Vacation Research Scholarships: Information Technology, Engineering and the Environment

These scholarships give you the opportunity to earn $300 a week undertaking research for up to 8 weeks with experienced researchers, usually between November and February, in a recognised research institute or centre within the University.

2019 Vacation Scholarships will become available in August 2019. 

How to find a placement

Think about areas in which you would like to research.

You may wish to select an existing project. View the projects available for 2019/2020 here and contact the person listed for the project(s) of your choice to find out more.

If you can’t find an existing project in your area of interest, or would like to develop your own project, you will need to identify a potential supervisor. You can:

- contact a school for more information:

- use the Directory of Research Expertise to identify experts in a given research area.

- approach academics who are known to you (eg lecturers, course coordinators, program directors)

 Important Information

  • Placement are a minimum of 4 weeks to a maximum of 8 weeks with experienced UniSA researchers, between November 2019 and February 2020. 
  • To accommodate the Christmas/New Year closure of 1.5 weeks, the period of tenure may be taken in two blocks of time, subject to approval from supervisory staff.
  • The scholarship is expected to be undertaken on a full-time basis (38 hours per week) for the period of the scholarship. Hours/duration of work are to be agreed upon with your supervisor prior to the acceptance and commencement of your project.
  • Students are eligible for the centrally funded Vacation Research Scholarship once only throughout their Undergraduate/Honours degree. However, as additional scholarships may be allocated funding from schools/divisions, applications form previous recipients will be accepted for consideration. Advice should be sought directly from the relevant school/division. Please refer to the project information links for contact details.
  • Your application will require the support of a project supervisor. If you were previously unknown to the researcher, you are encouraged to submit a separate supporting statement from an academic staff member who can comment on your academic abilities.
  • This scholarship is highly competitive based on academic merit and the availability of researchers in your area of interest and unfortunately not all applicants or projects will be funded.
  • Successful applicants cannot defer the scholarship and must take it up during the time nominated.

Projects

Further information

For more information about the scholarships and how to apply click here.

Vacation Research Project Descriptions

Information Technology & Mathematical Sciences

Project Title

Project description

Analysing comments and posts in online news stories

Project keyword(s): Social media data, sentiment analysis, metadata analysis, fake news, fake content

Social media content made by online news consumers is suitable for a range of purposes, including detecting of fake news, propaganda and illicit advertising. Analysis of the content of social media content helps detect when manipulation activities are present, who is doing it and what they aim to achieve. This project will trial suitable methods for detecting fake content, such as sentiment analysis, metadata analysis and text analysis. A number of data sets are available for this analysis, including comment data from the Guardian online, The Australian online, Twitter and the ABC Drum online. (Suitable for PhD, Masters, Honours, and Vacation Scholarship)

Contact: Prof Helen Ashman | E Helen.Ashman@unisa.edu.au
| T 8302 3741 | URL  http://people.unisa.edu.au/Helen.Ashman

Digital transformation and Strategy

Project key words: Holistic Digital Transformation, Strategy, Business Model

Project summary: Digital Transformation of a business can change the entire business model and transform its core business, i.e. how the business functions, its value proposition, how it reaches its target market how it makes a profit. An enterprise-wide digital transformation therefore requires a strategic approach and the development of a digital transformation strategy to fully leverage the opportunities and impact of new technologies. (suitable as Masters or PhD project or Vacation Scholarship)
Contact person Assoc Professor Nina Evans
E Nina.Evans@unisa.edu.au | T 8302 5070; URL https://people.unisa.edu.au/Nina.Evans

Digital Transformation and Business processes 

Project key words: Digital Transformation, Business Process, Business Process Management

Project summary: Digital transformation refers to the transformation of business functions (e.g. marketing, operations, human resources) and business processes (the operations, activities and tasks) to achieve a specific business goal. Business process management is essential in digital transformation and includes business process optimisation and business process automation that can be achieved with new technologies. (suitable as Masters or PhD project or Vacation Scholarship)
Contact person Assoc Professor Nina Evans
E Nina.Evans@unisa.edu.au | T 8302 5070; URL https://people.unisa.edu.au/Nina.Evans

Comparison of start-of-art anomaly detection algorithms in timeseries data

Project keyword(s): Timeseries, Anomaly detection, Matrix profile, Motif detection

Timeseries data is produced in many applications. For example, in water industries, sensors are used to detect water quality in terms of chemicals and turbidity, water flow rate and pressure in the supply network, and water level in pools and tanks etc. One of the problem in timeseries data is the detection of events. An example of an event is the sudden change in some water quality measurements, e.g., the sudden changes in water turbidity. The consequence of such an event is that the end users will get water that looks muddy or tests strangely. Early detection of such events in water is critical for early warning and adequate processing.

This project aims to implement and compare some newly developed algorithms in the research community for detecting anomalies. The data used in the comparison was collected from industry. The final goal of the project is to use these the implementation of the algorithms to detect events (or measurement changes) in the timeseries data. The algorithms are described in the references. If you work on the project, you will collect the implementation of these algorithms and make them run in our computer, and run on our data, and plot the final results as figures. Students must have software skills. (Suitable as Vacation Scholarship project)

Contact: Dr Jixue Liu | E jixue.liu@unisa.edu.au; T 8302 3054; URL  http://people.unisa.edu.au/jixue.liu

Digital Transformation and the key role of data, information and knowledge (information assets)

Project Key Words: Data, Information and Knowledge Management

Project Summary: Organisations deliver value to their clients by deploying four types of scarce and valuable resources, namely their Financial Assets (money), Human Assets (people), Physical Assets (property, infrastructure, hardware and software) and Information Assets (data, information and knowledge). In an increasingly complex and growing business landscape, the focus is on intangible information assets (the ‘lifeblood’ of any business). Data, information and knowledge are vital for communication, collaboration and innovation and managing information as a vital business asset enables organisations to improve decision making, mitigate risk, reduce costs and increase revenue. The exponential growth of unstructured data, the need to derive meaning and insights from the information and leverage it at the right time for the right reasons are critical. Information must be governed and managed by the business in a relentless drive to gain business benefit. (suitable as Masters or PhD project or Vacation Scholarship)

Contact person Assoc Professor Nina Evans
E Nina.Evans@unisa.edu.au | T 8302 5070; URL https://people.unisa.edu.au/Nina.Evans

Digital Transformation and Human Behaviour

Key Words: Digital Transformation, Organisational Behaviour, People

Project Summary: A study of Digital Transformation should include a focus on the way people use technologies and understanding the changing behaviour, expectations and needs of all stakeholders is crucial in change projects to enhance customer-centricity, user experience, worker empowerment and develop new workplace models. The human element also refers to key aspects of change management, namely collaboration, culture change, ecosystems, skills development and empowerment. People still value human and face-to-face interactions and Digital Transformation also plays a role in such non-digital interactions and transactions, for example by empowering customer-facing agents. (suitable as Masters or PhD project). 

Contact person Assoc Professor Nina Evans
E Nina.Evans@unisa.edu.au | T 8302 5070; URL https://people.unisa.edu.au/Nina.Evans

Exploring options for large dataset management for data sharing

Project keyword(s): Advanced database systems, distributed systems, data sharing, big data

Advanced remote sensing devices produce large amount of data. For example, when drones flying over a certain area can produce videos of large sizes. Airplane-born remote sensing  devices can record huge amount of data about geography and plantation of an area. High resolution satellite images also occupy large amount of storage spaces. Data from these devices needs to be collected, stored, retrieved, shared, and analysed by users from different locations with different needs. For example, some users are interested more in analysing images to find whether a person is in the images while other users are interested in analysing videos to find whether an event is related to the videos.

This project aims to explore options for the management and use of such data with the current state of the art technologies. The deliverables of the project are a report and slides that introduce these data management options, the cost for the options, and the advantages and disadvantages to the end users. The method for conducting the project work is mainly to search for information about advanced data management systems for such applications and summarize and compare these systems in different platforms such as cloud and local shared storage etc.

Contact: Dr Jixue Liu | jixue.liu@unisa.edu.au; T 8302 3054; URL http://people.unisa.edu.au/jixue.liu; | Prof Jiuyong Li | E Jiuyong.Li@unisa.edu.au; T 8302 3898; URL http://people.unisa.edu.au/Jiuyong.Li |  

Identifying cancer subtypes from multi-levelled biological data with computational methods 

Project keyword(s): Computer Science, Bioinformatics

Cancer is a leading cause of death, accounting for more than 8.2 million of deaths worldwide, or 22,000 people every day. In the past decade, personalised medicine, using genetic information to develop cancer-specific medication, has become a strong focus for health researchers. An important step in this personalised medicine framework is to identify cancer subtypes, as different cancer subtypes may have different treatment therapies. Since cancer is an extremely complex and heterogeneous disease, the personalised medicine framework relies heavily on achievements of advanced research in system biology (Wang, 2010).  System biology approaches use knowledge in Mathematics, Statistics and Computer Science to solve the biological problems. This project will study the computational methods for identifying cancer subtypes using multi types of biological data. Examples of related works are in (Wang et al. 2014, Liu et al. 2014). Background in Biology is an advantage but not a compulsory requirement. (Suitable as PhD, Masters, and Vacation Scholarship project)

Contact: Dr Thuc Le, Professor Jiuyong Li E Thuc.Le@unisa.edu.au; T 8302 3996; URL http://people.unisa.edu.au/Thuc.Le

Influencing Reddit readers through upvote manipulation

Project keyword(s): Social media manipulation, Reddit, fake news, fake content, vote manipulation

Reddit is one of the world's largest social media sites, organised into subject-based forums called subreddits. It has a content voting system as well as a commenting system that lets users upvote or downvote posts made by other users. We have already shown that it is possible to manipulate the Reddit voting system so that some posts are given the highly-desirable front page status, so they are seen by many more users than usual. However the level of possible manipulation seems to vary, with posts on the AskReddit forum being much more manipulable than posts on The_Donald. This project will extend this work by determining whether there are characteristics of different subreddits that make them easier to manipulate, such as the controls of sentiment and topic, being political or non-political, or perhaps the filter bubble effect. (Suitable for PhD, Masters, Honours, and Vacation Scholarship)

Contact: Dr Siu Wai Ho | Prof Helen Ashman | E Helen.Ashman@unisa.edu.au

| T 8302 3741 | URL  http://people.unisa.edu.au/Helen.Ashman

Digital transformation and digital leadership

Key Words: Digital Leadership, The role of the CIO, Business/IT alignment

Project Summary: Digital transformation is led from the top and the CEO, Chief Digital Officer or Chief Information Officer play important roles. Linear management thinking and siloed approaches should be replaced by hybrid, integrated, inclusive and fluid ecosystems. The main challenge for executives is to understand the impact of transformation on many areas, such as customer-centricity, business process reengineering, cybersecurity and IT. In modern day organisations much more is also expected of the IT executive/CIO as the role is transformed to that of a valuable business leader. The Business/IT relationship is key and closing the gap between them will ensure that business and IT professionals focus on the same goals. (suitable as Masters or PhD project). 

