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.

An advanced analytical study on linseed oil paint binder

Drying oils, and in particular linseed oil, were the most common binding media employed in painting between XVI and XIX centuries. Artists usually operated some pre-treatments on the oils to obtain binders with modified properties, such as different handling qualities or colour. Oil processing has a key role on the subsequent ageing of and degradation of linseed oil paints. In this thesis a multi-analytical approach was adopted to investigate the drying, polymerization and oxidative degradation of the linseed oil paints. In particular, thermogravimetry analysis (TGA), yielding information on the macromolecular scale, were compared with gas-chromatography mass-spectrometry (GC-MS) and direct exposure mass spectrometry (DEMS) providing information on the molecular scale. The study was performed on linseed oils and paint reconstructions prepared according to an accurate historical description of the painting techniques of the 19th century. TGA revealed that during ageing the molecular weight of the oils changes and that higher molecular weight fractions formed. TGA proved to be an excellent tool to compare the oils and paint reconstructions. This technique is able to highlight the different physical behaviour of oils that were processed using different methods and of paint layers on the basis of the different processed oil and /or the pigment used. GC/MS and DE-MS were used to characterise the soluble and non-polymeric fraction of the oils and paint reconstructions. GC/MS allowed us to calculate the ratios of palmitic to stearic acid (P/S), and azelaic to palmitic acid (A/P) and to evaluate effects produced by oil pre-treatments and the presence of different pigments. This helps to understand the role of the pre-treatments and of the pigments on the oxidative degradation undergone by siccative oils during ageing. DE-MS enabled the various molecular weight fractions of the samples to be simultaneously studied, and thus helped to highlight the presence of oxidation and hydrolysis reactions, and the formation of carboxylates that occur during ageing and with the changing of the oil pre-treatments and the pigments. The combination of thermal analysis with molecular techniques such as GC-MS, DEMS and FTIR enabled a model to be developed, for unravelling some crucial issues: 1) how oil pre-treatments produce binders with different physical-chemical qualities, and how this can influence the ageing of an oil paint film; 2) which is the role of the interaction between oil and pigments in the ageing and degradation process.

Proteolysis of the receptor for advanced glycation end products by matrix metalloproteinases

RAGE mediates diverse physiological and pathological effects by binding a variety of ligands. Despite incomplete understanding of RAGE-mediated disorders soluble RAGE (sRAGE) has been identified as a potential biomarker for RAGE-related diseases and possibly represents a hopeful pharmaceutical against RAGE-mediated disorders. Nevertheless, the source of sRAGE remains poorly investigated. Currently sRAGE is thought to be derived exclusively from alternative splicing of mRNA. In this thesis it was investigated whether sRAGE can also be released as a result of ectodomain shedding of full-length RAGE. Using cells overexpressing RAGE as a model system, it was demonstrated clearly that RAGE undergoes ectodomain shedding in both constitutive and regulated manner. Several stimuli including PMA, AMPA, calcium and chelerythrine stimulated the release of sRAGE into cell culture medium. Moreover, possible mechanisms that regulate ectodomain shedding of RAGE were investigated and it was found that shedding of RAGE is likely independent from PKC and MAPK pathways. By using gain of function and loss of function approaches MMP9 but not ADAM10, ADAM17 or MT1-MMP was characterized as the metalloproteinase that mediates shedding of RAGE. Furthermore, it was shown that cytoplasmic domain of RAGE is not essential for shedding of RAGE. In addition, the potential cleavage site of RAGE by MMP9 was investigated and a lack of sequence specificity for the RAGE processing proteinase was demonstrated by mutation analysis. Finally the physiopathological significance of shedding of RAGE is discussed. In conclusion, for the first time ectodomain shedding of human RAGE and the underlying regulatory mechanisms were investigated. The data open a new field for modulation of RAGE shedding as a novel intervention approach against RAGE-mediated diseases.

