Horizontal Grid Size Selection and its Influence on Mesoscale Model Simulations

The use of two-dimensional spectral analysis applied to terrain heights in order to determine characteristic terrain spatial scales and its subsequent use for the objective definition of an adequate grid size required to resolve terrain forcing are presented in this paper. In order to illustrate the influence of grid size, atmospheric flow in a complex terrain area of the Spanish east coast is simulated by the Regional Atmospheric Modeling System (RAMS) mesoscale numerical model using different horizontal grid resolutions. In this area, a grid size of 2 km is required to account for 95% of terrain variance. Comparison among results of the different simulations shows that, although the main wind behavior does not change dramatically, some small-scale features appear when using a resolution of 2 km or finer. Horizontal flow pattern differences are significant both in the nighttime, when terrain forcing is more relevant, and in the daytime, when thermal forcing is dominant. Vertical structures also are investigated, and results show that vertical advection is influenced highly by the horizontal grid size during the daytime period. The turbulent kinetic energy and potential temperature vertical cross sections show substantial differences in the structure of the planetary boundary layer for each model configuration

Subsampling the mean of heavy-tailed dependent observations

We establish the validity of subsampling confidence intervals for themean of a dependent series with heavy-tailed marginal distributions.Using point process theory, we study both linear and nonlinear GARCH-liketime series models. We propose a data-dependent method for the optimalblock size selection and investigate its performance by means of asimulation study.

Trade, firm selection, and innovation: the competition channel

The availability of rich firm-level data sets has recently led researchers to uncover new evidence on the effects of trade liberalization. First, trade openness forces the least productive firms to exit the market. Secondly, it induces surviving firms to increase their innovation efforts and thirdly, it increases the degree of product market competition. In this paper we propose a model aimed at providing a coherent interpretation of these findings. We introducing firm heterogeneity into an innovation-driven growth model, where incumbent firms operating in oligopolistic industries perform cost-reducing innovations. In this framework, trade liberalization leads to higher product market competition, lower markups and higher quantity produced. These changes in markups and quantities, in turn, promote innovation and productivity growth through a direct competition effect, based on the increase in the size of the market, and a selection effect, produced by the reallocation of resources towards more productive firms. Calibrated to match US aggregate and firm-level statistics, the model predicts that a 10 percent reduction in variable trade costs reduces markups by 1:15 percent, firm surviving probabilities by 1 percent, and induces an increase in productivity growth of about 13 percent. More than 90 percent of the trade-induced growth increase can be attributed to the selection effect.

Adaptive model selection using empirical complexities

Given $n$ independent replicates of a jointly distributed pair $(X,Y)\in {\cal R}^d \times {\cal R}$, we wish to select from a fixed sequence of model classes ${\cal F}_1, {\cal F}_2, \ldots$ a deterministic prediction rule $f: {\cal R}^d \to {\cal R}$ whose risk is small. We investigate the possibility of empirically assessingthe {\em complexity} of each model class, that is, the actual difficulty of the estimation problem within each class. The estimated complexities are in turn used to define an adaptive model selection procedure, which is based on complexity penalized empirical risk.The available data are divided into two parts. The first is used to form an empirical cover of each model class, and the second is used to select a candidate rule from each cover based on empirical risk. The covering radii are determined empirically to optimize a tight upper bound on the estimation error. An estimate is chosen from the list of candidates in order to minimize the sum of class complexity and empirical risk. A distinguishing feature of the approach is that the complexity of each model class is assessed empirically, based on the size of its empirical cover.Finite sample performance bounds are established for the estimates, and these bounds are applied to several non-parametric estimation problems. The estimates are shown to achieve a favorable tradeoff between approximation and estimation error, and to perform as well as if the distribution-dependent complexities of the model classes were known beforehand. In addition, it is shown that the estimate can be consistent,and even possess near optimal rates of convergence, when each model class has an infinite VC or pseudo dimension.For regression estimation with squared loss we modify our estimate to achieve a faster rate of convergence.