Contact person Assoc Professor Nina Evans
E Nina.Evans@unisa.edu.au | T 8302 5070; URL https://people.unisa.edu.au/Nina.Evans

Investigating genetic causes of cancer through complex gene regulatory networks 

Project keyword(s): Computer Science, Bioinformatics

This project will study the computational methods for identifying the genetic causes of cancer through gene regulatory networks containing multiple gene regulators. Gene regulatory networks play an important role in every process of life, and understanding the dynamics of these networks helps reveal the mechanisms of diseases. There have been tremendous works on inferring gene regulatory networks. However, most of the works consider the networks with only one type of gene regulator, such as transcription factors (Imam et al., 2015) or microRNAs (Le, 2013), thus only help reveal part of the whole regulatory network picture. This project aims to develop methods to construct gene regulatory networks that contain multiple types of gene regulators and methods to isolate sub-networks that are altered between normal and cancer patients. Examples of related works are in (Le et al. 2013, Ping et al. 2015). Background in Biology is an advantage but not a compulsory requirement. (Suitable as PhD, Masters, and Vacation Scholarship project)

Contact
: Dr Thuc Le, Professor Jiuyong Li E Thuc.Le@unisa.edu.au; T 8302 3996; URL http://people.unisa.edu.au/Thuc.Le

Deep neural networks for human emotion recognition 

Project keyword(s): Computer Science, Wearable Computing, Teleconferencing

Newly developed "deep learning" methods have reignited the field of neural networks in the last few years. For example, Google DeepMind recently announced the first computer program that plays the game of Go at human expert level, and this relied on deep learning.

The aim of this project is to design software that learns to robustly recognise human emotions, by making use of multiple types of sensor data, such as video, still images, and biometrics. It is expected that the main algorithm to be implemented will be a deep convolutional neural network. It will be based on the earlier work of Yu and Zhang [1] who have been able to get emotion recognition rates of up to 85% with a neural network technique.

The context of use will be explored if emotional recognition code can be developed that can run in near real time on live camera video and so provide feedback on user emotion while operating a computer interface. For example, using the video feel from a laptop camera to monitor the emotions of a person in front of it. (Suitable for Vacation Scholarship)

Contact: Professor Mark Billinghurst | E Mark.Billinghurst@unisa.edu.au | T 8302 3747 | URL http://people.unisa.edu.au/Mark.Billinghurst

Face to face collaboration using HoloLens 

Project keyword(s): Computer Science, Wearable Computing, Teleconferencing

The Microsoft HoloLens hardware combines a see-through head mounted display with excellent indoor tracking, and so provides an ideal platform for Augmented Reality. In this project we want to explore how the HoloLens could be used to enhance face-to-face collaboration.

The project will involve developing an example HoloLens application that will allow two people in the same room to view and interact with the same virtual content. This will build on earlier work that we have done in face-to-face AR interaction [1][2]. In addition we will explore novel interaction methods such as using virtual cues to show where people are looking, and enabling users to see from each other viewpoints. (Suitable for Vacation Scholarship)

Contact: Professor Mark Billinghurst | E Mark.Billinghurst@unisa.edu.au | T 8302 3747 | URL http://people.unisa.edu.au/Mark.Billinghurst

Gaze based remote conferencing 

Project keyword(s): Computer Science, Wearable Computing, Teleconferencing

For a number of years people have been studying how head worn cameras (HWCs) and head mounted displays (HMDs) can be used for remote collaboration on physical tasks. The HWC allows a remote expert to see what the local user is doing, while a HMD can allow the remote expert to provide Augmented Reality (AR) virtual cues overlaid on the local user’s view of the real world to help them complete the task. For example, in a remote maintenance task, workers using a wearable AR interface were able to reduce their task performance time by up to 30% [1].

In face-to-face conversation gaze provides information about where a person is directing his or her attention and so it could also be an important cue in remote collaboration. Previous research has found that sharing gaze between two remote collaborators significantly improved performance on a desktop visual search task, compared to audio only communication [2]. However there has been little research conducted on sharing gaze cues from a wearable collaborative system. In this project we want to explore the effect of adding gaze tracking to wearable systems for remote collaboration. 

The work would extend our earlier pilot work in this area [3] and involve the following: Background research on gaze tracking in collaborative systems, Create a prototype system integrating a HMD, HMC and eye-tracker, Conduct user studies with a variety of physical tasks, and Write research report. (Suitable for Vacation Scholarship)

Contact: Professor Mark Billinghurst | E Mark.Billinghurst@unisa.edu.au | T 8302 3747 | URL http://people.unisa.edu.au/Mark.Billinghurst

An Industry Standard Unstructured information management Implementation

Project keyword(s): Software Engineering, Natural Language Processing

Modern text analysis applications require many different tools (existing and novel) to work together across multiple platforms, programming languages, and architectures. These tools must work in parallel or sequential pipelines that can be configured and customised by the application developer. Therefore, standardised execution architectures, such as the UIMA (Unstructured Information Management Architecture) standard released by OASIS, are a necessity for ensuring the interoperability and consistent execution of different tools combined in an application. To this end, we are looking for a student to develop a standards compliant implementation of UIMA. Ability and willingness to learn at least one new programming language is expected.  The project is associated with multiple industry/defence application scenarios.  (Suitable for Vacation Scholarship)

Contact: Professor Markus Stumptner, Dr Matt Selway | E Markus.Stumptner@unisa.edu.au | T 8302 3965 | URL http://people.unisa.edu.au/Markus.Stumptner

Automated Transformation of Supply Chain Data

Project keyword(s): Automated Software Engineering, Data Integration, Industry 4.0

The digitalisation of industrial supply chains is moving rapidly towards providing end-to-end transparency for large transport customers.  Dedicated data models are being developed for data exchange along the supply chain, e.g. from trucking companies to customers, along the lines of data models already existing in the air traffic management domain.  The project will examine the development of particular data model adapters and business rules for supply chain interactions.  The project is associated with a major industrial supply chain customer company.  (Suitable for Vacation Scholarship)

 Contact: Professor Markus Stumptner, Dr Georg Grossmann | E Markus.Stumptner@unisa.edu.au | T 8302 3965 | URL http://people.unisa.edu.au/Markus.Stumptner

Beyond Google search: Learning semantic relations

Project keyword(s): Knowledge Acquisition, Machine Learning

A common technique in Natural Language Processing, popularised by Google’s word2vec, is to analyse a corpus of text to produce co-occurrence vectors for each word (basically the vectors contain one dimension per word pair), which enables us to determine the similarity between two words. Such vector models, or distributional semantics, demonstrates some neat properties: for example, subtracting the vector for 'man' from the vector for 'king' and adding the vector for 'woman' results in a vector very close to that of 'queen'. However, such models are limited, finding most use in indexing and information retrieval (such as Google's search engine), tend to focus on nouns, and are not truly semantic. For example, two word vectors may be close together, not because they are similar concepts, but because they are strongly related through some (unknown) relationship. These relationships are often identifiable by the verbs and prepositions used between them. This project aims to be a starting point for inferring semantic relations between word co-occurrence vectors by incorporating the co-occurrence information of prepositions and verbs. The candidate will develop an initial prototype that will identify basic semantic relations (such as part-whole relations, and more general/more specific terms) from key prepositions as well as a small set of domain specific relations of interest.) The project is associated with multiple potential applications in defence and industry. (Suitable for PhD, Masters, and Vacation Scholarship)

Contact: Professor Markus Stumptner, Dr Matt Selway | E Markus.Stumptner@unisa.edu.au | T 8302 3965 | URL http://people.unisa.edu.au/Markus.Stumptner

Comparative evaluation of natural language parsers

Project Keyword(s): Natural Language Processing, Software Engineering, Evaluation

Recently, Google has open-sourced their Natural Language Parser and many research groups promoted or released their own in response, providing a slew of state-of-the-art parsers for academics and organisations to use in their work. At the same time, there are many commonly used parsers, such as the Stanford Parser, that are widely considered to provide good results.

This project will produce a comparative evaluation of the currently available Natural Language Parsers, including both older parsers and those newly released. The project includes the design and execution of a systematic evaluation performed on common test corpora and will form the basis for the comparison of the Natural Language Understanding engine being developed within the Knowledge and Software Engineering Laboratory. The project is associated with current collaboration grants with Defence Science & Technology and international interoperability projects in the chemical industry, air and ground transport management.  (Suitable for PhD, Masters, and Vacation Scholarship). 

