Publication Bias in Meta-Analysis: Confidence Intervals for Rosenthal's Fail-Safe Number

The purpose of the present paper is to assess the efficacy of confidence intervals for Rosenthal's fail-safe number. Although Rosenthal's estimator is highly used by researchers, its statistical properties are largely unexplored. First of all, we developed statistical theory which allowed us to produce confidence intervals for Rosenthal's fail-safe number.This was produced by discerning whether the number of studies analysed in a meta-analysis is fixed or random. Each case produces different variance estimators. For a given number of studies and a given distribution, we provided five variance estimators. Confidence intervals are examined with a normal approximation and a nonparametric bootstrap. The accuracy of the different confidence interval estimates was then tested by methods of simulation under different distributional assumptions. The half normal distribution variance estimator has the best probability coverage. Finally, we provide a table of lower confidence intervals for Rosenthal's estimator.Comment: Published in the International Scholarly Research Notices in December 201

Geroch group for Einstein spaces and holographic integrability

We review how Geroch's reduction method is extended from Ricci-flat to Einstein spacetimes. The Ehlers-Geroch SL(2,R) group is still present in the three-dimensional sigma-model that captures the dynamics, but only a subgroup of it is solution-generating. Holography provides an alternative three-dimensional perspective to integrability properties of Einstein's equations in asymptotically anti-de Sitter spacetimes. These properties emerge as conditions on the boundary data (metric and energy-momentum tensor) ensuring that the hydrodynamic derivative expansion be resummed into an exact four-dimensional Einstein geometry. The conditions at hand are invariant under a set of transformations dubbed holographic U-duality group. The latter fills the gap left by the Ehlers-Geroch group in Einstein spaces, and allows for solution-generating maps mixing e.g. the mass and the nut charge.Comment: v1: 1+24 pages, Latex, imbrication with arXiv:1403.6511 in sections 2 and 3. arXiv admin note: text overlap with arXiv:1510.0645

MULTI AGENT-BASED ENVIRONMENTAL LANDSCAPE (MABEL) - AN ARTIFICIAL INTELLIGENCE SIMULATION MODEL: SOME EARLY ASSESSMENTS

The Multi Agent-Based Environmental Landscape model (MABEL) introduces a Distributed Artificial Intelligence (DAI) systemic methodology, to simulate land use and transformation changes over time and space. Computational agents represent abstract relations among geographic, environmental, human and socio-economic variables, with respect to land transformation pattern changes. A multi-agent environment is developed providing task-nonspecific problem-solving abilities, flexibility on achieving goals and representing existing relations observed in real-world scenarios, and goal-based efficiency. Intelligent MABEL agents acquire spatial expressions and perform specific tasks demonstrating autonomy, environmental interactions, communication and cooperation, reactivity and proactivity, reasoning and learning capabilities. Their decisions maximize both task-specific marginal utility for their actions and joint, weighted marginal utility for their time-stepping. Agent behavior is achieved by personalizing a dynamic utility-based knowledge base through sequential GIS filtering, probability-distributed weighting, joint probability Bayesian correlational weighting, and goal-based distributional properties, applied to socio-economic and behavioral criteria. First-order logics, heuristics and appropriation of time-step sequences employed, provide a simulation-able environment, capable of re-generating space-time evolution of the agents.Environmental Economics and Policy,

Factory Eco-Efficiency Modelling: Framework Development and Testing

Eco-efficiency is becoming an increasingly important organisational performance measure. Its indicators are regularly used alongside productivity, cost, quality, health and safety in operations and corporate social responsibility reporting. The purpose of this paper is to show an eco-efficiency modelling framework, and its application in the case of an automotive manufacturer. The framework composes, models and analyses resource and production data. Focus on energy, water distributions and material transformations in manufacturing, utility and facility assets are used to analyse eco-efficiency. Resources are examined in respect to three data granularity factors: subdivision, pulse, and magnitude. Models are linked with performance indicators to assess asset eco-efficiency. This work contributes to industrial sustainability literature by introducing a modelling framework that links with data granularity and eco-efficiency indicators

Efficient simulation of stochastic chemical kinetics with the Stochastic Bulirsch-Stoer extrapolation method

BackgroundBiochemical systems with relatively low numbers of components must be simulated stochastically in order to capture their inherent noise. Although there has recently been considerable work on discrete stochastic solvers, there is still a need for numerical methods that are both fast and accurate. The Bulirsch-Stoer method is an established method for solving ordinary differential equations that possesses both of these qualities.ResultsIn this paper, we present the Stochastic Bulirsch-Stoer method, a new numerical method for simulating discrete chemical reaction systems, inspired by its deterministic counterpart. It is able to achieve an excellent efficiency due to the fact that it is based on an approach with high deterministic order, allowing for larger stepsizes and leading to fast simulations. We compare it to the Euler ?-leap, as well as two more recent ?-leap methods, on a number of example problems, and find that as well as being very accurate, our method is the most robust, in terms of efficiency, of all the methods considered in this paper. The problems it is most suited for are those with increased populations that would be too slow to simulate using Gillespie’s stochastic simulation algorithm. For such problems, it is likely to achieve higher weak order in the moments.ConclusionsThe Stochastic Bulirsch-Stoer method is a novel stochastic solver that can be used for fast and accurate simulations. Crucially, compared to other similar methods, it better retains its high accuracy when the timesteps are increased. Thus the Stochastic Bulirsch-Stoer method is both computationally efficient and robust. These are key properties for any stochastic numerical method, as they must typically run many thousands of simulations