Oxidation of oleic acid at the air-water interface and its potential effects on cloud critical supersaturations

The oxidation of organic films on cloud condensation nuclei has the potential to affect climate and precipitation events. In this work we present a study of the oxidation of a monolayer of deuterated oleic acid (cis-9-octadecenoic acid) at the air-water interface by ozone to determine if oxidation removes the organic film or replaces it with a product film. A range of different aqueous sub-phases were studied. The surface excess of deuterated material was followed by neutron reflection whilst the surface pressure was followed using a Wilhelmy plate. The neutron reflection data reveal that approximately half the organic material remains at the air-water interface following the oxidation of oleic acid by ozone, thus cleavage of the double bond by ozone creates one surface active species and one species that partitions to the bulk (or gas) phase. The most probable products, produced with a yield of similar to(87 +/- 14)%, are nonanoic acid, which remains at the interface, and azelaic acid (nonanedioic acid), which dissolves into the bulk solution. We also report a surface bimolecular rate constant for the reaction between ozone and oleic acid of (7.3 +/- 0.9) x 10(-11) cm(2) molecule s(-1). The rate constant and product yield are not affected by the solution sub-phase. An uptake coefficient of ozone on the oleic acid monolayer of similar to 4 x 10(-6) is estimated from our results. A simple Kohler analysis demonstrates that the oxidation of oleic acid by ozone on an atmospheric aerosol will lower the critical supersaturation needed for cloud droplet formation. We calculate an atmospheric chemical lifetime of oleic acid of 1.3 hours, significantly longer than laboratory studies on pure oleic acid particles suggest, but more consistent with field studies reporting oleic acid present in aged atmospheric aerosol.

Contextual emergence of mental states from neurodynamics

The emergence of mental states from neural states by partitioning the neural phase space is analyzed in terms of symbolic dynamics. Well-defined mental states provide contexts inducing a criterion of structural stability for the neurodynamics that can be implemented by particular partitions. This leads to distinguished subshifts of finite type that are either cyclic or irreducible. Cyclic shifts correspond to asymptotically stable fixed points or limit tori whereas irreducible shifts are obtained from generating partitions of mixing hyperbolic systems. These stability criteria are applied to the discussion of neural correlates of consiousness, to the definition of macroscopic neural states, and to aspects of the symbol grounding problem. In particular, it is shown that compatible mental descriptions, topologically equivalent to the neurodynamical description, emerge if the partition of the neural phase space is generating. If this is not the case, mental descriptions are incompatible or complementary. Consequences of this result for an integration or unification of cognitive science or psychology, respectively, will be indicated.

Space partitioning for scalable K-means

K-Means is a popular clustering algorithm which adopts an iterative refinement procedure to determine data partitions and to compute their associated centres of mass, called centroids. The straightforward implementation of the algorithm is often referred to as `brute force' since it computes a proximity measure from each data point to each centroid at every iteration of the K-Means process. Efficient implementations of the K-Means algorithm have been predominantly based on multi-dimensional binary search trees (KD-Trees). A combination of an efficient data structure and geometrical constraints allow to reduce the number of distance computations required at each iteration. In this work we present a general space partitioning approach for improving the efficiency and the scalability of the K-Means algorithm. We propose to adopt approximate hierarchical clustering methods to generate binary space partitioning trees in contrast to KD-Trees. In the experimental analysis, we have tested the performance of the proposed Binary Space Partitioning K-Means (BSP-KM) when a divisive clustering algorithm is used. We have carried out extensive experimental tests to compare the proposed approach to the one based on KD-Trees (KD-KM) in a wide range of the parameters space. BSP-KM is more scalable than KDKM, while keeping the deterministic nature of the `brute force' algorithm. In particular, the proposed space partitioning approach has shown to overcome the well-known limitation of KD-Trees in high-dimensional spaces and can also be adopted to improve the efficiency of other algorithms in which KD-Trees have been used.

Water vapour self-continuum and water dimers: 1. Analysis of recent work

Recent laboratory observations and advances in theoretical quantum chemistry allow a reappraisal of the fundamental mechanisms that determine the water vapour self-continuum absorption throughout the infrared and millimetre wave spectral regions. By starting from a framework that partitions bimolecular interactions between water molecules into free-pair states, true bound and quasi-bound dimers, we present a critical review of recent observations, continuum models and theoretical predictions. In the near-infrared bands of the water monomer, we propose that spectral features in recent laboratory-derived self-continuum can be well explained as being due to a combination of true bound and quasi-bound dimers, when the spectrum of quasi-bound dimers is approximated as being double the broadened spectrum of the water monomer. Such a representation can explain both the wavenumber variation and the temperature dependence. Recent observations of the self-continuum absorption in the windows between these near-infrared bands indicate that widely used continuum models can underestimate the true strength by around an order of magnitude. An existing far-wing model does not appear able to explain the discrepancy, and although a dimer explanation is possible, currently available observations do not allow a compelling case to be made. In the 8–12 micron window, recent observations indicate that the modern continuum models either do not properly represent the temperature dependence, the wavelength variation, or both. The temperature dependence is suggestive of a transition from the dominance of true bound dimers at lower temperatures to quasibound dimers at higher temperatures. In the mid- and far-infrared spectral region, recent theoretical calculations indicate that true bound dimers may explain at least between 20% and 40% of the observed self-continuum. The possibility that quasi-bound dimers could cause an additional contribution of the same size is discussed. Most recent theoretical considerations agree that water dimers are likely to be the dominant contributor to the self-continuum in the mm-wave spectral range.

