Impact of Distributed Generation in the Transmission System Expansion Planning

In this work, the impact of distributed generation in the transmission expansion planning will be simulated through the performance of an optimization process for three different scenarios: the first without distributed generation, the second with distributed generation equivalent to 1% of the load, and the third with 5% of distributed generation. For modeling the expanding problem the load flow linearized method using genetic algorithms for optimization has been chosen. The test circuit used is a simplification of the south eastern Brazilian electricity system with 46 buses.

Probabilistic maintenance and optimization strategies for deteriorating civil infrastructures

In developed countries, civil infrastructures are one of the most significant investments of governments, corporations, and individuals. Among these, transportation infrastructures, including highways, bridges, airports, and ports, are of huge importance, both economical and social. Most developed countries have built a fairly complete network of highways to fit their needs. As a result, the required investment in building new highways has diminished during the last decade, and should be further reduced in the following years. On the other hand, significant structural deteriorations have been detected in transportation networks, and a huge investment is necessary to keep these infrastructures safe and serviceable. Due to the significant importance of bridges in the serviceability of highway networks, maintenance of these structures plays a major role. In this paper, recent progress in probabilistic maintenance and optimization strategies for deteriorating civil infrastructures with emphasis on bridges is summarized. A novel model including interaction between structural safety analysis,through the safety index, and visual inspections and non destructive tests, through the condition index, is presented. Single objective optimization techniques leading to maintenance strategies associated with minimum expected cumulative cost and acceptable levels of condition and safety are presented. Furthermore, multi-objective optimization is used to simultaneously consider several performance indicators such as safety, condition, and cumulative cost. Realistic examples of the application of some of these techniques and strategies are also presented.

Genetic variability of Triatoma flavida and Triatoma bruneri (Hemiptera: Reduviidae) by RAPD-PCR technique

The Triatominae (Hemiptera:Reduviidae) contains the principal and potential Chagas disease vectors present in Mexico, Central America and South America. Triatoma flavida and T. bruneri are Cuban species. These species are closely related according to morphology and were considered synonyms until 1981, when they were separated on the grounds of external characters of the body and the morphology of male genitalia. The present study seeks to analyze genetic polymorphism of T. flavida and T. bruneri populations using RAPD techniques, and to assess the genetic relationship between these species. Ten random primers were used to evaluate the genetic variability among species using RAPD-PCR. The genetic flow among them was calculated. The dendrogram based on calculated Jaccard distances showed two clearly distinguishable clusters which coincided with the studied species. Within each species, moderate genetic differentiation (Fst 0.05-0.15) and migration rates (N > 1) were found among populations, that reveal gene flow and genetic homogeneity. Between species, the Fst value showed a high genetic differentiation and the migration rate was insufficient to maintain genetic homogeneity, and confirmed the absence of gene flow between them. Our results confirm the genetic variability among T. flavida and T. bruneri species.

Genetic and functional analysis of the bacillus subtilis BSP1 gam cluster

Mannans (linear mannan, glucomannan, galactomannan and galactoglucomannan) are the major constituents of the hemicellulose fraction in softwoods and show great importance as a renewable resource for fuel or feedstock applications. As complex polysaccharides, mannans can only be degraded through a synergistic action of different mannan-degrading enzymes, mannanases. Microbial mannanases are mainly extracellular enzymes that can act in wide range of pH and temperature, contributing to pulp and paper, pharmaceutical, food and feed, oil and textile successful industrial applications. Knowing and controlling these microbial mannan-degrading enzymes are essential to take advantage of their great biotechnological potential. The genome of the laboratory 168 strain of Bacillus subtilis carries genes gmuA-G dedicated to the degradation and utilization of glucomannan, including an extracellular -mannanase. Recently, the genome sequence of an undomesticated strain of B. subtilis, BSP1, was determined. In BSP1, the gmuA-G operon is maintained, interestingly, however, a second cluster of genes was found (gam cluster), which comprise a second putative extracellular β-mannanase, and most likely specify a system for the degradation and utilization of a different mannan polymer, galactoglucomannan. The genetic organization and function of the gam cluster, and whether its presence in BSP1 strain results in new hemicellulolytic capabilities, compared to those of the laboratory strain, was address in this work. In silico and in vivo mRNA analyses performed in this study revealed that the gam cluster, comprising nine genes, is organized and expressed in at least six different transcriptional units. Furthermore, cloning, expression, and production of Bbsp2923 in Escherichia coli was achieved and preliminary characterization shows that the enzyme is indeed a β-mannanase. Finally, the high hemicellulolytic capacity of the undomesticated B. subtilis BSP1, demonstrated in this work by qualitative analyses, suggests potential to be used in the food and feed industries.

