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Natural building materials
No full textThe Earth Building Association of New Zealand (EBANZ) was set up to promote the use of earth and other natural building materials. EBANZ acts as a source of information on natural building methods and techniques especially for New Zealand conditions and as a network group for those interested in natural building here and overseas. Three of the more common natural building techniques include Mud or Adobe Brick, Rammed Earth, and Straw Bale. Presenters Min Hall from Unitec and Alan Drayton will discuss the design and construction techniques of the various natural building materials identifying issues that Building Inspectors need to be aware of when inspecting these types of properties.
MUDBRICK OR ADOBE EARTH BUILDINGS
Mud bricks, also known as adobe or just mudbrick, were made from a mixture of sand, clay, water, and frequently tempered with chopped straw and chaff branches. They were the most common method/material for constructing earthen buildings throughout the ancient Near East for millennia. Mud bricks are one of the oldest building materials in the world. They usually only require earth and the energy of the sun, so have very low embodied energy and environ mental impact.
RAMMED EARTH BUILDINGS
Rammed earth (or pisé) is an ancient technique that has been dated back to at least 7000 BC in Pakistan. It has been used in many structures around the world, most notably in parts of the Great Wall of China. Although most earth buildings are single or two-storied, a five-storey hotel was recently completed in Corralben, Australia.
Rammed earth walls are formed from soil that is just damp enough to hold together. The soil gets tamped between shutters with manual or pneumatic rammers. The mix is dry enough that once the material is rammed into place and a wall panel completed, the shuttering can be re moved immediately. One difficulty with the rammed earth method is that strict limits have to be placed on shrinkage to eliminate cracking. Sandy and gravelly material will have to be added to a lot of soils to reduce shrinkage. Often cement or hydrated lime is added to improve durability.
STRAW BALE
Straw bale houses were developed in America and are rising in popularity. They can be built with relative ease and speed, they may be load-bearing but more often they incorporate a post-and-beam frame with the bales being finished with a coat of plaster – often earth or lime based. The design issues are similar to earth buildings, but the need to avoid moisture and weathering is even more crucial. The construction detailing and plaster coatings make or break straw bale construction
Empowering homes: On navigating energy hardship with consumption monitoring in New Zealand
No full textRESEARCH QUESTIONS
To help understand EH in New Zealand, we focus in understanding:
• What are the best interventions needed to eradicate energy hardship in New Zealand?
• How does the MBIE concept of Energy Hardship Compare to its international counterparts?
• What types of interventions exist to minimize Energy Hardship?
• What is the demographic of households in energy hardship?
• What socioeconomic issues relate to energy hardship
Traffic signal phasing optimisation using enhanced q-network (EDQN)
No full textTraffic signal control is key in managing urban traffic flows and volumes. How ever, traditional traffic signal control methods typically struggle to face real-time traffic fluctuations, leading to frequent congestion. In contrast, reinforcement learn ing (RL), a machine learning approach that learns and optimises behaviour from its environment, offers an adaptive way to adjust signal control strategies. It can maximise traffic flow and minimise vehicle delay compared to conventional methods.
Against this backdrop, this research aims to optimise signal phasing by reducing the queue length of vehicles waiting at the intersection and improving travelling efficiency by using reinforcement learning (RL) used in traffic signal phasing control, summarising key theoretical foundations related to both fields. It then discusses commonly used microscopic traffic simulation software, such as Vissim, Simulation of Urban MObility (SUMO), and Paramics.
Focusing on optimising traffic signal phasing, this research proposes an enhanced Deep Q-networks (DQN) called EDQN. The control problem is modelled with a Markov Decision Process (MDP), having the speed and position of every vehicle within 140 meters of the intersection serving as inputs. To demonstrate the model’s optimisation, we define the reward function with the weighted sum of standardised metrics, including the average queue length of all vehicles waiting on the lane before entering the intersection.
The simulation results indicate that, when we compare with other models such as fixed-time control, actuated control, and the standard DQN algorithm, the improved EDQN algorithm consistently delivers the best performance that the queue length of vehicles has been decreasing obviously and convergence speed across light, moderate, and heavy traffic conditions that are faster than other control methods. The results conclude that the proposed EDQN model effectively enhances vehi cle throughput and improves overall traffic efficiency. Reducing the queue length significantly enhances traffic signal control at single intersections, achieving over 70% optimisation across three types of traffic conditions and significantly improving overall traffic signal phasing in the queue length metric
Scanning the Mt Eden Shot Tower
No full textRenata Jadresin Milic, Regan Potangaroa and Sam Smith report that February 2024 marked one year since Auckland and Aotearoa lost the Colonial Ammunition Company Shot Tower at Mt Eden.
