998 resultados para Zielinski, Anthony J.,


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The work of French sociologist, anthropologist and philosopher Pierre Bourdieu has been influential across a set of cognate disciplines that can be classified as physical culture studies. Concepts such as field, capital, habitus and symbolic violence have been used as theoretical tools by scholars and students looking to understand the nature and purpose of sport, leisure, physical education and human movement within wider society. Pierre Bourdieu and Physical Culture is the first book to focus on the significance of Bourdieu’s work for, and in, physical culture. Bringing together the work of leading and emerging international researchers, it introduces the core concepts in Bourdieu’s thought and work, and presents a series of fascinating demonstrations of the application of his theory to physical culture studies. A concluding section discusses the inherent difficulties of choosing and using theory to understand the world around us. By providing an in-depth and multi-layered example of how theory can be used across the many and varied components of sport, leisure, physical education and human movement, this book should help all serious students and researchers in physical culture to better understand the importance of social theory in their work.

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In the past two decades, complexity thinking has emerged as an important theoretical response to the limitations of orthodox ways of understanding educational phenomena. Complexity provides ways of understanding that embrace uncertainty, non-linearity and the inevitable ‘messiness’ that is inherent in educational settings, paying attention to the ways in which the whole is greater than the sum of its parts. This is the first book to focus on complexity thinking in the context of physical education, enabling fresh ways of thinking about research, teaching, curriculum and learning. Written by a team of leading international physical education scholars, the book highlights how the considerable theoretical promise of complexity can be reflected in the actual policies, pedagogies and practices of physical education (PE). It encourages teachers, educators and researchers to embrace notions of learning that are more organic and emergent, to allow the inherent complexity of pedagogical work in PE to be examined more broadly and inclusively. In doing so, Complexity Thinking in Physical Education makes a major contribution to our understanding of pedagogy, curriculum design and development, human movement and educational practice.

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Objective This paper presents an automatic active learning-based system for the extraction of medical concepts from clinical free-text reports. Specifically, (1) the contribution of active learning in reducing the annotation effort, and (2) the robustness of incremental active learning framework across different selection criteria and datasets is determined. Materials and methods The comparative performance of an active learning framework and a fully supervised approach were investigated to study how active learning reduces the annotation effort while achieving the same effectiveness as a supervised approach. Conditional Random Fields as the supervised method, and least confidence and information density as two selection criteria for active learning framework were used. The effect of incremental learning vs. standard learning on the robustness of the models within the active learning framework with different selection criteria was also investigated. Two clinical datasets were used for evaluation: the i2b2/VA 2010 NLP challenge and the ShARe/CLEF 2013 eHealth Evaluation Lab. Results The annotation effort saved by active learning to achieve the same effectiveness as supervised learning is up to 77%, 57%, and 46% of the total number of sequences, tokens, and concepts, respectively. Compared to the Random sampling baseline, the saving is at least doubled. Discussion Incremental active learning guarantees robustness across all selection criteria and datasets. The reduction of annotation effort is always above random sampling and longest sequence baselines. Conclusion Incremental active learning is a promising approach for building effective and robust medical concept extraction models, while significantly reducing the burden of manual annotation.

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This paper presents a new active learning query strategy for information extraction, called Domain Knowledge Informativeness (DKI). Active learning is often used to reduce the amount of annotation effort required to obtain training data for machine learning algorithms. A key component of an active learning approach is the query strategy, which is used to iteratively select samples for annotation. Knowledge resources have been used in information extraction as a means to derive additional features for sample representation. DKI is, however, the first query strategy that exploits such resources to inform sample selection. To evaluate the merits of DKI, in particular with respect to the reduction in annotation effort that the new query strategy allows to achieve, we conduct a comprehensive empirical comparison of active learning query strategies for information extraction within the clinical domain. The clinical domain was chosen for this work because of the availability of extensive structured knowledge resources which have often been exploited for feature generation. In addition, the clinical domain offers a compelling use case for active learning because of the necessary high costs and hurdles associated with obtaining annotations in this domain. Our experimental findings demonstrated that 1) amongst existing query strategies, the ones based on the classification model’s confidence are a better choice for clinical data as they perform equally well with a much lighter computational load, and 2) significant reductions in annotation effort are achievable by exploiting knowledge resources within active learning query strategies, with up to 14% less tokens and concepts to manually annotate than with state-of-the-art query strategies.