Contact: Professor Markus Stumptner, Dr Matt Selway | E Markus.Stumptner@unisa.edu.au | T 8302 3965 | URL http://people.unisa.edu.au/Markus.Stumptner 

The Cooking Simulator: A lightweight testbed for serious matters

Project Keyword(s): Behaviour Representation, Modelling, Simulation, Visualisation

The Knowledge and Software Engineering Laboratory, in conjunction with Defence Science & Technology, has developed a behaviour representation language for use with combat simulations. We invite students to participate in the development of a lightweight simulation engine and demonstration platform in which behaviours can be tested rapidly before being deployed to target simulation engines. Cooking recipes will be used as example simulations. The project will comprise: development of an event-driven simulation engine; a web-based front-end for visualising the cooking processes; and the modelling of a small set of recipes for demonstrating the simulation capability. Focus and scope of the project can be shaped according to the student’s interests and level of participation (Honours/Masters, Vacation Scholarship). Moreover, there is opportunity to contribute to research papers.

(Suitable for Honours/Masters, Vacation Scholarship.)

This work is aligned with the ‘Doctrine to Code’ project funded by Australian Defence Science and Technology (DST).

Contact: Professor Markus Stumptner, Dr Matt Selway | Dr Georg Grossmann | Dr Wolfgang Mayer |E Markus.Stumptner@unisa.edu.au | T 8302 3965 | URL http://people.unisa.edu.au/Markus.Stumptner

Cybersecurity policy

Project Keyword(s): Cybersecurity, Information Systems

Cybersecurity has become an increasingly important topic, not only for IT professionals, but for almost all organisations that utilise IT systems. In this project, students will explore a contemporary aspect of cybersecurity policy for a mutually agreed upon industry sector/client. For example, students may choose to investigate the practicality of a particular industry sector hosting its corporate data using a public cloud service from privacy, security and data sovereignty perspectives. (Suitable as PhD, Masters, Honours, and Vacation Scholarship project)

Contact: Dr Ben Martini, Dr Gaye Deegan| E Ben.Martini@unisa.edu.au; T 8302 5688; URL http://people.unisa.edu.au/Ben.Martini

Understanding and Supporting Vulnerable communities in the digital age

Project keyword(s)*: Information systems, ICT for development, information behaviour, vulnerable populations

Project summary: Digital technologies have a radical impact on vulnerable people's everyday lives including searching for and using valuable information. Questions of equality and social justice are as important as ever in the information age. There have been concerns about issues around bias, social exclusion, misinformation and information sharing hazards. Vulnerable people typically include women and children, ethnic people of colour, immigrants, gay men and lesbians [currently people from LBGTQI populations], the homeless, and the elderly (Du et al., 2017). Understanding information behaviours of vulnerable communities and its evolution in the digital age is a critical area of research and practice within the field of information science and technology which provides an excellent venue to discuss this crucial topic at the intersection of information, society, and technology. This project focuses on the understanding of needs and concerns of vulnerable populations in the digital age, and the creative use and design of emerging technologies for improving individual capabilities and social inclusion. (Suitable as Vacation Scholarship, PhD, Masters, and Honours project)

Contact: Dr Tina Du | E Tina.Du@unisa.edu.au; T 8302 5269; URL http://people.unisa.edu.au/Tina.Du

AR/VR Sports Visualisation

 

Project Keyword(s): Augmented Reality, Visualization, Athlete Skill Learning and Training.

This project will investigate the use of augmented and virtual reality systems to support athletes and recreational sports participants in skill learning and training.  AR may be employed to supplement training by providing a mechanism to visualize set plays, visualize body movements/position, and visualize game performance [1,2].  The affordances of AR technology equips an athlete with the ability practice both physically and mentally anywhere and at any time, e.g. while at home, going for a walk or on the field.  Visualisations may also be used by coaching staff to assess player performance data.  Research has shown that mental rehearsal or visualisation can improve athlete performance.  This project will explore new methods of AR for visualization with the aim of enhancing sports performance, i.e. AR as a support to mental rehearsal.  This project may also explore AR as a support to movement/skill learning (i.e. kicking a football) [3,4].  The project will explore how Augmented reality systems can be employed to develop athlete training applications.  This project will develop a prototype that will allow an athlete to visualise plays (i.e. in miniature or real size); or to provide visual representation of a movement, i.e. practise kicking a football, etc.

This project is well suited for anyone interested in learning or has experience with the Unity Game Engine; an interest in sports or visualization methods. You will be part of an enthusiastic group of talented developers with diverse experience in a very collaborative and supportive group. (Suitable as Vacation Scholarship project)

Contact: Dr Jo Zucco and Dr Ross Smith E Jo.Zucco@unisa.edu.au; T 8302 3447; URL http://people.unisa.edu.au/Jo.Zucco; E Ross.T.Smith@unisa.edu.au; T 8302 5551; URL http://people.unisa.edu.au/ross.t.smith

Causality based approach to adversarial machine learning

Project keyword(s)*: Computer Science, Machine Learning, Adversarial Machine Learning, Causal Inference, Data Mining, Cybersecurity

Project summary: While machine learning and AI technologies are starting to have real impact in many areas, how to protect a machine learning system from malicious attacks has become an urgent issue. Traditionally machine learning algorithms have been developed with the assumption that the environments of model training and model testing are the same (and benign). However, when an attacker is present, the training and/or testing environments could be changed and become different, leading to wrong models or wrong predictions. In the emerging area of adversarial machine learning, different types of attacks on machine learning algorithms are being studied and some defending algorithms have been proposed. However, the defending ability of the existing algorithms are limited by their generalizability beyond the assumed adversarial scenarios or predefined constraints on the testing sample distributions. More robust machine learning algorithms are needed in adversarial settings. Causal relationships imply the underlying mechanisms of a system, and thus they are stable across different environments. Additionally, causal relationships provide better interpretability on why things have happened. In this project, we will take a causality-based approach to achieving robust and secure machine learning, by discovering causal features from data and build robust and interpretable prediction models using the causal features. We will also study different adversarial attacks and examples and develop methods and a prototype system to test the discovered causal features and the causal prediction models, in comparison with existing defending algorithms. (Suitable as PhD, Masters, and Vacation Scholarship project)

Contact person and details: A/Prof Lin Liu, Prof Jiuyong Li
E lin.liu@unisa.edu.au; T 83023311; URL http://people.unisa.edu.au/Lin.Liu
| Prof Jiuyong Li | E Jiuyong.Li@unisa.edu.au; T 8302 3898; URL http://people.unisa.edu.au/Jiuyong.Li |  

Augmented Reality Tool to Support Student Learning of Arduino Microcontrollers

Project Keyword(s): Augmented Reality, Microcontrollers, Teaching and Learning.

This project will explore the use of augmented reality technology in education.  Augmented reality enhances the user's real world with virtual information.  Augmented reality has been explored in many educational settings (e.g. anatomy, chemistry, maintenance, assembly tasks [1,2,3]), and has been shown to positively influence and improve the learning process [4,5,6].  The project aims to leverage the capabilities of AR technology in an educational setting providing engaging, authentic and active learning experiences.  The research aims to explore whether an AR system improves learning over traditional approaches (e.g. paper based instruction, etc).  This project will develop an application that will allow a user to view the Arduino microcontroller using either a head-worn display or a handheld system (such as a mobile phone) and see the device with virtual information superimposed.  The system should provide information pertaining to the Arduino, how to connect it to other components (i.e. breadboards, LEDs, sensors, buzzers, etc) and meaningful interactions with virtual information. Through AR it will also reveal invisible elements such as electron flow direction, current and voltage information and allow manual annotations to be added to support the learning process.

This project is well suited for anyone interested in learning or has experience with the Unity Game Engine; an interest in teaching and learning or visualization methods. You will be part of an enthusiastic group of talented developers with diverse experience in a very collaborative and supportive group. (Suitable as Vacation Scholarship project)

Contact: Dr Jo Zucco and Dr Ross Smith E Jo.Zucco@unisa.edu.au; T 8302 3447; URL http://people.unisa.edu.au/Jo.Zucco; E Ross.T.Smith@unisa.edu.au; T 8302 5551; URL http://people.unisa.edu.au/ross.t.smith

Develop HoloLens Stroop Task Experiment Application 

Project Keyword(s): Computer Science, Augmented Reality, ACRC, Wearable Computer Lab

In psychology, the Stroop effect is a demonstration of interference in the reaction time of a task. When the name of a colour (e.g., "blue", "green", or "red") is printed in a colour which is not denoted by the name (i.e., the word "red" printed in blue ink instead of red ink), naming the colour of the word takes longer and is more prone to errors than when the colour of the ink matches the name of the colour. Naming the font colour of a printed word is an easier and quicker task if word meaning and font colour are not incongruent. If both are printed in red, the average time to say "RED" in response to the word 'Green' is greater than the time to say "RED" in response to the word 'Mouse'.

This project is to develop an Unity3D application that operates on a HoloLens optical see through head mounted display that performs the Stroop Task. The goal is to determine if there is a difference between the Stoop Task presented on HoloLens and on a traditional monitor. A research publication will be written after a formal user study is performed. The user study will run after the summer internship. The student is welcomed to help write the paper and help perform the user study. (Suitable as Vacation Scholarship Project)

Contact: Professor Bruce Thomas | E Bruce Thomas@unisa.edu.au |T 8302 3464; URL http://people.unisa.edu.au/Bruce.Thomas

Mixed Reality Physio-therapy Assistant 

Project Keyword(s): Augmented Reality, Visualization, Rehabilitation, Gamification, Health Sciences.

This project will explore the possibility of using a mixed reality application to help aid the recovery process after physical injury [1,2]. It will explore how gamification can be incorporated into exercise routines to make the experience of recovery more enjoyable. The system is intended to support exercises for areas such as the neck, shoulder, back, knees and other areas of the body. We aim to use features such as tracking the progress made during the therapy session to inform both the patient and clinicians (such as physiotherapists) of progress and provide analysis tools [3] to evaluate the data. The system will track movements and record information such as angle for how far patients can rotate their head. It will also, display angle data when a user finishes their input, and aims to visualize data in an entertaining way.

