Numerical modelling of single-layer folding: clarification of an issue regarding the possible effect of computer codes and the influence of initial irregularities

The influence of initial perturbation geometry and material propel-ties on final fold geometry has been investigated using finite-difference (FLAC) and finite-element (MARC) numerical models. Previous studies using these two different codes reported very different folding behaviour although the material properties, boundary conditions and initial perturbation geometries were similar. The current results establish that the discrepancy was not due to the different computer codes but due to the different strain rates employed in the two previous studies (i.e. 10(-6) s(-1) in the FLAC models and 10(-14) s(-1) in the MARC models). As a result, different parts of the elasto-viscous rheological field were bring investigated. For the same material properties, strain rate and boundary conditions, the present results using the two different codes are consistent. A transition in Folding behaviour, from a situation where the geometry of initial perturbation determines final fold shape to a situation where material properties control the final geometry, is produced using both models. This transition takes place with increasing strain rate, decreasing elastic moduli or increasing viscosity (reflecting in each case the increasing influence of the elastic component in the Maxwell elastoviscous rheology). The transition described here is mechanically feasible but is associated with very high stresses in the competent layer (on the order of GPa), which is improbable under natural conditions. (C) 2000 Elsevier Science Ltd. All rights reserved.

Using stochastic dynamic programming to determine optimal fire management for Banksia ornata

1. A model of the population dynamics of Banksia ornata was developed, using stochastic dynamic programming (a state-dependent decision-making tool), to determine optimal fire management strategies that incorporate trade-offs between biodiversity conservation and fuel reduction. 2. The modelled population of B. ornata was described by its age and density, and was exposed to the risk of unplanned fires and stochastic variation in germination success. 3. For a given population in each year, three management strategies were considered: (i) lighting a prescribed fire; (ii) controlling the incidence of unplanned fire; (iii) doing nothing. 4. The optimal management strategy depended on the state of the B. ornata population, with the time since the last fire (age of the population) being the most important variable. Lighting a prescribed fire at an age of less than 30 years was only optimal when the density of seedlings after a fire was low (< 100 plants ha(-1)) or when there were benefits of maintaining a low fuel load by using more frequent fire. 5. Because the cost of management was assumed to be negligible (relative to the value of the persistence of the population), the do-nothing option was never the optimal strategy, although lighting prescribed fires had only marginal benefits when the mean interval between unplanned fires was less than 20-30 years.

Modelling socially optimal land allocations for sugar cane growing in North Queensland: a linked mathematical programming and choice modelling study

A modelling framework is developed to determine the joint economic and environmental net benefits of alternative land allocation strategies. Estimates of community preferences for preservation of natural land, derived from a choice modelling study, are used as input to a model of agricultural production in an optimisation framework. The trade-offs between agricultural production and environmental protection are analysed using the sugar industry of the Herbert River district of north Queensland as an example. Spatially-differentiated resource attributes and the opportunity costs of natural land determine the optimal tradeoffs between production and conservation for a range of sugar prices.

Debt Maturity: Is Long-Term Debt Optimal?

We model and calibrate the arguments in favor and against short-term and long-term debt. These arguments broadly include: maturity premium, sustainability, and service smoothing. We use a dynamic-equilibrium model with tax distortions and government outlays uncertainty, and model maturity as the fraction of debt that needs to be rolled over every period. In the model, the benefits of defaulting are tempered by higher future interest rates. We then calibrate our artificial economy and solve for the optimal debt maturity for Brazil as an example of a developing country and the US as an example of a mature economy. We obtain that the calibrated costs from defaulting on long-term debt more than offset costs associated with short-term debt. Therefore, short-term debt implies higher welfare levels.

