Ottimizzazione delle reti di distribuzione idrica tramite algoritmi genetici multi-obiettivo

Water distribution networks optimization is a challenging problem due to the dimension and the complexity of these systems. Since the last half of the twentieth century this field has been investigated by many authors. Recently, to overcome discrete nature of variables and non linearity of equations, the research has been focused on the development of heuristic algorithms. This algorithms do not require continuity and linearity of the problem functions because they are linked to an external hydraulic simulator that solve equations of mass continuity and of energy conservation of the network. In this work, a NSGA-II (Non-dominating Sorting Genetic Algorithm) has been used. This is a heuristic multi-objective genetic algorithm based on the analogy of evolution in nature. Starting from an initial random set of solutions, called population, it evolves them towards a front of solutions that minimize, separately and contemporaneously, all the objectives. This can be very useful in practical problems where multiple and discordant goals are common. Usually, one of the main drawback of these algorithms is related to time consuming: being a stochastic research, a lot of solutions must be analized before good ones are found. Results of this thesis about the classical optimal design problem shows that is possible to improve results modifying the mathematical definition of objective functions and the survival criterion, inserting good solutions created by a Cellular Automata and using rules created by classifier algorithm (C4.5). This part has been tested using the version of NSGA-II supplied by Centre for Water Systems (University of Exeter, UK) in MATLAB® environment. Even if orientating the research can constrain the algorithm with the risk of not finding the optimal set of solutions, it can greatly improve the results. Subsequently, thanks to CINECA help, a version of NSGA-II has been implemented in C language and parallelized: results about the global parallelization show the speed up, while results about the island parallelization show that communication among islands can improve the optimization. Finally, some tests about the optimization of pump scheduling have been carried out. In this case, good results are found for a small network, while the solutions of a big problem are affected by the lack of constraints on the number of pump switches. Possible future research is about the insertion of further constraints and the evolution guide. In the end, the optimization of water distribution systems is still far from a definitive solution, but the improvement in this field can be very useful in reducing the solutions cost of practical problems, where the high number of variables makes their management very difficult from human point of view.

Metodi di ottimizzazione morfologica nel progetto preliminare di propulsori aeronautici avanzati

The aim of this Doctoral Thesis is to develop a genetic algorithm based optimization methods to find the best conceptual design architecture of an aero-piston-engine, for given design specifications. Nowadays, the conceptual design of turbine airplanes starts with the aircraft specifications, then the most suited turbofan or turbo propeller for the specific application is chosen. In the aeronautical piston engines field, which has been dormant for several decades, as interest shifted towards turboaircraft, new materials with increased performance and properties have opened new possibilities for development. Moreover, the engine’s modularity given by the cylinder unit, makes it possible to design a specific engine for a given application. In many real engineering problems the amount of design variables may be very high, characterized by several non-linearities needed to describe the behaviour of the phenomena. In this case the objective function has many local extremes, but the designer is usually interested in the global one. The stochastic and the evolutionary optimization techniques, such as the genetic algorithms method, may offer reliable solutions to the design problems, within acceptable computational time. The optimization algorithm developed here can be employed in the first phase of the preliminary project of an aeronautical piston engine design. It’s a mono-objective genetic algorithm, which, starting from the given design specifications, finds the engine propulsive system configuration which possesses minimum mass while satisfying the geometrical, structural and performance constraints. The algorithm reads the project specifications as input data, namely the maximum values of crankshaft and propeller shaft speed and the maximal pressure value in the combustion chamber. The design variables bounds, that describe the solution domain from the geometrical point of view, are introduced too. In the Matlab® Optimization environment the objective function to be minimized is defined as the sum of the masses of the engine propulsive components. Each individual that is generated by the genetic algorithm is the assembly of the flywheel, the vibration damper and so many pistons, connecting rods, cranks, as the number of the cylinders. The fitness is evaluated for each individual of the population, then the rules of the genetic operators are applied, such as reproduction, mutation, selection, crossover. In the reproduction step the elitist method is applied, in order to save the fittest individuals from a contingent mutation and recombination disruption, making it undamaged survive until the next generation. Finally, as the best individual is found, the optimal dimensions values of the components are saved to an Excel® file, in order to build a CAD-automatic-3D-model for each component of the propulsive system, having a direct pre-visualization of the final product, still in the engine’s preliminary project design phase. With the purpose of showing the performance of the algorithm and validating this optimization method, an actual engine is taken, as a case study: it’s the 1900 JTD Fiat Avio, 4 cylinders, 4T, Diesel. Many verifications are made on the mechanical components of the engine, in order to test their feasibility and to decide their survival through generations. A system of inequalities is used to describe the non-linear relations between the design variables, and is used for components checking for static and dynamic loads configurations. The design variables geometrical boundaries are taken from actual engines data and similar design cases. Among the many simulations run for algorithm testing, twelve of them have been chosen as representative of the distribution of the individuals. Then, as an example, for each simulation, the corresponding 3D models of the crankshaft and the connecting rod, have been automatically built. In spite of morphological differences among the component the mass is almost the same. The results show a significant mass reduction (almost 20% for the crankshaft) in comparison to the original configuration, and an acceptable robustness of the method have been shown. The algorithm here developed is shown to be a valid method for an aeronautical-piston-engine preliminary project design optimization. In particular the procedure is able to analyze quite a wide range of design solutions, rejecting the ones that cannot fulfill the feasibility design specifications. This optimization algorithm could increase the aeronautical-piston-engine development, speeding up the production rate and joining modern computation performances and technological awareness to the long lasting traditional design experiences.