This project is well suited for anyone interested in learning or has experience with the Unity Game Engine; an interest in gamification, rehabilitation or visualization methods. You will be part of an enthusiastic group of talented developers with diverse experience in a very collaborative and supportive group. (Suitable as Vacation Scholarship project)

Contact: Dr Jo Zucco and Dr Ross Smith E Jo.Zucco@unisa.edu.au; T 8302 3447; URL http://people.unisa.edu.au/Jo.Zucco; E Ross.T.Smith@unisa.edu.au; T 8302 5551; URL http://people.unisa.edu.au/ross.t.smith

Spatial Augmented Reality Experiments for Understanding How This Technology Improves Users Performance 

Project Keyword(s): Augmented Reality, Unity3D, User Studies

Augmented Reality is gaining acceptance within the general public. We do not understand how AR improves peoples lives. Spatial Augmented Reality is process of projecting digital information directly onto physical objects. We have been investigating how Spatial Augmented Reality usability compares with other forms of presenting Augment Reality, such head mounted displays. This project will be developing a set of Unity3D applications that are appropriate with Spatial Augmented Reality. One initial experiment to be developed is to change the user’s field of view (how much Spatial Augmented Reality) information is presented to the user.  The hypothesis is a smaller field of view would reduce the user’s ability to perform particular tasks. . A research publication will be written after a formal user study is performed. The user study will run after the summer internship. The student is welcomed to help write the paper and help perform the user study. (Suitable as Vacation Scholarship Project)

Contact: Professor Bruce Thomas | E Bruce Thomas@unisa.edu.au |T 8302 3464; URL http://people.unisa.edu.au/Bruce.Thomas

Telling Immersive Data Stories: Exploring Narrative Visualisation Techniques in Virtual Reality 

Project Keyword(s): Infographics, Virtual Reality, Vive Headsets

For over thirty years, computer-aided data visualisation has been a powerful tool in the data scientist’s toolbox to externalise thought processes and help build an understanding of real-world phenomena. Techniques like scatterplots, sparklines, and even the humble barchart, have been used to gain insight into social and world issues like poverty and global warming to great success. Communicating these finding, however, has often been a challenge in itself. These visualisations commonly require some level of interpretation and explanation — the insights must be communicated. To the layman, these visualisations can be inscrutable and do not translate into actionable knowledge.

Infographics — data stories using leveraging visualisation and narrative techniques — has been a recent solution to the dilemma of communicating data. Mastered by the likes of the New York Times, infographics, and more broadly “Narrative Visualisation”, have been successful in communicating complex insights into subjects as diverse as politics, crime, and even Netflix, to millions of readers around the world. More recently, these Narrative Visualisations have moved to the web to introduce elements of interactivity to aid the reader in internalising the underlying insights.

Virtual Reality (VR) has seen a recent surge in popularity with the introduction of the Oculus and HTC Vive headsets. The fidelity and level of immersion provided by these modern headsets is unprecedented, so much so that data visualisation researchers are beginning to explore the potential of the medium in a new research field known as Immersive Analytics. It then begs the question, what is Narrative Visualisation in the world of Virtual Reality?

This project will develop new techniques for navigating and interacting with data stories within virtual reality environments. The researcher will investigate modern techniques for storytelling used in VR and build a set of VR data stories that explore the influence that various methods of interaction and feedback (such as sound, walking and haptics) have on the delivery of the narrative.

Contact: Dr Andrew Cunningham | E: andrew.cunningham@unisa.edu.au; T: 8302 3081; URL: http://http://people.unisa.edu.au/Andrew.Cunningham

Probabilistic forecasting of wind farm output

Project keyword(s)*: windfarm, probabilistic forecasting, probability of exceedance (POE), quantile regression

Project summary: After two decades of operation and delivering reliable and secure energy to Australian customers, the National Electricity Market (NEM) is now undergoing considerable reform and review given the level and number of changes underway in the electricity industry. The rise in Distributed Energy Resources (DER); retirement of old fossil fuel plants; emissions reduction commitments; price competitiveness of renewable energy technologies and projected uptake, particularly wind and solar; are stirring the current market frameworks and creating the need for new models for forecasting short- and long-term generation from utility-scale wind and solar energy. We will use quantile regression techniques for forecasting wind farm output, in order to inform the operators of probability of exceedance of various levels of output. We will compare the results with other methods and also with the Australian Wind Energy Forecast System (AWEFS). (suitable as Masters or PhD project or Vacation Scholarship)

Contact: Professor John Boland | E John.Boland@unisa.edu.au; T 8302 3464; URL http://people.unisa.edu.au/John.Boland

Causal knowledge discovery across multiple domains

Project keyword(s)*:Computer Science, Data Mining, Machine Learning, Causal Discovery, Transfer Learning, Domain Adaption

Project summary:Causal knowledge discovery plays an essential role in evidence based decision making. Before a proposed decision is made, e.g. applying certain treatment to a cancer patient or deploying a new government policy in a region, if we can estimate or predict the effects of the treatment or the policy on the patient or the population in the region and identify the factors affecting the effects,  it would help with achieving the expected outcomes, and/or saving lives or money by avoiding wrong actions. However, how to obtain reliable estimation of causal effects from big observational data and how to identify those causal factors accurately are still open questions. Recently, there has been some exciting advancement in applying efficient data ming and machine learning techniques to causal knowledge discovery from big data. A new trend in this line of research is to exploit cross-domain and multiple sources of data to achieve reliable causal discovery.  The aim of this project is to develop novel machine learning and causal inference methods for accurate and fast discovery of causal knowledge from multiple domains and data sources, by leveraging the existing causal inference methods for single source data and the new developments machine learning and data mining, especially those in the area of transfer learning and domain adaption. (Suitable as PhD, Masters, and Vacation Scholarship project)

Contact person and details: Prof Jiuyong Li, A/Prof Lin Liu

E Jiuyong.Li@unisa.edu.au; T 8302 3898; URL http://people.unisa.edu.au/Jiuyong.Li

E lin.liu@unisa.edu.au; T 83023311; URL http://people.unisa.edu.au/Lin.Liu

Singular perturbations in differential equations 

Project Keyword(s): Applied Mathematics

The goal is to study the behaviour of solutions to a differential equation, when the equation itself depends on a parameter. The case of interest is when the parameter vanishes, and makes the higher derivatives disappear from the equation. Many examples are known, but not a full general theory. (Suitable for summer or semester-long research project)

Contact: Dr Jorge Aarao | E Jorge.Aarao@unisa.edu.au | T 8302 3741 | URL  http://people.unisa.edu.au/Jorge.Aarao

Machine learning and causal inference techniques for data-driven personalised decision making

With the rapid accumulation of big data, data-driven personalised decision making is becoming a reality in various areas, such as personalised online recommendation, precision medicine targeting specific patient subgroups and personalised teaching and learning. In the area of causal inference, heterogeneous treatment effect estimation has been studied extensively, with the goal of identifying the different effects of a treatment on different subpopulations or individuals. Recently machine learning techniques have been introduced for heterogeneous treatment effect estimation to deal with large and observational data, e.g. gene expression for identifying patient subgroups characterised by their distinct genetic features which possibly have led to the heterogeneous effects of a cancer treatment in the different subgroups. The existing machine learning techniques, however, are facing two major challenges: how to accurately identify subgroups from observational data and how to efficiently deal with large scale and multiple sources of data. This project aims to develop new machine learning and causal inference techniques to tackle the challenges. The outcome of the project can be applied to various application areas, e.g. medicine, particularly cancer treatment, business intelligence, and government policy making. (Suitable as PhD, Masters, and Vacation Scholarship project)

Contact person and details: Prof Jiuyong Li, A/Prof Lin Liu

E Jiuyong.Li@unisa.edu.au; T 8302 3898; URL http://people.unisa.edu.au/Jiuyong.Li

E lin.liu@unisa.edu.au; T 83023311; URL http://people.unisa.edu.au/Lin.Liu

Photorealism in Virtual Reality

Centre/Institute: IVE: Australian Research Centre for Interactive and Virtual Environments

Project keyword(s)*:  Virtual Reality, Photo Realism, Graphics

With the advent of the next generation graphics cards for NVidia, real-time raytracing is now in standard graphics cards. This project will investigate how this new capability will improve a user’s VR experience. The project will investigate how the new technology translates into the software development cycle. What are the performance effects of this technology? Are there impacts on user interactions? What other benefits are there for the development of VR applications. The current Scene Graph approach does not translate will to ray-tracing technologies, but asspects such as simulation and particle systems are well supported by ray-tracing. (Suitable as a final year or PhD project)

References: Wald, Ingo, et al. "Applying ray tracing for virtual reality and industrial design." 2006 IEEE Symposium on Interactive Ray Tracing. IEEE, 2006.

Sherman, William R., and Alan B. Craig. Understanding virtual reality: Interface, application, and design. Morgan Kaufmann, 2018.

Contact person and details: Professor Bruce Thomas

E Bruce.Thomas@unisa.edu.au; T 8302 3464; URL http://people.unisa.edu.au/bruce.thomas

Large Network Visualisation and Interaction for Data Analytics
 Centre/Institute: IVE: Australian Research Centre for Interactive and Virtual Environments

Project keyword(s)*: Large Network Visualisation

In this project, students will build an interactive network visualisation tool to explore large datasets. Networks form a fundamental structure for numerous problem domains. For example, social networks represent relationships that exist between people. Analysis of such networks can identify the potential spread of misinformation or can reveal hierarchy and cliques that were not immediately obvious. 

There are a class of analytical problems related to networks that cannot be solved through automated means. To illustrate, an analyst working in the domain of federal tax may want to identify complex tax fraud that might be occurring within a network, however the means by which fraud is occurring is unknown; the people within the network are constantly inventing new means to evade detection. This problem is only exacerbated in the global connected age, where social networks can involve tens of thousands of people. The analyst requires tools that support the interrogation of the network in order to develop new insights. 

Visualisation is the externalisation of information that supports the analytical process. Network visualisation is a well-explored area of research, however the visualisation of large networks remains an open question. Students in this project will build a performant, interactive network visualisation tool to visualise large network data. Students will be exposed to data analytics methods for understanding social network data as well as graph rendering techniques.

