The embodied hybrid space : designing social and digital interventions to facilitate connected learning in coworking spaces

This thesis developed and evaluated strategies for social and ubiquitous computing designs that can enhance connected learning and networking opportunities for users in coworking spaces. Based on a social and a technical design intervention deployed at the State Library of Queensland, the research findings illustrate the potential of combining social, spatial and digital affordances in order to nourish peer-to-peer learning, creativity, inspiration, and innovation. The study proposes a hybrid notion of placemaking as a new way of thinking about the design of coworking and interactive learning spaces.

Dynamic simulation of a light-weight, low-drag, hybrid-electric sports coupe

The University of Queensland UltraCommuter concept is an ultra- light, low-drag, hybrid-electric sports coupe designed to minimize energy consumption and environmental impact while enhancing the performance, styling, features and convenience that motorists enjoy. This paper presents a detailed simulation study of the vehicle's performance and fuel economy using ADVISOR, including a detailed description of the component models and parameters assumed. Results from the study include predictions of a 0-100 kph acceleration time of ≺9s, and top speed of 170 kph, an electrical energy consumption of ≺67 Wh/km in ZEV mode and a petrol-equivalent fuel consumption of ≺2.5 L/100 km in charge-sustaining HEV mode. Overall, the results of the ADVISOR modelling confirm the UltraCommuter's potential to achieve high performance with high efficiency, and the authors look forward to a confirmation of these estimates following completion of the vehicle.

A parametric analysis technique for design of fuel cell and hybrid-electric vehicles

This paper presents a new simplified parametric analysis technique for the design of fuel cell and hybrid-electric vehicles. The technique utilizes a comprehensive set of ∼30 parameters to fully characterize the vehicle platform, powertrain components, vehicle performance requirements and driving conditions. It is best applied to the sizing of powertrain components and prediction of energy consumption in a vehicle. This new parametric technique makes a good complement to existing vehicle simulation software packages and therefore represents a potentially valuable tool for the hybrid vehicle designer.

Brake system performance requirements of a lightweight electric/hybrid rear wheel drive vehicle

Investigates the braking performance requirements of the UltraCommuter, a lightweight series hybrid electric vehicle currently under development at the University of Queensland. With a predicted vehicle mass of 600 kg and two in-wheel motors each capable of 500 Nm of peak torque, decelerations up to 0.46 g are theoretically possible using purely regenerative braking. With 99% of braking demands less than 0.35 g, essentially all braking can be regenerative. The wheel motors have sufficient peak torque capability to lock the rear wheels in combination with front axle braking, eliminating the need for friction braking at the rear. Emergency braking levels approaching 1 g are achieved by supplementation with front disk brakes. This paper presents equations describing the peak front and rear axle braking forces which occur under straight line braking, including gradients. Conventionally, to guarantee stability, mechanical front/rear proportioning of braking effort ensures that the front axle locks first. In this application, all braking is initially regenerative at the rear, and an adaptive ''by-wire'' proportioning system presented ensures this stability requirement is still satisfied. Front wheel drive and all wheel drive systems are also discussed. Finally, peak and continuous performance measures, not commonly provided for friction brakes, are derived for the UltraCommuter's motor capability and range of operation.

Shadows : Re-visualising the shark. Developing a framework for hybrid photography in the illumination of nonhuman animal and ecological perspectives at Heron Island and Gladstone, Great Barrier Reef

This study investigated how and to what degree “hybrid photography”—the simultaneous use of indexical and fictional properties and strategies— innovates the representation of animals within animalcentric, ecocentric frameworks. Design theory structured this project’s Practice-led, Visual research methodology framework. Grounded theory processes articulated emerging categories of hybrid photography through systematically and comparatively treating animal photography works for reflexive analysis. Design theory then applied and clarified categories, developing practice that re-visualised shark perspectives as new ecological discourse. Shadows, a creative practice installation, realised a full-scale photographic investigation into shark and marine animal realities of a specific environment—Heron Island and Gladstone, Great Barrier Reef—facing ecological crisis from dredging and development at Gladstone Harbour. Works rendered and explored hybrid photography’s capacity for illuminating nonhuman animals, in particular, sharks, and comprise 65% of this project’s weighting. This exegetical paper offers a definition, strategies and evaluation of hybrid photography in unsettling animal perspectives as effective ecological discourse, and comprises 35%.

