Interplay of MLC, gantry and respiratory motion during DCAT delivery

This study investigated the possible interplay effects arising from the treatment of moving targets using the dynamic conformal arc therapy (DCAT) technique. Dose from a modulated test beam was measured, with and without phantom motion and with and without a 30o arc rotation, using a diode array placed on a sinusoidally moving platform. Measurements were repeated at five different collimator angles (0, 22.5, 45, 67.5 and 90o), at two different dose rates (300 and 600 MU/min). Results showed that the effect of respiratory motion on the measured dose distribution increased slightly when the beams were delivered as arcs, rather than with a static gantry angle, and that this effect increased substantially as the collimator angle was increased from 0o (MLC motion perpendicular to respiratory motion) to 90o (MLC motion parallel to respiratory motion). The dose oscillations arising from interplay between phantom and MLC motion were found to increase in magnitude when the dose rate was increased. These results led to the development of simple recommendations for minimizing the negative effects of motion interplay on DCAT dose distributions

Contributing to computing services to benchmarking asset management knowledge management

Asset management has broadened from a focus on maintenance management to whole of life cycle asset management requiring a suite of new competencies from asset procurement to management and disposal. Well developed skills and competencies as well as practical experience are a prerequisite to maintain capability, to manage demand as well to plan and set priorities and ensure on-going asset sustainability. This paper has as its focus to establish critical understandings of data, information and knowledge for asset management along with the way in which benchmarking these attributes through computer-aided design may aid a strategic approach to asset management. The paper provides suggestions to improve sharing, integration and creation of asset-related knowledge through the application of codification and personalization approaches.

The Pirate Bazaar: The Social Life of Copyright Law

This thesis provides a cultural history of Australian copyright law and related artistic controversies. It examines a number of disputes over authorship, collaboration, and appropriation across a variety of cultural fields. It considers legal controversies over the plagiarism of texts, the defacing of paintings, the sampling of musical works, the ownership of plays, the co-operation between film-makers, the sharing of MP3 files on the Internet, and the appropriation of Indigenous culture. Such narratives and stories relate to a broad range of works and subject matter that are protected by copyright law. This study offers an archive of oral histories and narratives of artistic creators about copyright law. It is founded upon interviews with creative artists and activists who have been involved in copyright litigation and policy disputes. This dialogical research provides an insight into the material and social effects of copyright law. This thesis concludes that copyright law is not just a ‘creature of statute’, but it is also a social and imaginative construct. In the lived experience of the law, questions of aesthetics and ethics are extremely important. Industry agreements are quite influential. Contracts play an important part in the operation of copyright law. The media profile of personalities involved in litigation and policy debates is pertinent. This thesis claims that copyright law can be explained by a mix of social factors such as ethical standards, legal regulations, market forces, and computer code. It can also be understood in terms of the personal stories and narratives that people tell about litigation and copyright law reform. Table of Contents Prologue 1 Introduction A Creature of Statute: Copyright Law and Legal Formalism 6 Chapter One The Demidenko Affair: Copyright Law and Literary Works 33 Chapter Two Daubism: Copyright Law and Artistic Works 67 Chapter Three The ABCs of Anarchism: Copyright Law and Musical Works 105 Chapter Four Heretic: Copyright Law and Dramatic Works 146 Chapter Five Shine: Copyright Law and Film 186 Chapter Six Napster: Infinite Digital Jukebox or Pirate Bazaar? Copyright Law and Digital Works 232 Chapter Seven Bangarra Dance Theatre: Copyright Law and Indigenous Culture 275 Chapter Eight The Cathedral and the Bazaar: The Future of Copyright Law 319

Use of 3D printed materials as tissue-equivalent phantoms

This study used the specific example of 3D printing with acrylonitrile butadiene styrene (ABS) as a means to investigate the potential usefulness of benchtop rapid prototyping as a technique for producing patient specific phantoms for radiotherapy dosimetry. Three small cylinders and one model of a human lung were produced via in-house 3D printing with ABS, using 90%, 50%, 30% and 10% ABS infill densities. These phantom samples were evaluated in terms of their geometric accuracy, tissue equivalence and radiation hardness, when irradiated using a range of clinical radiotherapy beams. The measured dimensions of the small cylindrical phantoms all matched their planned dimensions, within 1mm. The lung phantom was less accurately matched to the lung geometry on which it was based, due to simplifications introduced during the phantom design process. The mass densities, electron densities and linear attenuation coefficients identified using CT data, as well as the results of film measurements made using megavoltage photon and electron beams, indicated that phantoms printed with ABS, using infill densities of 30% or more, are potentially useful as lung- and tissue-equivalent phantoms for patient-specific radiotherapy dosimetry. All cylindrical 3D printed phantom samples were found to be unaffected by prolonged radiation and to accurately match their design specifications. However, care should be taken to avoid oversimplifying anatomical structures when printing more complex phantoms.

