Quantifying N2O and CO2 emissions from subtropical pasture

Greenhouse gas emissions from a well established, unfertilized tropical grass-legume pasture were monitored over two consecutive years using high resolution automatic sampling. Nitrous oxide emissions were highest during the summer months and were highly episodic, related more to the size and distribution of rain events than WFPS alone. Mean annual emissions were significantly higher during 2008 (5.7 ± 1.0 g N2O-N/ha/day) than 2007 (3.9 ± 0.4 and g N2O-N/ha/day) despite receiving nearly 500 mm less rain. Mean CO2 (28.2 ± 1.5 kg CO2 C/ha/day) was not significantly different (P < 0.01) between measurement years, emissions being highly dependent on temperature. A negative correlation between CO2 and WFPS at >70% indicated a threshold for soil conditions favouring denitrification. The use of automatic chambers for high resolution greenhouse gas sampling can greatly reduce emission estimation errors associated with temperature and WFPS changes.

Quantifying in-plane deformation of plate elements using vibration characteristics

Plate elements are used in many engineering applications. In-plane loads and deformations have significant influence on the vibration characteristics of plate elements. Numerous methods have been developed to quantify the effects of in-plane loads and deformations of individual plate elements with different boundary conditions based on their natural frequencies. However, these developments cannot be applied to the plate elements in a structural system as the natural frequency is a global parameter for the entire structure. This highlights the need for a method to quantify in-plane deformations of plate elements in structural framing systems. Motivated by this gap in knowledge, this research has developed a comprehensive vibration based procedure to quantify in-plane deformation of plate elements in a structural framing system. This procedure with its unique capabilities to capture the influence of load migration, boundary conditions and different tributary areas is presented herein and illustrated through examples.

A novel method for quantifying the criticality and vulnerability of the components of railway bridges

Conditions of bridges deteriorate with age, due to different critical factors including, changes in loading, fatigue, environmental effects and natural events. In order to rate a network of bridges, based on their structural condition, the condition of the components of a bridge and their effects on behaviour of the bridge should be reliably estimated. In this paper, a new method for quantifying the criticality and vulnerability of the components of the railway bridges in a network will be introduced. The type of structural analyses for identifying the criticality of the components for carrying train loads will be determined. In addition to that, the analytical methods for identifying the vulnerability of the components to natural events whose probability of occurrence is important, such as, flood, wind, earthquake and collision will be determined. In order to maintain the practicality of this method to be applied to a network of thousands of railway bridges, the simplicity of structural analysis has been taken into account. Demand by capacity ratios of the components at both safety and serviceability condition states as well as weighting factors used in current bridge management systems (BMS) are taken into consideration. It will be explained what types of information related to the structural condition of a bridge is required to be obtained, recorded and analysed. The authors of this paper will use this method in a new rating system introduced previously. Enhancing accuracy and reliability of evaluating and predicting the vulnerability of railway bridges to environmental effects and natural events will be the significant achievement of this research.

Interrupting malaria transmission: quantifying the impact of interventions in regions of low to moderate transmission

Malaria has been eliminated from over 40 countries with an additional 39 currently planning for, or committed to, elimination. Information on the likely impact of available interventions, and the required time, is urgently needed to help plan resource allocation. Mathematical modelling has been used to investigate the impact of various interventions; the strength of the conclusions is boosted when several models with differing formulation produce similar data. Here we predict by using an individual-based stochastic simulation model of seasonal Plasmodium falciparum transmission that transmission can be interrupted and parasite reintroductions controlled in villages of 1,000 individuals where the entomological inoculation rate is <7 infectious bites per person per year using chemotherapy and bed net strategies. Above this transmission intensity bed nets and symptomatic treatment alone were not sufficient to interrupt transmission and control the importation of malaria for at least 150 days. Our model results suggest that 1) stochastic events impact the likelihood of successfully interrupting transmission with large variability in the times required, 2) the relative reduction in morbidity caused by the interventions were age-group specific, changing over time, and 3) the post-intervention changes in morbidity were larger than the corresponding impact on transmission. These results generally agree with the conclusions from previously published models. However the model also predicted changes in parasite population structure as a result of improved treatment of symptomatic individuals; the survival probability of introduced parasites reduced leading to an increase in the prevalence of sub-patent infections in semi-immune individuals. This novel finding requires further investigation in the field because, if confirmed, such a change would have a negative impact on attempts to eliminate the disease from areas of moderate transmission.

An analytical framework for quantifying aquifer response time scales associated with transient boundary conditions

A major challenge in studying coupled groundwater and surface-water interactions arises from the considerable difference in the response time scales of groundwater and surface-water systems affected by external forcings. Although coupled models representing the interaction of groundwater and surface-water systems have been studied for over a century, most have focused on groundwater quantity or quality issues rather than response time. In this study, we present an analytical framework, based on the concept of mean action time (MAT), to estimate the time scale required for groundwater systems to respond to changes in surface-water conditions. MAT can be used to estimate the transient response time scale by analyzing the governing mathematical model. This framework does not require any form of transient solution (either numerical or analytical) to the governing equation, yet it provides a closed form mathematical relationship for the response time as a function of the aquifer geometry, boundary conditions, and flow parameters. Our analysis indicates that aquifer systems have three fundamental time scales: (i) a time scale that depends on the intrinsic properties of the aquifer; (ii) a time scale that depends on the intrinsic properties of the boundary condition, and; (iii) a time scale that depends on the properties of the entire system. We discuss two practical scenarios where MAT estimates provide useful insights and we test the MAT predictions using new laboratory-scale experimental data sets.

