Validation of the dietetic confidence scale

Dietitians have reported a lack of confidence in counselling clients with mental health issues. Standardised tools are needed to evaluate programs aiming to improve confidence. The Dietetic Confidence Scale (DCS) was developed to assess dietitians’perception of their capability about working with clients experiencing depression. Exploratory research revealed a 13-item, two-factor model. Dietetic confidence was associated with: 1) Confidence using the Nutrition Care Process; and 2) Confidence in Advocacy for Self-care and Client-care. This study aimed to validate the DCS using this two-factor model.The DCS was administered to 458 dietitians. Confirmatory factor analysis (CFA) assessed the scale’s psychometric validity. Reliability was measured using Cronbach’s alpha (α) co-efficient. CFA results supported the hypothesised two-factor, 13-item model. The Good Fit Index (GFI = 0.95) indicated a strong fit. Item-factor correlations ranged from r = 0.50 to 0.89. The overall scale and subscales showed good reliability (α = 0.93 to 0.76). This is the first study to validate an instrument that measures dietetic confidence about working with clients experiencing depression. The DCS can be used to measure changes in perceived confidence and identify where further training, mentoring or experience is needed. The findings also suggest that initiatives aimed at building dietitians' confidence about working with clients experiencing depression, should focus on improving client-focused nutrition care, foster advocacy, reflective practice, mentoring and encourage professional support networks. Avenues for future research include further validity and reliability testing to expand the generalisability of results; and modifying the scale for other disease or client populations.

Identifying the spatial scale of land use that most strongly influences overall river ecosystem health score

Catchment and riparian degradation has resulted in declining ecosystem health of streams worldwide. With restoration a priority in many regions, there is an increasing interest in the scale at which land use influences stream ecosystem health. Our goal was to use a substantial data set collected as part of a monitoring program (the Southeast Queensland, Australia, Ecological Health Monitoring Program data set, collected at 116 sites over six years) to identify the spatial scale of land use, or the combination of spatial scales, that most strongly influences overall ecosystem health. In addition, we aimed to determine whether the most influential scale differed for different aspects of ecosystem health. We used linear-mixed models and a Bayesian model-averaging approach to generate models for the overall aggregated ecosystem health score and for each of the five component indicators (fish, macroinvertebrates, water quality, nutrients, and ecosystem processes) that make up the score. Dense forest close to the survey site, mid-dense forest in the hydrologically active nearstream areas of the catchment, urbanization in the riparian buffer, and tree cover at the reach scale were all significant in explaining ecosystem health, suggesting an overriding influence of forest cover, particularly close to the stream. Season and antecedent rainfall were also important explanatory variables, with some land-use variables showing significant seasonal interactions. There were also differential influences of land use for each of the component indicators. Our approach is useful given that restoring general ecosystem health is the focus of many stream restoration projects; it allowed us to predict the scale and catchment position of restoration that would result in the greatest improvement of ecosystem health in the regions streams and rivers. The models we generated suggested that good ecosystem health can be maintained in catchments where 80% of hydrologically active areas in close proximity to the stream have mid-dense forest cover and moderate health can be obtained with 60% cover.

The analysis of large scale data taken from the world groundnut (Arachis hypogaea L.) germplasm collection I. Two-way quantitative data

Data associated with germplasm collections are typically large and multivariate with a considerable number of descriptors measured on each of many accessions. Pattern analysis methods of clustering and ordination have been identified as techniques for statistically evaluating the available diversity in germplasm data. While used in many studies, the approaches have not dealt explicitly with the computational consequences of large data sets (i.e. greater than 5000 accessions). To consider the application of these techniques to germplasm evaluation data, 11328 accessions of groundnut (Arachis hypogaea L) from the International Research Institute for the Semi-Arid Tropics, Andhra Pradesh, India were examined. Data for nine quantitative descriptors measured in the rainy and post-rainy growing seasons were used. The ordination technique of principal component analysis was used to reduce the dimensionality of the germplasm data. The identification of phenotypically similar groups of accessions within large scale data via the computationally intensive hierarchical clustering techniques was not feasible and non-hierarchical techniques had to be used. Finite mixture models that maximise the likelihood of an accession belonging to a cluster were used to cluster the accessions in this collection. The patterns of response for the different growing seasons were found to be highly correlated. However, in relating the results to passport and other characterisation and evaluation descriptors, the observed patterns did not appear to be related to taxonomy or any other well known characteristics of groundnut.