A Hybrid Queue-based Bayesian Network Framework for passenger facilitation modelling

This paper presents a novel framework for the modelling of passenger facilitation in a complex environment. The research is motivated by the challenges in the airport complex system, where there are multiple stakeholders, differing operational objectives and complex interactions and interdependencies between different parts of the airport system. Traditional methods for airport terminal modelling do not explicitly address the need for understanding causal relationships in a dynamic environment. Additionally, existing Bayesian Network (BN) models, which provide a means for capturing causal relationships, only present a static snapshot of a system. A method to integrate a BN complex systems model with stochastic queuing theory is developed based on the properties of the Poisson and Exponential distributions. The resultant Hybrid Queue-based Bayesian Network (HQBN) framework enables the simulation of arbitrary factors, their relationships, and their effects on passenger flow and vice versa. A case study implementation of the framework is demonstrated on the inbound passenger facilitation process at Brisbane International Airport. The predicted outputs of the model, in terms of cumulative passenger flow at intermediary and end points in the inbound process, are found to have an $R^2$ goodness of fit of 0.9994 and 0.9982 respectively over a 10 hour test period. The utility of the framework is demonstrated on a number of usage scenarios including real time monitoring and `what-if' analysis. This framework provides the ability to analyse and simulate a dynamic complex system, and can be applied to other socio-technical systems such as hospitals.

Hybrid antibacterial fabrics with extremely high aspect ratio Ag/AgTCNQ nanowires

This study reports the synthesis of extremely high aspect ratios (>3000) organic semiconductor nanowires of Ag–tetracyanoquinodimethane (AgTCNQ) on the surface of a flexible Ag fabric for the first time. These one-dimensional (1D) hybrid Ag/AgTCNQ nanostructures are attained by a facile, solution-based spontaneous reaction involving immersion of Ag fabrics in an acetonitrile solution of TCNQ. Further, it is discovered that these AgTCNQ nanowires show outstanding antibacterial performance against both Gram negative and Gram positive bacteria, which outperforms that of pristine Ag. The outcomes of this study also reflect upon a fundamentally important aspect that the antimicrobial performance of Ag-based nanomaterials may not necessarily be solely due to the amount of Ag+ ions leached from these nanomaterials, but that the nanomaterial itself may also play a direct role in the antimicrobial action. Notably, the applications of metal-organic semiconducting charge transfer complexes of metal-7,7,8,8-tetracyanoquinodimethane (TCNQ) have been predominantly restricted to electronic applications, except from our recent reports on their (photo)catalytic potential and the current case on antimicrobial prospects. This report on growth of these metal-TCNQ complexes on a fabric not only widens the window of these interesting materials for new biological applications, it also opens the possibilities for developing large-area flexible electronic devices by growing a range of metal-organic semiconducting materials directly on a fabric surface.

On parallel hybrid-electric propulsion system for unmanned aerial vehicles

This paper presents a review of existing and current developments and the analysis of Hybrid-Electric Propulsion Systems (HEPS) for small fixed-wing Unmanned Aerial Vehicles (UAVs). Efficient energy utilisation on an UAV is essential to its functioning, often to achieve the operational goals of range, endurance and other specific mission requirements. Due to the limitations of the space available and the mass budget on the UAV, it is often a delicate balance between the onboard energy available (i.e. fuel) and achieving the operational goals. One technology with potential in this area is with the use of HEPS. In this paper, information on the state-of-art technology in this field of research is provided. A description and simulation of a parallel HEPS for a small fixed-wing UAV by incorporating an Ideal Operating Line (IOL) control strategy is described. Simulation models of the components in a HEPS were designed in the MATLAB Simulink environment. An IOL analysis of an UAV piston engine was used to determine the most efficient points of operation for this engine. The results show that an UAV equipped with this HEPS configuration is capable of achieving a fuel saving of 6.5%, compared to the engine-only configuration.

Urban acupuncture: Hybrid social and technological practices for hyperlocal placemaking

This paper considers an emerging planning practice that uses networked connections to interact with urban places and re-create enlivened cities. The paper presents “urban acupuncture” as a new planning approach that broadens communication and strategically targets interventions across the city. Defined as an approach, which, through the use of digital social networks and interactions, involves citizens and planners in place activations in order to stimulate and reinvigorate place, thus creating meaningful relationships between citizens and their urban settings. This paper uses the UR[BNE] Brisbane Festival 2012 as a qualitative case study of urban acupuncture, best defined as a hyper-localized healing treatment through place activation to enliven and recreate cities. It examines the challenges faced and opportunities embraced by a network of urban professionals. Their aim was to activate the underused urban spaces of central Brisbane through the festival's activities and events. The findings identify the key elements required to design public spaces using socially and technologically networked interactions.

