Simulating the detailed chemical composition of secondary organic aerosol formed on a regional scale during the TORCH 2003 campaign in the southern UK

Following on from the companion study (Johnson et al., 2006), a photochemical trajectory model (PTM) has been used to simulate the chemical composition of organic aerosol for selected events during the 2003 TORCH (Tropospheric Organic Chemistry Experiment) field campaign. The PTM incorporates the speciated emissions of 124 nonmethane anthropogenic volatile organic compounds (VOC) and three representative biogenic VOC, a highly-detailed representation of the atmospheric degradation of these VOC, the emission of primary organic aerosol (POA) material and the formation of secondary organic aerosol (SOA) material. SOA formation was represented by the transfer of semi and non-volatile oxidation products from the gas-phase to a condensed organic aerosol-phase, according to estimated thermodynamic equilibrium phase-partitioning characteristics for around 2000 reaction products. After significantly scaling all phase-partitioning coefficients, and assuming a persistent background organic aerosol (both required in order to match the observed organic aerosol loadings), the detailed chemical composition of the simulated SOA has been investigated in terms of intermediate oxygenated species in the Master Chemical Mechanism, version 3.1 ( MCM v3.1). For the various case studies considered, 90% of the simulated SOA mass comprises between ca. 70 and 100 multifunctional oxygenated species derived, in varying amounts, from the photooxidation of VOC of anthropogenic and biogenic origin. The anthropogenic contribution is dominated by aromatic hydrocarbons and the biogenic contribution by alpha-and beta-pinene (which also constitute surrogates for other emitted monoterpene species). Sensitivity in the simulated mass of SOA to changes in the emission rates of anthropogenic and biogenic VOC has also been investigated for 11 case study events, and the results have been compared to the detailed chemical composition data. The role of accretion chemistry in SOA formation, and its implications for the results of the present investigation, is discussed.

Simulating regional scale secondary organic aerosol formation during the TORCH 2003 campaign in the southern UK

A photochemical trajectory model has been used to simulate the chemical evolution of air masses arriving at the TORCH field campaign site in the southern UK during late July and August 2003, a period which included a widespread and prolonged photochemical pollution episode. The model incorporates speciated emissions of 124 nonmethane anthropogenic VOC and three representative biogenic VOC, coupled with a comprehensive description of the chemistry of their degradation. A representation of the gas/aerosol absorptive partitioning of ca. 2000 oxygenated organic species generated in the Master Chemical Mechanism (MCM v3.1) has been implemented, allowing simulation of the contribution to organic aerosol (OA) made by semi- and non-volatile products of VOC oxidation; emissions of primary organic aerosol (POA) and elemental carbon (EC) are also represented. Simulations of total OA mass concentrations in nine case study events (optimised by comparison with observed hourly-mean mass loadings derived from aerosol mass spectrometry measurements) imply that the OA can be ascribed to three general sources: (i) POA emissions; (ii) a '' ubiquitous '' background concentration of 0.7 mu g m(-3); and (iii) gas-to-aerosol transfer of lower volatility products of VOC oxidation generated by the regional scale processing of emitted VOC, but with all partitioning coefficients increased by a species-independent factor of 500. The requirement to scale the partitioning coefficients, and the implied background concentration, are both indicative of the occurrence of chemical processes within the aerosol which allow the oxidised organic species to react by association and/or accretion reactions which generate even lower volatility products, leading to a persistent, non-volatile secondary organic aerosol (SOA). The contribution of secondary organic material to the simulated OA results in significant elevations in the simulated ratio of organic carbon (OC) to EC, compared with the ratio of 1.1 assigned to the emitted components. For the selected case study events, [OC]/[EC] is calculated to lie in the range 2.7-9.8, values which are comparable with the high end of the range reported in the literature.