Optimal reserve management and sovereign debt

Most models currently used to determine optimal foreign reserve holdings take the level of international debt as given. However, given the sovereign`s willingness-to-pay incentive problems, reserve accumulation may reduce sustainable debt levels. In addition, assuming constant debt levels does not allow addressing one of the puzzles behind using reserves as a means to avoid the negative effects of crisis: why do not sovereign countries reduce their sovereign debt instead? To study the joint decision of holding sovereign debt and reserves, we construct a stochastic dynamic equilibrium model calibrated to a sample of emerging markets. We obtain that the reserve accumulation does not play a quantitatively important role in this model. In fact, we find the optimal policy is not to hold reserves at all. This finding is robust to considering interest rate shocks, sudden stops, contingent reserves and reserve dependent output costs. (c) 2008 Elsevier B.V. All rights reserved.

Optimal estimation of one-parameter quantum channels

We explore the task of optimal quantum channel identification and in particular, the estimation of a general one-parameter quantum process. We derive new characterizations of optimality and apply the results to several examples including the qubit depolarizing channel and the harmonic oscillator damping channel. We also discuss the geometry of the problem and illustrate the usefulness of using entanglement in process estimation.

Effects of Optimal Medical Treatment With or Without Coronary Revascularization on Angina and Subsequent Revascularizations in Patients With Type 2 Diabetes Mellitus and Stable Ischemic Heart Disease

Background-In the Bypass Angioplasty Revascularization Investigation 2 Diabetes (BARI 2D) trial, an initial strategy of coronary revascularization and optimal medical treatment (REV) compared with an initial optimal medical treatment with the option of subsequent revascularization (MED) did not reduce all-cause mortality or the composite of cardiovascular death, myocardial infarction, and stroke in patients with type 2 diabetes mellitus and stable ischemic heart disease. In the same population, we tested whether the REV strategy was superior to the MED strategy in preventing worsening and new angina and subsequent coronary revascularizations. Methods and Results-Among the 2364 men and women (mean age, 62.4 years) with type 2 diabetes mellitus, documented coronary artery disease, and myocardial ischemia, 1191 were randomized to the MED and 1173 to the REV strategy preselected in the percutaneous coronary intervention (796) and coronary artery bypass graft (377) strata. Compared with the MED strategy, the REV strategy at the 3-year follow-up had a lower rate of worsening angina (8% versus 13%; P < 0.001), new angina (37% versus 51%; P = 0.001), and subsequent coronary revascularizations (18% versus 33%; P < 0.001) and a higher rate of angina-free status (66% versus 58%; P = 0.003). The coronary artery bypass graft stratum patients were at higher risk than those in the percutaneous coronary intervention stratum, and had the greatest benefits from REV. Conclusions-In these patients, the REV strategy reduced the occurrence of worsening angina, new angina, and subsequent coronary revascularizations more than the MED strategy. The symptomatic benefits were observed particularly for high-risk patients.

Optimal quantum measurements for phase-shift estimation in optical interferometry

Quantum information theory, applied to optical interferometry, yields a 1/n scaling of phase uncertainty Delta phi independent of the applied phase shift phi, where n is the number of photons in the interferometer. This 1/n scaling is achieved provided that the output state is subjected to an optimal phase measurement. We establish this scaling law for both passive (linear) and active (nonlinear) interferometers and identify the coefficient of proportionality. Whereas a highly nonclassical state is required to achieve optimal scaling for passive interferometry, a classical input state yields a 1/n scaling of phase uncertainty for active interferometry.

Optimal quantum trajectories for discrete measurements

Using the method of quantum trajectories we show that a known pure state can be optimally monitored through time when subject to a sequence of discrete measurements. By modifying the way that we extract information from the measurement apparatus we can minimize the average algorithmic information of the measurement record, without changing the unconditional evolution of the measured system. We define an optimal measurement scheme as one which has the lowest average algorithmic information allowed. We also show how it is possible to extract information about system operator averages from the measurement records and their probabilities. The optimal measurement scheme, in the limit of weak coupling, determines the statistics of the variance of the measured variable directly. We discuss the relevance of such measurements for recent experiments in quantum optics.

