A depth-integrated 2D coastal and estuarine model with conformal boundary-fitted mesh generation

Details are given of the development and application of a 2D depth-integrated, conformal boundary-fitted, curvilinear model for predicting the depth-mean velocity field and the spatial concentration distribution in estuarine and coastal waters. A numerical method for conformal mesh generation, based on a boundary integral equation formulation, has been developed. By this method a general polygonal region with curved edges can be mapped onto a regular polygonal region with the same number of horizontal and vertical straight edges and a multiply connected region can be mapped onto a regular region with the same connectivity. A stretching transformation on the conformally generated mesh has also been used to provide greater detail where it is needed close to the coast, with larger mesh sizes further offshore, thereby minimizing the computing effort whilst maximizing accuracy. The curvilinear hydrodynamic and solute model has been developed based on a robust rectilinear model. The hydrodynamic equations are approximated using the ADI finite difference scheme with a staggered grid and the solute transport equation is approximated using a modified QUICK scheme. Three numerical examples have been chosen to test the curvilinear model, with an emphasis placed on complex practical applications

Balance model for equatorial long waves

Geophysical fluid models often support both fast and slow motions. As the dynamics are often dominated by the slow motions, it is desirable to filter out the fast motions by constructing balance models. An example is the quasi geostrophic (QG) model, which is used widely in meteorology and oceanography for theoretical studies, in addition to practical applications such as model initialization and data assimilation. Although the QG model works quite well in the mid-latitudes, its usefulness diminishes as one approaches the equator. Thus far, attempts to derive similar balance models for the tropics have not been entirely successful as the models generally filter out Kelvin waves, which contribute significantly to tropical low-frequency variability. There is much theoretical interest in the dynamics of planetary-scale Kelvin waves, especially for atmospheric and oceanic data assimilation where observations are generally only of the mass field and thus do not constrain the wind field without some kind of diagnostic balance relation. As a result, estimates of Kelvin wave amplitudes can be poor. Our goal is to find a balance model that includes Kelvin waves for planetary-scale motions. Using asymptotic methods, we derive a balance model for the weakly nonlinear equatorial shallow-water equations. Specifically we adopt the ‘slaving’ method proposed by Warn et al. (Q. J. R. Meteorol. Soc., vol. 121, 1995, pp. 723–739), which avoids secular terms in the expansion and thus can in principle be carried out to any order. Different from previous approaches, our expansion is based on a long-wave scaling and the slow dynamics is described using the height field instead of potential vorticity. The leading-order model is equivalent to the truncated long-wave model considered previously (e.g. Heckley & Gill, Q. J. R. Meteorol. Soc., vol. 110, 1984, pp. 203–217), which retains Kelvin waves in addition to equatorial Rossby waves. Our method allows for the derivation of higher-order models which significantly improve the representation of Rossby waves in the isotropic limit. In addition, the ‘slaving’ method is applicable even when the weakly nonlinear assumption is relaxed, and the resulting nonlinear model encompasses the weakly nonlinear model. We also demonstrate that the method can be applied to more realistic stratified models, such as the Boussinesq model.

Insights into the molecular architecture of a peptide nanotube using FTIR and solid-state NMR spectroscopic measurements on an aligned sample

The self-assembly of proteins and peptides into b-sheet-rich amyloid fibers is a process that has gained notoriety because of its association with human diseases and disorders. Spontaneous self-assembly of peptides into nonfibrillar supramolecular structures can also provide a versatile and convenient mechanism for the bottom-up design of biocompatible materials with functional properties favoring a wide range of practical applications.[1] One subset of these fascinating and potentially useful nanoscale constructions are the peptide nanotubes, elongated cylindrical structures with a hollow center bounded by a thin wall of peptide molecules.[2] A formidable challenge in optimizing and harnessing the properties of nanotube assemblies is to gain atomistic insight into their architecture, and to elucidate precisely how the tubular morphology is constructed from the peptide building blocks. Some of these fine details have been elucidated recently with the use of magic-angle-spinning (MAS) solidstate NMR (SSNMR) spectroscopy.[3] MAS SSNMR measurements of chemical shifts and through-space interatomic distances provide constraints on peptide conformation (e.g., b-strands and turns) and quaternary packing. We describe here a new application of a straightforward SSNMR technique which, when combined with FTIR spectroscopy, reports quantitatively on the orientation of the peptide molecules within the nanotube structure, thereby providing an additional structural constraint not accessible to MAS SSNMR.

