A GIS-based model to assess electric energy consumptions and usable renewable energy potential in Lazio region at municipality scale

The ongoing energy transition processes need a rapprochement between the places of energy production and consumption with the aim of creating innovative and integrated territorial models. Consequentially, models and strategies for increasing the use of local and renewable energy sources (RES) play a key rule for enhancing energy independence and sustainability of the considered areas. The main objective of this study is to analyse the energy system of the Lazio Region in Italy, comparing electricity consumptions and production from renewable sources at municipality scale. In order to estimate the electricity consumptions and the local production by RES, the main sectors of electricity consumption together with the potential of the available RES for the electricity production have been analysed. The obtained results pinpointed the main critical aspects of the Lazio region, that are mainly focalized in the city of Rome and in the most densely inhabited municipalities. Furthermore, research outputs provide an overall framework on the regional RES potential and allowed the formulation of proposals aimed at overcoming the identified criticalities and increasing the share of electricity production from renewables. Finally, the research approach could be replicated in other areas, providing a useful process for decision makers and stakeholders

CosmoPower-JAX: high-dimensional Bayesian inference with differentiable cosmological emulators

We present CosmoPower-JAX, a JAX-based implementation of the CosmoPower framework, which accelerates cosmological inference by building neural emulators of cosmological power spectra. We show how, using the automatic differentiation, batch evaluation and just-in-time compilation features of JAX, and running the inference pipeline on graphics processing units (GPUs), parameter estimation can be accelerated by orders of magnitude with advanced gradient-based sampling techniques. These can be used to efficiently explore high-dimensional parameter spaces, such as those needed for the analysis of next-generation cosmological surveys. We showcase the accuracy and computational efficiency of CosmoPower-JAX on two simulated Stage IV configurations. We first consider a single survey performing a cosmic shear analysis totalling 37 model parameters. We validate the contours derived with CosmoPower-JAX and a Hamiltonian Monte Carlo sampler against those derived with a nested sampler and without emulators, obtaining a speed-up factor of O(103 ). We then consider a combination of three Stage IV surveys, each performing a joint cosmic shear and galaxy clustering (3x2pt) analysis, for a total of 157 model parameters. Even with such a high-dimensional parameter space, CosmoPower-JAX provides converged posterior contours in 3 days, as opposed to the estimated 6 years required by standard methods. CosmoPower-JAX is fully written in Python, and we make it publicly available to help the cosmological community meet the accuracy requirements set by next-generation surveys

Sviluppo di tecniche tomografiche per lo studio della turbolenza di bordo di RFX-mod

L’esperimento RFX-mod, per lo studio dei plasmi fusionistici in configurazione Reversed Field Pinch, è equipaggiato con una diagnostica Gas Puff Imaging ad alta risoluzione spaziale e temporale (5.0mm e 0.1 μs), utilizzata per misurare la densità nella zona di bordo del plasma. In particolare vengono misurati i blob: strutture coerenti con densità di particelle superiore alla media che si muovono nel piano perpendicolare al campo magnetico. L’apparato, tramite iniezione di He neutro, restituisce misure dell’integrale dell’emissività del plasma, funzione della sua densità elettronica, lungo 32 linee di vista differenti suddivise in tre ventagli di direzioni diverse. Il lavoro di tesi prevede lo sviluppo di tecniche di inversione tomografica adeguate a ricostruire le misure della GPI. Sono presi in considerazione tre algoritmi: ART, MART ed SVD. Vengono testati ricostruendo oggetti conosciuti, a partire da misure simulate su essi e confrontando i risultati viene identificata la tecnica più adatta allo scopo. In seguito si applica l’algoritmo migliore, MART, alle misure sperimentali della GPI. Il lavoro si conclude con la presentazione di un esempio del risultato finale di ricostruzione, con descrizione della forma e dell’evoluzione temporale di due blob.ope