Advanced Numerical Simulation of Silicon-Based Solar Cells

Photovoltaic (PV) conversion is the direct production of electrical energy from sun without involving the emission of polluting substances. In order to be competitive with other energy sources, cost of the PV technology must be reduced ensuring adequate conversion efficiencies. These goals have motivated the interest of researchers in investigating advanced designs of crystalline silicon solar (c-Si) cells. Since lowering the cost of PV devices involves the reduction of the volume of semiconductor, an effective light trapping strategy aimed at increasing the photon absorption is required. Modeling of solar cells by electro-optical numerical simulation is helpful to predict the performance of future generations devices exhibiting advanced light-trapping schemes and to provide new and more specific guidelines to industry. The approaches to optical simulation commonly adopted for c-Si solar cells may lead to inaccurate results in case of thin film and nano-stuctured solar cells. On the other hand, rigorous solvers of Maxwell equations are really cpu- and memory-intensive. Recently, in optical simulation of solar cells, the RCWA method has gained relevance, providing a good trade-off between accuracy and computational resources requirement. This thesis is a contribution to the numerical simulation of advanced silicon solar cells by means of a state-of-the-art numerical 2-D/3-D device simulator, that has been successfully applied to the simulation of selective emitter and the rear point contact solar cells, for which the multi-dimensionality of the transport model is required in order to properly account for all physical competing mechanisms. In the second part of the thesis, the optical problems is discussed. Two novel and computationally efficient RCWA implementations for 2-D simulation domains as well as a third RCWA for 3-D structures based on an eigenvalues calculation approach have been presented. The proposed simulators have been validated in terms of accuracy, numerical convergence, computation time and correctness of results.

Multidimensional Measures of Firm Competitiveness: a Model-Based Approach

The concept of competitiveness, for a long time considered as strictly connected to economic and financial performances, evolved, above all in recent years, toward new, wider interpretations disclosing its multidimensional nature. The shift to a multidimensional view of the phenomenon has excited an intense debate involving theoretical reflections on the features characterizing it, as well as methodological considerations on its assessment and measurement. The present research has a twofold objective: going in depth with the study of tangible and intangible aspect characterizing multidimensional competitive phenomena by assuming a micro-level point of view, and measuring competitiveness through a model-based approach. Specifically, we propose a non-parametric approach to Structural Equation Models techniques for the computation of multidimensional composite measures. Structural Equation Models tools will be used for the development of the empirical application on the italian case: a model based micro-level competitiveness indicator for the measurement of the phenomenon on a large sample of Italian small and medium enterprises will be constructed.

Advanced modelling of time domain electromagnetic data with updated hydrogeological interpretations

Several countries have acquired, over the past decades, large amounts of area covering Airborne Electromagnetic data. Contribution of airborne geophysics has dramatically increased for both groundwater resource mapping and management proving how those systems are appropriate for large-scale and efficient groundwater surveying. We start with processing and inversion of two AEM dataset from two different systems collected over the Spiritwood Valley Aquifer area, Manitoba, Canada respectively, the AeroTEM III (commissioned by the Geological Survey of Canada in 2010) and the “Full waveform VTEM” dataset, collected and tested over the same survey area, during the fall 2011. We demonstrate that in the presence of multiple datasets, either AEM and ground data, due processing, inversion, post-processing, data integration and data calibration is the proper approach capable of providing reliable and consistent resistivity models. Our approach can be of interest to many end users, ranging from Geological Surveys, Universities to Private Companies, which are often proprietary of large geophysical databases to be interpreted for geological and\or hydrogeological purposes. In this study we deeply investigate the role of integration of several complimentary types of geophysical data collected over the same survey area. We show that data integration can improve inversions, reduce ambiguity and deliver high resolution results. We further attempt to use the final, most reliable output resistivity models as a solid basis for building a knowledge-driven 3D geological voxel-based model. A voxel approach allows a quantitative understanding of the hydrogeological setting of the area, and it can be further used to estimate the aquifers volumes (i.e. potential amount of groundwater resources) as well as hydrogeological flow model prediction. In addition, we investigated the impact of an AEM dataset towards hydrogeological mapping and 3D hydrogeological modeling, comparing it to having only a ground based TEM dataset and\or to having only boreholes data.