Modelling ciphersuite and version negotiation in the TLS protocol

Real-world cryptographic protocols such as the widely used Transport Layer Security (TLS) protocol support many different combinations of cryptographic algorithms (called ciphersuites) and simultaneously support different versions. Recent advances in provable security have shown that most modern TLS ciphersuites are secure authenticated and confidential channel establishment (ACCE) protocols, but these analyses generally focus on single ciphersuites in isolation. In this paper we extend the ACCE model to cover protocols with many different sub-protocols, capturing both multiple ciphersuites and multiple versions, and define a security notion for secure negotiation of the optimal sub-protocol. We give a generic theorem that shows how secure negotiation follows, with some additional conditions, from the authentication property of secure ACCE protocols. Using this framework, we analyse the security of ciphersuite and three variants of version negotiation in TLS, including a recently proposed mechanism for detecting fallback attacks.

A decision support framework for prioritization of engineering asset management activities under uncertainty

Engineers and asset managers must often make decisions on how to best allocate limited resources amongst different interrelated activities, including repair, renewal, inspection, and procurement of new assets. The presence of project interdependencies and the lack of sufficient information on the true value of an activity often produce complex problems and leave the decision maker guessing about the quality and robustness of their decision. In this paper, a decision support framework for uncertain interrelated activities is presented. The framework employs a methodology for multi-criteria ranking in the presence of uncertainty, detailing the effect that uncertain valuations may have on the priority of a particular activity. The framework employs employing semi-quantitative risk measures that can be tailored to an organisation and enable a transparent and simple-to-use uncertainty specification by the decision maker. The framework is then demonstrated on a real world project set from a major Australian utility provider.

Business Process Management: 13th International Conference, BPM 2015, Innsbruck, Austria, August 31 -- September 3, 2015, Proceedings

BPM 2015 was the 13th International Conference on Business Process Management. It provided a global forum for researchers to meet and exchange views over research topics and outcomes in business process management. BPM 2015 was hosted by the University of Innsbruck and took place August 31 to September 3.

Complex Symbolic Sequence Encodings for Predictive Monitoring of Business Processes

This paper addresses the problem of predicting the outcome of an ongoing case of a business process based on event logs. In this setting, the outcome of a case may refer for example to the achievement of a performance objective or the fulfillment of a compliance rule upon completion of the case. Given a log consisting of traces of completed cases, given a trace of an ongoing case, and given two or more possible out- comes (e.g., a positive and a negative outcome), the paper addresses the problem of determining the most likely outcome for the case in question. Previous approaches to this problem are largely based on simple symbolic sequence classification, meaning that they extract features from traces seen as sequences of event labels, and use these features to construct a classifier for runtime prediction. In doing so, these approaches ignore the data payload associated to each event. This paper approaches the problem from a different angle by treating traces as complex symbolic sequences, that is, sequences of events each carrying a data payload. In this context, the paper outlines different feature encodings of complex symbolic sequences and compares their predictive accuracy on real-life business process event logs.

Genetic structure of the Korean black scraper Thamnaconus modestus inferred from microsatellite marker analysis