Hydrological consequences of land-cover change: Quantifying the influence of plants on soil moisture with time-lapse electrical resistivity

Electrical resistivity of soils and sediments is strongly influenced by the presence of interstitial water. Taking advantage of this dependency, electrical-resistivity imaging (ERI) can be effectively utilized to estimate subsurface soil-moisture distributions. The ability to obtain spatially extensive data combined with time-lapse measurements provides further opportunities to understand links between land use and climate processes. In natural settings, spatial and temporal changes in temperature and porewater salinity influence the relationship between soil moisture and electrical resistivity. Apart from environmental factors, technical, theoretical, and methodological ambiguities may also interfere with accurate estimation of soil moisture from ERI data. We have examined several of these complicating factors using data from a two-year study at a forest-grassland ecotone, a boundary between neighboring but different plant communities.At this site, temperature variability accounts for approximately 20-45 of resistivity changes from cold winter to warm summer months. Temporal changes in groundwater conductivity (mean=650 S/cm =57.7) and a roughly 100-S/cm spatial difference between the forest and grassland had only a minor influence on the moisture estimates. Significant seasonal fluctuations in temperature and precipitation had negligible influence on the basic measurement errors in data sets. Extracting accurate temporal changes from ERI can be hindered by nonuniqueness of the inversion process and uncertainties related to time-lapse inversion schemes. The accuracy of soil moisture obtained from ERI depends on all of these factors, in addition to empirical parameters that define the petrophysical soil-moisture/resistivity relationship. Many of the complicating factors and modifying variables to accurately quantify soil moisture changes with ERI can be accounted for using field and theoretical principles.

Quantifying uncertainty in parameter estimates for stochastic models of collective cell spreading using approximate Bayesian computation

Wound healing and tumour growth involve collective cell spreading, which is driven by individual motility and proliferation events within a population of cells. Mathematical models are often used to interpret experimental data and to estimate the parameters so that predictions can be made. Existing methods for parameter estimation typically assume that these parameters are constants and often ignore any uncertainty in the estimated values. We use approximate Bayesian computation (ABC) to estimate the cell diffusivity, D, and the cell proliferation rate, λ, from a discrete model of collective cell spreading, and we quantify the uncertainty associated with these estimates using Bayesian inference. We use a detailed experimental data set describing the collective cell spreading of 3T3 fibroblast cells. The ABC analysis is conducted for different combinations of initial cell densities and experimental times in two separate scenarios: (i) where collective cell spreading is driven by cell motility alone, and (ii) where collective cell spreading is driven by combined cell motility and cell proliferation. We find that D can be estimated precisely, with a small coefficient of variation (CV) of 2–6%. Our results indicate that D appears to depend on the experimental time, which is a feature that has been previously overlooked. Assuming that the values of D are the same in both experimental scenarios, we use the information about D from the first experimental scenario to obtain reasonably precise estimates of λ, with a CV between 4 and 12%. Our estimates of D and λ are consistent with previously reported values; however, our method is based on a straightforward measurement of the position of the leading edge whereas previous approaches have involved expensive cell counting techniques. Additional insights gained using a fully Bayesian approach justify the computational cost, especially since it allows us to accommodate information from different experiments in a principled way.

Quantifying progressive anterior overgrowth in the thoracic vertebrae of adolescent idiopathic scoliosis patients

Study design Anterior and posterior vertebral body heights were measured from sequential MRI scans of adolescent idiopathic scoliosis (AIS) patients and healthy controls. Objective To measure changes in vertebral body height over time during scoliosis progression to assess how vertebral body height discrepancies change during growth. Summary of background data Relative anterior overgrowth has been proposed as a potential driver for AIS initiation and progression. This theory proposes that the anterior column grows faster, and the posterior column slower, in AIS patients when compared to healthy controls. There is disagreement in the literature as to whether the anterior vertebral body heights are proportionally greater than posterior vertebral body heights in AIS patients when compared to healthy controls. To some extent, these discrepancies may be attributed to methodological differences. Methods MRI scans of the major curve of 21 AIS patients (mean age 12.5 ± 1.4 years, mean Cobb 32.2 ± 12.8º) and between T4 and T12 of 21 healthy adolescents (mean age 12.1 ± 0.5 years) were captured for this study. Of the 21 AIS patients, 14 had a second scan on average 10.8 ± 4.7 months after the first. Anterior and posterior vertebral body heights were measured from the true sagittal plane of each vertebra such that anterior overgrowth could be quantified. Results The difference between anterior and posterior vertebral body height in healthy, non-scoliotic children was significantly greater than in AIS patients with mild to moderate scoliosis. However there was no significant relationship between the overall anterior-posterior vertebral body height difference in AIS and either severity of the curve or its progression over time. Conclusions Whilst AIS patients have a proportionally longer anterior column than non-scoliotic controls, the degree of anterior overgrowth was not related to the rate of progression or the severity of the scoliotic curve.

Property cash flow studies : focusing on model consistency and data accuracy

The high degree of variability and inconsistency in cash flow study usage by property professionals demands improvement in knowledge and processes. Until recently limited research was being undertaken on the use of cash flow studies in property valuations but the growing acceptance of this approach for major investment valuations has resulted in renewed interest in this topic. Studies on valuation variations identify data accuracy, model consistency and bias as major concerns. In cash flow studies there are practical problems with the input data and the consistency of the models. This study will refer to the recent literature and identify the major factors in model inconsistency and data selection. A detailed case study will be used to examine the effects of changes in structure and inputs. The key variable inputs will be identified and proposals developed to improve the selection process for these key variables. The variables will be selected with the aid of sensitivity studies and alternative ways of quantifying the key variables explained. The paper recommends, with reservations, the use of probability profiles of the variables and the incorporation of this data in simulation exercises. The use of Monte Carlo simulation is demonstrated and the factors influencing the structure of the probability distributions of the key variables are outline. This study relates to ongoing research into functional performance of commercial property within an Australian Cooperative Research Centre.