Exploratory Research into the Resilience of Farming Systems during Periods of Hardship

This paper investigates the management strategies and responses used by New Zealand sheep and beef farmers to ensure resilience during periods of hardship. Using two, farm level surveys conducted in 1986 and 2010, some aspects of resilient farming systems were identified. Despite apparent hardship current farmers seemed more willing to take risks, with many more borrowing to invest in on farm developments than those in 1986. The main similarity between time periods was the greatest response to economic changes being the adoption of a low input policy. This result was quite significant, as conventional farmers are generally believed to resort to other strategies or responses.Resilience, New Zealand, indicators, sustainable agriculture, strategies, Agribusiness, Environmental Economics and Policy, Land Economics/Use, Production Economics,

Computer Network Optimisation with Artificial Intelligence and Optics

The last decade has seen a proliferation in data-intensive compute applications such as artificial intelligence (AI), genome sequencing, and the internet-of-things. The ever-growing throughput demand of these big-data jobs has coincided with a slow down in the development of powerful computer chips. Consequently, there has been a shift away from local computation with general-purpose CPUs towards remote pooling of specialised high-bandwidth processors in cloud data centres (DCs) and high-performance compute (HPC) clusters. Such computation relies on a computer network to facilitate data querying and parallel processing. The traditional Moore’s Law approach of evaluating compute power and cost purely in terms of individual end points is therefore no longer appropriate. Instead, compute must now be thought of as a system of interconnected resources which can be orchestrated to perform a task. However, there has been a lack of development in next-generation computer networks, leading to the performance bottleneck of these systems moving away from the end point processors themselves and into the network connecting them. Optical networking is a technology which can offer orders-of-magnitude improvement in computer network performance. For optical networks to be widely used in DCs and HPCs, several obstacles related to physical optical device characteristics and resource management must be overcome. In this thesis, we develop and evaluate novel AI approaches for addressing these challenges. The first part of the thesis looks at optimising the physical plane’s devices in an optical computer network. Concretely, three gradient-free AI signal control approaches (ant colony optimisation, a genetic algorithm, and particle swarm optimisation) are proposed to enable high-bandwidth, low-power optical switching technologies to operate on the sub-nanosecond timescales required to realise an optical circuit switched data centre network. The second part of the thesis considers the problem of optimising the orches- tration plane’s resource management methods used to control optical computer networks. A novel algorithm, retro branching, is proposed to improve the solve time performance of the canonical branch-and-bound exact solver using a graph neural network (GNN) trained with reinforcement learning (RL). State-of-the-art RL-for-branching results are achieved, opening the possibility for branch-and- bound to be applied to large NP-hard discrete optimisation problems such as those found in computer network resource management. We also propose another algorithm, PAC-ML (partitioning for asynchronous computing with machine learning), which trains a GNN with RL to automatically decide how much to distribute deep learning jobs in an optical HPC architecture in order to meet user-defined run time requirements, minimise the blocking rate, and maximise system throughput under dynamic scenarios; the first of its kind to consider such a problem setting. So far we have have considered optimising the devices in the physical plane and the resource managers in the orchestration plane of the computer network. These areas have both received research attention in prior works. However, what has not received much consideration is the underlying test bed in which physical and orchestration plane research and optimisation is typically conducted. Real DC and HPC environments are generally not available for research due to their proprietary nature and expensive cost of deployment. Consequently, researchers rely on simulated computer networks during novel system development. The fidelity, reproducibility, and flexibility of these simulations is therefore at least as important as the development and optimisation of the physical and orchestration systems for which they are used. Poor simulations will lead to the misguided development of network systems which do not perform as expected when deployed in real production environments. With this motivation, the third part of this thesis considers how to design and optimise the simulator used for computer network system research and development. A novel open source traffic generation framework and library, TrafPy, is presented, as well as a subsequent update to the generation algorithm to make it scalable to computer networks with thousands of nodes

