Description:
Kurrburra (aka Mr Ruffy) (1797-1849), Aboriginal wirrirrap (doctor, healer, bard), sage counsellor of his people, consultant with koalas, and heroic slayer of a feared orangutan-like cryptid that lived in the ranges north of Western Port, is believed to have been born in 1797, and was a member of the Yawen djirra clan, the eastern-most group of the Boonwurrung People whose Country stretched from Wirribi-yaluk (Werribee River) to Wammun (Wilsons Promontory) in Victoria. His moiety was Bunjil and in the early 1840s he had 2 wives: Kurundum (1819-?) and Bowyeup (1823-?), and 2 children, whose names are not known. Kurrburra's traditional Aboriginal name is the Boonwurrung word for the iconic marsupial Phascolarctos cinereus, more commonly known as the koala.
Description:
Kurrburra (aka Mr Ruffy) (1797-1849), Aboriginal wirrirrap (doctor, healer, bard), sage counsellor of his people, consultant with koalas, and heroic slayer of a feared orangutan-like cryptid that lived in the ranges north of Western Port, is believed to have been born in 1797, and was a member of the Yawen djirra clan, the eastern-most group of the Boonwurrung People whose Country stretched from Wirribi-yaluk (Werribee River) to Wammun (Wilsons Promontory) in Victoria. His moiety was Bunjil and in the early 1840s he had 2 wives: Kurundum (1819-?) and Bowyeup (1823-?), and 2 children, whose names are not known. Kurrburra's traditional Aboriginal name is the Boonwurrung word for the iconic marsupial Phascolarctos cinereus, more commonly known as the koala.
Description:
The use of spectral-domain optical coherence tomography (SD-OCT) is becoming commonplace for the in vivo longitudinal study of murine models of ophthalmic disease. Longitudinal studies, however, generate large quantities of data, the manual analysis of which is very challenging due to the time-consuming nature of generating delineations. Thus, it is of importance that automated algorithms be developed to facilitate accurate and timely analysis of these large datasets. Furthermore, as the models target a variety of diseases, the associated structural changes can also be extremely disparate. For instance, in the light damage (LD) model, which is frequently used to study photoreceptor degeneration, the outer retina appears dramatically different from the normal retina. To address these concerns, we have developed a flexible graph-based algorithm for the automated segmentation of mouse OCT volumes (ASiMOV). This approach incorporates a machine-learning component that can be easily trained for different disease models. To validate ASiMOV, the automated results were compared to manual delineations obtained from three raters on healthy and BALB/cJ mice post LD. It was also used to study a longitudinal LD model, where five control and five LD mice were imaged at four timepoints post LD. The total retinal thickness and the outer retina (comprising the outer nuclear layer, and inner and outer segments of the photoreceptors) were unchanged the day after the LD, but subsequently thinned significantly (p < 0.01). The retinal nerve fiber-ganglion cell complex and the inner plexiform layers, however, remained unchanged for the duration of the study.
Description:
The use of spectral-domain optical coherence tomography (SD-OCT) is becoming commonplace for the in vivo longitudinal study of murine models of ophthalmic disease. Longitudinal studies, however, generate large quantities of data, the manual analysis of which is very challenging due to the time-consuming nature of generating delineations. Thus, it is of importance that automated algorithms be developed to facilitate accurate and timely analysis of these large datasets. Furthermore, as the models target a variety of diseases, the associated structural changes can also be extremely disparate. For instance, in the light damage (LD) model, which is frequently used to study photoreceptor degeneration, the outer retina appears dramatically different from the normal retina. To address these concerns, we have developed a flexible graph-based algorithm for the automated segmentation of mouse OCT volumes (ASiMOV). This approach incorporates a machine-learning component that can be easily trained for different disease models. To validate ASiMOV, the automated results were compared to manual delineations obtained from three raters on healthy and BALB/cJ mice post LD. It was also used to study a longitudinal LD model, where five control and five LD mice were imaged at four timepoints post LD. The total retinal thickness and the outer retina (comprising the outer nuclear layer, and inner and outer segments of the photoreceptors) were unchanged the day after the LD, but subsequently thinned significantly (p < 0.01). The retinal nerve fiber-ganglion cell complex and the inner plexiform layers, however, remained unchanged for the duration of the study.
Description:
"The work in this thesis investigates whether a computational story model can be formulated that can overcome the limitations of existing story models and also interact with stories in multiple ways, similar to the ways in which people interact with them."
Description:
"The work in this thesis investigates whether a computational story model can be formulated that can overcome the limitations of existing story models and also interact with stories in multiple ways, similar to the ways in which people interact with them."
Description:
Many times, web services standards do not explicitly address core issues specific to the financial industrywhich makes it difficult to implement standards-compliant systems. But Web Services in Finance will bridge the gap in standards awareness. And you will acquire the skills to develop secure applications quickly. If you are a .NET or J2EE developer working in the financial industry, currently migrating applications to become Web services, or writing new Web services, then this book is your ideal companion! The authors thoroughly discuss crucial topics like data representation, messaging, security, privacy, management, monitoring, and more. What's more: the provided examples and API reviews will help you swiftly reach your goals.
