Genetic and environmental influences on blood pressure and physical activity: a study of nuclear families from Muzambinho, Brazil

Blood pressure (BP) and physical activity (PA) levels are inversely associated. Since genetic factors account for the observed variation in each of these traits, it is possible that part of their association may be related to common genetic and/or environmental influences. Thus, this study was designed to estimate the genetic and environmental correlations of BP and PA phenotypes in nuclear families from Muzambinho, Brazil. Families including 236 offspring (6 to 24 years) and their 82 fathers and 122 mothers (24 to 65 years) were evaluated. BP was measured, and total PA (TPA) was assessed by an interview (commuting, occupational, leisure time, and school time PA). Quantitative genetic modeling was used to estimate maximal heritability (h²), and genetic and environmental correlations. Heritability was significant for all phenotypes (systolic BP: h² = 0.37 ± 0.10, P < 0.05; diastolic BP: h² = 0.39 ± 0.09, P < 0.05; TPA: h² = 0.24 ± 0.09, P < 0.05). Significant genetic (r g) and environmental (r e) correlations were detected between systolic and diastolic BP (r g = 0.67 ± 0.12 and r e = 0.48 ± 0.08, P < 0.05). Genetic correlations between BP and TPA were not significant, while a tendency to an environmental cross-trait correlation was found between diastolic BP and TPA (r e = -0.18 ± 0.09, P = 0.057). In conclusion, BP and PA are under genetic influences. Systolic and diastolic BP share common genes and environmental influences. Diastolic BP and TPA are probably under similar environmental influences.

Analysis of genetic lineages and their correlation with virulence genes in enterococcus faecalis clinical isolates from root canal and systemic infections

Introduction: Enterococcus faecalis is a member of the mammalian gastrointestinal microbiota but has been considered a leading cause of hospital-acquired infections. In the oral cavity, it is commonly detected from root canals of teeth with failed endodontic treatment. However, little is known about the virulence and genetic relatedness among E. faecalis isolates from different clinical sources. This study compared the presence of enterococcal virulence factors among root canal strains and clinical isolates from hospitalized patients to identify virulent clusters of E. faecalis. Methods: Multilocus sequence typing analysis was used to determine genetic lineages of 40 E. faecalis clinical isolates from different sources. Virulence clusters were determined by evaluating capsule (cps) locus polymorphisms, pathogenicity island gene content, and antibiotic resistance genes by polymerase chain reaction. Results: The clinical isolates from hospitalized patients formed a phylogenetically separate group and were mostly grouped in the clonal complex 2, which is a known virulent cluster of E. faecalis that has caused infection outbreaks globally. The clonal complex 2 group comprised capsule-producing strains harboring multiple antibiotic resistance and pathogenicity island genes. On the other hand, the endodontic isolates were more diverse and harbored few virulence and antibiotic resistance genes. In particular, although more closely related to isolates from hospitalized patients, capsuleproducing E. faecalis strains from root canals did not carry more virulence/antibiotic genes than other endodontic isolates. Conclusions: E. faecalis isolates from endodontic infections have a genetic and virulence profile different from pathogenic clusters of hospitalized patients’ isolates, which is most likely due to niche specialization conferred mainly by variable regions in the genome.

Evolving decision trees with beam search-based initialization and lexicographic multi-objective evaluation

Decision tree induction algorithms represent one of the most popular techniques for dealing with classification problems. However, traditional decision-tree induction algorithms implement a greedy approach for node splitting that is inherently susceptible to local optima convergence. Evolutionary algorithms can avoid the problems associated with a greedy search and have been successfully employed to the induction of decision trees. Previously, we proposed a lexicographic multi-objective genetic algorithm for decision-tree induction, named LEGAL-Tree. In this work, we propose extending this approach substantially, particularly w.r.t. two important evolutionary aspects: the initialization of the population and the fitness function. We carry out a comprehensive set of experiments to validate our extended algorithm. The experimental results suggest that it is able to outperform both traditional algorithms for decision-tree induction and another evolutionary algorithm in a variety of application domains.

