Determination of pore size distributions by regularization and finite element collocation

The problem of extracting pore size distributions from characterization data is solved here with particular reference to adsorption. The technique developed is based on a finite element collocation discretization of the adsorption integral, with fitting of the isotherm data by least squares using regularization. A rapid and simple technique for ensuring non-negativity of the solutions is also developed which modifies the original solution having some negativity. The technique yields stable and converged solutions, and is implemented in a package RIDFEC. The package is demonstrated to be robust, yielding results which are less sensitive to experimental error than conventional methods, with fitting errors matching the known data error. It is shown that the choice of relative or absolute error norm in the least-squares analysis is best based on the kind of error in the data. (C) 1998 Elsevier Science Ltd. All rights reserved.

Monte Carlo evidence on cointegration and causation

The small sample performance of Granger causality tests under different model dimensions, degree of cointegration, direction of causality, and system stability are presented. Two tests based on maximum likelihood estimation of error-correction models (LR and WALD) are compared to a Wald test based on multivariate least squares estimation of a modified VAR (MWALD). In large samples all test statistics perform well in terms of size and power. For smaller samples, the LR and WALD tests perform better than the MWALD test. Overall, the LR test outperforms the other two in terms of size and power in small samples.

Applying linear time-varying constraints to econometric models: With an application to demand systems

When linear equality constraints are invariant through time they can be incorporated into estimation by restricted least squares. If, however, the constraints are time-varying, this standard methodology cannot be applied. In this paper we show how to incorporate linear time-varying constraints into the estimation of econometric models. The method involves the augmentation of the observation equation of a state-space model prior to estimation by the Kalman filter. Numerical optimisation routines are used for the estimation. A simple example drawn from demand analysis is used to illustrate the method and its application.

Trade relationships between the Gulf Cooperation Council and the European Union

This paper examines the trade relationship between the Gulf Cooperation Council (GCC) and the European Union (EU). A simultaneous equation regression model is developed and estimated to assist with the analysis. The regression results, using both the two stage least squares (2SLS) and ordinary least squares (OLS) estimation methods, reveal the existence of feedback effects between the two economic integrations. The results also show that during times of slack in oil prices, the GCC income from its investments overseas helped to finance its imports from the EU.

Using the EM Algorithm to Train Neural Networks: Misconceptions and a New Algorithm for Multiclass Classification

The expectation-maximization (EM) algorithm has been of considerable interest in recent years as the basis for various algorithms in application areas of neural networks such as pattern recognition. However, there exists some misconceptions concerning its application to neural networks. In this paper, we clarify these misconceptions and consider how the EM algorithm can be adopted to train multilayer perceptron (MLP) and mixture of experts (ME) networks in applications to multiclass classification. We identify some situations where the application of the EM algorithm to train MLP networks may be of limited value and discuss some ways of handling the difficulties. For ME networks, it is reported in the literature that networks trained by the EM algorithm using iteratively reweighted least squares (IRLS) algorithm in the inner loop of the M-step, often performed poorly in multiclass classification. However, we found that the convergence of the IRLS algorithm is stable and that the log likelihood is monotonic increasing when a learning rate smaller than one is adopted. Also, we propose the use of an expectation-conditional maximization (ECM) algorithm to train ME networks. Its performance is demonstrated to be superior to the IRLS algorithm on some simulated and real data sets.

Factors in the selection of patients for conditional release from their first psychiatric hospitalization

