Danielmccullough2338

We also showed that the term coordination used in computational and ecological perspectives has two different meanings, respectively. Each one had some similarities with biomechanical coordination. The findings of this study lead to an accurate understanding of the concept of coordination, which would help researchers formulate their empirical arguments for coordination in a more transparent manner. It would allow for accurate interpretation of data and theory development. By comprehensively providing multiple perspectives on coordination, this study intends to promote coordination studies in biomechanics.Notwithstanding their wide-spread use, it is unclear what level of empirical evidence exists to support sport participation and physical activity-based models. Sport participation and physical activity-based models characterize different stages of sport involvement based on sport activities (organized and unorganized) individuals take part in throughout their lifespan. The objectives of this scoping review was to explore the nature of empirical support for tenets of sport participation and physical activity-based models describing the evolution of an individuals' sport participation. Seventeen different sport participation models were identified through an iterative literature review, using a snowball search strategy and expert (n = 8) consultation. Of the identified models, three described the evolution of an individual's sport participation based on their participation in different activities at various stages of sport involvement and were retained for the review. A second literature review identified peer-reviewed publications supporting at least one tenet of these three models. Many tenets of retained models received some empirical support from some of the 38 publications identified, but some tenets were not tested. Most of the evidence supporting tenets originated from studies among elite-level athletes. Whereas some evidence exists to support current sport participation and physical activity models, more research is warranted, particularly among the general population of non-elite athletes, for the models to be used in full confidence to guide sport policies, programs, and practices.Objective This study aimed to estimate the number of weekly users of protein, creatine, and dieting supplements and to explore whether weekly use was related to eating disorder (ED) risk factors, exercise, sports participation, and immigrant status. Methods In total, 629 and 1,060 high school boys and girls, respectively, self-reported weekly frequency of protein, creatine, and dieting supplement use, and weight and shape concerns, appearance internalization and pressure, self-esteem, mental distress, physical activity level, exercise context, and the type and weekly frequency of sport played. Multiple hierarchical regression analyses were performed to investigate explanatory factors for supplement use. Results More boys than girls used protein and creatine supplements. Immigrant boys had more frequent use of all supplements than non-immigrant boys, and immigrant girls used creatine supplements more frequently than non-immigrant girls. In total, 23-40 and 5-6% of the variation in the weekly frequency of supplement use in boys and girls, respectively, was explained by immigrant status, ED risk factors, and exercise and sports participation. More frequent use of protein, creatine and dieting supplements in boys was significantly explained by more weight and shape concerns, fitness center exercise, and weight-sensitive sports participation. Depending on the type of supplement, more frequent use of supplements in girls was significantly explained by lower self-esteem, more engagement in weight-sensitive sports, and less engagement in general sport and exercise activities. Conclusion Weekly supplement use was common and more frequent among boys than girls. The weekly use of protein, creatine, and dieting supplements was related to ED risk factors, exercise and sports participation, and immigrant status in boys but not in girls.The purpose of this study was to investigate the relationship between volume regulatory biomarkers and the estrogen to progesterone ratio (EP) prior to and following varying methods and degrees of dehydration. Ten women (20 ± 1 year, 56.98 ± 7.25 kg, 164 ± 6 cm, 39.59 ± 2.96 mL•kg•min-1) completed four intermittent exercise trials (1.5 h, 33.8 ± 1.3°C, 49.5 ± 4.3% relative humidity). Testing took place in two hydration conditions, dehydrated via 24-h fluid restriction (Dehy, USG > 1.020) and euhydrated (Euhy, USG ≤ 1.020), and in two phases of the menstrual cycle, the late follicular phase (days 10-13) and midluteal phase (days 18-22). Change in body mass (%BMΔ), serum copeptin concentration, and plasma osmolality (Posm) were assessed before and after both dehydration stimuli (24-h fluid restriction and exercise heat stress). Saracatinib Serum estrogen and progesterone were analyzed pre-exercise only. Estrogen concentration did not differ between phases or hydration conditions. Progesterone was significantly elevated in luteal compared to follicular in both hydration conditions (Dehy-follicular 1.156 ± 0.31, luteal 5.190 ± 1.56 ng•mL-1, P less then 0.05; Euhy-follicular 0.915 ± 0.18, luteal 4.498 ± 1.38 ng·mL-1, P less then 0.05). As expected, EP was significantly greater in the follicular phase compared to luteal in both hydration conditions (Dehy-F138.94 ± 89.59, L 64.22 ± 84.55, P less then 0.01; Euhy-F158.13 ± 70.15, L 50.98 ± 39.69, P less then 0.01, [all •103]). Copeptin concentration was increased following 24-h fluid restriction and exercise heat stress (mean change 18 ± 9.4, P less then 0.01). We observed a possible relationship of lower EP and higher copeptin concentration following 24-h fluid restriction (r = -0.35, P = 0.054). While these results did not reach the level of statistical significance, these data suggest that the differing EP ratio may alter fluid volume regulation during low levels of dehydration but have no apparent impact after dehydrating exercise in the heat.This study was conducted to identify