Task acceptability and workload of driving city streets, rural roads, and expressways: ratings from video clips

SAfety VEhicles using adaptive Interface Technology (SAVE-IT Project) Task 2C: Develop and Validate EquationsSAVE-IT project, funded by U.S. Department of TransportationSubjects rated the workload of clips of forward road scenes (from the advanced collision avoidance system (ACAS) field operational test) in relation to 2 anchor clips of Level of Service (LOS) A and E (light and heavy traffic), and indicated if they would perform any of 3 tasks (dial a phone, manually tune a radio, enter a destination) in driving the scenes shown. After rating all of the clips, subjects rated a wider range of described situations (not shown in clips) and the relative contribution of road geometry, traffic, and other factors to workload. Using logistic regression, predictive equations for the refusal to engage in the 3 tasks were developed as a function of workload, driver age, and sex. Several equations were developed relating real-time driving statistics with workload, where workload was rated on a scale of 1 (minimum) to 10 (maximum). Some 87% of the rating variance was accounted for by the following expression: Mean Workload Rating=8.87-3.01(LogMeanRange)+ 0.48(MeanTrafficCount)+ 2.05(MeanLongitudinalAccleration), where range (to the lead vehicle) and traffic count were both determined by the adaptive cruise control radar. Other estimates were also generated from post-test ratings and adjustments, considering factors such as construction zones, lane drops, curves, and hills. From the results of this report alone, the workload estimates needed by a real-time workload manager could be developed using (1) the real time data, (2) look-up tables based on the clip ratings, (3) look-up tables based on the post-test data, or (4) some combination of those 3 sources.Delphi Delco Electronic Systemshttp://deepblue.lib.umich.edu/bitstream/2027.42/64467/1/102431.pd

Short Term High-Repetition Back Squat Protocol Does Not Improve 5-km Run Performance

International Journal of Exercise Science 13(7): 1770-1782, 2020. The purpose of this study was to evaluate the hypothesis that a novel high-repetition, low-resistance back squat training protocol, designed to stimulate high-intensity interval training, improves 5-km run performance. Fifteen runners [4 male, 11 female; 150 + minutes of endurance exercise/week; age = 22.7 ± 2.0 y; 21.5 ± 2.2 kg/m2 BMI] in this single-group test-retest design completed two weeks of back squats consisting of three sets of 15-24 repetitions at 60% of estimated one-repetition max (1RM), three times per week (1-2 days of rest between sessions). Outcome tests included a 5-km outdoor timed run, laboratory indirect calorimetry to quantify substrate oxidation rates during steady-state submaximal exercise (60% and 70% heart rate max (HRmax)), and estimated 1RM for back squats. Back squat estimated 1RM increased by 20% (58.3 ± 18.5 to 70.2 ± 16.7 kg, P \u3c 0.001). However, 5-km run times due to the back squat protocol did not significantly change (Pre-Squats: 23.9 ± 5.0 vs. Post-Squats: 23.7 ± 4.3 minutes, P = 0.71). Likewise, the squat training program did not significantly alter carbohydrate or lipid oxidation rates during steady-state submaximal exercise at 60% or 70% of HRmax (P values ranged from 0.36 - 0.99). Short term high-repetition back squat training does not appear to impact 5-km run performance or substrate utilization during submaximal exercise

Driving performance analysis of the ACAS FOT data and recommendations for a driving workload manager

This project was performed under a subcontract to Delphi. The primary sponsor was the U.S. Dept. of Transportation, RSPA/Volpe National Transportation Sys. Ctr., 55 Broadway, Kendall Square, Cambridge, MA 02142.SAfety VEhicles using adaptive Interface Technology (SAVE-IT Project) Tasks 2 and 3This report contains analyses of driving performance data from the Advanced Collision Avoidance System (ACAS) Field Operational Test (FOT), with data from nearly 100 drivers and over 100,000 miles of driving. The analyses compared normal and distracted situations and determined thresholds that distinguish between maneuvering and non-maneuvering situations. Four questions were addressed: 1. How are measures of driver input (steering wheel angle, etc.) and vehicle output (heading, speed, etc.) distributed as a function of 4 road types [(a) ramps, (b) interstates and freeways, (c) arterials and minor arterials, and (d) collectors and local roads]? 2. What is the effect of the number of tasks on measures of driver performance as a function of road type? (The distributions for 0 and 1 tasks were similar. For 2 tasks, the range was sometimes 50% less.) 3. How well do linear thresholds distinguish between maneuvering and non-maneuvering situations, and what should those values be? (It varies with the threshold; sometimes the odds were 10:1. Other times they were 1:1.) 4. How effectively do steering and throttle entropy predict distracted and normal driving? (Only steering entropy showed any differences.)Delphi Delco Electronics Systemshttp://deepblue.lib.umich.edu/bitstream/2027.42/64468/1/102432.pd

Second-generation UMTRI coding scheme for classifying driver tasks in distraction studies and application to the ACAS FOT video clips

