Dynamics of Oxygen Demand Within the Middle and Lower Savannah River Basins

2010 S.C. Water Resources Conference - Science and Policy Challenges for a Sustainable Futur

Relationship of VO2 Peak, Body Fat Percentage, and Power Output Measured During Repeated Bouts of a Wingate Protocol

The principle of specificity would indicate that being aerobically trained would not necessarily enhance performance in events relying principally on oxygen-independent metabolic pathways (i.e. “anaerobic” exercise). Body fatness may be associated with aerobic and anaerobic performance. VO2 Peak was determined with a graded cycle ergometry and, in a separate session 4 consecutive Wingate power tests (3 min recovery) in 31 males. Pearson correlations were calculated for VO2 Peak and Body Fat Percentage with Peak Power, Mean Power, Minimum Power, Fatigue Index, Peak Heart Rate, and Recovery Heart Rate. No significant correlations were found for VO2 Peak or Body Fat Percentage with Peak Power on any bout (p\u3e0.05). Significant correlations were found for VO2 Peak and Body Fat Percentage with Mean Power, Minimum Power, and Fatigue Index. Significant correlations were found for VO2 Peak with delta values of power performance and heart rates (peak and 3 min recovery). Results indicate that VO2 Peak is associated with repeated anaerobic performance, possibly due to greater capacity to recover between bouts. Body Fat Percentage was correlated with measures of power performance (strongest relationships existing in the earlier bouts), but is not strongly correlated with either the heart rate response to power performance or the change in performance over successive bouts

Understanding Hydrologic Variation Through Time-Series Analysis

2010 S.C. Water Resources Conference - Science and Policy Challenges for a Sustainable Futur

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

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

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

Modelling and mapping how common guillemots balance their energy budgets over a full annual cycle

The ability of individual animals to balance their energy budgets throughout the annual cycle is important for their survival, reproduction and population dynamics. However, the annual cycles of many wild, mobile animals are difficult to observe and our understanding of how individuals balance their energy budgets throughout the year therefore remains poor. We developed a hierarchical Bayesian state-space model to investigate how key components of animal energy budgets (namely individual energy gain and storage) varied in space and time. Our model used biologger-derived estimates of time-activity budgets, locations and energy expenditure to infer year-round time series of energy income and reserves. The model accounted for seasonality in environmental drivers such as sea surface temperature and daylength, allowing us to identify times and locations of high energy gain. Our study system was a population of common guillemots Uria aalge breeding at a western North Sea colony. These seabirds manage their energy budgets by adjusting their behaviour and accumulating fat reserves. However, typically during severe weather conditions, birds can experience an energy deficit over a sustained period, leading to starvation and large-scale mortality events. We show that guillemot energy gain varied in both time and space. Estimates of guillemot body mass varied throughout the annual cycle and birds periodically experienced losses in mass. Mass losses were likely to have either been adaptive, or due to energetic bottlenecks, the latter leading to increased susceptibility to mortality. Guillemots tended to be lighter towards the edge of their spatial distribution. We describe a framework that combines biologging data, time-activity budget analysis and Bayesian state-space modelling to identify times and locations of high energetic reward or potential energetic bottlenecks in a wild animal population. Our approach can be extended to address ecological and conservation-driven questions that were previously unanswerable due to logistical complexities in collecting data on wild, mobile animals across full annual cycles

Association of Exposure to Wildfire Air Pollution With Exacerbations of Atopic Dermatitis and Itch Among Older Adults.

This cross-sectional study evaluates the association of exposure to wildfire air pollution with exacerbations of atopic dermatitis and itch among adults aged 65 years or older

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