The importance of body-mass exponent optimization for evaluation of performance capability in cross-country skiing

Introduction Performance in cross-country skiing is influenced by the skier’s ability to continuously produce propelling forces and force magnitude in relation to the net external forces. A surrogate indicator of the “power supply” in cross-country skiing would be a physiological variable that reflects an important performance-related capability, whereas the body mass itself is an indicator of the “power demand” experienced by the skier. To adequately evaluate an elite skier’s performance capability, it is essential to establish the optimal ratio between the physiological variable and body mass. The overall aim of this doctoral thesis was to investigate the importance of body-mass exponent optimization for the evaluation of performance capability in cross-country skiing. Methods In total, 83 elite cross-country skiers (56 men and 27 women) volunteered to participate in the four studies. The physiological variables of maximal oxygen uptake (V̇O2max) and oxygen uptake corresponding to a blood-lactate concentration of 4 mmol∙l-1 (V̇O2obla) were determined while treadmill roller skiing using the diagonal-stride technique; mean oxygen uptake (V̇O2dp) and upper-body power output (Ẇ) were determined during double-poling tests using a ski-ergometer. Competitive performance data for elite male skiers were collected from two 15-km classical-technique skiing competitions and a 1.25-km sprint prologue; additionally, a 2-km double-poling roller-skiing time trial using the double-poling technique was used as an indicator of upper-body performance capability among elite male and female junior skiers. Power-function modelling was used to explain the race and time-trial speeds based on the physiological variables and body mass. Results The optimal V̇O2max-to-mass ratios to explain 15-km race speed were V̇O2max divided by body mass raised to the 0.48 and 0.53 power, and these models explained 68% and 69% of the variance in mean skiing speed, respectively; moreover, the 95% confidence intervals (CI) for the body-mass exponents did not include either 0 or 1. For the modelling of race speed in the sprint prologue, body mass failed to contribute to the models based on V̇O2max, V̇O2obla, and V̇O2dp. The upper-body power output-to-body mass ratio that optimally explained time-trial speed was Ẇ ∙ m-0.57 and the model explained 63% of the variance in speed. Conclusions The results in this thesis suggest that V̇O2max divided by the square root of body mass should be used as an indicator of performance in 15-km classical-technique races among elite male skiers rather than the absolute or simple ratio-standard scaled expression. To optimally explain an elite male skier’s performance capability in sprint prologues, power-function models based on oxygen-uptake variables expressed absolutely are recommended. Moreover, to evaluate elite junior skiers’ performance capabilities in 2-km double-poling roller-skiing time trials, it is recommended that Ẇ divided by the square root of body mass should be used rather than absolute or simple ratio-standard scaled expression of power output.

Convergence analysis of sampling-based decomposition methods for risk-averse multistage stochastic convex programs

We consider a class of sampling-based decomposition methods to solve risk-averse multistage stochastic convex programs. We prove a formula for the computation of the cuts necessary to build the outer linearizations of the recourse functions. This formula can be used to obtain an efficient implementation of Stochastic Dual Dynamic Programming applied to convex nonlinear problems. We prove the almost sure convergence of these decomposition methods when the relatively complete recourse assumption holds. We also prove the almost sure convergence of these algorithms when applied to risk-averse multistage stochastic linear programs that do not satisfy the relatively complete recourse assumption. The analysis is first done assuming the underlying stochastic process is interstage independent and discrete, with a finite set of possible realizations at each stage. We then indicate two ways of extending the methods and convergence analysis to the case when the process is interstage dependent.

Detection of glucose and triglycerides using information visualization methods to process impedance spectroscopy data

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Toward the Optimization of an e-Tongue System Using Information Visualization: A Case Study with Perylene Tetracarboxylic Derivative Films in the Sensing Units

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Multi-objective evolutionary particle swarm optimization in the assessment of the impact of distributed generation

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A common Tabu search algorithm for the global optimization of engineering problems

A novel common Tabu algorithm for global optimizations of engineering problems is presented. The robustness and efficiency of the presented method are evaluated by using standard mathematical functions and hy solving a practical engineering problem. The numerical results show that the proposed method is (i) superior to the conventional Tabu search algorithm in robustness, and (ii) superior to the simulated annealing algorithm in efficiency. (C) 2001 Elsevier B.V. B.V. All rights reserved.

Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi's orthogonal arrays

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Global optimization approach for the H2-norm model reduction problem

A branch and bound algorithm is proposed to solve the H2-norm model reduction problem for continuous-time linear systems, with conditions assuring convergence to the global optimum in finite time. The lower and upper bounds used in the optimization procedure are obtained through Linear Matrix Inequalities formulations. Examples illustrate the results.

Optimization of the mechanical properties of low-carbon steels by formation of a multiphase microstructure

In this work five methods of heat treatments are investigated in order to obtained convenient volume fractions of ferrite, bainite, martensite and retained austenite, starting with a low carbon steel and seeking the distinction of the phases, through optical microscopy. Specific chemical etching is improved. The results in tensile and fatigue tests were accomplished and the results were related with the microstructural parameters. The results show that the mechanical properties are closely related with the phases, grains size and the phases morphology. Copyright © 2001 Society of Automotive Engineers, Inc.

