Accounting and business practice. Incoming business forms. Tablet 1-4 for sets 3-7.

Mode of access: Internet.

Operator, organizational, direct support, and general support maintenance manual : motor generator, power supply generator, 60 kw, 120/208, 240/416 volt, 3-phase, 4 wire service, 400 hertz, DC component output, 28 volt, 10 amp, motor input AC, 416 volt, 3-phase, 60 hertz, portable skid mounted (General Dynamics Corp. model 14105008), FSN 6125-917-2244.

"June 1971."

Direct support and general support maintenance repair parts and special tools lists : dryer, aggregate, diesel engine driven, 4 wheel mtd., NSN 3895-00-236-8123, Barber-Greene MDL DA-60 : component of mixing plant asphalt, 100-150 ton per hour, NSN 3895-00-936-8613, Barber-Greene MDL KA-60.

Includes indexes.

Operator, organizational, and direct support maintenance manual : generator sets, gasoline engine w/carrying sound housing case, PU-422B/U, 0.4 KW, AC, 115V, 400 Hz and 100V, DC, 29.3V, Admiral model GJ4266 ... PU-532/PPS-4, 0.3 KW, AC, 115V, 400 Hz, 30V, 17 amp, 24V, 2.5 amp, DC, Admiral model G-2633, FSN 6115-889-1212, Henry Spen model 909238-1B, FSN 6115-436-4230.

"March 1973."

Operator, organizational, direct and general support, and depot maintenance manual : motor generator power supply, generator - 60 KW, AC, 120/208V, 240/416V, 3 phase, 4 wire service, 400 cycle, DC component output, 28V, 10 amp, motor input, AC, 120/208V, 240/416V, 3 phase, 60 cycle, skid mounted (John R. Hollingsworth Co. model JHMX60G) FSN 61225-963-6712.

Includes index.

Organizational, direct and general support, and depot maintenance repair parts and special tools lists : motor generator, power supply generator, 60 kw, 120/208, 240/416 volt, 3-phase, 4 wire service, 400 hertz, DC component output, 28 volt, 10 amp, motor input AC, 416 volt, 3-phase, 60 hertz, portable skid mounted (General Dynamics Corp. model 14105008), FSN 6125-917-2244.

"November 1970."

Instructions for installing installation kit MK-1412/VRC-24 installed in truck, 3/4 ton 4x4, M37, or M37B1 (for radio sets AN/VRC-24, or AN/VRC-24A alone, or for retransmission with radio sets AN/GRC-3 through-8).

"12 July 1972."

Organizational maintenance repair parts and special tools list for recorder-reproducer sets, sound, AN/TNH-24(V)1, (NSN 5835-01-017-0958), AN/TNH-24(V)2, (NSN 5835-01-018-4197), AN/TNH-24(V)3, (NSN 5835-01-021-9681), and AN/TNH-24(V)4, (NSN 5835-01-021-9680).

Includes index.

The empirical basis of substance use disorders diagnosis: research recommendations for the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-V)

Aims This paper presents the recommendations, developed from a 3-year consultation process, for a program of research to underpin the development of diagnostic concepts and criteria in the Substance Use Disorders section of the Diagnostic and Statistical Manual of Mental Disorders (DSM) and potentially the relevant section of the next revision of the International Classification of Diseases (ICD). Methods A preliminary list of research topics was developed at the DSM-V Launch Conference in 2004. This led to the presentation of articles on these topics at a specific Substance Use Disorders Conference in February 2005, at the end of which a preliminary list of research questions was developed. This was further refined through an iterative process involving conference participants over the following year. Results Research questions have been placed into four categories: (1) questions that could be addressed immediately through secondary analyses of existing data sets; (2) items likely to require position papers to propose criteria or more focused questions with a view to subsequent analyses of existing data sets; (3) issues that could be proposed for literature reviews, but with a lower probability that these might progress to a data analytic phase; and (4) suggestions or comments that might not require immediate action, but that could be considered by the DSM-V and ICD 11 revision committees as part of their deliberations. Conclusions A broadly based research agenda for the development of diagnostic concepts and criteria for substance use disorders is presented.

Organic reactions in ionic liquids; Ionic liquid-accelerated three-component reaction: a rapid one-pot synthesis of 3-alkyl-5-[(Z)-arylmethylidene]-1,3-thiazolidine-2,4-diones

A rapid one-pot synthesis of 3-alkyl-5-[(Z)-arylme­thylidene]-1,3-thiazolidine-2,4-dionesis described that occurs in recyclable ionic liquid [bmim]PF6 (1-butyl-3-methylimidazolium hexafluorophosphate).Significant rate enhancement and good selectivity have been observed.

Sequential, Bayesian geostatistics:a principled method for large data sets

The principled statistical application of Gaussian random field models used in geostatistics has historically been limited to data sets of a small size. This limitation is imposed by the requirement to store and invert the covariance matrix of all the samples to obtain a predictive distribution at unsampled locations, or to use likelihood-based covariance estimation. Various ad hoc approaches to solve this problem have been adopted, such as selecting a neighborhood region and/or a small number of observations to use in the kriging process, but these have no sound theoretical basis and it is unclear what information is being lost. In this article, we present a Bayesian method for estimating the posterior mean and covariance structures of a Gaussian random field using a sequential estimation algorithm. By imposing sparsity in a well-defined framework, the algorithm retains a subset of “basis vectors” that best represent the “true” posterior Gaussian random field model in the relative entropy sense. This allows a principled treatment of Gaussian random field models on very large data sets. The method is particularly appropriate when the Gaussian random field model is regarded as a latent variable model, which may be nonlinearly related to the observations. We show the application of the sequential, sparse Bayesian estimation in Gaussian random field models and discuss its merits and drawbacks.

Projected sequential Gaussian processes: flexible interpolation for large data sets

Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential framework for inference in such projected processes is presented, where the observations are considered one at a time. We introduce a C++ library for carrying out such projected, sequential estimation which adds several novel features. In particular we have incorporated the ability to use a generic observation operator, or sensor model, to permit data fusion. We can also cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the variogram parameters is based on maximum likelihood estimation. We illustrate the projected sequential method in application to synthetic and real data sets. We discuss the software implementation and suggest possible future extensions.

Discriminant Sets of Families of Hyperbolic Polynomials of Degree 4 and 5

∗ Research partially supported by INTAS grant 97-1644

A Mathematical Basis for an Interval Arithmetic Standard

Basic concepts for an interval arithmetic standard are discussed in the paper. Interval arithmetic deals with closed and connected sets of real numbers. Unlike floating-point arithmetic it is free of exceptions. A complete set of formulas to approximate real interval arithmetic on the computer is displayed in section 3 of the paper. The essential comparison relations and lattice operations are discussed in section 6. Evaluation of functions for interval arguments is studied in section 7. The desirability of variable length interval arithmetic is also discussed in the paper. The requirement to adapt the digital computer to the needs of interval arithmetic is as old as interval arithmetic. An obvious, simple possible solution is shown in section 8.