Assessing nutrient availability variations in landscapes

Non-Peer ReviewedA simple method was developed to assess the variability in nutrient availability in undulating landscapes using anion-exchange resin strip burial. Resin strips were buried in ten farm fields along transects at points in the landscape representing different landform elements present within the field. In all ten fields, strips were buried for one hour. In two of the fields, in addition to a one hour burial, another set of resin strips was buried and allowed to remain in the soil for two weeks. After burial, resin strips were removed and the nitrate accumulated on the strips was measured. Large variations in nutrient availability as predicted by resin strip burial were observed in the landscapes. The differences were closely related to the landscape position and landform element with the highest levels of available nitrate observed at lower slope positions where deposition of eroded soil has occurred. Two week burials revealed that mineralization contributes significantly to available nitrate in the soil. Resin strip burial appears to be a suitable tool for evaluating variations in nutrient availability in different landscape positions of a field

Prediction of blast loading in an internal environment using artificial neural networks

Explosive loading in a confined internal environment is highly complex and is driven by nonlinear physical processes associated with reflection and coalescence of multiple shock fronts. Prediction of this loading is not currently feasible using simple tools, and instead specialist computational software or practical testing is required, which are impractical for situations with a wide range of input variables. There is a need to develop a tool which balances the accuracy of experiments or physics-based numerical schemes with the simplicity and low computational cost of an engineering-level predictive approach. Artificial neural networks (ANNs) are formed of a collection of neurons that process information via a series of connections. When fully trained, ANNs are capable of replicating and generalising multi-parameter, high-complexity problems and are able to generate new predictions for unseen problems (within the bounds of the training variables). This article presents the development and rigorous testing of an ANN to predict blast loading in a confined internal environment. The ANN was trained using validated numerical modelling data, and key parameters relating to formulation of the training data and network structure were critically analysed in order to maximise the predictive capability of the network. The developed network was generally able to predict specific impulses to within 10% of the numerical data: 90% of specific impulses in the unseen testing data, and between 81% and 87% of specific impulses for data from four additional unseen test models, were predicted to this accuracy. The network was highly capable of generalising in areas adjacent to reflecting surfaces and as those close to ambient outflow boundaries. It is shown that ANNs are highly suited to modelling blast loading in a confined internal environment, with significant improvements in accuracy achievable if a robust, well distributed training dataset is used with a network structure that is tailored to the problem being solved

Inflationary potentials in DBI models

We study DBI inflation based upon a general model characterized by a power-law flow parameter $\epsilon(\phi)\propto\phi^{\alpha}$ and speed of sound $c_s(\phi)\propto\phi^{\beta}$, where $\alpha$ and $\beta$ are constants. We show that in the slow-roll limit this general model gives rise to distinct inflationary classes according to the relation between $\alpha$ and $\beta$ and to the time evolution of the inflaton field, each one corresponding to a specific potential; in particular, we find that the well-known canonical polynomial (large- and small-field), hybrid and exponential potentials also arise in this non-canonical model. We find that these non-canonical classes have the same physical features as their canonical analogs, except for the fact that the inflaton field evolves with varying speed of sound; also, we show that a broad class of canonical and D-brane inflation models are particular cases of this general non-canonical model. Next, we compare the predictions of large-field polynomial models with the current observational data, showing that models with low speed of sound have red-tilted scalar spectrum with low tensor-to-scalar ratio, in good agreement with the observed values. These models also show a correlation between large non-gaussianity with low tensor amplitudes, which is a distinct signature of DBI inflation with large-field polynomial potentials.Comment: Minor changes, reference added. Version submitted to JCA

Optimal point of insertion of the needle in neuraxial blockade using a midline approach: Study in a geometrical model