Contact person and details: Dr Andrew Cunningham

E: andrew.cunningham@unisa.edu.au; T: 8302 3081; URL: http://http://people.unisa.edu.au/Andrew.Cunningham

Underwater robotic vision 

Project keyword(s)*: computer vision, robotics, underwater
Marine ranching is a sustainable approach for fisheries. As oppose to traditional fish farming with a high density of a single type of fish in nets which raises issues such as pollution, marine ranching develops underwater infrastructure to facilitate an underwater ecosystem for sustainable fisheries. For marine ranching, fixed sensors face limitations in the range and distance of viewpoint. Underwater robotics equipped with visual and sonar sensors helps providing dynamic and close viewpoint to better perform the environment survey and monitoring. This project will apply 360 vision for underwater robots, and apply machine learning algorithms such as deep learning for monitoring the underwater ecosystem, 3D reconstruct the seabed with the presence of non-rigid objects like seagrass and underwater creatures, and provide accurate detection, recognition and counting among different fish species. The underwater robot can also be applied for urban water system monitoring, for instance automated inspection of waste-water tank. Students will be working on BlueROV underwater robot equipped with various sensors, and utilise GPU servers (including local servers equipped with Titan X and RTX 2080 Ti, and remote supercomputer equipped with K80/P100/V100) for processing and analysing the captured visual and sensing data. (Suitable for PhD, Masters, Honours, and Vacation Scholarship)

Contact person and details: Dr Ivan Lee, Dr Lin Xu

T 8302 3051 E Ivan.Lee@unisa.edu.au URL http://people.unisa.edu.au/Ivan.Lee

Scholarly big data analytics 

Project keyword(s)*: computer science, big data

Academic institutes play an important role in exploring new concepts and innovation through research activities. With limited resources such as financial support from public and private sectors, it is crucial to explore research concentration to help planning for strategic research priorities. By utilising the scholarly big data, this project investigates revealed symmetric comparative advantage of weighted research publications, and applies fitness and complexity models to analyse the research diversification and field sophistication among Australian universities. This project also explores the relationship between opportunity value, research complexity, and the fitness level among academic institutes. Other research extensions include, but not limited to, scholar social networks, research trend forecasting, scholar profiling and collaborator recommendation. (Suitable for PhD, Masters, Honours, and Vacation Scholarship)

Contact person and details: Dr Ivan Lee

T 8302 3051 E Ivan.Lee@unisa.edu.au URL http://people.unisa.edu.au/Ivan.Lee

Vision-based 3D reconstruction using deep learning 

Project keyword(s)*: computer vision, computer graphics, 3D reconstruction

Vision-based 3D reconstruction techniques such as structure from motion and visual SLAM relies on quality images for feature points matching. In the presence of low visibility conditions caused by fog, haze, dust, rain, and snow that degrade image quality, performance of 3D reconstruction will be affected. Effective image restoration, novel feature descriptor resilient to degraded image, and improved feature points matching, are feasible approaches to address the issue. Alternative approach by deploying deep convolutional neural network can be also applied. 3D reconstruction with other imaging techniques, such as holographic microscopy, stereoscopic vision, low resolution infra-red imaging, are potential extensions of this project. Students will be utilising GPU servers (including local servers equipped with Titan X and RTX 2080 Ti, and remote supercomputer equipped with K80/P100/V100) for this project. (Suitable for PhD, Masters, Honours, and Vacation Scholarship)

Contact person and details: Dr Ivan Lee

T 8302 3051 E Ivan.Lee@unisa.edu.au URL http://people.unisa.edu.au/Ivan.Lee

Multi-label classification of clinical time series data for patient phenotyping in the Intensive Care Unit using deep neural networks 

Project keyword(s)*: Electronic Health Records, Clinical time-series forecasting, Natural language processing, Long Short-Term Memory networks, Attention mechanism

 With most hospitals adopting Electronic Health Records (EHR), a vast amount of existing medical and biometric data has become available to researchers. This data makes it possible for researchers to build predictive models that can analyse patient data from charts, live monitoring equipment etc. This project uses one such dataset to predict patient phenotype – the overall picture a patient presents in the ICU; i.e. the conditions s/he may be currently experiencing, progressing towards or at a risk of developing. This project hopes to predict medications and interventions to be administered along the way. This can aid secondary analysis of patient management and may also assist medical inventory management. (Suitable for PhD, Masters, Honours, and Vacation Scholarship)

Contact person and details:Dr Ivan Lee

T 8302 3051 E Ivan.Lee@unisa.edu.au URL http://people.unisa.edu.au/Ivan.Lee

Dynamic scene saliency map for modelling visual attention

Project keyword(s)*: eye tracking, electroencephalogram, attention, wearable computing

 Saliency map by visual scene analysis models human selective visual attention, and the study attracts growing interests with applied machine learning algorithms to process static images for the analysis of spatial visual attention. This project aims to explore the dynamic visual attention with head and gaze movements. Combined analysis of gaze points and the EEG readings will be investigated, in conjunction with other wearable sensor readings such as from biometric sensors. Students will develop an embedded eye-tracking device, as well as utilising Emotiv EPOC Headset for the study. Students are also encouraged to discuss applications of other wearable sensing devices such as Perception Neuron motion capture device. (Suitable for PhD, Masters, Honours, and Vacation Scholarship)

Contact person and details: Dr Ivan Lee

T 8302 3051 E Ivan.Lee@unisa.edu.au URL http://people.unisa.edu.au/Ivan.Lee

Identifying and categorising radio interference

Project keyword(s)*: Computer science, radio spectral analysis, satellite

Project summary: The 406MHz frequency band is allocated world wide for satellite based Search and Rescue beacons.  Increasingly unwanted radio signals from ground emitters are interfering with the band.  A skilled operator can identify and characterise unwanted interferers by viewing the spectrogram.   Ideally this would happen continuously and in real-time.  The problem is to automate this process for spectrum monitoring.  Representative measured signals and spectrograms are available plus algorithms to detect wanted signals (Suitable for Vacation Scholarship)

Contact person and details: Adjunct Associate Research Professor Mark Rice, Associate Professor Mark McDonnell

E mark.rice@unisa.edu.au; mark.mcdonnell@unisa.edu.au; T 0418163980; URL https://people.unisa.edu.au/Mark.Rice  https://people.unisa.edu.au/mark.mcdonnell

Algorithms to encapsulate liquids in 3D printed materials

Centre/Institute: IVE: Australian Research Centre for Interactive and Virtual Environments

Project keyword(s)*: 3D printing, Software Algorithm, Materials Science

 This project aims to find methods of multi material 3D printing that will allow printed liquids to be enclosed in printed cells. Initially the project focus will investigating software algorithms and physical geometries that are suitable to support these types of operations. The work will be done in conjunction with the Future Industries Institute to provide support for the materials aspects of the project. Students will be given access to a range of 3D printers (both filament and resin based) and will be able to see the tangible outcomes of the software algorithms they develop. This project is well suited to students looking to gain programming experience and possibly have an existing interest or experience with 3D printers. (suitable as Masters or PhD project or Vacation Scholarship)

Contact: Dr Ross Smith

E ross.smith@unisa.edu.au Dr Nikki Stanford nikki.stanford@unisa.edu.au

Natural and Built Environments 

Project title

Project description

Aggregating transportation needs 

Project keyword(s)*: travel origin, travel destination, daily activity, transportation provision, on-demand services, IoT

In public transport, transportation demand and supply cannot be easily matched. As a consequence, many households who may want to travel by public transport have to drive private car to go everywhere. This issue leads to congestion, environmental and economic problems.
This project proposes an online based solution that helps capture spatial-temporal information of travel needs. This online tool can be utilized by inputting activity origin-destination, with preferred travel time for specific activity purposes to inform transport service providers to provide relevant services.
This study aims to develop an online based tool that can easily capture people’s travel information which include activities of:
• Review characteristics of the spatial temporal information regarding travel
• Develop an online tool to capture travel’s spatial temporal information
• Synthesise data captured into an aggregated travel pattern
The project results will contribute to better data for transport planning and a better understanding of travel behaviour. The project requires strong interests in IT-based app development and transport related application.

Contact person and details: Dr Li Meng, 83023223, li.meng@unisa.edu.au
Dr Wolfgang Mayer, Wolfgang.mayer@unisa.edu.au

Modelling human flow in the Jeffrey Smart building

Project keyword(s)*:  Human flow data, movement model, hot spot, building design, behaviour estimation.

Understanding users’ movement behaviour helps building design and improvements. The Jeffrey Smart Building has been equipped with the newest building design and smart technology and thereby has become the most popular destination of the University of South Australia. A large amount of data has been collected about people’s location and movement through intelligent technologies. Some of the data have been utilized to understand users’ preferences of location and length of stay in the Jeffrey Smart Building which can further contribute to building design and management, such an example is that the ISTS has generated a heat map.
In recent years, human movement in the building has drawn significant attention as a useful tool to help understand building design and facility plans and even disaster management such as fire evacuations.
Research aim and objectives
This project aims to construct a user’s movement model by using collected location data for better plan building design and equipment procurement.
Areas of focus:
• Review contemporary movement modelling and software and their contributions to building design and management
• Utilize spatial temporal data to develop a users’ movement model
• Test the model and seek the pattern of movement behaviour
The designed can be widely applied in many areas such as airport, shopping mall and urban street.

