Rangahau Aranga: AUT Graduate Review
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181 research outputs found
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Recovery After mTBI: What Does It Mean and How Do We Measure It?
Get PDFTraumatic brain injuries (TBIs) are proving to be a significant public health issue, with over 36,000 new cases each year in New Zealand. The majority (95%) of these cases are classified as mild (mTBI). Most patients recover within days to weeks after the injury. However, for some patients recovery is not achieved and they still experience symptoms months to years after their injury. One of the issues in determining why some individuals do not recover as quickly as others is that there is no comprehensive patient-reported measure of what it means to be recovered after mTBI. My research consists of three phases. In the first phase I interviewed 14 patients who had experienced at least one mTBI to determine what recovery means to them. Using thematic analysis, I identified three themes that were part of the recovery process: 1) an ability to function without limitations; 2) regaining a sense of self; and 3) symptom resolution. Most participants judged their recovery based on how they were feeling and felt there was a need to have a way of monitoring how they were doing over time. In the second phase I used the participants’ quotes and themes to create a questionnaire designed to measure recovery after mTBI. The initial set of 18 prototype items have been reviewed by five people with personal experience of mTBI and a panel of mTBI experts to refine the language used and ensure all major components of recovery are captured. In the third phase of my research, I will ask 200 people who have experienced a mTBI to complete the prototype questionnaire so we can establish its validity and reliability to determine its utility to be used as a tool in clinical practice to monitor people’s recovery. In this presentation I will summarise my results from the first and second studies and outline the plans for future research. 
Digital Entanglement: Thinking With and About Digital Artifacts
Get PDFObject-oriented cognition suggests that human thinking and perception are largely based on our interactions with objects in the world, and that our ability to recognise objects and understand their properties is fundamental to how we make sense of the world. Similarly, Enactivism emphasises the role of bodily interactions and experiences in shaping our cognitive processes, from imagining or remembering to scientific models that help advance our understanding of the world. However, the ubiquitous presence of digital technologies in our daily life implies that the nature and properties of artifacts as a means of directing cognition are impacted by a redefinition of the material properties of those artifacts. Digital Ontophany (Vial, 2018, 2019) argues that technologies generate cyclical changes in the way we perceive things (ontophanic shifts). These shifts affect our phenomenological experience of the world and have the power to change our idea of what is possible. Through a mix of micro-phenomenology and micro-ethnography, this research collects first-person accounts of the experience of learning with digital devices in a secondary school in Aotearoa New Zealand, as a way to shine light onto the black-boxed assemblage of learners and devices. The main aims of this presentation are to introduce a comprehensive literature review that supports this research and to discuss the initial findings, which indicate the emergence of a new dynamic of digital artifactuality that materialises new conditions of possibility and a redefinition of embodied dexterity. 
Exploring the effectiveness and resilience of integrated conservation and development projects to global disruptions: a comparative case study in the Cook Islands and Tonga
Get PDFRemote Coastal Communities (RCC) are at the forefront of humanitarian and climatic change. Faced with the stressors of sustaining growing populations in the context of natural resource degradation, RCC must also adapt to globalisation and the subsequent economic linkage and flows of resources and people. In an attempt to support the social-ecological wellbeing of marginalised communities, Integrated Conservation and Development Projects (ICDPs) were introduced in the late 1980s. Today, ICDPs, such as eco-tourism projects, are used to reduce reliance on natural resources, generate economic benefits, and increase local support for conservation. However, the effectiveness of ICDPs in meeting either conservation or development goals has long been debated. Furthermore, limited studies have explored the effectiveness and resilience of ICDPs to global disruptions. Based on the existing literature, it is unclear how major disruptions will impact RCCs in a globalised world and if ICDPs can effectively support the social-ecological wellbeing of host communities during and after global disruptions. This presentation will reveal preliminary findings from research in the Cook Islands and Tonga on the resilience and effectiveness of ICDPs in supporting the social-ecological wellbeing of two coastal communities in the wake of the disruption caused by the COVID-19 pandemic. 
