Synthesis of silica-coated ZnO nanocomposite : the resonance structure of polyvinyl pyrrolidone (PVP) as a coupling agent

Abstract The preparation of silica-coated ZnO nanocomposite using polyvinyl pyrrolidone (PVP) as a coupling agent was investigated. Transmission electron microscopy analysis revealed that silica has been deposited on the surface of PVP-capped ZnO nanoparticles as a continuous thin layer. Two-dimensional correlation analysis based on the time-dependent UV–vis spectra was introduced to study the interaction governing the deposition of silica on to PVP-capped ZnO. Strong hydrogen bonds formed between the amphiphilic PVP molecules and silica in the silicacoated PVP-capped ZnO composites. The reduced photocatalytic activity of silica-coated ZnO nanoparticles will enhance their performance as durable, safe, and nonreactive UV blockers in plastics, paints, and coating for outdoor textile and timber products.

Proof of Concept : developing a peer reviewed evidence-based interactive e-learning programme

Knowledge and skill acquisition related to vascular access are traditionally individual institutional educational initiatives. Australia currently has no national evidence based education programme for renal nurses. A survey of Australian and New Zealand Nephrology Educators' conducted in 2009, identified the need for more effective and consistent delivery of clinical education for nurses using innovative, web-based approaches supporting the tenets of e-learning methodologies. This paper discusses the development, implementation and proposed evaluation of a peer reviewed Australasian e-learning programme on buttonhole cannulation. It will further highlight the benefits of inter-organisational partnerships and how these partnerships can facilitate positive change in teaching and learning practices. This project has unique characteristics that collectively provide value, distinction and innovation to nurses, patients and renal departments. As the e-learning programme was founded on a platform of evidence-based practice it is therefore easily transferable to an international context.

Extraction of copper(II) ions from aqueous solutions with a methimazole-based ionic liquid

The recently synthesized ionic liquid (IL) 2-butylthiolonium bis(trifluoromethanesulfonyl)amide, [mimSBu][NTf2], has been used for the extraction of copper(II) from aqueous solution. The pH of the aqueous phase decreases upon addition of [mimSBu]+, which is attributed to partial release of the hydrogen attached to the N(3) nitrogen atom of the imidazolium ring. The presence of sparingly soluble water in [mimSBu][NTf2] also is required in solvent extraction studies to promote the incorporation of Cu(II) into the [mimSBu][NTf2] ionic liquid phase. The labile copper(II) system formed by interacting with both the water and the IL cation component has been characterized by cyclic voltammetry as well as UV−vis, Raman, and 1H, 13C, and 15N NMR spectroscopies. The extraction process does not require the addition of a complexing agent or pH control of the aqueous phase. [mimSBu][NTf2] can be recovered from the labile copper−water−IL interacting system by washing with a strong acid. High selectivity of copper(II) extraction is achieved relative to that of other divalent cobalt(II), iron(II), and nickel(II) transition-metal cations. The course of microextraction of Cu2+ from aqueous media into the [mimSBu][NTf2] IL phase was monitored in situ by cyclic voltammetry using a well-defined process in which specific interaction with copper is believed to switch from the ionic liquid cation component, [mimSBu], to the [NTf2] anion during the course of electrochemical reduction from Cu(II) to Cu(I). The microextraction−voltammetry technique provides a fast and convenient method to determine whether an IL is able to extract electroactive metal ions from an aqueous solution.

Fusion based 3D tracking of mobile transmitters via robust set-valued state estimation with RSS measurements

This paper investigates the problem of location and velocity detection of a mobile agent using Received Signal Strength (RSS) measurements captured by geographically distributed seed nodes. With inherently nonlinear power measurements, we derive a powerful linear measurement scheme using an analytical measurement conversion technique which can readily be used with RSS measuring sensors. We also employ the concept of sensor fusion in conjunction for the case of redundant measurements to further enhance the estimation accuracy.

Security framework for mobile agents-based applications

Mobile agents have been proposed for key applications such as forensics analysis, intrusion detection, e-commerce, and resource management. Yet, they are vulnerable to various security threats by malicious hosts or intruders. Conversely, genuine platforms may run malicious agents. It is essential to establish a truly secure framework for mobile agents to gain trust of clients in the system. Failure to accomplish a trustworthy secured framework for Mobile Agent System (MAS) will limit their deployment into the key applications. This chapter presents a comprehensive taxonomy of various security threats to Mobile Agent System and the existing implemented security mechanisms. Different mechanisms are discussed, and the related security deficiencies are highlighted. The various security properties of the agent and the agent platform are described. The chapter also introduces the properties, advantages, and roles of agents in various applications. It describes the infrastructure of the system and discusses several mobile agent frameworks and the accomplished security level.

