Running economy, which has traditionally been measured as the oxygen cost of running at a given velocity, has been accepted as the physiological criterion for 'efficient' performance and has been identified as a critical element of overall distance running performance. There is an intuitive link between running mechanics and energy cost of running, but research to date has not established a clear mechanical profile of an economic runner. It appears that through training, individuals are able to integrate and accommodate their own unique combination of dimensions and mechanical characteristics so that they arrive at a running motion which is most economical for them. Information in the literature suggests that biomechanical factors are likely to contribute to better economy in any runner. A variety of anthropometric dimensions could influence biomechanical effectiveness. These include: average or slightly smaller than average height for men and slightly greater than average height for women; high ponderal index and ectomorphic or ectomesomorphic physique; low percentage body fat; leg morphology which distributes mass closer to the hip joint; narrow pelvis and smaller than average feet. Gait patterns, kinematics and the kinetics of running may also be related to running economy. These factors include: stride length which is freely chosen over considerable running time; low vertical oscillation of body centre of mass; more acute knee angle during swing; less range of motion but greater angular velocity of plantar flexion during toe-off; arm motion of smaller amplitude; low peak ground reaction forces; faster rotation of shoulders in the transverse plane; greater angular excursion of the hips and shoulders about the polar axis in the transverse plane; and effective exploitation of stored elastic energy. Other factors which may improve running economy are: lightweight but well-cushioned shoes; more comprehensive training history; and the running surface of intermediate compliance. At the developmental level, this information might be useful in identifying athletes with favourable characteristics for economical distance running. At higher levels of competition, it is likely that 'natural selection' tends to eliminate athletes who failed to either inherit or develop characteristics which favour economy.

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Although it has stood the test of time for over 30 years, Moore's Law addresses but a single aspect of microprocessor design. As a proxy for technology, the number of transistors in an integrated circuit represents a limited perspective on the technology as a whole. Anderson et al. proposed a set of metrics by which to measure a technology, and a means to measure its progress over time utilizing data envelopment analysis. In this revised model, the assumption of state of the art (SOA) on product release is dropped, technical progress is measured iteratively over time, the effective time elapsed between the SOA and a no longer SOA has been refined to include a weighted average, and a means of utilizing proxy DMUs was implemented to maintain the dataset over time.

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The purpose of this study was to examine (a) the acute change in bat velocity (BV) following three types of warm-up procedures for baseball hitting (experiment 1), and (b) the effect of an 8-week training program of isometric contraction conditioning (ISO) on BV (experiment 2). In experiment 1, the BV of 24 collegiate baseball players was measured before and after one of the three warm-up procedures; five standard bat (mass = 850.5 g) dry swings (SBS), five weighted bat (mass = 850.5 g + 680.4 g) dry swings (WBS), and four sets of 5-second maximal voluntary isometric contractions mimicking the bat swing movement pattern (ISO). BV was measured just before ball-bat impact. Experiment 2 followed experiment 1 and used only the ISO warm-up. Twelve of the 24 subjects formed the experimental group and underwent an 8-week training program (3 days per week) of ISO training. Results of experiment 1 indicated (a) no significant change in post-SBS BV (-0.33 m·s-¹), (b) a significant decrease in post-WBS BV (-0.89 m·s-¹; p < 0.05), and (c) a significant increase in post-ISO BV (+0.39 m·s-¹; p < 0.05). In experiment 2, there was a significant increase in baseline BV after the 8-week training period (30.21 ± 1.83 m·s -¹ to 31.15 ± 1.57 m·s-¹). A comparison of BV before and after ISO warm-up revealed that change was significantly greater after the training period (100.17 ± 3.18% vs. 103.75 ± 1.91%). Our results suggest that warm-up with WBS does not increase BV and that using the ISO has both acute and chronic positive effects on BV as a warm-up procedure to improve BV.

Wireless communications technologies have undergone rapid changes over the last thirty years from analog approaches to digital-based systems. These technologies have improved on many fronts including bandwidth, range, and power requirements.Development of new telecommunications technologies is critical. It requires many years of efforts. In order to be competitive, it is critical to establish a roadmap of future technologies. This paper presents a framework to characterize, assess and forecast the wireless communication technologies. A DEA-based methodology was used for predicting the state of the art in future wireless communications technologies. Literature ReviewThere are many techniques that can be used to develop technology forecasts. Linstone (1999) provides an overview of methods evolving over time. Other researchers including Ayres (1999), Martino (1999) and Porter (1999) also provide comprehensive treatments of many approaches. Technology forecasting can be done both qualitatively as well as quantitatively. Linstone (2003) and du Preez (2003) provide examples of qualitative approaches such as multiple perspectives and threat/opportunity analysis which help to dissect problems so that further analysis can be done with quantitative models. Fildes (2006) provides an excellent review of forecasting research and outlets for publications. De Gooijer et al. (2006) add to this research by focusing on time series forecasting. Meade et al.(2006) provide a similar in-depth analysis for innovation diffusion. The Technology Futures Analysis Methods Working Group (Porter et al., 2004) provides a good review of integrating multiple methods and evolving new methods for technology forecasting. Methods used frequently include scenarios (Sager, 2003; Silberglitt et al.indicates the necessity of combining the forecasting model with the perceived future industry dynamics. He emphasizes that the quantitative forecasting methods such as time series and econometric modeling have become less accurate and cannot be relied upon because the industry no longer has the stable historical relationship that these models rely on. The literature suggests that including forecasts from different statistical methods generally improves accuracy when significant trends are involved. Useful information can be obtained using several sources of forecasts, adjusting for biases. Yoo and Moon (2006) suggest that instead of trying to choose the best single method, one should combine the results from different methods, which would help in reducing errors arising from faulty assumptions, biases, or mistakes in the data.The new product development literature is relevant because the efforts to create these new technologies are relatively similar to product development and it will provide technology platforms upon which other products will be developed. There is an extensive literature on new product development but for the sake of providing a context and linkage to this literature, we will provide a discussion of a few select papers. The importance and u...

Since its inception in 2001, technology forecasting using data envelopment analysis (TFDEA) has been used with a number of applications. This paper presents a formal comparison of TFDEA to a previously published application from Technological Forecasting and Social Change by Joseph Martino. Using the data and Martino's multiple regression model, we compare results obtained from TFDEA to those previously published. Both techniques predict the first flights of fighter jets introduced between 1960 and 1982 by using the first flights of aircraft introduced between 1944 and 1960. TFDEA was found to better predict the first flight dates than the forecast using multiple regression. These results indicate that TFDEA may be a powerful new technique for predicting complex technological trends and time to market for new products.