Analysis of influencing factors on reliability of bus unit distance running time

2021 ◽  
Author(s):  
Qin Wang
Keyword(s):  
Influencing Factors ◽  
Distance Running ◽  
Unit Distance ◽  
Running Time

Pacing Strategy During 24-Hour Ultramarathon-Distance Running

2017 ◽  
Vol 12 (5) ◽  
pp. 590-596 ◽  
Author(s):  
Arthur H. Bossi ◽  
Guilherme G. Matta ◽  
Guillaume Y. Millet ◽  
Pedro Lima ◽  
Leonardo C. Pertence ◽  
...  
Keyword(s):  
Herd Behavior ◽  
Age Group ◽  
Distance Running ◽  
Running Speed ◽  
Running Time ◽  
Pacing Strategy ◽  
Performance Group ◽  
General Behavior ◽  
Overall Performance ◽  
Shaped Pattern

Purpose:To describe pacing strategy in a 24-h running race and its interaction with sex, age group, athletes’ performance group, and race edition.Methods:Data from 398 male and 103 female participants of 5 editions were obtained based on a minimum 19.2-h effective-running cutoff. Mean running speed from each hour was normalized to the 24-h mean speed for analyses.Results:Mean overall performance was 135.6 ± 33.0 km with a mean effective-running time of 22.4 ± 1.3 h. Overall data showed a reverse J-shaped pacing strategy, with a significant reduction in speed from the second-to-last to the last hour. Two-way mixed ANOVAs showed significant interactions between racing time and both athlete performance group (F = 7.01, P < .001, ηp2 = .04) and race edition (F = 3.01, P < .001, ηp2 = .02) but not between racing time and either sex (F = 1.57, P = .058, ηp 2 < .01) or age group (F = 1.25, P = .053, ηp2 = .01). Pearson product–moment correlations showed an inverse moderate association between performance and normalized mean running speed in the first 2 h (r = –.58, P < .001) but not in the last 2 h (r = .03, P = .480).Conclusions:While the general behavior represents a rough reverse J-shaped pattern, the fastest runners start at lower relative intensities and display a more even pacing strategy than slower runners. The “herd behavior” seems to interfere with pacing strategy across editions, but not sex or age group of runners.

scholarly journals Delay recovery model for high-speed trains with compressed train dwell time and running time

2020 ◽  
Vol 28 (4) ◽  
pp. 424-434
Author(s):  
Yafei Hou ◽  
Chao Wen ◽  
Ping Huang ◽  
Liping Fu ◽  
Chaozhe Jiang
Keyword(s):  
Influencing Factors ◽  
High Speed ◽  
Dwell Time ◽  
Regression Tree ◽  
Recovery Model ◽  
Boosted Regression Tree ◽  
Running Time ◽  
High Speed Trains ◽  
Train Operation ◽  
Train Operation Adjustment

AbstractModeling the application of train operation adjustment actions to recover from delays is of great importance to supporting the decision-making of dispatchers. In this study, the effects of two train operation adjustment actions on train delay recovery were explored using train operation records from scheduled and actual train timetables. First, the modeling data were sorted to extract the possible influencing factors under two typical train operation adjustment actions, namely the compression of the train dwell time at stations and the compression of the train running time in sections. Stepwise regression methods were then employed to determine the importance of the influencing factors corresponding to the train delay recovery time, namely the delay time, the scheduled supplement time, the running interval, the occurrence time, and the place where the delay occurred, under the two train operation adjustment actions. Finally, the gradient-boosted regression tree (GBRT) algorithm was applied to construct a delay recovery model to predict the delay recovery effects of the train operation adjustment actions. A comparison of the prediction results of the GBRT model with those of a random forest model confirmed the better performance of the GBRT prediction model.

POSSIBLE INFLUENCING FACTORS OF BOWEL CLEANLINESS DURING COLONOSCOPY

2018 ◽  
Author(s):  
I Iozsef ◽  
O Ilyés ◽  
P Miheller ◽  
AV Patai
Keyword(s):  
Influencing Factors

Reliability-Based Assessment of the Truck Safe Following Distance Considering Multiple Influencing Factors

CICTP 2017 ◽  
2018 ◽  
Author(s):  
Bowen Dong ◽  
Wenjun Du ◽  
Feng Chen ◽  
Qi Deng ◽  
Xiaodong Pan
Keyword(s):  
Influencing Factors

Entrepreneurial Mindset

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Nusa FAIN ◽  
Michel ROD ◽  
Erik BOHEMIA
Keyword(s):  
Influencing Factors ◽  
Future Study ◽  
Engineering Students ◽  
Initial Study ◽  
Longitudinal Survey ◽  
Teaching Approaches ◽  
Approaches To Teaching ◽  
Design Students ◽  
Commerce Students ◽  
Case Based

This paper explores the influence of teaching approaches on entrepreneurial mindset of commerce, design and engineering students across 3 universities. The research presented in this paper is an initial study within a larger project looking into building ‘entrepreneurial mindsets’ of students, and how this might be influenced by their disciplinary studies. The longitudinal survey will measure the entrepreneurial mindset of students at the start of a course and at the end. Three different approaches to teaching the courses were employed – lecture and case based, blended online and class based and fully project-based course. The entrepreneurial mindset growth was surprisingly strongest within the engineering cohort, but was closely followed by the commerce students, whereas the design students were slightly more conservative in their assessments. Future study will focus on establishing what other influencing factors beyond the teaching approaches may relate to the observed change.

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