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Two-fluid Hydrodynamics of a quasi-1D unitary Fermi gas
No full textThis thesis is devoted to the study of the hydrodynamic behavior of the unitary Fermi gas trapped by a highly elongated harmonic potential. Propagation of sound is one of the most exciting features exhibited by interacting many-body systems. It provides crucial information on the dynamic behavior of the system as well as on key thermodynamic quantities. The propagation of sound is particularly interesting in superfluids where two-fluid hydrodynamic theory predicts the occurrence of two different sounds: first sound, where the normal and superfluid component oscillate in phase, and second sound, where the two components oscillate with opposite phase.
In the thesis, we investigate the propagation of sound waves of the unitary Fermi gas in a cylindrical geometry by solving the equations of two-fluid hydrodynamics in the `1D' scenario at finite temperature. The relevant thermodynamic functions entering the hydrodynamic equations are discussed in the superfluid and normal regimes in terms of universal scaling functions. Both the first sound and second sound solutions are calculated as a function of temperature and the role of the superfluid density is explicitly pointed out. The density fluctuations in the second sound wave are found to be large enough to be measured as a consequence of the finite thermal expansion coefficient of the gas, which is the strategy used in a recent experiment carried out at Innsbruck where second sound was detected in the unitary Fermi gas.
We also provide an investigation of the temperature dependence of the collective oscillations of first sound nature exhibited by a highly elongated harmonically trapped Fermi gas at unitarity, including the region below the critical temperature for superfluidity. Differently from the lowest axial breathing mode, the hydrodynamic frequencies of the higher-nodal excitations show a temperature dependence, which is calculated starting from Landau two-fluid theory and using the available experimental knowledge of the equation of state
Birational Maps in the Minimal Model Program.
No full textIn this dissertation I face three main arguments.
1) Classification of Fano-Mori contractions.
2) Chern numbers on smooth threefolds.
3) Pluricanonical systems
Molecular and cellular effects of supercritical carbon dioxide on some important food-borne pathogens
Get PDFIn the 2007 report of World Health Organization (WHO) it was reported that in 2005 a great proportion of 1.8 million people died because of food and drinking water contamination (Velusamy et al., 2010). Fresh food product such as, fruits and vegetables carry a natural non-pathogenic epiphytic micro-flora, but during the food chain: harvest, transportation and further processing and handling the produce can be contaminated with pathogens from human or animal sources (Anon, 2002).
While conventional methods used to evaluate pasteurization efficiency are based on cultivation in vitro, it has been ascertained that, under environmental stress conditions (e.g. nutrient limitation, pressure, temperature), a number of pathogens enter in a so-called Viable But Not Cultivable (VBNC) state, becoming eventually more resistant to stress and thus escaping to detection by cultivation methods. Improving health risk assessment associated with the increasing consumption of minimally processed fresh food products is a crucial need. To reach this objective, in the first part of my PhD project I set up and validated cultivation-independent bacterial viability assays, propidium monoazide quantitative PCR (PMA-qPCR) and flow cytometry (FCM), to monitor bacterial populations in food after Supercritical Carbon Dioxide (SC-CO2) treatment, that is one of the most promising non-thermal pasteurization technology in the age of the increasing demand for “ready-to-eat” and minimally-processed food products. The efficiency of SC-CO2 treatment was evaluated on bacterial liquid cultures, on bacteria spiked both on a synthetic solid substrate (LB agar) and on some fresh food products, including carrots, coconut and dry cured ham. The results indicated that the treatment is more efficient on bacteria spiked on LB agar, and that bacterial inactivation is accompanied by a reduction of their biovolume. Total bacterial inactivation on food products was reached for both Escherichia coli and Listeria monocytogenes, satisfying both the US and European requirements (CFSAN/FSIS, 2003; European Commission, 2005). Salmonella enterica was instead more resistant to treatment, suggesting future experiments consisting in the application of a combination between SC-CO2 and other techniques alternative to heat pasteurization, such as ultrasounds or Pulsed Electrical Field.