Geospatial analysis of woodland fire occurrence and recurrence in Italy

This research note aims to exemplify the potential of annual time series of wildfire geodatasets to quantify fire occurrence and recurrence amongst different woodland types at large scale, under an international forestry perspective. The study covers a time series of areas affected by wildfire between 2007 and 2014 in Italy. A GIS operation of geometric intersection was carried out between burned areas geodataset time series and Corine Land Cover. Mediterranean pine forest, high maquis, transitional woodland-shrub and high oro-Mediterranean pine forest are the woodland types most preferred in terms of fire occurrence and recurrence. Large fires and megafires hold a significant share of total burned area. An unexpected finding is the huge impact of fires in wildland-urban-interface areas. The proposed analysis provides spatial information that is central to any approach to fire management at large scale. Research findings provide support that can be used e.g. for advancements in research, prioritization of fire prevention, suppression measures, economic incentive allocation, and urban and peri-urban planning

EchoBoat and HYPACK: user guide v 1.0

Technical manual for deploying the Seafloor Echo Boat and processing associated data using Hypack

The Use of a Simplified Carbon Footprint Tool for Organic Waste Managers: Pros and Cons

Abstract: Given that the pressure of climate change action on companies is increasing, it is recom- mended to measure the improvement of mitigation activities in terms of GHG emissions. This paper aims to highlight the still-open aspects that characterise simplified GHG accounting tools, starting from the outcomes of a case study. This study was performed using a simplified Italian software for the CO2 eq accounting of composting and anaerobic digestion, two mitigation activities that contribute an important share of global GHG emissions reduction. The tool is based on the life-cycle thinking approach. It has been applied to an Italian company that treats the organic fraction of municipal solid waste. The tool analysis has made it possible to stress several issues that are currently the object of debate in the literature, for example, the trade-off between the flexibility of the software and its user friendliness or the multifunctionality issues and their different interpretations. However, focusing on just one impact category, i.e., climate change, may lead to an incomplete picture of the overall environmental performance of the process analysed. Therefore, this tool could be improved by including other impact categories, such as eutrophication and acidification, which may be affected by the studied activities

BDNF/TrkB axis activation promotes epithelial-mesenchymal transition in idiopathic pulmonary fibrosis

Background: Neurotrophins (NT) belongs to a family of growth factors which promotes neurons survival and differentiation. Increasing evidence show that NT and their receptor are expressed in lung tissues suggesting a possible role in lung health and disease. Here we investigated the expression and functional role of the TrkB/BDNF axis in idiopathic pulmonary fibrotic lung (myo)fibroblasts. Methods: Lung fibroblast were isolated from IPF patients and characterized for the expression of mesenchymal markers in comparison to normal lung fibroblasts isolated from non-IPF controls. Results: BDNF treatment promoted mesenchymal differentiation and this effect was counteracted by the TrkB inhibitor K252a. In this regard, we showed that K252a treatment was able to control the expression of transcription factors involved in epithelial to mesenchymal transition (EMT). Accordingly, K252a treatment reduced matrix metalloproteinase-9 enzyme activity and E-cadherin expression while increased cytoplasmic β-catenin expression. Conclusions: Our results suggest that BDNF/TrkB axis plays a role in EMT promoting the acquisition of (myo)fibroblast cell phenotype in IPF. Targeting BDNF/TrkB seems to represent a viable approach in order to prevent EMT dependent lung fibrosis

Towards fast machine-learning-assisted Bayesian posterior inference of realistic microseismic events

Bayesian inference applied to microseismic activity monitoring allows for principled estimation of the coordinates of microseismic events from recorded seismograms, and their associated uncertainties. However, forward modelling of these microseismic events, necessary to perform Bayesian source inversion, can be prohibitively expensive in terms of computational resources. A viable solution is to train a surrogate model based on machine learning techniques, to emulate the forward model and thus accelerate Bayesian inference. In this paper, we improve on previous work, which considered only sources with isotropic moment tensor. We train a machine learning algorithm on the power spectrum of the recorded pressure wave and show that the trained emulator allows for the complete and fast retrieval of the event coordinates for $\textit{any}$ source mechanism. Moreover, we show that our approach is computationally inexpensive, as it can be run in less than 1 hour on a commercial laptop, while yielding accurate results using less than $10^4$ training seismograms. We additionally demonstrate how the trained emulators can be used to identify the source mechanism through the estimation of the Bayesian evidence. This work lays the foundations for the efficient localisation and characterisation of any recorded seismogram, thus helping to quantify human impact on seismic activity and mitigate seismic hazard.Comment: 13 pages, 11 figures, 2 tables. Under revie