Ian D. Peake

Impact of nanoscale roughness of titanium thin film surfaces on bacterial retention

Two human pathogenic bacteria, Staphylococcus aureus CIP 68.5 and Pseudomonas aeruginosa ATCC 9025, were adsorbed onto surfaces containing Ti thin films of varying thickness to determine the extent to which nanoscale surface roughness influences the extent of bacterial attachment. A magnetron sputter thin film system was used to deposit titanium films with thicknesses of 3, 12, and 150 nm on glass substrata with corresponding surface roughness parameters of R(q) 1.6, 1.2, and 0.7 nm (on a 4 microm x 4 microm scanning area). The chemical composition, wettability, and surface architecture of titanium thin films were characterized using X-ray photoelectron spectroscopy, contact angle measurements, atomic force microscopy, three-dimensional interactive visualization, and statistical approximation of the topographic profiles. Investigation of the dynamic evolution of the Ti thin film topographic parameters indicated that three commonly used parameters, R(a), R(q), and R(max), were insufficient to effectively characterize the nanoscale rough/smooth surfaces. Two additional parameters, R(skw) and R(kur), which describe the statistical distributions of roughness character, were found to be useful for evaluating the surface architecture. Analysis of bacterial retention profiles indicated that bacteria responded differently to the surfaces on a scale of less than 1 nm change in the R(a) and R(q) Ti thin film surface roughness parameters by (i) an increased number of retained cells by a factor of 2-3, and (ii) an elevated level of secretion of extracellular polymeric substances.

Efficient incident handling in industrial automation through collaborative engineering

We present our monitoring and decision framework for collaborative engineering for globally distributed operation, support, maintenance, and services for industrial automation. The framework provides relevant information to plant operators, engineers, staff and stakeholders to support the handling of incidents, based on semantically-appropriate factors such as personnel skills, physical location of affected equipment and dependencies between plant elements. We discuss the proposed application and present the architecture and implementation. Based on incoming events the framework selects, aggregates and displays information automatically for human processing possibly at distant control centres. For example an alarm in a manufacturing facility can trigger the display of relevant information on multiple devices such as workstations, tablets, or large control-room screens to supervisors and experts. Devices can be potentially in different locations and can comprise different visualization capabilities. The core of our framework uses semantic models and formal methods-based techniques to aggregate and process this information.

Collaborative engineering through integration of architectural, social and spatial models

We present work towards using ontological information to facilitate collaborative tasks during operation, maintenance and service of industrial automation facilities. We use semantic models as an additional layer for a collaboration framework to enable automatic reasoning, decision support and knowledge sharing among multiple parties. Documents such as texts, workflows, images, social media profiles or models of production plants can be semantically annotated to facilitate their ontological classification. Our semantic models comprise behavior and space information, as well as links between documents and from documents to external data collections, such as logs, tables and sensor data. Our semantic models can be used to check consistency, confidentiality and security properties and to support collaborative tasks.

Domain-based change propagation analysis: An enterprise system case study

Change propagation has mainly been estimated by maintenance history or source code analysis. However, sometimes history and code are inaccessible, or impractical to analyse, such as for heterogeneous sources.

Change Propagation Analysis Using Domain Information

We propose a novel methodology for analysing change propagation in software using the domain-level behavioural model of a system. We hypothesize that change propagation analysis is feasible based purely on the information visible and understandable to domain experts, trading some accuracy for productivity. Such a method is independent of formal architectural representations and may be practical for applications with heterogeneous subsystems, or missing or undocumented source code. In this paper we introduce the first phase of the methodology: creating and evaluating a connection graph of conceptual relationships between user interface components. We provide results of case studies on two web-based systems which illustrate how our methodology can be applied, and how discovered conceptual relationships match the architectural dependencies.

Visualization, Simulation and Validation for Cyber-Virtual Systems

We present our framework for visualization, simulation and validation of cyber-physical systems in industrial automation during development, operation and maintenance. System models may represent an existing physical part – for example an existing robot installation – and a software simulated part – for example a possible future extension of the physical industrial automation setup. We call such systems cyber-virtual systems. Here, we present our VxLab infrastructure for visualization using combined large screens and its applications in industrial automation. The methodology for simulation and validation motivated in this paper is based on this infrastructure. We are targeting scenarios, where industrial sites which may be in remote locations are modeled, simulated and visualized. Modeling, simulation and the visualization can be done from different locations anywhere in the world. Here, we are also concentrating on software modeling challenges related to cyber-virtual systems and simulation, testing, validation and verification techniques applied to them. Software models of industrial sites require behavioral models of both human and machine oriented aspects such as workflows and the components of the industrial sites such as models for tools, robots, workpieces and other machinery as well as communication and sensor facilities. Furthermore, facilitating collaboration between sites and stakeholders, experts and operators is an important application of our work. This paper is an extension of our previously published work [1].

Evaluating recovery aware components for grid reliability

Failure in grids is costly and inevitable. Existing fault tolerance (FT) mechanisms are typically defensive and reactive, thus unnecessarily costly. In this paper we propose a hybrid FT approach, recovery aware component (RAC), combining reactive and proactive FT, with failure recovery or aversion of user-defined granularity, by component-orientation and architecture-level reasoning about FT, to increase reliability and availability without needless performance sacrifices. We model and analyse a parameterised RAC implementation combining prediction, proactive rejuvenation and reactive restarting to varying extents, calculating cost savings, reliability improvements and cost-benefit, under parameters such as prediction frequency and accuracy.

Architecture-based fault tolerance support for grid applications

Failure in long running grid applications is arguably inevitable and costly. Therefore, fault tolerance (FT) support for grid applications is needed. This paper evaluates an extension of our prior work on Recovery Aware Components (RAC), a component based FT approach. Our extension utilizes the grid application architecture according to a small number of architectural classes. In this paper, we evaluate the MapReduce architecture only and analyze the reliability improvement MapReduce applications would gain by adopting the RAC approach. Our analysis shows that significant increases in reliability are possible at moderate extra cost. Obviously the cost of FT depends on the failure rate of the managed system, i.e., the system to be protected from faults, and the FT strategy chosen. Our work aims to give High Performance Computing (HPC) software architects the tools to control these factors for dierent grid application architectures.

Visualization facilities for distributed and remote industrial automation: VxLab

The Virtual eXperiences Lab at RMIT is a “21st century lab scope,” an enabling platform for research and prototyping in industrial automation, focusing on software engineering, next generation human-machine interaction experiences, user interfaces, and training. VxLab combines high resolution visualization, industrial automation facilities and cloud-based simulation servers in a dedicated private network. In this paper we describe the architecture, use cases, and research and innovation projects. We also present experiences applying VxLab. We present capabilities, include connected infrastructure provided by industry partners.

A Candidate Architecture for Cloud-Based Monitoring in Industrial Automation

We describe a cloud-based architecture for monitoring large numbers of industrial automation devices. We are interested in massive parallel monitoring of software components controlling critical automation facilities such as factories, mines or oil rigs. Monitoring focuses on post-deployment verification, aiming at discovering specification violations indicating for example bugs or incompatibilities in the software-based control systems. Our work facilitates the goals of industry 4.0 and smart factories. We provide an evaluation of our architecture, realised in an experimental cloud-based automation facility, and based on a combination of real and simulated trials using a message broker commonly used in cloud computing.