Sitting to Participate

Over the years of education one of the most beneficial things one could do is participate in class. Many scholars have research the causes for participation in a classroom and what affects it has on student’s grades. For our study we are looking at participation and seat location. As current students we feel that majority of the students who participate inside the classroom sit in the front of the classroom. We defined the front of the classroom as the first three rows, the back of the classroom as the last three rows, and the middle of the classroom anything between the front and back. This brings us to our research question; is the placement of students in a classroom associated with how much they participate in the classroom? To give us a better idea of this subject we research six scholarly sources, handed out forty surveys to a convenience group, did participation observation, and interviewed two people

Connecting Cluster Substructure in Galaxy Cluster Cores at z=0.2 With Cluster Assembly Histories

We use semi-analytic models of structure formation to interpret gravitational lensing measurements of substructure in galaxy cluster cores (R<=250kpc/h) at z=0.2. The dynamic range of the lensing-based substructure fraction measurements is well matched to the theoretical predictions, both spanning f_sub~0.05-0.65. The structure formation model predicts that f_sub is correlated with cluster assembly history. We use simple fitting formulae to parameterize the predicted correlations: Delta_90 = tau_90 + alpha_90 * log(f_sub) and Delta_50 = tau_50 + alpha_50 * log(f_sub), where Delta_90 and Delta_50 are the predicted lookback times from z=0.2 to when each theoretical cluster had acquired 90% and 50% respectively of the mass it had at z=0.2. The best-fit parameter values are: alpha_90 = (-1.34+/-0.79)Gyr, tau_90 = (0.31+/-0.56)Gyr and alpha_50 = (-2.77+/-1.66)Gyr, tau_50 = (0.99+/-1.18)Gyr. Therefore (i) observed clusters with f_sub<~0.1 (e.g. A383, A1835) are interpreted, on average, to have formed at z>~0.8 and to have suffered <=10% mass growth since z~0.4, (ii) observed clusters with f_sub>~0.4 (e.g. A68, A773) are interpreted as, on average, forming since z~0.4 and suffering >10% mass growth in the ~500Myr preceding z=0.2, i.e. since z=0.25. In summary, observational measurements of f_sub can be combined with structure formation models to estimate the age and assembly history of observed clusters. The ability to ``age-date'' approximately clusters in this way has numerous applications to the large clusters samples that are becoming available.Comment: Accepted by ApJL, 4 pages, 2 figure

Physiological correlates of simulated sprint-distance triathlon

The purpose of this study was to examine the relationship between simulated triathlon performance and physiological variables measured during conventional laboratory tests. Seven non-elite, competitive male triathletes completed incremental cycling and running tests in a random order, in addition to a simulated sprint-distance triathlon trial (750 m swim, 500 kJ bike, 5 km run) using a 25 m pool, an electromagnetically braked cycle ergometer and motorised treadmill. There were no significant correlations between overall performance time and either running or cycling incremental tests, however significant correlations were found between triathlon run time and both running and cycling incremental tests (Vpeak, r = -.900, p<0.05; V4mmol, r = -.822, p<0.05; Wpeak, r = -.844, p<0.05). Total simulated triathlon time was highly correlated to cycle time (r = .930, p<0.05) and mean cycling power output (r = -.956, p<0.05), whilst there was no significant correlation between either swim time or run time and overall performance time. For non-elite, competitive male triathletes, a performance assessment which better reflects the demands of the cycle phase of triathlon (i.e. a time-trial protocol) may provide a better indication of simulated sprint-distance triathlon performance in comparison to commonly used incremental laboratory tests. Furthermore, cycling performance appears more important to overall performance in simulated sprint-distance triathlon than swimming or running