Genetic characterization and molecular identification of the bloodmeal sources of the potential bluetongue vector Culicoides obsoletus in the Canary Islands, Spain

[EN] Background: Culicoides (Diptera: Ceratopogonidae) biting midges are vectors for a diversity of pathogens including bluetongue virus (BTV) that generate important economic losses. BTV has expanded its range in recent decades, probably due to the expansion of its main vector and the presence of other autochthonous competent vectors. Although the Canary Islands are still free of bluetongue disease (BTD), Spain and Europe have had to face up to a spread of bluetongue with disastrous consequences. Therefore, it is essential to identify the distribution of biting midges and understand their feeding patterns in areas susceptible to BTD. To that end, we captured biting midges on two farms in the Canary Islands (i) to identify the midge species in question and characterize their COI barcoding region and (ii) to ascertain the source of their bloodmeals using molecular tools.Methods: Biting midges were captured using CDC traps baited with a 4-W blacklight (UV) bulb on Gran Canaria and on Tenerife. Biting midges were quantified and identified according to their wing patterns. A 688 bp segment of the mitochondrial COI gene of 20 biting midges (11 from Gran Canaria and 9 from Tenerife) were PCR amplified using the primers LCO1490 and HCO2198. Moreover, after selected all available females showing any rest of blood in their abdomen, a nested-PCR approach was used to amplify a fragment of the COI gene from vertebrate DNA contained in bloodmeals. The origin of bloodmeals was identified by comparison with the nucleotide-nucleotide basic alignment search tool (BLAST). Results: The morphological identification of 491 female biting midges revealed the presence of a single morphospecies belonging to the Obsoletus group. When sequencing the barcoding region of the 20 females used to check genetic variability, we identified two haplotypes differing in a single base. Comparison analysis using the nucleotide-nucleotide basic alignment search tool (BLAST) showed that both haplotypes belong to Culicoides obsoletus, a potential BTV vector. As well, using molecular tools we identified the feeding sources of 136 biting midges and were able to confirm that C. obsoletus females feed on goats and sheep on both islands.Conclusions: These results confirm that the feeding pattern of C. obsoletus is a potentially important factor in BTV transmission to susceptible hosts in case of introduction into the archipelago. Consequently, in the Canary Islands it is essential to maintain vigilance of Culicoides-transmitted viruses such as BTV and the novel Schmallenberg virus.

Analyzing the dependence structure of microarray data: a copula–based approach

The main aim of this Ph.D. dissertation is the study of clustering dependent data by means of copula functions with particular emphasis on microarray data. Copula functions are a popular multivariate modeling tool in each field where the multivariate dependence is of great interest and their use in clustering has not been still investigated. The first part of this work contains the review of the literature of clustering methods, copula functions and microarray experiments. The attention focuses on the K–means (Hartigan, 1975; Hartigan and Wong, 1979), the hierarchical (Everitt, 1974) and the model–based (Fraley and Raftery, 1998, 1999, 2000, 2007) clustering techniques because their performance is compared. Then, the probabilistic interpretation of the Sklar’s theorem (Sklar’s, 1959), the estimation methods for copulas like the Inference for Margins (Joe and Xu, 1996) and the Archimedean and Elliptical copula families are presented. In the end, applications of clustering methods and copulas to the genetic and microarray experiments are highlighted. The second part contains the original contribution proposed. A simulation study is performed in order to evaluate the performance of the K–means and the hierarchical bottom–up clustering methods in identifying clusters according to the dependence structure of the data generating process. Different simulations are performed by varying different conditions (e.g., the kind of margins (distinct, overlapping and nested) and the value of the dependence parameter ) and the results are evaluated by means of different measures of performance. In light of the simulation results and of the limits of the two investigated clustering methods, a new clustering algorithm based on copula functions (‘CoClust’ in brief) is proposed. The basic idea, the iterative procedure of the CoClust and the description of the written R functions with their output are given. The CoClust algorithm is tested on simulated data (by varying the number of clusters, the copula models, the dependence parameter value and the degree of overlap of margins) and is compared with the performance of model–based clustering by using different measures of performance, like the percentage of well–identified number of clusters and the not rejection percentage of H0 on . It is shown that the CoClust algorithm allows to overcome all observed limits of the other investigated clustering techniques and is able to identify clusters according to the dependence structure of the data independently of the degree of overlap of margins and the strength of the dependence. The CoClust uses a criterion based on the maximized log–likelihood function of the copula and can virtually account for any possible dependence relationship between observations. Many peculiar characteristics are shown for the CoClust, e.g. its capability of identifying the true number of clusters and the fact that it does not require a starting classification. Finally, the CoClust algorithm is applied to the real microarray data of Hedenfalk et al. (2001) both to the gene expressions observed in three different cancer samples and to the columns (tumor samples) of the whole data matrix.

