Reverse Engineering tools: development and experimentation of innovative methods for physical and geometrical data integration and post-processing

In recent years, the use of Reverse Engineering systems has got a considerable interest for a wide number of applications. Therefore, many research activities are focused on accuracy and precision of the acquired data and post processing phase improvements. In this context, this PhD Thesis deals with the definition of two novel methods for data post processing and data fusion between physical and geometrical information. In particular a technique has been defined for error definition in 3D points’ coordinates acquired by an optical triangulation laser scanner, with the aim to identify adequate correction arrays to apply under different acquisition parameters and operative conditions. Systematic error in data acquired is thus compensated, in order to increase accuracy value. Moreover, the definition of a 3D thermogram is examined. Object geometrical information and its thermal properties, coming from a thermographic inspection, are combined in order to have a temperature value for each recognizable point. Data acquired by an optical triangulation laser scanner are also used to normalize temperature values and make thermal data independent from thermal-camera point of view.

Complex chemical dynamics through engineering-like methods

Most of the problems in modern structural design can be described with a set of equation; solutions of these mathematical models can lead the engineer and designer to get info during the design stage. The same holds true for physical-chemistry; this branch of chemistry uses mathematics and physics in order to explain real chemical phenomena. In this work two extremely different chemical processes will be studied; the dynamic of an artificial molecular motor and the generation and propagation of the nervous signals between excitable cells and tissues like neurons and axons. These two processes, in spite of their chemical and physical differences, can be both described successfully by partial differential equations, that are, respectively the Fokker-Planck equation and the Hodgkin and Huxley model. With the aid of an advanced engineering software these two processes have been modeled and simulated in order to extract a lot of physical informations about them and to predict a lot of properties that can be, in future, extremely useful during the design stage of both molecular motors and devices which rely their actions on the nervous communications between active fibres.

Computational methods for genome screening

Motivation An actual issue of great interest, both under a theoretical and an applicative perspective, is the analysis of biological sequences for disclosing the information that they encode. The development of new technologies for genome sequencing in the last years, opened new fundamental problems since huge amounts of biological data still deserve an interpretation. Indeed, the sequencing is only the first step of the genome annotation process that consists in the assignment of biological information to each sequence. Hence given the large amount of available data, in silico methods became useful and necessary in order to extract relevant information from sequences. The availability of data from Genome Projects gave rise to new strategies for tackling the basic problems of computational biology such as the determination of the tridimensional structures of proteins, their biological function and their reciprocal interactions. Results The aim of this work has been the implementation of predictive methods that allow the extraction of information on the properties of genomes and proteins starting from the nucleotide and aminoacidic sequences, by taking advantage of the information provided by the comparison of the genome sequences from different species. In the first part of the work a comprehensive large scale genome comparison of 599 organisms is described. 2,6 million of sequences coming from 551 prokaryotic and 48 eukaryotic genomes were aligned and clustered on the basis of their sequence identity. This procedure led to the identification of classes of proteins that are peculiar to the different groups of organisms. Moreover the adopted similarity threshold produced clusters that are homogeneous on the structural point of view and that can be used for structural annotation of uncharacterized sequences. The second part of the work focuses on the characterization of thermostable proteins and on the development of tools able to predict the thermostability of a protein starting from its sequence. By means of Principal Component Analysis the codon composition of a non redundant database comprising 116 prokaryotic genomes has been analyzed and it has been showed that a cross genomic approach can allow the extraction of common determinants of thermostability at the genome level, leading to an overall accuracy in discriminating thermophilic coding sequences equal to 95%. This result outperform those obtained in previous studies. Moreover, we investigated the effect of multiple mutations on protein thermostability. This issue is of great importance in the field of protein engineering, since thermostable proteins are generally more suitable than their mesostable counterparts in technological applications. A Support Vector Machine based method has been trained to predict if a set of mutations can enhance the thermostability of a given protein sequence. The developed predictor achieves 88% accuracy.