Description:
"A bijection mapping that assigns natural numbers to vertices and/or edges of a graph is called a labeling. In this thesis, we consider graph labelings that have weights associated with each edge and/or vertex. If all the vertex weights (respectively, edge weights) have the same value then the labeling is called magic. If the weight is different for every vertex (respectively, every edge) then we called the labeling antimagic. In this thesis we introduce some variations of magic and antimagic labelings and discuss their properties and provide corresponding labeling schemes. There are two main parts in this thesis. One main part is on vertex labeling and the other main part is on edge labeling."
Description:
"A bijection mapping that assigns natural numbers to vertices and/or edges of a graph is called a labeling. In this thesis, we consider graph labelings that have weights associated with each edge and/or vertex. If all the vertex weights (respectively, edge weights) have the same value then the labeling is called magic. If the weight is different for every vertex (respectively, every edge) then we called the labeling antimagic. In this thesis we introduce some variations of magic and antimagic labelings and discuss their properties and provide corresponding labeling schemes. There are two main parts in this thesis. One main part is on vertex labeling and the other main part is on edge labeling."
Description:
Integration of embedded multimedia devices with powerful computing platforms, e.g., machine learning platforms, helps to build smart cities and transforms the concept of Internet of Things into Internet of Multimedia Things (IoMT). To provide different services to the residents of smart cities, the IoMT technology generates big multimedia data. The management of big multimedia data is a challenging task for IoMT technology. Without proper management, it is hard to maintain consistency, reusability, and reconcilability of generated big multimedia data in smart cities. Various machine learning techniques can be used for automatic classification of raw multimedia data and to allow machines to learn features and perform specific tasks. In this survey, we focus on various machine learning platforms that can be used to process and manage big multimedia data generated by different applications in smart cities. We also highlight various limitations and research challenges that need to be considered when processing big multimedia data in real-time.
Description:
Objectives: This study aims to support the training of Traditional Chinese Medicine practitioners by embedding an expert diagnostic model for arthritis into an Intelligent Interactive Learning Environment (IILE). Background: The increasing prevalence of Traditional Chinese Medicine (TCM) outside China is characterised by the emergence of university level practitioner training and stringent regulatory requirements. TCM differential diagnosis is a difficult task that was traditionally taught by exposure to large numbers of patients in a master-apprentice context. In university degree programs, students and novice diagnosticians cannot have the exposure to cases possible in the traditional context. An online system that engages students in the interactive construction of a virtual case and provides immediate feedback on the appropriateness of student actions and the accuracy of diagnostic conclusions can enhance student learning. The system, an Intelligent Interactive Learning Environment (IILE) is based on an approach that has been shown to improve learning outcomes in intensive care nurse training. Methods: An expert model of diagnostic reasoning elicited from TCM expert practitioners lies at the core of the IILE. The knowledge acquisition is performed using an argumentation tree representation that has been shown to be effective in structuring complex knowledge and facilitating engineer - expert interactions. Problems associated with keeping knowledge bases up to date are mitigated with the use of a knowledge model known as ripple down rules permits dynamic updating of knowledge so that knowledge bases evolve over time. A simple narrative model builds up the virtual case study as user interaction proceeds. Results and discussion: This article reports preliminary results in the study that includes an overview of TCM differential diagnosis, the argument tree, the ripple down rule representation and the narrative based IILE. Segments of the knowledge model based solely on TCM literature are illustrated.
Description:
Objectives: This study aims to support the training of Traditional Chinese Medicine practitioners by embedding an expert diagnostic model for arthritis into an Intelligent Interactive Learning Environment (IILE). Background: The increasing prevalence of Traditional Chinese Medicine (TCM) outside China is characterised by the emergence of university level practitioner training and stringent regulatory requirements. TCM differential diagnosis is a difficult task that was traditionally taught by exposure to large numbers of patients in a master-apprentice context. In university degree programs, students and novice diagnosticians cannot have the exposure to cases possible in the traditional context. An online system that engages students in the interactive construction of a virtual case and provides immediate feedback on the appropriateness of student actions and the accuracy of diagnostic conclusions can enhance student learning. The system, an Intelligent Interactive Learning Environment (IILE) is based on an approach that has been shown to improve learning outcomes in intensive care nurse training. Methods: An expert model of diagnostic reasoning elicited from TCM expert practitioners lies at the core of the IILE. The knowledge acquisition is performed using an argumentation tree representation that has been shown to be effective in structuring complex knowledge and facilitating engineer - expert interactions. Problems associated with keeping knowledge bases up to date are mitigated with the use of a knowledge model known as ripple down rules permits dynamic updating of knowledge so that knowledge bases evolve over time. A simple narrative model builds up the virtual case study as user interaction proceeds. Results and discussion: This article reports preliminary results in the study that includes an overview of TCM differential diagnosis, the argument tree, the ripple down rule representation and the narrative based IILE. Segments of the knowledge model based solely on TCM literature are illustrated.
