3,574 research outputs found
Interdependent binary choices under social influence: phase diagram for homogeneous unbiased populations
Coupled Ising models are studied in a discrete choice theory framework, where
they can be understood to represent interdependent choice making processes for
homogeneous populations under social influence. Two different coupling schemes
are considered. The nonlocal or group interdependence model is used to study
two interrelated groups making the same binary choice. The local or individual
interdependence model represents a single group where agents make two binary
choices which depend on each other. For both models, phase diagrams, and their
implications in socioeconomic contexts, are described and compared in the
absence of private deterministic utilities (zero opinion fields).Comment: 17 pages, 3 figures. This is the pre-peer reviewed version of the
following article: Ana Fern\'andez del R\'io, Elka Korutcheva and Javier de
la Rubia, Interdependent binary choices under social influence, Wiley's
Complexity, 2012; which has been published in final form at
http://onlinelibrary.wiley.com/doi/10.1002/cplx.21397/abstrac
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Kinetic multi-layer model of gas-particle interactions in aerosols and clouds (KM-GAP): linking condensation, evaporation and chemical reactions of organics, oxidants and water
We present a novel kinetic multi-layer model for gas-particle interactions in aerosols and clouds (KM-GAP) that treats explicitly all steps of mass transport and chemical reaction of semi-volatile species partitioning between gas phase, particle surface and particle bulk. KM-GAP is based on the PRA model framework (Pöschl-Rudich-Ammann, 2007), and it includes gas phase diffusion, reversible adsorption, surface reactions, bulk diffusion and reaction, as well as condensation, evaporation and heat transfer. The size change of atmospheric particles and the temporal evolution and spatial profile of the concentration of individual chemical species can be modelled along with gas uptake and accommodation coefficients. Depending on the complexity of the investigated system, unlimited numbers of semi-volatile species, chemical reactions, and physical processes can be treated, and the model shall help to bridge gaps in the understanding and quantification of multiphase chemistry and microphysics in atmo- spheric aerosols and clouds.
In this study we demonstrate how KM-GAP can be used to analyze, interpret and design experimental investigations of changes in particle size and chemical composition in response to condensation, evaporation, and chemical reaction. For the condensational growth of water droplets, our kinetic model results provide a direct link between laboratory observations and molecular dynamic simulations, confirming that the accommodation coefficient of water at 270 K is close to unity. Literature data on the evaporation of dioctyl phthalate as a function of particle size and time can be reproduced, and the model results suggest that changes in the experimental conditions like aerosol particle concentration and chamber geometry may influence the evaporation kinetics and can be optimized for eðcient probing of specific physical effects and parameters. With regard to oxidative aging of organic aerosol particles, we illustrate how the formation and evaporation of volatile reaction products like nonanal can cause a decrease in the size of oleic acid particles exposed to ozone
Evolution of statistical analysis in empirical software engineering research: Current state and steps forward
Software engineering research is evolving and papers are increasingly based
on empirical data from a multitude of sources, using statistical tests to
determine if and to what degree empirical evidence supports their hypotheses.
To investigate the practices and trends of statistical analysis in empirical
software engineering (ESE), this paper presents a review of a large pool of
papers from top-ranked software engineering journals. First, we manually
reviewed 161 papers and in the second phase of our method, we conducted a more
extensive semi-automatic classification of papers spanning the years 2001--2015
and 5,196 papers. Results from both review steps was used to: i) identify and
analyze the predominant practices in ESE (e.g., using t-test or ANOVA), as well
as relevant trends in usage of specific statistical methods (e.g.,
nonparametric tests and effect size measures) and, ii) develop a conceptual
model for a statistical analysis workflow with suggestions on how to apply
different statistical methods as well as guidelines to avoid pitfalls. Lastly,
we confirm existing claims that current ESE practices lack a standard to report
practical significance of results. We illustrate how practical significance can
be discussed in terms of both the statistical analysis and in the
practitioner's context.Comment: journal submission, 34 pages, 8 figure
In and out of Madagascar : dispersal to peripheral islands, insular speciation and diversification of Indian Ocean daisy trees (Psiadia, Asteraceae)
This study was supported by the European Union’s HOTSPOTS Training Network (MEST-2005-020561)Madagascar is surrounded by archipelagos varying widely in origin, age and structure. Although small and geologically young, these archipelagos have accumulated disproportionate numbers of unique lineages in comparison to Madagascar, highlighting the role of waif-dispersal and rapid in situ diversification processes in generating endemic biodiversity. We reconstruct the evolutionary and biogeographical history of the genus Psiadia (Asteraceae), a plant genus with near equal numbers of species in Madagascar and surrounding islands. Analyzing patterns and processes of diversification, we explain species accumulation on peripheral islands and aim to offer new insights on the origin and potential causes for diversification in the Madagascar and Indian Ocean Islands biodiversity hotspot. Our results provide support for an African origin of the group, with strong support for non-monophyly. Colonization of the Mascarenes took place by two evolutionary distinct lineages from Madagascar, via two independent dispersal events, each unique for their spatial and temporal properties. Significant shifts in diversification rate followed regional expansion, resulting in co-occurring and phenotypically convergent species on high-elevation volcanic slopes. Like other endemic island lineages, Psiadia have been highly successful in dispersing to and radiating on isolated oceanic islands, typified by high habitat diversity and dynamic ecosystems fuelled by continued geological activity. Results stress the important biogeographical role for Rodrigues in serving as an outlying stepping stone from which regional colonization took place. We discuss how isolated volcanic islands contribute to regional diversity by generating substantial numbers of endemic species on short temporal scales. Factors pertaining to the mode and tempo of archipelago formation and its geographical isolation strongly govern evolutionary pathways available for species diversification, and the potential for successful diversification of dispersed lineages, therefore, appears highly dependent on the timing of arrival, as habitat and resource properties change dramatically over the course of oceanic island evolution.Publisher PDFPeer reviewe
spam: A Sparse Matrix R Package with Emphasis on MCMC Methods for Gaussian Markov Random Fields
spam is an R package for sparse matrix algebra with emphasis on a Cholesky factorization of sparse positive definite matrices. The implemantation of spam is based on the competing philosophical maxims to be competitively fast compared to existing tools and to be easy to use, modify and extend. The first is addressed by using fast Fortran routines and the second by assuring S3 and S4 compatibility. One of the features of spam is to exploit the algorithmic steps of the Cholesky factorization and hence to perform only a fraction of the workload when factorizing matrices with the same sparsity structure. Simulations show that exploiting this break-down of the factorization results in a speed-up of about a factor 5 and memory savings of about a factor 10 for large matrices and slightly smaller factors for huge matrices. The article is motivated with Markov chain Monte Carlo methods for Gaussian Markov random fields, but many other statistical applications are mentioned that profit from an efficient Cholesky factorization as well.
