Calibration of a Hydraulic Model for Seasonal Flooding in a Lowland River with Natural Diversions and Bathymetric Uncertainty, for Dam Downstream Impact Assessment

A method is developed to generate bank-full river main channel geometry, to complement an open-source Digital Elevation Model (DEM) and produce a calibrated hydraulic model reproducing the extent of historically observed overbank flooding. This approach relies on limited surveyed cross section and flow rate information and is potentially suitable for projects in developing countries where the availability of measured data is limited. The method presented is applied to the case of the seasonal flooding of the Baro River in the Gambela floodplain in Ethiopia, modelled with a two-dimensional hydraulic model. The simulated flooding extent for the 1990 wet season is compared with the observed flooding from 1990 satellite imagery and the expected flow interaction patterns with the near Alwero River, showing good agreement. The calibrated model is also used to show the impact of the planned TAMS hydropower dam on the Baro River flooding

Ir-based nanoparticles for catalytic hydrogen generation using chemical hydrogen storage compounds

The objective of this thesis is to present and evaluate hydrous hydrazine as a suitable liquid storage compound for the production of hydrogen that can be utilised for hydrogen fuel cells. Catalytic decomposition of hydrous hydrazine at mild conditions is considered for application for hydrogen generation in portable devices, focusing on the (I) productivity, (II) reusability of the catalysts and the yield of hydrogen and (III) avoiding the production of ammonia which is harmful to the fuel cell membrane. Ir-based nanoparticles were synthesised and deposited on a range of supports and different preparation methods were used to tune their structural and catalytic properties. Moreover, computational calculations were used to elucidate the mechanism of reaction and the interaction of the catalytic surface with the hydrazine and intermediates. The catalytic performance of a synthesised Ir/CeO2 as reference material is presented in order to optimise the reaction conditions for identifying the kinetic regime of the studied reaction. Characterisation techniques and computational calculations are used to provide possible reaction pathways for tuning the yield of the reaction toward hydrogen production. After the determination of the optimal reaction parameters the preparation methods of supported monometallic Ir nanoparticles were modified in order (I) to tune the catalytic properties of active metal, (II) explore possible metal-support interactions by using different metal oxides as support and finally (III) optimising the yield of the reaction towards hydrogen. ATR-IR studies were carried out in order to identify adsorbed species on the surface of the desired model catalyst in specific chosen reaction conditions and validate the possible mechanisms of the reaction. Once Ir-based catalysts were optimised Ni-based catalysts were subsequently investigated to perform hydrous hydrazine catalytic decomposition with higher yield toward molecular hydrogen. From the knowledge obtained by the catalytic and characterisation studies of monometallic Ir and Ni catalysts bimetallic Ir-Ni catalysts were synthesised and characterised in order to combine the higher selectivity and higher activity of the two monometallic catalysts. Characterisations were performed to investigate and elucidate the properties of the bimetallic materials synthesised. Finally, the main conclusions of the results are reported and a set of options to continue this research is presented

Heat pumps for space heating and domestic hot water production in residential buildings, an environmental comparison in a present and future scenario

The hydrogen vector stands as a potentially important tool to achieve the decarbonization of the energy sector. It represents an option to store the periodic excesses of energy generation from renewable electrical sources to be used as it is as a substitute for fossil fuels in some applications or reconverted into electricity when needed. In this context, hydrogen can significantly decarbonize the building sector as an alternative fuel for gas-driven devices. Along with hydrogen, the European strategic vision indicates the electrification of heat among the main energy transition pathways. The potential environmental benefits achievable from renewable hydrogen in thermally-driven appliances and the electrification of residential heat through electric heat pumps were evaluated and compared in this work. The novelty of the research consists of a consequential comparative life cycle assessment (16 impact categories) evaluation for three buildings (old, old retrofitted, and new) supplied by three different appliances (condensing boiler, gas absorption heat pump, and electric heat pump), never investigated before. The energy transition was evaluated for 2020 and 2030 scenarios, considering the impact of gaseous fuels (natural gas and European green hydrogen) and electricity based on the pathway of the European electricity grid. (27 European member states plus the United Kingdom). The results allowed to compare the environmental profile in deterministic and stochastic approaches and confirm if the increase of renewables reduces the impact in the operational phase of the appliances. The results demonstrate that despite the increased renewable share, the use phase remains the most significant for both temporal scenarios, contributing to 91% of the environmental profile. Despite the higher footprint in 2020 compared to the electric heat pump (198-200 vs. 170-196 gCO2eq/kWhth), the gas absorption heat pump offered a lower environmental profile than the others in all the scenarios analyzed

Data-Driven, AI-Based Clinical Practice:Experiences, Challenges, and Research Directions

Clinical practice is evolving rapidly, away from the traditional but inefficient detect-and-cure approach, and towards a Preventive, Predictive, Personalised and Participative (P4) vision that focuses on extending people's wellness state. This vision is increasingly data-driven, AI-based, and is underpinned by many forms of "Big Health Data" including periodic clinical assessments and electronic health records, but also using new forms of self-assessment, such as mobile-based questionnaires and personal wearable devices. Over the last few years, we have been conducting a fruitful research collaboration with the Infectious Disease Clinic of the University Hospital of Modena having the main aim of exploring specific opportunities offered by data-driven AI-based approaches to support diagnosis, hospital organization and clinical research. Drawing from this experience, in this paper we provide an overview of the main research challenges that need to be addressed to design and implement data-driven healthcare applications. We present concrete instantiations of these challenges in three real-world use cases and summarise the specific solutions we devised to address them and, finally, we propose a research agenda that outlines the future of research in this field.</p

Data-driven, AI-based clinical practice: experiences, challenges, and research directions

Clinical practice is evolving rapidly, away from the traditional but inefficient detect-and-cure approach, and towards a Preventive, Predictive, Personalised and Participative (P4) vision that focuses on extending people’s wellness state. This vision is increasingly data-driven, AI-based, and is underpinned by many forms of "Big Health Data" including periodic clinical assessments and electronic health records, but also using new forms of self-assessment, such as mobile-based questionnaires and personal wearable devices. Over the last few years, we have been conducting a fruitful research collaboration with the Infectious Disease Clinic of the University Hospital of Modena having the main aim of exploring specific opportunities offered by data-driven AI-based approaches to support diagnosis, hospital organization and clinical research. Drawing from this experience, in this paper we provide an overview of the main research challenges that need to be addressed to design and implement data-driven healthcare applications. We present concrete instantiations of these challenges in three real-world use cases and summarise the specific solutions we devised to address them and, finally, we propose a research agenda that outlines the future of research in this field

Effects of axial torsion on sp carbon atomic nanowires

Ab-initio calculations within Density Functional Theory combined with experimental Raman spectra on cluster-beam deposited pure carbon films provide a consistent picture of sp-carbon chains stabilized by sp^3 or sp^2 terminations, the latter being sensitive to torsional strain. This unexplored effect promises many exciting applications since it allows one to modify the conductive states near the Fermi level and to switch on and off the on-chain pi-electron magnetism.Comment: in print in Phys Rev Let