Family Size Evolution in Drosophila Chemosensory Gene Families: A Comparative Analysis with a Critical Appraisal of Methods

Gene turnover rates and the evolution of gene family sizes are important aspects of genome evolution. Here, we use curated sequence data of the major chemosensory gene families from Drosophila-the gustatory receptor, odorant receptor, ionotropic receptor, and odorant-binding protein families-to conduct a comparative analysis among families, exploring different methods to estimate gene birth and death rates, including an ad hoc simulation study. Remarkably, we found that the state-of-the-art methods may produce very different rate estimates, which may lead to disparate conclusions regarding the evolution of chemosensory gene family sizes in Drosophila. Among biological factors, we found that a peculiarity of D. sechellia's gene turnover rates was a major source of bias in global estimates, whereas gene conversion had negligible effects for the families analyzed herein. Turnover rates vary considerably among families, subfamilies, and ortholog groups although all analyzed families were quite dynamic in terms of gene turnover. Computer simulations showed that the methods that use ortholog group information appear to be the most accurate for the Drosophila chemosensory families. Most importantly, these results reveal the potential of rate heterogeneity among lineages to severely bias some turnover rate estimation methods and the need of further evaluating the performance of these methods in a more diverse sampling of gene families and phylogenetic contexts. Using branch-specific codon substitution models, we find further evidence of positive selection in recently duplicated genes, which attests to a nonneutral aspect of the gene birth-and-death process.

Patterns of nucleotide diversity at the regions encompassing the Drosophila insulin-like peptide (dilp) genes: demography vs positive selection in Drosophila melanogaster.

In Drosophila, the insulin-signaling pathway controls some life history traits, such as fertility and lifespan, and it is considered to be the main metabolic pathway involved in establishing adult body size. Several observations concerning variation in body size in the Drosophila genus are suggestive of its adaptive character. Genes encoding proteins in this pathway are, therefore, good candidates to have experienced adaptive changes and to reveal the footprint of positive selection. The Drosophila insulin-like peptides (DILPs) are the ligands that trigger the insulin-signaling cascade. In Drosophila melanogaster, there are several peptides that are structurally similar to the single mammalian insulin peptide. The footprint of recent adaptive changes on nucleotide variation can be unveiled through the analysis of polymorphism and divergence. With this aim, we have surveyed nucleotide sequence variation at the dilp1-7 genes in a natural population of D. melanogaster. The comparison of polymorphism in D. melanogaster and divergence from D. simulans at different functional classes of the dilp genes provided no evidence of adaptive protein evolution after the split of the D. melanogaster and D. simulans lineages. However, our survey of polymorphism at the dilp gene regions of D. melanogaster has provided some evidence for the action of positive selection at or near these genes. The regions encompassing the dilp1-4 genes and the dilp6 gene stand out as likely affected by recent adaptive events.

Divergent evolution and purifying selection of the flaA gene sequences in Aeromonas

BACKGROUND: The bacterial flagellum is the most important organelle of motility in bacteria and plays a key role in many bacterial lifestyles, including virulence. The flagellum also provides a paradigm of how hierarchical gene regulation, intricate protein-protein interactions and controlled protein secretion can result in the assembly of a complex multi-protein structure tightly orchestrated in time and space. As if to stress its importance, plants and animals produce receptors specifically dedicated to the recognition of flagella. Aside from motility, the flagellum also moonlights as an adhesion and has been adapted by humans as a tool for peptide display. Flagellar sequence variation constitutes a marker with widespread potential uses for studies of population genetics and phylogeny of bacterial species. RESULTS: We sequenced the complete flagellin gene (flaA) in 18 different species and subspecies of Aeromonas. Sequences ranged in size from 870 (A. allosaccharophila) to 921 nucleotides (A. popoffii). The multiple alignment displayed 924 sites, 66 of which presented alignment gaps. The phylogenetic tree revealed the existence of two groups of species exhibiting different FlaA flagellins (FlaA1 and FlaA2). Maximum likelihood models of codon substitution were used to analyze flaA sequences. Likelihood ratio tests suggested a low variation in selective pressure among lineages, with an omega ratio of less than 1 indicating the presence of purifying selection in almost all cases. Only one site under potential diversifying selection was identified (isoleucine in position 179). However, 17 amino acid positions were inferred as sites that are likely to be under positive selection using the branch-site model. Ancestral reconstruction revealed that these 17 amino acids were among the amino acid changes detected in the ancestral sequence. CONCLUSION: The models applied to our set of sequences allowed us to determine the possible evolutionary pathway followed by the flaA gene in Aeromonas, suggesting that this gene have probably been evolving independently in the two groups of Aeromonas species since the divergence of a distant common ancestor after one or several episodes of positive selection. REVIEWERS: This article was reviewed by Alexey Kondrashov, John Logsdon and Olivier Tenaillon (nominated by Laurence D Hurst).