FCM and PMA-qPCR data showed that a fraction of bacterial cells not detectable by plate counts maintained the integrity of their membrane (at least 102 cells/g for each bacterial species) suggested that the cells entered in a VBNC state. Comprehensively, the FCM assay showed the best performance as a bacterial viability test method, permitting to evaluate with high sensitivity the efficiency of treatment, to discriminate subpopulations of cells with different level of membrane permeabilization, and to identify variations in biovolume and alterations of the cellular surface. The method could be applied, with some adjustments, to any field where determining microbial viability status is of importance, including food, environment or in the clinic.
Permeabilization of the cell membrane has been proposed to be the first event leading to cell inactivation or death after SC-CO2 treatment (Garcia-Gonzalez et al., 2007; Spilimbergo et al., 2009).The Permeabilization of membrane induced by SC-CO2 was also observed in Salmonella enterica (Kim et al., 2009a; Tamburini et al., 2013) and in Saccharomyces cerevisiae (Spilimbergo et al., 2010). Whether SC-CO2 has a direct effect on the bacterial membrane or permeabilization is a consequence of cell death remains an open question. In the second part of the Thesis to increase knowledge on the mechanism of bacterial inactivation mediated by SC-CO2 lipidomic profiles (HPLC-IT-ESI-MS), bacterial depolarization/permeabilization analysis (FCM) and gene expression studies of enzymes involved in phospholipids biosynthesis were performed on E. coli K12 MG1665. The data indicated that after 15 min of SC-CO2 treatment most of bacterial cells lost their membrane potential (95%) and membrane integrity (81% of permeabilized and 18% of partially-permeabilized cells). Bacterial permeabilization was associated to a 20% decrease of cellular biovolume and to a strong reduction (more than 50%) of all Phosphatidylglycerol (PG) membrane species, but without altering their average unsaturation index (1.30 ±0.02) and the average acyl chain on the glycerol backbone (33.30 ±0.03). The process acts more efficiently on PG than on PE (Phosphatidylethanolamine) head group phospholipids. Bacteria responded to treatment up-regulating the expression level of PssA gene, involved in PEs synthesis, since PssA activity is regulated by mole fraction of PGs and Cardiolin in the membrane. However still remains to understand why only PG species have been found to strongly decrease during the treatments. Further studies would be necessary, including phospholipid biosynthesis mutant analysis
Theoretical and Algorithmic Solutions for Null models in Network Theory
No full textThe graph-theoretical based formulation for the representation of the data-driven structure and the dynamics of complex systems is rapidly imposing as the paramount paradigm [1] across a variety of disciplines, from economics to neuroscience, with biological -omics as a major example. In this framework, the concept of Null Model
borrowed from the statistical sciences identifies the elective strategy to obtain a baseline points of modelling comparison [2]. Hereafter, a null model is a graph
which matches one specific graph in terms of some structural features, but which is otherwise taken to be generated as an instance of a random network. In this view, the network model introduced by Erdos & Renyi [3], where random edges are generated as independently and identically distributed Bernoulli trials, can be considered the simplest possible null model. In the following years,
other null models have been developed in the framework of graph theory, with the detection of the community structure as one of the most important target[4]. In particular, the model described in [5] introduces the concept of a randomized version of the original graph: edges are rewired at random, with each expected vertex degree matching the degree of the vertex in the original graph. Although aimed
at building a reference for the community detection, this approach will play a key role in one of the model considered in this thesis. Note that, although being the
first problem to be considered, designing null models for the community structures detection is still an open problem [6, 7]. Real world applications of null model in graph theory have also gained popularity in many different scientific areas, with ecology as the first example: see
[8] for a comprehensive overview. More recently, interest for network null models arose also in computational biology [9, 10], geosciences [11] and economics [12, 13],
just to name a few. In the present work the theoretical design and the practical implementation of
a series of algorithms for the construction of null models will be introduced, with applications ranging from functional genomics to game theory for social studies.