SAfety VEhicles using adaptive Interface Technology (SAVE-IT Project) Task 3C: PerformanceThis report describes the development of a new coding scheme to classify potentially distracting secondary tasks performed while driving, such as eating and using a cell phone. Compared with prior schemes (Stutts et al., first-generation UMTRI scheme), the new scheme has more distinctive endpoints for tasks and subtasks, is less subjective (e.g., no “high involvement” eating), includes codes for activities absent from prior schemes (e.g., chewing gum), and more closely links subtasks to visual, auditory, cognitive, and psychomotor task demands. The scheme has codes for 12 tasks (use a cell phone, eat/drink, smoke, chew gum, chew tobacco, groom, read, write, type, use an in-car system, internal distraction, and converse) plus codes for drowsiness. The scheme takes several factors into account, such as where the driver is looking, where the driver’s head is pointed, what the driver’s hands are doing, the weather, and the road surface condition. Each main task was divided into 3 to 17 subtasks (e.g., groom using tool, reach and get phone). This scheme was used to code video clips of drivers’ faces from the ACAS field operational test. In the first pass, 2,914 video clips were coded (for task, drowsiness, weather, and road) using custom UMTRI software. In the second pass, a sample of 403 distracted and 416 nondistracted clips were coded frame by frame (15,965 frames) for the subtasks performed, gaze direction, and where the head was pointed.Delphi Delco Electronic Systemshttp://deepblue.lib.umich.edu/bitstream/2027.42/64469/1/102433.pd

Reviews

Lilith in a New Light: Essays on the George Macdonald Fantasy Novel. Ed. Lucas H. Harriman. Reviewed by William Gray. Black & White Ogre Country: The Lost Tales of Hilary Tolkien. Edited by Angela Gardner. Illustrated by Jef Murray. Reviewed by Glen GoodKnight. C.S. Lewis and the Search for Rational Religion. John Beversluis. Reviewed by Donald T. Williams. Faith and Choice in the Works of Joss Whedon. K.. Dale Koontz. Reviewed by Amy H. Sturgis. Fritz Leiber, Critical Essays. Ed. Benjamin Szumskyj. Reviewed by Darrell Schweitzer. Myth and Magic: Art according to the Inklings. Eduardo Segura and Thomas Honegger. Reviewed by Jason Fisher. From Narnia to a Space Odyssey: The War of Ideas between Arthur C. Clarke And C. S. Lewis. Ed., and with introduction, by Ryder W. Miller. Reviewed by Joe R. Christopher. The Mirror Crack\u27d: Fear and Horror in JRR Tolkien\u27s Major Works. Ed. Lynn Forest-Hill. Reviewed by Edith L. Crowe. Arda Reconstructed: The Creation of the Published Silmarillion. Douglas Charles Kane. Reviewed by Jason Fisher. Night Operation. Owen Barfield. Reviewed by David Bratman. Eager Spring. Owen Barfield. Reviewed by David Bratman

Frequency of distracting tasks people do while driving: an analysis of the ACAS FOT data

SAfety VEhicles using adaptive Interface Technology (SAVE-IT Project)This report describes further analysis of data from the advanced collision avoidance system (ACAS) field operational test, a naturalistic driving study. To determine how distracted and nondistracted driving differ, a stratified sample of 2,914 video clips of the drivers’ faces and forward scene was coded to identify (1) where the driver was looking, (2) where their head was facing, (3) the secondary task performed, (4) what their hands were doing, and (5) the driving conditions. A sample of the clips from the first pass (balanced to equalize distracted and nondistracted clips) was examined frame by frame. Key findings include: 1. The most common secondary tasks were conversing, chewing gum, grooming, and using a cell phone, in that order. The most common subtasks were conversing on a cell phone, chewing gum, grooming with a hand, and biting one’s lips while chewing gum, in that order. 2. Depending on the analysis, 7 to 16% of all secondary tasks involved 2 or more secondary tasks occurring together, with 9 of the 10 most common combinations involving conversation or chewing gum. 3. Conversation tended to occur more frequently for older drivers and women, and on minor roads; and less often between midnight and 6:00 a.m., and when the outside temperature was below freezing. 4. Using the phone occurred more frequently for young drivers, for men, and in lighter traffic; and less often between midnight and 6:00 a.m.Delphi Delco Electronic Systemshttp://deepblue.lib.umich.edu/bitstream/2027.42/64457/1/102429.pd

IQCB1 and PDE6B Mutations Cause Similar Early Onset Retinal Degenerations in Two Closely Related Terrier Dog Breeds

