Eliminating discharge imbalances in predicting shallow water flows with shocks

The shallow water equations are widely used in modelling environmental flows. Being a hyperbolic system of differential equations, they admit shocks that represent hydraulic jumps and bores. Although the water surface can be solved satisfactorily with the modern shock-capturing schemes, the predicted flow rate often suffers from imbalances where shocks occur, eg the mass conservation is violated by failing to maintain a constant discharge rate at every cross-section in a steady open channel flow. A total-variation-diminishing Lax-Wendroff scheme is developed, and used to demonstrate how to achieve an exact flux balance. The performance of the proposed methods is inspected through some test cases, which include 1- and 2-dimensional, flat and irregular bed scenarios. The proposed methods are shown to preserve the mass exactly, and can be easily extended to other shock-capturing models.

Structure of coexisting liquid phases of supercooled water: analogy with ice polymorphs.

The structural changes occurring in supercooled liquid water upon moving from one coexisting liquid phase to the other have been investigated by computer simulation using a polarizable interaction potential model. The obtained results favorably compare with recent neutron scattering data of high and low density water. In order to assess the physical origin of the observed structural changes, computer simulation of several ice polymorphs has also been carried out. Our results show that there is a strict analogy between the structure of various disordered (supercooled) and ordered (ice) phases of water, suggesting that the occurrence of several different phases of supercooled water is rooted in the same physical origin that is responsible for ice polymorphism.

Light water breeding with nitride fuel

This scoping study proposes using mixed nitride fuel in Pu-based high conversion LWR designs in order to increase the breeding ratio. The higher density fuel reduces the hydrogen-to-heavy metal ratio in the reactor which results in a harder spectrum in which breeding is more effective. A Resource-renewable Boiling Water Reactor (RBWR) assembly was modeled in MCNP to demonstrate this effect in a typical high conversion LWR design. It was determined that changing the fuel from (U,TRU)O2 to (U,TRU)N in the assembly can increase its fissile inventory ratio (fissile Pu mass divided by initial fissile Pu mass) from 1.04 to up to 1.17. © 2011 Elsevier Ltd. All rights reserved.

Multiple grasp-specific representations of tool dynamics mediate skillful manipulation.

Skillful tool use requires knowledge of the dynamic properties of tools in order to specify the mapping between applied force and tool motion. Importantly, this mapping depends on the orientation of the tool in the hand. Here we investigate the representation of dynamics during skillful manipulation of a tool that can be grasped at different orientations. We ask whether the motor system uses a single general representation of dynamics for all grasp contexts or whether it uses multiple grasp-specific representations. Using a novel robotic interface, subjects rotated a virtual tool whose orientation relative to the hand could be varied. Subjects could immediately anticipate the force direction for each orientation of the tool based on its visual geometry, and, with experience, they learned to parameterize the force magnitude. Surprisingly, this parameterization of force magnitude showed limited generalization when the orientation of the tool changed. Had subjects parameterized a single general representation, full generalization would be expected. Thus, our results suggest that object dynamics are captured by multiple representations, each of which encodes the mapping associated with a specific grasp context. We suggest that the concept of grasp-specific representations may provide a unifying framework for interpreting previous results related to dynamics learning.

Detection of molecular interactions with modified ferrocene self-assembled monolayers.

Ferrocene-terminated self-assembled monolayers (Fc-SAMs) are one of the most studied molecular aggregates on metal electrodes. They are easy to fabricate and provide a stable and reproducible system to investigate the effect of the microenvironment on the electron transfer parameters. We propose a novel application for Fc-SAMs, the detection of molecular interactions, based on the modification of the SAM with target-specific receptors. Mixed SAMs were fabricated by coimmobilization on Au electrodes of thiolated alkane chains with three different head groups: hydroxy terminating head group, ferrocene head group, and a functional head group such as biotin. Upon binding, the intrinsic electric charge of the target (e.g., streptavidin) modifies the electrostatic potential at the plane of electron transfer, causing a shift in the formal potential E degrees '. The SAMs were characterized by AC voltammetry. The detection mechanism is confirmed by measurements of formal potential as a function of electrolyte pH.

Managing water resources and their connection to land and energy

Effective management is a key to ensuring the current and future sustainability of land, water and energy resources. Identifying the complexities of such management is not an easy task, especially since past studies have focussed on studying these resources in isolation from one another. However, with rapid population growth and an increase in the awareness of a potential change in climatic conditions that may affect the demand for and supply of food, water and energy, there has been a growing need to integrate the planning decisions relating to these three resources. The paper shows the visualisation of linked resources by drawing a set of interconnected Sankey diagrams for energy, water and land. These track the changes from basic resource (e.g. coal, surface water, groundwater and cropland) through transformations (e.g. fuel refining and desalination) to final services (e.g. sustenance, hygiene and transportation). The focus here is on the water analysis aspects of the tool, which uses California as a detailed case study. The movement of water in California is traced from its source to its services by mapping the different transformations of water from when it becomes available, through its use, to further treatment, to final sinks (including recycling and reuse of that resource). The connections that water has with energy and land resources for the state of California are highlighted. This includes the amount of energy used to pump and treat water, and the amount of water used for energy production and the land resources which create a water demand to produce crops for food. By mapping water in this way, policy-makers and resource managers can more easily understand the competing uses of water (environment, agriculture and urban use) through the identification of the services it delivers (e.g. sanitation, agriculture, landscaping), the potential opportunities for improving the management of the resource (e.g. building new desalination plants, reducing the demand for services), and the connections with other resources which are often overlooked in a traditional sector-based management strategy.

