Measuring and comparing the reliability of the structured walkthrough evaluation method with novices and experts

Effective evaluation of websites for accessibility remains problematic. Automated evaluation tools still require a significant manual element. There is also a significant expertise and evaluator effect. The Structured Walkthrough method is the translation of a manual, expert accessibility evaluation process adapted for use by novices. The method is embedded in the Accessibility Evaluation Assistant (AEA), a web accessibility knowledge management tool. Previous trials examined the pedagogical potential of the tool when incorporated into an undergraduate computing curriculum. The results of the evaluations carried out by novices yielded promising, consistent levels of validity and reliability. This paper presents the results of an empirical study that compares the reliability of accessibility evaluations produced by two groups (novices and experts). The main results of this study indicate that overall reliability of expert evaluations was 76% compared to 65% for evaluations produced by novices. The potential of the Structured Walkthrough method as a useful and viable tool for expert evaluators is also examined. Copyright 2014 ACM

Coal feed component testing for CDIF

Investigations conducted during the conceptual design of the Montana MHD Component Development and Integration Facility (CDIF) identified commercially available processing and feeding equipment potentially suitable for use in a reference design. Tests on sub-scale units of this equipment indicated that they would perform as intended

Comprehensive Organic Analysis of Antartic Micrometeorites

Introduction: Micrometeorites (MMs) are thought to be significant contributors of organic material to the early Earth [1], and a variety of techniques have been employed to identify their organic composition [2-6]. These include the identification of key organic groups using combinations of infrared, energy dispersive Xray, electron energy loss and Raman spectroscopy and scanning transmission X-ray microscopy [2-4], highlighting similarities between that of MMs and carbonaceous chondrites. Few studies, however, have focused on the characterisation of individual micrometeoritic organic components. Microscopic L2MS has been used to identify up to C5 polycyclic aromatic hydrocarbons and their alkyl derivatives [5]. A combination of ionexchange chromatography and fluorimetric detection has also been successful in identifying a number of protein amino acids including glycine and alanine [6]. We have previously reported a method to analyse ?g-sized quantities of extraterrestrial materials, with prior application to assessing organic volatile release from MM atmospheric entry heating simulations [7]. In this study we utilise this technique to characterise the organic composition of Antarctic terrestrial particles and MMs collected in 1994 from Cap-Prudhomme [8]

On the accuracy of retrieved wind information from Doppler lidar observations

A single pulsed Doppler lidar was successfully deployed to measure air flow and turbulence over the Malvern hills, Worcester, UK. The DERA Malvern lidar used was a CO2 µm pulsed Doppler lidar. The lidar pulse repetition rate was 120 Hz and had a pulse duration of 0.6 µs The system was set up to have 41 range gates with range resolution of 112 m. This gave a theoretical maximum range of approximately 4.6 km. The lidar site was 2 km east of the Malvern hill ridge which runs in a north-south direction and is approximately 6 km long. The maximum height of the ridge is 430 m. Two elevation scans (Range-Height Indicators) were carried out parallel and perpendicular to the mean surface flow. Since the surface wind was primarily westerly the scans were carried out perpendicular and parallel to the ridge of the Malvern hills. The data were analysed and horizontal winds, vertical winds and turbulent fluxes were calculated for profiles throughout the boundary layer. As an aid to evaluating the errors associated with the derivation of velocity and turbulence profiles, data from a simple idealized profile was also analysed using the same method. The error analysis shows that wind velocity profiles can be derived to an accuracy of 0.24 m s-1 in the horizontal and 0.3 m s-1 in the vertical up to a height of 2500 m. The potential for lidars to make turbulence measurements, over a wide area, through the whole depth of the planetary boundary layer and over durations from seconds to hours is discussed

Identification of blood biomarkers for use in point of care diagnosis tool for Alzheimer's disease.

Early diagnosis of Alzheimer's Disease (AD) is widely regarded as necessary to allow treatment to be started before irreversible damage to the brain occur and for patients to benefit from new therapies as they become available. Low-cost point-of-care (PoC) diagnostic tools that can be used to routinely diagnose AD in its early stage would facilitate this, but such tools require reliable and accurate biomarkers. However, traditional biomarkers for AD use invasive cerebrospinal fluid (CSF) analysis and/or expensive neuroimaging techniques together with neuropsychological assessments. Blood-based PoC diagnostics tools may provide a more cost and time efficient way to assess AD to complement CSF and neuroimaging techniques. However, evidence to date suggests that only a panel of biomarkers would provide the diagnostic accuracy needed in clinical practice and that the number of biomarkers in such panels can be large. In addition, the biomarkers in a panel vary from study to study. These issues make it difficult to realise a PoC device for diagnosis of AD. An objective of this paper is to find an optimum number of blood biomarkers (in terms of number of biomarkers and sensitivity/specificity) that can be used in a handheld PoC device for AD diagnosis. We used the Alzheimer's disease Neuroimaging Initiative (ADNI) database to identify a small number of blood biomarkers for AD. We identified a 6-biomarker panel (which includes A1Micro, A2Macro, AAT, ApoE, complement C3 and PPP), which when used with age as covariate, was able to discriminate between AD patients and normal subjects with a sensitivity of 85.4% and specificity of 78.6%

Dyslexia and Comorbid Dyscalculia: rate of comorbidity and underlying cognitive and learning profile

PURPOSE OF THE STUDY. Children diagnosed with a specific learning disorder (SLD) have four to five times higher chances of developing a comorbid condition. In particular, the high prevalence of comorbid dyscalculia (MD) in children with dyslexia (RD) has been documented. Nevertheless, the exact rate of MD comorbidity and the causes underlying the overlap remain unclear since most research has focused on studying them in isolation. Given the relevance of early identification and evidence-based interventions for further compensation of SLD, there is a need for studies on this matter. The study intended to fill this gap. METHOD. The study was a secondary data analysis of the standardised test scores of 215 neuropsychological assessments administered to grade 1 to 3 schoolchildren in Argentina who had a prior diagnosis of RD. For the purposes of the study, they were classified into 2 groups (RD only and comorbid RDMD). Scores were analyzed using SPSS Statistics to (i) explore the rate of MD comorbidity in children with RD; (ii) contrast the cognitive and learning profiles of the RD and the RDMD group; and (iii) assess the predictive value of each cognitive factor to the development of the RDMD comorbidity. RESULTS AND CONCLUSION. The study found that children with RD developed RDMD at a frequency of 33.5%. There was a significant difference in the two groups' learning and cognitive factors scores, with the comorbid group worst affected in all domains. Among these, verbal working memory, spatial skills, semantic long-term memory and phonological awareness were the most sensitive predictors; together they could account for 35% of the MD comorbidity. These findings are evidence of the high incidence of MD comorbidity in the population with RD and highlight the predictive value of specific cognitive markers