Discrete Conditional Phase-type model (DC_Ph) for patient waiting time with a logistic regression component to predict patient admission to hospital

Discrete Conditional Phase-type (DC-Ph) models are a family of models which represent skewed survival data conditioned on specific inter-related discrete variables. The survival data is modeled using a Coxian phase-type distribution which is associated with the inter-related variables using a range of possible data mining approaches such as Bayesian networks (BNs), the Naïve Bayes Classification method and classification regression trees. This paper utilizes the Discrete Conditional Phase-type model (DC-Ph) to explore the modeling of patient waiting times in an Accident and Emergency Department of a UK hospital. The resulting DC-Ph model takes on the form of the Coxian phase-type distribution conditioned on the outcome of a logistic regression model.

Assessment of the benefits of Discrete Conditional Survival Models in modelling ambulance response times

Many of the challenges faced in health care delivery can be informed through building models. In particular, Discrete Conditional Survival (DCS) models, recently under development, can provide policymakers with a flexible tool to assess time-to-event data. The DCS model is capable of modelling the survival curve based on various underlying distribution types and is capable of clustering or grouping observations (based on other covariate information) external to the distribution fits. The flexibility of the model comes through the choice of data mining techniques that are available in ascertaining the different subsets and also in the choice of distribution types available in modelling these informed subsets. This paper presents an illustrated example of the Discrete Conditional Survival model being deployed to represent ambulance response-times by a fully parameterised model. This model is contrasted against use of a parametric accelerated failure-time model, illustrating the strength and usefulness of Discrete Conditional Survival models.

Two-stage mixed discrete-continuous identification of radial basis function (RBF) neural models for nonlinear systems

The identification of nonlinear dynamic systems using radial basis function (RBF) neural models is studied in this paper. Given a model selection criterion, the main objective is to effectively and efficiently build a parsimonious compact neural model that generalizes well over unseen data. This is achieved by simultaneous model structure selection and optimization of the parameters over the continuous parameter space. It is a mixed-integer hard problem, and a unified analytic framework is proposed to enable an effective and efficient two-stage mixed discrete-continuous; identification procedure. This novel framework combines the advantages of an iterative discrete two-stage subset selection technique for model structure determination and the calculus-based continuous optimization of the model parameters. Computational complexity analysis and simulation studies confirm the efficacy of the proposed algorithm.

Discrete Conditional Phase-type models utilising classification trees: Application to modelling health service capacities

Discrete Conditional Phase-type (DC-Ph) models consist of a process component (survival distribution) preceded by a set of related conditional discrete variables. This paper introduces a DC-Ph model where the conditional component is a classification tree. The approach is utilised for modelling health service capacities by better predicting service times, as captured by Coxian Phase-type distributions, interfaced with results from a classification tree algorithm. To illustrate the approach, a case-study within the healthcare delivery domain is given, namely that of maternity services. The classification analysis is shown to give good predictors for complications during childbirth. Based on the classification tree predictions, the duration of childbirth on the labour ward is then modelled as either a two or three-phase Coxian distribution. The resulting DC-Ph model is used to calculate the number of patients and associated bed occupancies, patient turnover, and to model the consequences of changes to risk status.

Assessment of rolling resistance models in discrete element simulations

Particulate systems are of interest in many disciplines. They are often investigated using the discrete element method because of its capability to investigate particulate systems at the individual particle scale. To model the interaction between two particles and between a particle and a boundary, conventional discrete element models use springs and dampers in both the normal and tangential directions. The significance of particle rotation has been highlighted in both numerical studies and physical experiments. Several researchers have attempted to incorporate a rotational torque to account for the rolling resistance or rolling friction by developing different models. This paper presents a review of the commonly used models for rolling resistance and proposes a more general model. These models are classified into four categories according to their key characteristics. The robustness of these models in reproducing rolling resistance effects arising from different physical situations was assessed by using several benchmarking test cases. The proposed model can be seen to be more general and suitable for modelling problems involving both dynamic and pseudo-static regimes. An example simulation of the formation of a 2D sandpile is also shown. For simplicity, all formulations and examples are presented in 2D form, though the general conclusions are also applicable to 3D systems.

Adsorption isotherm models for basic dye adsorption by peat in single and binary component systems

Coloured effluents from textile industries are a problem in many rivers and waterways. Prediction of adsorption capacities of dyes by adsorbents is important in design considerations. The sorption of three basic dyes, namely Basic Blue 3, Basic Yellow 21 and Basic Red 22, onto peat is reported. Equilibrium sorption isotherms have been measured for the three single component systems. Equilibrium was achieved after twenty-one days. The experimental isotherm data were analysed using Langmuir, Freundlich, Redlich-Peterson, Temkin and Toth isotherm equations. A detailed error analysis has been undertaken to investigate the effect of using different error criteria for the determination of the single component isotherm parameters and hence obtain the best isotherm and isotherm parameters which describe the adsorption process. The linear transform model provided the highest R2 regression coefficient with the Redlich-Peterson model. The Redlich-Peterson model also yielded the best fit to experimental data for all three dyes using the non-linear error functions. An extended Langmuir model has been used to predict the isotherm data for the binary systems using the single component data. The correlation between theoretical and experimental data had only limited success due to competitive and interactive effects between the dyes and the dye-surface interactions.

