GIS-based approach for assessing the energy potential and the financial feasibility of run-off-river hydro-power in Alpine valleys

In the last decade, European attractive policies are favoring the construction of new run-off hydro-power plants. The realization cost of these plants is quite low in mountain areas thanks to small water discharges and high gross heads. For this reason, small rivers have been strongly exploited without considering an optimal use of the resource. Nowadays, available sites are often in areas with low accessibility and a greater specific cost of civil engineering works. However, during the planning of new small hydro-power plants, the dependency of physical, technical, legal and financial variable on space is often not assessed. The tool presented in this paper addresses this gap to support the planning of run-off-river plants. The method improves on previous approaches by (1) integrating all the legal, technical and financial analysis in a GIS tool, and (2) trying to validate the site-specific model with local knowledge. The tool is applied to the Gesso and Vermenagna valleys in the Alps. Information and data were collected and discussed with local stakeholders in order to improve the model results

Hmga2 protein loss alters nuclear envelope and 3D chromatin structure

The high-mobility group Hmga family of proteins are non-histone chromatin-interacting proteins which have been associated with a number of nuclear functions, including heterochromatin formation, replication, recombination, DNA repair, transcription, and formation of enhanceosomes. Due to its role based on dynamic interaction with chromatin, Hmga2 has a pathogenic role in diverse tumors and has been mainly studied in a cancer context; however, whether Hmga2 has similar physiological functions in normal cells remains less explored. Hmga2 was additionally shown to be required during the exit of embryonic stem cells (ESCs) from the ground state of pluripotency, to allow their transition into epiblast-like cells (EpiLCs), and here, we use that system to gain further understanding of normal Hmga2 function

The GRETA project: the contribution of near-surface geothermal energy for the energetic self-sufficiency of Alpine regions

The Alpine regions are deeply involved in the challenge set by climate change, which is a threat for their environment and for important economic activities such as tourism. The heating and cooling of buildings account for a major share of the total primary energy consumption in Europe, and hence the energy policies should focus on this sector to achieve the greenhouse gas reduction targets set by international agreements. Geothermal heat pump is one of the least carbon-intensive technologies for the heating and cooling of buildings. It exploits the heat stored within the ground, a local renewable energy source which is widely available across the Alpine territory. Nevertheless, it has been little considered by European policies and cooperation projects. GRETA (near-surface Geothermal REsources in the Territory of the Alpine space) is a cooperation project funded by the EU INTERREG-Alpine Space program, aiming at demonstrating the potential of shallow geothermal energy and to foster its integration into energy planning instruments. It started in December 2015 and will last three years, involving 12 partners from Italy, France, Switzerland, Germany, Austria, and Slovenia. In this paper, the project is presented, along with the results of the first year of work

Results of the workshop on strategies and tools for administrators of the territory of the italian alpine space for the shallow geothermal systems - GRETA Project - Near-surface Geothermal Resources in the Territory of the Alpine Space

I sistemi a pompa di calore che utilizzano la geotermia di bassa profondità (altrimenti nota come a "bassa entalpia") per il riscaldamento ed il raffrescamento degli edifici rappresentano una tecnologia efficiente e vantaggiosa che può contribuire significativamente alla riduzione delle emissioni di gas serra. Nonostante le bassissime emissioni e i costi energetici significativamente ridotti rispetto all'impiego di combustibili fossili, la diffusione dei sistemi che sfruttano l'energia geotermica a bassa entalpia (profondità generalmente comprese tra i 30 e i 150 m dal piano campagna), è ancora limitata da molteplici fattori. In questo ambito, gli amministratori del territorio esercitano un ruolo chiave in quanto, attraverso la predisposizione di strumenti di vario tipo (informativi, normativi e tecnici), essi possono imprimere un impulso importante allo sviluppo dei sistemi geotermici di bassa profondità, ampliando così il ventaglio di tecnologie attualmente disponibili per lo sfruttamento delle fonti energetiche rinnovabili [...

Natural History and Outcome of Hepatic Vascular Malformations in a Large Cohort of Patients with Hereditary Hemorrhagic Teleangiectasia

BACKGROUND: Hereditary hemorrhagic telangiectasia is a genetic disease characterized by teleangiectasias involving virtually every organ. There are limited data in the literature regarding the natural history of liver vascular malformations in hemorrhagic telangiectasia and their associated morbidity and mortality. AIM: This prospective cohort study sought to assess the outcome of liver involvement in hereditary hemorrhagic telangiectasia patients. METHODS: We analyzed 16 years of surveillance data from a tertiary hereditary hemorrhagic telangiectasia referral center in Italy. We considered for inclusion in this study 502 consecutive Italian patients at risk of hereditary hemorrhagic telangiectasia who presented at the hereditary hemorrhagic telangiectasia referral center and underwent a multidisciplinary screening protocol for the diagnosis of hereditary hemorrhagic telangiectasia. Of the 502 individuals assessed in the center, 154 had hepatic vascular malformations and were the subject of the study; 198 patients with hereditary hemorrhagic telangiectasia and without hepatic vascular malformations were the controls. Additionally, we report the response to treatment of patients with complicated hepatic vascular malformations. RESULTS: The 154 patients were included and followed for a median period of 44 months (range 12-181); of these, eight (5.2%) died from VM-related complications and 39 (25.3%) experienced complications. The average incidence rates of death and complications were 1.1 and 3.6 per 100 person-years, respectively. The median overall survival and event-free survival after diagnosis were 175 and 90 months, respectively. The rate of complete response to therapy was 63%. CONCLUSIONS: This study shows that substantial morbidity and mortality are associated with liver vascular malformations in hereditary hemorrhagic telangiectasia patients

