An alternative approach to describing agility in sports through establishment of a relationship between velocity and radius of curvature

Agility is an essential part of many athletic activities. Currently, agility drill duration is the sole criterion used for evaluation of agility performance. The relationship between drill duration and factors such as acceleration, deceleration and change of direction, however, has not been fully explored. This paper provides a mathematical description of the relationship between velocity and radius of curvatures in an agility drill through implementation of a power law (PL). Two groups of skilled and unskilled participants performed a cyclic forward/backward shuttle agility test. Kinematic data was recorded using motion capture system at a sampling rate of 200 Hz. The logarithmic relationship between tangential velocity and radius of curvature of participant trajectories in both groups was established using the PL. The slope of the regression line was found to be 0.26 and 0.36, for the skilled and unskilled groups, respectively. The magnitudes of regression line slope for both groups were approximately 0.3 which is close to the expected 1/3 value. Results are an indication of how the PL could be implemented in an agility drill thus opening the way for establishment of a more representative measure of agility performance instead of drill duration.

Evaluation of a skin self examination attitude scale using an item response theory model approach

Introduction The Skin Self-Examination Attitude Scale (SSEAS) is a brief measure that allows for the assessment of attitudes in relation to skin self-examination. This study evaluated the psychometric properties of the SSEAS using Item Response Theory (IRT) methods in a large sample of men ≥ 50 years in Queensland, Australia. Methods A sample of 831 men (420 intervention and 411 control) completed a telephone assessment at the 13-month follow-up of a randomized-controlled trial of a video-based intervention to improve skin self-examination (SSE) behaviour. Descriptive statistics (mean, standard deviation, item–total correlations, and Cronbach’s alpha) were compiled and difficulty parameters were computed with Winsteps using the polytomous Rasch Rating Scale Model (RRSM). An item person (Wright) map of the SSEAS was examined for content coverage and item targeting. Results The SSEAS have good psychometric properties including good internal consistency (Cronbach’s alpha = 0.80), fit with the model and no evidence for differential item functioning (DIF) due to experimental trial grouping was detected. Conclusions The present study confirms the SSEA scale as a brief, useful and reliable tool for assessing attitudes towards skin self-examination in a population of men 50 years or older in Queensland, Australia. The 8-item scale shows unidimensionality, allowing levels of SSE attitude, and the item difficulties, to be ranked on a single continuous scale. In terms of clinical practice, it is very important to assess skin cancer self-examination attitude to identify people who may need a more extensive intervention to allow early detection of skin cancer.

A data mining approach for fault diagnosis: An application of anomaly detection algorithm

Rolling-element bearing failures are the most frequent problems in rotating machinery, which can be catastrophic and cause major downtime. Hence, providing advance failure warning and precise fault detection in such components are pivotal and cost-effective. The vast majority of past research has focused on signal processing and spectral analysis for fault diagnostics in rotating components. In this study, a data mining approach using a machine learning technique called anomaly detection (AD) is presented. This method employs classification techniques to discriminate between defect examples. Two features, kurtosis and Non-Gaussianity Score (NGS), are extracted to develop anomaly detection algorithms. The performance of the developed algorithms was examined through real data from a test to failure bearing. Finally, the application of anomaly detection is compared with one of the popular methods called Support Vector Machine (SVM) to investigate the sensitivity and accuracy of this approach and its ability to detect the anomalies in early stages.

Autonomy-supportive pedagogical approach to sports coaching: Research, challenges and opportunities

There has been an increasing body of research on autonomy- or need-support specific to a coaching context that warrants some review of what we know and don't know, and what might be generative for future research. The previous studies reviewed within this article have shown consistent support for Self-determination theory with autonomy-supportive environments linked with adaptive outcomes, such as superior performance, enhanced self-worth, increased effort, and self-determined motivation; while controlling environments have been linked with increased attrition and extrinsic motivation or amotivation. In this way, much of the research in autonomy-supportive coaching has focused on the impact of coaching behaviours on athlete outcomes. While this is an important focus of inquiry, there has been a dearth of research examining those causal factors that impact coaches' pedagogical behaviours in the first case. This review underscores the need for future research to examine the antecedents to coaching behaviours, which is central to understanding the complexity and challenges in promoting an autonomy-supportive approach to sport coaching.

Zombies on the road: A holistic design approach to balancing gamification and safe driving [poster]

This paper explores novel driving experiences that make use of gamification and augmented reality in the car. We discuss our design considerations, which are grounded in road safety psychology and video game design theory. We aim to address the tension between safe driving practices and player engagement. Specifically, we propose a holistic, iterative thinking process inspired by game design cognition and share our insights generated through the application of this process. We present preliminary game concepts that blend digital components with physical elements from the driving environment. We further highlight how this design process helped us to iteratively evolve these concepts towards being safer while maintaining fun. These insights and game design cognition itself will be useful to the AutomotiveUI community investigating similar novel driving experiences.