Research methodologies in creative practice : literacy in the digital age of the twenty first century - learning from computer games

Literacy remains one of the central goals of schooling, but the ways in which it is understood are changing. The growth of the networked society, and the spread of Information and Communications Technologies (ICT), has brought about significant changes to traditional forms of literacy. Older, print based forms now take their place alongside a mix of newer multi-modal forms, where a wide range of elements such as image, sound, movement, light, colour and interactivity often supplant the printed word and contribute to the ways in which meaning is made. For young people to be fully literate in the twenty-first century, they need to have clear understandings about the ways in which these forms of literacy combine to persuade, present a point of view, argue a case or win the viewers’ sympathies. They need to know how to use them themselves, and to be aware of the ways in which others use them. They need to understand how digital texts organise and prioritise knowledge and information, and to recognise and be critically informed about the global context in which this occurs. That is, to be effective members of society, students need to become critical and capable users of both print and multimodal literacy, and be able to bring informed and analytic perspectives to bear on all texts, both print and digital, that they encounter in everyday life.

This is part of schools’ larger challenge to build robust connections between school and the world beyond, to meet the needs of all students, and to counter problems of alienation and marginalisation, particularly amongst students in the middle years. This means finding ways to be relevant and useful for all students, and to provide them with the skills and knowledge they will need in the ICT-based world of the Twentyfirst century. With respect to literacy education, engagement and technology, we urgently need more information as to how this might be best achieved.

Learning other agents' preferences in multi-agent negotiation using the bayesian classifier

In multi-agent systems, most of the time, an agent does not have complete information about the preferences and decision making processes of other agents. This prevents even the cooperative agents from making coordinated choices, purely due to their ignorance of what others want. To overcome this problem, traditional coordination methods rely heavily on inter-agent communication, and thus become very inefficient when communication is costly or simply not desirable (e.g. to preserve privacy). In this paper, we propose the use of learning to complement communication in acquiring knowledge about other agents. We augment the communication-intensive negotiating agent architecture with a learning module, implemented as a Bayesian classifier. This allows our agents to incrementally update models of other agents' preferences from past negotiations with them. Based on these models, the agents can make sound predictions about others' preferences, thus reducing the need for communication in their future interactions.

A unified formalism for landmark based representation of maps and navigation plans

We present a unified formalism for representing maps and using them for constructing plans of navigation for an autonomous agent. The foundation of this work lies in addressing key questions that an agent is confronted with when navigating. That is, besides the main task of how to reach the intended destination from the current position, the agent faces other questions like: where am I? what landmarks can I see? where is my destination relative to me and the landmarks I am seeing? Fundamental to this representation is the use of visual landmarks, which are used as pivotal points in the landscape being described. Further, in the representation of spatial information and navigation there are three different viewpoints: first, the localized representation from the viewpoint of a sighted, mobile agent; second, the static representation seen by the map-maker; and third, the view of an external agent giving directions on the basis of his own experience/knowledge. The major contribution of this map model and the associated navigation method lies in the framework which unifies these three different points of view. This unification enables the agent to make no distinction in terms of following implicit instructions contained in a map and the directions given by external agents.

Determining binding constants from 1H NMR titration data using global and local methods : a case study using [n]polynorbornane-based anion hosts

From data generated using 1H NMR titrations, different methodologies to calculate binding constants are compared. The ‘local’ analysis method that uses only a single isotherm (only one H-bond donor) is compared against the ‘global’ method (that includes many or all H-bond donors). The results indicate that for simple systems both methods are suitable, however, the global approach consistently provides a K a value with uncertainties up to 30% smaller. For more complex binding, the global analysis method gives much more robust results than the local methods. This study also highlights the need to explore several different modes when data do not fit well to a simple 1:1 complexation model and illustrates the need for better methods to estimate uncertainties in supramolecular binding experiments.

The role of nanomedicine in cell based therapeutics in cancer and inflammation.

Cell based therapeutics is one of the most rapidly advancing medical fields, bringing together a range of fields including transplantation, tissue engineering and regeneration, biomaterials and stem cell biology. However, traditional cell-based therapeutics have many limitations, one of which is their harmful effects exhibited on healthy body cells due to their lack of specificity. Nanomedicine is providing an alternative treatment strategy that is more targeted and specific to a range of diseases. Varying from polymers conjugated with drugs or tissue targeting molecules, to proteins encapsulated within a polymer shell, nanomedicine will without a doubt play a major role in designing effective cell-based therapeutics that can overcome certain classical problems. These may include from addressing the problem of non-specificity of contemporary treatments to overcoming mechanical barriers, such as crossing cell membranes. This review summarises the recent work on nano-based cell therapy as a regenerative agent and as a therapeutic for cancer and neurological diseases.