The four chapters devoted to the presentation of the examples of null model are preceded by an introductory chapter including a quick overview of graph theory,
together with all the required notations.
The first null model is the topic of the second chapter, where a suite of novel algorithms is shown, aimed at the efficient generation of complex networks under
different constraints on the node degrees. Although not the most important example in the thesis, the premiment position dedicated to this topic is due to its strict familiarity with the aforementioned classical null models for random graph construction. Together with the algorithms definition and examples, a thorough
theoretical analysis of the proposed solutions is shown, highlighting the improvements with respect to the state-of-the-art and the occurring limitations. Apart from its intrinsic mathematical value, the interest for these algorithms by the community of systems biology lies in the need for benchmark graphs resembling the real biological networks. They are in fact of uttermost importance when testing novel inference methods, and as testbeds for the network reconstruction challenges such as the DREAM series [14, 15, 16].
The following Chapter three includes the most complex application of null models presented in this thesis. The scientific workfield is again functional genomics,
namely the combinatorial approach to the modelling of patterns of mutations in cancer as detected by Next Generation Sequencing exome Data. This problem has
a natural mathematical representation in terms of rewiring of bipartite networks and mutual-exclusively mutated modules [17, 18], to which Markov chain updates
(switching-steps) are applied through a Switching Algorithm SA. Here we show some crucial improvements to the SA, we analytically derive an approximate lower
bound for the number of steps required, we introduce BiRewire, an R package implementing the improved SA and we demonstrate the effectiveness of the novel
solution on a breast cancer dataset. A novel threshold-selection method for the construction of co-expression net-
works based on the Pearson coefficient is the third and last biological example of null model, and it is outlined in Chapter four. Gene co-expression networks inferred by correlation from high-throughput profiling such as microarray data represent a simple but effective technique for discovering and interpreting linear gene relationships. In the last years several approach have been proposed to tackle the problem of deciding when the resulting correlation values are statistically significant. This is mostly crucial when the number of samples is small, yielding a non negligible chance that even high correlation values are due to random effects. Here we introduce a novel hard thresholding solution based on the assumption
that a coexpression network inferred by randomly generated data is expected to be empty. The theoretical derivation of the new bound by geometrical methods is shown together with two applications in oncogenomics. The last two chapters of the thesis are devoted to the presentation of null
models in non-biological contexts. In Chapter 5 a novel dynamic simulation model is introduced mimicking a random market in which sellers and buyers follow different price distributions and matching functions. The random marked is mathematically formulated by a dynamic bipartite graph, and the analytical formula for the evolution along time of the mean price exchange is derived, together with global likelihood function for retrieving the initial parameters under different assumptions. Finally in Chapter 6 we describe how graph tools can be used to model abstraction and strategy (see [19, 20, 21]) for a class of games in particular the TTT solitaire. We show that in this solitaire it is not possible to build an optimal (in
the sense of minimum number of moves) strategy dividing the big problems into smaller subproblems. Nevertheless, we find some subproblems and strategies for solving the TTT solitaire with a negligible increment in the number of moves. Although quite simple and far from simulating highly complex real-world situations of decision making, the TTT solitaire is an important tool for starting the exploration of the social analysis of the trajectories of the implementation of winning strategies through different learning procedures [22]
Non-Redundant Overlapping Clustering: Algorithms and Applications
No full textGiven a dataset, traditional clustering algorithms often only provide a single partitioning or a single view of the dataset. On complex tasks, many different clusterings of a dataset exist, thus alternative clusterings which are of high quality and different from given trivial clusterings are asked to have complementary views. The task is therefore a clear multi-objective optimization problem. However, most approaches in the literature optimize these objectives sequentially (one after another one) or indirectly (by some heuristic combination). This can result in solutions which are not Pareto- optimal. The problem is even more difficult for high-dimensional datasets as clusters can be located in various subspaces of the original feature space. Besides, many practical applications require that subspace clusters can still overlap but the overlap must be below a predefined threshold. Nonetheless, most of the state-of-the-art subspace clustering algorithms can only generate a set of disjoint or significantly overlapping subspace clusters.