Learning Large-Scale Bayesian Networks with the sparsebn Package
Learning graphical models from data is an important problem with wide
applications, ranging from genomics to the social sciences. Nowadays datasets
often have upwards of thousands---sometimes tens or hundreds of thousands---of
variables and far fewer samples. To meet this challenge, we have developed a
new R package called sparsebn for learning the structure of large, sparse
graphical models with a focus on Bayesian networks. While there are many
existing software packages for this task, this package focuses on the unique
setting of learning large networks from high-dimensional data, possibly with
interventions. As such, the methods provided place a premium on scalability and
consistency in a high-dimensional setting. Furthermore, in the presence of
interventions, the methods implemented here achieve the goal of learning a
causal network from data. Additionally, the sparsebn package is fully
compatible with existing software packages for network analysis.Comment: To appear in the Journal of Statistical Software, 39 pages, 7 figure
Technical Debt Prioritization: State of the Art. A Systematic Literature Review
Background. Software companies need to manage and refactor Technical Debt
issues. Therefore, it is necessary to understand if and when refactoring
Technical Debt should be prioritized with respect to developing features or
fixing bugs. Objective. The goal of this study is to investigate the existing
body of knowledge in software engineering to understand what Technical Debt
prioritization approaches have been proposed in research and industry. Method.
We conducted a Systematic Literature Review among 384 unique papers published
until 2018, following a consolidated methodology applied in Software
Engineering. We included 38 primary studies. Results. Different approaches have
been proposed for Technical Debt prioritization, all having different goals and
optimizing on different criteria. The proposed measures capture only a small
part of the plethora of factors used to prioritize Technical Debt qualitatively
in practice. We report an impact map of such factors. However, there is a lack
of empirical and validated set of tools. Conclusion. We observed that technical
Debt prioritization research is preliminary and there is no consensus on what
are the important factors and how to measure them. Consequently, we cannot
consider current research conclusive and in this paper, we outline different
directions for necessary future investigations
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Kinetic multi-layer model of aerosol surface and bulk chemistry (KM-SUB): the influence of interfacial transport and bulk diffusion on the oxidation of oleic acid by ozone
We present a novel kinetic multi-layer model that explicitly resolves mass transport
and chemical reaction at the surface and in the bulk of aerosol particles (KM-SUB).
The model is based on the PRA framework of gas–particle interactions (P¨oschl et al.,
5 2007), and it includes reversible adsorption, surface reactions and surface-bulk exchange
as well as bulk diffusion and reaction. Unlike earlier models, KM-SUB does
not require simplifying assumptions about steady-state conditions and radial mixing.
The temporal evolution and concentration profiles of volatile and non-volatile species
at the gas-particle interface and in the particle bulk can be modeled along with surface
10 concentrations and gas uptake coefficients.
In this study we explore and exemplify the effects of bulk diffusion on the rate of reactive
gas uptake for a simple reference system, the ozonolysis of oleic acid particles,
in comparison to experimental data and earlier model studies. We demonstrate how
KM-SUB can be used to interpret and analyze experimental data from laboratory stud15
ies, and how the results can be extrapolated to atmospheric conditions. In particular,
we show how interfacial transport and bulk transport, i.e., surface accommodation, bulk
accommodation and bulk diffusion, influence the kinetics of the chemical reaction. Sensitivity
studies suggest that in fine air particulate matter oleic acid and compounds with
similar reactivity against ozone (C=C double bonds) can reach chemical lifetimes of
20 multiple hours only if they are embedded in a (semi-)solid matrix with very low diffusion
coefficients (10−10 cm2 s−1).
Depending on the complexity of the investigated system, unlimited numbers of
volatile and non-volatile species and chemical reactions can be flexibly added and
treated with KM-SUB. We propose and intend to pursue the application of KM-SUB
25 as a basis for the development of a detailed master mechanism of aerosol chemistry
as well as for the derivation of simplified but realistic parameterizations for large-scale
atmospheric and climate models
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