An additive utility mixed integer programming model for nonlinear discriminant analysis

Mathematical programming (MP) discriminant analysis models can be used to develop classification models for assigning observations of unknown class membership to one of a number of specified classes using values of a set of features associated with each observation. Since most MP discriminant analysis models generate linear discriminant functions, these MP models are generally used to develop linear classification models. Nonlinear classifiers may, however, have better classification performance than linear classifiers. In this paper, a mixed integer programming model is developed to generate nonlinear discriminant functions composed of monotone piecewise-linear marginal utility functions for each feature and the cut-off value for class membership. It is also shown that this model can be extended for feature selection. The performance of this new MP model for two-group discriminant analysis is compared with statistical discriminant analysis and other MP discriminant analysis models using a real problem and a number of simulated problem sets.     

Efficient synthesis of a 7-azabicyclo[2.2.1]heptane based GlyT1 uptake inhibitor

An efficient synthetic route based on generation and subsequent electrophilic reaction of a Boc-protected azabicyclo[2.2.1]heptane anion to prepare a potent GlyT1 uptake inhibitor (1) is described.     

Nitrile functionalized methimazole-based ionic liquids

The alkylation reaction of 2-mercapto-1-methylimidazole 1b with 2-chloroacetonitrile and 2-chloropropionitrile produced S-alkyl methimazole chlorides 2a and 2b which were subjected to anion metathesis with lithium bis(trifluoromethanesulfonyl)amide, LiNTf(2), to afford nitrile functionalized methimazole-based room temperature ionic liquids 3a and 3b in 94% and 89% yields, respectively. Ionic liquids 3a and 3b have reasonably wide electrochemical windows. The efficient extraction of Ag(+) from aqueous media into 3a and 3b is also reported.     

Comparison of multimesh hp-FEM to interpolation and projection methods for spatial coupling of thermal and neutron diffusion calculations

Multiphysics solution challenges are legion within the field of nuclear reactor design and analysis. One major issue concerns the coupling between heat and neutron flow (neutronics) within the reactor assembly. These phenomena are usually very tightly interdependent, as large amounts of heat are quickly produced with an increase in fission events within the fuel, which raises the temperature that affects the neutron cross section of the fuel. Furthermore, there typically is a large diversity of time and spatial scales between mathematical models of heat and neutronics. Indeed, the different spatial resolution requirements often lead to the use of very different meshes for the two phenomena. As the equations are coupled, one must take care in exchanging solution data between them, or significant error can be introduced into the coupled problem. We propose a novel approach to the discretization of the coupled problem on different meshes based on an adaptive multimesh higher-order finite element method (hp-FEM), and compare it to popular interpolation and projection methods. We show that the multimesh hp-FEM method is significantly more accurate than the interpolation and projection approaches considered in this study.     

Asymptotic formulae for likelihood-based tests of new physics

We describe likelihood-based statistical tests for use in high energy physics for the discovery of new phenomena and for construction of confidence intervals on model parameters. We focus on the properties of the test procedures that allow one to account for systematic uncertainties. Explicit formulae for the asymptotic distributions of test statistics are derived using results of Wilks and Wald. We motivate and justify the use of a representative data set, called the “Asimov data set”, which provides a simple method to obtain the median experimental sensitivity of a search or measurement as well as fluctuations about this expectation.     

New concepts for radiometric measurements of environmental samples

The Radiation Detection and Nuclear Sciences Group at Pacific Northwest National Laboratory has a long history in conducting measurements of radioisotopes for various applications. This experience includes ultra-low background measurements, arrays of germanium detectors, automated sampling and measurement systems and coincidence measurement systems. A recent lab-supported effort has been studying how these capabilities, both in terms of hardware and experience, can be leveraged to enable environmental sampling measurements. One area of interest is the release of fission products and actinides into the environment from a reactor incident. While the initial survey of this area is still under way, one isotope of interest that surfaced early in the study is ²³⁸Pu. Existing techniques to assay this isotope suffer from measurement challenges. In alpha counting, there can be significant interference with ²⁴¹Am, while in mass spectrometry, there can be interference with ²³⁸U. The authors are developing the concept for a detector that through coincidence counting techniques can distinguish ²³⁸Pu and ²⁴¹Am. In addition, we will design the system to conduct radiometric measurements of other plutonium isotopes to enable a direct comparison of those isotopes. We will present our concept of the detector system for ²³⁸Pu, as well as discuss other radiometric measurements of fission products and actinides with which we intend to advance the state of the art for environmental measurements.     

A Linear Programming Model for an Integrated Crop and Intensive Beef Production Enterprise

In an integrated crop and intensive beef production enterprise, some of the feedstuffs used for cattle feeding are grown by the enterprise. In evaluating the performance of operations of this type, the interactions between the crop and beef production parts of the enterprise must be taken into account. A linear programming (L.P.) model of this type of enterprise is developed, with the coefficients of the animal feeding activities being derived from a dynamic programming (D.P.) model. This D.P. model is used to determine the least-cost method of feeding animals to produce a specified liveweight gain using rations formulated by L.P. from a given set of feedstuffs. This approach allows the interactions between crop and livestock production to be taken into account without the need to make assumptions about the source of feedstuffs or the rate of liveweight gain.     

Applications of rule-induction in the derivation of quantitative structure-activity relationships

Summary Recently, methods have been developed in the field of Artificial Intelligence (AI), specifically in the expert systems area using rule-induction, designed to extract rules from data. We have applied these methods to the analysis of molecular series with the objective of generating rules which are predictive and reliable.The input to rule-induction consists of a number of examples with known outcomes (a training set) and the output is a tree-structured series of rules. Unlike most other analysis methods, the results of the analysis are in the form of simple statements which can be easily interpreted. These are readily applied to new data giving both a classification and a probability of correctness.Rule-induction has been applied to in-house generated and published QSAR datasets and the methodology, application and results of these analyses are discussed.The results imply that in some cases it would be advantageous to use rule-induction as a complementary technique in addition to conventional statistical and pattern-recognition methods.     

Sustainable Yield Analysis in a Multicohort Single-species Fishery: A Mathematical Programming Approach

The concept of sustainable yield, i.e. the fish catch that can be maintained in the long run from a fishery in a steady state, is widely used in fisheries management. In traditional methods of sustainable yield analysis, based on the Schaefer model of a fish stock, the age structure of the stock is ignored. Approaches based on the Beverton-Holt multicohort fish population model take account of age structure but assume that instantaneous natural and fishing mortality rates are constant throughout the year. Using a fish population model in which this assumption is not required, a mixed integer programming model is developed for the analysis of a multicohort single-species fishery in a steady state. This new method of sustainable yield analysis is demonstrated using data for the western mackerel fishery. Comparisons with results from other studies of this fishery are presented.