whether team-wide or positional differences exist in simple or choice reactivity of collegiate soccer athletes when completed under various loads. Much research exists surrounding the assessment of reaction time in the general population, but given variations in training, little insight exists surrounding how unique and elite populations may differ based upon performance demands and task translatability to training. Reactive performance was assessed using the Dynavision D2 in 24 female soccer players (19.73 ± 1.05 years old) from a team within a power five conference of the National Collegiate Athletic Association. Evaluated loads included two conditions of simple reactivity (no additional load and with a concurrent lower body motor task) and three conditions of choice reactivity (no additional load, with a concurrent lower body motor task, and prolonged durations). Paired t-tests and ANOVAs were used to identify differences in task performance based upon load and positional group. No significant load-based or positional differences existed in measured simple reaction times. Performances in choice reaction tasks across the team were found to be slower when completed across extended durations (p less then 0.0001) and faster when completed concurrent with an added balance task (p = 0.0108), as compared to performance under normal conditions. By assessment of positional differences, goalkeepers tended to be slower than other positions in reactivity during choice tasks, despite no differences existing in simple task performance. Given the unique population utilized herein, measured reactivity in different tasks suggests a strong relation to the training demands of soccer, as well as those of goalkeepers as compared to field positions. Findings suggest that sport and positional demands may be substantial contributors to population- and individual-based reactivity performance.Recommender systems attempt to identify and recommend the most preferable item (product-service) to individual users. These systems predict user interest in items based on related items, users, and the interactions between items and users. We aim to build an auto-routine and color scheme recommender system for home-based smart lighting that leverages a wealth of historical data and machine learning methods. We utilize an unsupervised method to recommend a routine for smart lighting. Moreover, by analyzing users' daily logs, geographical location, temporal and usage information, we understand user preferences and predict their preferred light colors. To do so, users are clustered based on their geographical information and usage distribution. We then build and train a predictive model within each cluster and aggregate the results. Results indicate that models based on similar users increases the prediction accuracy, with and without prior knowledge about user preferences.For the segmentation of magnetic resonance brain images into anatomical regions, numerous fully automated methods have been proposed and compared to reference segmentations obtained manually. However, systematic differences might exist between the resulting segmentations, depending on the segmentation method and underlying brain atlas. This potentially results in sensitivity differences to disease and can further complicate the comparison of individual patients to normative data. In this study, we aim to answer two research questions 1) to what extent are methods interchangeable, as long as the same method is being used for computing normative volume distributions and patient-specific volumes? and 2) can different methods be used for computing normative volume distributions and assessing patient-specific volumes? To answer these questions, we compared volumes of six brain regions calculated by five state-of-the-art segmentation methods Erasmus MC (EMC), FreeSurfer (FS), geodesic information flows (GIF), multie AD patients' z-scores was high for regions of thalamus and putamen. This is encouraging as it indicates that the studied methods are interchangeable for these regions. For regions such as the hippocampus, amygdala, caudate nucleus and accumbens, and globus pallidus, not all method combinations showed a high ICC-z. Whether two methods are indeed interchangeable should be confirmed for the specific application and dataset of interest.This paper compares the predictive power of different models to forecast the real U.S. GDP. Using quarterly data from 1976 to 2020, we find that the machine learning K-Nearest Neighbour (KNN) model captures the self-predictive ability of the U.S. GDP and performs better than traditional time series analysis. We explore the inclusion of predictors such as the yield curve, its latent factors, and a set of macroeconomic variables in order to increase the level of forecasting accuracy. The predictions result to be improved only when considering long forecast horizons. The use of machine learning algorithm provides additional guidance for data-driven decision making.Memristors show great promise in neuromorphic computing owing to their high-density integration, fast computing and low-energy consumption. However, the non-ideal update of synaptic weight in memristor devices, including nonlinearity, asymmetry and device variation, still poses challenges to the in-situ learning of memristors, thereby limiting their broad applications. Although the existing offline learning schemes can avoid this problem by transferring the weight optimization process into cloud, it is difficult to adapt to unseen tasks and uncertain environments. Here, we propose a bi-level meta-learning scheme that can alleviate the non-ideal update problem, and achieve fast adaptation and high accuracy, named Rapid One-step Adaption (ROA). By introducing a special regularization constraint and a dynamic learning rate strategy for in-situ learning, the ROA method effectively combines offline pre-training and online rapid one-step adaption. Furthermore, we implemented it on memristor-based neural networks to solve few-shot learning tasks, proving its superiority over the pure offline and online schemes under noisy conditions.