Selecting valuable information to remember: age-related differences and similarities in self-regulated learning

It is often necessary to selectively attend to important information, at the expense of less important information, especially if you know you cannot remember large amounts of information. The present study examined how younger and older adults select valuable information to study, when given unrestricted choices about how to allocate study time. Participants were shown a display of point values ranging from 1–30. Participants could choose which values to study, and the associated word was then shown. Study time, and the choice to restudy words, was under the participant's control during the 2-minute study session. Overall, both age groups selected high value words to study and studied these more than the lower value words. However, older adults allocated a disproportionately greater amount of study time to the higher-value words, and age-differences in recall were reduced or eliminated for the highest value words. In addition, older adults capitalized on recency effects in a strategic manner, by studying high-value items often but also immediately before the test. A multilevel mediation analysis indicated that participants strategically remembered items with higher point value, and older adults showed similar or even stronger strategic process that may help to compensate for poorer memory. These results demonstrate efficient (and different) metacognitive control operations in younger and older adults, which can allow for strategic regulation of study choices and allocation of study time when remembering important information. The findings are interpreted in terms of life span models of agenda-based regulation and discussed in terms of practical applications. (PsycINFO Database Record (c) 2013 APA, all rights reserved)(journal abstract)

Stochastic climate theory and modeling

Stochastic methods are a crucial area in contemporary climate research and are increasingly being used in comprehensive weather and climate prediction models as well as reduced order climate models. Stochastic methods are used as subgrid-scale parameterizations (SSPs) as well as for model error representation, uncertainty quantification, data assimilation, and ensemble prediction. The need to use stochastic approaches in weather and climate models arises because we still cannot resolve all necessary processes and scales in comprehensive numerical weather and climate prediction models. In many practical applications one is mainly interested in the largest and potentially predictable scales and not necessarily in the small and fast scales. For instance, reduced order models can simulate and predict large-scale modes. Statistical mechanics and dynamical systems theory suggest that in reduced order models the impact of unresolved degrees of freedom can be represented by suitable combinations of deterministic and stochastic components and non-Markovian (memory) terms. Stochastic approaches in numerical weather and climate prediction models also lead to the reduction of model biases. Hence, there is a clear need for systematic stochastic approaches in weather and climate modeling. In this review, we present evidence for stochastic effects in laboratory experiments. Then we provide an overview of stochastic climate theory from an applied mathematics perspective. We also survey the current use of stochastic methods in comprehensive weather and climate prediction models and show that stochastic parameterizations have the potential to remedy many of the current biases in these comprehensive models.

Manipulation of lipids in animal-derived foods: can it contribute to public health nutrition?

Foods derived from animals are an important source of nutrients for humans. Concerns have been raised that due to their SFA content, dairy foods may increase the risk of cardiometabolic disease. Prospective studies do not indicate an association between milk consumption and increased disease risk although there are less data for other dairy foods. SFA in dairy products can be partially replaced by cis-MUFA through nutrition of the dairy cow although there are too few human studies to conclude that such modification leads to reduced chronic disease risk. Intakes of LCn-3 FA are sub-optimal in many countries and while foods such as poultry meat can be enriched by inclusion of fish oil in the diet of the birds, fish oil is expensive and has an associated risk that the meat will be oxidatively unstable. Novel sources of LCn-3 FA such as kirll oil, algae, and genetically modified plants may prove to be better candidates for meat enrichment. The value of FA-modified foods cannot be judged by their FA composition alone and there needs to be detailed human intervention studies carried out before judgements concerning improved health value can be made. Practical applications: The amount and FA composition of dietary lipids are known to contribute to the risk of chronic disease in humans which is increasing and becoming very costly to treat. The use of animal nutrition to improve the FA composition of staple foods such as dairy products and poultry meat has considerable potential to reduce chronic risk at population level although judgements must not be based simply on FA composition of the foods.

Environmentalities of urban climate governance in Maputo, Mozambique

Interest in the role that cities can play in climate change as sites of transformation has increased but research has been limited in its practical applications and there has been limited consideration of how policies and technologies play out. These challenges necessitate a re-thinking of existing notions of urban governance in order to account for the practices that emerge from governments and a plethora of other actors in the context of uncertainty. We understand these practices to constitute adaptive governance, underpinned by social learning guiding the actions of the multiplicity of actors. The aim here is to unpack how social learning for adaptive governance requires attention to competing understandings of risk and identity, and the multiplicity of mechanisms in which change occurs or is blocked in urban climate governance. We adopt a novel lens of 'environmentalities' which allows us to assess the historical and institutional context and power relations in the informal settlements of Maputo, Mozambique. Our findings highlight how environmental identities around urban adaptation to climate change are constituted in the social and physical divisions between the formal and informal settlements, whilst existing knowledge models prioritise dominant economic and political interests and lead to the construction of new environmental subjects. While the findings of this study are contextually distinct, the generalizable lessons are that governance of urban adaptation occurs and is solidified within a complex multiplicity of socioecological relations.