Sustainable improvement of water networks : to achieve best decisions for rehabilitation plans using advanced GIS and statistical techniques

Many of developing countries are facing crisis in water management due to increasing of population, water scarcity, water contaminations and effects of world economic crisis. Water distribution systems in developing countries are facing many challenges of efficient repair and rehabilitation since the information of water network is very limited, which makes the rehabilitation assessment plans very difficult. Sufficient information with high technology in developed countries makes the assessment for rehabilitation easy. Developing countries have many difficulties to assess the water network causing system failure, deterioration of mains and bad water quality in the network due to pipe corrosion and deterioration. The limited information brought into focus the urgent need to develop economical assessment for rehabilitation of water distribution systems adapted to water utilities. Gaza Strip is subject to a first case study, suffering from severe shortage in the water supply and environmental problems and contamination of underground water resources. This research focuses on improvement of water supply network to reduce the water losses in water network based on limited database using techniques of ArcGIS and commercial water network software (WaterCAD). A new approach for rehabilitation water pipes has been presented in Gaza city case study. Integrated rehabilitation assessment model has been developed for rehabilitation water pipes including three components; hydraulic assessment model, Physical assessment model and Structural assessment model. WaterCAD model has been developed with integrated in ArcGIS to produce the hydraulic assessment model for water network. The model have been designed based on pipe condition assessment with 100 score points as a maximum points for pipe condition. As results from this model, we can indicate that 40% of water pipeline have score points less than 50 points and about 10% of total pipes length have less than 30 score points. By using this model, the rehabilitation plans for each region in Gaza city can be achieved based on available budget and condition of pipes. The second case study is Kuala Lumpur Case from semi-developed countries, which has been used to develop an approach to improve the water network under crucial conditions using, advanced statistical and GIS techniques. Kuala Lumpur (KL) has water losses about 40% and high failure rate, which make severe problem. This case can represent cases in South Asia countries. Kuala Lumpur faced big challenges to reduce the water losses in water network during last 5 years. One of these challenges is high deterioration of asbestos cement (AC) pipes. They need to replace more than 6500 km of AC pipes, which need a huge budget to be achieved. Asbestos cement is subject to deterioration due to various chemical processes that either leach out the cement material or penetrate the concrete to form products that weaken the cement matrix. This case presents an approach for geo-statistical model for modelling pipe failures in a water distribution network. Database of Syabas Company (Kuala Lumpur water company) has been used in developing the model. The statistical models have been calibrated, verified and used to predict failures for both networks and individual pipes. The mathematical formulation developed for failure frequency in Kuala Lumpur was based on different pipeline characteristics, reflecting several factors such as pipe diameter, length, pressure and failure history. Generalized linear model have been applied to predict pipe failures based on District Meter Zone (DMZ) and individual pipe levels. Based on Kuala Lumpur case study, several outputs and implications have been achieved. Correlations between spatial and temporal intervals of pipe failures also have been done using ArcGIS software. Water Pipe Assessment Model (WPAM) has been developed using the analysis of historical pipe failure in Kuala Lumpur which prioritizing the pipe rehabilitation candidates based on ranking system. Frankfurt Water Network in Germany is the third main case study. This case makes an overview for Survival analysis and neural network methods used in water network. Rehabilitation strategies of water pipes have been developed for Frankfurt water network in cooperation with Mainova (Frankfurt Water Company). This thesis also presents a methodology of technical condition assessment of plastic pipes based on simple analysis. This thesis aims to make contribution to improve the prediction of pipe failures in water networks using Geographic Information System (GIS) and Decision Support System (DSS). The output from the technical condition assessment model can be used to estimate future budget needs for rehabilitation and to define pipes with high priority for replacement based on poor condition. rn

A linear O(N) model: a functional renormalization group approach for flat and curved space

