Assessing the variation in the load that produces maximal upper-body power

Substantial variation in the load that produces maximal power has been reported. It has been suggested that the variation observed may be due to differences in subject physical characteristics. Therefore the aim of this study was to determine the extent in which anthropometric measures correlate to the load that produces maximal power. Anthropometric measures (upper-arm length, forearm length, total arm length, upper-arm girth) and bench press strength were assessed in 26 professional rugby union players. Peak power was then determined in the bench press throw exercise using loads of 20 to 60% of one repetition maximum (1RM) in the bench press exercise. Maximal power occurred at 30 +/- 14 %1RM (mean +/- SD). Upper-arm length had the highest correlation with the load maximizing power: -0.61 (90% confidence limits -0.35 to -0.78), implying loads of 22 vs. 38 %1RM maximize power for players with typically long vs. short upper-arm length. Correlations for forearm length, total arm length and upper-arm girth to the load that maximized power were -0.29 (0.04 to -0.57), -0.56 (-0.28 to -0.75), and -0.29 (0.04 to -0.57), respectively. The relationship between 1RM and the load that produced maximal power was r = -0.23 (0.10 to -0.52). The between-subject variation in the load that maximised power observed (SD= +/- 14 %1RM) may have been due to differences in anthropometric characteristics, and absolute strength and power outputs. Indeed, athletes with longer limbs and larger girths, and greater maximal strength and power outputs utilised a lower percentage of 1RM loads to achieve maximum power. Therefore, we recommend individual assessment of the load that maximizes power output

Space geodesy constrains ice age terminal deglaciation: The global ICE-6G_C (VM5a) model

A new model of the last deglaciation event of the Late Quaternary ice age is here described and denoted as ICE-6G_C (VM5a). It differs from previously published models in this sequence in that it has been explicitly refined by applying all of the available Global Positioning System (GPS) measurements of vertical motion of the crust that may be brought to bear to constrain the thickness of local ice cover as well as the timing of its removal. Additional space geodetic constraints have also been applied to specify the reference frame within which the GPS data are described. The focus of the paper is upon the three main regions of Last Glacial Maximum ice cover, namely, North America, Northwestern Europe/Eurasia, and Antarctica, although Greenland and the British Isles will also be included, if peripherally, in the discussion. In each of the three major regions, the model predictions of the time rate of change of the gravitational field are also compared to that being measured by the Gravity Recovery and Climate Experiment satellites as an independent means of verifying the improvement of the model achieved by applying the GPS constraints. Several aspects of the global characteristics of this new model are also discussed, including the nature of relative sea level history predictions at far-field locations, in particular the Caribbean island of Barbados, from which especially high-quality records of postglacial sea level change are available but which records were not employed in the development of the model. Although ICE-6G_C (VM5a) is a significant improvement insofar as the most recently available GPS observations are concerned, comparison of model predictions with such far-field relative sea level histories enables us to identify a series of additional improvements that should follow from a further stage of model iteration

Effects of two contrast training programs on jump performance in rugby union players during a competition phase

Purpose: There is little literature comparing contrast training programs typically performed by team-sport athletes within a competitive phase. We compared the effects of two contrast training programs on a range of measures in high-level rugby union players during the competition season. Methods: The programs consisted of a higher volume-load (strength-power) or lower volume-load (speed-power) resistance training; each included a tapering of loading (higher force early in the week, higher velocity later in the week) and was performed twice a week for 4 wk. Eighteen players were assessed for peak power during a bodyweight countermovement jump (BWCMJ), bodyweight squat jump (BWSJ), 50 kg countermovement jump (50CMJ), 50 kg squat jump (50SJ), broad jump (BJ), and reactive strength index (RSI; jump height divided by contact time during a depth jump). Players were then randomized to either training group and were reassessed following the intervention. Inferences were based on uncertainty in outcomes relative to thresholds for standardized changes. Results: There were small between-group differences in favor of strength-power training for mean changes in the 50CMJ (8%; 90% confidence limits, ±8%), 50SJ (8%; ±10%), and BJ (2%; ±3%). Differences between groups for BWCMJ, BWSJ, and reactive strength index were unclear. For most measures there were smaller individual differences in changes with strength-power training. Conclusion: Our findings suggest that high-level rugby union athletes should be exposed to higher volume-load contrast training which includes one heavy lifting session each week for larger and more uniform adaptation to occur in explosive power throughout a competitive phase of the season

Production of Triply Charmed Ωccc\Omega_{ccc} Baryons in e+e−e^+e^- Annihilation

The total and differential cross sections for the production of triply charmed $\Omega_{ccc}$ baryons in $e^{+}e^{-}$ annihilation are calculated at the $Z$-boson pole.Comment: 13 pages, 2 figure

Advanced InSAR atmospheric correction: MERIS/MODIS combination and stacked water vapour models

