Predicting the orientation of lipid cubic phase films

Lipid cubic phase films are of increasingly widespread importance, both in the analysis of the cubic phases themselves by techniques including microscopy and X-ray scattering, and in their applications, especially as electrode coatings for electrochemical sensors and for templates for the electrodeposition of nanostructured metal. In this work we demonstrate that the crystallographic orientation adopted by these films is governed by minimization of interfacial energy. This is shown by the agreement between experimental data obtained using grazing-incidence small-angle X-ray scattering (GI-SAXS), and the predicted lowest energy orientation determined using a theoretical approach we have recently developed. GI-SAXS data show a high degree of orientation for films of both the double diamond phase and the gyroid phase, with the [111] and [110] directions respectively perpendicular to the planar substrate. In each case, this matches the lowest energy facet calculated for that particular phase.

The efficacy of shopping value in predicting destination loyalty

Recognizing the importance of tourism's experiential aspects, this research examines how hedonic and utilitarian values relate to tourist's overall shopping experience satisfaction and destination loyalty. Study findings suggest both hedonic and utilitarian shopping values are strongly linked to overall shopping satisfaction. Overall shopping satisfaction fully mediates utilitarian shopping value's effect on destination repatronage intention (DRI), destination word-of-mouth (DWoM), and partially mediates hedonic shopping value's (HSV) effect on DRI and DWoM. Study results advance consumer behavior theory and offer managerial implications for retailers operating in a rapidly maturing tourism destination in Turkey's Mediterranean region.

Predicting early data revisions to US GDP and the effects of releases on equity markets

The effects of data uncertainty on real-time decision-making can be reduced by predicting early revisions to US GDP growth. We show that survey forecasts efficiently anticipate the first-revised estimate of GDP, but that forecasting models incorporating monthly economic indicators and daily equity returns provide superior forecasts of the second-revised estimate. We consider the implications of these findings for analyses of the impact of surprises in GDP revision announcements on equity markets, and for analyses of the impact of anticipated future revisions on announcement-day returns.

Predicting fatty acid profiles in blood based on food intake and the FADS1 rs174546 SNP

SCOPE: A high intake of n-3 PUFA provides health benefits via changes in the n-6/n-3 ratio in blood. In addition to such dietary PUFAs, variants in the fatty acid desaturase 1 (FADS1) gene are also associated with altered PUFA profiles. METHODS AND RESULTS: We used mathematical modelling to predict levels of PUFA in whole blood, based on MHT and bolasso selected food items, anthropometric and lifestyle factors, and the rs174546 genotypes in FADS1 from 1,607 participants (Food4Me Study). The models were developed using data from the first reported time point (training set) and their predictive power was evaluated using data from the last reported time point (test set). Amongst other food items, fish, pizza, chicken and cereals were identified as being associated with the PUFA profiles. Using these food items and the rs174546 genotypes as predictors, models explained 26% to 43% of the variability in PUFA concentrations in the training set and 22% to 33% in the test set. CONCLUSIONS: Selecting food items using MHT is a valuable contribution to determine predictors, as our models' predictive power is higher compared to analogue studies. As unique feature, we additionally confirmed our models' power based on a test set.

Predicting how many animals will be where: how to build, calibrate and evaluate individual-based models

