Il conflitto tra ragione e passioni nel pensiero morale, economico e politico di Bernard Mandeville

Effects of the conflict between reason and passion in Bernard Mandeville’s moral, economic and political thought My PhD dissertation focuses on Bernard Mandeville (1670-1732), a Dutch philosopher who moved to London in his late twenties. The aspect of Mandeville’s thought I take into account in my research is the conflicting relation between reason and passions, and the consequences that Mandeville’s view of this conflict has in the development of his theory of human nature which, I argue, is what grounds his moral, economic and, above all, political theory. According to Mandeville, reason is fundamentally weak. Passions influence with more strength human actions, and, eventually, are the ones which motivate them. The role of reason is merely instrumental, restricted to finding appropriate means in order to reach the desired ends, which are capricious and inconstant, since they all come from unstable passions. Reason cannot take decisions meant to act in the long term, pursuing an object which has not a selfish and temporary nature. There is no possibility, thus, that men’s actions aim just to achieve a good and just society, without their interests being directly involved. The basically selfish root of every desire leads Mandeville to claim that there is neither benevolence nor altruism which guides human behaviour. Hence he expresses a judgement on the moral character of human beings, always busy with their self-satisfaction, and hardly ever considering what would be good on a wider perspective, including other people’s sake. The anthropological features ascribed to men by Mandeville, are those which lead him to prefer a political system where governors are not supposed to have particular abilities, either from an intellectual or from a moral point of view, and peace and order are preserved by the bureaucratic machine, which is meant to work with the least effort on the part of the politicians, and no big harm can be done even by corrupted or wicked governors. This system is adopted with an eye at remedying human deficiencies: Mandeville takes into primary account, when he thinks of how to build a peaceful and functioning society, that everyone is concerned with his selfish interest, and that the rationality of a single politician, or of a group of them belonging to a same generation, cannot find a good “solution” to govern men able to last over the long period, and to work in different ages. This implies a refusal of the Hobbesian theory of the pactum subjectionis, which has the character of a rational and definitive choice, and leads Mandeville to consider the order which arises spontaneously, without any plan or rational intervention.

Machine learning methods for prediction of disulphide bonding states of cysteine residues in proteins

The goal of this thesis work is to develop a computational method based on machine learning techniques for predicting disulfide-bonding states of cysteine residues in proteins, which is a sub-problem of a bigger and yet unsolved problem of protein structure prediction. Improvement in the prediction of disulfide bonding states of cysteine residues will help in putting a constraint in the three dimensional (3D) space of the respective protein structure, and thus will eventually help in the prediction of 3D structure of proteins. Results of this work will have direct implications in site-directed mutational studies of proteins, proteins engineering and the problem of protein folding. We have used a combination of Artificial Neural Network (ANN) and Hidden Markov Model (HMM), the so-called Hidden Neural Network (HNN) as a machine learning technique to develop our prediction method. By using different global and local features of proteins (specifically profiles, parity of cysteine residues, average cysteine conservation, correlated mutation, sub-cellular localization, and signal peptide) as inputs and considering Eukaryotes and Prokaryotes separately we have reached to a remarkable accuracy of 94% on cysteine basis for both Eukaryotic and Prokaryotic datasets, and an accuracy of 90% and 93% on protein basis for Eukaryotic dataset and Prokaryotic dataset respectively. These accuracies are best so far ever reached by any existing prediction methods, and thus our prediction method has outperformed all the previously developed approaches and therefore is more reliable. Most interesting part of this thesis work is the differences in the prediction performances of Eukaryotes and Prokaryotes at the basic level of input coding when ‘profile’ information was given as input to our prediction method. And one of the reasons for this we discover is the difference in the amino acid composition of the local environment of bonded and free cysteine residues in Eukaryotes and Prokaryotes. Eukaryotic bonded cysteine examples have a ‘symmetric-cysteine-rich’ environment, where as Prokaryotic bonded examples lack it.

Spectrum reconstruction from a scattering measurement using the adjoint Boltzmann transport equation for photons

Quality control of medical radiological systems is of fundamental importance, and requires efficient methods for accurately determine the X-ray source spectrum. Straightforward measurements of X-ray spectra in standard operating require the limitation of the high photon flux, and therefore the measure has to be performed in a laboratory. However, the optimal quality control requires frequent in situ measurements which can be only performed using a portable system. To reduce the photon flux by 3 magnitude orders an indirect technique based on the scattering of the X-ray source beam by a solid target is used. The measured spectrum presents a lack of information because of transport and detection effects. The solution is then unfolded by solving the matrix equation that represents formally the scattering problem. However, the algebraic system is ill-conditioned and, therefore, it is not possible to obtain a satisfactory solution. Special strategies are necessary to circumvent the ill-conditioning. Numerous attempts have been done to solve this problem by using purely mathematical methods. In this thesis, a more physical point of view is adopted. The proposed method uses both the forward and the adjoint solutions of the Boltzmann transport equation to generate a better conditioned linear algebraic system. The procedure has been tested first on numerical experiments, giving excellent results. Then, the method has been verified with experimental measurements performed at the Operational Unit of Health Physics of the University of Bologna. The reconstructed spectra have been compared with the ones obtained with straightforward measurements, showing very good agreement.