Aportaciones al control inverso con modelo de referencia basado en lógica borrosa, redes neuronales y algoritmos genéticos

Programa de doctorado: Ingeniería de Telecomunicación Avanzada.

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.

Genetic characterization, population history and evolutionary medicine perspective in two native south american populations: Yanesha and Wichi

Two Amerindian populations from the Peruvian Amazon (Yanesha) and from rural lowlands of the Argentinean Gran Chaco (Wichi) were analyzed. They represent two case study of the South American genetic variability. The Yanesha represent a model of population isolated for long-time in the Amazon rainforest, characterized by environmental and altitudinal stratifications. The Wichi represent a model of population living in an area recently colonized by European populations (the Criollos are the population of the admixed descendents), whose aim is to depict the native ancestral gene pool and the degree of admixture, in relation to the very high prevalence of Chagas disease. The methods used for the genotyping are common, concerning the Y chromosome markers (male lineage) and the mitochondrial markers (maternal lineage). The determination of the phylogeographic diagnostic polymorphisms was carried out by the classical techniques of PCR, restriction enzymes, sequencing and specific mini-sequencing. New method for the detection of the protozoa Trypanosoma cruzi was developed by means of the nested PCR. The main results show patterns of genetic stratification in Yanesha forest communities, referable to different migrations at different times, estimated by Bayesian analyses. In particular Yanesha were considered as a population of transition between the Amazon basin and the Andean Cordillera, evaluating the potential migration routes and the separation of clusters of community in relation to different genetic bio-ancestry. As the Wichi, the gene pool analyzed appears clearly differentiated by the admixed sympatric Criollos, due to strict social practices (deeply analyzed with the support of cultural anthropological tools) that have preserved the native identity at a diachronic level. A pattern of distribution of the seropositivity in relation to the different phylogenetic lineages (the adaptation in evolutionary terms) does not appear, neither Amerindian nor European, but in relation to environmental and living conditions of the two distinct subpopulations.

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).

Bioinformatische, genomweite Analyse genexpressionsregulierender Faktoren in Lebergewebe

