plants. Future uses of this approach range from functional classification to quantitative determination of plant communities in nature.

A foraminiferal δ(18)O record covering the last 2,200 years.

Thanks to the precise core dating and the high sedimentation rate of the drilling site (Gallipoli Terrace, Ionian Sea) we were able to measure a foraminiferal δ(18)O series covering the last 2,200 years with a time resolution shorter than 4 years. In order to support the quality of this data-set we link the δ(18)O values measured in the foraminifera shells to temperature and salinity measurements available for the last thirty years covered by the core. Moreover, we describe in detail the dating procedures based on the presence of volcanic markers along the core and on the measurement of (210)Pb and (137)Cs activity in the most recent sediment layers. The high time resolution allows for detecting a δ(18)O decennial-scale oscillation, together with centennial and multicentennial components. Due to the dependence of foraminiferal δ(18)O on environmental conditions, these oscillations can provide information about temperature and salinity variations in past millennia. The strategic location of the drilling area makes this record a unique tool for climate and oceanographic studies of the Central Mediterranean.

Radiotherapy dose enhancement using BNCT in conventional LINACs high-energy treatment: Simulation and experiment.

AIM: To employ the thermal neutron background that affects the patient during a traditional high-energy radiotherapy treatment for BNCT (Boron Neutron Capture Therapy) in order to enhance radiotherapy effectiveness. BACKGROUND: Conventional high-energy (15-25 MV) linear accelerators (LINACs) for radiotherapy produce fast secondary neutrons in the gantry with a mean energy of about 1 MeV due to (γ, n) reaction. This neutron flux, isotropically distributed, is considered as an unavoidable undesired dose during the treatment. Considering the moderating effect of human body, a thermal neutron fluence is localized in the tumour area: this neutron background could be employed for BNCT by previously administering (10)B-Phenyl-Alanine ((10)BPA) to the patient. MATERIALS AND METHODS: Monte Carlo simulations (MCNP4B-GN code) were performed to estimate the total amount of neutrons outside and inside human body during a traditional X-ray radiotherapy treatment. Moreover, a simplified tissue equivalent anthropomorphic phantom was used together with bubble detectors for thermal and fast neutron to evaluate the moderation effect of human body. RESULTS: Simulation and experimental results confirm the thermal neutron background during radiotherapy of 1.55E07 cm(-2) Gy(-1). The BNCT equivalent dose delivered at 4 cm depth in phantom is 1.5 mGy-eq/Gy, that is about 3 Gy-eq (4% of X-rays dose) for a 70 Gy IMRT treatment. CONCLUSIONS: The thermal neutron component during a traditional high-energy radiotherapy treatment could produce a localized BNCT effect, with a localized therapeutic dose enhancement, corresponding to 4% or more of photon dose, following tumour characteristics. This BNCT additional dose could thus improve radiotherapy, acting as a localized radio-sensitizer.