Preparation of nanomagnetic absorbent for partition coefficient measurement

In this paper, we report a new method based on supercritical carbon dioxide (scCO(2)) to fill and distribute the porous magnetic nanoparticles with n-octanol in a homogeneous manner. The high solubility of n-octanol in scCO(2) and high diffusivity and permeability of the fluid allow efficient delivery of n-octanol into the porous magnetic nanoparticles. Thus, the n-octanol-loaded magnetic nanoparticles can be readily dispersed into aqueous buffer (pH 7.40) to form a homogenous suspension consisting of nano-sized n-octanol droplets. We refer this suspension as the n-octanol stock solution. The n-octanol stock solution is then mixed with bulk aqueous phase (pH 7.40) containing an organic compound prior to magnetic separation. The small-size of the particles and the efficient mixing enable a rapid establishment of the partition equilibrium of the organic compound between the solid supported n-octanol nano-droplets and the bulk aqueous phase. UV-vis spectrophotometry is then applied to determine the concentration of the organic compound in the aqueous phase both before and after partitioning (after magnetic separation). As a result, log D values of organic compounds of pharmaceutical interest determined by this modified method are found to be in excellent agreement with the literature data. (c) 2006 Elsevier B.V. All rights reserved.

Antioxidant properties of Teaw (Cratoxylum formosum Dyer) extract in soybean oil and emulsions

The antioxidant activity of an extract from Teaw (Cratoxylum formosum Dyer) leaves was studied in soybean oil and soybean oil-in-water emulsions. Samples containing the extract or reference antioxidants including chlorogenic acid, which comprises 60% of the Teaw extract, were stored at 60 degrees C and analyzed periodically for peroxide value (PV) and thiobarbituric acid reactive substances (TBARS) to allow both hydroperoxides and hydroperoxide degradation products to be monitored. Chlorogenic acid and the Teaw extract were more effective than a-tocopherol in inhibiting lipid oxidation in bulk oil but were less effective in an oil-in-water emulsion in accordance with the polar paradox. The PV/TBARS ratio for oil samples containing chlorogenic acid was higher than for alpha-tocopherol and BHT because chlorogenic acid inhibits both hydroperoxide formation by radical scavenging and hydroperoxide decomposition by metal chelation. The importance of the metal-chelating activity in retarding hydroperoxide decomposition was confirmed by studying the decomposition of oil samples containing added ferric ions. The PV/TBARS ratio was higher for citric acid than for (x-tocopherol in the presence of added ferric chloride, but the order was reversed in samples lacking ferric chloride. Samples containing added chlorogenic acid gave the highest PV/TBARS ratios both in the presence and absence of ferric ions. The PV/TBARS ratios for the samples containing antioxidants fell rapidly to lower values in a soybean oil-in-water emulsion than in the soybean oil. This was due to increased hydroperoxide decomposition in the emulsion at the same PV. The Teaw extract contained 12% oil-soluble components, which contributed to a slightly higher oil-water partition coefficient than that of chlorogenic acid. The antioxidant activity of the aqueous phase of the Teaw extract was reduced more than that of chlorogenic acid by partitioning of the oil-soluble components into oil, which showed that the less-polar components contributed to the antioxidant activity of the Teaw extract in aqueous media.

Recovery of lactoferrin and lactoperoxidase from sweet whey using colloidal gas aphrons (CGAs) generated from an anionic surfactant, AOT

The recovery of lactoferrin and lactoperoxidase from sweet whey was studied using colloidal gas aphrons (CGAs), which are surfactant-stabilized microbubbles (10-100 mum). CGAs are generated by intense stirring (8000 rpm for 10 min) of the anionic surfactant AOT (sodium bis-2-ethylhexyl sulfosuccinate). A volume of CGAs (10-30 mL) is mixed with a given volume of whey (1 - 10 mL), and the mixture is allowed to separate into two phases: the aphron (top) phase and the liquid (bottom) phase. Each of the phases is analyzed by SDS-PAGE and surfactant colorimetric assay. A statistical experimental design has been developed to assess the effect of different process parameters including pH, ionic strength, the concentration of surfactant in the CGAs generating solution, the volume of CGAs and the volume of whey on separation efficiency. As expected pH, ionic strength and the volume of whey (i.e. the amount of total protein in the starting material) are the main factors influencing the partitioning of the Lf(.)Lp fraction into the aphron phase. Moreover, it has been demonstrated that best separation performance was achieved at pH = 4 and ionic strength = 0.1 mol/L i.e., with conditions favoring electrostatic interactions between target proteins and CGAs (recovery was 90% and the concentration of lactoferrin and lactoperoxidase in the aphron phase was 25 times higher than that in the liquid phase), whereas conditions favoring hydrophobic interactions (pH close to pI and high ionic strength) led to lower performance. However, under these conditions, as confirmed by zeta potential measurements, the adsorption of both target proteins and contaminant proteins is favored. Thus, low selectivity is achieved at all of the studied conditions. These results confirm the initial hypothesis that CGAs act as ion exchangers and that the selectivity of the process can be manipulated by changing main operating parameters such as type of surfactant, pH and ionic strength.