Contact: Dr Li Meng, 83023223, li.meng@unisa.edu.au
Dr Wolfgang Mayer, Wolfgang.mayer@unisa.edu.au

Mr Karl Sellmann, carl.sellmann@unisa.edu.au
Mr Justin Faggotter, justin.faggotter@unisa.edu.au

Innovative Method of Utilising Hydrogen Peroxide for Source Water Management of Blue-green algae

Project keyword(s): Water Management, Algal bloom control, Environmental Science and Analytical chemistry

Cyanobacteria (blue green algae) is an ongoing problem in water reservoirs, wastewater systems, and recreational water in South Australia. SA Water is currently developing a new innovative control method based on applying hydrogen peroxide. The use of hydrogen peroxide has a lot of advantages over the existing method of using copper sulphate algaecide. The aim of this research is to investigate the impact of different water chemistries on hydrogen peroxide decomposition and identifies the most efficient dose range in maintaining residuals to control algal blooms. The student will develop the project together with the SA Water team and also spending time at the SA Water laboratory and possible some field works with the objective to implement and provide an engineering solution to manage the blue-green algae issue

Contact: Dr Leslie Huang | E leslie.huang@unisa.edu.au;
| T 0451 595 426 | URL  http://people.unisa.edu.au/Leslie.huang

Dr Peter Hobson E Peter.Hobson@sawater.com.au 

The first record of subduction in the ancient Earth

Project keyword(s): Subduction, early Earth, Tanzania, metamorphism, high pressure

Subduction is a well-known process that recycles oceanic crust into the mantle. While the behaviour of subduction systems is well understood, what is poorly known is when modern-style subduction began on Earth. The record of this process is encoded in the formation of low geothermal gradient but high pressure rocks. These rocks seem to have emerged in the geological record only in the last 2 billion years, or put another way, in the most recent 40% of Earth’s history. Our research group has collected a suite of samples from central Tanzania that at face value represent the oldest products of modern-style subduction ever found. The aim of this project is to undertake thermodynamic modelling of the mineral assemblages in these ancient and unique rocks, to determine their thermal gradients and rates of exhumation, thereby constraining the thermal regimes that existed in probable early subduction systems.

Contact: Associate Professor Tom Raimondo | E Tom.Raimondo@unisa.edu.au
| T 0451 595 426 | URL  http://people.unisa.edu.au/Tom.Raimondo
Dr Laura Morrissey | E Laura.Morrissey@unisa.edu.au | T 0451 595 426 | URL  http://people.unisa.edu.au/Laura.Morrissey

Innovative treatment to reuse drinking water treatment sludge in concrete paver

Project keyword(s): Circular Economy, Materials Science, Concrete, Waste Disposal, Environmental Management.   

In the conventional coagulation-filtration treatment process, suspended solids and natural organic matter are removed from the raw water supply by the addition of aluminium and iron salts as coagulants, resulting in the production of water treatment residuals (WTR) or sludge. It has been estimated that SA Water generates 10,000 to 15,000 tons of sludge annually and the generation of sludge will increase with the rapid growth of population. The main solid components in sludge are irregular sized dust-like sand (~70% with size below 2mm) and stone particles (~10% with size of 9.5mm or above), and the main chemical components include aluminate (~10% by weight) mixed with or adhered to the sand and stone. The particle size and chemical composition analysis of the sludge suggested possible beneficial use of sludge to partially replace soil, sand and coarse aggregate (stone) in making concrete paver. The project aims to develop sustainable high-performance concrete pavers through recycling of drinking water sludge.  This is an innovative idea as no previous research was found in the literature and the proposed project will provide dual benefits to both water treatment and concrete construction industry.  If successfully implemented, this new type of concrete paver will have many advantages over normal concrete paver, such as higher strength, superior durability and superior corrosion resistance due to high content of aluminium in sludge. This project enable the student to gain experience with two different industries, particularly as a team member of this multidisciplinary research team.

Contact: Professor Yan Zhuge | E yan.zhuge@unisa.edu.au 
| T 8302 3093 | URL  http://people.unisa.edu.au/yan.zhuge

Dr Danda Li | E danda.li@unisa.edu.au | Dr Chris Chow | E chris.chow@unisa.edu.au

Asset Maintenance Data Platform: A case study to utilise multiple operating and maintenance data in asset management decision support

Project keyword(s): Asset Maintenance, Asset Management, Data Quality Assurance, Water Industry.   

Water utilities are generally asset intensive organisations and as such needs to ensure that the management of assets is carried out efficiently and effectively to deliver the required service levels at an affordable (lowest possible) price for the customer. Good business decision requires the support of data but often the first step in this process is to obtain good quality data, including a data quality assurance step, before the analyst can perform data analytics task for various purposes. Data management is becoming more important as we are producing larger and more complex data than ever before. There is a large volume of data being collected as part of the asset management process via monitoring, measurement, analysis and evaluation, to support decision making, however, part of this data can go into the situation of commonly described as ‘Data rich and information poor’ or at least not fully utilised. The project will provide mechanisms to identify high value data from the range of data being generated and collected. This collection of data is then processed by various data analytic / quality assurance techniques and create linkages between disparate sources of high value data to enable access to the data via a single portal. It is a step towards proactive in preventative maintenance. The student is required to party based in industry and this will enable the student to gain experience with industry, particularly as a team member of this multidisciplinary research team.

Contact: Dr Danda Li | E danda.li@unisa.edu.au  
| T 8302 3761 | URL  http://people.unisa.edu.au/danda.li

Asset Maintenance Data Platform: A case study to utilise multiple operating and maintenance data in asset management decision support.

Project keyword(s): Asset Maintenance, Asset Management, Data Quality Assurance, Water Industry.   

Water utilities are generally asset intensive organisations and as such needs to ensure that the management of assets is carried out efficiently and effectively to deliver the required service levels at an affordable (lowest possible) price for the customer. Good business decision requires the support of data but often the first step in this process is to obtain good quality data, including a data quality assurance step, before the analyst can perform data analytics task for various purposes. Data management is becoming more important as we are producing larger and more complex data than ever before. There is a large volume of data being collected as part of the asset management process via monitoring, measurement, analysis and evaluation, to support decision making, however, part of this data can go into the situation of commonly described as ‘Data rich and information poor’ or at least not fully utilised. The project will provide mechanisms to identify high value data from the range of data being generated and collected. This collection of data is then processed by various data analytic / quality assurance techniques and create linkages between disparate sources of high value data to enable access to the data via a single portal. It is a step towards proactive in preventative maintenance. The student is required to party based in industry and this will enable the student to gain experience with industry, particularly as a team member of this multidisciplinary research team.

Contact: Dr Danda Li | E danda.li@unisa.edu.au  
| T 8302 3761 | URL  http://people.unisa.edu.au/danda.li

Advanced technology and system for forestry supply chain management and logistics

Project keyword(s)*: logistics, supply chain system, methodology, operation system, forestry 

Amid increased environmental concerns, wood products could play an important role in achieving a carbon neutral future. The Australian government is committed to growing the wood industry to help create regional jobs and support regional communities while adding value to wood and fibre industries. At the same time, the government also wants to drive further technology innovation, research and development into new products and value‑adding in forest industries. Supply chain and logistics are identified as an important part in this sphere.

Research aim and objectives

This study is to investigate new technology in the logistics and supply chain and assess what can be applied in the forestry area. This project will focus on a review of past, present and further directions of technology in supply chain management and logistics including resource supply, logistics, storage, modelling and optimisation approaches of a supply chain system. The results illustrate up-to-date technology innovations and feasibilities in forestry supply chain systems.

Areas of focus:
The activities of the study include:
• Review world-wide supply chain and logistics advanced applications
• Explore forestry supply chain system issues affecting operation systems, storage and transportation
• Summarise the barriers and innovative solutions for the forestry supply chain

The output could help uncover hidden value in the forestry supply chain and provide information for decision makers to plan long-term strategies to underpin forestry financial viabilities, innovation and growth. The interested stakeholders include: Australia Forest Product Association, National Farmer's Federation, and Forest Research Mount Gambier.

Contact person and details: 
Li Meng, 83023223, li.meng@unisa.edu.au
Wolfgang Mayer, Wolfgang.mayer@unisa.edu.au

Modelling human flow in the Jeffrey Smart building

Project keyword(s)*: human flow data, movement model, hot spot, building design, behaviour estimation

Understanding users’ movement behaviour helps building design and improvements. The Jeffrey Smart Building has been equipped with the newest building design and smart technology and thereby has become the most popular destination of the University of South Australia. A large amount of data has been collected about people’s location and movement through intelligent technologies. Some of the data have been utilized to understand users’ preferences of location and length of stay in the Jeffrey Smart Building which can further contribute to building design and management, such an example is that the ISTS has generated a heat map.
In recent years, human movement in the building has drawn significant attention as a useful tool to help understand building design and facility plans and even disaster management such as fire evacuations.
Research aim and objectives
This project aims to construct a user’s movement model by using collected location data for better plan building design and equipment procurement.
Areas of focus:
• Review contemporary movement modelling and software and their contributions to building design and management
• Utilize spatial temporal data to develop a users’ movement model
• Test the model and seek the pattern of movement behaviour
The designed can be widely applied in many areas such as airport, shopping mall and urban street.

Contact person: Li Meng, 83023223, li.meng@unisa.edu.au
Ning Gu, ning.gu@unisa.edu.au
Wolfgang Mayer, Wolfgang.mayer@unisa.edu.au
Karl Sellmann, carl.sellmann@unisa.edu.au
Justin Faggotter, justin.faggotter@unisa.edu.au

Engineering

Project Title
Project description
Development of a modular viewer for HDR and esoteric images 

Project keyword(s): Software development, High dynamic range images

High Dynamic Range (HDR) imaging is the process of capturing scenes with greater contrast than traditional cameras allow, thereby retaining details in the shadows and overcoming saturation in bright areas. Although it is a field still within its relative infancy it has great potential in robotics and surveillance applications. Whereas the capture, creation, and compression of HDR images is receiving significant attention from the research community, the storage and visualisation of these images is lagging behind. This has created an ecosystem with a multitude of incompatible image formats, many often being created by individual research groups for internal use. This makes sharing data increasingly difficult. Software solutions do exist for viewing HDR images, however they are either expensive, closed-source, or non-expandable, generally only being compatible with older formats. In this project a student would develop a HDR image viewer, working alongside researchers within the field. The developed solution would need to incorporate the viewing and remapping of HDR images on traditional displays, be modular (enabling easy implementation of additional formats and compressors), and provide a consumer grade GUI, enabling non-researchers to use the software.