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We report the synthesis of new protic ionic liquids (PILs) based on aniline derivatives and the use of high-throughput (HT) techniques to screen possible candidates. In this work, a simple HT method was applied to rapidly screen different aniline derivatives against different acids in order to identify possible combinations that produce PILs. This was followed by repeating the HT process with Chemspeed robotic synthesis platform for more accurate results. One of the successful combinations were then chosen to be synthesised on full scale for further analysis. The new PILs are of interest to the fields of ionic liquids, energy storage and especially, conducting polymers as they serve as solvents, electrolytes and monomers in the same time for possible electropolymerisation (i.e. a self-contained polymer precursor).

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Here we demonstrate that commercial carbon supported Pt nanoparticles react with [AuCl4]- ions at room temperature to produce a highly active Au/Pt/C material with an ultralow coverage of elemental Au on the Pt nanoparticles that exhibits significantly enhanced activity for ethanol oxidation when compared to Pt/C.

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Automated digital recordings are useful for large-scale temporal and spatial environmental monitoring. An important research effort has been the automated classification of calling bird species. In this paper we examine a related task, retrieval of birdcalls from a database of audio recordings, similar to a user supplied query call. Such a retrieval task can sometimes be more useful than an automated classifier. We compare three approaches to similarity-based birdcall retrieval using spectral ridge features and two kinds of gradient features, structure tensor and the histogram of oriented gradients. The retrieval accuracy of our spectral ridge method is 94% compared to 82% for the structure tensor method and 90% for the histogram of gradients method. Additionally, this approach potentially offers a more compact representation and is more computationally efficient.

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A facile route to prepare catalystically active materials from a galinstan liquid metal alloy is introduced. Sonicating liquid galinstan in alkaline solution or treating it in reducing media results in the creation of solid In/Sn rich microspheres that show catalytic activity toward both potassium ferricyanide and 4-nitrophenol reduction.

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We investigated functional, morphological and molecular adaptations to strength training exercise and cold water immersion (CWI) through two separate studies. In one study, 21 physically active men strength trained for 12 weeks (2 d⋅wk–1), with either 10 min of CWI or active recovery (ACT) after each training session. Strength and muscle mass increased more in the ACT group than in the CWI group (P<0.05). Isokinetic work (19%), type II muscle fibre cross-sectional area (17%) and the number of myonuclei per fibre (26%) increased in the ACT group (all P<0.05) but not the CWI group. In another study, nine active men performed a bout of single-leg strength exercises on separate days, followed by CWI or ACT. Muscle biopsies were collected before and 2, 24 and 48 h after exercise. The number of satellite cells expressing neural cell adhesion molecule (NCAM) (10−30%) and paired box protein (Pax7)(20−50%) increased 24–48 h after exercise with ACT. The number of NCAM+ satellitecells increased 48 h after exercise with CWI. NCAM+- and Pax7+-positivesatellite cell numbers were greater after ACT than after CWI (P<0.05). Phosphorylation of p70S6 kinaseThr421/Ser424 increased after exercise in both conditions but was greater after ACT (P<0.05). These data suggest that CWI attenuates the acute changes in satellite cell numbers and activity of kinases that regulate muscle hypertrophy, which may translate to smaller long-term training gains in muscle strength and hypertrophy. The use of CWI as a regular post-exercise recovery strategy should be reconsidered.

