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Whatua te Muka Tāngata: Indigenous cloak-making as a site of healing and resistance
No full textThis article explores a collaborative arts-research exchange between Māori and Aboriginal women cloak-makers, positioning traditional cloaking practices as powerful sites of healing, resistance and cultural regeneration. Grounded in Kaupapa Māori and Whatuora (H. Smith, 2017; 2019) methodologies, the article weaves together the experiences of Māori weavers and Aboriginal possum-skin cloak-makers who came together on each other’s lands to share, learn and co-create. Through reciprocal exchange and community-engaged practice, three Aboriginal women came to wānanga in Aotearoa in April 2024, with two Māori women travelling to Victoria in Australia in the following month to experience their learning circles. The women revitalised intergenerational knowledge systems, language and creative pedagogies grounded in Indigenous maternities. Cloak-making processes serve not only as a tangible act of creation but as a metaphor for the binding of generations, reconnection to whenua (land), and reclamation of identity. The culminating collaborative cloak, Kahu–Kooramookyan, embodies the cultural narratives, relational ethics and artistic expressions that resonate across Māori and Aboriginal epistemologies. This article forwards cloaking as an artivism – activist arts practice – that nurtures Indigenous wellbeing and acts as a decolonising intervention, reconnecting communities through shared values of aroha, reciprocity and resistance. As ancestral knowledge is reactivated through hands, fibres and ceremony, cloak-making emerges as an educational, spiritual and political act of Indigenous sovereignty and resurgence.
Hinekura Smith acknowledges the Australian Research Council for the ARC Centre of Excellence for Indigenous Futures (Project ID: CE230100027)
ADHD in Aotearoa New Zealand: Challenges, support, and neurodivergent voices
No full textMihi (acknowledgments)
Pepeha (tribal parameters)
Undiagnosed Life
Pīwakawaka (NZ fantail) metaphor
Research
Insights from the survey (n=689)
Random things I’m up to these days..
AI, Māori education, and kaupapa Māori research
No full textWhat is kaupapa Māori?
AI in Māori education
Kaupapa Māori research & AI: Where are we now?
Government’s National AI Strategy (2025)
What this strategy tells us
A kaupapa Māori alternative —Munn’s Five Tests
What’s missing?
What might be needed next
Closing thought
The experiences of indigenous academics in the diaspora
No full textAs Indigenous outward migration expands, some diaspora groups are larger than their population back home which is the case for many in the Pacific diaspora. Research with Indigenous peoples is largely conducted in their homelands, with minimal research on their experiences in other countries. As Pacific Indigenous academics, we employed a dimension of talanoa in the written form to provide insights into our academic journeys. The direction of the talanoa highlight how we have successfully navigated various spaces in relation to decolonising and Indigenising education, and our intentions for standing in solidarity with the native people of the countries in which we reside. This article adds voice to Indigenous communities in diaspora who have been invisible both in the motherland and new homeland. It is envisioned that this work will add to Indigenous education scholarship, and better inform academic and professional practice
Explorations in Sustainable Design: Developing Solutions for Long Lasting Childrenswear
No full textIs adjustable clothing an effective approach for developing more sustainable solutions in children's apparel, particularly for the age group of 4 to 7 years?
Due to the rapid growth of children, parents, caregivers & guardians are compelled to acquire new garments frequently, often before the current clothing has reached the end of its lifespan. This practice not only imposes unnecessary financial burdens but also contributes to environmental degradation through excessive consumption and waste generation. Several studies have shown that the children’s clothing industry is experiencing significant growth. Creating garments intended for prolonged use by incorporating adjustable features can extend the product lifecycle, thereby enhancing both longevity and durability which is not currently addressed by many businesses. As a mother of two young daughters in a family that has recently relocated to New Zealand, my personal experience prompted me to investigate this gap. The frequent purchase of children's clothing has been a source of frustration, as it entails both monetary expenditure and considerable time investment, only to find that the garments become too small within a few months. Carrying seasonal children’s apparel from my homeland, Sri Lanka, and being compelled to acquire entirely new outfits for my growing daughters locally has been disheartening. Many parents adopt various strategies to economise, such as purchasing second-hand clothing; however, this practice is uncommon within the Asian community, where second-hand markets are seldom available. This inherent challenge has motivated me to seek more effective solutions. Informal discussions with fellow Asian parents who have migrated have revealed that this issue is prevalent. My focus has been to develop children apparel that emphasizes strategic longevity, ensuring durability and extended usability. Recognizing the practical benefits of adjustable children’s apparel, I have explored design solutions that support garments to be resized or reshaped to accommodate the child’s growth spurts.