A major source of error for repeat-pass Interferometric Synthetic Aperture Radar (InSAR) is the phase delay in radio signal propagation through the atmosphere (especially the part due to tropospheric water vapour). Based on experience with the Global Positioning System (GPS)/Moderate Resolution Imaging Spectroradiometer (MODIS) integrated model and the Medium Resolution Imaging Spectrometer (MERIS) correction model, two new advanced InSAR water vapour correction models are demonstrated using both MERIS and MODIS data: (1) the MERIS/MODIS combination correction model (MMCC); and (2) the MERIS/MODIS stacked correction model (MMSC). The applications of both the MMCC and MMSC models to ENVISAT Advanced Synthetic Aperture Radar (ASAR) data over the Southern California Integrated GPS Network (SCIGN) region showed a significant reduction in water vapour effects on ASAR interferograms, with the root mean square (RMS) differences between GPS- and InSAR-derived range changes in the line-of-sight (LOS) direction decreasing from ,10mm before correction to ,5mm after correction, which is similar to the GPS/MODIS integrated and MERIS correction models. It is expected that these two advanced water vapour correction models can expand the application of MERIS and MODIS data for InSAR atmospheric correction. A simple but effective approach has been developed to destripe Terra MODIS images contaminated by radiometric calibration errors. Another two limiting factors on the MMCC and MMSC models have also been investigated in this paper: (1) the impact of the time difference between MODIS and SAR data; and (2) the frequency of cloud-free conditions at the global scale

GPS displacement dataset for the study of elastic surface mass variations

Quantification of uncertainty in surface mass change signals derived from Global Positioning System (GPS) measurements poses challenges, especially when dealing with large datasets with continental or global coverage. We present a new GPS station displacement dataset that reflects surface mass load signals and their uncertainties. We assess the structure and quantify the uncertainty of vertical land displacement derived from 3045 GPS stations distributed across the continental US. Monthly means of daily positions are available for 15 years. We list the required corrections to isolate surface mass signals in GPS estimates and screen the data using GRACE(-FO) as external validation. Evaluation of GPS time series is a critical step, which identifies (a) corrections that were missed, (b) sites that contain non-elastic signals (e.g., close to aquifers), and (c) sites affected by background modeling errors (e.g., errors in the glacial isostatic model). Finally, we quantify uncertainty of GPS vertical displacement estimates through stochastic modeling and quantification of spatially correlated errors. Our aim is to assign weights to GPS estimates of vertical displacements, which will be used in a joint solution with GRACE(-FO). We prescribe white, colored, and spatially correlated noise. To quantify spatially correlated noise, we build on the common mode imaging approach by adding a geophysical constraint (i.e., surface hydrology) to derive an error estimate for the surface mass signal. We study the uncertainty of the GPS displacement time series and find an average noise level between 2 and 3 mm when white noise, flicker noise, and the root mean square (rms) of residuals about a seasonality and trend fit are used to describe uncertainty. Prescribing random walk noise increases the error level such that half of the stations have noise &gt; 4 mm, which is systematic with the noise level derived through modeling of spatially correlated noise. The new dataset is available at https://doi.org/10.5281/zenodo.8184285 (Peidou et al., 2023) and is suitable for use in a future joint solution with GRACE(-FO)-like observations.</p

Anthropometric and Physical Profiles of English Academy Rugby Union Players.

The purpose of the present study was to evaluate the anthropometric and physical characteristics of English regional academy rugby union academy players by age category (under 16, under 18 and under 21s). Data were collected on 67 academy players at the beginning of the pre-season period and comprised anthropometric (height, body mass and sum of 8 skinfolds) and physical (5 m, 10 m, 20 m & 40 m sprint, acceleration, velocity & momentum; agility 505; vertical jump; yo-yo intermittent recovery test level 1; 30-15 Intermittent Fitness Test; absolute and relative 3 repetition maximum (3RM) front squat, split squat, bench press, prone row and chin; and isometric mid-thigh pull). One way analysis of variance demonstrated significant increases across the three age categories (p < 0.05) for height (e.g., 16s = 178.8 ± 7.1; 18s = 183.5 ± 7.2; 21s = 186.7 ± 6.61 cm), body mass (e.g., 16s = 79.4 ± 12.8; 18s = 88.3 ± 11.9; 21s = 98.3 ± 10.4kg), countermovement jump height and peak power, sprint momentum, velocity and acceleration; absolute, relative and isometric (e.g., 16s = 2157.9 ± 309.9; 18s = 2561.3 ± 339.4; 21s = 3104.5 ± 354.0 N) strength. Momentum, maximal speed and the ability to maintain acceleration were all discriminating factors between age categories, suggesting that these variables may be more important to monitor rather than sprint times. These findings highlight that anthropometric and physical characteristics develop across age categories and provide comparative data for English academy Rugby Union players