Traffic generation for benchmarking data centre networks

Benchmarking is commonly used in research fields, such as computer architecture design and machine learning, as a powerful paradigm for rigorously assessing, comparing, and developing novel technologies. However, the data centre network (DCN) community lacks a standard open-access and reproducible traffic generation framework for benchmark workload generation. Driving factors behind this include the proprietary nature of traffic traces, the limited detail and quantity of open-access network-level data sets, the high cost of real world experimentation, and the poor reproducibility and fidelity of synthetically generated traffic. This is curtailing the community's understanding of existing systems and hindering the ability with which novel technologies, such as optical DCNs, can be developed, compared, and tested. We present TrafPy; an open-access framework for generating both realistic and custom DCN traffic traces. TrafPy is compatible with any simulation, emulation, or experimentation environment, and can be used for standardised benchmarking and for investigating the properties and limitations of network systems such as schedulers, switches, routers, and resource managers. We give an overview of the TrafPy traffic generation framework, and provide a brief demonstration of its efficacy through an investigation into the sensitivity of some canonical scheduling algorithms to varying traffic trace characteristics in the context of optical DCNs. TrafPy is open-sourced via GitHub and all data associated with this manuscript via RDR

Ovine footrot: new insights into bacterial colonisation

Ovine footrot is characterised by interdigital dermatitis (ID) and by the separation of the skin and hoof horn (under-running footrot). Dichelobacter nodosus is the essential pathogen causing footrot; the role of other microorganisms in this disease remains unclear. The aims of this study were (i) to investigate the colonisation of D nodosus, Fusobacterium necrophorum and Treponema species in biopsies from the ovine interdigital skin of healthy, ID and footrot-affected feet and (ii) to characterise the virulence of D nodosus strains in those biopsies. Postslaughter biopsy samples (n=241) were collected and analysed by real-time PCR to determine prevalence and load of the different bacterial species. The highest prevalence and load of D nodosus were found on feet with ID. The vast majority of samples contained virulent D nodosus and some samples contained both virulent and benign D nodosus. Notably, the more pathogenic subspecies of F necrophorum was found in samples from UK sheep. Our findings provide further insights into the role bacterial colonisation may play in the early stage of ID and in the progression towards footrot

More parasitic myositis cases in humans in Australia, and the definition of genetic markers for the causative agents as a basis for molecular diagnosis

Since 1998, there have been six reported human cases of myositis in Australia, attributable to infection with the nematode Haycocknema perplexum. However, an unequivocal diagnosis of H. perplexum infection and associated disease has been seriously compromised by a lack of molecular markers for this nematode. Here, we report new cases of disseminated myositis in two male patients from the states of Queensland and Tasmania in Australia, respectively; genetically characterize the causative agent from each case; and, also establish a PCR-based sequencing approach as a tool to support the diagnosis of future cases and to underpin epidemiological studies

A Vectorised Packing Algorithm for Efficient Generation of Custom Traffic Matrices

We propose a new algorithm for generating custom network traffic matrices which achieves 13×, 38×, and 70× faster generation times than prior work on networks with 64, 256, and 1024 nodes respectively

Exploring Cyber-Bullying: a Retrospective Study of First Year University Students

This was a retrospective study of cyber-bullying. Students enrolled in a first year course were selected to provide opinions on the issue of cyber-bullying as it pertained to social networking sites and young people. A mixed methods approach was applied to this study. Questionnaires provided quantitative data, and a focus group provided data for qualitative analysis. It was evident that students felt that cyber-bullying was not as prevalent as traditional bullying; however, it was identified as a serious issue. In relation to gender, traditional bullying was considered to be a problem for boys, more than cyber-bullying, whereas for girls cyber-bullying was considered to be a problem, more than traditional bullying. Social networking sites, solely, were not common tools used in cyber-bullying. Generally cell phones or a combination of cell phones and social networking sites were used. It was determined the age group at most risk from cyber-bullying to be early high school. Raising awareness of cyber-bullying was considered essential for prevention