A particular aim has been to explore whether adjustable clothing can be an impactful means of designing more sustainable solutions in childrenswear for age 4-7 years. Adjustable features, using shirring, snaps, buttons, zippers, loops, pleats, and straps have been employed and tested to identify potential product enhancements. In designing and developing these solutions, I prioritized understanding children’s preferences, aiming to incorporate features that promote playfulness, active engagement, and personal satisfaction, ultimately offering an enjoyable and adjustable wardrobe piece. Genuine feedback from my daughters and stakeholders has continually motivated me to refine and expand these solutions. I have utilised a mixed methods approach to design a range of clothing solutions with adjustable features. In this approach through wash and wear testing I assessed the longevity, durability, and the active engagement of these design solutions to check their meaningful impact on sustainability. As an Asian mother, within my unique position, I have also incorporated cultural and religious considerations, emphasizing modesty within all design aspects. During the sampling phase of my designs, I collaborated with a cut, make, and trim (CMT) manufacturer in Sri Lanka. This partnership provided technical support, flexibility in communication, and financial efficiency in achieving the desired quality. I recognise by coordinating design details from New Zealand, sourcing materials and manufacturing in Sri Lanka, and targeting the children’s market in New Zealand presents a promising business model which can be explored in the future for the needs of the Asian market. The experimental findings and challenges encountered in the CMT process have further informed the development of a sustainable business approach. I propose the concept of “Retain” and “Remain,” which emphasizes prolonging garments’ presence in children’s wardrobes and extending their wearability in advancing sustainability within the industry. This study, therefore, will be of value to sustainable practitioners for a step towards developing a business model of testing durability, longevity to cater to childrenswear market
Comparative performance evaluation of IPv4 and IPv6 transmission protocols
No full textThis 90-credit thesis presents a comparative analysis of the performance of IPv4 and IPv6, the two primary protocols for Internet networking, and evaluates transition mechanisms such as 6to4, ISATAP, and Dual-Stack. Utilizing a series of specially configured testbeds, the research measures and compares throughput, delay, and jitter across different packet sizes to assess each protocol’s performance.
We investigate the behavior of each testbed using two Cisco 2900 series gigabit routers and CAT8 shielded cables. We also investigate how the results of these protocols and mechanisms vary. Among all the experimental tests, the maximum throughput results are 948 Mbits/sec with IPv4 and Dual-Stack. These two have the same maximum throughput results. The minimum throughput is 914 Mbits/sec with 6to4. The maximum delay recorded is 39.55 ms with IPv6, and the minimum is 4.82 ms with Dual-Stack technology. The highest jitter recorded is 0.000406 ms with 6to4. The minimum jitter recorded is 0.000226 ms with ISATAP and IPv4.
Regarding transition mechanisms, the highest throughput observed is 948 Mbits/sec with Dual Stack, while the lowest is 914 Mbits/sec with 6to4. The minimum and the highest delays recorded are 4.82 ms and 27.14 ms, respectively, with Dual-Stack. For jitters, the lowest amount is 0.000226 ms with ISATAP, and the highest is 0.000406 ms with 6to4.
The findings underscore the necessity for a strategic approach to adopting IPv6, leveraging the strengths of transition protocols to ensure uninterrupted service and network scalability
Apple leaf disease detection: A comprehensive analysis of pre-trained models and platform development
No full textApples are one of the most popular and valuable fruits in the world, but they are also susceptible to many diseases affecting their leaves, for which it is important to identify and control them at an early stage. Conventional methods of disease classification are restrictive in the way that they are tedious, time-consuming, and require the expertise of professionals. This study explores the feasibility of applying deep learning models for apple leaf disease classification and how transfer learning can enhance the model accuracy across different datasets. It is important to determine how various architectures deal with real-world issues such as lighting variations, shadows, similar-looking disease symptoms, and intraclass variability.
For this purpose, different pre-trained Convolutional Neural Networks (CNNs), including InceptionV3, ResNet50V2, Xception, MobileNetV2, and InceptionResNetV2 were evaluated. These models were trained and then tested on various datasets with a set of different characteristics in terms of class imbalances, image quality, and augmentation techniques. Accuracy, precision, recall, and F1-score were used to measure the performance of the models and the study explored how the models perform with data augmentation, environmental noise, and class imbalances. Some models were efficient in distinguishing between different diseases to a certain level of detail, while others were more stable with respect to augmentation-induced distortions.
In addition, the research explores the practical implications through the development of an apple leaf disease detection platform. The platform enables users to upload an image and then this image can be processed to achieve an automated classification result. The platform integrates outputs from multiple models using a composite scoring approach to improve the reliability of predictions. This application shows how deep learning can be effectively applied in farming, plant disease control, and other areas of agricultural production. The study contributes to precision agriculture by providing insights into model robustness, dataset diversity, and deep learning-based disease classification. This research also reveals the strengths and weaknesses of different CNN architectures. The research demonstrates the effectiveness of using advanced deep-learning approaches for identifying apple diseases thus improving the scalability of disease management in agriculture
Decolonised Professional Framework of Practice for Computing
No full textDecolonised Professional Framework of Practice for Computing to support Story Re-telling: A decolonial approach to sustainable ICT. ICT4S Conference June 2025.
