Accuracy of SRM and power tap power monitoring systems for bicycling

Purpose: Although manufacturers of bicycle power monitoring devices SRM and Power Tap (PT) claim accuracy to within 2.5%, there are limited scientific data available in support. The purpose of this investigation was to assess the accuracy of SRM and PT under different conditions. Methods: First, 19 SRM were calibrated, raced for 11 months, and retested using a dynamic CALRIG (50-1000 W at 100 rpm). Second, using the same procedure, five PT were repeat tested on alternate days. Third, the most accurate SRM and PT were tested for the influence of cadence (60, 80, 100, 120 rpm), temperature (8 and 21degreesC) and time (1 h at similar to300 W) on accuracy. Finally, the same SRM and PT were downloaded and compared after random cadence and gear surges using the CALRIG and on a training ride. Results: The mean error scores for SRM and PT factory calibration over a range of 50-1000 W were 2.3 +/- 4.9% and -2.5 +/- 0.5%, respectively. A second set of trials provided stable results for 15 calibrated SRM after 11 months (-0.8 +/- 1.7%), and follow-up testing of all PT units confirmed these findings (-2.7 +/- 0.1%). Accuracy for SRM and PT was not largely influenced by time and cadence; however. power output readings were noticeably influenced by temperature (5.2% for SRM and 8.4% for PT). During field trials, SRM average and max power were 4.8% and 7.3% lower, respectively, compared with PT. Conclusions: When operated according to manufacturers instructions, both SRM and PT offer the coach, athlete, and sport scientist the ability to accurately monitor power output in the lab and the field. Calibration procedures matching performance tests (duration, power, cadence, and temperature) are, however, advised as the error associated with each unit may vary.

A bi-objective weighted model for improving the discrimination power in MCDEA

Lack of discrimination power and poor weight dispersion remain major issues in Data Envelopment Analysis (DEA). Since the initial multiple criteria DEA (MCDEA) model developed in the late 1990s, only goal programming approaches; that is, the GPDEA-CCR and GPDEA-BCC were introduced for solving the said problems in a multi-objective framework. We found GPDEA models to be invalid and demonstrate that our proposed bi-objective multiple criteria DEA (BiO-MCDEA) outperforms the GPDEA models in the aspects of discrimination power and weight dispersion, as well as requiring less computational codes. An application of energy dependency among 25 European Union member countries is further used to describe the efficacy of our approach. © 2013 Elsevier B.V. All rights reserved.

300-mW average output power hybrid mode-locked thulium-doped fiber laser

We report on ring thulium-doped fiber laser hybrid mode-locked by single-walled carbon nanotubes and nonlinear polarization evolution generating 600-fs pulses at 1910-1980nm wavelength band with 72.5MHz repetition rate. Average output power reached 300mW in single-pulse operation regime, corresponding to 4.88kW peak power and 2.93nJ pulse energy.

Az ellenzék ereje - általánosított súlyozott szavazási játékok (Minority power - generalized weighted voting games)

A hagyományos szavazási játékok speciális átruházható hasznosságú, kooperatív játékok, úgynevezett egyszerű játékok, ahol a játékosok a pártok, és az egyes koalíciók értéke 1 vagy 0 attól függően, hogy az adott koalíció elég erős-e az adott jogszabály elfogadásához, vagy sem. Ebben a cikkben bevezetjük az általánosított súlyozott szavazási játékok fogalmát, ahol a pártok mandátumainak száma a valószínűségi változó. Magyar példákon keresztül mutatjuk be az új megközelítés használhatóságát. / === / Voting games are cooperative games with transferable utility, so-called simple games, where the players are parties and the value of a coalition may be 0 or 1 depending on its ability to pass a new law. The authors introduce the concept of generalized weighted voting games where the parties' strengths are random variables. taking examples from Hungary to illustrate the use of this approach.

Estimation of average annual daily traffic (AADT) using geographically weighted regression (GWR) method and geographic information system (GIS)

Annual average daily traffic (AADT) is important information for many transportation planning, design, operation, and maintenance activities, as well as for the allocation of highway funds. Many studies have attempted AADT estimation using factor approach, regression analysis, time series, and artificial neural networks. However, these methods are unable to account for spatially variable influence of independent variables on the dependent variable even though it is well known that to many transportation problems, including AADT estimation, spatial context is important. ^ In this study, applications of geographically weighted regression (GWR) methods to estimating AADT were investigated. The GWR based methods considered the influence of correlations among the variables over space and the spatially non-stationarity of the variables. A GWR model allows different relationships between the dependent and independent variables to exist at different points in space. In other words, model parameters vary from location to location and the locally linear regression parameters at a point are affected more by observations near that point than observations further away. ^ The study area was Broward County, Florida. Broward County lies on the Atlantic coast between Palm Beach and Miami-Dade counties. In this study, a total of 67 variables were considered as potential AADT predictors, and six variables (lanes, speed, regional accessibility, direct access, density of roadway length, and density of seasonal household) were selected to develop the models. ^ To investigate the predictive powers of various AADT predictors over the space, the statistics including local r-square, local parameter estimates, and local errors were examined and mapped. The local variations in relationships among parameters were investigated, measured, and mapped to assess the usefulness of GWR methods. ^ The results indicated that the GWR models were able to better explain the variation in the data and to predict AADT with smaller errors than the ordinary linear regression models for the same dataset. Additionally, GWR was able to model the spatial non-stationarity in the data, i.e., the spatially varying relationship between AADT and predictors, which cannot be modeled in ordinary linear regression. ^