Probabilistic approaches in civil engineering: generation of random fields and structural identification with genetic algorithms

The inherent stochastic character of most of the physical quantities involved in engineering models has led to an always increasing interest for probabilistic analysis. Many approaches to stochastic analysis have been proposed. However, it is widely acknowledged that the only universal method available to solve accurately any kind of stochastic mechanics problem is Monte Carlo Simulation. One of the key parts in the implementation of this technique is the accurate and efficient generation of samples of the random processes and fields involved in the problem at hand. In the present thesis an original method for the simulation of homogeneous, multi-dimensional, multi-variate, non-Gaussian random fields is proposed. The algorithm has proved to be very accurate in matching both the target spectrum and the marginal probability. The computational efficiency and robustness are very good too, even when dealing with strongly non-Gaussian distributions. What is more, the resulting samples posses all the relevant, welldefined and desired properties of “translation fields”, including crossing rates and distributions of extremes. The topic of the second part of the thesis lies in the field of non-destructive parametric structural identification. Its objective is to evaluate the mechanical characteristics of constituent bars in existing truss structures, using static loads and strain measurements. In the cases of missing data and of damages that interest only a small portion of the bar, Genetic Algorithm have proved to be an effective tool to solve the problem.

Multiobjective genetic algorithms applied to heat transfer problems

In the present work, the multi-objective optimization by genetic algorithms is investigated and applied to heat transfer problems. Firstly, the work aims to compare different reproduction processes employed by genetic algorithms and two new promising processes are suggested. Secondly, in this work two heat transfer problems are studied under the multi-objective point of view. Specifically, the two cases studied are the wavy fins and the corrugated wall channel. Both these cases have already been studied by a single objective optimizer. Therefore, this work aims to extend the previous works in a more comprehensive study.