To deal with the above issues, for full-space alternative clustering, we develop an algorithm which fully acknowledges the multiple objectives, optimizes them directly and simultaneously, and produces solutions approximating the Pareto front. As for non-redundant subspace clustering, we propose a general framework for generating K overlapping subspace clusters where the maximum overlap between them is guaranteed to be below a predefined threshold. In both cases, our algorithms can be applied for several domains as different analyzing models can be used without modifying the main parts of the algorithms
Investigating Protein Folding Pathways at Atomistic Resolution: from a Small Domain to a Knotted Protein
No full textAlthough protein folding has been studied for decades many open issues still resist, and we yet lack a clear and general description of the mechanisms leading from the unfolded to the folded state. In particular, it is still under debate whether proteins fold through few well-defined pathways or trough a large multitude of independent ways. Answering these questions is made difficult by the fact that standard molecular dynamics (MD) simulations are very computationally expensive and often impracticable. Moreover, often even experimental techniques lack the necessary resolution to give a definitive answer.
We will introduce and develope the Dominant Reaction Pathway (DRP), which is an approach that permits to efficiently study the thermally activated conformational dynamics of bio-molecules in atomistic detail. In particular, it can be used to characterize and portray the folding pathways of a protein once the unfolded and folded configurations are given.
We firstly applied the DRP to a realistic protein studying the folding pathways of the Fip35 WW Domain, a 35 amino-acids long protein. Performing all atom simulations we were able to show that this small protein folds following only two pathways, defined by the order of formation of secondary structures. Notably, our results are compatible with ultra long MD simulations and consistent with the analysis of the experimental available data on the folding kinetics of the same system. Exploiting the efficiency of the DRP formalism, computing a folding trajectory of this protein only required about one hour on 48 CPU’s.
We applied then our simulation scheme to a much more challenging task: performing an all-atom folding simulation of a 82 amino-acids long protein displaying a topological knot in its native conformation. We were able to portray the folding mechanism and to identify the essential key contacts leading to the proper formation of this knot. Interestingly, we showed that non native contacts, i.e., transient contacts formed during the folding of the protein but absent in its native state, can sensibly enhance the probability of correctly forming the knot
Adaptive Brake By Wire: From Human Factors to Adaptive Implementation
No full textThe introduction of the Brake By Wire is replacing the traditional mechanical control systems with ECUs and it is raising the need to reproduce feelings of eliminated static mechanical components (i.e. hydraulic fluids, pumps and cylinders). Thanks to electromechanical actuators and human-machine interfaces (i.e. active pedal) it is possible to reproduce such feelings and, therefore, arbitrarily change their features. In this way it will be possible to customize the pedal feelings and the vehicle deceleration needed depending on several factors (i.e. surrounding braking scenario, driver characteristics, race vs day-by-day driving condition). Since braking maneuvers are typically critical and involve the driver, the design and development of brake by wire system must start from the consideration of human factors in order to increase acceptance and braking effectiveness. The objective of this research was to redesign the pedal feelings, making them adaptable to the surrounding. Driver acceptance and braking effectiveness could be highly improved by means of adaptive pedal feelings. The starting points of this research were humans factors in the braking domain. Literature and relevant studies have been taken into consideration to put into evidence human mechanisms and behaviors during braking phases. On such basis, two main results have been found out: braking use cases and pedal feeling curves.