Evolutionary methods for self-organizing cooperation in peer-to-peer networks

The Peer-to-Peer network paradigm is drawing the attention of both final users and researchers for its features. P2P networks shift from the classic client-server approach to a high level of decentralization where there is no central control and all the nodes should be able not only to require services, but to provide them to other peers as well. While on one hand such high level of decentralization might lead to interesting properties like scalability and fault tolerance, on the other hand it implies many new problems to deal with. A key feature of many P2P systems is openness, meaning that everybody is potentially able to join a network with no need for subscription or payment systems. The combination of openness and lack of central control makes it feasible for a user to free-ride, that is to increase its own benefit by using services without allocating resources to satisfy other peers’ requests. One of the main goals when designing a P2P system is therefore to achieve cooperation between users. Given the nature of P2P systems based on simple local interactions of many peers having partial knowledge of the whole system, an interesting way to achieve desired properties on a system scale might consist in obtaining them as emergent properties of the many interactions occurring at local node level. Two methods are typically used to face the problem of cooperation in P2P networks: 1) engineering emergent properties when designing the protocol; 2) study the system as a game and apply Game Theory techniques, especially to find Nash Equilibria in the game and to reach them making the system stable against possible deviant behaviors. In this work we present an evolutionary framework to enforce cooperative behaviour in P2P networks that is alternative to both the methods mentioned above. Our approach is based on an evolutionary algorithm inspired by computational sociology and evolutionary game theory, consisting in having each peer periodically trying to copy another peer which is performing better. The proposed algorithms, called SLAC and SLACER, draw inspiration from tag systems originated in computational sociology, the main idea behind the algorithm consists in having low performance nodes copying high performance ones. The algorithm is run locally by every node and leads to an evolution of the network both from the topology and from the nodes’ strategy point of view. Initial tests with a simple Prisoners’ Dilemma application show how SLAC is able to bring the network to a state of high cooperation independently from the initial network conditions. Interesting results are obtained when studying the effect of cheating nodes on SLAC algorithm. In fact in some cases selfish nodes rationally exploiting the system for their own benefit can actually improve system performance from the cooperation formation point of view. The final step is to apply our results to more realistic scenarios. We put our efforts in studying and improving the BitTorrent protocol. BitTorrent was chosen not only for its popularity but because it has many points in common with SLAC and SLACER algorithms, ranging from the game theoretical inspiration (tit-for-tat-like mechanism) to the swarms topology. We discovered fairness, meant as ratio between uploaded and downloaded data, to be a weakness of the original BitTorrent protocol and we drew inspiration from the knowledge of cooperation formation and maintenance mechanism derived from the development and analysis of SLAC and SLACER, to improve fairness and tackle freeriding and cheating in BitTorrent. We produced an extension of BitTorrent called BitFair that has been evaluated through simulation and has shown the abilities of enforcing fairness and tackling free-riding and cheating nodes.

Study of silicon-on-insulator multiple-gate MOS structures including band-gap engineering and self heating effects

The progresses of electron devices integration have proceeded for more than 40 years following the well–known Moore’s law, which states that the transistors density on chip doubles every 24 months. This trend has been possible due to the downsizing of the MOSFET dimensions (scaling); however, new issues and new challenges are arising, and the conventional ”bulk” architecture is becoming inadequate in order to face them. In order to overcome the limitations related to conventional structures, the researchers community is preparing different solutions, that need to be assessed. Possible solutions currently under scrutiny are represented by: • devices incorporating materials with properties different from those of silicon, for the channel and the source/drain regions; • new architectures as Silicon–On–Insulator (SOI) transistors: the body thickness of Ultra-Thin-Body SOI devices is a new design parameter, and it permits to keep under control Short–Channel–Effects without adopting high doping level in the channel. Among the solutions proposed in order to overcome the difficulties related to scaling, we can highlight heterojunctions at the channel edge, obtained by adopting for the source/drain regions materials with band–gap different from that of the channel material. This solution allows to increase the injection velocity of the particles travelling from the source into the channel, and therefore increase the performance of the transistor in terms of provided drain current. The first part of this thesis work addresses the use of heterojunctions in SOI transistors: chapter 3 outlines the basics of the heterojunctions theory and the adoption of such approach in older technologies as the heterojunction–bipolar–transistors; moreover the modifications introduced in the Monte Carlo code in order to simulate conduction band discontinuities are described, and the simulations performed on unidimensional simplified structures in order to validate them as well. Chapter 4 presents the results obtained from the Monte Carlo simulations performed on double–gate SOI transistors featuring conduction band offsets between the source and drain regions and the channel. In particular, attention has been focused on the drain current and to internal quantities as inversion charge, potential energy and carrier velocities. Both graded and abrupt discontinuities have been considered. The scaling of devices dimensions and the adoption of innovative architectures have consequences on the power dissipation as well. In SOI technologies the channel is thermally insulated from the underlying substrate by a SiO2 buried–oxide layer; this SiO2 layer features a thermal conductivity that is two orders of magnitude lower than the silicon one, and it impedes the dissipation of the heat generated in the active region. Moreover, the thermal conductivity of thin semiconductor films is much lower than that of silicon bulk, due to phonon confinement and boundary scattering. All these aspects cause severe self–heating effects, that detrimentally impact the carrier mobility and therefore the saturation drive current for high–performance transistors; as a consequence, thermal device design is becoming a fundamental part of integrated circuit engineering. The second part of this thesis discusses the problem of self–heating in SOI transistors. Chapter 5 describes the causes of heat generation and dissipation in SOI devices, and it provides a brief overview on the methods that have been proposed in order to model these phenomena. In order to understand how this problem impacts the performance of different SOI architectures, three–dimensional electro–thermal simulations have been applied to the analysis of SHE in planar single and double–gate SOI transistors as well as FinFET, featuring the same isothermal electrical characteristics. In chapter 6 the same simulation approach is extensively employed to study the impact of SHE on the performance of a FinFET representative of the high–performance transistor of the 45 nm technology node. Its effects on the ON–current, the maximum temperatures reached inside the device and the thermal resistance associated to the device itself, as well as the dependence of SHE on the main geometrical parameters have been analyzed. Furthermore, the consequences on self–heating of technological solutions such as raised S/D extensions regions or reduction of fin height are explored as well. Finally, conclusions are drawn in chapter 7.