Algorithms for UAS insertion in civil air space

One of the most interesting challenge of the next years will be the Air Space Systems automation. This process will involve different aspects as the Air Traffic Management, the Aircrafts and Airport Operations and the Guidance and Navigation Systems. The use of UAS (Uninhabited Aerial System) for civil mission will be one of the most important steps in this automation process. In civil air space, Air Traffic Controllers (ATC) manage the air traffic ensuring that a minimum separation between the controlled aircrafts is always provided. For this purpose ATCs use several operative avoidance techniques like holding patterns or rerouting. The use of UAS in these context will require the definition of strategies for a common management of piloted and piloted air traffic that allow the UAS to self separate. As a first employment in civil air space we consider a UAS surveillance mission that consists in departing from a ground base, taking pictures over a set of mission targets and coming back to the same ground base. During all mission a set of piloted aircrafts fly in the same airspace and thus the UAS has to self separate using the ATC avoidance as anticipated. We consider two objective, the first consists in the minimization of the air traffic impact over the mission, the second consists in the minimization of the impact of the mission over the air traffic. A particular version of the well known Travelling Salesman Problem (TSP) called Time-Dependant-TSP has been studied to deal with traffic problems in big urban areas. Its basic idea consists in a cost of the route between two clients depending on the period of the day in which it is crossed. Our thesis supports that such idea can be applied to the air traffic too using a convenient time horizon compatible with aircrafts operations. The cost of a UAS sub-route will depend on the air traffic that it will meet starting such route in a specific moment and consequently on the avoidance maneuver that it will use to avoid that conflict. The conflict avoidance is a topic that has been hardly developed in past years using different approaches. In this thesis we purpose a new approach based on the use of ATC operative techniques that makes it possible both to model the UAS problem using a TDTSP framework both to use an Air Traffic Management perspective. Starting from this kind of mission, the problem of the UAS insertion in civil air space is formalized as the UAS Routing Problem (URP). For this reason we introduce a new structure called Conflict Graph that makes it possible to model the avoidance maneuvers and to define the arc cost function of the departing time. Two Integer Linear Programming formulations of the problem are proposed. The first is based on a TDTSP formulation that, unfortunately, is weaker then the TSP formulation. Thus a new formulation based on a TSP variation that uses specific penalty to model the holdings is proposed. Different algorithms are presented: exact algorithms, simple heuristics used as Upper Bounds on the number of time steps used, and metaheuristic algorithms as Genetic Algorithm and Simulated Annealing. Finally an air traffic scenario has been simulated using real air traffic data in order to test our algorithms. Graphic Tools have been used to represent the Milano Linate air space and its air traffic during different days. Such data have been provided by ENAV S.p.A (Italian Agency for Air Navigation Services).

The impact of polymorphisms in P-gp, DNA repair and folic acid metabolism genes in newly diagnosed multiple myeloma patients treated with thalidomide plus dexamethasone, with or without bortezomib

The principle aim of this study was to investigate biological predictors of response and resistance to multiple myeloma treatment. Two hypothesis had been proposed as responsible of responsiveness: SNPs in DNA repair and Folate pathway, and P-gp dependent efflux. As a first objective, panel of SNPs in DNA repair and Folate pathway genes, were analyzed. It was a retrospective study in a group of 454, previously untreated, MM patients enrolled in a randomized phase III open-label study. Results show that some SNPs in Folate pathway are correlated with response to MM treatment. MTR genotype was associated with favorable response in the overall population of MM patients. However, this relation, disappear after adjustment for treatment response. When poor responder includes very good partial response, partial response and stable/progressive disease MTFHR rs1801131 genotype was associated with poor response to therapy. This relation - unlike in MTR – was still significant after adjustment for treatment response. Identification of this genetic variant in MM patients could be used as an independent prognostic factor for therapeutic outcome in the clinical practice. In the second objective, basic disposition characteristics of bortezomib was investigated. We demonstrated that bortezomib is a P-gp substrate in a bi-directional transport study. We obtain apparent permeability rate values that together with solubility values can have a crucial implication in better understanding of bortezomib pharmacokinetics with respect to the importance of membrane transporters. Subsequently, in view of the importance of P-gp for bortezomib responsiveness a panel of SNPs in ABCB1 gene - coding for P-gp - were analyzed. In particular we analyzed five SNPs, none of them however correlated with treatment responsiveness. However, we found a significant association between ABCB1 variants and cytogenetic abnormalities. In particular, deletion of chromosome 17 and t(4;14) translocation were present in patients harboring rs60023214 and rs2038502 variants respectively.