With regard to the pedal feelings curves, 4 different pedal curves which describe both force on brake pedal travel and acceleration on brake pedal travel are designed. The pedal feeling depends on several factors like the pedal travel, the pedal idle travel, the effort, responsiveness, deceleration perceived, ease of balance (i.e. ease of modulation), gradual braking and so on. Regarding braking use cases, they are described by vehicle data as speed, acceleration, angles and relevant rates, engine rpm, gas and brake pedal position/speed and so on. These use cases have been clustered in order to meet the 4 pedal curves.
The research continued on the implementation of a Matlab/Simulink/Stateflow model for the use case recognition. Basing on the vehicle data, the model is able to find out in which use case the vehicle is (parking, low speed maneuvers, emergency, downhill, and so on). Once it finds out the scenario, the model applies the most appropriate pedal feeling curve (both force feedback and deceleration needed). In the end, the model commands an EC brushless motor which is responsible of the changing of static springs force feedback of the pedal. The scenario recognition model has been validated through vehicle data on real road whereas the pedal feeling and relevant motor behaviors have been validate on bench tests
Study of dynamic and ground-state properties of dipolar Fermi gases using mean-field and quantum Monte Carlo methods
No full textIn this thesis I theoretically study the dynamic and ground state properties of ultracold dipolar Fermi gases. The mean-field approach based on the Thomas-Fermi energy functional is applied to consider the dynamic properties of bilayer harmonically trapped dipolar Fermi gases. The fixed-node Diffusion Monte Carlo method (FNDMC) is used instead to investigate the ground-state properties
of two dimensional dipolar Fermi gases. This technique is also applied to the problem of one impurity in a bilayer configuration with dipolar fermions
Homogenization of heterogeneous Cauchy-elastic materials leads to Mindlin second-gradient elasticity
No full textThrough a second-order homogenization procedure, the explicit relation is obtained between the non-local parameters of a second gradient elastic ma- terial and the microstructure of a composite material. This result is instru- mental for the definition of higher-order models, to be used for the analysis of mechanics at micro- and nano-scale, where size-effects become important.
The obtained relation is valid for both plane and three-dimensional prob- lems and generalizes earlier findings by Bigoni and Drugan (Analytical deriva- tion of Cosserat moduli via homogenization of heterogeneous elastic materials. J. Appl. Mech., 2007, 74, 741753) from several points of view:
i) the result holds for anisotropic phases with spherical or circular ellipsoid of inertia;
ii) the displacement boundary conditions considered in the homogenization procedure is independent of the characteristics of the material;
iii) a perfect energy match is found between heterogeneous and equivalent materials (instead of an optimal bound).
From the obtained solution it follows that the equivalent second-gradient Mindlin elastic solid:
a) is positive definite only when the discrepancy tensor is negative defined;
b) the non-local material symmetries are the same of the discrepancy tensor;
c) the non-local effective behaviour is affected by the shape of the RVE, which does not influence the first-order homogenized response.
Finally, explicit derivations of non-local parameters from heterogeneous Cauchy elastic composites are obtained in particular cases
Evolution of Arsenic nanometric distributions in Silicon under advanced ion implantation and annealing processes
No full textThe study presented in this thesis is focused on the investigation of Arsenic ultra-shallow distributions in Si for applications as source-drain extension dopant in CMOS technology. Using the Ultra-low energy SIMS measurements the evolution of arsenic shallow distribution was investigated with reference to the metastable electrical activation and the successive deactivation under moderate thermal treatment (550-700°C).
Three different approaches to form As USJ were investigated to understand their physical mechanisms to verify their possible application in next generation microelectronics devices.
First two activation approaches were based on low energy beamline ion implanted material. The first one is the low temperature (550°C) solid-phase epitaxial re-growth and the second activation approach is a sub-melt laser annealing at different temperatures. A range of deactivation studies was performed using these two classes of material with more attention given to the laser annealed ones.
Plasma ion immersion implantation together with the LA was considered as the third approach of arsenic ultra-shallow junction formation. Samples created by AsH3+ plasma were investigated with respect to arsenic distribution, silicon oxide thickness and arsenic local order using SIMS, INAA, and EXAFS analysis