RMIT University
  • Melbourne, Australia
Recent publications
Spatially separating electrons of different spins and efficiently generating spin currents are crucial steps towards building practical spintronics devices. Transverse magnetic focusing is a potential technique to accomplish both those tasks. In a material where there is significant Rashba spin–orbit interaction, electrons of different spins will traverse different paths in the presence of an external magnetic field. Experiments have demonstrated the viability of this technique by measuring conductance spectra that indicate the separation of spin-up and spin-down electrons. However the effect that the geometry of the leads has on these measurements is not well understood. By simulating an InGaAs-based transverse magnetic focusing device, we show that the resolution of features in the conductance spectra is affected by the shape, separation and width of the leads. Furthermore, the number of subbands occupied by the electrons in the leads affects the ratio between the amplitudes of the spin-split peaks in the spectra. We simulated devices with random onsite potentials and observed that transverse magnetic focusing devices are sensitive to disorder. Ultimately we show that careful choice and characterisation of device geometry are crucial for correctly interpreting the results of transverse magnetic focusing experiments.
Background The incidence of anterior cruciate ligament (ACL) injuries represents a large burden of knee injuries in both the general and sporting populations, often requiring surgical intervention. Although there is much research on complete ACL tears including outcomes and indications for surgery, little is known about the short- and long-term outcomes of non-operative, physiotherapy led intervention in partial ACL tears. The primary aim of this study was to evaluate studies looking at the effectiveness of physiotherapy led interventions in improving pain and function in young and middle-aged adults with partial ACL tears. Additionally, the secondary aim was to evaluate the completeness of exercise prescription in randomised trials for physiotherapy led interventions in the management in partial ACL tears. Methods A comprehensive and systematic search was performed on six databases ( Medline, CINAHL, EMBASE, PEDro, Scopus , SPORTDiscus and Cochrane). The search strategy consisted of two main concepts: (i) partial ACL tears, and (ii) non-operative management. 7,587 papers were identified by the search. After screening of eligible articles by two independent reviewers, 2 randomised studies were included for analysis. The same two reviewers assessed the completeness of reporting using the Toigio and Boutellier mechanobiological exercise descriptions and Template for Intervention Description and Replication (TIDieR) checklist. Group mean standard deviations (SD) for the main outcomes was extracted from both papers for analysis. Prospero Registration Number: CRD42020179892. Results The search strategy identified two studies; one looking at Tai Chi and the other Pilates. The analysis indicated that Tai Chi was significant in reducing pain scores and both Tai Chi and Pilates were found to increase Muscle Peak Torque Strength (MPTS) at 180 degrees. Furthermore, Tai Chi showed a significant increase in proprioception. Conclusions Physiotherapy led interventions such as Pilates, and Tai Chi may improve pain, proprioception and strength in young and middle-aged adults with partial ACL tears, however full scale, high-quality randomised studies are required with long term outcomes recorded.
Galvanic replacement reactions (GRRs) are spontaneous electroless reactions that proceed due to the favorable difference in the electrochemical potentials of the two chemical species participating in the reaction. To date, the reacting species in GRRs almost exclusively have been limited to a metal cation. In this work, for the first time, we provide a rare example of a redox mediated GRR where an anion in the original template is replaced by another anion. Extensive use of spectroscopies, microscopies and electrochemical techniques reveals that the exposure of metal-organic semiconductor of CuTCNQ (TCNQ = 7,7,8,8-tetracyanoquinodimethane) to neutral TCNQF4⁰ (TCNQF4 = 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane) dissolved in acetonitrile results in the spontaneous oxidation of the TCNQ¹– anion in CuTCNQ along with the concomitant reduction of the TCNQF4⁰ to TCNQF4¹⁻ anion. In this case, the exposure of phase I CuTCNQ grown on a Cu foil (Cu(foil)│CuTCNQ(solid)) to TCNQF4⁰ in acetonitrile results in a hybrid material containing Cu(foil)│CuTCNQx(TCNQF4)y(solid), where x + y = 1. The favorable difference in the redox potentials of the TCNQF41−/0 and TCNQ1−/0 processes in acetonitrile provides a strong driving force to facilitate the GRR between TCNQ⁻ and TCNQF4. The resulting hybrids show superior redox catalytic behavior in the reduction of [Fe(CN)6]³⁻ to [Fe(CN)6]⁴⁻ by S2O3²⁻ in comparison to CuTCNQ and CuTCNQF4 where the TCNQF4 content in the hybrid plays an important role in enhancing the catalytic rate. Mechanistic details related to the role of the Cu(foil)|CuTCNQ(F4) interface, and differences in the solubility of CuTCNQ and CuTCNQF4 in acetonitrile are presented.
