10,616 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Japanese Expert Teachers' Understanding of the Application of Rhythm in Judo: a New Pedagogy
Aim
The aim of this research is to understand the application of rhythm in judo through the experience of expert Japanese coaches.
Background
Scientists and experienced coaches agree rhythm is an important skill in peopleâs everyday life. There is currently no research that investigates the importance of rhythm in judo. People with a highly developed sense of rhythm, move properly, breathe properly, or begin and finish work at the right time. Where sport is concerned, motion and dance can play an important role not only in the improvement of performance, but also in the reduction, or even prevention of, injuries. Those who are naturally musically inclined (have a musical ear) may find they can improve their technique faster than others, and this is something that, by investigating the way expert coaches understand the application of rhythm in judo, this research seeks to understand.
As Lange, (1970) stated, factors of movement are âweight, space, time, and flow on the background of the general flux of movement in proportional arrangementsâ (Bradley, 2008; Selioni, 2013; Youngerman, 1976), therefore, this research will investigate the interaction of body and mind. Dance training as well as judo are somatic experiences that have as their ultimate goal the attainment of a skilled body. With quality training an athlete gains an increased awareness of their body which leads to better control of movement and is very important for judo athletes. This training is found in Japanese kabuki dance (Hahn, 2007), the Greek syrtaki dance (Zografou & Pateraki, 2007), and in walking techniques used in the traditional and Olympic sports of Japanese judo and Greek wrestling.
Methods
Interpretative phenomenological analysis (IPA) was the most suitable data analysis approach for this study for a number of reasons, mainly because it was considered to most closely reflect the author's realist epistemological view. The idiographic approach and framework, particularly on IPA, was regarded as a useful framework in which the current topic could meaningfully be explored.
As this study is one of the first to explore this new thematic area, IPA was the preferred approach to address the goal of providing a detailed account of the expertâs experience. Therefore, semi-structured interviews were used as a data source. This is the most conventional form of data collection using IPA and most closely reflects the researcher-participant relationship. Semi-structured interviews provide considerable flexibility by allowing the researcher to be guided by the phenomena of interest to the participant.
In this study, purposive sampling was achieved using inclusion criteria pertaining to the research question.
Using the ranking system criteria based on the belt in combination with age employed by the International Judo Federation (IJF) and Kodokan Judo Institute, six expert coaches of forty years old and over with a minimum belt rank of 6th dan were selected as a sample.
Results
Both interviews and the codification process contributed to new findings regarding the application of rhythm to judo, and judo itself as a pedagogical tool.
The diagrammatic model can be considered a 'guideline' to the phenomena deemed most significant. The personal significance of rhythm in judo was evidenced by the frequency with which the interviewees naturally referred to it during the interviews. A number of interviewees said that it was important for rhythm to be second nature. Rhythm was also described as an integrated and representative
element in the context of training. This framework was seen as essential in providing the reader with a contextualised understanding of the phenomena considered most important for the current research. Interviewees reported various motives for employing training in rhythm such as faster technical development, better attack/defence, fitness, speed, skills acquisition, personal and spiritual growth, competition results.
Conclusions
This study offers first-hand accounts from professional coaches of a previously unknown phenomena, namely the use of rhythm in judo, and sheds insight on how judo experts understand rhythm in terms of training, competition, and personal growth. These findings suggest that outside of training, coaches play an important role in teaching, mentoring, and leading students. In conclusion, the research revealed four important points which form the basis of a new method of teaching judo: pedagogy, skills, rhythm and movement
Where to place emergency ambulance vehicles: use of a capacitated maximum covering location model with real call data
This study integrates geographical information systems (GIS) with a mathematical optimization technique to enhance emergency medical services (EMS) coverage in a county in the northeast of Iran. EMS demand locations were determined through one-year EMS call data analysis. We formulated a maximal covering location problem (MCLP) as a mixed-integer linear programming model with a capacity threshold for vehicles using the CPLEX optimizer, an optimization software package from IBM. To ensure applicability to the EMS setting, we incorporated a constraint that maintains an acceptable level of service for all EMS calls. Specifically, we implemented two scenarios: a relocation model for existing ambulances and an allocation model for new ambulances, both using a list of candidate locations. The relocation model increased the proportion of calls within the 5-minute coverage standard from 69% to 75%. With the allocation model, we found that the coverage proportion could rise to 84% of total calls by adding ten vehicles and eight new stations. The incorporation of GIS techniques into optimization modelling holds promise for the efficient management of scarce healthcare resources, particularly in situations where time is of the essence
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Production networks in the cultural and creative sector: case studies from the publishing industry
The CICERONE project investigates cultural and creative industries through case study research, with a focus on production networks. This report, part of WP2, examines the publishing industry within this framework. It aims to understand the industryâs hidden aspects, address statistical issues in measurement, and explore the industryâs transformation and integration of cultural and economic values. The report provides an overview of the production network, explores statistical challenges, and presents qualitative analyses of two case studies. It concludes by highlighting the potential of the Global Production Network (GPN) approach for analyzing, researching, policymaking, and intervening in the European publishing network.