Für eine Reihe einzelner genetischer Faktoren und Promotorelemente wurde in der Vergangenheit eine Regulation der Genexpression in der Leber (und auch in anderen Geweben) gezeigt. Mit der Verfügbarkeit des gesamten humanen Genoms sowie dessen Expressionsdaten in großen Microarray- und SAGE-Datenbanken bietet sich die Möglichkeit, solche Regulationsmechanismen in großem, genomweitem Maßstab zu untersuchen. Dabei geht diese Arbeit der Frage nach, ob es übergeordnete, eine Expression speziell in der Leber fördernde oder hemmende Faktoren gibt oder ob jedes Gen von einer unabhängigen Kombination von Faktoren reguliert wird, in dessen Summe die Expression des individuellen Gens in der Leber am stärksten ist. Sollten sich übergeordnete, eine Expression in der Leber stimulierende Faktoren finden, wären diese interessant für die Entwicklung neuer Behandlungskonzepte bei Lebererkrankungen. Zur Untersuchung dieser Fragestellung wurden aus einem Affymetrix Microarray Datenset für 12 Gewebe die Expressiondaten von insgesamt jeweils 15.472 Genen extrahiert. In einem zweiten Schritt wurden zusätzlich die Promotorsequenzen der einzelnen zugehörigen Gene, definiert als eine 1000 bp Region upstream des Transkriptionsstarts, in dieselbe Datenbank abgelegt. Die Promotorsequenzen wurden über den PromotorScan-Algorithmus analysiert. Auf diese Weise wurden Transkriptionsfaktorbindungsstellen auf 7042 der Promotoren identifiziert. Es fand sich eine Gesamtzahl von 241.984 Transkriptionsfaktorbindungsstellen. Anhand der Microarray-Expressionsdaten wurde die Gesamtgruppe der verfügbaren Gene und Promotoren in zwei Gruppen unterteilt, nämlich in die Gruppe der Gene, deren Expression in der Leber deutlich am höchsten gefunden wurde und in die Gruppe der Gene, die in anderen Geweben am höchsten exprimiert waren. Jeder potentiell bindende Transkriptionsfaktor wurde auf unterschiedliches Vorkommen in diesen beiden Gruppen hin untersucht. Dies geschah unter der Vorstellung, dass übergeordnete Faktoren, die eine Expression in der Leber stimulieren in der Gruppe der Gene, die in der Leber am höchsten exprimiert sind, verhältnismäßig wesentlich häufiger zu finden sein könnten. Eine solches häufigeres Vorkommen ließ sich jedoch für keinen einzigen Faktor nachweisen. Transkriptionsfaktorbindungsstellen sind typischerweise zwischen 5 und 15 bp lang. Um auszuschließen, dass mit dem verwendeten PromotorScan-Algorithmus Transkriptionsfaktorbindungsstellen, die bisher nicht bekannt sind, nicht übersehen wurden, wurden die Häufigkeit sämtlicher möglicher 8 bp (48) und 10 bp (410) Nukleotid-Kombinationen in diesen Promotoren untersucht. Biologisch relevante Unterschiede fanden sich zwischen den beiden Gruppen nicht. In gleicher Weise wurde auch die Bedeutung von TATA-Boxen untersucht. TATA-Boxen kommt bei der Transkriptionsinitiierung eine wichtige Rolle zu, indem über sie die Bindung des initialen Transkriptionskomplexes vermittelt wird. Insgesamt 1033 TATA-Boxen wurden ebenfalls mittels PromotorScan vorausgesagt. Dabei waren 57 auf Promotoren von Genen, die in der Leber überexprimiert waren und 976 auf Promotoren von Genen, die in anderen Geweben überexprimiert waren. Der Vergleich dieser beiden Gruppen ließ keine signifikant unterschiedliche Häufigkeit an TATA-Boxen erkennen. Im weiteren wurde die Bedeutung von CpG-Islands für eine potentiell differentielle Regulation untersucht. Insgesamt wurden 8742 CpG-Islands in einem Bereich von bis zu 5 kb upstream des Transkriptionsstarts identifiziert, 364 davon auf Promotoren von Genen, die am höchsten in der Leber exprimiert waren, 8378 auf Promotoren von Genen, die in anderen Geweben am höchsten exprimiert waren. Signifikante Unterschiede in der Verteilung von CpG-Islands auf Promotoren dieser beiden Gengruppen ließen sich nicht nachweisen. Schließlich wurden die RNA- und Proteinsequenzen des Transkriptoms und Proteoms hinsichtlich ihrer Zusammensetzung aus einzelnen Nukleotiden bzw. Aminosäuren analysiert. Auch hierbei fanden sich keine signifikanten Unterschiede in der Verteilung zwischen beiden Gengruppen. Die Zusammenschau der Ergebnisse zeigt, dass die Regulation der einzelnen Gene im Lebergewebe im wesentlichen individuell erfolgt. Im Rahmen der vorgelegten bioinformatischen Analysen fanden sich keine übergeordneten genetischen „Leberfaktoren“, die speziell eine Expression von Genen in der Leber stimulieren. Neue therapeutische Ansätze, die auf eine Regulation der Genexpression in der Leber zielen, werden somit auch weiterhin auf die Beeinflussung individueller Gene fokussiert bleiben.

Numerical optimization,modeling and system evaluation of a thermophotovoltaic hybrid panel

Photovoltaic (PV) solar panels generally produce electricity in the 6% to 16% efficiency range, the rest being dissipated in thermal losses. To recover this amount, hybrid photovoltaic thermal systems (PVT) have been devised. These are devices that simultaneously convert solar energy into electricity and heat. It is thus interesting to study the PVT system globally from different point of views in order to evaluate advantages and disadvantages of this technology and its possible uses. In particular in Chapter II, the development of the PVT absorber numerical optimization by a genetic algorithm has been carried out analyzing different internal channel profiles in order to find a right compromise between performance and technical and economical feasibility. Therefore in Chapter III ,thanks to a mobile structure built into the university lab, it has been compared experimentally electrical and thermal output power from PVT panels with separated photovoltaic and solar thermal productions. Collecting a lot of experimental data based on different seasonal conditions (ambient temperature,irradiation, wind...),the aim of this mobile structure has been to evaluate average both thermal and electrical increasing and decreasing efficiency values obtained respect to separate productions through the year. In Chapter IV , new PVT and solar thermal equation based models in steady state conditions have been developed by software Dymola that uses Modelica language. This permits ,in a simplified way respect to previous system modelling softwares, to model and evaluate different concepts about PVT panel regarding its structure before prototyping and measuring it. Chapter V concerns instead the definition of PVT boundary conditions into a HVAC system . This was made trough year simulations by software Polysun in order to finally assess the best solar assisted integrated structure thanks to F_save(solar saving energy)factor. Finally, Chapter VI presents the conclusion and the perspectives of this PhD work.