Sustainable ICT is an emerging field that integrates ethical, environmental, and technical perspec- tives, yet there is little guidance on the role of computing professionals in advancing sustainable practice. Despite the increasing focus on sustainability in computing, there is no established framework to define the responsibilities of ICT professionals in this space. Existing research in ICT for Sustainability (ICT4S) has explored the environmental impacts of computing and the role of technology in supporting sustainability goals, but it has not adequately addressed how ICT professionals should navigate their ethical and professional responsibilities in this evolving field. This research introduces a story re-telling methodology, drawing from Indigenous and decolonial storytelling traditions, to bridge the gap between sustainability principles and professional practice in ICT. Using an action-based, iterative process, we used storytelling cycles to critically reflect on their roles, responsibilities, and ethical decision-making in sustainable computing, capturing insights through qualitative narratives, and articulating a decolonised professional framework of practice for ICT professionals. This approach provides a decolonial framework that clarifies the role of computing professionals in sustainable ICT, offering practical guidance for future practitioners and contributing to a more inclusive and ethically grounded professional practice
Grape & apple disease detection using CNN-ViT hybrid model with RF classifier
No full textRESEARCH QUESTIONS
1 How much does the hybrid CNN–ViT model improve classification accuracy for grape and apple leaf diseases compared to standalone CNN and ViT models?
2 What is the impact of using a Random Forest classifier on generalization 4 and overfitting mitigation in disease classification tasks?
3 How do CNN, ViT and CNN-ViT-RF hybrid models compare in terms of accuracy and generalization on grape and apple disease classification?
ABSTRACT
As agriculture increasingly adopts intelligent and automated systems, the early detection of crop diseases has become critical to maintaining yield and quality. In New Zealand, where apple and grape cultivation is central to both local and international horticulture, crops are highly vulnerable to diseases such as Black Rot, ESCA, and Leaf Blight. Manual inspection methods are time-consuming and error-prone, especially over large farm areas. To address this challenge, this study proposes a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and a Random Forest (RF) classifier for multi-class plant disease detection using RGB leaf images.
CNNs are leveraged for extracting fine-grained local features, such as texture and lesions, while ViTs model global dependencies using attention-based mechanisms. The complementary feature sets are fused and passed through a Random Forest classifier, enhancing model generalization across disease classes. The proposed CNN–ViT–RF model was evaluated across ten curated datasets comprising grape and apple leaf images with multiple disease categories, class imbalance, and varying quality conditions. The training pipeline incorporated PCA-based clustering, CycleGAN-based augmentation, and 5-fold cross-validation to ensure robustness and generalizability.
Experimental results show the hybrid model significantly outperforms individual CNN, ViT, and CNN+RF baselines, achieving up to 98% classification accuracy and macro-averaged F1-scores consistently above 0.95. The model also demonstrated superior AUC-ROC and class separation in confusion matrix analyses. Robustness tests under conditions of noise, brightness variation, and rotation confirmed the system’s resilience in real-world settings. Inference speed and memory profiling on the NVIDIA Jetson Nano further indicate the model’s suitability for edge deployment in smart farming applications.
Overall, the fusion of local and global feature extractors with an ensemble classifier presents a scalable and effective solution for precision agriculture and automated plant health monitorin
Fluralaner and its potential threat to native invertebrates
No full textFluralaner is a broad-spectrum isoxazoline insecticide and acaricide approved for use in poultry and companion animals in Aotearoa New Zealand due to its efficacy and favourable safety profile in mammals. However, its high environmental persistence and potential toxicity to non target invertebrates raise ecological concerns. Particularly given limited data on its ecotoxicology and environmental fate. This study analysed and reported on the toxicity of fluralaner in Teleogryllus commodus (field crickets) following exposure. Field crickets were selected as a model for native invertebrates that fill similar ecological and behavioral niches. Behavioural observations (e.g., involuntary leg movement without body movement, inability to right themselves, or hyperextended legs while upright) and lethality (no movement after air puffing and probing) were recorded over 72 hour time frame revealed a time-dependent increase in toxicity, with the concentration to cause 50% of test individuals to perish (LC₅₀) values declining from 15.92 mg/ml at 48 hours to 11.67 mg/ml at 72 hours, indicating delayed or cumulative effects. A clear concentration-effect relationship observed, with affect rates rising from 66.7% at 2 mg/ml at 24 hours to 100% at ≥8 mg/ml at 72 hours. These findings underscore the need for more comprehensive environmental risk assessments and consideration for the pharmacokinetic (absorption, distribution, metabolism, and excretion) in the approval process of veterinary compounds like fluralaner