Speaking of supervision: A dialogic approach to building higher degree research supervision capacity in the creative arts

In the emergent field of creative practice higher degrees by research, first generation supervisors have developed new models of supervision for an unprecedented form of research that combines creative practice and written thesis. In a national research project, entitled 'Effective supervision of creative practice higher research degrees', we set out to capture and share early supervisors' insights, strategies and approaches to supporting their creative practice PhD students. From the insights we gained during the early interview process, we expanded our research methods in line with a distributed leadership model and developed a dialogic framework. This led us to unanticipated conclusions and unexpected recommendations. In this study, we primarily draw on philosopher and literary theorist Mikhail Bakhtin's dialogics to explain how giving precedence to the voices of supervisors not only facilitated the articulation of dispersed tacit knowledge, but also led to other 20 discoveries. These include the nature of supervisors' resistance to prescribed models, policies and central academic development programmes; the importance of polyvocality and responsive dialogue in enabling continued innovation in the field; the benefits to supervisors of reflecting, discussing and sharing practices with colleagues; and the value of distributed leadership and dialogue to academic development and supervision capacity building in research education.

A holistic, institution-wide approach to building sessional staff capacity

Sessional Academics enhance students’ learning experience by bringing a diverse range of perspectives and expertise into the classroom. As industry specialists, research students, and recent graduates who have excelled in their courses, they complement the discipline expertise of career academics. With increasing casualization of the academic workforce, Sessional Academics now deliver the majority of face-to-face undergraduate teaching in Australian Universities. To enable them to realize their full potential as effective contributors to student learning and course quality, universities need to offer effective training and access to advice and support and facilitate engagement in university life. However, in the face of complex and diverse contexts, overwhelming numbers, and the transitory nature of sessional cohorts, few universities have developed a comprehensive, systematic approach. During the past three years at QUT, we have set out to develop a multifaceted approach to Sessional Academic support and development. In this paper I will explain why and how we have done so, and describe the range of strategies and programs we have developed. They include a central academic development program, which is structured and scaffolded with learning objectives and outcomes, and aligned with a graduate certificate in Academic Practice; a Sessional Academic Success program, which deploys experienced, school-based sessional academic success advisors to provide local support, build a sense of community, and offer discipline focused academic development; an online, dialogic communication strategy; and opportunities to present and be acknowledged for good learning and teaching practices. Together, these strategies have impacted on sessional academics’ confidence, learning and teaching capacity, reflection and engagement.

Performance enhancement: A whole of person approach to first year dance students’ transition into tertiary training

The transition into university presents very particular challenges for students. The First Year Experience (FYE) is a transitional liminal phase, fraught with uncertainty, ripe with potential. The complexity inherent in this initial phase of tertiary education is well documented and continues to be interrogated. Providing timely and effective support and interventions for potentially at-risk first year students as they transition into tertiary study is a key priority for universities across the globe (Gale et al., 2015). This article outlines the evolution of an established and highly successful Transitional Training Program (TTP) for first year tertiary dance students, with particular reference to the 2015 iteration of the program. TTP design embraces three dimensions: physical training in transition, learning in transition, and teaching for transition, with an emphasis on developing and encouraging a mindset that enables information to be transferred into alternative settings for practice and learning throughout life. The aim of the 2015 TTP was to drive substantial change in first year Dance students’ satisfaction, connectedness, and overall performance within the Bachelor of Fine Arts (BFA) Dance course, through the development and delivery of innovative curriculum and pedagogical practices that promote the successful transition of dance students into their first year of university. The program targeted first year BFA Dance students through the integration of specific career guidance; performance psychology; academic skills support; practical dance skills support; and specialized curricula and pedagogy.

Exploring the nexus between tourism and output in Cook Islands: An ARDL bounds approach

Tourism plays an important role in the development of Cook Islands. In this paper we examine the nexus between tourism and growth using quarterly data over the period 2009Q1–2014Q2 using the recently upgraded ARDL bounds test to cointegration tool, Microfit 5.01, which provides sample adjusted bounds and hence is more reliable for small sample size studies. We perform the cointegration using the ARDL bounds test and examine the direction of causality. Using visitor arrival and output in per capita terms as respective proxy for tourism development and growth, we examine the long-run association and report the elasticity coefficient of tourism and causality nexus, accordingly. Using unit root break tests, we note that 2011Q1 and 2011Q2 are two structural break periods in the output series. However, we note that this period is not statistically significant in the ARDL model and hence excluded from the estimation. Subsequently, the regression results show the two series are cointegrated. The long-run elasticity coefficient of tourism is estimated to be 0.83 and the short-run is 0.73. A bidirectional causality between tourism and income is noted for Cook Islands which indicates that tourism development and income mutually reinforce each other. In light of this, socio-economic policies need to focus on broad-based, inclusive and income-generating tourism development projects which are expected to have feedback effect.