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.

An evolutionary approach to the design of experiments for combinatorial optimization with an application to enzyme engineering

In a large number of problems the high dimensionality of the search space, the vast number of variables and the economical constrains limit the ability of classical techniques to reach the optimum of a function, known or unknown. In this thesis we investigate the possibility to combine approaches from advanced statistics and optimization algorithms in such a way to better explore the combinatorial search space and to increase the performance of the approaches. To this purpose we propose two methods: (i) Model Based Ant Colony Design and (ii) Naïve Bayes Ant Colony Optimization. We test the performance of the two proposed solutions on a simulation study and we apply the novel techniques on an appplication in the field of Enzyme Engineering and Design.

Preparation of new crystal forms via photochemical, mechanochemical and sol-gel methods

This work of thesis involves various aspects of crystal engineering. Chapter 1 focuses on crystals containing crown ether complexes. Aspects such as the possibility of preparing these materials by non-solution methods, i.e. by direct reaction of the solid components, thermal behavior and also isomorphism and interconversion between hydrates are taken into account. In chapter 2 a study is presented aimed to understanding the relationship between hydrogen bonding capability and shape of the building blocks chosen to construct crystals. The focus is on the control exerted by shape on the organization of sandwich cations such as cobalticinium, decamethylcobalticinium and bisbenzenchromium(I) and on the aggregation of monoanions all containing carboxylic and carboxylate groups, into 0-D, 1-D, 2-D and 3-D networks. Reactions conducted in multi-component molecular assemblies or co-crystals have been recognized as a way to control reactivity in the solid state. The [2+2] photodimerization of olefins is a successful demonstration of how templated solid state synthesis can efficiently synthesize unique materials with remarkable stereoselectivity and under environment-friendly conditions. A demonstration of this synthetic strategy is given in chapter 3. The combination of various types of intermolecular linkages, leading to formation of high order aggregation and crystalline materials or to a random aggregation resulting in an amorphous precipitate, may not go to completeness. In such rare cases an aggregation process intermediate between crystalline and amorphous materials is observed, resulting in the formation of a gel, i.e. a viscoelastic solid-like or liquid-like material. In chapter 4 design of new Low Molecular Weight Gelators is presented. Aspects such as the relationships between molecular structure, crystal packing and gelation properties and the application of this kind of gels as a medium for crystal growth of organic molecules, such as APIs, are also discussed.

Crystal Engineering of bright luminescent copper iodide clusters with phosphorus and nitrogen-based ligands

Copper(I) halide clusters are recently considered as good candidate for optoelectronic devices such as OLEDs . Although the copper halide clusters, in particular copper iodide, are very well known since the beginning of the 20th century, only in the late ‘70s the interest on these compounds grew dramatically due their particular photophysical behaviour. These complexes are characterized by a dual triplet emission bands, named Cluster Centred (3CC) and Halogen-to-Ligand charge transfer (3XLCT), the intensities of which are strictly related with the temperature. The CC transition, due to the presence of a metallophylic interactions, is prevalent at ambient temperature while the XLCT transition, located preferentially on the ligand part, became more prominent at low temperature. Since these pioneering works, it was easy to understand the photophysical properties of this compounds became more interesting in solid-state respect to solution with an improvement in emission efficiency. In this work we aim to characterize in SS organocopper(I)iodide compounds to valuate the correlation between the molecular crystal structure and the photophysical properties. It is also considered to hike new strategies to synthesize CuI complexes from the wet reactions to the more green solvent free methods. The advantages in using these strategies are evident but, obtain a single crystal suitable for SCXRD analysis from these batches is quite impossible. The structure solution still remains the key point in this research so we tackle this problem solving the structure by X-ray powder diffraction data. When the sample was fully characterized we moved to design and development of the associated OLED-device. Since copper iodide complexes are often insoluble in organic solvents, the high vacuum deposition technique is preferred. A new non-conventional deposition process have also been proposed to avoid the low complex stability in this practice with an in-situ complex formation in a layer-by layer deposition route.