Wave-height hazard analysis in Eastern Coast of Spain : Bayesian approach using generalized Pareto distribution

Standard practice of wave-height hazard analysis often pays little attention to the uncertainty of assessed return periods and occurrence probabilities. This fact favors the opinion that, when large events happen, the hazard assessment should change accordingly. However, uncertainty of the hazard estimates is normally able to hide the effect of those large events. This is illustrated using data from the Mediterranean coast of Spain, where the last years have been extremely disastrous. Thus, it is possible to compare the hazard assessment based on data previous to those years with the analysis including them. With our approach, no significant change is detected when the statistical uncertainty is taken into account. The hazard analysis is carried out with a standard model. Time-occurrence of events is assumed Poisson distributed. The wave-height of each event is modelled as a random variable which upper tail follows a Generalized Pareto Distribution (GPD). Moreover, wave-heights are assumed independent from event to event and also independent of their occurrence in time. A threshold for excesses is assessed empirically. The other three parameters (Poisson rate, shape and scale parameters of GPD) are jointly estimated using Bayes' theorem. Prior distribution accounts for physical features of ocean waves in the Mediterranean sea and experience with these phenomena. Posterior distribution of the parameters allows to obtain posterior distributions of other derived parameters like occurrence probabilities and return periods. Predictives are also available. Computations are carried out using the program BGPE v2.0

Bayesian genetic parameters for body weight and survival of Nile tilapia farmed in Brazil

The objective of this study was to estimate genetic parameters for survival and weight of Nile tilapia (Oreochromis niloticus), farmed in cages and ponds in Brazil, and to predict genetic gain under different scenarios. Survival was recorded as a binary response (dead or alive), during harvest time in the 2008 grow-out period. Genetic parameters were estimated using a Bayesian mixed linear-threshold animal model via Gibbs sampling. The breeding population consisted of 2,912 individual fish, which were analyzed together with the pedigree of 5,394 fish. The heritabilities estimates, with 95% posterior credible intervals, for tagging weight, harvest weight and survival were 0.17 (0.09-0.27), 0.21 (0.12-0.32) and 0.32 (0.22-0.44), respectively. Credible intervals show a 95% probability that the true genetic correlations were in a favourable direction. The selection for weight has a positive impact on survival. Estimated genetic gain was high when selecting for harvest weight (5.07%), and indirect gain for tagging weight (2.17%) and survival (2.03%) were also considerable.

Combining conservative and variable markers to inferthe evolutionary history of Prunus subgen. Amygdalus s.l.under domestication

The genus Prunus L. is large and economically important. However, phylogenetic relationships within Prunus at low taxonomic level, particularly in the subgenus Amygdalus L. s.l., remain poorly investigated. This paper attempts to document the evolutionary history of Amygdalus s.l. and establishes a temporal framework, by assembling molecular data from conservative and variable molecular markers. The nuclear s6pdh gene in combination with the plastid trnSG spacer are analyzed with bayesian and maximum likelihood methods. Since previous phylogenetic analysis with these markers lacked resolution, we additionally analyzed 13 nuclear SSR loci with the δµ2 distance, followed by an unweighted pair group method using arithmetic averages algorithm. Our phylogenetic analysis with both sequence and SSR loci confirms the split between sections Amygdalus and Persica, comprising almonds and peaches, respectively. This result is in agreement with biogeographic data showing that each of the two sections is naturally distributed on each side of the Central Asian Massif chain. Using coalescent based estimations, divergence times between the two sections strongly varied when considering sequence data only or combined with SSR. The sequence-only based estimate (5 million years ago) was congruent with the Central Asian Massif orogeny and subsequent climate change. Given the low level of differentiation within the two sections using both marker types, the utility of combining microsatellites and data sequences to address phylogenetic relationships at low taxonomic level within Amygdalus is discussed. The recent evolutionary histories of almond and peach are discussed in view of the domestication processes that arose in these two phenotypically-diverging gene pools: almonds and peaches were domesticated from the Amygdalus s.s. and Persica sections, respectively. Such economically important crops may serve as good model to study divergent domestication process in close genetic pool.