Structure matters: Adoption of structured classification Approach in the context of cognitive presence classification

Within online learning communities, receiving timely and meaningful insights into the quality of learning activities is an important part of an effective educational experience. Commonly adopted methods – such as the Community of Inquiry framework – rely on manual coding of online discussion transcripts, which is a costly and time consuming process. There are several efforts underway to enable the automated classification of online discussion messages using supervised machine learning, which would enable the real-time analysis of interactions occurring within online learning communities. This paper investigates the importance of incorporating features that utilise the structure of on-line discussions for the classification of "cognitive presence" – the central dimension of the Community of Inquiry framework focusing on the quality of students' critical thinking within online learning communities. We implemented a Conditional Random Field classification solution, which incorporates structural features that may be useful in increasing classification performance over other implementations. Our approach leads to an improvement in classification accuracy of 5.8% over current existing techniques when tested on the same dataset, with a precision and recall of 0.630 and 0.504 respectively.

Ink-jet printing of graphene for flexible electronics: An environmentally-friendly approach

Mechanical flexibility is considered an asset in consumer electronics and next-generation electronic systems. Printed and flexible electronic devices could be embedded into clothing or other surfaces at home or office or in many products such as low-cost sensors integrated in transparent and flexible surfaces. In this context inks based on graphene and related two-dimensional materials (2DMs) are gaining increasing attention owing to their exceptional (opto)electronic, electrochemical and mechanical properties. The current limitation relies on the use of solvents, providing stable dispersions of graphene and 2DMs and fitting the proper fluidic requirements for printing, which are in general not environmentally benign, and with high boiling point. Non-toxic and low boiling point solvents do not possess the required rheological properties (i.e., surface tension, viscosity and density) for the solution processing of graphene and 2DMs. Such solvents (e.g., water, alcohols) require the addition of stabilizing agents such as polymers or surfactants for the dispersion of graphene and 2DMs, which however unavoidably corrupt their properties, thus preventing their use for the target application. Here, we demonstrate a viable strategy to tune the fluidic properties of water/ethanol mixtures (low-boiling point solvents) to first effectively exfoliate graphite and then disperse graphene flakes to formulate graphene-based inks. We demonstrate that such inks can be used to print conductive stripes (sheet resistance of ~13 kΩ/□) on flexible substrates (polyethylene terephthalate), moving a step forward towards the realization of graphene-based printed electronic devices.

“It’s in the syllabus”: Identifying information literacy and data information literacy opportunities using a grounded theory approach

Developing innovative library services requires a real world understanding of faculty members' desired curricular goals. This study aimed to develop a comprehensive and deeper understanding of Purdue's nutrition science and political science faculties' expectations for student learning related to information and data information literacies. Course syllabi were examined using grounded theory techniques that allowed us to identify how faculty were addressing information and data information literacies in their courses, but it also enabled us to understand the interconnectedness of these literacies to other departmental intentions for student learning, such as developing a professional identity or learning to conduct original research. The holistic understanding developed through this research provides the necessary information for designing and suggesting information literacy and data information literacy services to departmental faculty in ways supportive of curricular learning outcomes.

Studying change in popular culture: A "middle-range" approach

"This chapter reviews the capacity of the discipline field to account for the velocity and quality of digitally-driven transformations, while making a case for a "middle range" approach that steers between unbridled optimism ("all-change") and determined scepticism ("Continuity") about the potential of such change. The chapter focuses on online screen distribution as a case study, considering the evidence for, and significance of, change in industry structure and the main payers, how content is produced and by whom, the nature of content, and the degree to which online screen distribution has reached thresholds of mainstream popularity."

A biomechanical approach to iris normalization

The richness of the iris texture and its variability across individuals make it a useful biometric trait for personal authentication. One of the key stages in classical iris recognition is the normalization process, where the annular iris region is mapped to a dimensionless pseudo-polar coordinate system. This process results in a rectangular structure that can be used to compensate for differences in scale and variations in pupil size. Most iris recognition methods in the literature adopt linear sampling in the radial and angular directions when performing iris normalization. In this paper, a biomechanical model of the iris is used to define a novel nonlinear normalization scheme that improves iris recognition accuracy under different degrees of pupil dilation. The proposed biomechanical model is used to predict the radial displacement of any point in the iris at a given dilation level, and this information is incorporated in the normalization process. Experimental results on the WVU pupil light reflex database (WVU-PLR) indicate the efficacy of the proposed technique, especially when matching iris images with large differences in pupil size.

Automating marine mammal detection in aerial images captured during wildlife surveys: A deep learning approach

Aerial surveys conducted using manned or unmanned aircraft with customized camera payloads can generate a large number of images. Manual review of these images to extract data is prohibitive in terms of time and financial resources, thus providing strong incentive to automate this process using computer vision systems. There are potential applications for these automated systems in areas such as surveillance and monitoring, precision agriculture, law enforcement, asset inspection, and wildlife assessment. In this paper, we present an efficient machine learning system for automating the detection of marine species in aerial imagery. The effectiveness of our approach can be credited to the combination of a well-suited region proposal method and the use of Deep Convolutional Neural Networks (DCNNs). In comparison to previous algorithms designed for the same purpose, we have been able to dramatically improve recall to more than 80% and improve precision to 27% by using DCNNs as the core approach.