Applications of computational modeling in ballistics

The development of the technology of ballistics as applied to gun launched Army weapon systems is the main objective of research at the U.S. Army Ballistic Research Laboratory (BRL). The primary research programs at the BRL consist of three major ballistic disciplines: exterior, interior, and terminal. The work done at the BRL in these areas was traditionally highly dependent on experimental testing. A considerable emphasis was placed on the development of computational modeling to augment the experimental testing in the development cycle; however, the impact of the computational modeling to this date is modest. With the availability of supercomputer computational resources recently installed at the BRL, a new emphasis on the application of computational modeling to ballistics technology is taking place. The major application areas are outlined which are receiving considerable attention at the BRL at present and to indicate the modeling approaches involved. An attempt was made to give some information as to the degree of success achieved and indicate the areas of greatest need

Artificial neural networks and physical modeling for determination of baseline consumption of CHP plants

An effective modeling technique is proposed for determining baseline energy consumption in the industry. A CHP plant is considered in the study that was subjected to a retrofit, which consisted of the implementation of some energy-saving measures. This study aims to recreate the post-retrofit energy consumption and production of the system in case it would be operating in its past configuration (before retrofit) i.e., the current consumption and production in the event that no energy-saving measures had been implemented. Two different modeling methodologies are applied to the CHP plant: thermodynamic modeling and artificial neural networks (ANN). Satisfactory results are obtained with both modeling techniques. Acceptable accuracy levels of prediction are detected, confirming good capability of the models for predicting plant behavior and their suitability for baseline energy consumption determining purposes. High level of robustness is observed for ANN against uncertainty affecting measured values of variables used as input in the models. The study demonstrates ANN great potential for assessing baseline consumption in energyintensive industry. Application of ANN technique would also help to overcome the limited availability of on-shelf thermodynamic software for modeling all specific typologies of existing industrial processes

SBML Reaction Finder: Retrieve and extract specific reactions from the BioModels database

Summary: The SBML Reaction Finder (SRF) application leverages the deep semantic annotations in the BioModels database to provide efficient retrieval and extraction of individual reactions from SBML models. We hope that the SRF will be useful to quantitative modelers who seek to accelerate their modeling efforts by reusing previously published representations of specific chemical reactions.

Availability and Implementation: The SRF is open source, coded in Java, and distributed under the Mozilla Pubic License Version 1.1. Windows, Macintosh and Linux distributions are available for download at 
http://sourceforge.net/projects/sbmlrxnfinder.
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The volatility of realized volatility

Using unobservable conditional variance as measure, latent-variable approaches, such as GARCH and stochastic-volatility models, have traditionally been dominating the empirical finance literature. In recent years, with the availability of high-frequency financial market data modeling realized volatility has become a new and innovative research direction. By constructing "observable" or realized volatility series from intraday transaction data, the use of standard time series models, such as ARFIMA models, have become a promising strategy for modeling and predicting (daily) volatility. In this paper, we show that the residuals of the commonly used time-series models for realized volatility exhibit non-Gaussianity and volatility clustering. We propose extensions to explicitly account for these properties and assess their relevance when modeling and forecasting realized volatility. In an empirical application for S&P500 index futures we show that allowing for time-varying volatility of realized volatility leads to a substantial improvement of the model's fit as well as predictive performance. Furthermore, the distributional assumption for residuals plays a crucial role in density forecasting. Klassifikation: C22, C51, C52, C5

A finite-valued solver for disjunctive fuzzy answer set programs

Fuzzy Answer Set Programming (FASP) is a declarative programming paradigm which extends the flexibility and expressiveness of classical Answer Set Programming (ASP), with the aim of modeling continuous application domains. In contrast to the availability of efficient ASP solvers, there have been few attempts at implementing FASP solvers. In this paper, we propose an implementation of FASP based on a reduction to classical ASP. We also develop a prototype implementation of this method. To the best of our knowledge, this is the first solver for disjunctive FASP programs. Moreover, we experimentally show that our solver performs well in comparison to an existing solver (under reasonable assumptions) for the more restrictive class of normal FASP programs