Genetic algorithm for shipping route estimation with long-range tracking data : automatic reconstruction of shipping routes based on the historical ship positions for maritime safety applications

Ship tracking systems allow Maritime Organizations that are concerned with the Safety at Sea to obtain information on the current location and route of merchant vessels. Thanks to Space technology in recent years the geographical coverage of the ship tracking platforms has increased significantly, from radar based near-shore traffic monitoring towards a worldwide picture of the maritime traffic situation. The long-range tracking systems currently in operations allow the storage of ship position data over many years: a valuable source of knowledge about the shipping routes between different ocean regions. The outcome of this Master project is a software prototype for the estimation of the most operated shipping route between any two geographical locations. The analysis is based on the historical ship positions acquired with long-range tracking systems. The proposed approach makes use of a Genetic Algorithm applied on a training set of relevant ship positions extracted from the long-term storage tracking database of the European Maritime Safety Agency (EMSA). The analysis of some representative shipping routes is presented and the quality of the results and their operational applications are assessed by a Maritime Safety expert.

A novel approach for honey pollen profile assessment using an electronic tongue and chemometric tools

Nowadays the main honey producing countries require accurate labeling of honey before commercialization, including floral classification. Traditionally, this classification is made by melissopalynology analysis, an accurate but time-consuming task requiring laborious sample pre-treatment and high-skilled technicians. In this work the potential use of a potentiometric electronic tongue for pollinic assessment is evaluated, using monofloral and polyfloral honeys. The results showed that after splitting honeys according to color (white, amber and dark), the novel methodology enabled quantifying the relative percentage of the main pollens (Castanea sp., Echium sp., Erica sp., Eucaliptus sp., Lavandula sp., Prunus sp., Rubus sp. and Trifolium sp.). Multiple linear regression models were established for each type of pollen, based on the best sensors sub-sets selected using the simulated annealing algorithm. To minimize the overfitting risk, a repeated K-fold cross-validation procedure was implemented, ensuring that at least 10-20% of the honeys were used for internal validation. With this approach, a minimum average determination coefficient of 0.91 ± 0.15 was obtained. Also, the proposed technique enabled the correct classification of 92% and 100% of monofloral and polyfloral honeys, respectively. The quite satisfactory performance of the novel procedure for quantifying the relative pollen frequency may envisage its applicability for honey labeling and geographical origin identification. Nevertheless, this approach is not a full alternative to the traditional melissopalynologic analysis; it may be seen as a practical complementary tool for preliminary honey floral classification, leaving only problematic cases for pollinic evaluation.

Monovarietal extra-virgin olive oil classification: a fusion of human sensory attributes and an electronic tongue

Olive oil quality grading is traditionally assessed by human sensory evaluation of positive and negative attributes (olfactory, gustatory, and final olfactorygustatory sensations). However, it is not guaranteed that trained panelist can correctly classify monovarietal extra-virgin olive oils according to olive cultivar. In this work, the potential application of human (sensory panelists) and artificial (electronic tongue) sensory evaluation of olive oils was studied aiming to discriminate eight single-cultivar extra-virgin olive oils. Linear discriminant, partial least square discriminant, and sparse partial least square discriminant analyses were evaluated. The best predictive classification was obtained using linear discriminant analysis with simulated annealing selection algorithm. A low-level data fusion approach (18 electronic tongue signals and nine sensory attributes) enabled 100 % leave-one-out cross-validation correct classification, improving the discrimination capability of the individual use of sensor profiles or sensory attributes (70 and 57 % leave-one-out correct classifications, respectively). So, human sensory evaluation and electronic tongue analysis may be used as complementary tools allowing successful monovarietal olive oil discrimination.

Electronic tongue: a versatile tool for mineral and fruit-flavored waters recognition

Natural mineral waters (still), effervescent natural mineral waters (sparkling) and aromatized waters with fruit-flavors (still or sparkling) are an emerging market. In this work, the capability of a potentiometric electronic tongue, comprised with lipid polymeric membranes, to quantitatively estimate routinely quality physicochemical parameters (pH and conductivity) as well as to qualitatively classify water samples according to the type of water was evaluated. The study showed that a linear discriminant model, based on 21 sensors selected by the simulated annealing algorithm, could correctly classify 100 % of the water samples (leave-one out cross-validation). This potential was further demonstrated by applying a repeated K-fold cross-validation (guaranteeing that at least 15 % of independent samples were only used for internal-validation) for which 96 % of correct classifications were attained. The satisfactory recognition performance of the E-tongue could be attributed to the pH, conductivity, sugars and organic acids contents of the studied waters, which turned out in significant differences of sweetness perception indexes and total acid flavor. Moreover, the E-tongue combined with multivariate linear regression models, based on sub-sets of sensors selected by the simulated annealing algorithm, could accurately estimate waters pH (25 sensors: R 2 equal to 0.99 and 0.97 for leave-one-out or repeated K-folds cross-validation) and conductivity (23 sensors: R 2 equal to 0.997 and 0.99 for leave-one-out or repeated K-folds cross-validation). So, the overall satisfactory results achieved, allow envisaging a potential future application of electronic tongue devices for bottled water analysis and classification.