From machine learning to deep learning: experimental comparison of machine learning and deep learning for skin cancer image segmentation
Get PDFSkin lesion analysis is a tedious and challenging task, thus, in this research the suitability of employing machine learning or deep learning approaches for automatic lesion segmentation on dermoscopic skin cancer images is determined. The segmented region can assist clinical experts in understanding the complex lesion structure and internal pattern to find the correct skin cancer type for its early diagnosis and prevention. In this study, I present two methodologies for performing lesion segmentation: machine learning-based optimized K-means with Firefly Algorithm (FA) and Convolutional Neural Network (CNN). In the first model, the FA is hybridized with K-means clustering based on the novel average intensity fitness function to optimize the segmentation map. It is observed in the experimental results that the K-means algorithm may lead to poor results due to the wrong selection of initial centroid value, thus FA is hybridized into it to improve the performance. The second model is an enhanced encoder-decoder-based CNN framework implemented in an end-to-end fashion. These two models are compared to understand whether machine learning or deep learning is suitable to perform medical image segmentation based on a few performance metrics such as accuracy, Intersection over Union (IoU), and DICE index. These methods are evaluated and compared on two benchmark datasets provided by the International Skin Imaging Collaboration (ISIC) named ISIC 2016 [1] and ISIC 2017 [2]. Experimental results showed that the CNN model outperformed the machine learning model with an accuracy difference of 7.98% on ISIC 2016, and 7.32% on ISIC 2017. I concluded from the experimental findings that the deep learning model is more accurate and efficient in segmenting the lesion area as compared to the machine learning model. Thus, findings from this experimental work will be considered for the design of an automatic classification system by incorporating a deep learning-based segmentation approach as a pre-processing step. 
Logical Coding & Emotional Poetry: An exploration of poetic expression in the digital
Get PDFThis practice-oriented design research investigates opportunities for meaningful poetic expression within a digital medium. It hypothesises that the digital medium offers more opportunities for personal expression than in traditional print as poetry can become dynamic with animation, interactivity, and discovery. However, these come with added complexities. This project sits within digital poetry but borrows ideas from other realms of knowledge and practice, such as aesthetic expressivism (Collingwood, 2017) and hypertext (Landow, 2006). The practice focuses on testing, reflecting, and articulating the poetic expression present in three distinct characteristics of the digital environment: ephemerality, hidden content, and non-linearity. These characteristics are further investigated through prototypes, where the research tests techniques that reflect their qualities to create poetic experiences. Within this project, the researcher takes on two roles: the poet and the designer. The former brings expressive and subjective lenses, while the latter introduces objectivity and attention to technical skills. At times, these artistic and technical voices felt dissonant, however there were glimpses of their symbiosis during the practice. Arguably, finding ways to encourage this symbiosis can ensure greater synergy and meaningful connections between the poem and its form. To investigate this further, the research explores when poetic writing occurs in relation to choosing techniques of the medium. Within this presentation, I will map and discuss explored workflows used for approaching digital poetry that consider both the outlined characteristics of the medium and the relationship between artistic and technical voices in practice. It is believed that without a clear understanding of what the digital space could offer, a poem could have no difference in its reading experience from its traditional form, or it could become jarring and meaningless
Calculating the Environmental Impact of Apparel Products
Get PDFAll of the components of a garment’s life cycle, from material production through to disposal, at the end of its life, have an impact on the environment. Consumer awareness and increasing sustainability concerns have prompted fashion companies to seek solutions. Designers have an important role in shaping the life cycle of a product from its inception. It is crucial that sustainable design strategies and tools can accurately measure the impact of a product during its complete lifecycle. One of the most effective methods for determining a product's environmental impact is a Life Cycle Assessment (LCA). It is a methodological tool that describes a product's environmental impact, from ‘cradle-to-grave’. Existing tools that use LCA to aid designers are highly sophisticated, entail considerable time and skills, and are prohibitively expensive. There is a need for a simpler and more generic tool that can be used by designers on a day-to-day basis. My research addresses this gap through the development of a design tool that can assess the environmental impact of apparel products over their complete life cycle from ‘cradle-to-grave’. Recognising the need for fashion designers to measure the impact of their products, this presentation will discuss the term ‘reckon’ as a critical component for achieving fashion sustainability. Along with reduce, reuse and recycle; ‘reckon’ is the fourth ‘r’ that brings new insights and decisions about the way things are designed and manufactured. Accurately measuring a product’s environmental impact, will empower designers to reduce it and ensure their long-term viability
Leadership Affective Influences on Followers: A Daily Diary Study
Get PDFLeadership is inherently an affective phenomenon, and there is rising interest in understanding how affective processes can help leaders mobilize followers. Organizational leaders perhaps use affective displays to motivate and guide followers for achieving organizational goals. While the theory and evidence on the effectiveness of leadership affect is growing rapidly, extant research takes a static view of these affective processes. An emerging realization is that leadership factors can fluctuate in the short term and cast unique influences on followers. Therefore, the current study adopted an atypical approach and utilized a daily diary design (five days) to understand leader-follower daily affective influences. Specifically, I test leaders' positive and negative affect moderated by their use of natural emotions and then focus on transfer to followers' perceptions of interpersonal justice, the followers' positive and negative affect, and ultimately their job satisfaction. These relationships are tested using a sample of 75 leaders and 212 followers from Pakistan using multi-level analysis across three levels (leader, follower, days). Findings provide strong support for the daily affect-transfer model. I found that leaders' positive and negative affect varies on a daily basis, and it produces distinct influences on followers' daily job satisfaction directly and through mediation paths of followers' daily affect (i.e., emotional contagion) and followers' perception of leadership daily justice (i.e., cognitive interpretations). Moreover, results also highlight the key moderating role of leaders' use of natural emotions to enhance the effectiveness of positive affect and mitigate the adverse effects of leaders' negative affect. In this presentation, I will illustrate the significance of daily fluctuations in workplace affective displays and how leaders can use these displays to guide and motivate followers.  