The analysis of large scale data taken from the world groundnut (Arachis hypogaea L.) germplasm collection. II. Two-way data with mixed data types

As a sequel to a paper that dealt with the analysis of two-way quantitative data in large germplasm collections, this paper presents analytical methods appropriate for two-way data matrices consisting of mixed data types, namely, ordered multicategory and quantitative data types. While various pattern analysis techniques have been identified as suitable for analysis of the mixed data types which occur in germplasm collections, the clustering and ordination methods used often can not deal explicitly with the computational consequences of large data sets (i.e. greater than 5000 accessions) with incomplete information. However, it is shown that the ordination technique of principal component analysis and the mixture maximum likelihood method of clustering can be employed to achieve such analyses. Germplasm evaluation data for 11436 accessions of groundnut (Arachis hypogaea L.) from the International Research Institute of the Semi-Arid Tropics, Andhra Pradesh, India were examined. Data for nine quantitative descriptors measured in the post-rainy season and five ordered multicategory descriptors were used. Pattern analysis results generally indicated that the accessions could be distinguished into four regions along the continuum of growth habit (or plant erectness). Interpretation of accession membership in these regions was found to be consistent with taxonomic information, such as subspecies. Each growth habit region contained accessions from three of the most common groundnut botanical varieties. This implies that within each of the habit types there is the full range of expression for the other descriptors used in the analysis. Using these types of insights, the patterns of variability in germplasm collections can provide scientists with valuable information for their plant improvement programs.

A broad-scale analysis of links between coastal fisheries production and mangrove extent : a case-study for northeastern Australia

The paradigm that mangroves are critical for sustaining production in coastal fisheries is widely accepted, but empirical evidence has been tenuous. This study showed that links between mangrove extent and coastal fisheries production could be detected for some species at a broad regional scale (1000s of kilometres) on the east coast of Queensland, Australia. The relationships between catch-per-unit-effort for different commercially caught species in four fisheries (trawl, line, net and pot fisheries) and mangrove characteristics, estimated from Landsat images were examined using multiple regression analyses. The species were categorised into three groups based on information on their life history characteristics, namely mangrove-related species (banana prawns Penaeus merguiensis, mud crabs Scylla serrata and barramundi Lates calcarifer), estuarine species (tiger prawns Penaeus esculentus and Penaeus semisulcatus, blue swimmer crabs Portunus pelagicus and blue threadfin Eleutheronema tetradactylum) and offshore species (coral trout Plectropomus spp.). For the mangrove-related species, mangrove characteristics such as area and perimeter accounted for most of the variation in the model; for the non-mangrove estuarine species, latitude was the dominant parameter but some mangrove characteristics (e.g. mangrove perimeter) also made significant contributions to the models. In contrast, for the offshore species, latitude was the dominant variable, with no contribution from mangrove characteristics. This study also identified that finer scale spatial data for the fisheries, to enable catch information to be attributed to a particular catchment, would help to improve our understanding of relationships between mangroves and fisheries production.

Developing a multidimensional scale of customer-oriented deviance (COD)

Although frontline employees' bending of organizational rules and norms for customers is an important phenomenon, marketing scholars to date only broadly describe over-servicing behaviors and provide little distinction among deviant behavioral concepts. Drawing on research on pro-social and pro-customer behaviors and on studies of positive deviance, this paper develops and validates a multi-faceted, multi-dimensional construct term customer-oriented deviance. Results from two samples totaling 616 frontline employees (FLEs) in the retail and hospitality industries demonstrate that customer-oriented deviance is a four-dimensional construct with sound psychometric properties. Evidence from a test of a theoretical model of key antecedents establishes nomological validity with empathy/perspective-taking, risk-taking propensity, role conflict, and job autonomy as key predictors. Results show that the dimensions of customer-oriented deviance are distinct and have significant implications for theory and practice.

Exploring the factor structure of a new self- and other-oriented alcohol expectancy scale relating to sexual coercion and vulnerability

Introduction and Aim: Sexual assaults commonly involve alcohol use by the perpetrator, victim, or both. Beliefs about alcohol’s effects may impact on people’s perceptions of and responses to men and women who have had such experiences while intoxicated from alcohol. This study aimed to develop an alcohol expectancy scale that captures young adults’ beliefs about alcohol’s role in sexual aggression and victimisation. Design and Methods: Based on pilot focus groups, an initial pool of 135 alcohol expectancy items was developed, checked for readability and face validity, and administered via a cross-sectional survey to 201 male and female university students (18-25 years). Items were specified in terms of three target drinkers: self, men, and women. In addition, a social desirability measure was included. Results: Principal Axis Factoring revealed a 4-factor solution for the targets men and women and a 5-factor solution for the target self with 72 items retained. Factors related to sexual coercion, sexual vulnerability, confidence, self-centredness, and negative cognitive and behavioural effects. Social desirability issues were evident for the target self, but not for the targets men and women. Discussion and Conclusions: Young adults link alcohol’s effects with sexual vulnerabilities via perceived risky cognitions and behaviours. Due to social desirability, these expectancies may be difficult to explicate for the self but may be accessible instead via other-oriented assessment. The Sexual Coercion and Vulnerability Alcohol Expectancy Scale has potential as a tool to elucidate the established tendency for observers to excuse intoxicated sexual perpetrators while blaming intoxicated victims.

