Search results for: Richard C. W. James

Authors: Clorinda Hogan, Shaun Abbott, Mark Halaki, Marcela Torres Catiglioni, Goshi Yamauchi, Lachlan Mitchell, James Salter, Michael Romann, Stephen Cobley

Abstract:

Introduction: Common annual age-group competition structures unintentionally introduce participation inequalities, performance (dis)advantages and selection biases due to the effect of maturational variation between youth swimmers. On this basis, there are implications for improving performance evaluation strategies. Therefore the aim was to: (1) To determine maturity timing distributions in female youth swimming; (2) quantify the relationship between maturation status and 100-m FC performance; (3) apply Maturational-based Corrective Adjustment Procedures (Mat-CAPs) for removal of maturational status performance influences. Methods: (1) Cross-sectional analysis of 663 female (10-15 years) swimmers who underwent assessment of anthropometrics (mass, height and sitting height) and estimations of maturity timing and offset. (2) 100-m front-crawl performance (seconds) was assessed at Australian regional, state, and national-level competitions between 2016-2020. To determine the relationship between maturation status and 100-m front-crawl performance, MO was plotted against 100-m FC performance time. The expected maturity status - performance relationship for females aged 10-15 years of age was obtained through a quadratic function (y = ax2 + bx + c) from unstandardized coefficients. The regression equation was subsequently used for Mat-CAPs. (3) Participants aged 10-13 years were categorised into maturity-offset categories. Maturity offset distributions for Raw (‘All’, ‘Top 50%’ & ‘Top 25%’) and Correctively Adjusted swim times were examined. Chi-square, Cramer’s V and ORs determined the occurrence of maturation biases for each age group and selection level. Results—: (1) Maturity timing distributions illustrated overrepresentation of ‘normative’ maturing swimmers (11.82 ± 0.40 years), with a descriptive shift toward the early maturing relative to the normative population. (2) A curvilinear relationship between maturity-offset and swim performance was identified (R2 = 0.53, P < 0.001) and subsequently utilised for Mat-CAPs. (3) Raw maturity offset categories identified partial maturation status skewing towards biologically older swimmers at 10/11 and 12 years, with effect magnitudes increasing in the ‘Top 50%’ and ‘25%’ of performance times. Following Mat-CAPs application, maturity offset biases were removed in similar age groups and selection levels. When adjusting performance times for maturity offset, Mat-CAPs was successful in mitigating against maturational biases until approximately 1-year post Peak Height Velocity. The overrepresentation of ‘normative’ maturing female swimmers contrasted with the substantial overrepresentation of ‘early’ maturing male swimmers found previously in 100-m front-crawl. These findings suggest early maturational timing is not advantageous in females, but findings associated with Aim 2, highlight how advanced maturational status remained beneficial to performance. Observed differences between female and male maturational biases may relate to the differential impact of physiological development during pubertal years. Females experience greater increases of fat mass and potentially differing changes in body shape which can negatively affect swim performance. Conclusions: Transient maturation status-based participation and performance advantages were apparent within a large sample of Australian female youth 100-m FC swimmers. By removing maturity status performance biases within female youth swimming, Mat-CAPs could help improve participation experiences and the accuracy of identifying genuinely skilled female youth swimmers.

Keywords: athlete development, long-term sport participation, performance evaluation, talent identification, youth competition

Procedia PDF Downloads 182

Authors: Sitira Williams, Georgina Cherry, Andrea Wright, Kevin Wells, Taran Rai, Richard Brown, Travis Street, Alasdair Cook