Performance of neuraxial blockade using a midline approach can be technically difficult. It is therefore important to optimize factors that are under the influence of the clinician performing the procedure. One of these factors might be the chosen point of insertion of the needle. Surprisingly few data exist on where between the tips of two adjacent spinous processes the needle should be introduced. A geometrical model was adopted to gain more insight into this issue. Spinous processes were represented by parallelograms. The length, the steepness relative to the skin, and the distance between the parallelograms were varied. The influence of the chosen point of insertion of the needle on the range of angles at which the epidural and subarachnoid space could be reached was studied. The optimal point of insertion was defined as the point where this range is the widest. The geometrical model clearly demonstrated, that the range of angles at which the epidural or subarachnoid space can be reached, is dependent on the point of insertion between the tips of the adjacent spinous processes. The steeper the spinous processes run, the more cranial the point of insertion should be. Assuming that the model is representative for patients, the performance of neuraxial blockade using a midline approach might be improved by choosing the optimal point of insertion

Spatial uncertainty propagation analysis with the spup R package

Many environmental and geographical models, such as those used in land degradation, agro ecological and climate studies, make use of spatially distributed inputs that are known imperfectly. The R package spup provides functions for examining the uncertainty propagation from input data and model parameters onto model outputs via the environmental model. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques. Uncertain variables are described by probability distributions. Both numerical and categorical data types are handled. The package also accommodates spatial auto-correlation within a variable and cross-correlation between variables. The MC realizations may be used as input to the environmental models written in or called from R. This article provides theoretical background and three worked examples that guide users through the application of spup

Direct Learning for Parameter-Varying Feedforward Control:A Neural-Network Approach

The performance of a feedforward controller is primarily determined by the extent to which it can capture the relevant dynamics of a system. The aim of this paper is to develop an input-output linear parameter-varying (LPV) feedforward parameterization and a corresponding data-driven estimation method in which the dependency of the coefficients on the scheduling signal are learned by a neural network. The use of a neural network enables the parameterization to compensate a wide class of constant relative degree LPV systems. Efficient optimization of the neural-network-based controller is achieved through a Levenberg-Marquardt approach with analytic gradients and a pseudolinear approach generalizing Sanathanan-Koerner to the LPV case. The performance of the developed feedforward learning method is validated in a simulation study of an LPV system showing excellent performance

Pretreatment integrase strand transfer inhibitor resistance in North Carolina from 2010-2016

Objective: We sought to define the prevalence of pretreatment integrase strand transfer inhibitor (INSTI) resistance and assess the transmission networks of those with pretreatment INSTI resistance. Design: A retrospective cohort study of HIV-positive patients with genotypic resistance testing sent to a single referral laboratory in North Carolina between 2010 and 2016. Methods: We linked genotype and public health data for in-care HIV-positive individuals to determine the prevalence of INSTI resistance among treatment-naive (defined as those with a first genotype ≤3 months after diagnosis) and treatment-experienced (defined as those with a first genotype >3 months after diagnosis) patients. We performed molecular and phylogenetic analyses to assess whether pretreatment INSTI resistance mutations represented clustered HIV transmission. Results: Of 8825 individuals who contributed sequences for protease, reverse transcriptase, or INSTI genotypic resistance testing during the study period, 2784 (31%) contributed at least one sequence for INSTI resistance testing. Of these, 840 were treatment-naive individuals and 20 [2.4%, 95% confidence interval (CI): 1.5, 3.6%] had INSTI mutations; only two (0.2%, 95% CI: 0.02, 0.9%) had major mutations. Of 1944 treatment-experienced individuals, 9.6% (95% CI: 8.3, 11.0%) had any INSTI mutation and 7.0% (95% CI: 5.9, 8.3%) had major mutations; the prevalence of INSTI mutations among treatment-experienced patients decreased overtime (P<0.001). In total 12 of 20 individuals with pretreatment INSTI mutations were part of 10 molecular transmission clusters; only one cluster shared identical minor mutations. Conclusion: The prevalence of major pretreatment INSTI resistance is very low. Pretreatment INSTI mutations do not appear to represent clustered HIV transmission