The current account, fiscal policy, and medium-run income determination

This article presents a new framework for analyzing the simultaneous determination of current account imbalances and the path of national income. Using standard macroeconomic behavioral relationships, it first examines how and why current account deficits matter by investigating links between domestic consumption, government spending, output, saving, investment, interest rates, and capital flows. Central to the model is the distinction between aggregate output and expenditure that enables dissection of the effects of discretionary fiscal change on the current account and national income. The framework yields results relevant to the twin deficits hypothesis that are contrary to those of standard models.

Adaptive responses of wild mungbean (Vigna radiata ssp sublobata) to photo-thermal environment. I. Phenology

The phenology of 11 diverse accessions of wild mungbean was observed under natural and artificial photoperiod - temperature conditions, in order to examine whether genotypic differences might be attributed to adaptive responses to photo-thermal conditions. There was large variation in phenological response among accessions and across environments, much of which was due to differences in the duration of the pre-flowering phase. Accessions that flowered earlier tended to flower for longer, apart from 2 earlier flowering, inland Australian lines that were also earlier maturing. The patterns of response in time from sowing to flowering over environment were consistent with quantitative short-day photoperiodic adaptation, a conclusion supported by the effects of artificial day-length extension and by 'goodness of fit' of the observed responses to standard models relating rate of development to photoperiod and temperature. The fitted models indicated that rate of development towards flowering was hastened by warmer temperatures, and delayed by longer day lengths, with differential sensitivity between accessions to both factors. The models also suggested that photoperiod was more important for accessions collected closer to the equator, which were generally later flowering as a consequence. Conversely, temperature was relatively more important in lines from higher latitudes. Modelling also suggested that the period from first flowering to maturity was sensitive to photoperiod and temperature. Again, longer days appeared to prolong growth and delay maturity. However, cooler temperatures accelerated rather than slowed maturity, by suppressing further vegetative growth. The variation observed indicated that there is considerable scope for using the wild population to broaden the adaptation of cultivated mungbean. In particular, the unusual response of a late-flowering, photoperiod-insensitive accession warrants further study to establish whether the wild population contains a unique 'long juvenile' trait analogous to that being used for improving phenological adaptation in soybean.

Quantitative justification for target concentration intervention - parameter variability and predictive performance using population pharmacokinetic models for aminoglycosides

Aims [1] To quantify the random and predictable components of variability for aminoglycoside clearance and volume of distribution [2] To investigate models for predicting aminoglycoside clearance in patients with low serum creatinine concentrations [3] To evaluate the predictive performance of initial dosing strategies for achieving an aminoglycoside target concentration. Methods Aminoglycoside demographic, dosing and concentration data were collected from 697 adult patients (>=20 years old) as part of standard clinical care using a target concentration intervention approach for dose individualization. It was assumed that aminoglycoside clearance had a renal and a nonrenal component, with the renal component being linearly related to predicted creatinine clearance. Results A two compartment pharmacokinetic model best described the aminoglycoside data. The addition of weight, age, sex and serum creatinine as covariates reduced the random component of between subject variability (BSVR) in clearance (CL) from 94% to 36% of population parameter variability (PPV). The final pharmacokinetic parameter estimates for the model with the best predictive performance were: CL, 4.7 l h(-1) 70 kg(-1); intercompartmental clearance (CLic), 1 l h(-1) 70 kg(-1); volume of central compartment (V-1), 19.5 l 70 kg(-1); volume of peripheral compartment (V-2) 11.2 l 70 kg(-1). Conclusions Using a fixed dose of aminoglycoside will achieve 35% of typical patients within 80-125% of a required dose. Covariate guided predictions increase this up to 61%. However, because we have shown that random within subject variability (WSVR) in clearance is less than safe and effective variability (SEV), target concentration intervention can potentially achieve safe and effective doses in 90% of patients.

Using the canonical modelling approach to simplify the simulation of function in functional-structural plant models

Functional-structural plant models that include detailed mechanistic representation of underlying physiological processes can be expensive to construct and the resulting models can also be extremely complicated. On the other hand, purely empirical models are not able to simulate plant adaptability and response to different conditions. In this paper, we present an intermediate approach to modelling plant function that can simulate plant response without requiring detailed knowledge of underlying physiology. Plant function is modelled using a 'canonical' modelling approach, which uses compartment models with flux functions of a standard mathematical form, while plant structure is modelled using L-systems. Two modelling examples are used to demonstrate that canonical modelling can be used in conjunction with L-systems to create functional-structural plant models where function is represented either in an accurate and descriptive way, or in a more mechanistic and explanatory way. We conclude that canonical modelling provides a useful, flexible and relatively simple approach to modelling plant function at an intermediate level of abstraction.

Optimal Paths and Costs of Adjustment in Dynamic DEA Models: With Application to Chilean Department Stores

In this paper we propose a range of dynamic data envelopment analysis (DEA) models which allow information on costs of adjustment to be incorporated into the DEA framework. We first specify a basic dynamic DEA model predicated on a number or simplifying assumptions. We then outline a number of extensions to this model to accommodate asymmetric adjustment costs, non-static output quantities, non-static input prices, and non-static costs of adjustment, technological change, quasi-fixed inputs and investment budget constraints. The new dynamic DEA models provide valuable extra information relative to the standard static DEA models-they identify an optimal path of adjustment for the input quantities, and provide a measure of the potential cost savings that result from recognising the costs of adjusting input quantities towards the optimal point. The new models are illustrated using data relating to a chain of 35 retail department stores in Chile. The empirical results illustrate the wealth of information that can be derived from these models, and clearly show that static models overstate potential cost savings when adjustment costs are non-zero.

Designs for generalized linear models with several variables and model uncertainty

Standard factorial designs sometimes may be inadequate for experiments that aim to estimate a generalized linear model, for example, for describing a binary response in terms of several variables. A method is proposed for finding exact designs for such experiments that uses a criterion allowing for uncertainty in the link function, the linear predictor, or the model parameters, together with a design search. Designs are assessed and compared by simulation of the distribution of efficiencies relative to locally optimal designs over a space of possible models. Exact designs are investigated for two applications, and their advantages over factorial and central composite designs are demonstrated.

Prediction of protein continuum secondary structure with probabilistic models based on NMR solved structures

Background: The structure of proteins may change as a result of the inherent flexibility of some protein regions. We develop and explore probabilistic machine learning methods for predicting a continuum secondary structure, i.e. assigning probabilities to the conformational states of a residue. We train our methods using data derived from high-quality NMR models. Results: Several probabilistic models not only successfully estimate the continuum secondary structure, but also provide a categorical output on par with models directly trained on categorical data. Importantly, models trained on the continuum secondary structure are also better than their categorical counterparts at identifying the conformational state for structurally ambivalent residues. Conclusion: Cascaded probabilistic neural networks trained on the continuum secondary structure exhibit better accuracy in structurally ambivalent regions of proteins, while sustaining an overall classification accuracy on par with standard, categorical prediction methods.