Nanoscale phase domain structure and associated device performance of organic solar cells based on a diketopyrrolopyrrole polymer

We investigate the blend morphology and performance of bulk heterojunction organic photovoltaic devices comprising the donor polymer, pDPP-TNT (poly{3,6-dithiophene-2-yl-2,5-di(2-octyldodecyl)-pyrrolo[3,4-c]pyrrole-1, 4-dione-alt-naphthalene}) and the fullerene acceptor, [70]PCBM ([6,6]-phenyl C71-butyric acid methyl ester). The blend morphology is heavily dependent upon the solvent system used in the fabrication of thin films. Thin films spin-coated from chloroform possess a cobblestone-like morphology, consisting of thick, round-shaped [70]PCBM-rich mounds separated by thin polymer-rich valleys. The size of the [70]PCBM domains is found to depend on the overall film thickness. Thin films spin-coated from a chloroform:dichlorobenzene mixed solvent system are smooth and consist of a network of pDPP-TNT nanofibers embedded in a [70]PCBM-rich matrix. Rinsing the films in hexane selectively removes [70]PCBM and allows for analysis of domain size and purity. It also provides a means for investigating exciton dissociation efficiency through relative photoluminescence yield measurements. Devices fabricated from chloroform solutions show much poorer performance than the devices fabricated from the mixed solvent system; this disparity in performance is seen to be more pronounced with increasing film thickness. The primary cause for the improved performance of devices fabricated from mixed solvents is attributed to the greater donor-acceptor interfacial area and resulting greater capacity for charge carrier generation.

Exploring the properties of macroscopic fundamental diagram : analysing Brisbane urban network

This thesis explored traffic characteristics at the aggregate level for area-wide traffic monitoring of large urban area. It focused on three aspects: understanding a macroscopic network performance under real-time traffic information provision, measuring traffic performance of a signalised arterial network using available data sets, and discussing network zoning for monitoring purposes in the case of Brisbane, Australia. This work presented the use of probe vehicle data for estimating traffic state variables, and illustrated dynamic features of regional traffic performance of Brisbane. The results confirmed the viability and effectiveness of area-wide traffic monitoring.

Antisocial behaviour orders : Unanticipated directions in social network site development

Abstract: Social network technologies, as we know them today have become a popular feature of everyday life for many people. As their name suggests, their underlying premise is to enable people to connect with each other for a variety of purposes. These purposes however, are generally thought of in a positive fashion. Based on a multi-method study of two online environments, Habbo Hotel and Second Life, which incorporate social networking functionality, we she light on forms of what can be conceptualized as antisocial behaviours and the rationales for these. Such behaviours included: scamming, racist/homophobic attacks, sim attacks, avatar attacks, non-conformance to contextual norms, counterfeiting and unneighbourly behaviour. The rationales for sub behaviours included: profit, fun, status building, network disruption, accidental acts and prejudice. Through our analysis we are able to comment upon the difficulties of defining antisocial behaviour in such environments, particularly when such environments are subject to interpretation vis their use and expected norms. We also point to the problems we face in conducting our public and private lives given the role ICTs are playing in the convergence of these two spaces and also the convergence of ICTs themselves.

CubIT presentation and collaboration framework

Cubit is a public installation developed for QUT's Cube. It allows QUT staff and students to upload and exhibit media content on the Cube's display surfaces. Interact with the work of QUT's Science, Technology, Engineering and Mathematics (STEM) research projects with CubIT, a unique system at the Cube that allows the general public to collaborate with and access research content shared by QUT's students and academics. QUT students and staff can easily present and share their work at The Cube on a set of large multi-touch displays. To access The Cube, all they need to do is swipe their staff or student card at the CubIT system. They will then be able to instantly upload presentations, videos or visualisation of their work. CubIT boasts a host of collaborative features that allows users to share content across user accounts, annotate content and create shared presentations. Interactive features allow the public to engage and collaborate with content hands-on. In addition to being accessible through The Cube, CubIT allows users to interact with their work through alternative mediums and devices, including mobile phones, tablets and Dropbox. Please note you must be on the QUT network to access CubIT. CubIT can be booked to appear on the Cube Level 5 at specfic times for student and staff purposes. Please email booking requests