Multi-scale bio-inspired place recognition

This paper presents a novel place recognition algorithm inspired by the recent discovery of overlapping and multi-scale spatial maps in the rodent brain. We mimic this hierarchical framework by training arrays of Support Vector Machines to recognize places at multiple spatial scales. Place match hypotheses are then cross-validated across all spatial scales, a process which combines the spatial specificity of the finest spatial map with the consensus provided by broader mapping scales. Experiments on three real-world datasets including a large robotics benchmark demonstrate that mapping over multiple scales uniformly improves place recognition performance over a single scale approach without sacrificing localization accuracy. We present analysis that illustrates how matching over multiple scales leads to better place recognition performance and discuss several promising areas for future investigation.

A Combined Renormalization Group-Multiple Scale Method for Singularly Perturbed Problems

This paper introduces a straightforward method to asymptotically solve a variety of initial and boundary value problems for singularly perturbed ordinary differential equations whose solution structure can be anticipated. The approach is simpler than conventional methods, including those based on asymptotic matching or on eliminating secular terms. © 2010 by the Massachusetts Institute of Technology.

Amplitude equations and asymptotic expansions for multi-scale problems

In this paper we introduce a new technique to obtain the slow-motion dynamics in nonequilibrium and singularly perturbed problems characterized by multiple scales. Our method is based on a straightforward asymptotic reduction of the order of the governing differential equation and leads to amplitude equations that describe the slowly-varying envelope variation of a uniformly valid asymptotic expansion. This may constitute a simpler and in certain cases a more general approach toward the derivation of asymptotic expansions, compared to other mainstream methods such as the method of Multiple Scales or Matched Asymptotic expansions because of its relation with the Renormalization Group. We illustrate our method with a number of singularly perturbed problems for ordinary and partial differential equations and recover certain results from the literature as special cases. © 2010 - IOS Press and the authors. All rights reserved.

Discussion on full-scale internal pressure measurements

The wind loading on most structural elements is made up of both an external and internal pressure. Internal pressures are also important for the design of naturally ventilated buildings. The internal pressure is the interaction between the external pressure propagating through the building envelope and any internal plant causing building pressurization. Although the external pressure field can be well defined through a series of wind tunnel tests, modeling complexities makes accurate prediction of the internal pressure difficult. For commercial testing for the determination of design cladding pressures, an internal pressure coefficient is generally assumed from wind loading standards. Several theories regarding the propagation of internal pressures through single and multiple dominant openings have been proposed for small and large flexible buildings (Harris (1990), Holmes, (1979), Liu & Saathoff (1981 ), Vickery (1986, 1994), Vickery & Bloxham (1992), Vickery & Georgiou (1991))...

The large-scale production of graphene flakes using magnetically-enhanced arc discharge between carbon electrodes

A novel approach to large-scale production of high-quality graphene flakes in magnetically-enhanced arc discharges between carbon electrodes is reported. A non-uniform magnetic field is used to control the growth and deposition zones, where the Y-Ni catalyst experiences a transition to the ferromagnetic state, which in turn leads to the graphene deposition in a collection area. The quality of the produced material is characterized by the SEM, TEM, AFM, and Raman techniques. The proposed growth mechanism is supported by the nucleation and growth model.

Multi-scale hybrid numerical simulation of the growth of high-aspect-ratio nanostructures

Recently, a variety high-aspect-ratio nanostructures have been grown and profiled for various applications ranging from field emission transistors to gene/drug delivery devices. However, fabricating and processing arrays of these structures and determining how changing certain physical parameters affects the final outcome is quite challenging. We have developed several modules that can be used to simulate the processes of various physical vapour deposition systems from precursor interaction in the gas phase to gas-surface interactions and surface processes. In this paper, multi-scale hybrid numerical simulations are used to study how low-temperature non-equilibrium plasmas can be employed in the processing of high-aspect-ratio structures such that the resulting nanostructures have properties suitable for their eventual device application. We show that whilst using plasma techniques is beneficial in many nanofabrication processes, it is especially useful in making dense arrays of high-aspect-ratio nanostructures.

Electrostatic nanoparticle filter for atomic scale fabrication in low-temperature plasmas

The means of reducing nanoparticle contamination in the synthesis of carbon nanostructures in reactive Ar + H2 + CH4 plasmas are studied. It is shown that by combining the electrostatic filtering and thermophoretic manipulation of nanoparticles, one can significantly improve the quality of carbon nanopatterns. By increasing the substrate heating power, one can increase the size of deposited nanoparticles and eventually achieve nanoparticle-free nanoassemblies. This approach is generic and is applicable to other reactive plasma-aided nanofabrication processes.