Special issue : Computational intelligence models for image processing and information reasoning

Computational Intelligence (CI) models comprise robust computing methodologies with a high level of machine learning quotient. CI models, in general, are useful for designing computerized intelligent systems/machines that possess useful characteristics mimicking human behaviors and capabilities in solving complex tasks, e.g., learning, adaptation, and evolution. Examples of some popular CI models include fuzzy systems, artificial neural networks, evolutionary algorithms, multi-agent systems, decision trees, rough set theory, knowledge-based systems, and hybrid of these models. This special issue highlights how different computational intelligence models, coupled with other complementary techniques, can be used to handle problems encountered in image processing and information reasoning.

"Recognition" : Interactive motion tracking installation with neural network agent performer

Public installation running throughout the night at Cube 37 gallery. Morphing forms are projected onto the glass front of the gallery, facing the street. Human perticipants interact with the forms whose morphology changes with the movements of the participants. When no human participants are present, a neural network based agent that has learnt how to dance from a trained dancer, takes over and the forms follow the agent's movement.

A nanoliposome-based drug delivery system for cancer

The current project shows that the sulfatide-containing nanoliposome (SCL) drug delivery system could be an effective and safe nanocarrier for the anticancer agent doxorubicin to target tumours with high expression of tenascin-C. Moreover, SCL encapsulation could be a new strategy for the treatment of diseases in the central nervous system.

Time correlated anomaly detection based on inferences

Anomaly detection techniques are used to find the presence of anomalous activities in a network by comparing traffic data activities against a "normal" baseline. Although it has several advantages which include detection of "zero-day" attacks, the question surrounding absolute definition of systems deviations from its "normal" behaviour is important to reduce the number of false positives in the system. This study proposes a novel multi-agent network-based framework known as Statistical model for Correlation and Detection (SCoDe), an anomaly detection framework that looks for timecorrelated anomalies by leveraging statistical properties of a large network, monitoring the rate of events occurrence based on their intensity. SCoDe is an instantaneous learning-based anomaly detector, practically shifting away from the conventional technique of having a training phase prior to detection. It does acquire its training using the improved extension of Exponential Weighted Moving Average (EWMA) which is proposed in this study. SCoDe does not require any previous knowledge of the network traffic, or network administrators chosen reference window as normal but effectively builds upon the statistical properties from different attributes of the network traffic, to correlate undesirable deviations in order to identify abnormal patterns. The approach is generic as it can be easily modified to fit particular types of problems, with a predefined attribute, and it is highly robust because of the proposed statistical approach. The proposed framework was targeted to detect attacks that increase the number of activities on the network server, examples which include Distributed Denial of Service (DDoS) and, flood and flash-crowd events. This paper provides a mathematical foundation for SCoDe, describing the specific implementation and testing of the approach based on a network log file generated from the cyber range simulation experiment of the industrial partner of this project.

Development of microfluidic-based analytical methodology for studying the effects of chemotherapy agents on cancer tissue

Whilst a multitude of techniques have been employed to study the biology of tumour tissue and its response to chemotherapeutic reagents, most current methodologies do not capture the sophistication of the in vivo environment. Microfluidics however offers the ability to maintain and interrogate primary tissue samples in an environment with biomimetic flow characteristics. In this study head and neck squamous cell carcinoma (HNSCC) tumour biopsies have been used to investigate the performance of a microfluidic device for generating clinically-useful information. The response of fresh and cryogenically-frozen primary HNSCC or metastatic lymph node samples to chemotherapy drugs (cisplatin, 5-flurouracil or docetaxel), alone and in combination, were monitored for both proliferation (water-soluble tetrazolium salt metabolism) and cell death biomarker release (lactate dehydrogenase, LDH) “off-chip”. The frozen tissue showed no significant difference in terms of either proliferation or LDH release in comparison with the matched fresh samples. Administration of all drugs caused cell death, in a dose-response manner, with the combination showing the greatest amount of cytotoxicity particularly at days 8 and 9; correlating well with published clinical data. The system described here offers an innovative method for studying the tumour microenvironment in vitro and, through incorporation of relevant analytical modules, provides the basis of a pre-clinical device that can be used to define personalised treatment regimens.