Modeling Diminishing Marginal Returns: An Application to the Aircraft Availability Model

The Aircraft Availability Model (AAM) provides the Air Force with a worldwide peacetime requirement for reparable spare parts. This research models AAM methodology as it relates to the concept of diminishing marginal returns in resource application. Three separate modeling techniques are investigated with the goal of reformulating the AAM as a mathematical programming model that provides a comparable solution and a capable tool for the conduct of sensitivity analysis. The general formulations presented here are continuous non-linear, continuous linear, and piecewise linear discrete/continuous models. Two formulations of the piecewise linear discrete/continuous model are presented. The piecewise linear model based on AAM sort values shows the dominance of an optimization routine relative to the AAM shopping list greedy heuristic. The piecewise linear model based on availability rates provides the capability to maximize the mission design series (MDS) availability level. It has the potential to obtain the highest possible MDS availability relative to reparable spares inventory levels. This mathematical model is discussed in complete detail as a robust platform for conducting extensive post-optimality analysis

The Volatility of Realized Volatility

Using unobservable conditional variance as measure, latent–variable approaches, such as GARCH and stochastic–volatility models, have traditionally been dominating the empirical finance literature. In recent years, with the availability of high–frequency financial market data modeling realized volatility has become a new and innovative research direction. By constructing “observable” or realized volatility series from intraday transaction data, the use of standard time series models, such as ARFIMA models, have become a promising strategy for modeling and predicting (daily) volatility. In this paper, we show that the residuals of the commonly used time–series models for realized volatility exhibit non–Gaussianity and volatility clustering. We propose extensions to explicitly account for these properties and assess their relevance when modeling and forecasting realized volatility. In an empirical application for S&P500 index futures we show that allowing for time–varying volatility of realized volatility leads to a substantial improvement of the model’s fit as well as predictive performance. Furthermore, the distributional assumption for residuals plays a crucial role in density forecasting.Finance, Realized Volatility, Realized Quarticity, GARCH, Normal Inverse Gaussian Distribution, Density Forecasting

Estimation of soil phosphorus availability via visible and near-infrared spectroscopy

Spectroscopic techniques have great potential to evaluate soil properties. However, there are still questions regarding the applicability of spectroscopy to analyze soil phosphorous (P) availability, especially in tropical soils with low nutrient contents. Therefore, this study evaluated the possibility to estimate P availability in soil and its pools (labile, moderately labile and non-labile) via Vis-NIR spectroscopy based on intra-field calibration. We used soils from two different locations, a plot experiment that received application of phosphate fertilizers (Field-A) and a cultivated field where a grid soil sampling was performed (Field-B). We used the technique of diffuse reflectance in the visible and near-infrared (Vis-NIR) to obtain the spectra of soil samples. Predictive modeling for P availability and labile, moderately labile and non-labile pools of P in soil were obtained via partial least squares (PLS) regression; classification modeling was performed via Soft Independent Modeling of Class Analogy (SIMCA) on three P availability levels in order to overcome the limitation on quantifying P via Vis-NIR spectroscopy. We found that isolating P contents as the only variable (Field-A), Vis-NIR spectroscopy does not allow estimating P pools in the soil. In addition, quantification of P available in the soil via predictive modeling has limitations in tropical soils. On the other hand, estimating P content in soil through classes of availability is a feasible and promising alternative

Modeling emergency departments using discrete event simulation techniques

This paper discusses the application of Discrete Event Simulation (DES) for modeling the operations of an Emer-gency Department (ED). The model was developed to help the ED managers understand the behavior of the system with regards to the hidden causes of excessive waiting times. It served as a tool for assessing the impact of major departmental resources on Key Performance Indicators (KPIs), and was also used as a cost effective method for testing various what-if scenarios for possible system im-provement. The study greatly enhanced managers’ under-standing of the system and how patient flow is influenced by process changes and resource availability. The results of this work also helped managers to either reverse or modify some proposed changes to the system that were previously being considered. The results also show a possible reduc-tion of more than 20% in patients waiting times