Accurate stratospheric particle size distributions from two separate flat-plate collection surfaces

Over the past two decades, flat-plate particle collections have revealed the presence of a remarkable variety of both terrestrial and extraterrestrial material in the stratosphere [1-6]. The ratio of terrestrial to extraterrestrial material and the nature of material collected may vary over observable time scales. Variations in particle number density can be important since the earth’s atmospheric radiation balance, and therefore the earth’s climate, can be influenced by articulate absorption and scattering of radiation from the sun and earth [7-9]. In order to assess the number density of solid particles in the stratosphere, we have examined a representative fraction of the so1id particles from two flat-plate collection surfaces, whose collection dates are separated in time by 5 years.

A flexible asset maintenance decision-making process model

Optimal Asset Maintenance decisions are imperative for efficient asset management. Decision Support Systems are often used to help asset managers make maintenance decisions, but high quality decision support must be based on sound decision-making principles. For long-lived assets, a successful Asset Maintenance decision-making process must effectively handle multiple time scales. For example, high-level strategic plans are normally made for periods of years, while daily operational decisions may need to be made within a space of mere minutes. When making strategic decisions, one usually has the luxury of time to explore alternatives, whereas routine operational decisions must often be made with no time for contemplation. In this paper, we present an innovative, flexible decision-making process model which distinguishes meta-level decision making, i.e., deciding how to make decisions, from the information gathering and analysis steps required to make the decisions themselves. The new model can accommodate various decision types. Three industrial case studies are given to demonstrate its applicability.

General belongingness, workplace belongingness, and depressive symptoms

Research has shown that a strong relationship exists between belongingness and depressive symptoms; however, the contribution of specific types of belongingness remains unknown. Participants (N=369) completed the sense of belonging instrument, psychological sense of organizational membership, and the depression scale of the depression anxiety stress scales. Factor analysis demonstrated that workplace and general belongingness are distinct constructs. When regressed onto depressive symptoms, these belongingness types made independent contributions, together accounting for 45% of variance, with no moderation effects evident. Hence, general belongingness and specific workplace belongingness appear to have strong additive links to depressive symptoms. These results add support to the belongingness hypothesis and sociometer theory and have significant implication for depression prevention and treatment

Benefit cost analysis applied to behavioral and engineering safety countermeasures in San Francisco, California

The state of the practice in safety has advanced rapidly in recent years with the emergence of new tools and processes for improving selection of the most cost-effective safety countermeasures. However, many challenges prevent fair and objective comparisons of countermeasures applied across safety disciplines (e.g. engineering, emergency services, and behavioral measures). These countermeasures operate at different spatial scales, are funded often by different financial sources and agencies, and have associated costs and benefits that are difficult to estimate. This research proposes a methodology by which both behavioral and engineering safety investments are considered and compared in a specific local context. The methodology involves a multi-stage process that enables the analyst to select countermeasures that yield high benefits to costs, are targeted for a particular project, and that may involve costs and benefits that accrue over varying spatial and temporal scales. The methodology is illustrated using a case study from the Geary Boulevard Corridor in San Francisco, California. The case study illustrates that: 1) The methodology enables the identification and assessment of a wide range of safety investment types at the project level; 2) The nature of crash histories lend themselves to the selection of both behavioral and engineering investments, requiring cooperation across agencies; and 3) The results of the cost-benefit analysis are highly sensitive to cost and benefit assumptions, and thus listing and justification of all assumptions is required. It is recommended that a sensitivity analyses be conducted when there is large uncertainty surrounding cost and benefit assumptions.

A known-groups evaluation of the Response Bias Scale in a neuropsychological setting

We evaluated the Minnesota Multiphasic Personality Inventory-Second Edition (MMPI-2) Response Bias Scale (RBS). Archival data from 83 individuals who were referred for neuropsychological assessment with no formal diagnosis (n = 10), following a known or suspected traumatic brain injury (n = 36), with a psychiatric diagnosis (n = 20), or with a history of both trauma and a psychiatric condition (n = 17) were retrieved. The criteria for malingered neurocognitive dysfunction (MNCD) were applied, and two groups of participants were formed: poor effort (n = 15) and genuine responders (n = 68). Consistent with previous studies, the difference in scores between groups was greatest for the RBS (d = 2.44), followed by two established MMPI-2 validity scales, F (d = 0.25) and K (d = 0.23), and strong significant correlations were found between RBS and F (rs = .48) and RBS and K (r = −.41). When MNCD group membership was predicted using logistic regression, the RBS failed to add incrementally to F. In a separate regression to predict group membership, K added significantly to the RBS. Receiver-operating curve analysis revealed a nonsignificant area under the curve statistic, and at the ideal cutoff in this sample of >12, specificity was moderate (.79), sensitivity was low (.47), and positive and negative predictive power values at a 13% base rate were .25 and .91, respectively. Although the results of this study require replication because of a number of limitations, this study has made an important first attempt to report RBS classification accuracy statistics for predicting poor effort at a range of base rates.