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

Brain size and evolutionary diversification in birds

The role of behavior in evolution remains controversial, despite that some ideas are over 100 years old. Changes in behavior are generally believed to enhance evolution by exposing individuals to new selective pressures and by facilitating range expansions. However, this hypothesis lacks firm empirical evidence. Moreover, behavioral changes can also inhibit evolution by hiding heritable variation from natural selection. Taking advantage of the complete phylogeny of extant birds, a new species-level measure of past diversification rate and the best existing measures of brain size (n = 1326 species), I show here that relative brain size is associated (albeit weakly) with diversification rates. Assuming that brain relative size reflects behavioral flexibility, an assumption well-supported by evidence, this finding supports the idea that behavior can enhance evolutionary diversification. This view is further supported by the discovery that the most important factor influencing diversification rates is ecological generalism, which is believed to require behavioral flexibility. Thus, behavioral changes that expose animals to a variety of environments can have played an important role in the evolution of birds.

Single period Markowitz portfolio selection, performance gauging and duality: a variation on Luenberger's shortage function

Markowitz portfolio theory (1952) has induced research into the efficiency of portfolio management. This paper studies existing nonparametric efficiency measurement approaches for single period portfolio selection from a theoretical perspective and generalises currently used efficiency measures into the full mean-variance space. Therefore, we introduce the efficiency improvement possibility function (a variation on the shortage function), study its axiomatic properties in the context of Markowitz efficient frontier, and establish a link to the indirect mean-variance utility function. This framework allows distinguishing between portfolio efficiency and allocative efficiency. Furthermore, it permits retrieving information about the revealed risk aversion of investors. The efficiency improvement possibility function thus provides a more general framework for gauging the efficiency of portfolio management using nonparametric frontier envelopment methods based on quadratic optimisation.

Firm Size and Geographical Aggregation: An Empirical Appraisal in Industrial Location

This paper assesses empirically the importance of size discrimination and disaggregate data for deciding where to locate a start-up concern. We compare three econometric specifications using Catalan data: a multinomial logit with 4 and 41 alternatives (provinces and comarques, respectively) in which firm size is the main covariate; a conditional logit with 4 and 41 alternatives including attributes of the sites as well as size-site interactions; and a Poisson model on the comarques and the full spatial choice set (942 municipalities) with site-specific variables. Our results suggest that if these two issues are ignored, conclusions may be misleading. We provide evidence that large and small firms behave differently and conclude that Catalan firms tend to choose between comarques rather than between municipalities. Moreover, labour-intensive firms seem more likely to be located in the city of Barcelona. Keywords: Catalonia, industrial location, multinomial response model. JEL: C250, E30, R00, R12

The Determinants of Entry are not Independent of Start-up Size: Some evidence from Spanish manufacturing

In this paper we explore the determinants of firm start-up size of Spanish manufacturing industries. The industries' barriers to entry affect the ability of potential entrants to enter the markets and the size range at which they decide to enter. In order to examine the relationships between barriers to entry and size we applied the quantile regression techniques. Our results indicate that the variables that characterize the structure of the market, the variables that are related to the behaviour of the incumbent firms and the rate of growth of the industries generate different barriers depending on the initial size of the entrants. Keywords: Entry, regression quantiles, start-up size. JEL classification: L110, L600

A territorial approach to firm entry: The effect of city size

This paper concerns the effects of territorial factors on the processes involved in the creation of manufacturing firms in Spanish cities. Most contributions have focused on regional factors rather than urban ones. Here we assume that it is possible to identify certain urban factors that attract new firms. We use data for the entry of firms in Spanish manufacturing industries between 1994 and 2002. This paper contributes to the existing literature on market entry. Key words: cities, regions, firm entry and Spanish economy. JEL: R0, R12, L60

Firm Size and Short-Term Dynamics in Aggregate Entry and Exit

Much of the research on industry dynamics focuses on the interdependence between the sectorial rates of entry and exit. This paper argues that the size of firms and the reaction-adjustment period are important conditions missed in this literature. I illustrate the effects of this omission using data from the Spanish manufacturing industries between 1994 and 2001. Estimates from systems of equations models provide evidence of a conical revolving door phenomenon and of partial adjustments in the replacement-displacement of large firms. KEYWORDS: aggregation, industry dynamics, panel data, symmetry, simultaneity. JEL CLASSIFICATION: C33, C52, L60, L11