A knowledge-based decision support system for payload scheduling

This paper presents the development of a prototype Knowledge-based Decision Support System, currently under development, for scheduling payloads/experiments on space station missions. The DSS is being built on Symbolics, a Lisp machine, using KEE, a commercial knowledge engineering tool

LISP based simulation generators for modeling complex space processes

The development of a simulation assistant for modeling discrete event processes is presented. Included are an overview of the system, a description of the simulation generators, and a sample process generated using the simulation assistant

Automatic programming of simulation models

The objective of automatic programming is to improve the overall environment for describing the program. This improved environment is realized by a reduction in the amount of detail that the programmer needs to know and is exposed to. Furthermore, this improved environment is achieved by a specification language that is more natural to the user's problem domain and to the user's way of thinking and looking at the problem. The goal of this research is to apply the concepts of automatic programming (AP) to modeling discrete event simulation system. Specific emphasis is on the design and development of simulation tools to assist the modeler define or construct a model of the system and to then automatically write the corresponding simulation code in the target simulation language, GPSS/PC. A related goal is to evaluate the feasibility of various languages for constructing automatic programming simulation tools

Automatic programming of simulation models

The concepts of software engineering were used to improve the simulation modeling environment. Emphasis was placed on the application of an element of rapid prototyping, or automatic programming, to assist the modeler define the problem specification. Then, once the problem specification has been defined, an automatic code generator is used to write the simulation code. The following two domains were selected for evaluating the concepts of software engineering for discrete event simulation: manufacturing domain and a spacecraft countdown network sequence. The specific tasks were to: (1) define the software requirements for a graphical user interface to the Automatic Manufacturing Programming System (AMPS) system; (2) develop a graphical user interface for AMPS; and (3) compare the AMPS graphical interface with the AMPS interactive user interface

Study of the continuous grinding of steeped endosperm from sorghum grain in the production of starch

Digitized by Kansas State University Librarie

Using automatic programming for simulating reliability network models

This paper presents the development of an automatic programming system for assisting modelers of reliability networks to define problems and then automatically generate the corresponding code in the target simulation language GPSS/PC

Selecting normalization genes for small diagnostic microarrays

BACKGROUND: Normalization of gene expression microarrays carrying thousands of genes is based on assumptions that do not hold for diagnostic microarrays carrying only few genes. Thus, applying standard microarray normalization strategies to diagnostic microarrays causes new normalization problems. RESULTS: In this paper we point out the differences of normalizing large microarrays and small diagnostic microarrays. We suggest to include additional normalization genes on the small diagnostic microarrays and propose two strategies for selecting them from genomewide microarray studies. The first is a data driven univariate selection of normalization genes. The second is multivariate and based on finding a balanced diagnostic signature. Finally, we compare both methods to standard normalization protocols known from large microarrays. CONCLUSION: Not including additional genes for normalization on small microarrays leads to a loss of diagnostic information. Using house keeping genes from the literature for normalization fails to work for certain datasets. While a data driven selection of additional normalization genes works well, the best results were obtained using a balanced signature

Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors

Penalized regression is an attractive framework for variable selection problems. Often, variables possess a grouping structure, and the relevant selection problem is that of selecting groups, not individual variables. The group lasso has been proposed as a way of extending the ideas of the lasso to the problem of group selection. Nonconvex penalties such as SCAD and MCP have been proposed and shown to have several advantages over the lasso; these penalties may also be extended to the group selection problem, giving rise to group SCAD and group MCP methods. Here, we describe algorithms for fitting these models stably and efficiently. In addition, we present simulation results and real data examples comparing and contrasting the statistical properties of these methods

Preparation of porous thin-film polymethylsiloxane microparticles in a W/O emulsion system

Porous thin-film polymethylsiloxane microparticles have been prepared successfully from octyltrichlorosilane and methyltrichlorosilane in (water/oil) W/O emulsion systems by using several oil phases and changing the amount of the silanes or of the surfactant Span 60. Hollow microspheres of various shell thicknesses (120-180 nm) and high surface area were prepared by using four types of nonpolar solvents as the oil phase of the W/O emulsion system. The diameter of the spheres can also be controlled (1-1.6 mu m) by using different oil phases. The results of thermal analysis, nitrogen adsorption isotherm, infrared spectra and X-ray diffraction data showed that hollow microspheres of amorphous polymethylsiloxane with high surface area (360-385 m(2)g(-1)) can be obtained by heating the spheres in air at 673 K; the polymethylsiloxane microspheres become nonporous silica particles after calcination at 873 K for 3 h. Cup-shape microparticles of polymethylsiloxane with nano-order thickness (20-120 nm) were prepared by reducing the amount of silanes in the mixture. Small hollow particles were prepared by replacing a portion of the octyltrichlorosilane with Span 60.ArticlePOLYMER JOURNAL. 47(6): 449-455 (2015)journal articl