NEURAL NETWORKS IN FORECASTING AND DECISION MAKING

Neural networks (NN) have been widely touted as solving many forecasting and decision modeling problems. For example, they are argued to be able to model easily any type of parametric or non-parametric process and also automatically and optimally transform the input data. Also, they are easy to embed in information systems and they can learn how to perform simple forecasting and decision making tasks without human input. Our research-in-progress evaluates these claims. We will spend the first half of the session reviewing our work comparing neural networks to classical techniques in time series forecasting, regression-based causal forecasting, and regression-based decision models. In tile second half of the session, we will discuss the art and science of building these models. In Hill, O\u27Connor and Remus (1992), time series forecasts based on neural networks were compared with forecasts from six statistical time series methods (including exponential smoothing and Box-Jenkins) and two judgment-based methods; we did this for 111 real financial time series. The classical methods were all estimated by experts. Across all series, the neural networks did better than or as good as statistical and judgment methods. In Marquez et al. (forthcoming), data representing three common bivariate functional forms used in causal forecasting (linear, log-linear, and reciprocal) were generated and the performance of the neural network models was compared against the true regression model across differing functional forms, sample sizes, and noise levels. The results showed that neural network models perform within 2% of the mean absolute percentage error (MAPE); this is very good performance in the real world. This work is continuing as Marquez studies issues such as the vulnerability of neural networks and regression to multicolinearity, outliers, and other data problems. In Remus and Hill (forthcoming), tile production scheduling decisions as modeled by neural networks and regression-based decision rules for sixty-two decision makers were compared. Neural network models performed as well as but not better than those using the linear regression models. In Hill and Remus (forthcoming), the above research was continued and composite neural network models were estimated. The neural networks performed better than both the classical models and neural networks from the earlier study. The coinposite neural network also performed at least as well as classical composite models

Telling the tale of the first stars

HE 0107-5240 is a star in more than once sense of the word. Chemically, it is the most primitive object yet discovered, and it is at the centre of debate about the origins of the first elements in the Universe.Comment: 3 pages, 0 figures, published in Nature "News and Views," Apr. 24, 200

A modelling approach to carbon, water and energy feedbacks and interactions across the land-atmosphere interface.

The climate is changing and the rate of this change is expected to increase. In the 20th century global surface temperatures rose by 0.6 (±0.2) K. Based on current model predictions, and economic forecasts, global temperature increases of 1.4 to 5.8 K are expected over the period 1990 – 2100. One of the main drivers for this temperature increase is the build up of CO2 in the atmosphere which has been increasing since pre-industrial times. Pre-industrial concentrations of CO2 were bounded between 180 ppm and 300 ppm, however the current concentrations of 380 ppm are far in excess of these bounds. Further more, forecasts indicates that a further doubling in the next century is a distinct possibility. However making predictions about the future climate is difficult. Predicting the trajectory that the climate will take uses assumptions of economic growth, technological advances and ecological and physical processes. If we are to make informed decisions regarding the future of the planet, we have to account not only for future anthropogenic emissions and land use, but we also have to identify the response of the Earth system. By its very nature the Earth is immensely complex; processes, interactions and feedbacks exist which operate on vastly different spatial and temporal scales. Each of these processes has an associated level of uncertainty. This uncertainty propagates through models and the processes and feedbacks they simulate. One of our jobs as environmental scientists is to quantify and then reduce these uncertainties. Consequently it is critical to quantify the interactions of the land-surface and the atmosphere. The role of the land-surface is critical to the response of the Earth’s climate. All general circulation models and regional scale models need representations of the land-surface. A lot of the work concerning the land-surface aims to determine the land-surface partitioning of energy, the evapotranspiration of water and if the land-surface is a sink or a source of CO2. To do achieve this we need to understand (1) the underlying processes governing the response of the land-surface, (2) the response of these processes to perturbations from climate change and humans, (3) the temporal and spatial heterogeneity in these processes, and (4) the feedbacks that land-surface processes have with the climate. In this thesis I use a coupled atmosphere-biosphere model to show current understanding of the carbon, water and energy dynamics of the biosphere and the atmosphere to be consistent with both PBL and stand-based measurements. I then use the CAB model to investigate the strength of different feedbacks between the atmosphere and biosphere. Finally the model is then used in a Monte Carlo Bayesian inversion scheme to invert atmospheric measurements to infer information about surface parameters

Potential Sinks for Geologic Storage of CO2 Generated in the Carolinas

This document summarizes a scoping study of the current state of knowledge of carbon storage options for our geographic area. The focus is on one aspect of carbon capture and storage—identification of deep saline aquifers in which carbon dioxide (CO2 ) generated in the Carolinas might be stored. The study does not address other aspects of CO2 storage projects, such as capture and compression of the gas, well construction and development, or injection. Transport of CO2 is touched upon in this study but has not been fully addressed. The information contained in this document is primarily from review of published geologic literature and unpublished data. No field data collection has been completed as part of this study. Further work will be necessary to increase confidence in the suitability of the potential CO2 storage sites identified in this report. This study does not address the regulatory, environmental, or public policy issues associated with carbon storage, which are under development at this time.Duke Energy, Progress Energy, Santee Cooper Power, South Carolina Electric and Gas, Electric Power Research Institute (EPRI), Southern States Energy Board (SSEB)Bureau of Economic Geolog