Exploring the effect of companion robots on emotional expression in older people with dementia : a pilot randomized controlled trial

This pilot study aimed to compare the effect of companion robots (PARO) to participation in an interactive reading group on emotions in people living with moderate to severe dementia in a residential care setting. A randomized crossover design, with PARO and reading control groups, was used. Eighteen residents with mid- to late-stage dementia from one aged care facility in Queensland, Australia, were recruited. Participants were assessed three times using the Quality of Life in Alzheimer’s Disease, Rating Anxiety in Dementia, Apathy Evaluation, Geriatric Depression, and Revised Algase Wandering Scales. PARO had a moderate to large positive influence on participants’ quality of life compared to the reading group. The PARO intervention group had higher pleasure scores when compared to the reading group. Findings suggest PARO may be useful as a treatment option for people with dementia; however, the need for a larger trial was identified.

The prevalence and perception of pain amongst hospital in-patients

The aim of this paper is to describe the prevalence and perceptions of pain and pain management amongst hospital in-patients. A cross-sectional descriptive survey of 205 patients was conducted. Presence and severity of pain was assessed using verbal descriptor and visual analogue scales, and perceptions of pain were assessed using multi-item scales. Although the severity of pain reported was consistent across age groups and clinical areas, women in the study sample were significantly more likely to report high levels of pain than men. Differences in how men and women communicate their pain were observed, with women indicating that they were less willing to ask for help with their pain. Results suggest that pain continues to be an important problem for a large number of men and women in hospital, and that the experience of pain impacts negatively upon their well-being. Gender differences in the experience of and response to pain remain important considerations for clinical nurses who have major responsibilities for the management of pain in hospitalized patients.

Uncertainty analysis of pollutant build-up modelling based on a Bayesian Weighted Least Squares approach

Reliable pollutant build-up prediction plays a critical role in the accuracy of urban stormwater quality modelling outcomes. However, water quality data collection is resource demanding compared to streamflow data monitoring, where a greater quantity of data is generally available. Consequently, available water quality data sets span only relatively short time scales unlike water quantity data. Therefore, the ability to take due consideration of the variability associated with pollutant processes and natural phenomena is constrained. This in turn gives rise to uncertainty in the modelling outcomes as research has shown that pollutant loadings on catchment surfaces and rainfall within an area can vary considerably over space and time scales. Therefore, the assessment of model uncertainty is an essential element of informed decision making in urban stormwater management. This paper presents the application of a range of regression approaches such as ordinary least squares regression, weighted least squares Regression and Bayesian Weighted Least Squares Regression for the estimation of uncertainty associated with pollutant build-up prediction using limited data sets. The study outcomes confirmed that the use of ordinary least squares regression with fixed model inputs and limited observational data may not provide realistic estimates. The stochastic nature of the dependent and independent variables need to be taken into consideration in pollutant build-up prediction. It was found that the use of the Bayesian approach along with the Monte Carlo simulation technique provides a powerful tool, which attempts to make the best use of the available knowledge in the prediction and thereby presents a practical solution to counteract the limitations which are otherwise imposed on water quality modelling.

Avoiding uninformed responses in destination image questionnaires

Since the first destination image studies were published in the early 1970s, the field has become one of the most popular in the tourism literature. While reviews of the destination image literature show no commonly agreed conceptualisation of the construct, researchers have predominantly used structured questionnaires for measurement. There has been criticism that the way some of these scales have been selected means a greater likelihood of attributes being irrelevant to participants. This opens up the risk of stimulating uninformed responses. The issue of uninformed response was first raised as a source of error 60 years ago. However, there has been little, if any, discussion in relation to destination image measurement, studies of which often require participants to provide opinion-driven rather than fact-based responses. This paper reports the trial of a ‘don’t know’ (DK) non-response option for participants in two destination image questionnaires. It is suggested the use of a DK option provides participants with an alternative to i) skipping the question, ii) using the scale midpoint to denote neutrality, or iii) providing an uninformed response. High levels of DK usage by participants can then alert the marketer of the need to improve awareness of destination performance for potential salient attributes.