Contact: Dr Daniel Griffiths T 8302 5664 | E daniel.griffiths@unisa.edu.au URL | http://people.unisa.edu.au/Daniel.Griffiths

High Dynamic Range Video dataset for Autonomous Vehicles

Project keyword(s): autonomous vehicles, visual navigation

Autonomous vehicles are a cutting-edge implementation of mechatronic technologies. However, whereas we primarily rely on our vision to drive, current self-driving cars are largely reliant of LIDAR and sonar based solutions. This creates issues when the vast majority of our roadways are designed around visual feedback: such as road markings, traffic signs, traffic lights, and even other vehicles. Traditional cameras lack the dynamic range required to operate on unconstrained roads. They can lose information in high contract environments and have difficulty detecting light objects against a bright sky or dark objects in shadows, difficulties that can have dire consequences. On the other hand High Dynamic Range (HDR) imaging techniques greatly exceed the limits of traditional cameras and can meet---if not exceed---human vision when applied correctly. Computer generated HDR driving datasets exist, but there is a lack of real-world HDR driving data. A student on this project would work alongside university staff capturing real-world HDR video from a vehicle using research grade cameras, cataloguing the data, and publishing the dataset to the wider academic community. This work will contribute to making safe autonomous vehicles a reality.

Contact: Dr Daniel Griffiths T 8302 5664 | E daniel.griffiths@unisa.edu.au URL | http://people.unisa.edu.au/Daniel.Griffiths

Electromagnetic Interference Shielding Performance of MXene – Elastomer Nanocomposites

Project keyword(s): Electromagnetic Interference, MXene, Elastomers, Nanocomposites

With the increase in smart electronic devices that usually make use of electromagnetic irradiations, electromagnetic interference remains an environmental problem owing to the effect it poses on humans and the environment. The research community has long investigated the electromagnetic interference phenomenon and various attempts have been made to shield this effect. Conductive metals have previously been used extensively for EMI purposes based on the Faraday cage effect[1-3], however, metals contribute greatly to the overall weight of the resulting components making them undesirable in many applications.

Due to their high strength to density ratio, polymers have been the materials of choice in recent times for EMI applications[4]. Most polymers however have insulating properties, making them unideal in their neat forms. Conductive fillers are thus used to improve the conducting properties of nanocomposites with an advantage of an increased strength [5-7]. This project seeks to make use of a recently synthesised two-dimensional material, MXene, in synthesising elastomer nanocomposites with high electromagnetic shielding properties. The flexibility and the envisaged shielding effectiveness of the resulting nanocomposites will improve EMI properties and broaden their application. Preliminary studies have shown good electrical conductivity, permittivity and permeability properties of the nanocomposites with highly improved mechanical properties, making them potential replacements of heavy metallic devices.  

Contact Associate Professor Jun Ma | T 8320 3380 | E jun.ma@unisa.edu.au URL | http://people.unisa.edu.au/Jun.Ma 

Future Industries Institute 

Project title

Project description

Exploring the abundance and diversity of octopus in remote South Australia

Project keyword(s): environment, conservation, fisheries, marine biology, octopus

Octopus could support South Australia’s newest fishery. However, we know very little about the biology and abundance of octopus in South Australia to support sustainable fisheries management. Over the next 12 months, my team and I will be working with the fishing industry in Port Lincoln, as well as the Department of Environment and Water, to collect vital biological data on octopus in SA. As a summer scholar, you would assist us to collect these data and may include field work and travel to Port Lincoln.

Contact: Dr Zoë Doubleday | T 8302 5061 | 
E: zoe.doubleday@unisa.edu.au 
URL: https://people.unisa.edu.au/Zoe.Doubleday

3D printing of blood microsampling/processing cartridge for point of care diagnostic of preeclampsia 

Project keyword(s): 3D design and printing, microcapillary, solid-state biosensor, bioassay

We have recently developed a prototype of a point of care preeclampsia diagnostic technology as featured  in the Herald Sun. This is significant as ten million women  are affected worldwide by preeclampsia each year and the condition is the direct cause of death for over 500,000 infants and 76,000 pregnant women, mostly in  low resource countries. Despite its significance and global prevalence, effective diagnosis of preeclampsia remains a challenge and our technology based on solid-state sensing nanotechnology can measure blood biomarkers with very high sensitivity. An integral part of the decentralized testing of preeclampsia is the ability to accurately sample and process droplet of finger-prick blood without using any of external equipment. We have developed the 1st generation of the sample processing using the state-of-the art Markforged 3D printer. We are seeking an engineering/science student with some knowledge in 3D CAD design (i.e. Solidworks ) and/or microfabrication, who will be involved in designing the 2nd generation of the preeclampsia testing platform. The student will experience a vibrant research environment, conduct experiments, and report their results to supervisors.

Contact: Dr Duy Tran 
T: 08 8302 6867
E: Duy.Tran@unisa.edu.au
URL: https://people.unisa.edu.au/Duy.Tran

Optimisation of a sample preparation procedure for low abundant protein samples

The ability to prepare high quality protein samples is an important process in the discipline of proteomics. Filter Aided Sample Preparation (FASP) is a method where cells/tissues are processed in a single reactor vessel to efficiently and consistently produce protein samples for mass spectrometry (MS) analysis. The aim of the project is to develop optimised FASP protocols that produce samples highly representative of their protein content. After this prerequisite has been demonstrated with standard protein samples, it is envisaged that cancerous cell lines and tissues will also be processed by FASP, where the problem of low abundant proteins are a major issue in the field. This project would provide experience in sample preparation, MS data acquisition and processing and would suit a motivated student interested in the biological sciences or analytical/organic chemistry fields.

Contact: Dr Mark Condina or Dr Clifford Young
T: 8302 5508
E: mark.condina@unisa.edu.au, Clifford.Young@unisa.edu.au
URL: https://people.unisa.edu.au/Mark.Condina | https://people.unisa.edu.au/Clifford.Young

Microfluidic 3D cell culture platform for mimicking tissue microenvironment

Project key words:
Device fabrication, microfluidics, organ-on-a-chip, 3D cell culture, bioimaging, cancer, toxicology, drug testing, biomaterials, cancer research

Project description:
The project will use novel microfluidic 3D cell culture platform to mimic tissue microenvironments. The project aims to develop and optimize the advanced 3D culture system for different organ system culture in vitro. This platform potentially provides a greater understanding in tissue microenvironment in cancer and disease for evaluating the outcomes of toxicology and drug treatment. The candidate will participate the cell culture for microfluidic device and the characterizations of the micro-environments in 3D cell culture models.
The project is associated with multiple potential applications in multidisciplinary fields. This project is relevant to the students’ fields and skill sets including engineering, bioengineering, biomedical engineering, cell biology, medical biotechnology, medical science, microscopy, biomaterials, materials science, environmental science, toxicology, cancer research and pharmaceutical science. (Suitable for PhD, Masters, and Vacation Scholarship)

Contact: Dr Chia-Chi Chien
E: Chia-Chi.Chien@unisa.edu.au
T: 08-83023319
URL: https://people.unisa.edu.au/Chia-Chi.Chien

Developing a protocol for the application of conducting paints and the measurement of target sensitivity and EMI interference of metal detector coils

 Project keyword(s): Magnetic Coils, EMI shielding, Conductive Paint, Conducting Polymers, Electronics

Magnetic coils form part of the sensor head/assembly for metal detectors.  To improve the signal to noise (S/N) performance of the coils they are typically coated with a conductive paint. The conductive paint or conducting polymer is used as an EMI coating.  Optimising the performance of these coils is somewhat of a “black art” and involves a trade-off between target sensitivity and immunity to false triggering due to EMI sources.  This project will develop a measurement protocol that will correlate coil sensitivity and EMI suppression against the applied conductive coating resistance.  The aim will be to benchmark an existing conductive paint against a newly developed conducting polymer paint and to optimise both products with respect to coil performance.  The project is well suited for a materials science student with a basic understanding of electrical concepts or an electrical engineering student with a basic understanding of material properties.    

Contact: Dr Rick Fabretto | T8302 3675 | E rick.fabretto@unisa.edu.au URL | http://people.unisa.edu.au/rick.fabretto

Real-time ion sensing, using conducting polymers

Project keyword(s): Sensors, Conducting Polymers, Electronics, Manufacturing 

Conducting polymers are an interesting class of materials that bring the electrical properties of metals together with the mechanical properties of polymers. More recently they have shown promise to do new things, such as selective interaction with certain salts and ions in water. When this selectivity is combined with a monitoring process, a novel range of sensors can be developed.

This project will explore ways to tune the selectivity, investigate the electrical or optical techniques to detect the polymer response, and importantly develop processes to manufacture the materials and device. The overall aim is to enhance the manufacturability and performance of conducting polymers so our industry partners can exploit them in new commercial products.

Contact: Associate Prof Drew Evans | T 83025719 | E drew.evans@unisa.edu.au URL | http://people.unisa.edu.au/Drew.Evans 

Macroporous polymer monoliths for the separation of biomolecules

Project keyword(s): Materials science, porous polymers, monoliths, protein separation; capillary chromatography

Monolithic polymers have become generation of stationary phase to be used for the separation of large biomolecules. Their best-known advantage is a rigid structure with high permeability due to the presence of large through pores, which permit the use of high liquid flow rates at low backpressures. Even though this column technology has recognised advantages over particulate counterparts, it is well established that one of the limiting factors in preparing reproducible polymer monoliths with good chromatographic performance is the degree of bed heterogeneity. Therefore, alternative polymerization methods are needed to improve the structural homogeneity of polymer monoliths.

This study will explore the preparation of polymer monoliths by controlled living radical polymerisation aiming to obtain materials with well-defined macroporous structure. The materials will be used for the separation of biomolecules by capillary liquid chromatography.