Swarm Intelligent Selection and Optimization of Machining System Parameters for Microchannel Fabrication in Medical Devices

Current technology trends in medical device industry calls for fabrication of massive arrays of microfeatures such as microchannels on to nonsilicon material substrates with high accuracy, superior precision, and high throughput. Microchannels are typical features used in medical devices for medication dosing into the human body, analyzing DNA arrays or cell cultures. In this study, the capabilities of machining systems for micro-end milling have been evaluated by conducting experiments, regression modeling, and response surface methodology. In machining experiments by using micromilling, arrays of microchannels are fabricated on aluminium and titanium plates, and the feature size and accuracy (width and depth) and surface roughness are measured. Multicriteria decision making for material and process parameters selection for desired accuracy is investigated by using particle swarm optimization (PSO) method, which is an evolutionary computation method inspired by genetic algorithms (GA). Appropriate regression models are utilized within the PSO and optimum selection of micromilling parameters; microchannel feature accuracy and surface roughness are performed. An analysis for optimal micromachining parameters in decision variable space is also conducted. This study demonstrates the advantages of evolutionary computing algorithms in micromilling decision making and process optimization investigations and can be expanded to other applications

Selection of microorganisms producer of lipase for fat removal from biodiesel purification water

The objective this study has been the selection of lipase productor microorganism, for removal of oils and grease, in the pre-treatment of biodiesel wastewater washing. For this, analyses of the physicist-chemistries characteristics had been made with the wastewater of the biodiesel washing, and then it had been isolated and chosen, by means of determinations of the lipase activity. Following, it was made a test of fat biodegradation, in the conditions: pH (5.95), temperature (35 ºC), rotation (180 rpm) and ammonium sulfate as nitrogen source (3 g L-1) and establishing as variable the two microorganism preselected and the time (24; 48; 72; 96 and 120 h). The biodiesel purification wastewater had presented high potential of environmental impact, presenting a concentration of O of 6.76 g L-1. From the six isolated microbiological cultures, two microorganisms (A and B) had been selected, with enzymatic index of 0.56 and 0.57, respectively. The treatment of the wastewater using the isolated microorganism (Klebsiella oxytoca) had 80% of the fatty removal in 48 h.

Expression of anti-Z-DNA single chain antibody variable fragment on the filamentous phage surface

We describe the expression of an anti-Z-DNA single chain variable region antibody fragment (scFv) on a filamentous phage surface. Four vectors for phage display were constructed. Two of them are able to display multiple copies of the antibody fragment, and the others can be used to make monovalent libraries. The vectors use different promoter/leader sequences to direct the expression of the fused proteins. All were able to promote the assembly of fusion virion particles. In this paper we also show the affinity selection (biopanning) of those phage-antibodies based on the capacity of their products to recognize the antigen. We used biotinylated Z-DNA and the selection was performed in a solution phase fashion. The data presented here indicate that these vectors can be further used to construct anti-nucleic acid antibody fragment libraries that can be used to study the basis of nucleic acid-protein interaction and its role in autoimmunity mechanisms.

Sélection de modèle d'imputation à partir de modèles bayésiens hiérarchiques linéaires multivariés

Les logiciels utilisés sont Splus et R.

Une nouvelle perspective sur la personnalité comme variable prévisionnelle du rendement individuel au travail : l'approche holistique comparée à l'approche traditionnelle centrée sur les variables

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

Bias from farmer self-selection in genetically modified crop productivity estimates: Evidence from Indian data

In the continuing debate over the impact of genetically modified (GM) crops on farmers of developing countries, it is important to accurately measure magnitudes such as farm-level yield gains from GM crop adoption. Yet most farm-level studies in the literature do not control for farmer self-selection, a potentially important source of bias in such estimates. We use farm-level panel data from Indian cotton farmers to investigate the yield effect of GM insect-resistant cotton. We explicitly take into account the fact that the choice of crop variety is an endogenous variable which might lead to bias from self-selection. A production function is estimated using a fixed-effects model to control for selection bias. Our results show that efficient farmers adopt Bacillus thuringiensis (Bt) cotton at a higher rate than their less efficient peers. This suggests that cross-sectional estimates of the yield effect of Bt cotton, which do not control for self-selection effects, are likely to be biased upwards. However, after controlling for selection bias, we still find that there is a significant positive yield effect from adoption of Bt cotton that more than offsets the additional cost of Bt seed.

Development of smart variable stiffness actuators using polymer hydrogels

In this work, compliant actuators are developed by coupling braided structures and polymer gels, able to produce work by controlled gel swelling in the presence of water. A number of aspects related to the engineering of gel actuators were studied, including gel selection, modelling and experimentation of constant force and constant displacement behaviour, and response time. The actuator was intended for use as vibration neutralizer: with this aim, generation of a force of 10 N in a time not exceeding a second was needed. Results were promising in terms of force generation, although response time was still longer than required. In addition, the easiest way to obtain the reversibility of the effect is still under discussion: possible routes for improvement are suggested and will be the object of future work.