Pipeline renewal decision support : a case study using LinEAR in a water pipeline network

This paper presents a case study for the application of a Linear Engineering Asset Renewal decision support software tool (LinEAR) at a water distribution network in Australia. This case study examines how the LinEAR can assist water utilities to minimise their total pipeline management cost, to make a long-term budget based on mathematically predicted expenditure, and to present calculated evidence for supporting their expenditure requirements. The outcomes from the study on pipeline renewal decision support demonstrate that LinEAR can help water utilities to improve the decision process and save renewal costs over a long-term by providing an optimum renewal schedules. This software can help organisation to accumulate technical knowledge and prediction future impact of the decision using what-if analysis.

Demand Response for Residential Appliances in a Smart Electricity Distribution Network: Utility and Customer Perspectives

This thesis introduces advanced Demand Response algorithms for residential appliances to provide benefits for both utility and customers. The algorithms are engaged in scheduling appliances appropriately in a critical peak day to alleviate network peak, adverse voltage conditions and wholesale price spikes also reducing the cost of residential energy consumption. Initially, a demand response technique via customer reward is proposed, where the utility controls appliances to achieve network improvement. Then, an improved real-time pricing scheme is introduced and customers are supported by energy management schedulers to actively participate in it. Finally, the demand response algorithm is improved to provide frequency regulation services.

Acceptability-based QoE management for user-centric mobile video delivery : a field study evaluation

Effective Quality of Experience (QoE) management for mobile video delivery – to optimize overall user experience while adapting to heterogeneous use contexts – is still a big challenge to date. This paper proposes a mobile video delivery system to emphasize the use of acceptability as the main indicator of QoE to manage the end-to-end factors in delivering mobile video services. The first contribution is a novel framework for user-centric mobile video system that is based on acceptability-based QoE (A-QoE) prediction models, which were derived from comprehensive subjective studies. The second contribution is results from a field study that evaluates the user experience of the proposed system during realistic usage circumstances, addressing the impacts of perceived video quality, loading speed, interest in content, viewing locations, network bandwidth, display devices, and different video coding approaches, including region-of-interest (ROI) enhancement and center zooming

A convolutional neural network for automatic analysis of aerial imagery

This paper introduces a new method to automate the detection of marine species in aerial imagery using a Machine Learning approach. Our proposed system has at its core, a convolutional neural network. We compare this trainable classifier to a handcrafted classifier based on color features, entropy and shape analysis. Experiments demonstrate that the convolutional neural network outperforms the handcrafted solution. We also introduce a negative training example-selection method for situations where the original training set consists of a collection of labeled images in which the objects of interest (positive examples) have been marked by a bounding box. We show that picking random rectangles from the background is not necessarily the best way to generate useful negative examples with respect to learning.

Evaluation of saliva collection devices for the analysis of proteins

Background: Human saliva mirrors the body's health and can be collected non-invasively, does not require specialized skills and is suitable for large population based screening programs. The aims were twofold: to evaluate the suitability of commercially available saliva collection devices for quantifying proteins present in saliva and to provide levels for C-reactive protein (CRP), myoglobin, and immunoglobin E (IgE) in saliva of healthy individuals as a baseline for future studies. Methods: Saliva was collected from healthy volunteers (n = 17, ages 18-33 years). The following collection methods were evaluated: drool; Salimetrics (R) Oral Swab (SOS); Salivette (R) Cotton and Synthetic (Sarstedt) and Greiner Bio-One Saliva Collection System (GBO SCS (R)). We used AlphaLISA (R) assays to measure CRP, IgE and myoglobin levels in human saliva. Results: Significant (p<0.05) differences in the salivary flow rates were observed based on the method of collection, Le. salivary flow rates were significantly lower (p<0.05) in unstimulated saliva (Le. drool and SOS), when compared with mechanically stimulated methods (p<0.05) (Salivette (R) Cotton and Synthetic) and acid stimulated method (p<0.05) (SCS (R)). Saliva collected using SOS yielded significantly (p<0.05) lower concentrations of myoglobin and CRP, whilst, saliva collected using the Salivette (R) Cotton and Synthetic swab yielded significantly (p<0.05) lower myoglobin and IgE concentrations respectively. Conclusions: The results demonstrated significantly relevant differences in analyte levels based on the collection method. Significant differences in the salivary flow rates were also observed depending on the saliva collection method. The data provide preliminary baseline values for salivary CRP, myoglobin, and IgE levels in healthy participants and based on the collection method. (C) 2012 Elsevier B.V. All rights reserved.