Morphometric and genetic differentiation among populations of Eupemphix nattereri (Amphibia, Anura, Leiuperidae) from central Brazil

To assess genetic structure and phenotypic diversity of Eupemphix nattereri Steindachner, 1863, morphometric and molecular analyses were carried out for nine populations from the State of Goiás. A total of 11 morphometric traits were evaluated and genetic information was estimated using RAPD markers. Genetic and phenotypic distances were determined as a function of geographical origin. Correlation among genetic, morphometric, micro, and macroenviromental were analyzed by the Mantel test. Genetic data indicated high levels of genetic diversity (Φst= 0.3) among the nine populations. Mantel tests did not reveal a significant positive correlation between genetic and geographical distances, indicating that locally geographical populations were not genetically similar, even in distances smaller than 50 km. Discriminant analysis on 11 morphometric measurements showed a high divergence among the nine populations. However, a marginally significant correlation (P=0.08) between genetic and morphometric distances was found. The observed correlation was not causal in terms of the relationship between phenotype and genotype, but indicated common spatial structures. Thus, our results suggest that isolation-by-distance processes may explain population divergence in Eupemphix nattereri.

Consumers' behavior and the Bertrand paradox: an ACE approach

We analyze the classical Bertrand model when consumers exhibit some strategic behavior in deciding from which seller they will buy. We use two related but different tools. Both consider a probabilistic learning (or evolutionary) mechanism, and in the two of them consumers' behavior in uences the competition between the sellers. The results obtained show that, in general, developing some sort of loyalty is a good strategy for the buyers as it works in their best interest. First, we consider a learning procedure described by a deterministic dynamic system and, using strong simplifying assumptions, we can produce a description of the process behavior. Second, we use nite automata to represent the strategies played by the agents and an adaptive process based on genetic algorithms to simulate the stochastic process of learning. By doing so we can relax some of the strong assumptions used in the rst approach and still obtain the same basic results. It is suggested that the limitations of the rst approach (analytical) provide a good motivation for the second approach (Agent-Based). Indeed, although both approaches address the same problem, the use of Agent-Based computational techniques allows us to relax hypothesis and overcome the limitations of the analytical approach.

Restriction site-associated DNA sequencing, genotyping error estimation and de novo assembly optimization for population genetic inference.

Restriction site-associated DNA sequencing (RADseq) provides researchers with the ability to record genetic polymorphism across thousands of loci for nonmodel organisms, potentially revolutionizing the field of molecular ecology. However, as with other genotyping methods, RADseq is prone to a number of sources of error that may have consequential effects for population genetic inferences, and these have received only limited attention in terms of the estimation and reporting of genotyping error rates. Here we use individual sample replicates, under the expectation of identical genotypes, to quantify genotyping error in the absence of a reference genome. We then use sample replicates to (i) optimize de novo assembly parameters within the program Stacks, by minimizing error and maximizing the retrieval of informative loci; and (ii) quantify error rates for loci, alleles and single-nucleotide polymorphisms. As an empirical example, we use a double-digest RAD data set of a nonmodel plant species, Berberis alpina, collected from high-altitude mountains in Mexico.

The effect of host social system on parasite population genetic structure: comparative population genetics of two ectoparasitic mites and their bat hosts.

BACKGROUND: The population genetic structure of a parasite, and consequently its ability to adapt to a given host, is strongly linked to its own life history as well as the life history of its host. While the effects of parasite life history on their population genetic structure have received some attention, the effect of host social system has remained largely unstudied. In this study, we investigated the population genetic structure of two closely related parasitic mite species (Spinturnix myoti and Spinturnix bechsteini) with very similar life histories. Their respective hosts, the greater mouse-eared bat (Myotis myotis) and the Bechstein's bat (Myotis bechsteinii) have social systems that differ in several substantial features, such as group size, mating system and dispersal patterns. RESULTS: We found that the two mite species have strongly differing population genetic structures. In S. myoti we found high levels of genetic diversity and very little pairwise differentiation, whereas in S. bechsteini we observed much less diversity, strongly differentiated populations and strong temporal turnover. These differences are likely to be the result of the differences in genetic drift and dispersal opportunities afforded to the two parasites by the different social systems of their hosts. CONCLUSIONS: Our results suggest that host social system can strongly influence parasite population structure. As a result, the evolutionary potential of these two parasites with very similar life histories also differs, thereby affecting the risk and evolutionary pressure exerted by each parasite on its host.