Machine-learning methods for structure prediction of β-barrel membrane proteins

Different types of proteins exist with diverse functions that are essential for living organisms. An important class of proteins is represented by transmembrane proteins which are specifically designed to be inserted into biological membranes and devised to perform very important functions in the cell such as cell communication and active transport across the membrane. Transmembrane β-barrels (TMBBs) are a sub-class of membrane proteins largely under-represented in structure databases because of the extreme difficulty in experimental structure determination. For this reason, computational tools that are able to predict the structure of TMBBs are needed. In this thesis, two computational problems related to TMBBs were addressed: the detection of TMBBs in large datasets of proteins and the prediction of the topology of TMBB proteins. Firstly, a method for TMBB detection was presented based on a novel neural network framework for variable-length sequence classification. The proposed approach was validated on a non-redundant dataset of proteins. Furthermore, we carried-out genome-wide detection using the entire Escherichia coli proteome. In both experiments, the method significantly outperformed other existing state-of-the-art approaches, reaching very high PPV (92%) and MCC (0.82). Secondly, a method was also introduced for TMBB topology prediction. The proposed approach is based on grammatical modelling and probabilistic discriminative models for sequence data labeling. The method was evaluated using a newly generated dataset of 38 TMBB proteins obtained from high-resolution data in the PDB. Results have shown that the model is able to correctly predict topologies of 25 out of 38 protein chains in the dataset. When tested on previously released datasets, the performances of the proposed approach were measured as comparable or superior to the current state-of-the-art of TMBB topology prediction.

Fluid-structure interaction by a coupled lattice Boltzmann-finite element approach

In this thesis, a strategy to model the behavior of fluids and their interaction with deformable bodies is proposed. The fluid domain is modeled by using the lattice Boltzmann method, thus analyzing the fluid dynamics by a mesoscopic point of view. It has been proved that the solution provided by this method is equivalent to solve the Navier-Stokes equations for an incompressible flow with a second-order accuracy. Slender elastic structures idealized through beam finite elements are used. Large displacements are accounted for by using the corotational formulation. Structural dynamics is computed by using the Time Discontinuous Galerkin method. Therefore, two different solution procedures are used, one for the fluid domain and the other for the structural part, respectively. These two solvers need to communicate and to transfer each other several information, i.e. stresses, velocities, displacements. In order to guarantee a continuous, effective, and mutual exchange of information, a coupling strategy, consisting of three different algorithms, has been developed and numerically tested. In particular, the effectiveness of the three algorithms is shown in terms of interface energy artificially produced by the approximate fulfilling of compatibility and equilibrium conditions at the fluid-structure interface. The proposed coupled approach is used in order to solve different fluid-structure interaction problems, i.e. cantilever beams immersed in a viscous fluid, the impact of the hull of the ship on the marine free-surface, blood flow in a deformable vessels, and even flapping wings simulating the take-off of a butterfly. The good results achieved in each application highlight the effectiveness of the proposed methodology and of the C++ developed software to successfully approach several two-dimensional fluid-structure interaction problems.

Innovative Man Machine Interfaces In Aeronautics

The research activity focused on the study, design and evaluation of innovative human-machine interfaces based on virtual three-dimensional environments. It is based on the brain electrical activities recorded in real time through the electrical impulses emitted by the brain waves of the user. The achieved target is to identify and sort in real time the different brain states and adapt the interface and/or stimuli to the corresponding emotional state of the user. The setup of an experimental facility based on an innovative experimental methodology for “man in the loop" simulation was established. It allowed involving during pilot training in virtually simulated flights, both pilot and flight examiner, in order to compare the subjective evaluations of this latter to the objective measurements of the brain activity of the pilot. This was done recording all the relevant information versus a time-line. Different combinations of emotional intensities obtained, led to an evaluation of the current situational awareness of the user. These results have a great implication in the current training methodology of the pilots, and its use could be extended as a tool that can improve the evaluation of a pilot/crew performance in interacting with the aircraft when performing tasks and procedures, especially in critical situations. This research also resulted in the design of an interface that adapts the control of the machine to the situation awareness of the user. The new concept worked on, aimed at improving the efficiency between a user and the interface, and gaining capacity by reducing the user’s workload and hence improving the system overall safety. This innovative research combining emotions measured through electroencephalography resulted in a human-machine interface that would have three aeronautical related applications: • An evaluation tool during the pilot training; • An input for cockpit environment; • An adaptation tool of the cockpit automation.