Individual-based models (IBMs) can simulate the actions of individual animals as they interact with one another and the landscape in which they live. When used in spatially-explicit landscapes IBMs can show how populations change over time in response to management actions. For instance, IBMs are being used to design strategies of conservation and of the exploitation of fisheries, and for assessing the effects on populations of major construction projects and of novel agricultural chemicals. In such real world contexts, it becomes especially important to build IBMs in a principled fashion, and to approach calibration and evaluation systematically. We argue that insights from physiological and behavioural ecology offer a recipe for building realistic models, and that Approximate Bayesian Computation (ABC) is a promising technique for the calibration and evaluation of IBMs. IBMs are constructed primarily from knowledge about individuals. In ecological applications the relevant knowledge is found in physiological and behavioural ecology, and we approach these from an evolutionary perspective by taking into account how physiological and behavioural processes contribute to life histories, and how those life histories evolve. Evolutionary life history theory shows that, other things being equal, organisms should grow to sexual maturity as fast as possible, and then reproduce as fast as possible, while minimising per capita death rate. Physiological and behavioural ecology are largely built on these principles together with the laws of conservation of matter and energy. To complete construction of an IBM information is also needed on the effects of competitors, conspecifics and food scarcity; the maximum rates of ingestion, growth and reproduction, and life-history parameters. Using this knowledge about physiological and behavioural processes provides a principled way to build IBMs, but model parameters vary between species and are often difficult to measure. A common solution is to manually compare model outputs with observations from real landscapes and so to obtain parameters which produce acceptable fits of model to data. However, this procedure can be convoluted and lead to over-calibrated and thus inflexible models. Many formal statistical techniques are unsuitable for use with IBMs, but we argue that ABC offers a potential way forward. It can be used to calibrate and compare complex stochastic models and to assess the uncertainty in their predictions. We describe methods used to implement ABC in an accessible way and illustrate them with examples and discussion of recent studies. Although much progress has been made, theoretical issues remain, and some of these are outlined and discussed.

Predicting sizes of hexagonal and gyroid metal nanostructures from liquid crystal templating

We describe a method to predict and control the lattice parameters of hexagonal and gyroid mesoporous materials formed by liquid crystal templating. In the first part, we describe a geometric model with which the lattice parameters of different liquid crystal mesophases can be predicted as a function of their water/surfactant/oil volume fractions, based on certain geometric parameters relating to the constituent surfactant molecules. We demonstrate the application of this model to the lamellar (LR), hexagonal (H1), and gyroid bicontinuous cubic (V1) mesophases formed by the binary Brij-56 (C16EO10)/water system and the ternary Brij-56/hexadecane/water system. In this way, we demonstrate predictable and independent control over the size of the cylinders (with hexadecane) and their spacing (with water). In the second part, we produce mesoporous platinum using as templates hexagonal and gyroid phases with different compositions and show that in each case the symmetry and lattice parameter of the metal nanostructure faithfully replicate those of the liquid crystal template, which is itself in agreement with the model. This demonstrates a rational control over the geometry, size, and spacing of pores in a mesoporous metal.

Nepotism and turnover intentions amongst knowledge workers in Saudi Arabia

This chapter discusses how international assignment was used as tool to expand knowledge within the organisation using the example of the Kingdom of Saudi Arabia, we focus in particularly on the case of repatriation and problems with subsequent staff turnover, among repatriates in Saudi Arabia’s private sector. Before doing so, the chapter provides a background to the Saudi labour market and the impact of Saudization policies that aimed to reduce relying on foreign labour. Following this, the chapter discusses the Saudi government attempt create a national knowledgeable labour force through international assignment. Finally, using the example of an organisation in Saudi Arabia, this chapter illustrates the possible role of Wasta - a prevalent form of nepotism that permeates organizational life in Saudi Arabia - in repatriates managers turnover intention. Our focus is on unravelling the impact of Wasta on HRM practices with a particular focus on the management of the repatriation process of Saudi employees upon their completion of international assignments.

Is behavioral ecology important for understanding and predicting population dynamics?

Population ecology is a discipline that studies changes in the number and composition (age, sex) of the individuals that form a population. Many of the mechanisms that generate these changes are associated with individual behavior, for example how individuals defend their territories, find mates or disperse. Therefore, it is important to model population dynamics considering the potential influence of behavior on the modeled dynamics. This study illustrates the diversity of behaviors that influence population dynamics describing several methods that allow integrating behavior into population models and range from simpler models that only consider the number of individuals to complex individual-based models that capture great levels of detail. A series of examples shows the importance of explicitly considering behavior in population modeling to avoid reaching erroneous conclusions. This integration is particularly relevant for conservation, as incorrect predictions regarding the dynamics of populations of conservation interest can lead to inadequate assessment and management. Improved predictions can favor effective protection of species and better use of the limited financial and human conservation resources.