Microstructures and strengthening mechanisms of Cu/Ni/W nanolayered composites

Cu/Ni/W nanolayered composites with individual layer thickness ranging from 5nm to 300nm were prepared by a magnetron sputtering system. Microstructures and strength of the nanolayered composites were investigated by using the nanoindentation method combined with theoretical analysis. Microstructure characterization revealed that the Cu/Ni/W composite consists of a typical Cu/Ni coherent interface and Cu/W and Ni/W incoherent interfaces. Cu/Ni/W composites have an ultrahigh strength and a large strengthening ability compared with bi-constituent Cu–X(X¼Ni, W, Au, Ag, Cr, Nb, etc.) nanolayered composites. Summarizing the present results and those reported in the literature, we systematically analyze the origin of the ultrahigh strength and its length scale dependence by taking into account the constituent layer properties, layer scales and heterogeneous layer/layer interface characteristics, including lattice and modulus mismatch as well as interface structure.

Exponential integrators and a dual-scale model for wood drying

For the timber industry, the ability to simulate the drying of wood is invaluable for manufacturing high quality wood products. Mathematically, however, modelling the drying of a wet porous material, such as wood, is a diffcult task due to its heterogeneous and anisotropic nature, and the complex geometry of the underlying pore structure. The well{ developed macroscopic modelling approach involves writing down classical conservation equations at a length scale where physical quantities (e.g., porosity) can be interpreted as averaged values over a small volume (typically containing hundreds or thousands of pores). This averaging procedure produces balance equations that resemble those of a continuum with the exception that effective coeffcients appear in their deffnitions. Exponential integrators are numerical schemes for initial value problems involving a system of ordinary differential equations. These methods differ from popular Newton{Krylov implicit methods (i.e., those based on the backward differentiation formulae (BDF)) in that they do not require the solution of a system of nonlinear equations at each time step but rather they require computation of matrix{vector products involving the exponential of the Jacobian matrix. Although originally appearing in the 1960s, exponential integrators have recently experienced a resurgence in interest due to a greater undertaking of research in Krylov subspace methods for matrix function approximation. One of the simplest examples of an exponential integrator is the exponential Euler method (EEM), which requires, at each time step, approximation of φ(A)b, where φ(z) = (ez - 1)/z, A E Rnxn and b E Rn. For drying in porous media, the most comprehensive macroscopic formulation is TransPore [Perre and Turner, Chem. Eng. J., 86: 117-131, 2002], which features three coupled, nonlinear partial differential equations. The focus of the first part of this thesis is the use of the exponential Euler method (EEM) for performing the time integration of the macroscopic set of equations featured in TransPore. In particular, a new variable{ stepsize algorithm for EEM is presented within a Krylov subspace framework, which allows control of the error during the integration process. The performance of the new algorithm highlights the great potential of exponential integrators not only for drying applications but across all disciplines of transport phenomena. For example, when applied to well{ known benchmark problems involving single{phase liquid ow in heterogeneous soils, the proposed algorithm requires half the number of function evaluations than that required for an equivalent (sophisticated) Newton{Krylov BDF implementation. Furthermore for all drying configurations tested, the new algorithm always produces, in less computational time, a solution of higher accuracy than the existing backward Euler module featured in TransPore. Some new results relating to Krylov subspace approximation of '(A)b are also developed in this thesis. Most notably, an alternative derivation of the approximation error estimate of Hochbruck, Lubich and Selhofer [SIAM J. Sci. Comput., 19(5): 1552{1574, 1998] is provided, which reveals why it performs well in the error control procedure. Two of the main drawbacks of the macroscopic approach outlined above include the effective coefficients must be supplied to the model, and it fails for some drying configurations, where typical dual{scale mechanisms occur. In the second part of this thesis, a new dual{scale approach for simulating wood drying is proposed that couples the porous medium (macroscale) with the underlying pore structure (microscale). The proposed model is applied to the convective drying of softwood at low temperatures and is valid in the so{called hygroscopic range, where hygroscopically held liquid water is present in the solid phase and water exits only as vapour in the pores. Coupling between scales is achieved by imposing the macroscopic gradient on the microscopic field using suitably defined periodic boundary conditions, which allows the macroscopic ux to be defined as an average of the microscopic ux over the unit cell. This formulation provides a first step for moving from the macroscopic formulation featured in TransPore to a comprehensive dual{scale formulation capable of addressing any drying configuration. Simulation results reported for a sample of spruce highlight the potential and flexibility of the new dual{scale approach. In particular, for a given unit cell configuration it is not necessary to supply the effective coefficients prior to each simulation.