Objective: This study examined a sample of patients in Victoria, Australia, to identify factors in selection for conditional release from an initial hospitalization that occurred within 30 days of entry into the mental health system. Methods: Data were from the Victorian Psychiatric Case Register. All patients first hospitalized and conditionally released between 1990 and 2000 were identified (N = 8,879), and three comparison groups were created. Two groups were hospitalized within 30 days of entering the system: those who were given conditional release and those who were not. A third group was conditionally released from a hospitalization that occurred after or extended beyond 30 days after system entry. Logistic regression identified characteristics that distinguished the first group. Ordinary least-squares regression was used to evaluate the contribution of conditional release early in treatment to reducing inpatient episodes, inpatient days, days per episode, and inpatient days per 30 days in the system. Results: Conditional release early in treatment was used for 11 percent of the sample, or more than a third of those who were eligible for this intervention. Factors significantly associated with selection for early conditional release were those related to a better prognosis ( initial hospitalization at a later age and having greater than an 11th grade education), a lower likelihood of a diagnosis of dementia or schizophrenia, involuntary status at first inpatient admission, and greater community involvement ( being employed and being married). When the analyses controlled for these factors, use of conditional release early in treatment was significantly associated with a reduction in use of subsequent inpatient care.

Theory manual to OctVCE – a cartesian cell CFD code with special application to blast wave problems, Department of Mechanical Engineering Report 2007/12

OctVCE is a cartesian cell CFD code produced especially for numerical simulations of shock and blast wave interactions with complex geometries, in particular, from explosions. Virtual Cell Embedding (VCE) was chosen as its cartesian cell kernel for its simplicity and sufficiency for practical engineering design problems. The code uses a finite-volume formulation of the unsteady Euler equations with a second order explicit Runge-Kutta Godonov (MUSCL) scheme. Gradients are calculated using a least-squares method with a minmod limiter. Flux solvers used are AUSM, AUSMDV and EFM. No fluid-structure coupling or chemical reactions are allowed, but gas models can be perfect gas and JWL or JWLB for the explosive products. This report also describes the code’s ‘octree’ mesh adaptive capability and point-inclusion query procedures for the VCE geometry engine. Finally, some space will also be devoted to describing code parallelization using the shared-memory OpenMP paradigm. The user manual to the code is to be found in the companion report 2007/13.

Residence time distributions of gas flowing through rotating drum bioreactors

Residence time distribution studies of gas through a rotating drum bioreactor for solid-state fermentation were performed using carbon monoxide as a tracer gas. The exit concentration as a function of time differed considerably from profiles expected for plug flow, plug flow with axial dispersion, and continuous stirred tank reactor (CSTR) models. The data were then fitted by least-squares analysis to mathematical models describing a central plug flow region surrounded by either one dead region (a three-parameter model) or two dead regions (a five-parameter model). Model parameters were the dispersion coefficient in the central plug flow region, the volumes of the dead regions, and the exchange rates between the different regions. The superficial velocity of the gas through the reactor has a large effect on parameter values. Increased superficial velocity tends to decrease dead region volumes, interregion transfer rates, and axial dispersion. The significant deviation from CSTR, plug flow, and plug flow with axial dispersion of the residence time distribution of gas within small-scale reactors can lead to underestimation of the calculation of mass and heat transfer coefficients and hence has implications for reactor design and scaleup. (C) 2001 John Wiley & Sons, Inc.

Stochastic structure and individual-tree growth models

The majority of past and current individual-tree growth modelling methodologies have failed to characterise and incorporate structured stochastic components. Rather, they have relied on deterministic predictions or have added an unstructured random component to predictions. In particular, spatial stochastic structure has been neglected, despite being present in most applications of individual-tree growth models. Spatial stochastic structure (also called spatial dependence or spatial autocorrelation) eventuates when spatial influences such as competition and micro-site effects are not fully captured in models. Temporal stochastic structure (also called temporal dependence or temporal autocorrelation) eventuates when a sequence of measurements is taken on an individual-tree over time, and variables explaining temporal variation in these measurements are not included in the model. Nested stochastic structure eventuates when measurements are combined across sampling units and differences among the sampling units are not fully captured in the model. This review examines spatial, temporal, and nested stochastic structure and instances where each has been characterised in the forest biometry and statistical literature. Methodologies for incorporating stochastic structure in growth model estimation and prediction are described. Benefits from incorporation of stochastic structure include valid statistical inference, improved estimation efficiency, and more realistic and theoretically sound predictions. It is proposed in this review that individual-tree modelling methodologies need to characterise and include structured stochasticity. Possibilities for future research are discussed. (C) 2001 Elsevier Science B.V. All rights reserved.