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Social media sources (50) were identified, keywords defined by veterinarians and organised into 6 topics known to be indicative of feline pruritus: body areas, behaviors, symptoms, diagnosis, and treatments. These were augmented using academic literature, a cat owner survey, synonyms, and Google Trends. The content was collected using a social intelligence solution, with keywords tagged and filtered. Data were aggregated and de-duplicated. SL content matching body areas, behaviors and symptoms were reviewed manually, and posts were marked relevant if: posted by a pet owner, identifying an itchy cat and not duplicated. A sub-set of 493 posts published from 2009-2022 was used for reflexive thematic analysis in NVIVO (Burlington, MA) to identify themes. Five themes were identified: allergy, pruritus, additional behaviors, unusual or undesirable behaviors, diagnosis, and treatment. Most (258) posts reported the cat was excessively licking, itching, and scratching. The majority were indoor cats and were less playful and friendly when itchy. Half of these posts did not indicate a known cause of pruritus. Bald spots and scabs (123) were reported, often causing swelling and fur loss, and 56 reported bumps, lumps, and dry patches. Other impacts on the cat’s quality of life were ear mites, cat self-trauma and stress. Seven posts reported their cats’ symptoms caused them ongoing anxiety and depression. Cats with food allergies to poultry (often chicken and beef) causing bald spots featured in 23 posts. Veterinarians advised switching to a raw food diet and/or changing their bowls. Some cats got worse after switching, leaving owners’ needs unmet. Allergic reactions to flea bites causing excessive itching, red spots, scabs, and fur loss were reported in 13 posts. Some (3) posts indicated allergic reactions to medication. Cats with seasonal and skin allergies, causing sneezing, scratching, headshaking, watery eyes, and nasal discharge, were reported 17 times. Eighty-five posts identified additional behaviors. Of these, 13 reported their cat’s burst pimple or insect bite. Common behaviors were headshaking, rubbing, pawing at their ears, and aggressively chewing. In some cases, bites or pimples triggered previously unseen itchiness, making the cat irritable. Twenty-four reported their cat had anxiety: overgrooming, itching, losing fur, hiding, freaking out, breathing quickly, sleeplessness, hissing and vocalising. Most reported these cats as having itchy skin, fleas, and bumps. Cats were commonly diagnosed with an ear infection, ringworm, acne, or kidney disease. Acne was diagnosed in cats with an allergy flare-up or overgrooming. Ear infections were diagnosed in itchy cats with mites or other parasites. Of the treatments mentioned, steroids were most frequently used, then anti-parasitics, including flea treatments and oral medication (steroids, antibiotics). Forty-six posts reported distress following poor outcomes after medication or additional vet consultations. SL provides veterinarians with unique insights. Verbatim comments highlight the detrimental effects of pruritus on pets and owner quality of life. This study demonstrates the need for veterinarians to communicate management and treatment options more effectively to relieve owner frustrations. Data analysis could be scaled up using machine learning for topic modeling.

Keywords: content analysis, feline, itch, pruritus, social media, thematic analysis, veterinary dermatology

Procedia PDF Downloads 188

Authors: James Forren

Abstract:

This research paper details a recent demonstrator project in which digital form found textile structures were built by human craftspersons wearing augmented reality (AR) head-worn displays (HWDs). The project utilized a wet-state natural fiber / cementitious matrix composite to generate minimal bending shapes in tension which, when cured and rotated, performed as minimal-bending compression members. The significance of the project is that it synthesizes computational structural simulations with visually guided handcraft production. Computational and physical form-finding methods with textiles are well characterized in the development of architectural form. One difficulty, however, is physically building computer simulations: often requiring complicated digital fabrication workflows. However, AR HWDs have been used to build a complex digital form from bricks, wood, plastic, and steel without digital fabrication devices. These projects utilize, instead, the tacit knowledge motor schema of the human craftsperson. Computational simulations offer unprecedented speed and performance in solving complex structural problems. Human craftspersons possess highly efficient complex spatial reasoning motor schemas. And textiles offer efficient form-generating possibilities for individual structural members and overall structural forms. This project proposes that the synthesis of these three modalities of structural problem-solving – computational, human, and material - may not only develop efficient structural form but offer further creative potentialities when the respective intelligence of each modality is productively leveraged. The project methodology pertains to its three modalities of production: 1) computational, 2) human, and 3) material. A proprietary three-dimensional graphic statics simulator generated a three-legged arch as a wireframe model. This wireframe was discretized into nine modules, three modules per leg. Each module was modeled as a woven matrix of one-inch diameter chords. And each woven matrix was transmitted to a holographic engine running on HWDs. Craftspersons wearing the HWDs then wove wet cementitious chords within a simple falsework frame to match the minimal bending form displayed in front of them. Once the woven components cured, they were demounted from the frame. The components were then assembled into a full structure using the holographically displayed computational model as a guide. The assembled structure was approximately eighteen feet in diameter and ten feet in height and matched the holographic model to under an inch of tolerance. The construction validated the computational simulation of the minimal bending form as it was dimensionally stable for a ten-day period, after which it was disassembled. The demonstrator illustrated the facility with which computationally derived, a structurally stable form could be achieved by the holographically guided, complex three-dimensional motor schema of the human craftsperson. However, the workflow traveled unidirectionally from computer to human to material: failing to fully leverage the intelligence of each modality. Subsequent research – a workshop testing human interaction with a physics engine simulation of string networks; and research on the use of HWDs to capture hand gestures in weaving seeks to develop further interactivity with rope and chord towards a bi-directional workflow within full-scale building environments.