COMPOSITION OF SUPERCRITICAL CARBON DIOXIDE EXTRACTS OF PITANGA (EUGENIA UNIFLORA L.) LEAVES

The leaves of the Pitanga bush (Eugenia uniflora L.) are considered to be effective against many diseases. Extracts from Pitanga leaves have been found to show pronounced anti-inflammatory action and to have antimicrobial and antifungal activities, among other properties. In this work, extracts from Pitanga leaves were obtained by hydrodistillation and by extraction with supercritical carbon dioxide (SC-CO(2)) at three conditions of temperature and pressure. In the SC-CO(2) extractions also were collected the components that are lost with the CO(2) in the exit of the system using Porapak-Q polymer trap. All extracts were analyzed by gas chromatography-mass spectrometry (GC-MS). Thirty-nine compounds were found in the extracts and twenty-six were identified. The main components identified in the extracts in decreasing quantitative order were: curzerene, germacrene B, C(15)H(20)O(2) and beta-elemene for hydrodistillation; C(15)H(20)O(2) and curzerene for SC-CO(2) extracts and 3-hexen-1-ol, curzerene, C(15)H(20)O(2), beta-elemene and germacrene B for SC-CO(2) extracts captured in Porapak-Q. PRACTICAL APPLICATIONS The natural extracts are a potential source of compounds possessing biological activities. They can be used in foods, pharmaceutics and cosmetics. Pitanga is an exotic fruit from Brazil and extracts from its leaves have been used against many diseases in Brazilian folk medicine. Supercritical extraction is an interesting process for the production of natural extracts because it is a clean process and the knowledge of composition of extracts is crucial for the identification of the probable active components.

ESTIMATING SENSORY SHELF LIFE OF CHOCOLATE AND CARROT CUPCAKES USING ACCEPTANCE TESTS

This study determined the sensory shelf life of a commercial brand of chocolate and carrot cupcakes, aiming at increasing the current 120 days of shelf life to 180. Appearance, texture, flavor and overall quality of cakes stored at six different storage times were evaluated by 102 consumers. The data were analyzed by analysis of variance and linear regression. For both flavors, the texture presented a greater loss in acceptance during the storage period, showing an acceptance mean close to indifference on the hedonic scale at 120 days. Nevertheless, appearance, flavor and overall quality stayed acceptable up to 150 days. The end of shelf life was estimated at about 161 days for chocolate cakes and 150 days for carrot cakes. This study showed that the current 120 days of shelf life can be extended to 150 days for carrot cake and to 160 days for chocolate cake. However, the 180 days of shelf life desired by the company were not achieved. PRACTICAL APPLICATIONS This research shows the adequacy of using sensory acceptance tests to determine the shelf life of two food products (chocolate and carrot cupcakes). This practical application is useful because the precise determination of the shelf life of a food product is of vital importance for its commercial success. The maximum storage time should always be evaluated in the development or reformulation of new products, changes in packing or storage conditions. Once the physical-chemical and microbiological stability of a product is guaranteed, sensorial changes that could affect consumer acceptance will determine the end of the shelf life of a food product. Thus, the use of sensitive and reliable methods to estimate the sensory shelf life of a product is very important. Findings show the importance of determining the shelf life of each product separately and to avoid using the shelf time estimated for a specific product on other, similar products.

Complexity and anisotropy in host morphology make populations less susceptible to epidemic outbreaks

One of the challenges in epidemiology is to account for the complex morphological structure of hosts such as plant roots, crop fields, farms, cells, animal habitats and social networks, when the transmission of infection occurs between contiguous hosts. Morphological complexity brings an inherent heterogeneity in populations and affects the dynamics of pathogen spread in such systems. We have analysed the influence of realistically complex host morphology on the threshold for invasion and epidemic outbreak in an SIR (susceptible-infected-recovered) epidemiological model. We show that disorder expressed in the host morphology and anisotropy reduces the probability of epidemic outbreak and thus makes the system more resistant to epidemic outbreaks. We obtain general analytical estimates for minimally safe bounds for an invasion threshold and then illustrate their validity by considering an example of host data for branching hosts (salamander retinal ganglion cells). Several spatial arrangements of hosts with different degrees of heterogeneity have been considered in order to separately analyse the role of shape complexity and anisotropy in the host population. The estimates for invasion threshold are linked to morphological characteristics of the hosts that can be used for determining the threshold for invasion in practical applications.

