10,173 research outputs found
Explaining consequences of employment insecurity: The dynamics of scarring in the United Kingdom, Poland and Norway
This deliverable presents three country studies on scarring effects of early employment insecurity in the United Kingdom, Poland and Norway. Traditional analysis of scarring effects has favoured the analysis of the impact of the experience of unemployment on the experience of subsequent unemployment (state dependence) and the monetary costs of previous unemployment in terms of lower subsequent wages (see e.g. Arulampalam, Booth and Taylor 2000; Arulampalam, Gregg and Gregory 2001). The three present country studies go beyond the traditional analysis of scarring effects in order to better understand the trade-offs experienced by young female and male workers when faced with an insecure labour market integration. With national longitudinal data, original methodological designs and research focus, each study contributes in an original way to the research literature. All three studies pay special attention to gender and education as potential moderating variables of scarring effects
Markovian Dynamics on Complex Reaction Networks
Complex networks, comprised of individual elements that interact with each
other through reaction channels, are ubiquitous across many scientific and
engineering disciplines. Examples include biochemical, pharmacokinetic,
epidemiological, ecological, social, neural, and multi-agent networks. A common
approach to modeling such networks is by a master equation that governs the
dynamic evolution of the joint probability mass function of the underling
population process and naturally leads to Markovian dynamics for such process.
Due however to the nonlinear nature of most reactions, the computation and
analysis of the resulting stochastic population dynamics is a difficult task.
This review article provides a coherent and comprehensive coverage of recently
developed approaches and methods to tackle this problem. After reviewing a
general framework for modeling Markovian reaction networks and giving specific
examples, the authors present numerical and computational techniques capable of
evaluating or approximating the solution of the master equation, discuss a
recently developed approach for studying the stationary behavior of Markovian
reaction networks using a potential energy landscape perspective, and provide
an introduction to the emerging theory of thermodynamic analysis of such
networks. Three representative problems of opinion formation, transcription
regulation, and neural network dynamics are used as illustrative examples.Comment: 52 pages, 11 figures, for freely available MATLAB software, see
http://www.cis.jhu.edu/~goutsias/CSS%20lab/software.htm
Why Money Trickles Up - Wealth & Income Distributions
This paper combines ideas from classical economics and modern finance with
the general Lotka-Volterra models of Levy & Solomon to provide straightforward
explanations of wealth and income distributions. Using a simple and realistic
economic formulation, the distributions of both wealth and income are fully
explained. Both the power tail and the log-normal like body are fully captured.
It is of note that the full distribution, including the power law tail, is
created via the use of absolutely identical agents. It is further demonstrated
that a simple scheme of compulsory saving could eliminate poverty at little
cost to the taxpayer.Comment: 45 pages of text, 36 figure
Logistic regression models to predict solvent accessible residues using sequence- and homology-based qualitative and quantitative descriptors applied to a domain-complete X-ray structure learning set
A working example of relative solvent accessibility (RSA) prediction for proteins is presented. Novel logistic regression models with various qualitative descriptors that include amino acid type and quantitative descriptors that include 20- and six-term sequence entropy have been built and validated. A domain-complete learning set of over 1300 proteins is used to fit initial models with various sequence homology descriptors as well as query residue qualitative descriptors. Homology descriptors are derived from BLASTp sequence alignments, whereas the RSA values are determined directly from the crystal structure. The logistic regression models are fitted using dichotomous responses indicating buried or accessible solvent, with binary classifications obtained from the RSA values. The fitted models determine binary predictions of residue solvent accessibility with accuracies comparable to other less computationally intensive methods using the standard RSA threshold criteria 20 and 25% as solvent accessible. When an additional non-homology descriptor describing Lobanov–Galzitskaya residue disorder propensity is included, incremental improvements in accuracy are achieved with 25% threshold accuracies of 76.12 and 74.45% for the Manesh-215 and CASP(8+9) test sets, respectively. Moreover, the described software and the accompanying learning and validation sets allow students and researchers to explore the utility of RSA prediction with simple, physically intuitive models in any number of related applications
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