The Korean black scraper, Thamnaconus modestus, is one of the most economically important maricultural fish species in Korea. However, the annual catch of this fish has been continuously declining over the past several decades. In this study, the genetic diversity and relationships among four wild populations and two hatchery stocks of Korean black scraper were assessed based on 16 microsatellite (MS) markers. A total of 319 different alleles were detected over all loci with an average of 19.94 alleles per locus. The hatchery stocks [mean number of alleles (N A) = 12, allelic richness (A R) = 12, expected heterozygosity (He) = 0.834] showed a slight reduction (P > 0.05) in genetic variability in comparison with wild populations (mean N A = 13.86, A R = 12.35, He = 0.844), suggesting a sufficient level of genetic variation in the hatchery populations. Similarly low levels of inbreeding and significant Hardy–Weinberg equilibrium deviations were detected in both wild and hatchery populations. The genetic subdivision among all six populations was low but significant (overall F ST = 0.008, P < 0.01). Pairwise F ST, a phylogenetic tree, and multidimensional scaling analysis suggested the existence of three geographically structured populations based on different sea basin origins, although the isolation-by-distance model was rejected. This result was corroborated by an analysis of molecular variance. This genetic differentiation may result from the co-effects of various factors, such as historical dispersal, local environment and ocean currents. These three geographical groups can be considered as independent management units. Our results show that MS markers may be suitable not only for the genetic monitoring of hatchery stocks but also for revealing the population structure of Korean black scraper populations. These results will provide critical information for breeding programs, the management of cultured stocks and the conservation of this species.

‘Blind Faith’. An experiment with narrative agency in game design

This paper reports on the current field of narrative-based game design through case study analysis with a particular focus on balancing high narrative agency with low production resources.

Application of simulated annealing to data distribution for all-to-all comparison problems in homogeneous systems

Distributed systems are widely used for solving large-scale and data-intensive computing problems, including all-to-all comparison (ATAC) problems. However, when used for ATAC problems, existing computational frameworks such as Hadoop focus on load balancing for allocating comparison tasks, without careful consideration of data distribution and storage usage. While Hadoop-based solutions provide users with simplicity of implementation, their inherent MapReduce computing pattern does not match the ATAC pattern. This leads to load imbalances and poor data locality when Hadoop's data distribution strategy is used for ATAC problems. Here we present a data distribution strategy which considers data locality, load balancing and storage savings for ATAC computing problems in homogeneous distributed systems. A simulated annealing algorithm is developed for data distribution and task scheduling. Experimental results show a significant performance improvement for our approach over Hadoop-based solutions.

Using genetic algorithm in profile-based assignment of applications to virtual machines for greener data centers

The increase in data center dependent services has made energy optimization of data centers one of the most exigent challenges in today's Information Age. The necessity of green and energy-efficient measures is very high for reducing carbon footprint and exorbitant energy costs. However, inefficient application management of data centers results in high energy consumption and low resource utilization efficiency. Unfortunately, in most cases, deploying an energy-efficient application management solution inevitably degrades the resource utilization efficiency of the data centers. To address this problem, a Penalty-based Genetic Algorithm (GA) is presented in this paper to solve a defined profile-based application assignment problem whilst maintaining a trade-off between the power consumption performance and resource utilization performance. Case studies show that the penalty-based GA is highly scalable and provides 16% to 32% better solutions than a greedy algorithm.

A penalty-based genetic algorithm for the migration cost-aware virtual machine placement problem in cloud data centers

In the past few years, the virtual machine (VM) placement problem has been studied intensively and many algorithms for the VM placement problem have been proposed. However, those proposed VM placement algorithms have not been widely used in today's cloud data centers as they do not consider the migration cost from current VM placement to the new optimal VM placement. As a result, the gain from optimizing VM placement may be less than the loss of the migration cost from current VM placement to the new VM placement. To address this issue, this paper presents a penalty-based genetic algorithm (GA) for the VM placement problem that considers the migration cost in addition to the energy-consumption of the new VM placement and the total inter-VM traffic flow in the new VM placement. The GA has been implemented and evaluated by experiments, and the experimental results show that the GA outperforms two well known algorithms for the VM placement problem.

A Strength Pareto Evolutionary Algorithm for live migration of multiple interdependent virtual machines in data centers

Although live VM migration has been intensively studied, the problem of live migration of multiple interdependent VMs has hardly been investigated. The most important problem in the live migration of multiple interdependent VMs is how to schedule VM migrations as the schedule will directly affect the total migration time and the total downtime of those VMs. Aiming at minimizing both the total migration time and the total downtime simultaneously, this paper presents a Strength Pareto Evolutionary Algorithm 2 (SPEA2) for the multi-VM migration scheduling problem. The SPEA2 has been evaluated by experiments, and the experimental results show that the SPEA2 can generate a set of VM migration schedules with a shorter total migration time and a shorter total downtime than an existing genetic algorithm, namely Random Key Genetic Algorithm (RKGA). This paper also studies the scalability of the SPEA2.