Keywords: augmented reality, cementitious composites, computational form finding, textile structures

Procedia PDF Downloads 175

Authors: Davide Palma, Dimitra Papagiannaki, Marco Minella, Manuel Lai, Rita Binetti, Claire Richard

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Modern analytical technologies allow us to detect water contaminants at trace and ultra-trace concentrations highlighting how a large number of organic compounds is not efficiently abated by most wastewater treatment facilities relying on biological processes; we usually refer to these micropollutants as contaminants of emerging concern (CECs). The availability of reliable end effective technologies, able to guarantee the high standards of water quality demanded by legislators worldwide, has therefore become a primary need. In this context, water plasma stands out among developing technologies as it is extremely effective in the abatement of numerous classes of pollutants, cost-effective, and environmentally friendly. In this work, a custom-built non-thermal pulsed plasma discharge generator was used to abate the concentration of selected CECs in the water samples. Samples were treated in a 50 mL pyrex reactor using two different types of plasma discharge occurring at the surface of the treated solution or, underwater, working with positive polarity. The distance between the tips of the electrodes determined where the discharge was formed: underwater when the distance was < 2mm, at the water surface when the distance was > 2 mm. Peak voltage was in the 100-130kV range with typical current values of 20-40 A. The duration of the pulse was 500 ns, and the frequency of discharge could be manually set between 5 and 45 Hz. Treatment of 100 µM diclofenac solution in MilliQ water, with a pulse frequency of 17Hz, revealed that surface discharge was more efficient in the degradation of diclofenac that was no longer detectable after 6 minutes of treatment. Over 30 minutes were required to obtain the same results with underwater discharge. These results are justified by the higher rate of H₂O₂ formation (21.80 µmolL⁻¹min⁻¹ for surface discharge against 1.20 µmolL⁻¹min⁻¹ for underwater discharge), larger discharge volume and UV light emission, high rate of ozone and NOx production (up to 800 and 1400 ppb respectively) observed when working with surface discharge. Then, the surface discharge was used for the treatment of the three selected perfluoroalkyl compounds, namely, perfluorooctanoic acid (PFOA), perfluorohexanoic acid (PFHxA), and pefluorooctanesulfonic acid (PFOS) both individually and in mixture, in ultrapure and groundwater matrices with initial concentration of 1 ppb. In both matrices, PFOS exhibited the best degradation reaching complete removal after 30 min of treatment (degradation rate 0.107 min⁻¹ in ultrapure water and 0.0633 min⁻¹ in groundwater), while the degradation rate of PFOA and PFHxA was slower of around 65% and 80%, respectively. Total nitrogen (TN) measurements revealed levels up to 45 mgL⁻¹h⁻¹ in water samples treated with surface discharge, while, in analogous samples treated with underwater discharge, TN increase was 5 to 10 times lower. These results can be explained by the significant NOx concentrations (over 1400 ppb) measured above functioning reactor operating with superficial discharge; rapid NOx hydrolysis led to nitrates accumulation in the solution explaining the observed evolution of TN values. Ionic chromatography measures confirmed that the vast majority of TN was under the form of nitrates. In conclusion, non-thermal pulsed plasma discharge, obtained with a custom-built generator, was proven to effectively degrade diclofenac in water matrices confirming the potential interest of this technology for wastewater treatment. The surface discharge was proven to be more effective in CECs removal due to the high rate of formation of H₂O₂, ozone, reactive radical species, and strong UV light emission. Furthermore, nitrates enriched water obtained after treatment could be an interesting added-value product to be used as fertilizer in agriculture. Acknowledgment: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 765860.

Keywords: CECs removal, nitrogen fixation, non-thermal plasma, water treatment

Procedia PDF Downloads 119

Authors: Liane J. Ioannou, Jonathan Serpell, Cino Bendinelli, David Walters, Jenny Gough, Dean Lisewski, Win Meyer-Rochow, Julie Miller, Duncan Topliss, Bill Fleming, Stephen Farrell, Andrew Kiu, James Kollias, Mark Sywak, Adam Aniss, Linda Fenton, Danielle Ghusn, Simon Harper, Aleksandra Popadich, Kate Stringer, David Watters, Susannah Ahern