Tensile properties of graphene nanotube hybrid structures : a molecular dynamics study

Graphene has been reported with record-breaking properties which have opened up huge potential applications. A considerable research has been devoted to manipulate or modify the properties of graphene to target a more smart nanoscale device. Graphene and carbon nanotube hybrid structure (GNHS) is one of the promising graphene derivates, while their mechanical properties have been rarely discussed in literature. Therefore, such a studied is conducted in this paper basing on the large-scale molecular dynamics simulation. The target GNHS is constructed by considering two separate graphene layers that being connected by single-wall carbon nanotubes (SWCNTs) according to the experimental observations. It is found that the GNHSs exhibit a much lower yield strength, Young’s modulus, and earlier yielding comparing with a bilayer graphene sheet. Fracture of studied GNHSs is found to fracture located at the connecting region between carbon nanotubes (CNTs) and graphene. After failure, monatomic chains are normally observed at the front of the failure region, and the two graphene layers at the failure region without connecting CNTs will adhere to each other, generating a bilayer graphene sheet scheme (with a layer distance about 3.4 Å). This study will enrich the current understanding of the mechanical performance of GNHS, which will guide the design of GNHS and shed lights on its various applications.

Pathway to sustainability : the Lady Elliot Island hybrid solar power station

The Lady Elliot Island eco-resort, on the Great Barrier Reef, operates with a strong sustainability ethic, and has broken away from its reliance on diesel generators, an initiative which has ongoing and substantial economic benefit. The first step was an energy audit that led to a 35% reduction in energy usage, to an average of 575 kWh per day. The eco-resort then commissioned a hybrid solar power station, in 2008, with energy storage in battery banks. Solar power is currently (2013) providing about 160 kWh of energy per day, and the eco-resort’s diesel fuel usage has decreased from 550 to 100 litres per day, enabling the power station to pay for itself in 3 years. The eco-resort plans to complete its transition to renewable energy by 2015, by installing additional solar panels, and a 10-15 kW wind turbine. This paper starts by discussing why the eco-resort chose a hybrid solar power station to transition to renewable energy, and the barriers to change. It then describes the power station, upgrades through to 2013, the power control system, the problems that were solved to realise the potential of a facility operating in a harsh and remote environment, and its performance. The paper concludes by outlining other eco-resort sustainability practices, including education and knowledge-sharing initiatives, and monitoring the island’s environmental and ecological condition.

Hybrid model for motorway travel time estimation considering increased detector spacing

Travel time estimation and prediction on motorways has long been a topic of research. Prediction modeling generally assumes that the estimation is perfect. No matter how good is the prediction modeling- the errors in estimation can significantly deteriorate the accuracy and reliability of the prediction. Models have been proposed to estimate travel time from loop detector data. Generally, detectors are closely spaced (say 500m) and travel time can be estimated accurately. However, detectors are not always perfect, and even during normal running conditions few detectors malfunction, resulting in increase in the spacing between the functional detectors. Under such conditions, error in the travel time estimation is significantly large and generally unacceptable. This research evaluates the in-practice travel time estimation model during different traffic conditions. It is observed that the existing models fail to accurately estimate travel time during large detector spacing and congestion shoulder periods. Addressing this issue, an innovative Hybrid model that only considers loop data for travel time estimation is proposed. The model is tested using simulation and is validated with real Bluetooth data from Pacific Motorway Brisbane. Results indicate that during non free flow conditions and larger detector spacing Hybrid model provides significant improvement in the accuracy of travel time estimation.

A hybrid genetic algorithm for the energy-efficient virtual machine placement problem in data centers

Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation technology. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches consider the energy consumption by physical machines only, but do not consider the energy consumption in communication network, in a data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement. In our preliminary research, we have proposed a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both physical machines and the communication network in a data center. Aiming at improving the performance and efficiency of the genetic algorithm, this paper presents a hybrid genetic algorithm for the energy-efficient virtual machine placement problem. Experimental results show that the hybrid genetic algorithm significantly outperforms the original genetic algorithm, and that the hybrid genetic algorithm is scalable.

Development of an integrated GIS and land use planning course : impacts of hybrid instructional methods

This study reports an action research undertaken at Queensland University of Technology. It evaluates the effectiveness of the integration of GIS within the substantive domains of an existing land use planning course in 2011. Using student performance, learning experience survey, and questionnaire survey data, it also evaluates the impacts of incorporating hybrid instructional methods (e.g., in-class and online instructional videos) in 2012 and 2013. Results show that: students (re)iterated the importance of GIS in the course justifying the integration; the hybrid methods significantly increased student performance; and unlike replacement, the videos are more suitable as a complement to in-class activity.

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS–SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS–SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65–85% for hybrid PLS–SVM model respectively. Also it was found that the hybrid PLS–SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS–SVM model.