Purpose.: To identify the causative mutations in two early-onset canine retinal degenerations, crd1 and crd2, segregating in the American Staffordshire terrier and the Pit Bull Terrier breeds, respectively. Methods.: Retinal morphology of crd1- and crd2-affected dogs was evaluated by light microscopy. DNA was extracted from affected and related unaffected controls. Association analysis was undertaken using the Illumina Canine SNP array and PLINK (crd1 study), or the Affymetrix Version 2 Canine array, the “MAGIC” genotype algorithm, and Fisher\u27s Exact test for association (crd2 study). Positional candidate genes were evaluated for each disease. Results.: Structural photoreceptor abnormalities were observed in crd1-affected dogs as young as 11-weeks old. Rod and cone inner segment (IS) and outer segments (OS) were abnormal in size, shape, and number. In crd2-affected dogs, rod and cone IS and OS were abnormal as early as 3 weeks of age, progressing with age to severe loss of the OS, and thinning of the outer nuclear layer (ONL) by 12 weeks of age. Genome-wide association study (GWAS) identified association at the telomeric end of CFA3 in crd1-affected dogs and on CFA33 in crd2-affected dogs. Candidate gene evaluation identified a three bases deletion in exon 21 of PDE6B in crd1-affected dogs, and a cytosine insertion in exon 10 of IQCB1 in crd2-affected dogs. Conclusions.: Identification of the mutations responsible for these two early-onset retinal degenerations provides new large animal models for comparative disease studies and evaluation of potential therapeutic approaches for the homologous human diseases

How do distracted and normal driving differ: an analysis of the ACAS naturalistic driving data

SAfety VEhicles using adaptive Interface Technology (SAVE-IT Project)To determine how distracted and normal driving differ, this report re-examines driving performance data from the advanced collision avoidance system (ACAS) field operational test (FOT), a naturalistic driving study (96 drivers, 136,792 miles). In terms of overall driving performance statistics, distraction (defined as 4 successive video frames where the driver’s head was not oriented to the forward scene) had almost no effect, except for decreasing mean throttle opening by 36% and mean speed by 6%. No consistent normal/distracted differences were found in the parameters that fit the distributions of steering wheel angle, heading, and speed (all double exponential) and throttle opening (gamma) for each road type by driver age combination. In contrast, logistic regression identified other statistics and factors that discriminated between normal and distracted driving. They included (a) turn signal use and age group for expressways, (b) gender and if the lead vehicle range exceeded 60 m for major roads, and (c) lane width, lane offset, and lead vehicle velocity for minor roads. Finally, in a supplemental analysis, throttle holds (1 - 4 s periods of essentially no throttle change suggesting the driver may not be attending to driving) were actually more common for normal driving when a single time window (1 s) by threshold change combination (4 %) was selected. However, when settings (time windows of 1 – 4 s, thresholds of 1 – 4 %) were tailored for each age group by road class combination, throttle holds could identify when the driver was distracted.Delphi Delco Electronic Systemshttp://deepblue.lib.umich.edu/bitstream/2027.42/64458/1/102430.pd

Low Temperature Opacities

Previous computations of low temperature Rosseland and Planck mean opacities from Alexander & Ferguson (1994) are updated and expanded. The new computations include a more complete equation of state with more grain species and updated optical constants. Grains are now explicitly included in thermal equilibrium in the equation of state calculation, which allows for a much wider range of grain compositions to be accurately included than was previously the case. The inclusion of high temperature condensates such as Al$_2$O$_3$ and CaTiO$_3$ significantly affects the total opacity over a narrow range of temperatures before the appearance of the first silicate grains. The new opacity tables are tabulated for temperatures ranging from 30000 K to 500 K with gas densities from 10$^{-4}$ g cm$^{-3}$ to 10$^{-19}$ g cm$^{-3}$. Comparisons with previous Rosseland mean opacity calculations are discussed. At high temperatures, the agreement with OPAL and Opacity Project is quite good. Comparisons at lower temperatures are more divergent as a result of differences in molecular and grain physics included in different calculations. The computation of Planck mean opacities performed with the opacity sampling method are shown to require a very large number of opacity sampling wavelength points; previously published results obtained with fewer wavelength points are shown to be significantly in error. Methods for requesting or obtaining the new tables are provided.Comment: 39 pages with 12 figures. To be published in ApJ, April 200

A Next-generation Marker Genotyping Platform (AmpSeq) in Heterozygous Crops: A Case Study for Marker-assisted Selection in Grapevine

Marker-assisted selection (MAS) is often employed in crop breeding programs to accelerate and enhance cultivar development, via selection during the juvenile phase and parental selection prior to crossing. Next-generation sequencing and its derivative technologies have been used for genome-wide molecular marker discovery. To bridge the gap between marker development and MAS implementation, this study developed a novel practical strategy with a semi-automated pipeline that incorporates traitassociated single nucleotide polymorphism marker discovery, low-cost genotyping through amplicon sequencing (AmpSeq) and decision making. The results document the development of a MAS package derived from genotyping-by-sequencing using three traits (flower sex, disease resistance and acylated anthocyanins) in grapevine breeding. The vast majority of sequence reads ( ⩾99%) were from the targeted regions. Across 380 individuals and up to 31 amplicons sequenced in each lane of MiSeq data, most amplicons (83 to 87%) had o10% missing data, and read depth had a median of 220–244 × . Several strengths of the AmpSeq platform that make this approach of broad interest in diverse crop species include accuracy, flexibility, speed, high-throughput, lowcost and easily automated analysis