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BACKGROUND: There are significant variations in the management, treatment and outcomes of thyroid cancer, particularly in the role of: diagnostic investigation and pre-treatment scanning; optimal extent of surgery (total or hemi-thyroidectomy); use of active surveillance for small low-risk cancers; central lymph node dissections (therapeutic or prophylactic); outcomes following surgery (e.g. recurrent laryngeal nerve palsy, hypocalcaemia, hypoparathyroidism); post-surgical hormone, calcium and vitamin D therapy; and provision and dosage of radioactive iodine treatment. A proven strategy to reduce variations in the outcome and to improve survival is to measure and compare it using high-quality clinical registry data. Clinical registries provide the most effective means of collecting high-quality data and are a tool for quality improvement. Where they have been introduced at a state or national level, registries have become one of the most clinically valued tools for quality improvement. To benchmark clinical care, clinical quality registries require systematic measurement at predefined intervals and the capacity to report back information to participating clinical units. OBJECTIVE: The aim of this study was to develop a core set clinical indicators that enable measurement and reporting of quality of care for patients with thyroid cancer. We hypothesise that measuring clinical quality indicators, developed to identify differences in quality of care across sites, will reduce variation and improve patient outcomes and survival, thereby lessening costs and healthcare burden to the Australian community. METHOD: Preparatory work and scoping was conducted to identify existing high quality, clinical guidelines and best practice for thyroid cancer both nationally and internationally, as well as relevant literature. A bi-national panel was invited to participate in a modified Delphi process. Panelists were asked to rate each proposed indicator on a Likert scale of 1–9 in a three-round iterative process. RESULTS: A total of 236 potential quality indicators were identified. One hundred and ninety-two indicators were removed to reflect the data capture by the Australian and New Zealand Thyroid Cancer Registry (ANZTCR) (from diagnosis to 90-days post-surgery). The remaining 44 indicators were presented to the panelists for voting. A further 21 indicators were later added by the panelists bringing the total potential quality indicators to 65. Of these, 21 were considered the most important and feasible indicators to measure quality of care in thyroid cancer, of which 12 were recommended for inclusion in the final set. The consensus indicator set spans the spectrum of care, including: preoperative; surgery; surgical complications; staging and post-surgical treatment planning; and post-surgical treatment. CONCLUSIONS: This study provides a core set of quality indicators to measure quality of care in thyroid cancer. This indicator set can be applied as a tool for internal quality improvement, comparative quality reporting, public reporting and research. Inclusion of these quality indicators into monitoring databases such as clinical quality registries will enable opportunities for benchmarking and feedback on best practice care to clinicians involved in the management of thyroid cancer.

Keywords: clinical registry, Delphi survey, quality indicators, quality of care

Procedia PDF Downloads 179

Authors: Manavjit Singh Dhindsa, Ursula Das, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

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These days, wildland fires, also known as forest fires, are more prevalent than ever. Wildfires have major repercussions that affect ecosystems, communities, and the environment in several ways. Wildfires lead to habitat destruction and biodiversity loss, affecting ecosystems and causing soil erosion. They also contribute to poor air quality by releasing smoke and pollutants that pose health risks, especially for individuals with respiratory conditions. Wildfires can damage infrastructure, disrupt communities, and cause economic losses. The economic impact of firefighting efforts, combined with their direct effects on forestry and agriculture, causes significant financial difficulties for the areas impacted. This research explores different forest fire spread models and presents a comprehensive review of various techniques and methodologies used in the field. A forest fire spread model is a computational or mathematical representation that is used to simulate and predict the behavior of a forest fire. By applying scientific concepts and data from empirical studies, these models attempt to capture the intricate dynamics of how a fire spreads, taking into consideration a variety of factors like weather patterns, topography, fuel types, and environmental conditions. These models assist authorities in understanding and forecasting the potential trajectory and intensity of a wildfire. Emphasizing the need for a comprehensive understanding of wildfire dynamics, this research explores the approaches, assumptions, and findings derived from various models. By using a comparison approach, a critical analysis is provided by identifying patterns, strengths, and weaknesses among these models. The purpose of the survey is to further wildfire research and management techniques. Decision-makers, researchers, and practitioners can benefit from the useful insights that are provided by synthesizing established information. Fire spread models provide insights into potential fire behavior, facilitating authorities to make informed decisions about evacuation activities, allocating resources for fire-fighting efforts, and planning for preventive actions. Wildfire spread models are also useful in post-wildfire mitigation strategies as they help in assessing the fire's severity, determining high-risk regions for post-fire dangers, and forecasting soil erosion trends. The analysis highlights the importance of customized modeling approaches for various circumstances and promotes our understanding of the way forest fires spread. Some of the known models in this field are Rothermel’s wildland fuel model, FARSITE, WRF-SFIRE, FIRETEC, FlamMap, FSPro, cellular automata model, and others. The key characteristics that these models consider include weather (includes factors such as wind speed and direction), topography (includes factors like landscape elevation), and fuel availability (includes factors like types of vegetation) among other factors. The models discussed are physics-based, data-driven, or hybrid models, also utilizing ML techniques like attention-based neural networks to enhance the performance of the model. In order to lessen the destructive effects of forest fires, this initiative aims to promote the development of more precise prediction tools and effective management techniques. The survey expands its scope to address the practical needs of numerous stakeholders. Access to enhanced early warning systems enables decision-makers to take prompt action. Emergency responders benefit from improved resource allocation strategies, strengthening the efficacy of firefighting efforts.

Keywords: artificial intelligence, deep learning, forest fire management, fire risk assessment, fire simulation, machine learning, remote sensing, wildfire modeling

Procedia PDF Downloads 81