Relationship between Thermodynamic Driving Force and One-Way Fluxes in Reversible Chemical Reactions

Chemical reaction systems operating in nonequilibrium open-system states arise in a great number of contexts, including the study of living organisms, in which chemical reactions, in general, are far from equilibrium. Here we introduce a theorem that relates forward and re-verse fluxes and free energy for any chemical process operating in a steady state. This rela-tionship, which is a generalization of equilibrium conditions to the case of a chemical process occurring in a nonequilibrium steady state, provides a novel equivalent definition for chemical reaction free energy. In addition, it is shown that previously unrelated theories introduced by Ussing and Hodgkin and Huxley for transport of ions across membranes, Hill for catalytic cycle fluxes, and Crooks for entropy production in microscopically reversible systems, are united in a common framework based on this relationship.Comment: 11 page

Tuning the Clock: Uranium and Thorium Chronometers Applied to CS 31082-001

We obtain age estimates for the progenitor(s) of the extremely metal-poor ([Fe/H = -2.9) halo star CS 31082-001, based on the recently reported first observation of a Uranium abundance in this (or any other) star. Age estimates are derived by application of the classical r-process model with updated nuclear physics inputs. The [U/Th] ratio yields an age of 13+-4 Gyr or 8+-4 Gyr, based on the use of the ETFSI-Q or the new HFBCS-1 nuclear mass models, respectively. Implications for Thorium chronometers are discussed.Comment: 5 pages incl. 1 figure, a shorter 3 page version will be published in the proceedings of the "Astrophysical Ages and Timescales" conference held in Hilo, Hawaii, Feb 5-9, 200

Near-Infrared Spectroscopy of Carbon-Enhanced Metal-Poor Stars. I. A SOAR/OSIRIS Pilot Study

We report on an abundance analysis for a pilot study of seven Carbon-Enhanced Metal-Poor (CEMP) stars, based on medium-resolution optical and near-infrared spectroscopy. The optical spectra are used to estimate [Fe/H], [C/Fe], [N/Fe], and [Ba/Fe] for our program stars. The near-infrared spectra, obtained during a limited early science run with the new SOAR 4.1m telescope and the Ohio State Infrared Imager and Spectrograph (OSIRIS), are used to obtain estimates of [O/Fe] and 12C/13C. The chemical abundances of CEMP stars are of importance for understanding the origin of CNO in the early Galaxy, as well as for placing constraints on the operation of the astrophysical s-process in very low-metallicity Asymptotic Giant Branch (AGB) stars. This pilot study includes a few stars with previously measured [Fe/H], [C/Fe], [N/Fe],[O/Fe], 12C/13C, and [Ba/Fe], based on high-resolution optical spectra obtained with large-aperture telescopes. Our analysis demonstrates that we are able to achieve reasonably accurate determinations of these quantities for CEMP stars from moderate-resolution optical and near-infrared spectra. This opens the pathway for the study of significantly larger samples of CEMP stars in the near future. Furthermore, the ability to measure [Ba/Fe] for (at least the cooler) CEMP stars should enable one to separate stars that are likely to be associated with s-process enhancements (the CEMP-s stars) from those that do not exhibit neutron-capture enhancements (the CEMP-no stars).Comment: 27 pages, including 5 tables, 6 figures, accepted for publication in The Astronomical Journa

A Search for Nitrogen-Enhanced Metal-Poor Stars

Theoretical models of very metal-poor intermediate-mass Asymptotic Giant Branch (AGB) stars predict a large overabundance of primary nitrogen. The very metal-poor, carbon-enhanced, s-process-rich stars, which are thought to be the polluted companions of now-extinct AGB stars, provide direct tests of the predictions of these models. Recent studies of the carbon and nitrogen abundances in metal-poor stars have focused on the most carbon-rich stars, leading to a potential selection bias against stars that have been polluted by AGB stars that produced large amounts of nitrogen, and hence have small [C/N] ratios. We call these stars Nitrogen-Enhanced Metal-Poor (NEMP) stars, and define them as having [N/Fe] > +0.5 and [C/N] < -0.5. In this paper, we report on the [C/N] abundances of a sample of 21 carbon-enhanced stars, all but three of which have [C/Fe] < +2.0. If NEMP stars were made as easily as Carbon-Enhanced Metal-Poor (CEMP) stars, then we expected to find between two and seven NEMP stars. Instead, we found no NEMP stars in our sample. Therefore, this observational bias is not an important contributor to the apparent dearth of N-rich stars. Our [C/N] values are in the same range as values reported previously in the literature (-0.5 to +2.0), and all stars are in disagreement with the predicted [C/N] ratios for both low-mass and high-mass AGB stars. We suggest that the decrease in [C/N] from the low-mass AGB models is due to enhanced extra-mixing, while the lack of NEMP stars may be caused by unfavorable mass ratios in binaries or the difficulty of mass transfer in binary systems with large mass ratios.Comment: 14 pages, 7 figures, to be published in Ap