MUSCLE ENERGY OF TENNIS-STOPS WITH DIFFERENT MOVEMENT PATTERNS

INTRODUCTION: Fast runs starting with high accelerations and ending with abrupt stops are essential elements of tennis. While acceleration is achieved in a unique way without sliding, stops are performed in various ways. Depending on the surface, shoes and anthropometry, the stopping motion may or may not sliding. Stopping without sliding is a motion in which muscles are shortened or stretched while contracting. During sliding stops, muscles also contract, but almost no shortening or stretching of the muscles is involved. This has a major influence on energy consumption. Muscle energy becomes a limiting factor for the speed and quality of performance during prolonged matches or tournaments. The purpose of this study was to approximate the relative differences in the energy consumption of tennis stops with different stopping patterns. METHOD: Five male tennis players participated in this study. All played at either the state or national level. Their ages were between 23 and 28 years (24.2±1.9). For each subject, 38 anthropometric measurements were taken. Reflective markers were placed on 17 landmarks. Each participant performed three stops in the university gymnasium (almost no sliding) and three stops on an indoor tennis court with a floor of loose rubber granulate designed to permit sliding. Movements were filmed using three 50 Hz digital cameras with a shutter speed of 1/3500 sec. Digitizing was done using an automatic WinAnalyze system. The resulting marker coordinates and 38 anthropometric measurements per athlete were the input for the SDS-98 simulation system. SDS-98 created the Hanavan model and calculated the inverse dynamics in accordance with the filmed movements. Muscle energies were computed for the joints (neck, shoulder, elbow, hip, knee, ankle, spine) using the equation: [formula], which is the integral of the absolute of the scalar product between the relative angular velocity and the torque of the joint. The efficiency 0 of a stop was calculated as the quotient of muscle energy divided by the total mechanical energy change from the beginning of the movement to the stop. RESULTS AND DISCUSSION: Significant differences were found for muscle energy/efficiency between stopping motions with and without sliding (0stop/0slide between 1.3 and 4.0). CONCLUSIONS: Deviations between real world data and research calculations can occur. They may be caused by the simplicity of the body model, by digitizing errors, and by uncertainty in calculating the center of pressure for the feet during ground contact. However, the results show quantitatively that sliding stops are favorable for players with low endurance

Laser-sintered thin films of doped SiGe nanoparticles

We present a study of the morphology and the thermoelectric properties of short-pulse laser-sintered (LS) nanoparticle (NP) thin films, consisting of SiGe alloy NPs or composites of Si and Ge NPs. Laser-sintering of spin-coated NP films in vacuum results in a macroporous percolating network with a typical thickness of 300 nm. The Seebeck coefficient is independent of the sintering process and typical for degenerate doping. The electrical conductivity of LS films rises with increasing temperature, best described by a power-law and influenced by two-dimensional percolation effects.Comment: 4 pages, 4 figure

Study of the footprints of short-term variation in XCO₂ observed by TCCON sites using NIES and FLEXPART atmospheric transport models

The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier Transform Spectrometers (FTS) that record near-infrared (NIR) spectra of the Sun. From these spectra, accurate and precise observations of CO2 column-averaged dry-air mole fraction (denoted XCO2) are retrieved. TCCON FTS observations have previously been used to validate satellite estimations of XCO2; however, our knowledge of the short-term spatial and temporal variations in XCO2 surrounding the TCCON sites is limited. In this work, we use the National Institute for Environmental Studies (NIES) Eulerian three-dimensional transport model and the FLEXPART (FLEXible PARTicle) Lagrangian Particle Dispersion Model (LPDM) to determine the footprints of short-term variations in XCO2 observed by operational, past, future, and possible TCCON sites. We propose a footprint-based method for the colocation of satellite and TCCON XCO2 observations, and estimate the performance of the method using the NIES model and five GOSAT XCO2 product datasets. Comparison of the proposed approach with a standard geographic method shows higher number of colocation points and average bias reduction up to 0.15 ppm for a subset of 16 stations for the period from January 2010 to January 2014. Case studies of the Darwin and La Réunion sites reveal that when the footprint area is rather curved, non-uniform and significantly different from a geographical rectangular area, the differences between these approaches are more noticeable. This emphasizes that the colocation is sensitive to local meteorological conditions and flux distributions

Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003-2018) for carbon and climate applications