The predictive power of homework assignments on student achievement in grade three

Homework has been a controversial issue in education for the past century. Research has been scarce and has yielded results at both ends of the spectrum. This study examined the relationship between homework performance (percent of homework completed and percent of homework correct), student characteristics (SAT-9 score, gender, ethnicity, and socio-economic status), perceptions, and challenges and academic achievement determined by the students' average score on weekly tests and their score on the FCAT NRT mathematics assessment. ^ The subjects for this study consisted of 143 students enrolled in Grade 3 at a suburban elementary school in Miami, Florida. Pearson's correlations were used to examine the associations of the predictor variables with average test scores and FCAT NRT scores. Additionally, simultaneous regression analyses were carried out to examine the influence of the predictor variables on each of the criterion variables. Hierarchical regression analyses were performed on the criterion variables from the predictor variables. ^ Homework performance was significantly correlated with average test score. Controlling for the other variables homework performance was highly related to average test score and FCAT NRT score. ^ This study lends support to the view that homework completion is highly related to student academic achievement at the lower elementary level. It is suggested that at the elementary level more consideration be given to the amount of homework completed by students and to utilize the information in formulating intervention strategies for student who may not be achieving at the appropriate levels. ^

The Predictive Power of Homework Assignments on Student Achievement in Mathematics

This study examined the relationship between homework performance (percent of homework completed and percent of homework correct), student characteristics (Stanford Achievement Test score, gender, ethnicity, and socio-economic status), perceptions, and challenges and academic achievement determined by the students’ average score on weekly tests and their score on the Florida Comprehensive Assessment Test (FCAT) Norm Reference Test (NRT) mathematics assessment.

Assessing Methods for Assigning SNPs to Genes in Gene-Based Tests of Association Using Common Variants

Gene-based tests of association are frequently applied to common SNPs (MAF>5%) as an alternative to single-marker tests. In this analysis we conduct a variety of simulation studies applied to five popular gene-based tests investigating general trends related to their performance in realistic situations. In particular, we focus on the impact of non-causal SNPs and a variety of LD structures on the behavior of these tests. Ultimately, we find that non-causal SNPs can significantly impact the power of all gene-based tests. On average, we find that the “noise” from 6–12 non-causal SNPs will cancel out the “signal” of one causal SNP across five popular gene-based tests. Furthermore, we find complex and differing behavior of the methods in the presence of LD within and between non-causal and causal SNPs. Ultimately, better approaches for a priori prioritization of potentially causal SNPs (e.g., predicting functionality of non-synonymous SNPs), application of these methods to sequenced or fully imputed datasets, and limited use of window-based methods for assigning inter-genic SNPs to genes will improve power. However, significant power loss from non-causal SNPs may remain unless alternative statistical approaches robust to the inclusion of non-causal SNPs are developed.

Average-case analysis of power consumption in embedded systems

Power efficiency is one of the most important constraints in the design of embedded systems since such systems are generally driven by batteries with limited energy budget or restricted power supply. In every embedded system, there are one or more processor cores to run the software and interact with the other hardware components of the system. The power consumption of the processor core(s) has an important impact on the total power dissipated in the system. Hence, the processor power optimization is crucial in satisfying the power consumption constraints, and developing low-power embedded systems. A key aspect of research in processor power optimization and management is “power estimation”. Having a fast and accurate method for processor power estimation at design time helps the designer to explore a large space of design possibilities, to make the optimal choices for developing a power efficient processor. Likewise, understanding the processor power dissipation behaviour of a specific software/application is the key for choosing appropriate algorithms in order to write power efficient software. Simulation-based methods for measuring the processor power achieve very high accuracy, but are available only late in the design process, and are often quite slow. Therefore, the need has arisen for faster, higher-level power prediction methods that allow the system designer to explore many alternatives for developing powerefficient hardware and software. The aim of this thesis is to present fast and high-level power models for the prediction of processor power consumption. Power predictability in this work is achieved in two ways: first, using a design method to develop power predictable circuits; second, analysing the power of the functions in the code which repeat during execution, then building the power model based on average number of repetitions. In the first case, a design method called Asynchronous Charge Sharing Logic (ACSL) is used to implement the Arithmetic Logic Unit (ALU) for the 8051 microcontroller. The ACSL circuits are power predictable due to the independency of their power consumption to the input data. Based on this property, a fast prediction method is presented to estimate the power of ALU by analysing the software program, and extracting the number of ALU-related instructions. This method achieves less than 1% error in power estimation and more than 100 times speedup in comparison to conventional simulation-based methods. In the second case, an average-case processor energy model is developed for the Insertion sort algorithm based on the number of comparisons that take place in the execution of the algorithm. The average number of comparisons is calculated using a high level methodology called MOdular Quantitative Analysis (MOQA). The parameters of the energy model are measured for the LEON3 processor core, but the model is general and can be used for any processor. The model has been validated through the power measurement experiments, and offers high accuracy and orders of magnitude speedup over the simulation-based method.