Slowness: an objective for spike-timing-dependent plasticity?

Our nervous system can efficiently recognize objects in spite of changes in contextual variables such as perspective or lighting conditions. Several lines of research have proposed that this ability for invariant recognition is learned by exploiting the fact that object identities typically vary more slowly in time than contextual variables or noise. Here, we study the question of how this "temporal stability" or "slowness" approach can be implemented within the limits of biologically realistic spike-based learning rules. We first show that slow feature analysis, an algorithm that is based on slowness, can be implemented in linear continuous model neurons by means of a modified Hebbian learning rule. This approach provides a link to the trace rule, which is another implementation of slowness learning. Then, we show analytically that for linear Poisson neurons, slowness learning can be implemented by spike-timing-dependent plasticity (STDP) with a specific learning window. By studying the learning dynamics of STDP, we show that for functional interpretations of STDP, it is not the learning window alone that is relevant but rather the convolution of the learning window with the postsynaptic potential. We then derive STDP learning windows that implement slow feature analysis and the "trace rule." The resulting learning windows are compatible with physiological data both in shape and timescale. Moreover, our analysis shows that the learning window can be split into two functionally different components that are sensitive to reversible and irreversible aspects of the input statistics, respectively. The theory indicates that irreversible input statistics are not in favor of stable weight distributions but may generate oscillatory weight dynamics. Our analysis offers a novel interpretation for the functional role of STDP in physiological neurons.

Using sub-word-level information for confidence estimation with conditional random field models

The task of word-level confidence estimation (CE) for automatic speech recognition (ASR) systems stands to benefit from the combination of suitably defined input features from multiple information sources. However, the information sources of interest may not necessarily operate at the same level of granularity as the underlying ASR system. The research described here builds on previous work on confidence estimation for ASR systems using features extracted from word-level recognition lattices, by incorporating information at the sub-word level. Furthermore, the use of Conditional Random Fields (CRFs) with hidden states is investigated as a technique to combine information for word-level CE. Performance improvements are shown using the sub-word-level information in linear-chain CRFs with appropriately engineered feature functions, as well as when applying the hidden-state CRF model at the word level.

Multilinear function factorisation for time series feature extraction

This work applies a variety of multilinear function factorisation techniques to extract appropriate features or attributes from high dimensional multivariate time series for classification. Recently, a great deal of work has centred around designing time series classifiers using more and more complex feature extraction and machine learning schemes. This paper argues that complex learners and domain specific feature extraction schemes of this type are not necessarily needed for time series classification, as excellent classification results can be obtained by simply applying a number of existing matrix factorisation or linear projection techniques, which are simple and computationally inexpensive. We highlight this using a geometric separability measure and classification accuracies obtained though experiments on four different high dimensional multivariate time series datasets. © 2013 IEEE.

Effect of the interplay between protein and surface on the properties of adsorbed protein layers.

Although protein adsorption to surface is a common phenomenon, investigation of the process is challenging due to the complexity of the interplay between external factors, protein and surface properties. Therefore experimental approaches have to measure the properties of adsorbed protein layers with high accuracy in order to achieve a comprehensive description of the process. To this end, we used a combination of two biosensing techniques, dual polarization interferometry and quartz crystal microbalance with dissipation. From this, we are able to extract surface coverage values, layer structural parameters, water content and viscoelastic properties to examine the properties of protein layers formed at the liquid/solid interface. Layer parameters were examined upon adsorption of proteins of varying size and structural properties, on surfaces with opposite polarity. We show that "soft" proteins such as unfolded α-synuclein and high molecular weight albumin are highly influenced by the surface polarity, as they form a highly diffuse and hydrated layer on the hydrophilic silica surface as opposed to the denser, less hydrated layer formed on a hydrophobic methylated surface. These layer properties are a result of different orientations and packing of the proteins. By contrast, lysozyme is barely influenced by the surface polarity due to its intrinsic structural stability. Interestingly, we show that for a similar molecular weight, the unfolded α-synuclein forms a layer with the highest percentage of solvation not related to surface coverage but resulting from the highest water content trapped within the protein. Together, these data reveal a trend in layer properties highlighting the importance of the interplay between protein and surface for the design of biomaterials.

Effect of the interplay between protein and surface on the properties of adsorbed protein layers

Although protein adsorption to surface is a common phenomenon, investigation of the process is challenging due to the complexity of the interplay between external factors, protein and surface properties. Therefore experimental approaches have to measure the properties of adsorbed protein layers with high accuracy in order to achieve a comprehensive description of the process. To this end, we used a combination of two biosensing techniques, dual polarization interferometry and quartz crystal microbalance with dissipation. From this, we are able to extract surface coverage values, layer structural parameters, water content and viscoelastic properties to examine the properties of protein layers formed at the liquid/solid interface. Layer parameters were examined upon adsorption of proteins of varying size and structural properties, on surfaces with opposite polarity. We show that "soft" proteins such as unfolded α-synuclein and high molecular weight albumin are highly influenced by the surface polarity, as they form a highly diffuse and hydrated layer on the hydrophilic silica surface as opposed to the denser, less hydrated layer formed on a hydrophobic methylated surface. These layer properties are a result of different orientations and packing of the proteins. By contrast, lysozyme is barely influenced by the surface polarity due to its intrinsic structural stability. Interestingly, we show that for a similar molecular weight, the unfolded α-synuclein forms a layer with the highest percentage of solvation not related to surface coverage but resulting from the highest water content trapped within the protein. Together, these data reveal a trend in layer properties highlighting the importance of the interplay between protein and surface for the design of biomaterials. © 2014 The Authors.