Satellite retrievals of column-averaged dry-air mole fractions of carbon dioxide (CO₂) and methane (CH₄), denoted XCO₂ and XCH₄, respectively, have been used in recent years to obtain information on natural and anthropogenic sources and sinks and for other applications such as comparisons with climate models. Here we present new data sets based on merging several individual satellite data products in order to generate consistent long-term climate data records (CDRs) of these two Essential Climate Variables (ECVs). These ECV CDRs, which cover the time period 2003–2018, have been generated using an ensemble of data products from the satellite sensors SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT and (for XCO₂) for the first time also including data from the Orbiting Carbon Observatory 2 (OCO-2) satellite. Two types of products have been generated: (i) Level 2 (L2) products generated with the latest version of the ensemble median algorithm (EMMA) and (ii) Level 3 (L3) products obtained by gridding the corresponding L2 EMMA products to obtain a monthly 5∘×5∘ data product in Obs4MIPs (Observations for Model Intercomparisons Project) format. The L2 products consist of daily NetCDF (Network Common Data Form) files, which contain in addition to the main parameters, i.e., XCO₂ or XCH₄, corresponding uncertainty estimates for random and potential systematic uncertainties and the averaging kernel for each single (quality-filtered) satellite observation. We describe the algorithms used to generate these data products and present quality assessment results based on comparisons with Total Carbon Column Observing Network (TCCON) ground-based retrievals. We found that the XCO₂ Level 2 data set at the TCCON validation sites can be characterized by the following figures of merit (the corresponding values for the Level 3 product are listed in brackets) – single-observation random error (1σ): 1.29 ppm (monthly: 1.18 ppm); global bias: 0.20 ppm (0.18 ppm); and spatiotemporal bias or relative accuracy (1σ): 0.66 ppm (0.70 ppm). The corresponding values for the XCH₄ products are single-observation random error (1σ): 17.4 ppb (monthly: 8.7 ppb); global bias: −2.0 ppb (−2.9 ppb); and spatiotemporal bias (1σ): 5.0 ppb (4.9 ppb). It has also been found that the data products exhibit very good long-term stability as no significant long-term bias trend has been identified. The new data sets have also been used to derive annual XCO₂ and XCH₄ growth rates, which are in reasonable to good agreement with growth rates from the National Oceanic and Atmospheric Administration (NOAA) based on marine surface observations. The presented ECV data sets are available (from early 2020 onwards) via the Climate Data Store (CDS, https://cds.climate.copernicus.eu/, last access: 10 January 2020) of the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/, last access: 10 January 2020)

Consistent regional fluxes of CH4 and CO2 inferred from GOSAT proxy XCH4 : XCO2 retrievals, 2010–2014

We use the GEOS-Chem global 3-D model of atmospheric chemistry and transport and an ensemble Kalman filter to simultaneously infer regional fluxes of methane (CH4) and carbon dioxide (CO2) directly from GOSAT retrievals of XCH4:XCO2, using sparse ground-based CH4 and CO2 mole fraction data to anchor the ratio. This work builds on previously reported theory that takes advantage that: (1) these ratios are less prone to systematic error than either the full physics data products or the proxy CH4 data products; and (2) the resulting CH4 and CO2 fluxes are self-consistent. We show that a posteriori fluxes inferred from the GOSAT data generally outperform the fluxes inferred only from in situ data, as expected. GOSAT CH4 and CO2 fluxes are consistent with global growth rates for CO2 and CH4 reported by NOAA, and with a range of independent data including in particular new profile measurements (0–7 km) over the Amazon basin that were collected specifically to help validate GOSAT over this geographical region. We find that large-scale multi-year annual a posteriori CO2 fluxes inferred from GOSAT data are similar to those inferred from the in situ surface data but with smaller uncertainties, particularly over the tropics. GOSAT data are consistent with smaller peak-to-peak seasonal amplitudes of CO2 than either a priori or the in situ inversion, particularly over the tropics and the southern extra-tropics. Over the northern extra-tropics, GOSAT data show larger uptake than the a priori but less than the in situ inversion, resulting in small net emissions over the year. We also find evidence that the carbon balance of tropical South America was perturbed following the droughts of 2010 and 2012 with net annual fluxes not returning to an approximate annual balance until 2013. In contrast, GOSAT data significantly changed the a priori spatial distribution of CH4 emission with a 40 % increase over tropical South America and tropical Asia and smaller decrease over Eurasia and temperate South America. We find no evidence from GOSAT that tropical South American CH4 fluxes were dramatically affected by the two large-scale Amazon droughts. However, we find that GOSAT data are consistent with double seasonal peaks in fluxes that are reproduced over the five years we studied: a small peak in January to April and a larger peak in June to October, which is likely due to superimposed emissions from different geographical regions