Efeitos de diferentes tamanhos de palmares nos indicadores tecnicos do nado crawl em esforços maximos de 50 e 100m

Universidade Estadual de Campinas . Faculdade de Educação Física

Da preparação preliminar ao nivel olimpico : um estudo de caso no remo brasileiro feminino

Universidade Estadual de Campinas . Faculdade de Educação Física

Relações entre marcadores digito-palamres e aptidão fisica em atletas de judo de elite

Universidade Estadual de Campinas . Faculdade de Educação Física

Reliability and discriminatory power of methods for dental plaque quantification

OBJECTIVE: This in situ study evaluated the discriminatory power and reliability of methods of dental plaque quantification and the relationship between visual indices (VI) and fluorescence camera (FC) to detect plaque. MATERIAL AND METHODS: Six volunteers used palatal appliances with six bovine enamel blocks presenting different stages of plaque accumulation. The presence of plaque with and without disclosing was assessed using VI. Images were obtained with FC and digital camera in both conditions. The area covered by plaque was assessed. Examinations were done by two independent examiners. Data were analyzed by Kruskal-Wallis and Kappa tests to compare different conditions of samples and to assess the inter-examiner reproducibility. RESULTS: Some methods presented adequate reproducibility. The Turesky index and the assessment of area covered by disclosed plaque in the FC images presented the highest discriminatory powers. CONCLUSION: The Turesky index and images with FC with disclosing present good reliability and discriminatory power in quantifying dental plaque.

Association between neuromuscular tests and kumite performance on the Brazilian Karate National Team

The aim of this study was to verify the relationship of strength and power with performance on an international level karate team during official kumite simulations. Fourteen male black belt karate athletes were submitted to anthropometric data collection and then performed the following tests on two different days: vertical jump test, bench press and squat maximum dynamic strength (1RM) tests. We also tested power production for both exercises at 30 and 60% 1RM and performed a kumite match simulation. Blood samples were obtained at rest and immediately after the kumite matches to measure blood lactate concentration. Karate players were separated by performance (winners vs. defeated) on the kumite matches. We found no significant differences between winners and defeated for strength, vertical jump height, anthropometric data and blood lactate concentration. Interestingly, winners were more powerful in the bench press and squat exercises at 30% 1RM. Maximum strength was correlated with absolute (30% 1RM r = 0.92; 60% 1RM r = 0.63) and relative power (30% 1RM r = 0.74; 60% 1RM r = 0.11, p > 0.05) for the bench press exercise. We concluded that international level karate players' kumite match performance are influenced by higher levels of upper and lower limbs power production.

Effect of GaAlAs Laser Irradiation on the Epiphyseal Cartilage of Rats

Objective: To study the effect of an 830-nm gallium-aluminum-arsenic (GaAlAs) diode laser at two different energy densities (5 and 15 J/cm(2)) on the epiphyseal cartilage of rats by evaluating bone length and the number of chondrocytes and thickness of each zone of the epiphyseal cartilage. Background Data: Few studies have been conducted on the effects of low-level laser therapy on the epiphyseal cartilage at different irradiation doses. Materials and Methods: A total of 30 male Wistar rats with 23 days of age and weighing 90 g on average were randomly divided into 3 groups: control group (CG, no stimulation), G5 group (energy density, 5 J/cm(2)), and G15 group (energy density, 15 J/cm(2)). Laser treatment sessions were administered every other day for a total of 10 sessions. The animals were killed 24 h after the last treatment session. Histological slides of the epiphyseal cartilage were stained with hematoxylin-eosin (HE), photographed with a Zeiss photomicroscope, and subjected to histometric and histological analyses. Statistical analysis was performed using one-way analysis of variance followed by Tukey's post hoc test. All statistical tests were performed at a significance level of 0.05. Results: Histological analysis and x-ray radiographs revealed an increase in thickness of the epiphyseal cartilage and in the number of chondrocytes in the G5 and G15 groups. Conclusion: The 830-nm GaAlAs diode laser, within the parameters used in this study, induced changes in the thickness of the epiphyseal cartilage and increased the number of chondrocytes, but this was not sufficient to induce changes in bone length.