A spatial decision support system to assess personal exposure to air pollution integrating sensor measurements

Recent epidemiological studies have reinforced the link between short and long-term exposure to air pollutants and adverse effects on public health especially over the weaker part of the population, like children and older adults. The creation of simple tools to locate sensible areas as well as of dedicated Spatial Decision Support System (SDSS) to improve the management of pollution risk areas system is strongly advised. The aim of this work is to develop a SDSS methodology, based on easy to find data and usable by decision makers, to assess and reduce the impact of air pollutants in a urban context. To achieve this goals I tested the exploitability of a set of low-cost sensors for outdoor air quality monitoring, I characterized the urban micro-environments and the spatial variability of air pollutants using remote sensing compared to field data and eventually I developed a SDSS to improve the public health designing and comparing different scenarios. The city centre of Edinburgh has been used as study case for the purposed methodology. To test the reliability and applicability of low cost sensors as proxies for remote sensed data, we conducted a measurements campaign to compare the observed data between an official measurements station (OMS) in Trento (Italy) and electrochemical and thick film sensors respectively of Carbon Monoxide (CO) and Ozone ($O_3$). Due to data quality and availability we decided to characterize the urban micro-environments of Edinburgh (Scotland, UK) in eight main classes (water, grass, vegetation, road, car, bus, buildings and shadow) combining the Geographic Object-Based Image Analysis (GEOBIA) with Machine Learning algorithms to process the high resolution (0.25m x 0.25m) RGB aerial ortho-rectified images. This land-use characterization combined with other geographical informations, like the classification of the roads and the urban morphology, were compared with 37 Nitrogen Dioxide (NO2) concentration data, collected using passive tubes during a six week campaign of measurements conducted by the school of Chemistry of the University of Edinburgh. I developed a new open-source GIS python library (PyGRASS), integrated in the stable release of GRASS GIS, to speed-up the prototyping phase and to create and test new GIS tools and methodologies. Different studies on SDSS were carried out to implement procedures and models. Based on these models and data all the factors (land-use, roads and geo-morphological features) were ranked to identify which are driving forces for urban air quality and to help decision makers to develop new policies. The sensor tested in Trento revealed an evident drift in measurement residues for CO, furthermore the measurements were also quite sensitive to external factors such as temperature and humidity. Since these sensors required frequent recalibration in order to obtain reliable results, their use was not as low-cost as expected. The characterization of urban land-use in Edinburgh with GEOBIA and machine learning provided an overall accuracy of 93.71\% with a Cohen's k of 0.916 using a train/test dataset of 9301 objects. The $NO_2$ data confirm the assumption that air concentration is strongly dependent on geographical position and it is strongly influenced by the position of the pollutant's source. Using the results of the tests and remote sensing analysis, I developed an SDSS. Starting from the current situation, I designed three scenarios to assess the effect that different policies and actions could have on improving air quality at on the local and district level. The outcomes of this work can be used to define and compare different scenarios and develop effective policies to reduce the impact of air pollutants in an urban context using simple and easy to find data. The GIS-based tool can help to identify critical areas before deploying sensors and splitting the study area in homogeneous micro-environments clusters. The model is easy to expand following different procedures

Pygrass: An Object Oriented Python Application Programming Interface (API) for Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS)

PyGRASS is an object-oriented Python Application Programming Interface (API) for Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS), a powerful open source GIS widely used in academia, commercial settings and governmental agencies. We present the architecture of the PyGRASS library, covering interfaces to GRASS modules, vector and raster data, with a focus on the new capabilities that it provides to GRASS users and developers. Our design concept of the module interface allows the direct linking of inputs and outputs of GRASS modules to create process chains, including compatibility checks, process control and error handling. The module interface was designed to be easily extended to work with remote processing services (Web Processing Service (WPS), Web Service Definition Language (WSDL)/Simple Object Access Protocol (SOAP)). The new object-oriented Python programming API introduces an abstract layer that opens the possibility to use and access transparently the efficient raster and vector functions of GRASS that are implemented in C. The design goal was to provide an easy to use, but powerful, Python interface for users and developers who are not familiar with the programming language C and with the GRASS C-API. We demonstrate the capabilities, scalability and performance of PyGRASS with several dedicated tests and benchmarks. We compare and discuss the results of the benchmarks with dedicated C implementations

Pygrass: An Object Oriented Python Application Programming Interface (API) for Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS)

PyGRASS is an object-oriented Python Application Programming Interface (API) for Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS), a powerful open source GIS widely used in academia, commercial settings and governmental agencies. We present the architecture of the PyGRASS library, covering interfaces to GRASS modules, vector and raster data, with a focus on the new capabilities that it provides to GRASS users and developers. Our design concept of the module interface allows the direct linking of inputs and outputs of GRASS modules to create process chains, including compatibility checks, process control and error handling. The module interface was designed to be easily extended to work with remote processing services (Web Processing Service (WPS), Web Service Definition Language (WSDL)/Simple Object Access Protocol (SOAP)). The new object-oriented Python programming API introduces an abstract layer that opens the possibility to use and access transparently the efficient raster and vector functions of GRASS that are implemented in C. The design goal was to provide an easy to use, but powerful, Python interface for users and developers who are not familiar with the programming language C and with the GRASS C-API. We demonstrate the capabilities, scalability and performance of PyGRASS with several dedicated tests and benchmarks. We compare and discuss the results of the benchmarks with dedicated C implementations