Comparing genetic and linguistic diversity in African populations with a focus on the Khoisan of southern Africa

The interaction between disciplines in the study of human population history is of primary importance, profiting from the biological and cultural characteristics of humankind. In fact, data from genetics, linguistics, archaeology and cultural anthropology can be combined to allow for a broader research perspective. This multidisciplinary approach is here applied to the study of the prehistory of sub-Saharan African populations: in this continent, where Homo sapiens originally started his evolution and diversification, the understanding of the patterns of human variation has a crucial relevance. For this dissertation, molecular data is interpreted and complemented with a major contribution from linguistics: linguistic data are compared to the genetic data and the research questions are contextualized within a linguistic perspective. In the four articles proposed, we analyze Y chromosome SNPs and STRs profiles and full mtDNA genomes on a representative number of samples to investigate key questions of African human variability. Some of these questions address i) the amount of genetic variation on a continental scale and the effects of the widespread migration of Bantu speakers, ii) the extent of ancient population structure, which has been lost in present day populations, iii) the colonization of the southern edge of the continent together with the degree of population contact/replacement, and iv) the prehistory of the diverse Khoisan ethnolinguistic groups, who were traditionally understudied despite representing one of the most ancient divergences of modern human phylogeny. Our results uncover a deep level of genetic structure within the continent and a multilayered pattern of contact between populations. These case studies represent a valuable contribution to the debate on our prehistory and open up further research threads.

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.

Lernen mit einer Hybride aus Lernendem Klassifizierendem System und Selbstorganisierender Karte

Im Forschungsgebiet der Künstlichen Intelligenz, insbesondere im Bereich des maschinellen Lernens, hat sich eine ganze Reihe von Verfahren etabliert, die von biologischen Vorbildern inspiriert sind. Die prominentesten Vertreter derartiger Verfahren sind zum einen Evolutionäre Algorithmen, zum anderen Künstliche Neuronale Netze. Die vorliegende Arbeit befasst sich mit der Entwicklung eines Systems zum maschinellen Lernen, das Charakteristika beider Paradigmen in sich vereint: Das Hybride Lernende Klassifizierende System (HCS) wird basierend auf dem reellwertig kodierten eXtended Learning Classifier System (XCS), das als Lernmechanismus einen Genetischen Algorithmus enthält, und dem Wachsenden Neuralen Gas (GNG) entwickelt. Wie das XCS evolviert auch das HCS mit Hilfe eines Genetischen Algorithmus eine Population von Klassifizierern - das sind Regeln der Form [WENN Bedingung DANN Aktion], wobei die Bedingung angibt, in welchem Bereich des Zustandsraumes eines Lernproblems ein Klassifizierer anwendbar ist. Beim XCS spezifiziert die Bedingung in der Regel einen achsenparallelen Hyperquader, was oftmals keine angemessene Unterteilung des Zustandsraumes erlaubt. Beim HCS hingegen werden die Bedingungen der Klassifizierer durch Gewichtsvektoren beschrieben, wie die Neuronen des GNG sie besitzen. Jeder Klassifizierer ist anwendbar in seiner Zelle der durch die Population des HCS induzierten Voronoizerlegung des Zustandsraumes, dieser kann also flexibler unterteilt werden als beim XCS. Die Verwendung von Gewichtsvektoren ermöglicht ferner, einen vom Neuronenadaptationsverfahren des GNG abgeleiteten Mechanismus als zweites Lernverfahren neben dem Genetischen Algorithmus einzusetzen. Während das Lernen beim XCS rein evolutionär erfolgt, also nur durch Erzeugen neuer Klassifizierer, ermöglicht dies dem HCS, bereits vorhandene Klassifizierer anzupassen und zu verbessern. Zur Evaluation des HCS werden mit diesem verschiedene Lern-Experimente durchgeführt. Die Leistungsfähigkeit des Ansatzes wird in einer Reihe von Lernproblemen aus den Bereichen der Klassifikation, der Funktionsapproximation und des Lernens von Aktionen in einer interaktiven Lernumgebung unter Beweis gestellt.