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This work describes the fabrication of nanostructured copper electrodes using a simple potential cycling protocol that involves oxidation and reduction of the surface in an alkaline solution. It was found that the inclusion of additives, such as benzyl alcohol and phenylacetic acid, has a profound effect on the surface oxidation process and the subsequent reduction of these oxides. This results in not only a morphology change, but also affects the electrocatalytic performance of the electrode for the reduction of nitrate ions. In all cases, the electrocatalytic performance of the restructured electrodes was significantly enhanced compared with the unmodified electrode. The most promising material was formed when phenylacetic acid was used as the additive. In addition, the reduction of residual oxides on the surface after the modification procedure to expose freshly active reaction sites on the surface before nitrate reduction was found to be a significant factor in dictating the overall electrocatalytic activity. It is envisaged that this approach offers an interesting way to fabricate other nanostructured electrode surfaces.

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Light gauge Steel Frame (LSF) walls are extensively used in the building industry due to the many advantages they provide over other wall systems. Although LSF walls have been used widely, fire design of LSF walls is based on approximate prescriptive methods based on limited fire tests. Also these fire tests were conducted using the standard fire curve [1] and the applicability of available design rules to realistic design fire curves has not been verified. This paper investigates the accuracy of existing fire design rules in the current cold-formed steel standards and the modifications proposed by previous researchers. Of these the recently developed design rules by Gunalan and Mahendran [2] based on Eurocode 3 Part 1.3 [3] and AS/NZS 4600 [4] for standard fire exposure [1] were investigated in detail to determine their applicability to predict the axial compression strengths and fire resistance ratings of LSF walls exposed to realistic design fire curves. This paper also presents the fire performance results of LSF walls exposed to a range of realistic fire curves obtained using a finite element analysis based parametric study. The results from the parametric study were used to develop a simplified design method based on the critical hot flange temperature to predict the fire resistance ratings of LSF walls exposed to realistic fire curves. Finally, the stud failure times (fire resistance rating) obtained from the fire design rules and the simplified design method were compared with parametric study results for LSF walls lined with single and double plasterboards, and externally insulated with rock fibres under realistic fire curves.

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An introduction to the journal, its goals, mission, and vision.

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In this paper it is demonstrated how the Bayesian parametric bootstrap can be adapted to models with intractable likelihoods. The approach is most appealing when the semi-automatic approximate Bayesian computation (ABC) summary statistics are selected. After a pilot run of ABC, the likelihood-free parametric bootstrap approach requires very few model simulations to produce an approximate posterior, which can be a useful approximation in its own right. An alternative is to use this approximation as a proposal distribution in ABC algorithms to make them more efficient. In this paper, the parametric bootstrap approximation is used to form the initial importance distribution for the sequential Monte Carlo and the ABC importance and rejection sampling algorithms. The new approach is illustrated through a simulation study of the univariate g-and- k quantile distribution, and is used to infer parameter values of a stochastic model describing expanding melanoma cell colonies.

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Structural fire safety has become one of the key considerations in the design and maintenance of the built infrastructure. Conventionally the fire resistance rating of load bearing Light gauge Steel Frame (LSF) walls is determined based on the standard time-temperature curve given in ISO 834. Recent research has shown that the true fire resistance of building elements exposed to building fires can be less than their fire resistance ratings determined based on standard fire tests. It is questionable whether the standard time-temperature curve truly represents the fuel loads in modern buildings. Therefore an equivalent fire severity approach has been used in the past to obtain fire resistance rating. This is based on the performance of a structural member exposed to a realistic design fire curve in comparison to that of standard fire time-temperature curve. This paper presents the details of research undertaken to develop an energy based time equivalent approach to obtain the fire resistance ratings of LSF walls exposed to realistic design fire curves with respect to standard fire exposure. This approach relates to the amount of energy transferred to the member. The proposed method was used to predict the fire resistance ratings of single and double layer plasterboard lined and externally insulated LSF walls. The predicted fire ratings were compared with the results from finite element analyses and fire design rules for three different wall configurations exposed to both rapid and prolonged fires. The comparison shows that the proposed energy method can be used to obtain the fire resistance ratings of LSF walls in the case of prolonged fires.