In questa tesi sono state applicate le tecniche del gruppo di rinormalizzazione funzionale allo studio della teoria quantistica di campo scalare con simmetria O(N) sia in uno spaziotempo piatto (Euclideo) che nel caso di accoppiamento ad un campo gravitazionale nel paradigma dell'asymptotic safety. Nel primo capitolo vengono esposti in breve alcuni concetti basilari della teoria dei campi in uno spazio euclideo a dimensione arbitraria. Nel secondo capitolo si discute estensivamente il metodo di rinormalizzazione funzionale ideato da Wetterich e si fornisce un primo semplice esempio di applicazione, il modello scalare. Nel terzo capitolo è stato studiato in dettaglio il modello O(N) in uno spaziotempo piatto, ricavando analiticamente le equazioni di evoluzione delle quantità rilevanti del modello. Quindi ci si è specializzati sul caso N infinito. Nel quarto capitolo viene iniziata l'analisi delle equazioni di punto fisso nel limite N infinito, a partire dal caso di dimensione anomala nulla e rinormalizzazione della funzione d'onda costante (approssimazione LPA), già studiato in letteratura. Viene poi considerato il caso NLO nella derivative expansion. Nel quinto capitolo si è introdotto l'accoppiamento non minimale con un campo gravitazionale, la cui natura quantistica è considerata a livello di QFT secondo il paradigma di rinormalizzabilità dell'asymptotic safety. Per questo modello si sono ricavate le equazioni di punto fisso per le principali osservabili e se ne è studiato il comportamento per diversi valori di N.

Oxidative stress and inflammation response after nanoparticle exposure: differences between human lung cell monocultures and an advanced three-dimensional model of the human epithelial airways

Combustion-derived and manufactured nanoparticles (NPs) are known to provoke oxidative stress and inflammatory responses in human lung cells; therefore, they play an important role during the development of adverse health effects. As the lungs are composed of more than 40 different cell types, it is of particular interest to perform toxicological studies with co-cultures systems, rather than with monocultures of only one cell type, to gain a better understanding of complex cellular reactions upon exposure to toxic substances. Monocultures of A549 human epithelial lung cells, human monocyte-derived macrophages and monocyte-derived dendritic cells (MDDCs) as well as triple cell co-cultures consisting of all three cell types were exposed to combustion-derived NPs (diesel exhaust particles) and to manufactured NPs (titanium dioxide and single-walled carbon nanotubes). The penetration of particles into cells was analysed by transmission electron microscopy. The amount of intracellular reactive oxygen species (ROS), the total antioxidant capacity (TAC) and the production of tumour necrosis factor (TNF)-alpha and interleukin (IL)-8 were quantified. The results of the monocultures were summed with an adjustment for the number of each single cell type in the triple cell co-culture. All three particle types were found in all cell and culture types. The production of ROS was induced by all particle types in all cell cultures except in monocultures of MDDCs. The TAC and the (pro-)inflammatory reactions were not statistically significantly increased by particle exposure in any of the cell cultures. Interestingly, in the triple cell co-cultures, the TAC and IL-8 concentrations were lower and the TNF-alpha concentrations were higher than the expected values calculated from the monocultures. The interplay of different lung cell types seems to substantially modulate the oxidative stress and the inflammatory responses after NP exposure.

Use of the Supermolecule Approach To Model the Syn and Anti Conformations of Solvated Cyclic 3‘,5‘-Adenosine Monophosphate

The supermolecule approach has been used to model the hydration of cyclic 3‘,5‘-adenosine monophosphate, cAMP. Model building combined with PM3 optimizations predict that the anti conformer of cAMP is capable of hydrogen bonding to an additional solvent water molecule compared to the syn conformer. The addition of one water to the syn superstructure with concurrent rotation of the base about the glycosyl bond to form the anti superstructure leads to an additional enthalpy of stabilization of approximately −6 kcal/mol at the PM3 level. This specific solute−solvent interaction is an example of a large solvent effect, as the method predicts that cAMP has a conformational preference for the anti isomer in solution. This conformational preference results from a change in the number of specific solute−solvent interactions in this system. This prediction could be tested by NMR techniques. The number of waters predicted to be in the first hydration sphere around cAMP is in agreement with the results of hydration studies of nucleotides in DNA. In addition, the detailed picture of solvation about this cyclic nucleotide is in agreement with infrared experimental results.

