Test-Retest Reliability of Simulated Driving Performance: A Pilot Study

Twenty-seven volunteers completed three simulated driving tests to determine test-retest reliability of performance on a low-cost, fixed-base computerized driving simulator. One retest was completed a few hours after the initial drive, and the final retest was completed 7 days following the initial test drive. Driving performance was compared using measures of vehicle control, speed, and reaction time to critical events. A measure of participants’ ability to inhibit a pre-potent response was also assessed using an inhibition task during each drive, with the number of incorrect inhibition responses recorded. Practice effects were evident for measures of vehicle control (deviation of lane position and number of line crossings) and participants’ ability to withhold responses to inhibition tasks. Good test-retest reliability was observed for measures of vehicle control, speed, reaction time, and variability measures. Poor test-retest reliability was observed for the number of stopping failures observed during driving. The findings from this study suggest that the driving scenario used provides reliable assessment tasks that could be used to track the effects of pharmacological treatments on driving performance. However, an additional familiarization drive should be included as part of future study protocols employing this driving scenario to reduce learning effects during trials. Care should also be taken when interpreting results from tasks with low test-retest reliabilit

Memetic: from meeting memory to virtual ethnography & distributed video analysis

The JISC-funded Memetic2 project was designed as knowledge management and project memory support for teams meeting via the Access Grid environment (Buckingham Shum et al, 2006). This paper describes how these capabilities also enable it to serve as a novel distributed video analysis tool to support interaction analysis. Memetic technologies can be used to record, annotate and discuss sessions recorded within a flexible, visual hypermedia environment called Compendium. We propose that beyond the use originally conceived, the Memetic toolset could find wide ranging applications within social science for virtual ethnography and data analysis

Sector level cost of equity in African financial markets

This paper assesses the effectiveness of Liu (2006) metrics in measuring illiquidity within a multifactor CAPM pricing model. Costs of equity are estimated using this model for the major sectors within Africa’s larger equity markets: Morocco, Tunisia, Egypt, Kenya, Nigeria, Zambia, Botswana and South Africa. In all countries, the cost of equity is found to be highest in the financial sector and lowest in the blue chip stocks of Tunisia, Morocco, Namibia and South Africa. At an aggregate level, Nigeria and Zambia have the highest cost of capital

Sixteen years of social and ecological dynamics reveal challenges and opportunities for adaptive management in sustaining the commons

Efforts to confront the challenges of environmental change and uncertainty include attempts to adaptively manage social–ecological systems. However, critical questions remain about whether adaptive management can lead to sustainable outcomes for both ecosystems and society. Here, we make a contribution to these efforts by presenting a 16-y analysis of ecological outcomes and perceived livelihood impacts from adaptive coral reef management in Papua New Guinea. The adaptive management system we studied was a customary rotational fisheries closure system (akin to fallow agriculture), which helped to increase the biomass of reef fish and make fish less wary (more catchable) relative to openly fished areas. However, over time the amount of fish in openly fished reefs slowly declined. We found that, overall, resource users tended to have positive perceptions about this system, but there were negative perceptions when fishing was being prohibited. We also highlight some of the key traits of this adaptive management system, including 1) strong social cohesion, whereby leaders played a critical role in knowledge exchange; 2) high levels of compliance, which was facilitated via a “carrot-and-stick” approach that publicly rewarded good behavior and punished deviant behavior; and 3) high levels of participation by community actors

Sensory Processing Problems in Children with ADHD, a Systematic Review

One of the most common psychiatric disorders in children is attention deficit hyperactivity disorder (ADHD). Its course and outcome are heterogeneous. Sensory processing problems impact the nature of response to daily events. ADHD and sensory problems may occur together and interact. No published review article about sensory processing problems in children with ADHD were found. A systematic search, conducted on Pub-Med (up to January 2010), and Google Scholar, yielded 255 abstracts on sensory processing problems in children including 11 studies about sensory problems in children with ADHD. Sensory processing problems in children with ADHD is not a well studied area. Sensory processing problems in children with ADHD are more common than in typically developing children. Findings do not support that ADHD subtypes are distinct disorders with regard to sensory processing problems. However, co-morbidity with oppositional defiant disorder and anxiety are predictors of more severe sensory processing problems in children with ADHD

Aligning the Goals of Learning Analytics with its Research Scholarship: An Open Peer Commentary Approach

To promote cross-community dialogue on matters of significance within the field of learning analytics], we as editors-in- chief of the Journal of Learning Analytics have introduced a section for papers that are open to peer commentary. The first of these papers, “A LAK of Direction: Misalignment Between the Goals of Learning Analytics and its Research Scholarship” by Motz et al. (2023), appeared in the journal’s early access section in March 2023, a few days before the start of the 13th International Learning Analytics and Knowledge Conference (LAK ’23). “A LAK of Direction” takes as its starting point the definition of learning analytics used in the call for papers of the first LAK conference (LAK ’11) and used since then by the Society for Learning Analytics Research (SoLAR): “Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs” (Long & Siemens, 2011, p. 24). Following the conference, an invitation to submit proposals for commentaries on the paper was released, and 12 of these proposals were accepted. This paper brings those commentaries togethe

Aligning the Goals of Learning Analytics with its Research Scholarship: An Open Peer Commentary Approach

To promote cross-community dialogue on matters of significance within the field of learning analytics (LA), we as editors-in-chief of the Journal of Learning Analytics (JLA) have introduced a section for papers that are open to peer commentary. An invitation to submit proposals for commentaries on the paper was released, and 12 of these proposals were accepted. The 26 authors of the accepted commentaries are based in Europe, North America, and Australia. They range in experience from PhD students and early-career researchers to some of the longest-standing, most senior members of the learning analytics community. This paper brings those commentaries together, and we recommend reading it as a companion piece to the original paper by Motz et al. (2023), which also appears in this issu

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac