Faculty of Design and Creative Technologies (Te Ara Auaha)
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The Faculty of Design and Creative Technologies (Te Ara Auaha) is comprised of four school; Colab, the School of Art and Design, the School of Communication Studies and the School of Engineering, Computer and Mathematical Sciences. It also has Institutes, Centres and Labs across the Arts and Sciences in a mix that blends the traditional and the new, praxis and theory.
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Browsing Faculty of Design and Creative Technologies (Te Ara Auaha) by Subject "0502 Environmental Science and Management"
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- ItemA Metasurface-Based LTC Polarization Converter With S-shaped Split Ring Resonator Structure for Flexible Applications(MDPI AG, 2023-07-10) Li, Erfeng; Li, Xue Jun; Seet, Boon-Chong; Ghaffar, Adnan; Aneja, AayushThis paper presents a metasurface-based linear-to-circular polarization converter with a flexible structure for conformal and wearable applications. The converter consists of nested S- and C-shaped split ring resonators in the unit cell and can convert linearly polarized incident waves into left-handed circularly polarized ones at 12.4 GHz. Simulation results show that the proposed design has a high polarization conversion rate and efficiency at the operating frequency. Conformal tests are also conducted to evaluate the performance under curvature circumstances. A minor shift in the operating frequency is observed when the converter is applied on a sinusoidal wavy surface.
- ItemA Preliminary Investigation into the Degradation of Asbestos Fibres in Soils, Rocks and Building Materials Associated with Naturally Occurring Biofilms(MDPI, 2024-01-19) Berry, TA; Wallis, S; Doyle, E; de Lange, P; Steinhorn, G; Vigliaturo, R; Belluso, E; Blanchon, DBioremediation utilizes living organisms such as plants, microbes and their enzymatic products to reduce toxicity in xenobiotic compounds. Microbial-mediated bioremediation is cost effective and sustainable and in situ application is easily implemented. Either naturally occurring metabolic activity can be utilized during bioremediation for the degradation, transformation or accumulation of substances, or microbial augmentation with non-native species can be exploited. Despite the perceived low potential for the biological degradation of some recalcitrant compounds, successful steps towards bioremediation have been made, including with asbestos minerals, which are prevalent in building stock (created prior to the year 2000) in New Zealand. Evidence of the in situ biodegradation of asbestos fibres was investigated in samples taken from a retired asbestos mine, asbestos-contaminated soils and biofilm or lichen-covered asbestos-containing building materials. Microbial diversity within the biofilms to be associated with the asbestos-containing samples was investigated using internal transcribed spacer and 16S DNA amplicon sequencing, supplemented with isolation and culturing on agar plates. A range of fungal and bacterial species were found, including some known to produce siderophores. Changes to fibre structure and morphology were analysed using Transmission Electron Microscopy and Energy-Dispersive X-ray Spectroscopy. Chrysotile fibrils from asbestos-containing material (ACMs), asbestos-containing soils, and asbestos incorporated into lichen material showed signs of amorphisation and dissolution across their length, which could be related to biological activity.
- ItemA Unified Efficient Deep Learning Architecture for Rapid Safety Objects Classification Using Normalized Quantization-Aware Learning(MDPI, 2023-11-05) Okeke, Stephen; Nguyen, MinhThe efficient recognition and classification of personal protective equipment are essential for ensuring the safety of personnel in complex industrial settings. Using the existing methods, manually performing macro-level classification and identification of personnel in intricate spheres is tedious, time-consuming, and inefficient. The availability of several artificial intelligence models in recent times presents a new paradigm shift in object classification and tracking in complex settings. In this study, several compact and efficient deep learning model architectures are explored, and a new efficient model is constructed by fusing the learning capabilities of the individual, efficient models for better object feature learning and optimal inferencing. The proposed model ensures rapid identification of personnel in complex working environments for appropriate safety measures. The new model construct follows the contributory learning theory whereby each fussed model brings its learned features that are then combined to obtain a more accurate and rapid model using normalized quantization-aware learning. The major contribution of the work is the introduction of a normalized quantization-aware learning strategy to fuse the features learned by each of the contributing models. During the investigation, a separable convolutional driven model was constructed as a base model, and then the various efficient architectures were combined for the rapid identification and classification of the various hardhat classes used in complex industrial settings. A remarkable rapid classification and accuracy were recorded with the new resultant model.
- ItemAn Adaptive Traffic-flow Management System with a Cooperative Transitional Maneuver for Vehicular Platoons(MDPI AG, ) Hota, Lopamudra; Nayak, Biraja Prasad; Sahoo, Bibhudatta; Chong, Peter HJ; Kumar, ArunGlobally, the increases in vehicle numbers, traffic congestion, and road accidents are serious issues. Autonomous vehicles (AVs) traveling in platoons provide innovative solutions for efficient traffic flow management, especially for congestion mitigation, thus reducing accidents. In recent years, platoon-based driving, also known as vehicle platoon, has emerged as an extensive research area. Vehicle platooning reduces travel time and increases road capacity by reducing the safety distance between vehicles. For connected and automated vehicles, cooperative adaptive cruise control (CACC) systems and platoon management systems play a significant role. Platoon vehicles can maintain a closer safety distance due to CACC systems, which are based on vehicle status data obtained through vehicular communications. This paper proposes an adaptive traffic flow and collision avoidance approach for vehicular platoons based on CACC. The proposed approach considers the creation and evolution of platoons to govern the traffic flow during congestion and avoid collisions in uncertain situations. Different obstructing scenarios are identified during travel, and solutions to these challenging situations are proposed. The merge and join maneuvers are performed to help the platoon’s steady movement. The simulation results show a significant improvement in traffic flow due to the mitigation of congestion using platooning, minimizing travel time, and avoiding collisions.
- ItemAutomated Road Defect and Anomaly Detection for Traffic Safety: A Systematic Review(MDPI AG, 2023-06-16) Rathee, Munish; Bačić, Boris; Doborjeh, MaryamRecently, there has been a substantial increase in the development of sensor technology. As enabling factors, computer vision (CV) combined with sensor technology have made progress in applications intended to mitigate high rates of fatalities and the costs of traffic-related injuries. Although past surveys and applications of CV have focused on subareas of road hazards, there is yet to be one comprehensive and evidence-based systematic review that investigates CV applications for Automated Road Defect and Anomaly Detection (ARDAD). To present ARDAD’s state-of-the-art, this systematic review is focused on determining the research gaps, challenges, and future implications from selected papers (N = 116) between 2000 and 2023, relying primarily on Scopus and Litmaps services. The survey presents a selection of artefacts, including the most popular open-access datasets (D = 18), research and technology trends that with reported performance can help accelerate the application of rapidly advancing sensor technology in ARDAD and CV. The produced survey artefacts can assist the scientific community in further improving traffic conditions and safety.
- ItemCentring Localised Indigenous Concepts of Wellbeing in Urban Nature-Based Solutions for Climate Change Adaptation: Case-Studies from Aotearoa New Zealand and the Cook Islands(Frontiers Media SA, 2024-02-02) Mihaere, Shannon; Holman-Wharehoka, Māia-te-oho; Mataroa, Jovaan; Kiddle, Gabriel Luke; Pedersen Zari, Maibritt; Blaschke, Paul; Bloomfield, SibylNature-based solutions (NbS) offer significant potential for climate change adaptation and resilience. NbS strengthen biodiversity and ecosystems, and premise approaches that centre human wellbeing. But understandings and models of wellbeing differ and continue to evolve. This paper reviews wellbeing models and thinking from Aotearoa New Zealand, with focus on Te Ao Māori (the Māori world and worldview) as well as other Indigenous models of wellbeing from wider Te Moana-nui-a-Kiwa Oceania. We highlight how holistic understandings of human-ecology-climate connections are fundamental for the wellbeing of Indigenous peoples of Te Moana-nui-a-Kiwa Oceania and that they should underpin NbS approaches in the region. We profile case study experience from Aotearoa New Zealand and the Cook Islands emerging out of the Nature-based Urban design for Wellbeing and Adaptation in Oceania (NUWAO) research project, that aims to develop nature-based urban design solutions, rooted in Indigenous knowledges that support climate change adaptation and wellbeing. We show that there is great potential for nature-based urban adaptation agendas to be more effective if linked closely to Indigenous ecological knowledge and understandings of wellbeing.
- ItemCylindrical Piezoelectric PZT Transducers for Sensing and Actuation(MDPI AG, 2023-03-11) Meshkinzar, Ata; Al-Jumaily, Ahmed MPiezoelectric transducers have numerous applications in a wide range of sensing and actuation applications. Such a variety has resulted in continuous research into the design and development of these transducers, including but not limited to their geometry, material and configuration. Among these, cylindrical-shaped piezoelectric PZT transducers with superior features are suitable for various sensor or actuator applications. However, despite their strong potential, they have not been thoroughly investigated and fully established. The aim of this paper is to shed light on various cylindrical piezoelectric PZT transducers, their applications and design configurations. Based on the latest literature, different design configurations such as stepped-thickness cylindrical transducers and their potential application areas will be elaborated on to propose future research trends for introducing new configurations that meet the requirements for biomedical applications, the food industry, as well as other industrial fields.
- ItemImpact Assessment of Climate Change on Energy Performance and Thermal Load of Residential Buildings in New Zealand(Elsevier, 2023-07-17) Jalali, Z; Shamseldin, AY; Ghaffarianhoseini, AWhile it is evident that climate change will have an impact on the energy demand for heating and cooling in buildings, the exact extent of this impact is not yet fully understood. Quantification of future cooling and heating need in buildings provides a basis for taking appropriate measures for building climate change adaptation. The focus of this study is to examine how future climate change scenarios will impact the heating and cooling of residential buildings across different climatic regions in New Zealand. The future weather data under changing climate were generated for six climatic zones of New Zealand employing the statistical downscaling method. The study used various climate change scenarios, which represent concentration pathways (RCPs), to generate weather data. Specifically, the RCP8.5 and RCP4.5 scenarios were employed in the building performance simulations for different prototypes of residential buildings. The results showed there would be a significant change in the thermal performance of residential buildings, with a noticeable increase in cooling load and a decrease in heating load. These changes include a maximum thermal load change of 3 kWh/m2 in Auckland by 2090, 2.7 kWh/m2 in Hamilton, 8.3 kWh/m2 in Wellington, 4.2 kWh/m2 in Rotorua, 11 kWh/m2 in Christchurch, and 11.6 kWh/m2 in Queenstown. The warmer climatic zones are expected to change from a heating dominated to a cooling-dominated zone. The results indicated the importance of considering present and future climatic conditions in design and establishing a foundation for actions for the resilience of buildings to climate change.
- ItemImproving Urban Habitat Connectivity for Native Birds: Using Least-Cost Path Analyses to Design Urban Green Infrastructure Networks(MDPI AG, 2023-07-21) MacKinnon, M; Pedersen Zari, M; Brown, DKHabitat loss and fragmentation are primary threats to biodiversity in urban areas. Least-cost path analyses are commonly used in ecology to identify and protect wildlife corridors and stepping-stone habitats that minimise the difficulty and risk for species dispersing across human-modified landscapes. However, they are rarely considered or used in the design of urban green infrastructure networks, particularly those that include building-integrated vegetation, such as green walls and green roofs. This study uses Linkage Mapper, an ArcGIS toolbox, to identify the least-cost paths for four native keystone birds (kererū, tūī, korimako, and hihi) in Wellington, New Zealand, to design a network of green roof corridors that ease native bird dispersal. The results identified 27 least-cost paths across the central city that connect existing native forest habitats. Creating 0.7 km2 of green roof corridors along these least-cost paths reduced cost-weighted distances by 8.5–9.3% for the kererū, tūī, and korimako, but there was only a 4.3% reduction for the hihi (a small forest bird). In urban areas with little ground-level space for green infrastructure, this study demonstrates how least-cost path analyses can inform the design of building-integrated vegetation networks and quantify their impacts on corridor quality for target species in cities.