The CICERONE projectâs case study research delves into the publishing industry, investigating its production networks and examining key aspects often unseen by the public. The report addresses statistical challenges in measuring the industry and sheds light on its ongoing transformations and integration of cultural and economic values. It presents an overview of the production network, explores statistical issues, and provides qualitative analyses of two case studies. The report emphasizes the potential of the GPN approach for analyzing and intervening in the European publishing network, ultimately contributing to research, policymaking, and understanding within the industry
Facilitating prosociality through technology: Design to promote digital volunteerism
Volunteerism covers many activities involving no financial rewards for volunteers but which contribute
to the common good. There is existing work in designing technology for volunteerism in HumanComputer Interaction (HCI) and related disciplines that focuses on motivation to improve
performance, but it does not account for volunteer wellbeing. Here, I investigate digital volunteerism
in three case studies with a focus on volunteer motivation, engagement, and wellbeing. My research
involved volunteers and others in the volunteering context to generate recommendations for a
volunteer-centric design for digital volunteerism. The thesis has three aims:
1. To investigate motivational aspects critical for enhancing digital volunteersâ experiences
2. To identify digital platform attributes linked to volunteer wellbeing
3. To create guidelines for effectively supporting volunteer engagement in digital volunteering
platforms
In the first case study I investigate the design of a chat widget for volunteers working in an
organisation with a view to develop a design that improves their workflow and wellbeing. The second
case study investigates the needs, motivations, and wellbeing of volunteers who help medical
students improve their medical communication skills. An initial mixed-methods study was followed by
an experiment comparing two design strategies to improve volunteer relatedness; an important
indicator of wellbeing. The third case study looks into volunteer needs, experiences, motivations, and
wellbeing with a focus on volunteer identity and meaning-making on a science-based research
platform. I then analyse my findings from these case studies using the lens of care ethics to derive
critical insights for design.
The key contributions of this thesis are design strategies and critical insights, and a volunteer-centric
design framework to enhance the motivation, wellbeing and engagement of digital volunteers
Innovating within Institutional Voids: A Digital Health Platform in India
Most of the literature on digital innovation assumes availability of resources and access to markets and intermediaries. Institutional voids â lack of formal and informal arrangements â are generally seen as detrimental to digital innovation. While the extant literature provides insights about how some innovation can take place within institutional voids, it largely ignores the role of digital platforms. Based on field work in India, we examine how digital platforms can interface with institutional voids to create social and economic impacts. We find that platforms can address socio-economic challenges by framing, aggregating, and networking within institutional voids. Using an illustrative case study in rural India, where voids and constraints are prevalent, our research highlights how platforms can take strategic actions to develop socio-digital solutions to serve marginalized populations while earning sustainable revenues. We highlight dynamic interactions among physical, social, and digital layers that help platforms reframe constraints and address institutional voids
Federated Learning for Medical Image Analysis: A Survey
Machine learning in medical imaging often faces a fundamental dilemma, namely
the small sample size problem. Many recent studies suggest using multi-domain
data pooled from different acquisition sites/datasets to improve statistical
power. However, medical images from different sites cannot be easily shared to
build large datasets for model training due to privacy protection reasons. As a
promising solution, federated learning, which enables collaborative training of
machine learning models based on data from different sites without cross-site
data sharing, has attracted considerable attention recently. In this paper, we
conduct a comprehensive survey of the recent development of federated learning
methods in medical image analysis. We first introduce the background and
motivation of federated learning for dealing with privacy protection and
collaborative learning issues in medical imaging. We then present a
comprehensive review of recent advances in federated learning methods for
medical image analysis. Specifically, existing methods are categorized based on
three critical aspects of a federated learning system, including client end,
server end, and communication techniques. In each category, we summarize the
existing federated learning methods according to specific research problems in
medical image analysis and also provide insights into the motivations of
different approaches. In addition, we provide a review of existing benchmark
medical imaging datasets and software platforms for current federated learning
research. We also conduct an experimental study to empirically evaluate typical
federated learning methods for medical image analysis. This survey can help to
better understand the current research status, challenges and potential
research opportunities in this promising research field.Comment: 19 pages, 6 figure
AMEE: A Robust Framework for Explanation Evaluation in Time Series Classification
This paper aims to provide a framework to quantitatively evaluate and rank
explanation methods for the time series classification task, which deals with a
prevalent data type in critical domains such as healthcare and finance. The
recent surge of research interest in explanation methods for time series
classification has provided a great variety of explanation techniques.
Nevertheless, when these explanation techniques disagree on a specific problem,
it remains unclear which of them to use. Comparing the explanations to find the
right answer is non-trivial. Two key challenges remain: how to quantitatively
and robustly evaluate the informativeness (i.e., relevance for the
classification task) of a given explanation method, and how to compare
explanation methods side-by-side. We propose AMEE, a Model-Agnostic Explanation
Evaluation framework for quantifying and comparing multiple saliency-based
explanations for time series classification. Perturbation is added to the input
time series guided by the saliency maps (i.e., importance weights for each
point in the time series). The impact of perturbation on classification
accuracy is measured and used for explanation evaluation. The results show that
perturbing discriminative parts of the time series leads to significant changes
in classification accuracy. To be robust to different types of perturbations
and different types of classifiers, we aggregate the accuracy loss across
perturbations and classifiers. This allows us to objectively quantify and rank
different explanation methods. We provide a quantitative and qualitative
analysis for synthetic datasets, a variety of UCR benchmark datasets, as well
as a real-world dataset with known expert ground truth.Comment: Pre-prin
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