A Bayesian approach to fuzzy hypotheses testing for the estimation of optimal age for vaccination against measles

Fuzzy Bayesian tests were performed to evaluate whether the mother`s seroprevalence and children`s seroconversion to measles vaccine could be considered as ""high"" or ""low"". The results of the tests were aggregated into a fuzzy rule-based model structure, which would allow an expert to influence the model results. The linguistic model was developed considering four input variables. As the model output, we obtain the recommended age-specific vaccine coverage. The inputs of the fuzzy rules are fuzzy sets and the outputs are constant functions, performing the simplest Takagi-Sugeno-Kang model. This fuzzy approach is compared to a classical one, where the classical Bayes test was performed. Although the fuzzy and classical performances were similar, the fuzzy approach was more detailed and revealed important differences. In addition to taking into account subjective information in the form of fuzzy hypotheses it can be intuitively grasped by the decision maker. Finally, we show that the Bayesian test of fuzzy hypotheses is an interesting approach from the theoretical point of view, in the sense that it combines two complementary areas of investigation, normally seen as competitive. (C) 2007 IMACS. Published by Elsevier B.V. All rights reserved.

A decision support system to facilitate management of patients with acute gastrointestinal bleeding

Objective: To develop a model to predict the bleeding source and identify the cohort amongst patients with acute gastrointestinal bleeding (GIB) who require urgent intervention, including endoscopy. Patients with acute GIB, an unpredictable event, are most commonly evaluated and managed by non-gastroenterologists. Rapid and consistently reliable risk stratification of patients with acute GIB for urgent endoscopy may potentially improve outcomes amongst such patients by targeting scarce health-care resources to those who need it the most. Design and methods: Using ICD-9 codes for acute GIB, 189 patients with acute GIB and all. available data variables required to develop and test models were identified from a hospital medical records database. Data on 122 patients was utilized for development of the model and on 67 patients utilized to perform comparative analysis of the models. Clinical data such as presenting signs and symptoms, demographic data, presence of co-morbidities, laboratory data and corresponding endoscopic diagnosis and outcomes were collected. Clinical data and endoscopic diagnosis collected for each patient was utilized to retrospectively ascertain optimal management for each patient. Clinical presentations and corresponding treatment was utilized as training examples. Eight mathematical models including artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor, linear discriminant analysis (LDA), shrunken centroid (SC), random forest (RF), logistic regression, and boosting were trained and tested. The performance of these models was compared using standard statistical analysis and ROC curves. Results: Overall the random forest model best predicted the source, need for resuscitation, and disposition with accuracies of approximately 80% or higher (accuracy for endoscopy was greater than 75%). The area under ROC curve for RF was greater than 0.85, indicating excellent performance by the random forest model Conclusion: While most mathematical models are effective as a decision support system for evaluation and management of patients with acute GIB, in our testing, the RF model consistently demonstrated the best performance. Amongst patients presenting with acute GIB, mathematical models may facilitate the identification of the source of GIB, need for intervention and allow optimization of care and healthcare resource allocation; these however require further validation. (c) 2007 Elsevier B.V. All rights reserved.

Tooth Tissue Engineering: Optimal Dental Stem Cell Harvest Based on Tooth Development

Our long-term objective is to devise reliable methods to generate biological replacement teeth exhibiting the physical properties and functions of naturally formed human teeth. Previously, we demonstrated the successful use of tissue engineering approaches to generate small, bioengineered tooth crowns from harvested pig and rat postnatal dental stem cells (DSCs). To facilitate characterizations of human DSCs, we have developed a novel radiographic staging system to accurately correlate human third molar tooth developmental stage with anticipated harvested DSC yield. Our results demonstrated that DSC yields were higher in less developed teeth (Stages 1 and 2), and lower in more developed teeth (Stages 3, 4, and 5). The greatest cell yields and colony-forming units (CFUs) capability was obtained from Stages 1 and 2 tooth dental pulp. We conclude that radiographic developmental staging can be used to accurately assess the utility of harvested human teeth for future dental tissue engineering applications.