- ItemIntegrating Energy Retrofit with Seismic Upgrades to Future-Proof Built Heritage: Case Studies of Unreinforced Masonry Buildings in Aotearoa New Zealand(Elsevier BV, 2023-06) Besen, P; Boarin, PDeep energy retrofit can improve historic buildings’ indoor environmental quality and protect them from decay and obsolescence while reducing their energy use and related greenhouse gas emissions. Although this practice has been growing internationally, in Aotearoa New Zealand there are currently no policies or initiatives to encourage energy retrofit in historic buildings and no substantial examples of projects. Most retrofits currently focus on much-needed earthquake strengthening, due to high seismic risks and national policies which mandate all existing earthquake-prone buildings to be either structurally retrofitted or demolished over the next decades. As seismic upgrade projects are widespread, this study explores the potential of applying energy retrofit concurrently with seismic strengthening, with a focus on unreinforced masonry (URM) – the main type of earthquake-prone historic construction in the country. The research investigates three case studies of listed heritage URM buildings using Post-Occupancy Evaluation and simulation. Their current performance was investigated, and retrofit scenarios were analysed through energy and hygrothermal simulation, utilising the EnerPHit standard as a guide. The energy models demonstrated a potential reduction of up to 92% in heating demand when comparing the most comprehensive retrofit scenario with the baseline in the coldest climate. The potential energy savings from each intervention were balanced against their heritage impact, based on the standard EN16883:2017. The study provides a methodology for balancing several considerations in integrated retrofit to make historic buildings more resilient not only to seismic threats, but also to a changing climate, while keeping a respectful approach to heritage.
- ItemOptimizing Wireless Connectivity: A Deep Neural Network-Based Handover Approach for Hybrid LiFi and WiFi Networks(MDPI AG, 2024-03-22) Khan, Mohammad Usman Ali; Babar, Mohammad Inayatullah; Rehman, Saeed Ur; Komosny, Dan; Chong, Peter Han JooA Hybrid LiFi and WiFi network (HLWNet) integrates the rapid data transmission capabilities of Light Fidelity (LiFi) with the extensive connectivity provided by Wireless Fidelity (WiFi), resulting in significant benefits for wireless data transmissions in the designated area. However, the challenge of decision-making during the handover process in HLWNet is made more complex due to the specific characteristics of electromagnetic signals' line-of-sight transmission, resulting in a greater level of intricacy compared to previous heterogeneous networks. This research work addresses the problem of handover decisions in the Hybrid LiFi and WiFi networks and treats it as a binary classification problem. Consequently, it proposes a handover method based on a deep neural network (DNN). The comprehensive handover scheme incorporates two sets of neural networks (ANN and DNN) that utilize input factors such as channel quality and the mobility of users to enable informed decisions during handovers. Following training with labeled datasets, the neural-network-based handover approach achieves an accuracy rate exceeding 95%. A comparative analysis of the proposed scheme against the benchmark reveals that the proposed method considerably increases user throughput by approximately 18.58% to 38.5% while reducing the handover rate by approximately 55.21% to 67.15% compared to the benchmark artificial neural network (ANN); moreover, the proposed method demonstrates robustness in the face of variations in user mobility and channel conditions.
- ItemSurvey on Intrusion Detection Systems Based on Machine Learning Techniques for the Protection of Critical Infrastructure(MDPI AG, ) Pinto, Andrea; Herrera, Luis-Carlos; Donoso, Yezid; Gutierrez, Jairo AIndustrial control systems (ICSs), supervisory control and data acquisition (SCADA) systems, and distributed control systems (DCSs) are fundamental components of critical infrastructure (CI). CI supports the operation of transportation and health systems, electric and thermal plants, and water treatment facilities, among others. These infrastructures are not insulated anymore, and their connection to fourth industrial revolution technologies has expanded the attack surface. Thus, their protection has become a priority for national security. Cyber-attacks have become more sophisticated and criminals are able to surpass conventional security systems; therefore, attack detection has become a challenging area. Defensive technologies such as intrusion detection systems (IDSs) are a fundamental part of security systems to protect CI. IDSs have incorporated machine learning (ML) techniques that can deal with broader kinds of threats. Nevertheless, the detection of zero-day attacks and having technological resources to implement purposed solutions in the real world are concerns for CI operators. This survey aims to provide a compilation of the state of the art of IDSs that have used ML algorithms to protect CI. It also analyzes the security dataset used to train ML models. Finally, it presents some of the most relevant pieces of research on these topics that have been developed in the last five years.