Unit, direct support, and general support maintenance repair parts and special tools lists : truck, forklift, electric, solid rubber tires, 6000 lb capacity, 180 in. lift (Drexel Dynamics model FL60-EE5600) (Army model MHE206), NSN 3930-00-935-7867.

"November 1992."

Operator and organizational maintenance manual : truck, lift, fork, electric, solid rubber tires, 6,000 lbs. capacity, 180 in. lift, Drexel Dynamics model FL-60-EE5600, Army model MHE 206, FSN 3930-935-7867.

Includes index.

Organizational maintenance repair parts and special tools lists : trucks, lift, fork, electric, solid rubber tires, 4000 lb capacity, 100 in. lift, Army model MHE 218, NSN 3930-00-403-5661, Drexel Dynamics model FL-40-EE-6550 : 180 in. lift, Army model MHE 218, NSN 3930-00-403-5662, Drexel Dynamics model FL-40-EE-6250.

Includes index.

Direct and general support maintenance manual : truck, lift, fork, electric, solid rubber tires, Army model MHE-218, 4,000 lbs. capacity, 100 in. lift, Drexel Dynamics model FL-40-EE-6550, FSN 3930-403-5661, 4,000 lbs. capacity, 180 in. lift, Drexel Dynamics model FL-40-EE-6250, FSN 3930-403-5662.

Includes index.

Technical manual, operator and organizational maintenance manual : truck, lift, fork, electric, solid rubber tires, Army model MHE 218, 4,000 lbs. capacity, 100 in. lift, Drexel Dynamics model FL-40-EE-6550, FSN 3930-403-5661, 4,000 lbs. capacity, 180 in. lift, Drexel Dynamics model FL-40-EE-6250, FSN 3930-403-5662.

"April 1971."

Finite mixture regression model with random effects: application to neonatal hospital length of stay

A two-component mixture regression model that allows simultaneously for heterogeneity and dependency among observations is proposed. By specifying random effects explicitly in the linear predictor of the mixture probability and the mixture components, parameter estimation is achieved by maximising the corresponding best linear unbiased prediction type log-likelihood. Approximate residual maximum likelihood estimates are obtained via an EM algorithm in the manner of generalised linear mixed model (GLMM). The method can be extended to a g-component mixture regression model with the component density from the exponential family, leading to the development of the class of finite mixture GLMM. For illustration, the method is applied to analyse neonatal length of stay (LOS). It is shown that identification of pertinent factors that influence hospital LOS can provide important information for health care planning and resource allocation. (C) 2002 Elsevier Science B.V. All rights reserved.

Exact solution of the XXZ Gaudin model with generic open boundaries

The XXZ Gaudin model with generic integrable boundaries specified by generic non-diagonal K-matrices is studied. The commuting families of Gaudin operators are diagonalized by the algebraic Bethe ansatz method. The eigenvalues and the corresponding Bethe ansatz equations are obtained. (C) 2004 Elsevier B.V. All rights reserved.

A carbon activation model with application to longan seed char gasification

In this paper a new structural model is presented to describe the evolution of porosity of char during the gasification process. The model assumes the char structure to be composed of bundles of parallel graphite layers, and the reactivities of each layer with the gasification agent are assumed to be different to represent the different degree of heterogeneity of each layer (i.e. each layer will react with the gasification agent at a different rate). It is this difference in the reactivity that allows micropores to be created during the course of gasification. This simple structural model enables the evolution of pore volume, pore geometrical surface area and the pore size distribution to be described with respect to the extent of char burn-off. The model is tested against the experimental data of gasification of longan seed-derived char with carbon dioxide and it is found that the agreement between the model and the data is reasonably satisfactory, especially the evolution of surface area and pore volume with burn-off.

A(n-1) Gaudin model with open boundaries

The A(n-1) Gaudin model with integrable boundaries specified by non-diagonal K-matrices is studied. The commuting families of Gaudin operators are diagonalized by the algebraic Bethe ansatz method. The eigenvalues and the corresponding Bethe ansatz equations are obtained. (c) 2005 Elsevier B.V. All rights reserved.

Finding the balance: Living with memory loss

Since the late 1980s, it has been increasingly recognized that the experiences of people with dementia have been omitted from research in the area of dementia and memory loss. More recently, it has been accepted that people with dementia have insight into their condition and, therefore, the ability to contribute to research. A qualitative research project was undertaken with nine participants to explore the experiences and coping strategies of people with dementia. Interviews were undertaken and the data analysed using thematic analysis. Three major themes emerged: coming to terms with memory loss, maintaining control and independence, and the impact of illness on relationships. Understanding the reality for people is essential given that representations of the catastrophic impact of dementia generate high levels of anxiety and depression. Implications for nurses' practice include the need for skilled, well-paced, sensitive and ongoing information about the condition, along with the need to recognize and support the active coping strategies of people with memory loss.

A Mixture model with random-effects components for clustering correlated gene-expression profiles

Motivation: The clustering of gene profiles across some experimental conditions of interest contributes significantly to the elucidation of unknown gene function, the validation of gene discoveries and the interpretation of biological processes. However, this clustering problem is not straightforward as the profiles of the genes are not all independently distributed and the expression levels may have been obtained from an experimental design involving replicated arrays. Ignoring the dependence between the gene profiles and the structure of the replicated data can result in important sources of variability in the experiments being overlooked in the analysis, with the consequent possibility of misleading inferences being made. We propose a random-effects model that provides a unified approach to the clustering of genes with correlated expression levels measured in a wide variety of experimental situations. Our model is an extension of the normal mixture model to account for the correlations between the gene profiles and to enable covariate information to be incorporated into the clustering process. Hence the model is applicable to longitudinal studies with or without replication, for example, time-course experiments by using time as a covariate, and to cross-sectional experiments by using categorical covariates to represent the different experimental classes. Results: We show that our random-effects model can be fitted by maximum likelihood via the EM algorithm for which the E(expectation) and M(maximization) steps can be implemented in closed form. Hence our model can be fitted deterministically without the need for time-consuming Monte Carlo approximations. The effectiveness of our model-based procedure for the clustering of correlated gene profiles is demonstrated on three real datasets, representing typical microarray experimental designs, covering time-course, repeated-measurement and cross-sectional data. In these examples, relevant clusters of the genes are obtained, which are supported by existing gene-function annotation. A synthetic dataset is considered too.

A scatterometer neural network sensor model with input noise

The ERS-1 satellite carries a scatterometer which measures the amount of radiation scattered back toward the satellite by the ocean's surface. These measurements can be used to infer wind vectors. The implementation of a neural network based forward model which maps wind vectors to radar backscatter is addressed. Input noise cannot be neglected. To account for this noise, a Bayesian framework is adopted. However, Markov Chain Monte Carlo sampling is too computationally expensive. Instead, gradient information is used with a non-linear optimisation algorithm to find the maximum em a posteriori probability values of the unknown variables. The resulting models are shown to compare well with the current operational model when visualised in the target space.