Adsorption of frog foam nest proteins at the air-water interface

The surfactant properties of aqueous protein mixtures ( ranaspumins) from the foam nests of the tropical frog Physalaemus pustulosus have been investigated by surface tension, two-photon excitation. uorescence microscopy, specular neutron reflection, and related biophysical techniques. Ranaspumins lower the surface tension of water more rapidly and more effectively than standard globular proteins under similar conditions. Two- photon excitation. uorescence microscopy of nest foams treated with fluorescent marker ( anilinonaphthalene sulfonic acid) shows partitioning of hydrophobic proteins into the air-water interface and allows imaging of the foam structure. The surface excess of the adsorbed protein layers, determined from measurements of neutron reflection from the surface of water utilizing H2O/D2O mixtures, shows a persistent increase of surface excess and layer thickness with bulk concentration. At the highest concentration studied ( 0.5 mg ml(-1)), the adsorbed layer is characterized by three distinct regions: a protruding top layer of similar to20 Angstrom, a middle layer of similar to30 Angstrom, and a more diffuse submerged layer projecting some 25 Angstrom into bulk solution. This suggests a model involving self-assembly of protein aggregates at the air-water interface in which initial foam formation is facilitated by specific surfactant proteins in the mixture, further stabilized by subsequent aggregation and cross-linking into a multilayer surface complex.

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.

An on-line algorithm of uncertain time delay estimation in a continuous system

In this paper, we present an on-line estimation algorithm for an uncertain time delay in a continuous system based on the observational input-output data, subject to observational noise. The first order Pade approximation is used to approximate the time delay. At each time step, the algorithm combines the well known Kalman filter algorithm and the recursive instrumental variable least squares (RIVLS) algorithm in cascade form. The instrumental variable least squares algorithm is used in order to achieve the consistency of the delay parameter estimate, since an error-in-the-variable model is involved. An illustrative example is utilized to demonstrate the efficacy of the proposed approach.

Modeling the effects of node heterogeneity on the performance of a real grid application

The performance benefit when using Grid systems comes from different strategies, among which partitioning the applications into parallel tasks is the most important. However, in most cases the enhancement coming from partitioning is smoothed by the effect of the synchronization overhead, mainly due to the high variability of completion times of the different tasks, which, in turn, is due to the large heterogeneity of Grid nodes. For this reason, it is important to have models which capture the performance of such systems. In this paper we describe a queueing-network-based performance model able to accurately analyze Grid architectures, and we use the model to study a real parallel application executed in a Grid. The proposed model improves the classical modelling techniques and highlights the impact of resource heterogeneity and network latency on the application performance.

Parallel Monte Carlo approach for integration of the rendering equation

This paper is addressed to the numerical solving of the rendering equation in realistic image creation. The rendering equation is integral equation describing the light propagation in a scene accordingly to a given illumination model. The used illumination model determines the kernel of the equation under consideration. Nowadays, widely used are the Monte Carlo methods for solving the rendering equation in order to create photorealistic images. In this work we consider the Monte Carlo solving of the rendering equation in the context of the parallel sampling scheme for hemisphere. Our aim is to apply this sampling scheme to stratified Monte Carlo integration method for parallel solving of the rendering equation. The domain for integration of the rendering equation is a hemisphere. We divide the hemispherical domain into a number of equal sub-domains of orthogonal spherical triangles. This domain partitioning allows to solve the rendering equation in parallel. It is known that the Neumann series represent the solution of the integral equation as a infinity sum of integrals. We approximate this sum with a desired truncation error (systematic error) receiving the fixed number of iteration. Then the rendering equation is solved iteratively using Monte Carlo approach. At each iteration we solve multi-dimensional integrals using uniform hemisphere partitioning scheme. An estimate of the rate of convergence is obtained using the stratified Monte Carlo method. This domain partitioning allows easy parallel realization and leads to convergence improvement of the Monte Carlo method. The high performance and Grid computing of the corresponding Monte Carlo scheme are discussed.

