Unidimensional model of the ad-atom diffusion on a substrate submitted to a standing acoustic wave I. Derivation of the ad-atom motion equation

The effect of a standing acoustic wave on the diffusion of an ad-atom on a crystalline surface is theoretically studied. We used an unidimensional space model to study the ad-atom+substrate system. The dynamic equation of the ad-atom, a Generalized Langevin equation, is analytically derived from the full Hamiltonian of the ad-atom+substrate system submitted to the acoustic wave. A detailed analysis of each term of this equation, as well as of their properties, is presented. Special attention is devoted to the expression of the effective force induced by the wave on the ad-atom. It has essentially the same spatial and time dependences as its parent standing acoustic wave

Flow Structure of an Impinging Plane Jet

Sixth Conference on Design and Modeling of Mechanical Systems (CMSM 2015), Hammamet, TUNISIA, MAR 23-25, 2015International audienceThe current study attempt to examine and investigate the flow field of a plane air jet impinging normally on a flat surface. In order to understand the development of the flow generated from a rectangle turbulent jet impinging a flat plate, a detailed dynamic and turbulent simulation is presented. The plate is appropriately larger than the nozzle exit diameter. The ground plate can be moved vertically in order to simulate the height ratio h/e=28. Another aim of this study is to explore the effect of the Reynolds number (Re=1000, 2000, 3000) on the flow structure. A Computational Fluid Dynamics study is performed using the Reynolds-averaged Navier-Stokes equations by means of the RSM (Reynolds Stress Model) second order turbulent closure model. The results include mean and turbulent velocities and quantify the large effects of flow distortion on the turbulent structure of complex, three-dimensional impingement flow

Effect of gas exchange data selection methods on resting metabolic rate estimation in young athletes.

This cross-sectional study analysed the effect of the gas exchange data selection methods on the resting metabolic rate (RMR) estimation and proposed a protocol shortening providing a suitable RMR estimation for young athletes. Sixty-six healthy young Brazilian athletes performed a 30-minute RMR assessment. Different methods of gas exchange data selection were applied: short and long-time intervals, steady-state (SSt), and filtering. A mixed one-way ANOVA was used to analyse the mean differences in gas exchange, RMR, respiratory exchange ratio (RER), and coefficients of variation across all methods. Additionally, paired Student's t-test were used to compare the first and best SSt RMR values for each SSt method (3, 4, and 5-min). The 5-min SSt method provided the lowest RMR estimate (1454 kcal.day-1). There was a statistical difference between methods (F = 2.607, p = 0.04), but they presented a clinically irrelevant absolute difference (~36 kcal.day-1). There were no differences in RER among methods. In addition, using the SSt method, 12 minutes of assessment were enough to obtain a valid estimation of RMR. The 5-min SSt method should be employed for assessing the RMR among young athletes, considering the possibility of obtaining a shortened assessment (~12 min) with an acceptable and low coefficient of variation

Predictive equations for resting metabolic rate are not appropriate to use in Brazilian male adolescent football athletes.

High accuracy in estimating energy expenditure is essential for enhancing sports performance. The resting metabolic rate (RMR), as a primary component of total energy expenditure (TEE), is commonly estimated using predictive equations. However, these references may not be applicable to adolescent athletes. The purpose of this cross-sectional study was to analyse the differences between predicted RMR in relation to energy expenditure measured by indirect calorimetry (IC) among 45 Brazilian male adolescent football athletes. Indirect calorimetry (IC) and anthropometric (bioimpedance) measurements were recorded at a single visit to the laboratory after fasting overnight. The mean age was 15.6 ± 1.14 years, body mass was 63.05 ± 7.8 kg, and height was 172 ± 7.5 cm. The RMR values predicted by equations proposed by the Food and Agriculture Organization (FAO) (United Nations), Henry and Rees (HR), Harris Benedict (HB), and Cunningham (CUN) were compared with IC RMR values, by correlation analysis. The FAO and HR predictive equations yielded different values from IC (IC: 1716.26 ± 202.58, HR: 1864.87 ± 147.78, FAO: 1854.28 ± 130.19, p = 0.001). A moderate correlation of 0.504 was found between the results of HB and IC. In the survival-agreement model, the CUN equation showed low disagreement with the IC RMR, with error values between 200 and 300 kcal/day. The results showed that HB and CUN yielded similar values as IC, with the CUN equation showing low disagreement with IC; hence, adolescent athletes should undergo evaluation with precise laboratory methods to ensure that accurate information about RMR is recorded

The period from the resting metabolic rate assessment in which the First Steady-State (SSt) was achieved for 3 min SSt (A), 4 min SSt (B), and 5 min SSt (C).

The First SSt was achieved when the first period in which the coefficients of variation (CV) for volume of oxygen consumption (VO2) and volume of carbon dioxide production (VCO2) were lower than 10% and the CV for respiratory exchange ratio (RER) was lower than 5%. Best SSt represents the SSt period presenting the lowest mean of the CVs as mentioned. Boxes represent the cumulative percentage for the Best SSt; circles represent the cumulative percentage for the First SSt. Effect size: S—Small, M—Medium, L—Large.</p

S1 Data -

This cross-sectional study analysed the effect of the gas exchange data selection methods on the resting metabolic rate (RMR) estimation and proposed a protocol shortening providing a suitable RMR estimation for young athletes. Sixty-six healthy young Brazilian athletes performed a 30-minute RMR assessment. Different methods of gas exchange data selection were applied: short and long-time intervals, steady-state (SSt), and filtering. A mixed one-way ANOVA was used to analyse the mean differences in gas exchange, RMR, respiratory exchange ratio (RER), and coefficients of variation across all methods. Additionally, paired Student’s t-test were used to compare the first and best SSt RMR values for each SSt method (3, 4, and 5-min). The 5-min SSt method provided the lowest RMR estimate (1454 kcal.day-1). There was a statistical difference between methods (F = 2.607, p = 0.04), but they presented a clinically irrelevant absolute difference (~36 kcal.day-1). There were no differences in RER among methods. In addition, using the SSt method, 12 minutes of assessment were enough to obtain a valid estimation of RMR. The 5-min SSt method should be employed for assessing the RMR among young athletes, considering the possibility of obtaining a shortened assessment (~12 min) with an acceptable and low coefficient of variation.</div

Differences among gas exchange data selection methods for RMR (A), VO2 (B), VCO2 (C), and RER (D) across the different methods.

Black columns represent the short Time Interval (TI) methods; Dark grey columns represent the long TI methods; Light grey columns represent the Steady-State (SSt) methods; White columns represent the Filtering methods. P-values come from the One-Way repeated measures analysis of variance. Data are presented as mean ± standard deviation (SD). Effect size: S—Small, M—Medium, L—Large. Min—minutes; VO2—volume of oxygen consumption; VCO2—volume of carbon dioxide production; RMR—resting metabolic rate; RER—respiratory exchange ratio.</p