Measuring time series predictability using support vector regression

Most studies involving statistical time series analysis rely on assumptions of linearity, which by its simplicity facilitates parameter interpretation and estimation. However, the linearity assumption may be too restrictive for many practical applications. The implementation of nonlinear models in time series analysis involves the estimation of a large set of parameters, frequently leading to overfitting problems. In this article, a predictability coefficient is estimated using a combination of nonlinear autoregressive models and the use of support vector regression in this model is explored. We illustrate the usefulness and interpretability of results by using electroencephalographic records of an epileptic patient.

Inflated beta distributions

This paper considers the issue of modeling fractional data observed on [0,1), (0,1] or [0,1]. Mixed continuous-discrete distributions are proposed. The beta distribution is used to describe the continuous component of the model since its density can have quite different shapes depending on the values of the two parameters that index the distribution. Properties of the proposed distributions are examined. Also, estimation based on maximum likelihood and conditional moments is discussed. Finally, practical applications that employ real data are presented.

Flow injection analysis of ethyl xanthate by gas diffusion and UV detection as CS(2) for process monitoring of sulfide ore flotation

A sensitive and robust analytical method for spectrophotometric determination of ethyl xanthate, CH(3)CH(2)OCS(2)(-) at trace concentrations in pulp solutions from froth flotation process is proposed. The analytical method is based on the decomposition of ethyl xanthate. EtX(-), with 2.0 mol L(-1) HCl generating ethanol and carbon disulfide. CS(2). A gas diffusion cell assures that only the volatile compounds diffuse through a PTFE membrane towards an acceptor stream of deionized water, thus avoiding the interferences of non-volatile compounds and suspended particles. The CS(2) is selectively detected by UV absorbance at 206 nm (epsilon = 65,000 L mol(-1) cm(-1)). The measured absorbance is directly proportional to EtX(-) concentration present in the sample solutions. The Beer`s law is obeyed in a 1 x 10(-6) to 2 x 10(-4) mol L(-1) concentration range of ethyl xanthate in the pulp with an excellent correlation coefficient (r = 0.999) and a detection limit of 3.1 x 10(-7) mol L(-1), corresponding to 38 mu g L. At flow rates of 200 mu L min(-1) of the donor stream and 100 mu L min(-1) of the acceptor channel a sampling rate of 15 injections per hour could be achieved with RSD < 2.3% (n = 10, 300 mu L injections of 1 x 10(-5) mol L(-1) EtX(-)). Two practical applications demonstrate the versatility of the FIA method: (i) evaluation the free EtX(-) concentration during a laboratory study of the EtX(-) adsorption capacity on pulverized sulfide ore (pyrite) and (ii) monitoring of EtX(-) at different stages (from starting load to washing effluents) of a flotation pilot plant processing a Cu-Zn sulfide ore. (C) 2010 Elsevier By. All rights reserved.

Effect of the catalyst composition in the Pt-x(Ru-Ir)(1-x)/C system on the electro-oxidation of methanol in acid media

The effect of variations in the composition for ternary catalysts of the type Pt-x(Ru-Ir)(1-x)/C on the methanol oxidation reaction in acid media for x values of 0.25, 0.50 and 0.75 is reported. The catalysts were prepared by the sol-gel method and characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), atomic absorption spectroscopy (AAS) and energy dispersive X-ray (EDX) analyses. The nanometric character (2.8-3.2 nm) of the sol-gel deposits was demonstrated by XRD and TEM while EDX and AAS analyses showed that the metallic ratio in the compounds was very near to the expected one. Cyclic voltammograms for methanol oxidation revealed that the reaction onset occur at less positive potentials in all the ternary catalysts tested here when compared to a Pt-0.75-Ru-0.25/C (E-Tek) commercial composite. Steady-state polarization experiments (Tafel plots) showed that the Pt-0.25(Ru-Ir)(0.75)/C catalyst is the more active one for methanol oxidation as revealed by the shift of the reaction onset towards lower potentials. In addition, constant potential electrolyses suggest that the addition of Ru and Ir to Pt decreases the poisoning effect of the strongly adsorbed species generated during methanol oxidation. Consequently, the Pt-0.25 (Ru-Ir)(0.75)/C Composite catalyst is a very promising one for practical applications. (c) 2007 Elsevier B.V. All rights reserved.