Component modeling for SCADA network mapping

Supervisory Control and Data Acquisition systems (SCADA) are widely used to control critical infrastructure automatically. Capturing and analyzing packet-level traffic flowing through such a network is an essential requirement for problems such as legacy network mapping and fault detection. Within the framework of captured network traffic, we present a simple modeling technique, which supports the mapping of the SCADA network topology via traffic monitoring. By characterizing atomic network components in terms of their input-output topology and the relationship between their data traffic logs, we show that these modeling primitives have good compositional behaviour, which allows complex networks to be modeled. Finally, the predictions generated by our model are found to be in good agreement with experimentally obtained traffic.

Identification of pre-spike network in patients with mesial temporal lobe epilepsy

Background: Seizures and interictal spikes in mesial temporal lobe epilepsy (MTLE) affect a network of brain regions rather than a single epileptic focus. Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) studies have demonstrated a functional network in which hemodynamic changes are time-locked to spikes. However, whether this reflects the propagation of neuronal activity from a focus, or conversely the activation of a network linked to spike generation remains unknown. The functional connectivity (FC) changes prior to spikes may provide information about the connectivity changes that lead to the generation of spikes. We used EEG-fMRI to investigate FC changes immediately prior to the appearance of interictal spikes on EEG in patients with MTLE. Methods/principal findings: Fifteen patients with MTLE underwent continuous EEG-fMRI during rest. Spikes were identified on EEG and three 10 s epochs were defined relative to spike onset: spike (0–10 s), pre-spike (−10 to 0 s), and rest (−20 to −10 s, with no previous spikes in the preceding 45s). Significant spike-related activation in the hippocampus ipsilateral to the seizure focus was found compared to the pre-spike and rest epochs. The peak voxel within the hippocampus ipsilateral to the seizure focus was used as a seed region for FC analysis in the three conditions. A significant change in FC patterns was observed before the appearance of electrographic spikes. Specifically, there was significant loss of coherence between both hippocampi during the pre-spike period compared to spike and rest states. Conclusion/significance: In keeping with previous findings of abnormal inter-hemispheric hippocampal connectivity in MTLE, our findings specifically link reduced connectivity to the period immediately before spikes. This brief decoupling is consistent with a deficit in mutual (inter-hemispheric) hippocampal inhibition that may predispose to spike generation.

A Memetic Algorithm for the location-based continuously operating reference stations placement problem in Network Real-Time Kinematic

Network Real-Time Kinematic (NRTK) is a technology that can provide centimeter-level accuracy positioning services in real time, and it is enabled by a network of Continuously Operating Reference Stations (CORS). The location-oriented CORS placement problem is an important problem in the design of a NRTK as it will directly affect not only the installation and operational cost of the NRTK, but also the quality of positioning services provided by the NRTK. This paper presents a Memetic Algorithm (MA) for the location-oriented CORS placement problem, which hybridizes the powerful explorative search capacity of a genetic algorithm and the efficient and effective exploitative search capacity of a local optimization. Experimental results have shown that the MA has better performance than existing approaches. In this paper we also conduct an empirical study about the scalability of the MA, effectiveness of the hybridization technique and selection of crossover operator in the MA.