Political Economy of Happiness: Greatest happiness for the greatest number
Get PDFNations, institutions and researchers around the world are increasingly demanding their governments set out a systemic change to humanize the present order of the world. Bhutan’s Gross National Happiness (GNH) index is a deliberate attempt to embed Bhutanese values into national governance structures. GNH provides clarity of what it means to be a politician, a public servant as citizens and government machineries and as individual human being, and that clarity is primarily the need to pursue everything in moderation, and the need to provide policy, focus and sharpness. Bhutan measures happiness through nationwide GNH surveys every five years. The latest GNH survey report shows that the perception of government performance is among the lowest of the 33 indicators, and was the most prominent decrease in the sufficiency level on people’s perception of the government performance across the 33 indicators. For Bhutan to achieve GNH, this indicator must be investigated in depth, therefore, this thesis centres on a significantly understudied context of government performance as it explores the nature of GNH qualitatively. This work is of national importance to Bhutan, representing a high-level and intellectually rigorous engagement with national policy for social good. The qualitative exploration in this study offers a unique examination into the interpretations and complexities of perceptions of the political economy of GNH, underpinning an intricate and textured picture of the lives of Bhutanese. The greatest strength of this thesis is that I have been able to conduct in-depth interviews with policy experts: such as the Presidents of political parties, Members of Parliament, Chief Policy Officers of government, and leadership of government, corporate and private institutions. In addition, this study has wider educational, economic, and social policy implications for countries seeking to structure national identities which go beyond employability, clearly aligning with AUT’s vision for its graduates
Combining local and scientific knowledge in ecosystem based DRR in Sri Lanka.
Get PDFIn many countries ‘green’ ecosystem-oriented solutions are preferred for sustainable Disaster Risk Reduction (DRR) over ‘grey’ infrastructure protective mechanisms. Sustainably managed ecosystems decrease disaster risks by mitigating the impacts of hazards and reducing community exposure. The potential of ecosystems in DRR is increasingly recognized but still poorly understood and researched compared to dominant physical scientific and engineering approaches. Furthermore, a significant gap exists in terms of combining local/indigenous and scientific knowledge for ecosystem-based DRR. Local and indigenous knowledge for ecosystem-based DRR evolves from local communities’ experiences, local-based observations and inter-generational knowledge. Scientific knowledge for ecosystem-based DRR is based on scientific methods and principles, the detached deductive, observational, data analytical, and systems approach. The two knowledge domains should be viewed as two halves of a more holistic approach to contribute towards sustainable ecosystem based-DRR. This research will adopt a case-study design in the Rathnapura District and Kolonnawa Divisional Secretariat Division of Sri Lanka. Primary data will be collected through semi-structured interviews and focus group discussions. Secondary data will be collected through governmental agencies’ and departments’ databases. Research outcomes are expected to fill significant gaps in ecosystem-based DRR literature and contribute to the advancement of theoretical knowledge in DRR globally. In this presentation, I will talk about how disaster risk reduction has evolved, ecosystem-based approaches for disaster risk reduction, and integrating local knowledge and scientific knowledge for ecosystem-based disaster risk reduction. I will also take examples from disasters all over the world.