Sequential Flavour Symmetry Breaking

The gauge sector of the Standard Model (SM) exhibits a flavour symmetry which allows for independent unitary transformations of the fermion multiplets. In the SM the flavour symmetry is broken by the Yukawa couplings to the Higgs boson, and the resulting fermion masses and mixing angles show a pronounced hierarchy. In this work we connect the observed hierarchy to a sequence of intermediate effective theories, where the flavour symmetries are broken in a step-wise fashion by vacuum expectation values of suitably constructed spurion fields. We identify the possible scenarios in the quark sector and discuss some implications of this approach.Comment: 22 pages latex, no figure

An automatic deep learning approach for coronary artery calcium segmentation

Coronary artery calcium (CAC) is a significant marker of atherosclerosis and cardiovascular events. In this work we present a system for the automatic quantification of calcium score in ECG-triggered non-contrast enhanced cardiac computed tomography (CT) images. The proposed system uses a supervised deep learning algorithm, i.e. convolutional neural network (CNN) for the segmentation and classification of candidate lesions as coronary or not, previously extracted in the region of the heart using a cardiac atlas. We trained our network with 45 CT volumes; 18 volumes were used to validate the model and 56 to test it. Individual lesions were detected with a sensitivity of 91.24%, a specificity of 95.37% and a positive predicted value (PPV) of 90.5%; comparing calcium score obtained by the system and calcium score manually evaluated by an expert operator, a Pearson coefficient of 0.983 was obtained. A high agreement (Cohen's k = 0.879) between manual and automatic risk prediction was also observed. These results demonstrated that convolutional neural networks can be effectively applied for the automatic segmentation and classification of coronary calcifications

Properties of short-range and long-range correlation energy density functionals from electron-electron coalescence

The combination of density functional theory with other approaches to the many-electron problem through the separation of the electron-electron interaction into a short-range and a long-range contribution is a promising method, which is raising more and more interest in recent years. In this work some properties of the corresponding correlation energy functionals are derived by studying the electron-electron coalescence condition for a modified (long-range-only) interaction. A general relation for the on-top (zero electron-electron distance) pair density is derived, and its usefulness is discussed with some examples. For the special case of the uniform electron gas, a simple parameterization of the on-top pair density for a long-range only interaction is presented and supported by calculations within the ``extended Overhauser model''. The results of this work can be used to build self-interaction corrected short-range correlation energy functionals.Comment: revised version, to appear in Phys. Rev.

Energy Density Functionals From the Strong-Coupling Limit Applied to the Anions of the He Isoelectronic Series

Anions and radicals are important for many applications including environmental chemistry, semiconductors, and charge transfer, but are poorly described by the available approximate energy density functionals. Here we test an approximate exchange-correlation functional based on the exact strong-coupling limit of the Hohenberg-Kohn functional on the prototypical case of the He isoelectronic series with varying nuclear charge $Z<2$, which includes weakly bound negative ions and a quantum phase transition at a critical value of $Z$, representing a big challenge for density functional theory. We use accurate wavefunction calculations to validate our results, comparing energies and Kohn-Sham potentials, thus also providing useful reference data close to and at the quantum phase transition. We show that our functional is able to bind H$^-$ and to capture in general the physics of loosely bound anions, with a tendency to strongly overbind that can be proven mathematically. We also include corrections based on the uniform electron gas which improve the results.Comment: Accepted for the JCP Special Topic Issue "Advances in DFT Methodology

Local-spin-density functional for multideterminant density functional theory

Based on exact limits and quantum Monte Carlo simulations, we obtain, at any density and spin polarization, an accurate estimate for the energy of a modified homogeneous electron gas where electrons repel each other only with a long-range coulombic tail. This allows us to construct an analytic local-spin-density exchange-correlation functional appropriate to new, multideterminantal versions of the density functional theory, where quantum chemistry and approximate exchange-correlation functionals are combined to optimally describe both long- and short-range electron correlations.Comment: revised version, ti appear in PR

Towards A New Decision Support System for Design, Management and Operation of Wastewater Treatment Plants for the Reduction of Greenhouse Gases Emission

The increasing attention paid to the environment has led to a reduction in the emissions from wastewater treatment plants (WWTPs). Moreover, the increasing interest in the greenhouse gas (GHG) emissions from WWTPs suggests that we reconsider the traditional tools used for designing and managing WWTPs. Indeed, nitrous oxide, carbon dioxide and methane can be emitted from wastewater treatment, significantly contributing to the greenhouse gas (GHG) footprint. The reduction of energy consumption as well as GHG emission are of particular concern for large WWTPs which treat the majority of wastewater in terms of both volume and pollution load. Nowadays, there is an increasing need to develop new tools that include additional performance indicators related to GHG emissions and energy consumption as well as traditional effluent quality parameters. Energy consumption, in fact, can be considered as an indirect source of GHGs. This paper presents the development of an ongoing research project aiming at setting-up an innovative mathematical model platform for the design and management of WWTPs. The final goal of the project by means of this platform is to minimize the environmental impact of WWTPs through their optimization in terms of energy consumptions and emissions, which can be regarded as discharged pollutants, sludge and GHGs

Towards a Reduction of Greenhouse Gases: a New Decision Support System for Design, Management and Operation of Wastewater Treatment Plants

The increasing attention for the environment has led to reduce the emissions from wastewater treatment plants (WWTPs). Moreover, the increasing interest towards the greenhouse gas (GHG) emissions from WWTPs suggests to reconsider the traditional tools used for designing and managing WWTPs. Indeed, nitrous oxide (N2O), carbon dioxide (CO2) and methane (CH4) can be emitted from wastewater treatment significantly contributing to the greenhouse gas (GHG) footprint. The reduction of energy consumption as well as GHG emission are of particular concern for large WWTPs which treat the majority of wastewater in terms of both volume and pollution load. Nowadays, there is an increasing need to develop new tools that include additional performance indicators related to GHG emissions and energy consumption as well as traditional effluent quality parameters. Energy consumption, in fact, can be considered as an indirect source of GHGs. This paper presents the development of a research project aiming at setting-up an innovative mathematical model platform for the design and management of WWTPs. The final goal of the project by means of this platform is to minimize the environmental impact of WWTPs through their optimization in terms of energy consumptions and emissions, which can be regarded as discharged pollutants, sludge and GHGs