Description:
The interrelationship between the public and private sectors, and formal and informal healthcare sectors effects market-level service quality, pricing behaviour and referral networks. However, health utilisation analysis of national survey data from many low and middle income countries is constrained by the lack of disaggregated health provider data. This study is concerned with the pattern of repeat outpatient consultations for a single episode of fever from public and private qualified providers and private unqualified providers. Cross-sectional survey data from 1173 adult respondents sampled from three districts within India's most populous state-Uttar Pradesh is analysed. Data was collected during the monsoon season-September to October-in 2012. Regression analysis focuses on the pattern of repeats visits for a single episode of mild-sever fever as the dependent variable. Results show that Women and Muslims in rural north India are more likely to not access healthcare, and if they do, consult with low quality unqualified outpatient healthcare providers. For fever durations of four or more days, men are more likely to access unqualified providers compared to women. Results of the current study supports the literature that women's utilisation of outpatient healthcare for communicable illnesses in LMICs is often less than men. A relative lack of access to household resources explains why fever duration parameter estimates for women and men differ.
Description:
This paper presents a novel development of a new machine learning-based control system for the Agoraphilic (free-space attraction) concept of navigating robots in unknown dynamic environments with a moving goal. Furthermore, this paper presents a new methodology to generate training and testing datasets to develop a machine learning-based module to improve the performances of Agoraphilic algorithms. The new algorithm presented in this paper utilises the free-space attraction (Agoraphilic) concept to safely navigate a mobile robot in a dynamically cluttered environment with a moving goal. The algorithm uses tracking and prediction strategies to estimate the position and velocity vectors of detected moving obstacles and the goal. This predictive methodology enables the algorithm to identify and incorporate potential future growing free-space passages towards the moving goal. This is supported by the new machine learning-based controller designed specifically to efficiently account for the high uncertainties inherent in the robot’s operational environment with a moving goal at a reduced computational cost. This paper also includes comparative and experimental results to demonstrate the improvements of the algorithm after introducing the machine learning technique. The presented experiments demonstrated the success of the algorithm in navigating robots in dynamic environments with the challenge of a moving goal.
Description:
This paper presents a novel development of a new machine learning-based control system for the Agoraphilic (free-space attraction) concept of navigating robots in unknown dynamic environments with a moving goal. Furthermore, this paper presents a new methodology to generate training and testing datasets to develop a machine learning-based module to improve the performances of Agoraphilic algorithms. The new algorithm presented in this paper utilises the free-space attraction (Agoraphilic) concept to safely navigate a mobile robot in a dynamically cluttered environment with a moving goal. The algorithm uses tracking and prediction strategies to estimate the position and velocity vectors of detected moving obstacles and the goal. This predictive methodology enables the algorithm to identify and incorporate potential future growing free-space passages towards the moving goal. This is supported by the new machine learning-based controller designed specifically to efficiently account for the high uncertainties inherent in the robot’s operational environment with a moving goal at a reduced computational cost. This paper also includes comparative and experimental results to demonstrate the improvements of the algorithm after introducing the machine learning technique. The presented experiments demonstrated the success of the algorithm in navigating robots in dynamic environments with the challenge of a moving goal.
Description:
Explores how building a data culture can support teachers to intentionally gather and use data from a rich array of sources to inform their understandings of children and strengthen their own practices. Focuses on how using data can promote quality across ECE curriculum, pedagogy, assessment, and evaluation. Refers to the authors' Data, Knowledge, Action programme of research to discuss how researchers and teachers have been working together to design and try out new data systems, and the insights gained about the processes and supports needed to develop a data culture and use data effectively in ECE settings. Source: National Library of New Zealand Te Puna Matauranga o Aotearoa, licensed by the Department of Internal Affairs for re-use under the Creative Commons Attribution 3.0 New Zealand Licence.
Description:
This paper details the research design of a multidisciplinary, multi-method, collaborative research project investigating health communication from the experiences of the early childhood education (ECE) sector during the COVID-19 pandemic. With the rapidly evolving pandemic, the ECE sector was instantly tasked with expanding their required health practices to prevent the spread of infection. It was evident that the sector needed a system to communicate health advice in a timely, consistent and effective manner. Founded on a partnership model based on 'knowledge brokering' theory, this project demonstrates the value of a multidisciplinary research team collaborating with stakeholder organisations to investigate how COVID-19 health information traversed through complexities of organisational layers and diverse communities of families and staff. Detailing our data collection and analysis protocols, we conclude by outlining how our innovative research design is generating actionable and impactful recommendations for both the ECE and health sectors. [Author abstract]