Testing market efficiency: Evidence from the NFL sports betting market

This article examines the efficiency of the National Football League (NFL) betting market. The standard ordinary least squares (OLS) regression methodology is replaced by a probit model. This circumvents potential econometric problems, and allows us to implement more sophisticated betting strategies where bets are placed only when there is a relatively high probability of success. In-sample tests indicate that probit-based betting strategies generate statistically significant profits. Whereas the profitability of a number of these betting strategies is confirmed by out-of-sample testing, there is some inconsistency among the remaining out-of-sample predictions. Our results also suggest that widely documented inefficiencies in this market tend to dissipate over time.

A systematic study of the microwave and thermal cure kinetics of the TGDDM/DDS and TGDDM/DDM epoxy-amine resin systems

The microwave and thermal cure processes for the epoxy-amine systems N,N,N',N'-tetraglycidyl-4,4'-diaminodiphenyl methane (TGDDM) with diaminodiphenyl sulfone (DDS) and diaminodiphenyl methane (DDM) have been investigated. The DDS system was studied at a single cure temperature of 433 K and a single stoichiometry of 27 wt% and the DDM system was studied at two stoichiometries, 19 and 32 wt%, and a range temperatures between 373 and 413 K. The best values the kinetic rate parameters for the consumption of amines have been determined by a least squares curve Ft to a model for epoxy-amine cure. The activation energies for the rate parameters for the MY721/DDM system were determined as was the overall activation energy for the cure reaction which was found to be 62 kJ mol(-1). No evidence was found for any specific effect of the microwave radiation on the rate parameters, and the systems were both found to be characterized by a negative substitution effect. Copyright (C) 2001 John Wiley & Sons, Ltd.

The copolymerization of N-vinyl-2-pyrrolidone with 2-hydroxyethyl methacrylate

The bulk free radical copolymerization of 2-hydroxyethyl methacrylate (HEMA) with N-vinyl-2-pyrrolidone (VP) was carried out to low conversions at 50 degreesC, using benzoyl peroxide (BPO) as initiator. The compositions of the copolymers; were determined using C-13 NMR spectroscopy. The conversion of monomers to polymers was studied using FT-NIR spectroscopy in order to predict the extent of conversion of monomer to polymer. From model fits to the composition data, a statistical F-test revealed that die penultimate model describes die copolymerization better than die terminal model. Reactivity ratios were calculated by using a non-linear least squares analysis (NLLS) and r(H) = 8.18 and r(V) = 0.097 were found to be the best fit values of the reactivity ratios for the terminal model and r(HH) = 12.0, r(VH) = 2.20, r(VV) = 0.12 and r(HV) = 0.03 for the penultimate model. Predictions were made for changes in compositions as a function of conversion based upon the terminal and penultimate models.

A systematic study of the microwave and thermal cure kinetics of the DGEBA/DDS and DGEBA/DDM epoxy-amine resin systems

The microwave and thermal cure processes for the epoxy-amine systems (epoxy resin diglycidyl ether of bisphenol A, DGEBA) with 4,4'-diaminodiphenyl sulphone (DDS) and 4,4'-diaminodiphenyl methane (DDM) have been investigated for 1:1 stoichiometries by using fiber-optic FT-NIR spectroscopy. The DGEBA used was in the form of Ciba-Geigy GY260 resin. The DDM system was studied at a single cure temperature of 373 K and a single stoichiometry of 20.94 wt% and the DDS system was studied at a stoichiometry of 24.9 wt% and a range of temperatures between 393 and 443 K. The best values of the kinetic rate parameters for the consumption of amines have been determined by a least squares curve fit to a model for epoxy/amine cure. The activation energies for the polymerization of the DGEBA/DDS system were determined for both cure processes and found to be 66 and 69 kJ mol(-1) for the microwave and thermal cure processes, respectively. No evidence was found for any specific effect of the microwave radiation on the rate parameters, and the systems were both found to be characterized by a negative substitution effect. Copyright (C) 2002 John Wiley Sons, Ltd.