Bias from farmer self-selection in genetically modified crop productivity estimates: Evidence from Indian data

In the continuing debate over the impact of genetically modified (GM) crops on farmers of developing countries, it is important to accurately measure magnitudes such as farm-level yield gains from GM crop adoption. Yet most farm-level studies in the literature do not control for farmer self-selection, a potentially important source of bias in such estimates. We use farm-level panel data from Indian cotton farmers to investigate the yield effect of GM insect-resistant cotton. We explicitly take into account the fact that the choice of crop variety is an endogenous variable which might lead to bias from self-selection. A production function is estimated using a fixed-effects model to control for selection bias. Our results show that efficient farmers adopt Bacillus thuringiensis (Bt) cotton at a higher rate than their less efficient peers. This suggests that cross-sectional estimates of the yield effect of Bt cotton, which do not control for self-selection effects, are likely to be biased upwards. However, after controlling for selection bias, we still find that there is a significant positive yield effect from adoption of Bt cotton that more than offsets the additional cost of Bt seed.

Genetic analysis of complex demographic scenarios: Spatially expanding populations of the cane toad, Bufo marinus

Inferring the spatial expansion dynamics of invading species from molecular data is notoriously difficult due to the complexity of the processes involved. For these demographic scenarios, genetic data obtained from highly variable markers may be profitably combined with specific sampling schemes and information from other sources using a Bayesian approach. The geographic range of the introduced toad Bufo marinus is still expanding in eastern and northern Australia, in each case from isolates established around 1960. A large amount of demographic and historical information is available on both expansion areas. In each area, samples were collected along a transect representing populations of different ages and genotyped at 10 microsatellite loci. Five demographic models of expansion, differing in the dispersal pattern for migrants and founders and in the number of founders, were considered. Because the demographic history is complex, we used an approximate Bayesian method, based on a rejection-regression algorithm. to formally test the relative likelihoods of the five models of expansion and to infer demographic parameters. A stepwise migration-foundation model with founder events was statistically better supported than other four models in both expansion areas. Posterior distributions supported different dynamics of expansion in the studied areas. Populations in the eastern expansion area have a lower stable effective population size and have been founded by a smaller number of individuals than those in the northern expansion area. Once demographically stabilized, populations exchange a substantial number of effective migrants per generation in both expansion areas, and such exchanges are larger in northern than in eastern Australia. The effective number of migrants appears to be considerably lower than that of founders in both expansion areas. We found our inferences to be relatively robust to various assumptions on marker. demographic, and historical features. The method presented here is the only robust, model-based method available so far, which allows inferring complex population dynamics over a short time scale. It also provides the basis for investigating the interplay between population dynamics, drift, and selection in invasive species.

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

Dynamic Niche Clustering: a fuzzy variable radius niching technique for multimodal optimisation in GAs

This paper describes the recent developments and improvements made to the variable radius niching technique called Dynamic Niche Clustering (DNC). DNC is fitness sharing based technique that employs a separate population of overlapping fuzzy niches with independent radii which operate in the decoded parameter space, and are maintained alongside the normal GA population. We describe a speedup process that can be applied to the initial generation which greatly reduces the complexity of the initial stages. A split operator is also introduced that is designed to counteract the excessive growth of niches, and it is shown that this improves the overall robustness of the technique. Finally, the effect of local elitism is documented and compared to the performance of the basic DNC technique on a selection of 2D test functions. The paper is concluded with a view to future work to be undertaken on the technique.

A comparative review of dimension reduction methods in approximate Bayesian computation

Approximate Bayesian computation (ABC) methods make use of comparisons between simulated and observed summary statistics to overcome the problem of computationally intractable likelihood functions. As the practical implementation of ABC requires computations based on vectors of summary statistics, rather than full data sets, a central question is how to derive low-dimensional summary statistics from the observed data with minimal loss of information. In this article we provide a comprehensive review and comparison of the performance of the principal methods of dimension reduction proposed in the ABC literature. The methods are split into three nonmutually exclusive classes consisting of best subset selection methods, projection techniques and regularization. In addition, we introduce two new methods of dimension reduction. The first is a best subset selection method based on Akaike and Bayesian information criteria, and the second uses ridge regression as a regularization procedure. We illustrate the performance of these dimension reduction techniques through the analysis of three challenging models and data sets.