Genetic analysis of the breeding system of an invasive subterranean termite, Reticulitermes santonensis, in urban and natural habitats.

Reticulitermes santonensis is a subterranean termite that invades urban areas in France and elsewhere where it causes damage to human-built structures. We investigated the breeding system, colony and population genetic structure, and mode of dispersal of two French populations of R. santonensis. Termite workers were sampled from 43 and 31 collection points, respectively, from a natural population in west-central France (in and around the island of Oleron) and an urban population (Paris). Ten to 20 workers per collection point were genotyped at nine variable microsatellite loci to determine colony identity and to infer colony breeding structure. There was a total of 26 colonies, some of which were spatially expansive, extending up to 320 linear metres. Altogether, the analysis of genotype distribution, F-statistics and relatedness coefficients suggested that all colonies were extended families headed by numerous neotenics (nonwinged precocious reproductives) probably descended from pairs of primary (winged) reproductives. Isolation by distance among collection points within two large colonies from both populations suggested spatially separated reproductive centres with restricted movement of workers and neotenics. There was a moderate level of genetic differentiation (F(ST) = 0.10) between the Oleron and Paris populations, and the number of alleles was significantly higher in Oleron than in Paris, as expected if the Paris population went through bottlenecks when it was introduced from western France. We hypothesize that the diverse and flexible breeding systems found in subterranean termites pre-adapt them to invade new or marginal habitats. Considering that R. santonensis may be an introduced population of the North American species R. flavipes, a breeding system consisting primarily of extended family colonies containing many neotenic reproductives may facilitate human-mediated spread and establishment of R. santonensis in urban areas with harsh climates.

Structural prediction of peptides bound to MHC class I.

An ab initio structure prediction approach adapted to the peptide-major histocompatibility complex (MHC) class I system is presented. Based on structure comparisons of a large set of peptide-MHC class I complexes, a molecular dynamics protocol is proposed using simulated annealing (SA) cycles to sample the conformational space of the peptide in its fixed MHC environment. A set of 14 peptide-human leukocyte antigen (HLA) A0201 and 27 peptide-non-HLA A0201 complexes for which X-ray structures are available is used to test the accuracy of the prediction method. For each complex, 1000 peptide conformers are obtained from the SA sampling. A graph theory clustering algorithm based on heavy atom root-mean-square deviation (RMSD) values is applied to the sampled conformers. The clusters are ranked using cluster size, mean effective or conformational free energies, with solvation free energies computed using Generalized Born MV 2 (GB-MV2) and Poisson-Boltzmann (PB) continuum models. The final conformation is chosen as the center of the best-ranked cluster. With conformational free energies, the overall prediction success is 83% using a 1.00 Angstroms crystal RMSD criterion for main-chain atoms, and 76% using a 1.50 Angstroms RMSD criterion for heavy atoms. The prediction success is even higher for the set of 14 peptide-HLA A0201 complexes: 100% of the peptides have main-chain RMSD values < or =1.00 Angstroms and 93% of the peptides have heavy atom RMSD values < or =1.50 Angstroms. This structure prediction method can be applied to complexes of natural or modified antigenic peptides in their MHC environment with the aim to perform rational structure-based optimizations of tumor vaccines.

Complementarities between mathematical and computational modeling: the case of the repeated Prisoners' Dilemma

We study the properties of the well known Replicator Dynamics when applied to a finitely repeated version of the Prisoners' Dilemma game. We characterize the behavior of such dynamics under strongly simplifying assumptions (i.e. only 3 strategies are available) and show that the basin of attraction of defection shrinks as the number of repetitions increases. After discussing the difficulties involved in trying to relax the 'strongly simplifying assumptions' above, we approach the same model by means of simulations based on genetic algorithms. The resulting simulations describe a behavior of the system very close to the one predicted by the replicator dynamics without imposing any of the assumptions of the mathematical model. Our main conclusion is that mathematical and computational models are good complements for research in social sciences. Indeed, while computational models are extremely useful to extend the scope of the analysis to complex scenarios hard to analyze mathematically, formal models can be useful to verify and to explain the outcomes of computational models.