Markers of disease severity are associated with malnutrition in Parkinson's disease

Objective In Parkinson's disease (PD), commonly reported risk factors for malnutrition in other populations commonly occur. Few studies have explored which of these factors are of particular importance in malnutrition in PD. The aim was to identify the determinants of nutritional status in people with Parkinson's disease (PWP). Methods Community-dwelling PWP (>18 years) were recruited (n = 125; 73M/52F; Mdn 70 years). Self-report assessments included Beck's Depression Inventory (BDI), Spielberger Trait Anxiety Inventory (STAI), Scales for Outcomes in Parkinson's disease – Autonomic (SCOPA-AUT), Modified Constipation Assessment Scale (MCAS) and Freezing of Gait Questionnaire (FOG-Q). Information about age, PD duration, medications, co-morbid conditions and living situation was obtained. Addenbrooke's Cognitive Examination (ACE-R), Unified Parkinson's Disease Rating Scale (UPDRS) II and UPDRS III were performed. Nutritional status was assessed using the Subjective Global Assessment (SGA) as part of the scored Patient-Generated Subjective Global Assessment (PG-SGA). Results Nineteen (15%) were malnourished (SGA-B). Median PG-SGA score was 3. More of the malnourished were elderly (84% vs. 71%) and had more severe disease (H&Y: 21% vs. 5%). UPDRS II and UPDRS III scores and levodopa equivalent daily dose (LEDD)/body weight(mg/kg) were significantly higher in the malnourished (Mdn 18 vs. 15; 20 vs. 15; 10.1 vs. 7.6 respectively). Regression analyses revealed older age at diagnosis, higher LEDD/body weight (mg/kg), greater UPDRS III score, lower STAI score and higher BDI score as significant predictors of malnutrition (SGA-B). Living alone and higher BDI and UPDRS III scores were significant predictors of a higher log-adjusted PG-SGA score. Conclusions In this sample of PWP, the rate of malnutrition was higher than that previously reported in the general community. Nutrition screening should occur regularly in those with more severe disease and depression. Community support should be provided to PWP living alone. Dopaminergic medication should be reviewed with body weight changes.

Laboratory and pilot scale pretreatment of sugarcane bagasse by acidified aqueous glycerol solutions

Pretreatment of sugarcane bagasse with acidified aqueous glycerol solution was evaluated at both laboratory and pilot scales. Laboratory scale pretreatment (4.00 g dry mass in 40.00 g liquid) with glycerol solutions containing ≤ 20 wt% water and 1.2 wt% HCl at 130 °C for 60 min resulted in biomass having glucan digestibilities of ≥ 88%. Comparable glucan enzymatic digestibility of 90% was achieved with bagasse pretreated at pilot scale (10 kg dry mass in 60 kg liquid) using a glycerol solution containing 0.4 wt% HCl and 17 wt% water at 130 °C for 15 min. We attribute more efficient pretreatment at pilot scale (despite shorter reaction time and reduced acid content) to improved mixing and heat transfer in a horizontal reactor. Pretreatment of sugarcane bagasse with acid-catalysed glycerol solutions likely produces glycerol-glycosides, which together with hydrolysed lignin are potential substrates for the production of biopolymers.

Efficient articulated trajectory reconstruction using dynamic programming and filters

This paper considers the problem of reconstructing the motion of a 3D articulated tree from 2D point correspondences subject to some temporal prior. Hitherto, smooth motion has been encouraged using a trajectory basis, yielding a hard combinatorial problem with time complexity growing exponentially in the number of frames. Branch and bound strategies have previously attempted to curb this complexity whilst maintaining global optimality. However, they provide no guarantee of being more efficient than exhaustive search. Inspired by recent work which reconstructs general trajectories using compact high-pass filters, we develop a dynamic programming approach which scales linearly in the number of frames, leveraging the intrinsically local nature of filter interactions. Extension to affine projection enables reconstruction without estimating cameras.

Identifying an appropriate driving behaviour scale for the occupational driving context : the DBQ vs. the ODBQ

Self reported driving behaviour in the occupational driving context has typically been measured through scales adapted from the general driving population (i.e. the Manchester Driver Behaviour Questionnaire (DBQ)). However, research suggests that occupational driving is influenced by unique factors operating within the workplace environment, and thus, a behavioural scale should reflect those behaviours prevalent and unique within the driving context. To overcome this limitation, developed the Occupational Driver Behaviour Questionnaire (ODBQ) which utilises a relevant theoretical model to assess the impact of the broader workplace context on driving behaviour. Although the theoretical argument has been established, research is yet to examine whether the ODBQ or the DBQ is a more sensitive measure of the workplace context. As such, this paper identifies selected organisational factors (i.e. safety climate and role overload) as predictors of the DBQ and the ODBQ and compares the relative predictive value in both models. In undertaking this task, 248 occupational drivers were recruited from a community-oriented nursing population. As predicted, hierarchical regression analyses revealed that the organisational factors accounted for a significantly greater proportion of variance in the ODBQ than the DBQ. These findings offer a number of practical and theoretical applications for occupational driving practice and future research.