Reliability of simulated sprint-distance triathlon

To assess reliability of simulated sprint-distance triathlon seven non-elite, male triathletes completed three trials (750 m swim, 20 km bike, 5 km run), using a 25 m pool, an electromagnetically braked cycle ergometer and motorised treadmill. Total times (h, min and s) were 1:17:37 ± 0:06:41, 1:18:22 ± 0:08:59 and 1:18:47 ± 0:09:56. Coefficient of variation (CV) for total performance time was 2.7% between trials 1&2 (CI = 1.7-6.0) and 2.3% between trials 2&3 (CI = 1.5-5.1). Performance CV’s for swim, cycle and run phases were also <5% between trials 2&3. These results show that for non-elite, competitive male triathletes, performance time in simulated sprint-distance triathlon is highly reproducible, with a CV comparable to endurance performances of similar duration (<5%)

Effects of residual fatigue on pace regulation during sprint-distance triathlon running

Introduction: It has been suggested that unique relationships exist between perceived exertion, pacing and physiological responses during triathlon. However, research to date has not clearly established how the interaction of these factors is affected by residual physiological fatigue, particularly during running performance over distances relevant to sprint-distance triathlon. This study therefore investigated the effects of the preceding swim and cycle on pacing strategy, perceived exertion, and physiological status during sprint-distance triathlon running. Methods: Eight amateur male triathletes (mean ± SD: age 36.0 ± 5.7 yrs, mass 75.7 ± 5.3 kg) completed two field-based performance trials. The first was a sprint-distance triathlon (0.75 km swim, 20 km cycle, 5 km run) and the second an isolated 5 km run time-trial, each separated by 7-18 days and utilising the same flat out-and-back road course. Wrist-mounted GPS devices (Garmin 310XT, UK) recorded performance time, running speed (km•h-1) and heart rate during each trial. Participants recorded ratings of perceived exertion (Borg 6-20 scale) every kilometre using a wrist-mounted recording sheet and pen. Core temperature (CorTemp, HQInc, USA), blood lactate concentration (Lactate Pro, Kodak, Japan) and body mass (to 0.1 kg; Seca 875) were also measured immediately prior to, and after, each run. Results: Performance time for isolated running (19:28 ± 00:32) was ~7% quicker than triathlon running (20:48 ± 00:43) (p<0.01), with a similar positive pacing strategy displayed throughout both trials (figure 1). Initial core temperature, blood lactate concentration and heart rate values were all significantly higher for the triathlon run compared to the isolated run (p<0.01), with no differences in final values for these measures. No significant differences were observed for initial RPE, rate of RPE increase, or final RPE between runs. Discussion/Conclusion: Prior swimming and cycling impair performance but do not affect pacing strategy during sprint-distance triathlon running. Reduced performance may be attributed to the residual physiological strain observed at the start of the triathlon run. However, the maintenance of scalar-linear increases in RPE appears to be the primary regulator of pacing strategy during triathlon running, with physiological responses only indirectly related to this process

How Do Pharmacists Construct, Facilitate and Consolidate Their Professional Identity?

The pharmacy profession has, and continues to experience change regarding roles and responsibilities. The supply of medicines still remains a central function but patient facing, clinical roles are now becoming more common place where pharmacists use their knowledge to maximise patient use of medicines. This transitional state from supplier of medicine to medicine advisor raises questions over the professional identity of pharmacists. This literature-informed commentary highlights current understanding of how identity is formed and reinforced.We propose the profession needs to be clearer in articulating what pharmacy does and advocate the need for strong branding that the profession, public and other healthcare practitioners understand

The Long and the Short End of the Term Structure of Policy Rules

We first document a large secular shift in the estimated response of the entire term structure of interest rates to inflation and output in the United States. The shift occurred in the early 1980s. We then derive an equation that links these responses to the coefficients of the central bank's monetary policy rule for the short-term interest rate. The equation reveals two countervailing forces that help explain and understand the nature of the link and how its sign is determined. Using this equation, we show that a shift in the policy rule in the early 1980s provides an explanation for the observed shift in the term structure. We also explore a shift in the policy rule in the 2002-2005 period and its possible effect on long-term rates.