This research demonstrates the development of a tectonic approach to architecture through an experimental, iterative methodology. It is a synthetic approach where tectonics and form are engaged in a non-hierarchical negotiation. An architecture where expression, ornament, structure and their spatial consequences are intertwined and inseparable. The design research posited here has been conducted over the past nine years through the sustained development of a series of architectural tectonic experiments called the agentBody Prototypes. These prototypes reify an ambition to compress surface, structure and ornament into a single irreducible assemblage. The agentBody Prototypes are a series of fourteen proto-architectural projects, or fragments, with lead design by Roland Snooks, and research, development and fabrication by the RMIT Architecture | Tectonic Formation Lab. The paper describes the wider context of this work and includes a brief chronological overview of this trajectory, followed by a series of observations drawn from critical reflection. This paper attempts to draw out the architectural design implications that have emerged through a specific interaction of algorithmic design, and robotic fabrication.
Digital transformation is among the most pervasive forces disrupting business models across every industrial sector. While prior research has explored the digital transformation of business models, the effect on the value proposition as a key element of the business model has received only limited attention. Drawing on an extensive single case study in the regional media industry involving 59 interviews with one service provider and its customers, this study explores the digital transformation of the provider’s value proposition and how this process was collectively enacted by the provider and its customers. This study develops an empirically grounded framework illustrating the key value proposition transformation drivers, provider and customer sense-making practices, and value element reshaping implications. Overall, this study advances contemporary digital transformation and value proposition research by demonstrating how the process of digital transformation changes the nature and content of the value proposition and how managers can facilitate this process.
Service employees (SEs) are instrumental in shaping customer brand perceptions. However, to deliver favorable brand experiences, SEs may not always abide by socially constructed norms and guidelines—called institutions—that coordinate service interactions. We explore how SEs navigate internal and external institutions, and the potential implications for brand meaning outcomes. Drawing on qualitative interviews with SEs from five local and international bank brands in Vietnam, and archival data, we discover 10 practices that function as institutional work and identify potential implications for brand meaning outcomes of authenticity, relevance, and legitimacy. Using institutional theory as an enabling lens, we demonstrate how these practices either disrupt or maintain internal and external institutions with dark-side or light-side consequences for brands. Specifically, our findings uncover how dark-side practices may place brand meaning outcomes at risk and how light-side practices, even those that disrupt institutions, can potentially enhance brand meaning, providing significant theoretical and managerial implications.
This work investigates the design and adaptive control of a miniature robot with multi-modal locomotion which has the ability to climb inside train bogies for inspection purposes. We propose and analyse a kinematically redundant mechanism with six 2-DOF couple joints. The robot can squeeze through narrow spaces and also climb on surfaces with transitions, irregularities and discontinuities. The unique design allows desirable self-motion close to obstacles but imposes strict requirements in motion control and precise path following. This paper applies such redundancy and self-motion by constructing an adaptive controller with time-varying safety constraints for all twelve joints of the mechanism. The control strategy relies on the time-varying Barrier Lyapunov Function to bound the trajectory error. It also deploys an adaptive radial basis function neural network to estimate the system parameters of the robot. Various simulation experiments show that the proposed controller satisfies all safety and physical joint constraints. It also minimises trajectory tracking error irrespective of initial conditions, disturbances, and unmodelled dynamic effects. Finally, we compare the tracking results with those obtained by a Feedback Linearisation controller and a Quadratic Lyapunov Function-based controller. Results demonstrate enhanced locomotion and trajectory tracking for collision-free manoeuvring in tight spaces.
Topology optimization techniques are typically performed on a design domain discretized with finite element meshes to generate efficient and innovative structural designs. The optimized structural topologies usually exhibit zig-zag boundaries formed from straight element edges. Existing techniques to obtain smooth structural topologies are limited. Most methods are computationally expensive, as they are performed iteratively with topology optimization. Other methods, such as post-processing methods, are applied after topology optimization, but they cannot guarantee to obtain equivalent structural designs, as the volume and geometric features may be changed. This study presents a new method that uses pre-built lookup tables to transform the shape of boundary elements obtained from topology optimization to create smoothed structural topologies. The new method is developed based on the combination of the bi-directional evolutionary structural optimization (BESO) technique and marching geometries to determine structural topologies and lookup tables, respectively. An additional step is used to ensure that the generated result meets a target volume. A variety of 2D and 3D examples are presented to demonstrate the effectiveness of the new method. This research shows that the new method is highly efficient, as it can be directly added to the last step of topology optimization with a low computational cost, and the volume and geometric features can be preserved in smoothed topologies. Finite element models are also created for original and smoothed structural topologies to show that the structural stiffness can be significantly enhanced after smoothing.
The increasing plastic wastes have become a serious concern to the environment. Plastic wastes could be efficiently converted to fuel via pyrolysis. In the process of plastic pyrolysis, the key process parameters will affect the production of the final product, the distribution of pyrolysis gas, pyrolysis oil, and pyrolytic wax. Based on previous experiments, the discrete element model and computational fluid model were established in this study to compare the influence of heat carrier loading and furnace rolling speed on the pyrolysis of waste plastics in a rotary furnace. The simulation results indicate that when the granular heat carriers were loaded, as the heat carrier loading increased, the translational velocity and angular velocity of the waste plastic particles increased. Compared with the heat carrier-free case, the heating rate of the waste plastic particles is greatly increased when the heat carrier was loaded. The increase in the heat carrier loading also increased the residence time of the pyrolysis volatiles in the rotary furnace. With the acceleration of the rolling speed of the furnace, the translational velocity and angular velocity of plastic particles all increased, but the improvement of heat transfer efficiency is not obvious. This is consistent with the insignificant change in the pyrolysis product in the previous experimental results. The CFD-DEM simulation (excluded the pyrolysis reaction model) provided detailed information on particle movement, heat transfer and volatile residence time which could be used as an effective tool to interpret the experimental observation and optimize the process parameters.
Interest is increasing among researchers and industry regarding packaging's potential to reduce household food waste (HFW). Researchers have recommended packaging solutions and the food/beverage–packaging industry have developed packaging solutions; are the recommendations and developments related? This review paper connects academic recommendations to industry practice by identifying and comparing packaging solutions from industry press-release articles to the HFW drivers and packaging solutions identified in primary consumer empirical studies. The review covers a 16-year timespan from 2006–2021, globally, to collect data on packaging functions/formats, materials, and food groups. The analysis shows that industry developments differ from research recommendations. While most of the packaging functions/formats suggested in the empirical literature have precedence for commercial availability, many packaging solutions developed by industry are not acknowledged in the empirical literature. Combining the unique contributions of research within and external to industry creates a fuller picture of HFW, supporting more effective implementation of packaging solutions to help reduce it. There is an opportunity for industry to implement a greater number of packaging formats aligned to the most frequently reported HFW drivers. Enabling greater collaboration between the research community and industry by bringing together this literature in a critical review is a major contribution of this paper.
Thermal management system generally ensures the safe operating conditions and heat resilience of battery packs in hybrid electric vehicles (HEVs). The current study raised a novel approach to reduce fire risks related to HEVs through a novel battery thermal management system powered by low-grade combustion waste heat running on steam ejectors for the first time. In this paper, an ejector operating at a low temperature under 100 °C for HEV's battery thermal management system is proposed and investigated. An in-house wet-steam model considering the condensation effect has been developed to characterise the ejector's internal flow structure and further analyse its feasibility as a thermal management system. The results show that the model considering the condensation process is more feasible in evaluating the performance of the steam ejector than the dry gas assumption. To improve the performance of the proposed ejector battery thermal management system, the effect of superheating of primary steam has been investigated. The results showed that an optimum point exists with 11 K superheating between improvement of entrainment ratio, the system's coefficient of performance and the power efficiency for the current case. The entrainment ratio at that point reaches around 0.45, while the coefficient of performance reaches 0.225.
Groundwater pollution poses a serious threat to the main source of clean water globally. Nanoparticles have the potential for remediation of polluted aquifers; however, environmental safety concerns associated with in situ deployments of such technology include potential detrimental effects on microorganisms in terms of toxicity and functional disruptions. In this work, we evaluated a new and ecofriendly approach using carbon dots (CDs) as Fenton-like catalysts to catalyse the degradation of dye-containing groundwater samples. This investigation aimed at evaluating the efficacy of a novel remediation technology in terms of dye degradation and toxicity reduction while assessing its impacts on aquatic microorganisms. Uncontaminated Australian groundwater samples were spiked with methylene blue and incubated in the dark, at 18 °C, under slow agitation, using CDs at 0.5 mg mL⁻¹ and H2O2 at 73.5 mM for 25 h. The dye degradation rate was determined as well as the toxicity of the treated solutions using the Microtox® bioassay. Further, to determine the changes in the groundwater microbial community, 16 S rRNA sequencing was used and evenness and diversity indices were analysed using Pielou's evenness and Simpson index, respectively. This study revealed that dye-containing groundwater were effectively treated by CDs showing a degradation rate of 78–82% and a significant 4-fold reduction in the toxicity. Characterisation of the groundwater microbiota revealed a predominance of at least 60% Proteobacteria phylum in all samples where diversity and evenness were maintained throughout the remediation process. The results showed that CDs could be an efficient approach to treat polluted groundwater and potentially have minimum impact on the environmental microbiome.
Cyberbullying behavior (CB) on social media is complex because its perpetrators exhibit varied demographic characteristics and personalities. Prior studies have applied Big Five (Big5) and Dark Tetrads (Dark4) personality traits (PTs) along with demographic attributes, using symmetrical modelling, but revealed mixed and inconsistent results. This paper applies an asymmetric modelling approach using complexity and configurational theories to develop configurations of PTs and demography to predict CB. The online survey data have been analyzed using fuzzy set qualitative comparative analysis (fsQCA) technique. Regarding Big5 PTs, our findings reveal that, for instance, people scoring high in conscientiousness, neuroticism, openness and low in agreeableness undertake cyberbullying. For Dark4 PTs, the combination of either psychopathy and sadism or Machiavellianism and psychopathy leads to cyberbullying. As for demographic attributers, educated young married people, irrespective of gender, are likely to commit cyberbullying. Our all-inclusive model reveals that social media bullies, regardless of their gender, marital status, and social media experience, are young, educated, neurotic, conscientious, psychopathic, and sadistic with high Machiavellianism and low agreeableness. Further, we suggest configurations to reduce cyberbullying. The findings are discussed with implications for theory and practice.
Optimal hybrid renewable microgrids Sustainable development Techno-econo-socio-environmental model Water-energy-carbon nexus A B S T R A C T Combined technological, economic, sociological, and environmental (TESE) models can play a unique role in leveraging renewable energies and supporting sustainable development. Yet, a multi-aspect TESE model has never been used in Korea to adapt to climate change through sustainable energy policies. This comprehensive TESE study addresses several sustainability challenges in the Korean energy sector. First, demand electricity is predicted using four deep and stacked neural networks to develop a smart demand-based model. Second, optimal hybrid renewable microgrids (HRMGs) are simulated at 17 sites to evaluate five renewable energy sources in three scenarios. Third, hybrid assessment results are clustered using a K-means algorithm to generate hybrid renewable energy maps for South Korea. Fourth, the TESE model is analyzed with more than 13 variables using a cascading multi-criteria decision-making approach to prorate a budget and develop a prioritized roadmap for a sustainable 2030 in Korea. Fifth, a stochastic linear mathematical model is developed to propose a sustainable energy policy that considers the water-energy-carbon nexus. The results show that a convolutional neural network can efficiently predict sequential demand electricity (R 2 = 98.79%), with respective biogas, solar, hy-drostatic, wind, and hydrokinetic energy fractions of 45.7%, 34.5%, 14%, 5.78%, and 0.01% under optimal conditions in Korea. The present free market-based policies are recommended to be revised in favor of domestic production of renewable energy facilities if new jobs can be created for more than $7500 each, and the carbon penalty cost can be kept below $83/ton CO 2-eq in Korea.
Shell structures are widely used in architectural design and civil engineering. However, it remains challenging to simultaneously optimize their shape, thickness, and topology under various design constraints and construction requirements. This work presents a method for the shape–thickness–topology coupled optimization of shell structures. In this method, the shape of shells is described by the non-uniform rational B-splines surface. Both shell elements and brick elements are used to discretize the design domain, so that the effect of shell thickness can be accounted for during the form-finding process. The structural self-weight is taken into consideration due to its practical importance. The minimum thickness is constrained to improve the constructability of the obtained designs. Several numerical examples are used to demonstrate the effectiveness of the proposed method. The results show that this method is capable of designing structurally efficient and aesthetically pleasing shells. This work holds potential applications in architectural design.
Labour and production costs are considered major production challenges for strawberry (Fragaria sp.) farmers, due to the reliance on manual harvesting methods. Automation has been proposed as a desirable solution, in particular robotic-driven harvesting with in-built decision making for determination of fruit ripeness and early-prediction of harvest timing and conformity to industry quality parameters (fruit weight and length). To support the development of these automated processes, the work presented herein explored the capacity to utilise automated image analysis for the prediction of strawberry quality measures. This involved the hydroponic growth of strawberry plants under controlled conditions and the daily collection of photographs of individual flowers and fruit. Machine learning (ML)-driven image colour extraction from the collected 1685 strawberry images utilised object detection to identify flowers and fruit within images, followed by cropping and counting of remaining image pixels, which were assigned based on pixel RGB to one of 10 pre-defined groups: achromatic, blue, cyan, green, orange, pink, purple, red, white, and yellow. These colour measures were utilised as inputs for general regression with 10-fold cross-validation to generate 3 models: for the prediction of current-state fruit developmental stage (R² = 0.9071), current-state fruit length (R² = 0.8565), and days remaining until harvest (R² = 0.8694). Additionally, current-state fruit development stage and current-state length were utilised as inputs for general regression with 10-fold cross-validation to develop predictive models for the endpoint (harvest) key quality measures: fruit harvest-length (R² = 0.8817) and fruit harvest-weight (R² = 0.7252). Noting that days to harvest could be accurately predicted up to 15 days prior to harvest, and the harvest quality measures could be accurately predicted up to 22 days prior to harvest, the models presented herein may be utilised to increase automation and thereby improve efficiency in the scheduling of harvesting and quality control of strawberry farming.
A deposition strategy is demonstrated for fabricating perovskite solar cells (PSCs) under ambient laboratory conditions. A nitrogen-flow treatment followed by a short (3 min), low-temperature (135 °C) anneal enables control of the nucleation and crystal growth of slot-die coated perovskite layers without the need for an anti-solvent for devices. PSCs using either glass or poly(ethylene terephthalate) (PET) substrates were prepared with an n-i-p device architecture and comprised a perovskite layer containing formamidinium and cesium cations, with or without the additive of 2-(2,3,4,5,6-pentafluorophenyl)-ethylammonium iodide (FEAI). The perovskite layer in glass-based devices was slot-die coated onto a stationary substrate with a moving coating head, whereas the deposition of the perovskite layer in PET-based devices was achieved by slot-die coating onto a moving (roll-to-roll, R2R) web using a stationary coating head. The processing conditions enabled the fabrication of PSCs displaying excellent temperature and humidity tolerance (up to 50% relative humidity). Devices fabricated on the glass substrates had power-conversion efficiencies (PCEs) of more than 18%, with PCEs of more than 15% measured for cells on the PET film substrates. The use of the FEAI additive improved the environmental stability of encapsulated glass-based PSC devices, which retained up to 70% of their initial PCE after 1000 h at maximum power point under 1-sun illumination in air. The scalability of the fabrication techniques used in this work, along with the low annealing temperature and ability to process in an ambient laboratory environment, makes this a promising approach for translation to industrial-scale R2R production processes.
Unmanned aerial vehicle (UAV) wind measurement is a newly developed wind measurement method that can improve the efficiency of conventional wind measurements. A method for measuring the wake characteristics of wind turbines based on UAV is proposed. The research results show that when the downstream distance was 1D, the wake wind speed and turbulence intensity at the hub height are about 60–70% and 130–150% of the incoming flow, respectively. When the downstream distance was 10D, the wake wind speed and turbulence intensity at the hub height gradually recovered to about 90% and 110% of the incoming flow, respectively, and the wake width in the area was about 2.5 times the diameter of the turbine. The wind speed measured by UAV was in good agreement with that predicted by the AV model and the 3DJG model, indicating that the Gaussian-shape curve could better describe the wake wind speed distribution. The UAV anemometry system provides a new research approach for wind turbine wake wind field measurement and the assessment of in-service wind farm resources. Moreover, the results from this study provide a reference for the determination of wind turbine wake, generation power prediction, and structure design.
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20,129 members
James Chapman
  • School of Science
Ben Cooke
  • Centre for Urban Research
Mohammad Al Kobaisi
  • School of Applied Sciences
Mehran Ghasemlou
  • School of Science
Ascelin Gordon
  • School of Global, Urban and Social Studies
3001, Melbourne, Australia
Head of institution
Martin Bean CBE
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