Microscopic and spectroscopic investigation of poly(3-hexylthiophene) interaction with carbon nanotubes

The inclusion of carbon nanotubes in polymer matrix has been proposed to enhance the polymer’s physical and electrical properties. In this study, microscopic and spectroscopic techniques are used to investigate the interaction between poly(3-hexylthiophene) (P3HT) and nanotubes and the reciprocal modification of physical properties. The presence of P3HT-covered nanotubes dispersed in the polymer matrix has been observed by atomic force microscopy and transmission electron microscopy. Then, the modification of P3HT optical properties due to nanotube inclusion has been evidenced with spectroscopic techniques like absorption and Raman spectroscopy. The study is completed with detailed nanoscale analysis by scanning probe techniques. The ordered self assembly of polymer adhering on the nanotube is unveiled by showing an example of helical wrapping of P3HT. Scanning tunneling spectroscopy study provides information on the electronic structure of nanotube-polymer assembly, revealing the charge transfer from P3HT to the nanotube.

Computational approaches for modelling intrinsic noise and delays in genetic regulatory networks

This chapter focuses on the interactions and roles between delays and intrinsic noise effects within cellular pathways and regulatory networks. We address these aspects by focusing on genetic regulatory networks that share a common network motif, namely the negative feedback loop, leading to oscillatory gene expression and protein levels. In this context, we discuss computational simulation algorithms for addressing the interplay of delays and noise within the signaling pathways based on biological data. We address implementational issues associated with efficiency and robustness. In a molecular biology setting we present two case studies of temporal models for the Hes1 gene (Monk, 2003; Hirata et al., 2002), known to act as a molecular clock, and the Her1/Her7 regulatory system controlling the periodic somite segmentation in vertebrate embryos (Giudicelli and Lewis, 2004; Horikawa et al., 2006).

Emergent task-specific object semantics through distributed experience networks

Autonomous development of sensorimotor coordination enables a robot to adapt and change its action choices to interact with the world throughout its lifetime. The Experience Network is a structure that rapidly learns coordination between visual and haptic inputs and motor action. This paper presents methods which handle the high dimensionality of the network state-space which occurs due to the simultaneous detection of multiple sensory features. The methods provide no significant increase in the complexity of the underlying representations and also allow emergent, task-specific, semantic information to inform action selection. Experimental results show rapid learning in a real robot, beginning with no sensorimotor mappings, to a mobile robot capable of wall avoidance and target acquisition.

Extreme ultraviolet (EUV) degradation of poly(olefin sulfone)s : towards applications as EUV photoresists

Poly(olefin sulfone)s, formed by the reaction of sulfur dioxide (SO2) and an olefin, are known to be highly susceptible to degradation by radiation and thus have been identified as candidate materials for chain scission-based extreme ultraviolet lithography (EUVL) resist materials. In order to investigate this further, the synthesis and characterisation of two poly(olefin sulfone)s namely poly(1-pentene sulfone) (PPS) and poly(2-methyl-1-pentene sulfone) (PMPS), was achieved and the two materials were evaluated for possible chain scission EUVL resist applications. It was found that both materials possess high sensitivities to EUV photons; however; the rates of outgassing were extremely high. The only observed degradation products were found to be SO2 and the respective olefin suggesting that depolymerisation takes place under irradiation in a vacuum environment. In addition to depolymerisation, a concurrent conversion of SO2 moieties to a sulfide phase was observed using XPS.

Evolving noisy oscillatory dynamics in genetic regulatory networks

We introduce a genetic programming (GP) approach for evolving genetic networks that demonstrate desired dynamics when simulated as a discrete stochastic process. Our representation of genetic networks is based on a biochemical reaction model including key elements such as transcription, translation and post-translational modifications. The stochastic, reaction-based GP system is similar but not identical with algorithmic chemistries. We evolved genetic networks with noisy oscillatory dynamics. The results show the practicality of evolving particular dynamics in gene regulatory networks when modelled with intrinsic noise.

Poly[aqua([mu]3-2-hydroxy-5-nitrobenzoato-[kappa]3O1:O1':O2)rubidium]

In the structure of title compound [Rb2(C7H4NO2)2(H2O)2]n the centrosymmetric cyclic dimeric repeating unit comprises two irregular RbO4 complex centres bridged by the carboxylate groups of the 5-nitrosalicylate ligands. The coordination about each Rb is completed by a monodentate water molecule and a phenolic O donor which gives a bridging extension [Rb-O range 3.116(7)-3.135(5)A]. The two-dimensional polymeric structure is stabilized by intermolecular water O-H...O(carboxyl) hydrogen bonds and weak inter-ring pi--pi interactions [minimum ring centroid separation, 3.620(4)A].

Aerial image analysis using spiking neural networks with application to power line corridor monitoring

Trees, shrubs and other vegetation are of continued importance to the environment and our daily life. They provide shade around our roads and houses, offer a habitat for birds and wildlife, and absorb air pollutants. However, vegetation touching power lines is a risk to public safety and the environment, and one of the main causes of power supply problems. Vegetation management, which includes tree trimming and vegetation control, is a significant cost component of the maintenance of electrical infrastructure. For example, Ergon Energy, the Australia’s largest geographic footprint energy distributor, currently spends over $80 million a year inspecting and managing vegetation that encroach on power line assets. Currently, most vegetation management programs for distribution systems are calendar-based ground patrol. However, calendar-based inspection by linesman is labour-intensive, time consuming and expensive. It also results in some zones being trimmed more frequently than needed and others not cut often enough. Moreover, it’s seldom practicable to measure all the plants around power line corridors by field methods. Remote sensing data captured from airborne sensors has great potential in assisting vegetation management in power line corridors. This thesis presented a comprehensive study on using spiking neural networks in a specific image analysis application: power line corridor monitoring. Theoretically, the thesis focuses on a biologically inspired spiking cortical model: pulse coupled neural network (PCNN). The original PCNN model was simplified in order to better analyze the pulse dynamics and control the performance. Some new and effective algorithms were developed based on the proposed spiking cortical model for object detection, image segmentation and invariant feature extraction. The developed algorithms were evaluated in a number of experiments using real image data collected from our flight trails. The experimental results demonstrated the effectiveness and advantages of spiking neural networks in image processing tasks. Operationally, the knowledge gained from this research project offers a good reference to our industry partner (i.e. Ergon Energy) and other energy utilities who wants to improve their vegetation management activities. The novel approaches described in this thesis showed the potential of using the cutting edge sensor technologies and intelligent computing techniques in improve power line corridor monitoring. The lessons learnt from this project are also expected to increase the confidence of energy companies to move from traditional vegetation management strategy to a more automated, accurate and cost-effective solution using aerial remote sensing techniques.

Evolving genetic regulatory networks performing as stochastic switches

Recent studies have shown that small genetic regulatory networks (GRNs) can be evolved in silico displaying certain dynamics in the underlying mathematical model. It is expected that evolutionary approaches can help to gain a better understanding of biological design principles and assist in the engineering of genetic networks. To take the stochastic nature of GRNs into account, our evolutionary approach models GRNs as biochemical reaction networks based on simple enzyme kinetics and simulates them by using Gillespie’s stochastic simulation algorithm (SSA). We have already demonstrated the relevance of considering intrinsic stochasticity by evolving GRNs that show oscillatory dynamics in the SSA but not in the ODE regime. Here, we present and discuss first results in the evolution of GRNs performing as stochastic switches.

The undirected feedback vertex set problem has a Poly(k) Kernel

Resolving a noted open problem, we show that the Undirected Feedback Vertex Set problem, parameterized by the size of the solution set of vertices, is in the parameterized complexity class Poly(k), that is, polynomial-time pre-processing is sufficient to reduce an initial problem instance (G, k) to a decision-equivalent simplified instance (G', k') where k' � k, and the number of vertices of G' is bounded by a polynomial function of k. Our main result shows an O(k11) kernelization bound.

An efficient public key management regime for vehicular ad hoc networks (VANETS)

The primary goal of the Vehicular Ad Hoc Network (VANET) is to provide real-time safety-related messages to motorists to enhance road safety. Accessing and disseminating safety-related information through the use of wireless communications technology in VANETs should be secured, as motorists may make critical decisions in dealing with an emergency situation based on the received information. If security concerns are not addressed in developing VANET systems, an adversary can tamper with, or suppress, the unprotected message to mislead motorists to cause traffic accidents and hazards. Current research on secure messaging in VANETs focuses on employing the certificate-based Public Key Infrastructure (PKI) scheme to support message encryption and digital signing. The security overhead of such a scheme, however, creates a transmission delay and introduces a time-consuming verification process to VANET communications. This thesis has proposed a novel public key verification and management approach for VANETs; namely, the Public Key Registry (PKR) regime. Compared to the VANET PKI scheme, this new approach can satisfy necessary security requirements with improved performance and scalability, and at a lower cost by reducing the security overheads of message transmission and eliminating digital certificate deployment and maintenance issues. The proposed PKR regime consists of the required infrastructure components, rules for public key management and verification, and a set of interactions and associated behaviours to meet these rule requirements. This is achieved through a system design as a logic process model with functional specifications. The PKR regime can be used as development guidelines for conforming implementations. An analysis and evaluation of the proposed PKR regime includes security features assessment, analysis of the security overhead of message transmission, transmission latency, processing latency, and scalability of the proposed PKR regime. Compared to certificate-based PKI approaches, the proposed PKR regime can maintain the necessary security requirements, significantly reduce the security overhead by approximately 70%, and improve the performance by 98%. Meanwhile, the result of the scalability evaluation shows that the latency of employing the proposed PKR regime stays much lower at approximately 15 milliseconds, whether operating in a huge or small environment. It is therefore believed that this research will create a new dimension to the provision of secure messaging services in VANETs.

Frequency response function based structural damage detection using artificial neural networks

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.

Academic faculty’s teaching social networks : what is the extent of library faculty’s inclusion?

Collaboration between academic and library faculty is an important topic of discussion and research among academic librarians. Partnerships are vital for developing effective information literacy education. The research reported in this paper aims to develop an understanding of academic collaborators by analyzing academic faculty’s teaching social network. Academic faculty teaching social networks have not been previously described through the lens of social network analysis. A teaching social network is comprised of people and their communication channels that affect academic faculty when they design and deliver their courses. Social network analysis was the methodology used to describe the teaching social networks. The preliminary results show academic faculty were more affected by the channels of communication in how they taught (pedagogy) than what they taught (course content). This study supplements the existing research on collaboration and information literacy. It provides both academic and library faculty with added insight into their relationships.