Performance of young male swimmers in the 100-meters front crawl

Youth swimming performance may be influenced by anthropometric, physiology and technical factors. The present paper examined the role of these factors in performance of 100m freestyle in swimmers 12-14 years of age (n = 24). Multiple regression analysis (forward method) was used to examine the variance of the 100 meters front crawl. Anaerobic power, swimming index and critical speed explained 88% (p < .05) of the variance in the average speed of 100 meters front crawl among young male pubertal swimmers. To conclude, performance of young swimmers in the 100 meters front crawl is determined predominantly by physiological factors and swimming technique.

Integrating technology roadmapping and portfolio management at the front-end of new product development

Many authors point out that the front-end of new product development (NPD) is a critical success factor in the NPD process and that numerous companies face difficulties in carrying it out appropriately. Therefore, it is important to develop new theories and proposals that support the effective implementation of this earliest phase of NPD. This paper presents a new method to support the development of front-end activities based on integrating technology roadmapping (TRM) and project portfolio management (PPM). This new method, called the ITP Method, was implemented at a small Brazilian high-tech company in the nanotechnology industry to explore the integration proposal. The case study demonstrated that the ITP Method provides a systematic procedure for the fuzzy front-end and integrates innovation perspectives into a single roadmap, which allows for a better alignment of business efforts and communication of product innovation goals. Furthermore, the results indicated that the method may also improve quality, functional integration and strategy alignment. (C) 2010 Elsevier Inc. All rights reserved.

An estimate of biometric parameters in eucalyptus clone plantations in Southern Bahia: an application of the airborne laser scanning (ALS) technology

The application of airborne laser scanning (ALS) technologies in forest inventories has shown great potential to improve the efficiency of forest planning activities. Precise estimates, fast assessment and relatively low complexity can explain the good results in terms of efficiency. The evolution of GPS and inertial measurement technologies, as well as the observed lower assessment costs when these technologies are applied to large scale studies, can explain the increasing dissemination of ALS technologies. The observed good quality of results can be expressed by estimates of volumes and basal area with estimated error below the level of 8.4%, depending on the size of sampled area, the quantity of laser pulses per square meter and the number of control plots. This paper analyzes the potential of an ALS assessment to produce certain forest inventory statistics in plantations of cloned Eucalyptus spp with precision equal of superior to conventional methods. The statistics of interest in this case were: volume, basal area, mean height and dominant trees mean height. The ALS flight for data assessment covered two strips of approximately 2 by 20 Km, in which clouds of points were sampled in circular plots with a radius of 13 m. Plots were sampled in different parts of the strips to cover different stand ages. The clouds of points generated by the ALS assessment: overall height mean, standard error, five percentiles (height under which we can find 10%, 30%, 50%,70% and 90% of the ALS points above ground level in the cloud), and density of points above ground level in each percentile were calculated. The ALS statistics were used in regression models to estimate mean diameter, mean height, mean height of dominant trees, basal area and volume. Conventional forest inventory sample plots provided real data. For volume, an exploratory assessment involving different combinations of ALS statistics allowed for the definition of the most promising relationships and fitting tests based on well known forest biometric models. The models based on ALS statistics that produced the best results involved: the 30% percentile to estimate mean diameter (R(2)=0,88 and MQE%=0,0004); the 10% and 90% percentiles to estimate mean height (R(2)=0,94 and MQE%=0,0003); the 90% percentile to estimate dominant height (R(2)=0,96 and MQE%=0,0003); the 10% percentile and mean height of ALS points to estimate basal area (R(2)=0,92 and MQE%=0,0016); and, to estimate volume, age and the 30% and 90% percentiles (R(2)=0,95 MQE%=0,002). Among the tested forest biometric models, the best fits were provided by the modified Schumacher using age and the 90% percentile, modified Clutter using age, mean height of ALS points and the 70% percentile, and modified Buckman using age, mean height of ALS points and the 10% percentile.