Complementarity of Subjective Global Assessment (SGA) and Nutritional Risk Screening 2002 (NRS 2002) for predicting poor clinical outcomes in hospitalized patients

Background & aims: We evaluated the ability of Nutritional Risk Screening 2002 (NRS 2002) and Subjective Global Assessment (SGA) to predict malnutrition related to poor clinical outcomes. Methods: We assessed 705 patients at a public university hospital within 48 h of admission. Logistic regression and number needed to screen (NNS) were calculated to test the complementarity between the tools and their ability to predict very long length of hospital stay (VLLOS), complications, and death. Results: Of the patients screened, 27.9% were at nutritional risk (NRS+) and 38.9% were malnourished (SGA B or C). Compared to those patients not at nutritional risk, NRS+, SGA B or C patients were at increased risk for complications (p = 0.03, 0.02, and 0.003, respectively). NRS+ patients had an increased risk of death (p = 0.03), and SGA B and C patients had an increased likelihood of VLLOS (p = 0.008 and p < 0.0001, respectively). Patients who were both NRS+ and SGA C had lower estimates of NNS than patients who were NRS+ or SGA C only, though their confidence intervals did overlap. Conclusions: The concurrent application of SGA in NRS+ patients might enhance the ability to predict poor clinical outcomes in hospitalized patients in Brazil. (C) 2010 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Predicting spectral features in galaxy spectra from broad-band photometry

We explore the prospects of predicting emission-line features present in galaxy spectra given broad-band photometry alone. There is a general consent that colours, and spectral features, most notably the 4000 angstrom break, can predict many properties of galaxies, including star formation rates and hence they could infer some of the line properties. We argue that these techniques have great prospects in helping us understand line emission in extragalactic objects and might speed up future galaxy redshift surveys if they are to target emission-line objects only. We use two independent methods, Artificial Neural Networks (based on the ANNz code) and Locally Weighted Regression (LWR), to retrieve correlations present in the colour N-dimensional space and to predict the equivalent widths present in the corresponding spectra. We also investigate how well it is possible to separate galaxies with and without lines from broad-band photometry only. We find, unsurprisingly, that recombination lines can be well predicted by galaxy colours. However, among collisional lines some can and some cannot be predicted well from galaxy colours alone, without any further redshift information. We also use our techniques to estimate how much information contained in spectral diagnostic diagrams can be recovered from broad-band photometry alone. We find that it is possible to classify active galactic nuclei and star formation objects relatively well using colours only. We suggest that this technique could be used to considerably improve redshift surveys such as the upcoming Fibre Multi Object Spectrograph (FMOS) survey and the planned Wide Field Multi Object Spectrograph (WFMOS) survey.

Effects of species turnover on reserve site selection in a fragmented landscape

Changes in species composition is an important process in many ecosystems but rarely considered in systematic reserve site selection. To test the influence of temporal variability in species composition on the establishment of a reserve network, we compared network configurations based on species data of small mammals and frogs sampled during two consecutive years in a fragmented Atlantic Forest landscape (SE Brazil). Site selection with simulated annealing was carried out with the datasets of each single year and after merging the datasets of both years. Site selection resulted in remarkably divergent network configurations. Differences are reflected in both the identity of the selected fragments and in the amount of flexibility and irreplaceability in network configuration. Networks selected when data for both years were merged did not include all sites that were irreplaceable in one of the 2 years. Results of species number estimation revealed that significant changes in the composition of the species community occurred. Hence, temporal variability of community composition should be routinely tested and considered in systematic reserve site selection in dynamic systems.