Another look at the relative importance of sectors and regions in determining property returns

This paper re-examines the relative importance of sector and regional effects in determining property returns. Using the largest property database currently available in the world, we decompose the returns on individual properties into a national effect, common to all properties, and a number of sector and regional factors. However, unlike previous studies, we categorise the individual property data into an ever-increasing number of property-types and regions, from a simple 3-by-3 classification, up to a 10 by 63 sector/region classification. In this way we can test the impact that a finer classification has on the sector and regional effects. We confirm the earlier findings of previous studies that sector-specific effects have a greater influence on property returns than regional effects. We also find that the impact of the sector effect is robust across different classifications of sectors and regions. Nonetheless, the more refined sector and regional partitions uncover some interesting sector and regional differences, which were obscured in previous studies. All of which has important implications for property portfolio construction and analysis.

Application of complex extreme learning machine to multiclass classification problems with high dimensionality: a THz spectra classification problem

We extend extreme learning machine (ELM) classifiers to complex Reproducing Kernel Hilbert Spaces (RKHS) where the input/output variables as well as the optimization variables are complex-valued. A new family of classifiers, called complex-valued ELM (CELM) suitable for complex-valued multiple-input–multiple-output processing is introduced. In the proposed method, the associated Lagrangian is computed using induced RKHS kernels, adopting a Wirtinger calculus approach formulated as a constrained optimization problem similarly to the conventional ELM classifier formulation. When training the CELM, the Karush–Khun–Tuker (KKT) theorem is used to solve the dual optimization problem that consists of satisfying simultaneously smallest training error as well as smallest norm of output weights criteria. The proposed formulation also addresses aspects of quaternary classification within a Clifford algebra context. For 2D complex-valued inputs, user-defined complex-coupled hyper-planes divide the classifier input space into four partitions. For 3D complex-valued inputs, the formulation generates three pairs of complex-coupled hyper-planes through orthogonal projections. The six hyper-planes then divide the 3D space into eight partitions. It is shown that the CELM problem formulation is equivalent to solving six real-valued ELM tasks, which are induced by projecting the chosen complex kernel across the different user-defined coordinate planes. A classification example of powdered samples on the basis of their terahertz spectral signatures is used to demonstrate the advantages of the CELM classifiers compared to their SVM counterparts. The proposed classifiers retain the advantages of their ELM counterparts, in that they can perform multiclass classification with lower computational complexity than SVM classifiers. Furthermore, because of their ability to perform classification tasks fast, the proposed formulations are of interest to real-time applications.

Improved simulation of fire–vegetation interactions in the Land surface Processes and eXchanges dynamic global vegetation model (LPX-Mv1)

The Land surface Processes and eXchanges (LPX) model is a fire-enabled dynamic global vegetation model that performs well globally but has problems representing fire regimes and vegetative mix in savannas. Here we focus on improving the fire module. To improve the representation of ignitions, we introduced a reatment of lightning that allows the fraction of ground strikes to vary spatially and seasonally, realistically partitions strike distribution between wet and dry days, and varies the number of dry days with strikes. Fuel availability and moisture content were improved by implementing decomposition rates specific to individual plant functional types and litter classes, and litter drying rates driven by atmospheric water content. To improve water extraction by grasses, we use realistic plant-specific treatments of deep roots. To improve fire responses, we introduced adaptive bark thickness and post-fire resprouting for tropical and temperate broadleaf trees. All improvements are based on extensive analyses of relevant observational data sets. We test model performance for Australia, first evaluating parameterisations separately and then measuring overall behaviour against standard benchmarks. Changes to the lightning parameterisation produce a more realistic simulation of fires in southeastern and central Australia. Implementation of PFT-specific decomposition rates enhances performance in central Australia. Changes in fuel drying improve fire in northern Australia, while changes in rooting depth produce a more realistic simulation of fuel availability and structure in central and northern Australia. The introduction of adaptive bark thickness and resprouting produces more realistic fire regimes in Australian savannas. We also show that the model simulates biomass recovery rates consistent with observations from several different regions of the world characterised by resprouting vegetation. The new model (LPX-Mv1) produces an improved simulation of observed vegetation composition and mean annual burnt area, by 33 and 18% respectively compared to LPX.