Built about 1916, it was the only 20th century shot tower in Australasia and the last shot tower standing in Aotearoa New Zealand. Believed to be the only steel-framed tower of its kind in the Southern Hemisphere and unlike the brick towers in Australia and other parts of the world, it was a rare example internationally to have been built using steel-framed construction
From surviving to thriving: How digitalisation supports retail SMEs
No full textThis study explores the effects of digitalisation on small and medium-sized retail enterprises in Auckland, New Zealand. It focuses on three key variables: Information and Computer Technology adoption, digital literacy and technical resources and explores their impact on company resilience and revenue growth. The success and development of small and medium-sized retail enterprises have been a point of discussion, especially in the light of challenges to brick-and-mortar retail following COVID-19 and markets increasingly dominated by large national and international retailers. Various digitalisation tools have the potential to enhance the performance of small and medium-sized retail enterprises if used effectively or waste scarce resources if applied without the right skills or not operationalised effectively. This study aimed to collect empirical evidence of the impact of digitalisation on small and medium-sized retail enterprises in Auckland, New Zealand to provide a better understanding of factors that can either help or hinder the companies. The study employed a survey-based approach to gather primary data from small and medium-sized retail enterprises in Auckland, New Zealand. The study collected survey data from sales representative, managers and owners of 88 different small and medium retail companies across Auckland, New Zealand and used a Likert scale to measure Information and Computer Technology adoption, digital literacy, technical resources, resilience and revenue growth. The results demonstrated that Information and Computer Technology adoption and digital literacy considerably contribute to company resilience and revenue growth, with Information and Computer Technology adoption having the most significant effect. While technical resources were crucial for revenue growth, the impact on company resilience was not significant. The study provided empirical evidence that digitalisation is critical for preparing small and medium-sized retail enterprises for market disruptions and maintaining financial stability and enhances the understanding of the individual impact and relative usefulness of various aspects of digitalisation. It also provides guidance and recommendations for more targeted and meaningful investment in digitalisation for small and medium-sized retail enterprises in Auckland, New Zealand and elsewhere
Toi ki roto, toi ki waho = Art in, art out: Breathing through contemporary art practices as rongoā (healing) embedded in kaupapa Māori methodologies
No full textRESEARCH QUESTION
What can contemporary art practices contribute to transforming experiences of anti-social behaviour within kāinga?
TUHINGA WHAKARĀPOPOTO (Abstract)
He toi rongoa. He toi kaiao. He toi tairongo.
He toi tuku iho. He toi nō nanahi, nō naiānei mo apōpō.
Art is healing. Art is living. Art is sensory. Art is traditional. Art is contemporary. Art is our history, present and future.
(Whakatāuākī, Tonina Ngatai and Te Waimarie Ngatai-Callaghan, 2023)
As part of a larger conversation about contemporary art, culture and society, I explore a kaupapa Māori approach to the creative process of sculptural installations, investigating its potential for transformation in wellness. The underlying reason for this inquiry is a response to anti-social behaviours within communities that impact the personal space of kāinga. These research findings can lead to new developments in rongoā based on creative practices that promote overall health and well-being from a Māori-centered perspective. This study will employ whakapapa, pūrākau and rongoā as methods of enquiry, alongside those of contemporary art, informed by kaupapa Māori principles. The objective is to create three distinct, but related installation works that align with the research undertaking.
