Exploring Predictors of Older Adults\u27 Performance on a Novel Driving Simulator Task

On a per-mile driven basis, older adults are at increased risk of being involved in an automobile accident. The development and implementation of driving assessment tools is necessary to inform decisions about driving reduction and cessation. Driving simulators are one method of assessing driving performance and safety, however many simulators are cost-prohibitive for most researchers and clinicians. Additionally, while driving performance has been previously explored with respect to clinical populations (e.g., Alzheimer’s Disease), less work has evaluated this topic in a cognitively healthy sample. The present study sought to determine whether a novel, cost-effective driving simulator (Assetto Corsa (AC)) might be useful in the evaluation of driving performance in a sample of cognitively healthy older adults. A total of 53 participants completed a battery of paper-and-pencil and computerized cognitive performance measures and self-reports regarding their driving safety and behaviors, and a subset of participants (n = 35) completed the driving simulator task. Hierarchical regressions revealed that paper-and-pencil measures of simple attention and executive functioning and a computerized measure of processing speed were associated with aspects of driving simulator performance. Pearson correlation coefficients revealed that lower self-rated driving was associated with slower completion of the simulator task, and decrements in several cognitive domains were associated with greater self-reported difficulty driving in various conditions, greater aberrant driving behaviors, and higher likelihood of having legal difficulties as a result of driving (e.g., traffic tickets). Implications for future work are discussed

Assessing Changes in Self-Reported Driving Ability After Mild Traumatic Brain Injury

The ability to safely drive a car requires intact cognitive functioning across a variety of domains, many of which are adversely affected following a moderate-to-severe traumatic brain injury (TBI). Mild traumatic brain injury (mTBI) impacts similar cognitive facets, albeit to a less severe extent, and preliminary evidence suggests that mTBI may also have a deleterious effect on driving abilities immediately following injury. However, changes in driving ability over the course of recovery from mTBI have not been adequately examined. The present study addressed this dearth in the literature through examination of self-reported driving ability in 18 participants with a recent mTBI and 25 orthopedic injury (OI) comparison participants both immediately following injury and at two-week follow-up. Participants were recruited from a local emergency department, at which they completed self-report measures of driving ability and an assessment of post-concussive symptoms (PCS). Participants also completed the driving ability self-report at two-week follow-up. Participants with an mTBI reported more driving problems than those with an OI and both groups increased in driving problems reported from baseline to follow-up. Greater PCS was associated with more driving problems at follow-up. Results indicate a possible deleterious effect of injury on driving ability. Implications for future work and clinical practice are discussed

Data Warehousing Scenarios for Model Management

Model management is a framework for supporting meta-data related applications where models and mappings are manipulated as first class objects using operations such as Match, Merge, ApplyFunction, and Compose. To demonstrate the approach, we show how to use model management in two scenarios related to loading data warehouses. The case study illustrates the value of model management as a methodology for approaching meta-data related problems. It also helps clarify the required semantics of key operations. These detailed scenarios provide evidence that generic model management is useful and, very likely, implementable

An Online Bibliography on Scheme Evolution

We briefly motivate and present a new online bibliography on schema evolution, an area which has recently gained much interest in both research and practice

Generic Schema Matching with Cupid

Schema matching is a critical step in many applications, such as XML message mapping, data warehouse loading, and schema integration. In this paper, we investigate algorithms for generic schema matching, outside of any particular data model or application. We first present a taxonomy for past solutions, showing that a rich range of techniques is available. We then propose a new algorithm, Cupid, that discovers mappings between schema elements based on their names, data types, constraints, and schema structure, using a broader set of techniques than past approaches. Some of our innovations are the integrated use of linguistic and structural matching, context-dependent matching of shared types, and a bias toward leaf structure where much of the schema content resides. After describing our algorithm, we present experimental results that compare Cupid to two other schema matching systems

Prospectus, December 1, 1999

https://spark.parkland.edu/prospectus_1999/1032/thumbnail.jp

A survey of approaches to automatic schema matching

Schema matching is a basic problem in many database application domains, such as data integration, E-business, data warehousing, and semantic query processing. In current implementations, schema matching is typically performed manually, which has significant limitations. On the other hand, previous research papers have proposed many techniques to achieve a partial automation of the match operation for specific application domains. We present a taxonomy that covers many of these existing approaches, and we describe the approaches in some detail. In particular, we distinguish between schema-level and instance-level, element-level and structure-level, and language-based and constraint-based matchers. Based on our classification we review some previous match implementations thereby indicating which part of the solution space they cover. We intend our taxonomy and review of past work to be useful when comparing different approaches to schema matching, when developing a new match algorithm, and when implementing a schema matching component

PLoS Biology—We're Open

With this first issue of PLoS Biology, the editors present the aims and scope of the journa

Optimization-Based Correction of a Segmented Optical Telescope Using Image-Plane Sensing

Abstract- The apertures of many optical telescopes are constructed of multiple mirror segments. To attain the performance of a monolithic mirror, the segments must be corrected in tip, tilt, and piston. In this paper we consider a three-segment optical telescope and develop an optimizationbased algorithm for determining the initial error of the segmented aperture through a sequence of displacements, thereby facilitating correction of the initial error. The optimization algorithm is used to investigate the effects of sensor quantization on the achievable correction accuracy