Building better wildlife-habitat models

Wildlife-habitat models are an important tool in wildlife management toda?, and by far the majority of these predict aspects of species distribution (abundance or presence) as a proxy measure of habitat quality. Unfortunately, few are tested on independent data, and of those that are, few show useful predictive st;ill. We demonstrate that six critical assumptions underlie distribution based wildlife-habitat models, all of which must be valid for the model to predict habitat quality. We outline these assumptions in a mete-model, and discuss methods for their validation. Even where all sis assumptions show a high level of validity, there is still a strong likelihood that the model will not predict habitat quality. However, the meta-model does suggest habitat quality can be predicted more accurately if distributional data are ignored, and variables more indicative of habitat quality are modelled instead.

Estimation of contribution of changes in classic risk factors to trends in coronary-event rates across the WHO MONICA Project populations

Background From the mid-1980s to mid-1990s, the WHO MONICA Project monitored coronary events and classic risk factors for coronary heart disease (CHD) in 38 populations from 21 countries. We assessed the extent to which changes in these risk factors explain the variation in the trends in coronary-event rates across the populations. Methods In men and women aged 35-64 years, non-fatal myocardial infarction and coronary deaths were registered continuously to assess trends in rates of coronary events. We carried out population surveys to estimate trends in risk factors. Trends in event rates were regressed on trends in risk score and in individual risk factors. Findings Smoking rates decreased in most male populations but trends were mixed in women; mean blood pressures and cholesterol concentrations decreased, body-mass index increased, and overall risk scores and coronary-event rates decreased. The model of trends in 10-year coronary-event rates against risk scores and single risk factors showed a poor fit, but this was improved with a 4-year time lag for coronary events. The explanatory power of the analyses was limited by imprecision of the estimates and homogeneity of trends in the study populations. Interpretation Changes in the classic risk factors seem to partly explain the variation in population trends in CHD. Residual variance is attributable to difficulties in measurement and analysis, including time lag, and to factors that were not included, such as medical interventions. The results support prevention policies based on the classic risk factors but suggest potential for prevention beyond these.

Response to price and production risk: The case of Australian wheat

A model of Australian wheat grower supply response was specified under the constraints of price and yield uncertainty, risk aversion, partial adjustment, and quadratic costs. The model was solved to obtain area planted. The results of estimation indicate that risk arising from prices and climate have had a significant influence on producer decision making. The coefficient of relative risk aversion and short-run and long-run elasticities of supply with respect to price were calculated. Wheat growers' risk premium, expected at the start of the season for exposed price and yield risk, was 2.8 percent of revenue or 10.4 percent of profit as measured by producer surplus. (C) 2000 John Wiley & Sons, Inc.

A model for predicting the future incidence of coronary heart disease within percentiles of coronary heart disease risk

Background We present a method (The CHD Prevention Model) for modelling the incidence of fatal and nonfatal coronary heart disease (CHD) within various CHD risk percentiles of an adult population. The model provides a relatively simple tool for lifetime risk prediction for subgroups within a population. It allows an estimation of the absolute primary CHD risk in different populations and will help identify subgroups of the adult population where primary CHD prevention is most appropriate and cost-effective. Methods The CHD risk distribution within the Australian population was modelled, based on the prevalence of CHD risk, individual estimates of integrated CHD risk, and current CHD mortality rates. Predicted incidence of first fatal and nonfatal myocardial infarction within CHD risk strata of the Australian population was determined. Results Approximately 25% of CHD deaths were predicted to occur amongst those in the top 10 percentiles of integrated CHD risk, regardless of age group or gender. It was found that while all causes survival did not differ markedly between percentiles of CHD risk before the ages of around 50-60, event-free survival began visibly to differ about 5 years earlier. Conclusions The CHD Prevention Model provides a means of predicting future CHD incidence amongst various strata of integrated CHD risk within an adult population. It has significant application both in individual risk counselling and in the identification of subgroups of the population where drug therapy to reduce CHD risk is most cost-effective. J Cardiovasc Risk 8:31-37 (C) 2001 Lippincott Williams & Wilkins.

The moderating role of social support in Karasek's job strain model

This paper examines whether social support is a boundary-determining criterion in the job strain model of Karasek (1979). The particular focus is the extent to which different sources of social support, work overload and task control influence job satisfaction, depersonalization and supervisor assessments of work performance. Hypotheses are tested using prospective survey data from 80 clerical staff in a university setting. Results revealed 3-way interactions among levels of support (supervisor, co-worker, non-work), perceived task control and work overload on levels of work performance and employee adjustment (self-report). After controlling for levels of negative affect in all analyses, there was evidence that high levels of supervisor support mitigated against the negative effects of high strain jobs on levels of job satisfaction and reduced reported levels of depersonalization. Moreover, high levels of non-work support and co-worker support also mitigated against the negative effects of high strain jobs on levels of work performance. The results are discussed in terms of the importance of social support networks both at, and beyond, the work context.

Estimation of crystalline phase present in the glucose crystal-solution mixture by water activity measurement

The amount of crystalline fraction present in monohydrate glucose crystal-solution mixture up to 110% crystal in relation to solution (crystal:solution=110:100) was determined by water activity measurement. It was found that the water activity had a strong linear correlation (R-2=0.994) with the amount of glucose present above saturation. Difference in the water activities of the crystal-solution mixture (a(w1)) and the supersaturated solution (a(w2)) by re-dissolving the crystalline fraction allowed calculation of the amount of crystalline phase present (DeltaG) in the mixture by an equation DeltaG=846.97(a(w1)-a(w2)). Other methods such as Raoult's, Norrish and Money-Born equations were also tested for the prediction of water activity of supersaturated glucose solution. (C) 2003 Swiss Society of Food Science and Technology. Published by Elsevier Science Ltd. All rights reserved.

Analysis of dynamic process models for structural insight and model reduction .1. Structural identification measures

An important consideration in the development of mathematical models for dynamic simulation, is the identification of the appropriate mathematical structure. By building models with an efficient structure which is devoid of redundancy, it is possible to create simple, accurate and functional models. This leads not only to efficient simulation, but to a deeper understanding of the important dynamic relationships within the process. In this paper, a method is proposed for systematic model development for startup and shutdown simulation which is based on the identification of the essential process structure. The key tool in this analysis is the method of nonlinear perturbations for structural identification and model reduction. Starting from a detailed mathematical process description both singular and regular structural perturbations are detected. These techniques are then used to give insight into the system structure and where appropriate to eliminate superfluous model equations or reduce them to other forms. This process retains the ability to interpret the reduced order model in terms of the physico-chemical phenomena. Using this model reduction technique it is possible to attribute observable dynamics to particular unit operations within the process. This relationship then highlights the unit operations which must be accurately modelled in order to develop a robust plant model. The technique generates detailed insight into the dynamic structure of the models providing a basis for system re-design and dynamic analysis. The technique is illustrated on the modelling for an evaporator startup. Copyright (C) 1996 Elsevier Science Ltd

Estimation of the pore size of the large-conductance mechanosensitive ion channel of Escherichia coli

The open channel diameter of Escherichia coli recombinant large-conductance mechanosensitive ion channels (MscL) was estimated using the model of Hille (Hille, B. 1968. Pharmacological modifications of the sodium channels of frog nerve. J. Gen. Physiol. 51:199-219)that relates the pore size to conductance. Based on the MscL conductance of 3.8 nS, and assumed pore lengths, a channel diameter of 34 to 46 Angstrom was calculated. To estimate the pore size experimentally, the effect of large organic ions on the conductance of MscL was examined. Poly-L-lysines (PLLs) with a diameter of 37 Angstrom or larger significantly reduced channel conductance, whereas spermine (similar to 15 Angstrom), PLL19 (similar to 25 Angstrom) and 1,1'-bis-(3-(1'-methyl-(4,4'-bipyridinium)-1-yl)-propyl)-4,4'-bipyridinium (similar to 30 Angstrom) had no effect. The smaller organic ions putrescine, cadaverine, spermine, and succinate all permeated the channel. We conclude that the open pore diameter of the MscL is similar to 40 Angstrom, indicating that the MscL has one of the largest channel pores yet described. This channel diameter is consistent with the proposed homohexameric model of the MscL.

Potential demand for hedging by Australian wheat producers

The potential for hedging Australian wheat with the new Sydney Futures Exchange wheat contract is examined using a theoretical hedging model parametised from previous studies. The optimal hedging ratio for an 'average' wheat farmer was found to be zero under reasonable assumptions about transaction costs and based on previously published measures of risk aversion. The estimated optimal hedging ratios were found by simulation to be quite sensitive to assumptions about the degree of risk aversion. If farmers are significantly more risk averse than is currently believed, then there is likely to be an active interest in the new futures market.