These works are tributes to the art of storytelling - pūrākau. In the realm of kaupapa Māori creative practice, the reclamation and revitalisation of our ancestral narratives are akin to awakening the mana of our tūpuna and breathing life into the whispers of our whenua. Through storytelling, we gather the fragments of our past, weaving them into our traditions and strengthening our identity. Everybody has a story - a narrative of life. Incorporating visual arts, stories of my ancestors and acknowledging the direct relationship to haukāinga weaves together
Diagnosis of schizophrenia and psychosis in Māori people using speech assay: A natural language processing approach
No full textIndigenous peoples and ethnic minorities worldwide have a higher incidence of psychosis, primarily schizophrenia. In New Zealand, Māori individuals experience greater rates of anxiety and depression compared to non-Māori, with a significantly higher incidence of mental illness among Māori. We propose natural language processing (NLP) trained on speech samples from Māori patients as a potential solution to the problem of culturally biased psychometric screening tools for psychosis and schizophrenia. This research examines NLP's ability to diagnose psychosis in Māori patients by analyzing speech and language abnormalities as indicators of severe mental illnesses such as schizophrenia. Our research emphasizes the need for inclusive language models and investigates cross-cultural applicability. We employed a three-part method: conducting clinical interviews, pre-processing data with the Natural Language Toolkit (NLTK), and applying language classifiers. The study's results demonstrate the promise of NLP, but limited patient data necessitate further research, including standardizing datasets and integrating NLP with indigenous languages. This research represents a step towards improving diagnostic accuracy and support for Māori people suffering from psychosis, aligning with healthcare's goal of fair and culturally responsive mental health screening
Hand-made temporalities
No full textWe all store photos from our past – often in shoe boxes tucked away up in the attic. I store my dad’s hand-drawn cards - addressed to me (and later to me and / or my partner). This paper proposes the idea that my dad’s personal card drawings (and associated texts) are a visual memory vault for me from various moments of my early life. Because the cards are almost always dated, they allow a type of memory time travel backwards into that exact temporality. The written text topics vary: my father may be thanking me for a phone call to my Mum; or thanking me for a gift to him; or notification of a death of some local person his town; or simply describing the weather of that weekend; or a fatherly rebuke to do something that I had forgotten to do! The rediscovery of these artifacts is a visual signpost to exactly what was happening at date in time. As we all know, this can happen in association with a particular scent or a certain song, our memories and emotions flood back to that special moment with a few moments of intense nostalgia. This paper will unpack some of my shoe boxes and present a few of my Father’s cards to the reader showing the humility and grace of his early morning hand-made doodles that have lain dormant for years. Will we do the same with the myriads of emails or text messages that bombarde our current uber paced lives
Two painters
No full textLISTS OF WORKS
Sotto [ink on paper framed]
Seam [ink on paper framed]
Cite [ink on paper framed]
Leger [ink on paper framed]
Riff [ink on paper framed]
Breve [ink on paper framed]
Seem/Seme [ink on canvas framed]
Flume [ink on canvas framed]
Cache [ink on canvas framed]
Stave [ink on canvas framed
A novel hybrid deep learning model for earlier accident prediction using computer vision on surveillance camera
No full textFrequent incidence of road accidents worldwide drives continuous efforts toward accident prediction to ensure road safety. Road accidents pose a significant global issue that causes millions of causalities with huge economic losses. Human errors, unsafe roads, and risky weather conditions usually cause those accidents. Early accident detection is a significant factor in preventing road accidents by enabling real-time alerts and faster response and reducing the causality from road accidents. Using advanced computer vision and object recognition models, early accident detection systems provide practical solutions for preventing road accidents using real-time alerts and faster responses.
In this research, we’ll present a hybrid model that can detect accidents earlier by focusing on improving accuracy and prediction time. We collected videos from the CADP dataset and used a custom dataset for forecasting, and we pre-processed the data by labeling, modifying, and converting it into images. The study reviews computer vision, machine learning methods, and technologies used in earlier traffic accident prediction by proposing a hybrid model using multiple versions of the YOLO model, like YOLOv8, YOLOv10, YOLOv10, YOLOv11, and Faster R-CNN. We trained all those models on fifty custom datasets multiple times with different parameters for better detection accu racy and output.
In the result analysis, the individual models like YOLOv8, YOLOv10, YOLOv11, and Faster R-CNN, achieved an accuracy of 57.14%, 60%, and 42.86% for both YOLOv11 and Faster R-CNN, respectively. In individual comparison, YOLOv10 has the best maximum detection time at 2.228 seconds, and YOLOv11 has the best average accident detection time at 1.125 seconds. From comparison, the hybrid model has the best performance, with the best average detection time of 2.067 seconds and the best maximum detection time of 3.567 seconds. The proposed Hybrid model contains three trained models, which consist of YOLOv8, YOLOv10, and Faster R-CNN, with an accuracy of accident forecasting 88.57% accuracy, where this model predicts thirty-one incident videos out of thirty-five incidents in video dat