Genome characterization through a mathematical model of the genetic code: an analysis of the whole chromosome 1 of A. thaliana

The objective of this work is to characterize the genome of the chromosome 1 of A.thaliana, a small flowering plants used as a model organism in studies of biology and genetics, on the basis of a recent mathematical model of the genetic code. I analyze and compare different portions of the genome: genes, exons, coding sequences (CDS), introns, long introns, intergenes, untranslated regions (UTR) and regulatory sequences. In order to accomplish the task, I transformed nucleotide sequences into binary sequences based on the definition of the three different dichotomic classes. The descriptive analysis of binary strings indicate the presence of regularities in each portion of the genome considered. In particular, there are remarkable differences between coding sequences (CDS and exons) and non-coding sequences, suggesting that the frame is important only for coding sequences and that dichotomic classes can be useful to recognize them. Then, I assessed the existence of short-range dependence between binary sequences computed on the basis of the different dichotomic classes. I used three different measures of dependence: the well-known chi-squared test and two indices derived from the concept of entropy i.e. Mutual Information (MI) and Sρ, a normalized version of the “Bhattacharya Hellinger Matusita distance”. The results show that there is a significant short-range dependence structure only for the coding sequences whose existence is a clue of an underlying error detection and correction mechanism. No doubt, further studies are needed in order to assess how the information carried by dichotomic classes could discriminate between coding and noncoding sequence and, therefore, contribute to unveil the role of the mathematical structure in error detection and correction mechanisms. Still, I have shown the potential of the approach presented for understanding the management of genetic information.

A numerical methodology for the multi-objective optimization of the DI Diesel engine combustion

DI Diesel engine are widely used both for industrial and automotive applications due to their durability and fuel economy. Nonetheless, increasing environmental concerns force that type of engine to comply with increasingly demanding emission limits, so that, it has become mandatory to develop a robust design methodology of the DI Diesel combustion system focused on reduction of soot and NOx simultaneously while maintaining a reasonable fuel economy. In recent years, genetic algorithms and CFD three-dimensional combustion simulations have been successfully applied to that kind of problem. However, combining GAs optimization with actual CFD three-dimensional combustion simulations can be too onerous since a large number of calculations is usually needed for the genetic algorithm to converge, resulting in a high computational cost and, thus, limiting the suitability of this method for industrial processes. In order to make the optimization process less time-consuming, CFD simulations can be more conveniently used to generate a training set for the learning process of an artificial neural network which, once correctly trained, can be used to forecast the engine outputs as a function of the design parameters during a GA optimization performing a so-called virtual optimization. In the current work, a numerical methodology for the multi-objective virtual optimization of the combustion of an automotive DI Diesel engine, which relies on artificial neural networks and genetic algorithms, was developed.

Genetic basis of variation for root traits and response to heat stress in durum wheat

Durum wheat is the second most important wheat species worldwide and the most important crop in several Mediterranean countries including Italy. Durum wheat is primarily grown under rainfed conditions where episodes of drought and heat stress are major factors limiting grain yield. The research presented in this thesis aimed at the identification of traits and genes that underlie root system architecture (RSA) and tolerance to heat stress in durum wheat, in order to eventually contribute to the genetic improvement of this species. In the first two experiments we aimed at the identification of QTLs for root trait architecture at the seedling level by studying a bi-parental population of 176 recombinant inbred lines (from the cross Meridiano x Claudio) and a collection of 183 durum elite accessions. Forty-eight novel QTLs for RSA traits were identified in each of the two experiments, by means of linkage- and association mapping-based QTL analysis, respectively. Important QTLs controlling the angle of root growth in the seedling were identified. In a third experiment, we investigated the phenotypic variation of root anatomical traits by means of microscope-based analysis of root cross sections in 10 elite durum cultivars. The results showed the presence of sizeable genetic variation in aerenchyma-related traits, prompting for additional studies aimed at mapping the QTLs governing such variation and to test the role of aerenchyma in the adaptive response to abiotic stresses. In the fourth experiment, an association mapping experiment for cell membrane stability at the seedling stage (as a proxy trait for heat tolerance) was carried out by means of association mapping. A total of 34 QTLs (including five major ones), were detected. Our study provides information on QTLs for root architecture and heat tolerance which could potentially be considered in durum wheat breeding programs.

