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211.
Gerfeson Mendonça Alex Antonio Florindo Cassiano Ricardo Rech Delma Katiana Silva de Freitas José Cazuza de Farias Júnior 《Journal of sports sciences》2018,36(9):1068-1075
Little is known about the association between environmental characteristics and types of physical activity in adolescents in a Latin American context. The aim of this study was to examine the association between perceived neighborhood environmental characteristics and different types of physical activity in 2,874 adolescents from Joao Pessoa, Paraiba State, Northeastern Brazil. The types of activity measured by questionnaire (≥10 min/day) included sports, physical exercises, active commuting and recreational activities. Neighborhood characteristics were measured by a 15-item scale. Multilevel analyses showed that adolescents who reported “having places they liked to go to” (OR = 1.41; 95%CI: 1.10–1.79) and “places with opportunities to practice” (OR = 1.29; 95%CI: 1.01–1.65) were more likely to play sports. “Seeing interesting things while walking” (OR = 1.24; 95%CI: 1.01–1.53) and “Seeing other adolescents engaged in physical activity” (OR = 1.47; 95%CI: 1,05–2,06) were associated with exercises. “Seeing other adolescents engaged in physical activity” (OR = 1.47; 95%CI: 1.18–1.82), “the neighborhood is not violent” (OR = 1.29; 95%CI: 1.04–1.60) and “having places they like to go to” (OR = 1.59; 95%CI: 1.13–2.25) were positively associated and “places with opportunities to practice” (OR = 0.79; 95%CI: 0.63–0.98) inversely related to active commuting. “Seeing other adolescents engaged in physical activities” (OR = 1.31; 95%CI: 1.05–1.63) and “seeing interesting things while walking” (OR = 1.26; 95%CI: 1.02–1.56) were associated with recreational activities. Neighborhood environmental characteristics associated with the physical activity vary with the type of practices adopted by adolescents. 相似文献
212.
Ricardo Colomo-Palacios Ángel García-Crespo Pedro Soto-Acosta Marcos Ruano-Mayoral Diego Jiménez-López 《International Journal of Information Management》2010
It is evident that companies and research centres can find a myriad of instruments to fund their R&D (Research & Development) activities. Due to the dynamics of the information management in these circumstances there is a need for intermediary businesses intervention. Consequently, intermediary companies are thus involved in a complex process that needs to be managed and controlled. Within this scenario an Information Technology (IT) platform, named RDi-Advise, was developed by EgeoIT using semantic technologies to support R&D process decisions. This case analysis describes the development and implementation of the platform including the lessons learned during the activity period. 相似文献
213.
Kelly de Jesus Helon Vicente Hultmann Ayala Leandro dos Santos Coelho João Paulo Vilas-Boas Ricardo Jorge Pinto Fernandes 《Journal of sports sciences》2019,37(13):1512-1520
We aimed to compare multilayer perceptron (MLP) neural networks, radial basis function neural networks (RBF) and linear models (LM) accuracy to predict the centre of mass (CM) horizontal speed at low-moderate, heavy and severe swimming intensities using physiological and biomechanical dataset. Ten trained male swimmers completed a 7 × 200 m front crawl protocol (0.05 m.s?1 increments and 30 s intervals) to assess expiratory gases and blood lactate concentrations. Two surface and four underwater cameras recorded independent images subsequently processed focusing a three-dimensional reconstruction of two upper limb cycles at 25 and 175 m laps. Eight physiological and 13 biomechanical variables were inputted to predict CM horizontal speed. MLP, RBF and LM were implemented with the Levenberg-Marquardt algorithm (feed forward with a six-neuron hidden layer), orthogonal least squares algorithm and decomposition of matrices. MLP revealed higher prediction error than LM at low-moderate intensity (2.43 ± 1.44 and 1.67 ± 0.60%), MLP and RBF depicted lower mean absolute percentage errors than LM at heavy intensity (2.45 ± 1.61, 1.82 ± 0.92 and 3.72 ± 1.67%) and RBF neural networks registered lower errors than MLP and LM at severe intensity (2.78 ± 0.96, 3.89 ± 1.78 and 4.47 ± 2.36%). Artificial neural networks are suitable for speed model-fit at heavy and severe swimming intensities when considering physiological and biomechanical background. 相似文献