A Comparison of Bayesian and Sampling Theory Inferences in a Probit Model

HE PROBIT MODEL IS A POPULAR DEVICE for explaining binary choice decisions in econometrics. It has been used to describe choices such as labor force participation, travel mode, home ownership, and type of education. These and many more examples can be found in papers by Amemiya (1981) and Maddala (1983). Given the contribution of economics towards explaining such choices, and given the nature of data that are collected, prior information on the relationship between a choice probability and several explanatory variables frequently exists. Bayesian inference is a convenient vehicle for including such prior information. Given the increasing popularity of Bayesian inference it is useful to ask whether inferences from a probit model are sensitive to a choice between Bayesian and sampling theory techniques. Of interest is the sensitivity of inference on coefficients, probabilities, and elasticities. We consider these issues in a model designed to explain choice between fixed and variable interest rate mortgages. Two Bayesian priors are employed: a uniform prior on the coefficients, designed to be noninformative for the coefficients, and an inequality restricted prior on the signs of the coefficients. We often know, a priori, whether increasing the value of a particular explanatory variable will have a positive or negative effect on a choice probability. This knowledge can be captured by using a prior probability density function (pdf) that is truncated to be positive or negative. Thus, three sets of results are compared:those from maximum likelihood (ML) estimation, those from Bayesian estimation with an unrestricted uniform prior on the coefficients, and those from Bayesian estimation with a uniform prior truncated to accommodate inequality restrictions on the coefficients.

Long-term survivor mixture model with random effects: application to a multi-centre clinical trial of carcinoma

A mixture model incorporating long-term survivors has been adopted in the field of biostatistics where some individuals may never experience the failure event under study. The surviving fractions may be considered as cured. In most applications, the survival times are assumed to be independent. However, when the survival data are obtained from a multi-centre clinical trial, it is conceived that the environ mental conditions and facilities shared within clinic affects the proportion cured as well as the failure risk for the uncured individuals. It necessitates a long-term survivor mixture model with random effects. In this paper, the long-term survivor mixture model is extended for the analysis of multivariate failure time data using the generalized linear mixed model (GLMM) approach. The proposed model is applied to analyse a numerical data set from a multi-centre clinical trial of carcinoma as an illustration. Some simulation experiments are performed to assess the applicability of the model based on the average biases of the estimates formed. Copyright (C) 2001 John Wiley & Sons, Ltd.

Communicating trustworthiness and building trust in interorganizational virtual organizations

We propose a theory of trust in interorganizational virtual organizations that focuses on how trustworthiness can be communicated and trust built in this environment. The theory highlights three issues that must be dealt with if the potential obstacles to the development of trust in the virtual context are to be overcome. These are communication of trustworthiness facilitated by reliable Information and Communication Technology (ICT), establishment of a common business understanding, and strong business ethics. We propose four specific propositions relating to these issues, and suggest topics to be explored in future research. (C) 2001 Elsevier Science Inc. All rights reserved.

A dynamic model with on-line identification for rotary sugar drying processes

A dynamic modelling methodology, which combines on-line variable estimation and parameter identification with physical laws to form an adaptive model for rotary sugar drying processes, is developed in this paper. In contrast to the conventional rate-based models using empirical transfer coefficients, the heat and mass transfer rates are estimated by using on-line measurements in the new model. Furthermore, a set of improved sectional solid transport equations with localized parameters is developed in this work to reidentified on-line using measurement data, the model is able to closely track the dynamic behaviour of rotary drying processes within a broad range of operational conditions. This adaptive model is validated against experimental data obtained from a pilot-scale rotary sugar dryer. The proposed modelling methodology can be easily incorporated into nonlinear model based control schemes to form a unified modelling and control framework.place the global correlation for the computation of solid retention time. Since a number of key model variables and parameters are identified on-line using measurement data, the model is able to closely track the dynamic behaviour of rotary drying processes within a broad range of operational conditions. This adaptive model is validated against experimental data obtained from a pilot-scale rotary sugar dryer. The proposed modelling methodology can be easily incorporated into nonlinear model based control schemes to form a unified modelling and control framework.

An adaptive neural-wavelet model for short term load forecasting

This paper proposed a novel model for short term load forecast in the competitive electricity market. The prior electricity demand data are treated as time series. The forecast model is based on wavelet multi-resolution decomposition by autocorrelation shell representation and neural networks (multilayer perceptrons, or MLPs) modeling of wavelet coefficients. To minimize the influence of noisy low level coefficients, we applied the practical Bayesian method Automatic Relevance Determination (ARD) model to choose the size of MLPs, which are then trained to provide forecasts. The individual wavelet domain forecasts are recombined to form the accurate overall forecast. The proposed method is tested using Queensland electricity demand data from the Australian National Electricity Market. (C) 2001 Elsevier Science B.V. All rights reserved.

n-fold Weibull multiplicative model

In this paper we study the n-fold multiplicative model involving Weibull distributions and examine some properties of the model. These include the shapes for the density and failure rate functions and the WPP plot. These allow one to decide if a given data set can be adequately modelled by the model. We also discuss the estimation of model parameters based on the WPP plot. (C) 2001 Elsevier Science Ltd. All rights reserved.