Evaluation of clinical implications correlate to genetic polymorphisms and pharmacokinetic of transdermal fentanyl

ABSTRACT Background:Strong opioids are the treatment of choice for moderate to severe cancer-related pain. Fentanyl is a synthetic opioid with high affinity for the μ-opioid receptor and is 75–100 times more potent than morphine. Fentanyl is metabolised rapidly, particularly in the liver and only 10% is excreted as intact substance. The use of CYP3A4 inhibitors and inducers, impaired liver function, and heating of the patch potentially influence fentanyl pharmacokinetics in a clinically relevant way. The influence of BMI and gender on fentanyl pharmacokinetics is questionable. Pharmacogenetic, may influence fentanyl pharmacokinetic and other factors have been studied but did not show significant and clinically relevant effects on fentanyl pharmacokinetic. Method: This is a biological interventional prospective, single-center study in 49 patients with solid or haematological neoplasm treated with transdermal fentanyl undergoing 5-step pharmacokinetic and pharmacogenetic withdrawals from administration of the fentanyl patch. Objective:to evaluate the pharmacokinetic and pharmacogenetic of transdermal fentanyl in relation to the patient's clinical response on pain Results: Sex was the only parameter with evidence of different distribution between responders and non-responders , showing a major chance for male to be responders than females. We found some correlation with pharmacokinetic parameters and sex, regarding adverse events and NRS correlation with BPI. NAT2 and UGT2B7 polymorphisms are associated with AUC and Cmax kinetics parameters, NAT2 and CYP4F2 showed some evidence of association with the fentanyl dosage and CYP2B6 polymorphism seemed to be correlate with side effects. Conclusion: Small sample size of study population make difficult do find some significant correlation between pharmacogenetic, pharmacokinetic and clinical response. Larger studies are needed to increase knowledge about response to opioid treatment in cancer patients to better individualized pain treatment.

The genetic prehistory of Northern Calabria. Archeogenomic investigation of the human remains from Grotta di Pietra Sant’Angelo and Grotta della Monaca.

Given its strategic position at the center of the Mediterranean Basin, and its unique history of contacts and migrations, Calabria is an ideal region to decipher the genetic traces of at least some of the numerous demographic prehistoric events in Southern Italy. This thesis focuses on the genetic and social changes of ancient inhabitants of Calabria, covering a timeline of approximately 7000 to 3300 years ago, ranging from the Middle Neolithic to the Middle Bronze Age. We generated the first genome-wide data from Calabria, by focusing on the single inhumation of “Grotta di Pietra Sant’Angelo” (San Lorenzo Bellizzi, Cosenza) and on the vast community found buried in “Grotta della Monaca” (Sant’Agata di Esaro, Cosenza). Supported by archaeological evidence, the primary objective of this research was to employ paleogenomic evidence to decipher funerary customs, social organization, family ties, and demographic shifts in Southern Italy over a period extending beyond three millennia. The possibility of gender-related burial practices and kinship ties among the deceased was also explored. Subsequently, the biogeographical origin and ancestry of prehistoric people of Calabria was contextualized within the broad landscape of existing data on Mediterranean populations. By generating the first genomic evidence from prehistoric Calabria, unresolved questions were addressed, related to the appearance and persistence of distinct genomic components, such as the Iran-related and the Steppe-related ancestry, whose impact on ancient Southern Italian genomes remains uncharted.