Microscopic models for dielectric relaxation in disordered systems

It is shown how the Debye rotational diffusion model of dielectric relaxation of polar molecules (which may be described in microscopic fashion as the diffusion limit of a discrete time random walk on the surface of the unit sphere) may be extended to yield the empirical Havriliak-Negami (HN) equation of anomalous dielectric relaxation from a microscopic model based on a kinetic equation just as in the Debye model. This kinetic equation is obtained by means of a generalization of the noninertial Fokker-Planck equation of conventional Brownian motion (generally known as the Smoluchowski equation) to fractional kinetics governed by the HN relaxation mechanism. For the simple case of noninteracting dipoles it may be solved by Fourier transform techniques to yield the Green function and the complex dielectric susceptibility corresponding to the HN anomalous relaxation mechanism.

Willingness to Pay for Rural Landscape Improvements: Combining Mixed Logit and Random-Effects Models

This paper reports the findings from a discrete-choice experiment designed to estimate the economic benefits associated with rural landscape improvements in Ireland. Using a mixed logit model, the panel nature of the dataset is exploited to retrieve willingness-to-pay values for every individual in the sample. This departs from customary approaches in which the willingness-to-pay estimates are normally expressed as measures of central tendency of an a priori distribution. Random-effects models for panel data are subsequently used to identify the determinants of the individual-specific willingness-to-pay estimates. In comparison with the standard methods used to incorporate individual-specific variables into the analysis of discrete-choice experiments, the analytical approach outlined in this paper is shown to add considerable explanatory power to the welfare estimates.

Solving the advection-dispersion equation using the discrete puff particle method

A flexible, mass-conservative numerical technique for solving the advection-dispersion equation for miscible contaminant transport is presented. The method combines features of puff transport models from air pollution studies with features from the random walk particle method used in water resources studies, providing a deterministic time-marching algorithm which is independent of the grid Peclet number and scales from one to higher dimensions simply. The concentration field is discretised into a number of particles, each of which is treated as a point release which advects and disperses over the time interval. The dispersed puff is itself discretised into a spatial distribution of particles whose masses can be pre-calculated. Concentration within the simulation domain is then calculated from the mass distribution as an average over some small volume. Comparison with analytical solutions for a one-dimensional fixed-duration concentration pulse and for two-dimensional transport in an axisymmetric flow field indicate that the algorithm performs well. For a given level of accuracy the new method has lower computation times than the random walk particle method.

Thermogravimetric evaluation of perennial ryegrass (Lolium perenne) for the prediction of in vitro dry matter digestibility

Thermogravimetry (TG) can be used for assessing the compositional differences in grasses that relate to dry matter digestibility (DMD) determined by pepsin-cellulase assay. This investigation developed regression models for predicting DMD of herbage grass during one growing season using TG results. The calibration samples were obtained from a field trial of eight cultivars and two breeding lines. The harvested materials from five cuts were analysed by TG to identify differences in the combustion patterns within the range of 30-600 degrees C. The discrete results including weight loss, peak height, area, temperature, widths and residue of three decomposition peaks were regressed against the measured DMD values of the calibration samples. Similarly, continuous weight loss results of the same samples were also utilised to generate DMD models. The r(2) for validation of the discrete and the best continuous models were 0.90 and 0.95, respectively, and the two calibrations were validated using independent samples from 24 plots from a trial carried out in 2004. The standard error for prediction of the 24 samples by the discrete model (4.14%) was higher than that by the continuous model (2.98%). This study has shown that DMD of grass could be predicted from the TG results. The benefit of thermal analysis is the ability to detect and show changes in composition of cell wall fractions of grasses during different cuts in a year.

Testing the stability of the benefit transfer function for discrete choice contingent valuation data

This paper examines the stability of the benefit transfer function across 42 recreational forests in the British Isles. A working definition of reliable function transfer is Put forward, and a suitable statistical test is provided. A novel split sample method is used to test the sensitivity of the models' log-likelihood values to the removal of contingent valuation (CV) responses collected at individual forest sites, We find that a stable function improves Our measure of transfer reliability, but not by much. We conclude that, in empirical Studies on transferability, considerations of function stability are secondary to the availability and quality of site attribute data. Modellers' can study the advantages of transfer function stability vis-a-vis the value of additional information on recreation site attributes. (c) 2008 Elsevier GmbH. All rights reserved.

Differences in mouse models of diabetes mellitus in studies of male reproduction

Diabetes Mellitus (DM) has been found to have subtle yet profound effects on the metabolic status of the testis, the expression of numerous spermatogenic genes and is associated with increased numbers of sperm with nuclear DNA damage. The precise mechanism causing these detrimental effects remains unknown. The presence of increased levels of the most prominent member (carboxymethyllysine - CML) of the advanced glycation end product adducts and their receptor (RAGE) in the reproductive tract of DM men has provided a new avenue for research. As there are suspicions that the antibiotic (streptozotocin - STZ) employed to induce DM is also capable of causing oxidative stress and DNA damage, we compared CML and RAGE levels in the reproductive tract and sperm nDNA status of STZ mice with the levels in the Ins(2Akita) mouse to determine which more closely mimics the situation described in the human diabetic. CML was observed in the testes, epididymes and sperm of all animals. Sperm from DM mice showed particularly strong CML immunolocalization in the acrosomal cap, the equatorial region and whenever present, cytoplasmic droplets. Although increased, the level of CML on the sperm of the STZ and Ins(2Akita) DM mice did not reach statistical significance. RAGE was present on the developing acrosome and epididymal sperm of all animals and in discrete regions of the epididymes of the DM models. Only the epididymal sperm of the Ins(2Akita) mice were found to have significantly increased (p < 0.0001) nDNA damage. The Ins(2Akita) mouse therefore appears to more accurately reflect the conditions found in the human and, as such, is a more representative model for the study of diabetes and glycation's influence on male fertility.