Optimization of neutral hydrolysis reaction of post-consumer PET for chemical recycling

The neutral hydrolysis reaction of post-consumer poly(ethylene terephthalate) in solid state was studied through the reaction of the polymer with water at the molar ratio 1:91 with autogenous pressure. Two sizes of post-consumer PET flakes and temperatures of 135 °C, 170°C and 205°C with pressures of 4.0 atm, 7.5 atm and 13.5 atm, respectively, were considered. With reaction time equal to 6h, the method reached 99% depolymerization at 205°C, 8.2% at 170 °C and 1.7% at 135°C. The reaction extension was measured by separating the terephthalic acid formed in the process and calculating by gravimetry how much material could still be reacted. Through the viscosimetry of diluted, solutions and the counting of carboxylic end groups in the remaining material from the gravimetric assay, it was possible to suggest that the reaction occurs randomly and in the whole volume of the polymeric particle and not solely on the surface. The terephthalic acid obtained and then purified was characterized by elemental analysis, magnetic nuclear resonance, size and panicle size distribution and spectrophotometry in the visible spectrum, and it was similar to the petrochemical equivalent, with purity recorded in carbon base equal to 99.9%.

A new implementation of Population Based Incremental Learning method for optimization studies in electromagnetics

To enhance the global search ability of Population Based Incremental Learning (PBIL) methods, It Is proposed that multiple probability vectors are to be Included on available PBIL algorithms. As a result, the strategy for updating those probability vectors and the negative learning and mutation operators are redefined as reported. Numerical examples are reported to demonstrate the pros and cons of the newly Implemented algorithm. ©2006 IEEE.

Global optimization for the ℋ∞-norm model reduction problem

A branch and bound algorithm is proposed to solve the [image omitted]-norm model reduction problem for continuous and discrete-time linear systems, with convergence to the global optimum in a finite time. The lower and upper bounds in the optimization procedure are described by linear matrix inequalities (LMI). Also proposed are two methods with which to reduce the convergence time of the branch and bound algorithm: the first one uses the Hankel singular values as a sufficient condition to stop the algorithm, providing to the method a fast convergence to the global optimum. The second one assumes that the reduced model is in the controllable or observable canonical form. The [image omitted]-norm of the error between the original model and the reduced model is considered. Examples illustrate the application of the proposed method.

Application of an iterative method and an evolutionary algorithm in fuzzy optimization

This work develops two approaches based on the fuzzy set theory to solve a class of fuzzy mathematical optimization problems with uncertainties in the objective function and in the set of constraints. The first approach is an adaptation of an iterative method that obtains cut levels and later maximizes the membership function of fuzzy decision making using the bound search method. The second one is a metaheuristic approach that adapts a standard genetic algorithm to use fuzzy numbers. Both approaches use a decision criterion called satisfaction level that reaches the best solution in the uncertain environment. Selected examples from the literature are presented to compare and to validate the efficiency of the methods addressed, emphasizing the fuzzy optimization problem in some import-export companies in the south of Spain. © 2012 Brazilian Operations Research Society.

Multi-choice mixed integer goal programming optimization for real problems in a sugar and ethanol milling company

Goal Programming (GP) is an important analytical approach devised to solve many realworld problems. The first GP model is known as Weighted Goal Programming (WGP). However, Multi-Choice Aspirations Level (MCAL) problems cannot be solved by current GP techniques. In this paper, we propose a Multi-Choice Mixed Integer Goal Programming model (MCMI-GP) for the aggregate production planning of a Brazilian sugar and ethanol milling company. The MC-MIGP model was based on traditional selection and process methods for the design of lots, representing the production system of sugar, alcohol, molasses and derivatives. The research covers decisions on the agricultural and cutting stages, sugarcane loading and transportation by suppliers and, especially, energy cogeneration decisions; that is, the choice of production process, including storage stages and distribution. The MCMIGP allows decision-makers to set multiple aspiration levels for their problems in which the more/higher, the better and the less/lower, the better in the aspiration levels are addressed. An application of the proposed model for real problems in a Brazilian sugar and ethanol mill was conducted; producing interesting results that are herein reported and commented upon. Also, it was made a comparison between MCMI GP and WGP models using these real cases. © 2013 Elsevier Inc.

Molecularly designed layer-by-layer (LbL) films to detect catechol using information visualization methods

The control of molecular architectures has been exploited in layer-by-layer (LbL) films deposited on Au interdigitated electrodes, thus forming an electronic tongue (e-tongue) system that reached an unprecedented high sensitivity (down to 10-12 M) in detecting catechol. Such high sensitivity was made possible upon using units containing the enzyme tyrosinase, which interacted specifically with catechol, and by processing impedance spectroscopy data with information visualization methods. These latter methods, including the parallel coordinates technique, were also useful for identifying the major contributors to the high distinguishing ability toward catechol. Among several film architectures tested, the most efficient had a tyrosinase layer deposited atop LbL films of alternating layers of dioctadecyldimethylammonium bromide (DODAB) and 1,2-dipalmitoyl-sn-3-glycero-fosfo-rac-(1-glycerol) (DPPG), viz., (DODAB/DPPG)5/DODAB/Tyr. The latter represents a more suitable medium for immobilizing tyrosinase when compared to conventional polyelectrolytes. Furthermore, the distinction was more effective at low frequencies where double-layer effects on the film/liquid sample dominate the electrical response. Because the optimization of film architectures based on information visualization is completely generic, the approach presented here may be extended to designing architectures for other types of applications in addition to sensing and biosensing. © 2013 American Chemical Society.