Mobile-agent based multi-constraint one-to-many bilateral e-Negotiation framework

The thesis proposes a multi-constraint one-to-many bilateral e-Trade negotiation framework. It deploys mobile agents in negotiation, considers trading competition between vendors and search space, efficiently manages the risk of losing top utility offers that expire before the negotiation deadline, accurately evaluates offers, and truly maintains the security of negotiation data.

"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.

Emergent effects in massive agent swarms in real-time game environments

 Computational efficiency and hence the scale of agent-based swarm simulations is bound by the nearest neighbour computation for each agent. This article proposes the use of GPU texture memory to implement lookup tables for a spatial partitioning based k-Nearest Neighbours algorithm. These improvements allow simulation of swarms of 220 agents at higher rates than the current best alternative algorithms. This approach is incorporated into an existing framework for simulating steering behaviours allowing for a complete implementation of massive agent swarm simulations, with per agent behaviour preferences, on a Graphics Processing Unit. These simulations have enabled an investigation of the emergent dynamics that occur when massive swarms interact with a choke point in their environment. Various modes of sustained dynamics with temporal and spatial coherence are identified when a critical mass of agents is simulated and some elementary properties are presented. The algorithms presented in this article enable researchers and content designers in games and movies to implement truly massive agent swarms in real time and thus provide a basis for further identification and analysis of the emergent dynamics in these swarms. This will improve not only the scale of swarms used in commercial games and movies but will also improve the reliability of swarm behaviour with respect to content design goals.

Multifunctional and multitargeted nanoparticles for drug delivery to overcome barriers of drug resistance in human cancers

The recurrence and metastatic spread of cancer are major drawbacks in cancer treatment. Although chemotherapy is one of the most effective methods for the treatment of metastatic cancers, it is nonspecific and causes significant toxic damage. The development of drug resistance to chemotherapeutic agents through various mechanisms also limits their therapeutic potential. However, as we discuss here, the use of nanodelivery systems that are a combination of diagnostics and therapeutics (theranostics) is as relatively novel concept in the treatment of cancer. Such systems are likely to improve the therapeutic benefits of encapsulated drugs and can transit to the desired site, maintaining their pharmaceutical properties. The specific targeting of malignant cells using multifunctional nanoparticles exploits theranostics as an improved agent for delivering anticancer drugs and as a new solution for overriding drug resistance.

Ethical trust and social moral norms simulation : a bio-inspired agent-based modelling approach

The understanding of the micro-macro link is an urgent need in the study of social systems. The complex adaptive nature of social systems adds to the challenges of understanding social interactions and system feedback and presents substantial scope and potential for extending the frontiers of computer-based research tools such as simulations and agent-based technologies. In this project, we seek to understand key research questions concerning the interplay of ethical trust at the individual level and the development of collective social moral norms as representative sample of the bigger micro-macro link of social systems. We outline our computational model of ethical trust (CMET) informed by research findings from trust, machine ethics and neural science. Guided by the CMET architecture, we discuss key implementation ideas for the simulations of ethical trust and social moral norms.

Interplay of ethical trust and social moral norms : environment modelling and computational mechanisms in agent-based social simulation

In many agent-based models theoretical and computational mechanisms are needed for model abstraction and design. However, it can be challenging to arrive at the appropriate mechanisms and models. This research on the interplay of ethical trust and social moral norms addresses that challenge via an analytical framework on the spread of moral norms, the modelling of social environment and the selection of spread mechanisms as applied to agent-based social simulation. We describe the mechanism alignment mapping, two forms of interaction modelling between the social environment and agents, and the results obtained from the simulation of our computational model. These results provide an insight into how the agent-based paradigm can be applied as a technique of investigation for normative moral processes in computational social sciences.

Identification of unprecedented anticancer properties of high molecular weight biomacromolecular complex containing bovine lactoferrin (HMW-bLf)

With the successful clinical trials, multifunctional glycoprotein bovine lactoferrin is gaining attention as a safe nutraceutical and biologic drug targeting cancer, chronic-inflammatory, viral and microbial diseases. Interestingly, recent findings that human lactoferrin oligomerizes under simulated physiological conditions signify the possible role of oligomerization in the multifunctional activities of lactoferrin molecule during infections and in disease targeting signaling pathways. Here we report the purification and physicochemical characterization of high molecular weight biomacromolecular complex containing bovine lactoferrin (≥250 kDa), from bovine colostrum, a naturally enriched source of lactoferrin. It showed structural similarities to native monomeric iron free (Apo) lactoferrin (∼78-80 kDa), retained anti-bovine lactoferrin antibody specific binding and displayed potential receptor binding properties when tested for cellular internalization. It further displayed higher thermal stability and better resistance to gut enzyme digestion than native bLf monomer. High molecular weight bovine lactoferrin was functionally bioactive and inhibited significantly the cell proliferation (p<0.01) of human breast and colon carcinoma derived cells. It induced significantly higher cancer cell death (apoptosis) and cytotoxicity in a dose-dependent manner in cancer cells than the normal intestinal cells. Upon cellular internalization, it led to the up-regulation of caspase-3 expression and degradation of actin. In order to identify the cutting edge future potential of this bio-macromolecule in medicine over the monomer, its in-depth structural and functional properties need to be investigated further.

Nucleic acid aptamer-guided cancer therapeutics and diagnostics: the next generation of cancer medicine

Conventional anticancer therapies, such as chemo- and/or radio-therapy are often unable to completely eradicate cancers due to abnormal tumor microenvironment, as well as increased drug/radiation resistance. More effective therapeutic strategies for overcoming these obstacles are urgently in demand. Aptamers, as chemical antibodies that bind to targets with high affinity and specificity, are a promising new and novel agent for both cancer diagnostic and therapeutic applications. Aptamer-based cancer cell targeting facilitates the development of active targeting in which aptamer-mediated drug delivery could provide promising anticancer outcomes. This review is to update the current progress of aptamer-based cancer diagnosis and aptamer-mediated active targeting for cancer therapy in vivo, exploring the potential of this novel form of targeted cancer therapy.

Applying a multi-agent classifier system with a novel trust measurement method to classifying medical data

In this paper, we present the application of a Multi-Agent Classifier System (MACS) to medical data classification tasks. The MACS model comprises a number of Fuzzy Min-Max (FMM) neural network classifiers as its agents. A trust measurement method is used to integrate the predictions from multiple agents, in order to improve the overall performance of the MACS model. An auction procedure based on the sealed bid is adopted for the MACS model in determining the winning agent. The effectiveness of the MACS model is evaluated using the Wisconsin Breast Cancer (WBC) benchmark problem and a real-world heart disease diagnosis problem. The results demonstrate that stable results are produced by the MACS model in undertaking medical data classification tasks. © 2014 Springer Science+Business Media Singapore.