A Cox Model for Biostatistics of the Future

Professor Sir David R. Cox (DRC) is widely acknowledged as among the most important scientists of the second half of the twentieth century. He inherited the mantle of statistical science from Pearson and Fisher, advanced their ideas, and translated statistical theory into practice so as to forever change the application of statistics in many fields, but especially biology and medicine. The logistic and proportional hazards models he substantially developed, are arguably among the most influential biostatistical methods in current practice. This paper looks forward over the period from DRC's 80th to 90th birthdays, to speculate about the future of biostatistics, drawing lessons from DRC's contributions along the way. We consider "Cox's model" of biostatistics, an approach to statistical science that: formulates scientific questions or quantities in terms of parameters gamma in probability models f(y; gamma) that represent in a parsimonious fashion, the underlying scientific mechanisms (Cox, 1997); partition the parameters gamma = theta, eta into a subset of interest theta and other "nuisance parameters" eta necessary to complete the probability distribution (Cox and Hinkley, 1974); develops methods of inference about the scientific quantities that depend as little as possible upon the nuisance parameters (Barndorff-Nielsen and Cox, 1989); and thinks critically about the appropriate conditional distribution on which to base infrences. We briefly review exciting biomedical and public health challenges that are capable of driving statistical developments in the next decade. We discuss the statistical models and model-based inferences central to the CM approach, contrasting them with computationally-intensive strategies for prediction and inference advocated by Breiman and others (e.g. Breiman, 2001) and to more traditional design-based methods of inference (Fisher, 1935). We discuss the hierarchical (multi-level) model as an example of the future challanges and opportunities for model-based inference. We then consider the role of conditional inference, a second key element of the CM. Recent examples from genetics are used to illustrate these ideas. Finally, the paper examines causal inference and statistical computing, two other topics we believe will be central to biostatistics research and practice in the coming decade. Throughout the paper, we attempt to indicate how DRC's work and the "Cox Model" have set a standard of excellence to which all can aspire in the future.

Theoretical estimation of optical absorption and photoluminescence in nanostructured silicon; an approach to improve efficiency in nanostructured solar cells

Renewable energy is growing in demand, and thus the the manufacture of solar cells and photovoltaic arrays has advanced dramatically in recent years. This is proved by the fact that the photovoltaic production has doubled every 2 years, increasing by an average of 48% each year since 2002. Covering the general overview of solar cell working, and its model, this thesis will start with the three generations of photovoltaic solar cell technology, and move to the motivation of dedicating research to nanostructured solar cell. For the current generation solar cells, among several factors, like photon capture, photon reflection, carrier generation by photons, carrier transport and collection, the efficiency also depends on the absorption of photons. The absorption coefficient,α, and its dependence on the wavelength, λ, is of major concern to improve the efficiency. Nano-silicon structures (quantum wells and quantum dots) have a unique advantage compared to bulk and thin film crystalline silicon that multiple direct and indirect band gaps can be realized by appropriate size control of the quantum wells. This enables multiple wavelength photons of the solar spectrum to be absorbed efficiently. There is limited research on the calculation of absorption coefficient in nano structures of silicon. We present a theoretical approach to calculate the absorption coefficient using quantum mechanical calculations on the interaction of photons with the electrons of the valence band. One model is that the oscillator strength of the direct optical transitions is enhanced by the quantumconfinement effect in Si nanocrystallites. These kinds of quantum wells can be realized in practice in porous silicon. The absorption coefficient shows a peak of 64638.2 cm-1 at = 343 nm at photon energy of ξ = 3.49 eV ( = 355.532 nm). I have shown that a large value of absorption coefficient α comparable to that of bulk silicon is possible in silicon QDs because of carrier confinement. Our results have shown that we can enhance the absorption coefficient by an order of 10, and at the same time a nearly constant absorption coefficient curve over the visible spectrum. The validity of plots is verified by the correlation with experimental photoluminescence plots. A very generic comparison for the efficiency of p-i-n junction solar cell is given for a cell incorporating QDs and sans QDs. The design and fabrication technique is discussed in brief. I have shown that by using QDs in the intrinsic region of a cell, we can improve the efficiency by a factor of 1.865 times. Thus for a solar cell of efficiency of 26% for first generation solar cell, we can improve the efficiency to nearly 48.5% on using QDs.