Parallel Monte Carlo sampling scheme for sphere and hemisphere

The sampling of certain solid angle is a fundamental operation in realistic image synthesis, where the rendering equation describing the light propagation in closed domains is solved. Monte Carlo methods for solving the rendering equation use sampling of the solid angle subtended by unit hemisphere or unit sphere in order to perform the numerical integration of the rendering equation. In this work we consider the problem for generation of uniformly distributed random samples over hemisphere and sphere. Our aim is to construct and study the parallel sampling scheme for hemisphere and sphere. First we apply the symmetry property for partitioning of hemisphere and sphere. The domain of solid angle subtended by a hemisphere is divided into a number of equal sub-domains. Each sub-domain represents solid angle subtended by orthogonal spherical triangle with fixed vertices and computable parameters. Then we introduce two new algorithms for sampling of orthogonal spherical triangles. Both algorithms are based on a transformation of the unit square. Similarly to the Arvo's algorithm for sampling of arbitrary spherical triangle the suggested algorithms accommodate the stratified sampling. We derive the necessary transformations for the algorithms. The first sampling algorithm generates a sample by mapping of the unit square onto orthogonal spherical triangle. The second algorithm directly compute the unit radius vector of a sampling point inside to the orthogonal spherical triangle. The sampling of total hemisphere and sphere is performed in parallel for all sub-domains simultaneously by using the symmetry property of partitioning. The applicability of the corresponding parallel sampling scheme for Monte Carlo and Quasi-D/lonte Carlo solving of rendering equation is discussed.

Full interference cancellation for two-path cooperative communications

This paper proposes a full interference cancellation (FIC) approach for two-path cooperative communications. Unlike the single relay schemes, the two-path cooperative scheme involves two relay nodes, so that the source can continuously transmit data to the two relays alternatively and the full bandwidth efficiency with respect to the direct transmission can be retained. The two-path relay scheme may however suffer from inter-relay interference which is caused by the simultaneous transmission of the source and one of the relays at any time. In this paper, first the inter-relay interference is expressed as a single recursive term in the received signal, and then the FIC approach is proposed to fully remove the inter-relay interference. The FIC has not only better performance but also less complexity than existing approaches. Numerical examples are also given to verify the proposed approach.

Modeling the effects of node heterogeneity on the performance of grid applications

The performance benefit when using grid systems comes from different strategies, among which partitioning the applications into parallel tasks is the most important. However, in most cases the enhancement coming from partitioning is smoothed by the effects of synchronization overheads, mainly due to the high variability in the execution times of the different tasks, which, in turn, is accentuated by the large heterogeneity of grid nodes. In this paper we design hierarchical, queuing network performance models able to accurately analyze grid architectures and applications. Thanks to the model results, we introduce a new allocation policy based on a combination between task partitioning and task replication. The models are used to study two real applications and to evaluate the performance benefits obtained with allocation policies based on task replication.

A kernel-based two-class classifier for imbalanced data sets

Many kernel classifier construction algorithms adopt classification accuracy as performance metrics in model evaluation. Moreover, equal weighting is often applied to each data sample in parameter estimation. These modeling practices often become problematic if the data sets are imbalanced. We present a kernel classifier construction algorithm using orthogonal forward selection (OFS) in order to optimize the model generalization for imbalanced two-class data sets. This kernel classifier identification algorithm is based on a new regularized orthogonal weighted least squares (ROWLS) estimator and the model selection criterion of maximal leave-one-out area under curve (LOO-AUC) of the receiver operating characteristics (ROCs). It is shown that, owing to the orthogonalization procedure, the LOO-AUC can be calculated via an analytic formula based on the new regularized orthogonal weighted least squares parameter estimator, without actually splitting the estimation data set. The proposed algorithm can achieve minimal computational expense via a set of forward recursive updating formula in searching model terms with maximal incremental LOO-AUC value. Numerical examples are used to demonstrate the efficacy of the algorithm.

A fast linear-in-the-parameters classifier construction algorithm using orthogonal forward selection to minimize leave-one-out misclassification rate

We propose a simple and computationally efficient construction algorithm for two class linear-in-the-parameters classifiers. In order to optimize model generalization, a forward orthogonal selection (OFS) procedure is used for minimizing the leave-one-out (LOO) misclassification rate directly. An analytic formula and a set of forward recursive updating formula of the LOO misclassification rate are developed and applied in the proposed algorithm. Numerical examples are used to demonstrate that the proposed algorithm is an excellent alternative approach to construct sparse two class classifiers in terms of performance and computational efficiency.