Contact: Professor Emily Hilder | T 8320 6292 | E emily.hilder@unisa.edu.au URL |  http://people.unisa.edu.au/Emily.Hilder

Porous polymers for CO 2  
 

 Project keyword(s): Materials science, porous polymers, monoliths, carbon capture

High internal phase emulsions (HIPEs) are concentrated mixtures of liquid droplets dispersed in another liquid, defined by a minimum droplet volume fraction of 74%. These HIPEs are commonly stabilized by commercially available non-ionic surfactants, where these amphiphilic species decrease the interfacial tension between the oil and the aqueous phases, allowing emulsification. However, stabilization of a new system often requires a different choice of surfactant.

Novel amphiphilic polymeric emulsifiers with versatile synthesis and ease of functionalization are highly desired, as this may allow the stabilization of multiple systems by simply altering the ratio of hydrophilic to hydrophobic portions of the molecule. The reversible addition-fragmentation chain transfer (RAFT) process allows the synthesis of such amphiphilic polymers, with the most common method through sequential polymerization of two monomers.

This study will explore the preparation of polymeric surfactant by controlled living radical polymerization to allow straightforward functionalization of the obtained polyHIPE via surfactant-assisted functionalization strategy as a new CO 2 capturing material.

Contact: Professor Emily Hilder | T 8320 6292 | E emily.hilder@unisa.edu.au URL |  http://people.unisa.edu.au/Emily.Hilder

Biomonitoring of recycled municipal wastewater

Project keywords: recycled water; pathogens; microbiome

 Waterborne pathogens are a critical management issue for the horticultural industry and are of key concern for suppliers and users of recycled water supplies. A growing body of research has demonstrated the presence of human pathogens in/on salad vegetables that are eaten raw or with minimal processing. Irrigation water is one of the main pathways by which pathogens can be transferred to vegetable leaf tissues and subsequently colonise humans. A comprehensive understanding of this pathway is crucial to develop appropriate management options to mitigate the potential for disease outbreaks linked to fresh produce, and is particularly important in the Australian context where impaired irrigation water sources (e.g. drought impacted) and recycled water are increasingly the norm.

This project will use a combination of molecular microbiology and imaging techniques to advance our understanding of the complex relationships between irrigation water and opportunistic human pathogens in and on fresh produce.

Please note that applicants will be required to handle wastewater samples as part of this project. Due to the limited time frame project applicants should ideally have previous training in biosafety, microbiology and molecular biology, good aseptic technique and biosafety knowledge, and be comfortable working in a PC2 lab. This project will be conducted in the Future Industries Institute at UniSA’s Mawson Lakes Campus.

Contact: Dr Barbara Drigo, A/Prof Erica Donner
email: Erica.Donner@unisa.edu.au |
T |  8302 3624 | URL: https://people.unisa.edu.au/Erica.Donner
https://people.unisa.edu.au/Barbara.Drigo

Environmental Contaminants Group Projects 
  • Development of cost efficient adsorbents for wastewater treatment
  • Distribution and speciation of metals in traditional medicinal plants
  • Interactions of Engineered Nanomaterials with Cereal Crops – Agronomic game changer or environmental disaster?
  • Transforming Agricultural Wastes: Biogenic synthesis of nanocomposites for wastewater treatment.

 

An ever increasing global population coupled with increased worldwide industrialization places ever increasing waste and contaminant stresses on the already fragile and finite natural land and water resources. Within the Environmental Contaminants Group (ECG) we are dedicated to understanding the fate, transport and toxicity of existing and emerging contaminants in order to protect the world’s valuable land and water resources for future generations. The group focuses on the protection of both ecosystem and human health, together with strong interests in strengthening Australia’s manufacturing industry via the development of innovative and cost-effective remediation solutions and technologies for soil and water contamination.

 Our core research capabilities include biogeochemistry, contaminated site remediation, ecotoxicology, environmental and analytical chemistry, environmental risk assessment, human health risk assessment, remote sensing, and soil-plant contaminant dynamics.

Webpage: https://www.unisa.edu.au/Research/research-archive/Mawson-Institute/Research/Key-Research-Projects/owens-Laboratories/

Contact: Dr Gary Owens 
email: gary.owens@unisa.edu.au
T |  8302 5043  https://people.unisa.edu.au/Gary.Owens

Development of cost efficient adsorbents for wastewater treatment

Project keywords: Environmental Science and Engineering: adsorbents, azo dyes, biochar, environmental remediation, heavy metal, materials science, nanomaterials and nanotechnology, recycling, surface modification, textile effluent, water treatment.

Worldwide, the excessive consumption of water, compounded with a general deterioration in the quality of both surface and ground waters due to anthropogenic activities, is a major threat to water security and ecosystem health. In particular, industrial pollution of water continues to be of major issue; especially in developing countries where limited water resources are already stretched to breaking point and governments lack the resources to implement full wastewater treatment. Thus in many of these developing countries access to clean drinking water is not always guaranteed due to the high costs associated with water treatment. Thus access to cheap but efficient adsorbents for water purification is essential and consequently there is a need to develop cost effective and efficient adsorbents for a variety of inorganic, metallic and organic pollutants.

This project adopts a multi-faceted approach which considers water contaminant issues on a case-by-case basis and recognizes that there is no wonder adsorbent that would suit every potential contaminant situation. Thus contaminant issues that may potentially be considered include 1) the removal of azo dyes, which are common to the waste streams of textile and tanning industries, 2) the treatment of As contaminated drinking water in Bangladesh, 3) reducing excessive nitrate or phosphate pesticide levels in farm effluents, or more simply, 4) excessive heavy metal efflux from ongoing industrial processes. In all cases the project attempts to develop a tailored cost–effective treatment technology suited to the specific contaminant issue and prevailing environmental conditions.

RELATED ARTICLES
1. Xiaoying Jin, Yong Liu, Jeanette Tan , Gary Owens and Zuliang Chen (2018) Removal of Cr(VI) from aqueous solutions via reduction and absorption by green synthesized iron nanoparticles, Journal of Cleaner Production, 176, 929-936.
2. Li Wang, Guangcai Chen, Gary Owens and Jianfeng Zhang (2016) Enhanced antibiotic removal by the addition of bamboo charcoal during pig manure composting RSC Advances, 6(33), 27575-27583.
3. Zhi-Guo Pei, Xiao-Quan Shan, JingJing Kong, Bei Wen, Shuzhen Zhang and Gary Owens (2010) Co-adsorption of Ciprofloxacin and Cu(II) on Montmorillonite and Kaolinite as Affected by Solution pH, Environmental Science and Technology, 44(3), 915-920
4. Liang-guo Yan, Xiao-quan Shan, Bei Wen and Gary Owens (2008) Adsorption of cadmium onto Al13-pillared acid-activated montmorillonite, Journal of Hazardous Materials, 156(1-3), 499-508

 

Distribution and speciation of metals in traditional medicinal plants

 In many countries worldwide, traditional medicine using locally grown plants continues to be a common practice. However, there is a growing concern that this practice can potentially lead to detrimental health effects related to heavy metal toxicity when medicinal plants are consumed which accumulate high levels of heavy metals. The aim of this project is to determine the magnitude and severity of heavy metal contamination in medicinal plants and to understand the soil-to-plant transfer patterns of these heavy metals for a number of common medicinal plants. This study could be applied to medicinal plants from India or traditional Chinese medicines, or indeed any cultural background that has a tradition of medicinal plant use.

Figure 1. Two masters students examinine the growth of Perilla frutescens, a member of the mint family, which is widely cropped and used throughout South East Asia in both cooking and as a traditional medicinal herb. Of potential concern is that the exact same plant species is also identified as an efficient hyperaccumulator of the toxic heavy metal Cadmium (Cd). Thus there is concern that Perilla cropped on contaminated soils may accumulate metals in its edible parts and thus pose a potential threat to human health.

 RELATED ARTICLES

1. Preeti Tripathi, Sanjay Dwivedi, Aradhana Mishra, Amit Kumar, Richa Dave, Sudhakar Srivastava, Mridul Kumar Shukla, Pankaj Kumar Srivastava, Debasis Chakrabarty, Prabodh Kumar Trivedi, Rudra Deo Tripathi (2012) Arsenic accumulation in native plants of West Bengal, India: prospects for phytoremediation but concerns with the use of medicinal plants, Environ Monit Assess, 184, 2617–2631.
2. P. Dzomba, T. Chayamiti and E. Togarepi (2012) Heavy Metal Content of Selected Raw Medicinal Plant Materials: Implication for Patient Health, Bulletin of Environment, Pharmacology and Life Sciences, 1(10), 28-33.
3. Mohammad Rahimi, Reza Farhadi and Mojib Salehi balashahri (2012) Effects of heavy metals on the medicinal plant, International Journal of Agronomy and Plant Production, 3(4), 154-158.

 

Interactions of Engineered Nanomaterials with Cereal Crops – Agronomic game changer or environmental disaster?

Project Keywords: Environmental Science and Engineering: agriculture, biogeochemistry, fertilizers, food security, nanomaterials, plant nutrients, soils.

RESEARCH PROJECT
By the end of the 21st century, in excess of 9.5 billion people will inhabit the planet. This will result in increased demand for food, and a reduction in the area and resources available for food production. While many studies have advocated engineered nanomaterials (ENMs) as a game changing, novel means of improving agricultural productivity, many other studies have equally advocated ENMs as agents for environmental disaster due to significant physical and chemical interactions with both inorganic and organic species in aquatic and soil environments. Thus since the long-term effect of the efflux of such ENMs is uncertain, this project seeks to definitively assess whether the presence of ENMs in agricultural soils significantly alters soil nutrient and contaminant cycles and ascertain what effect this is likely to have on agricultural crop productivity and environmental health. Unravelling the complex interactions between ENMs, soils and cereal crops will provide fundamental new knowledge of the mechanisms involved; so as to protect Australian food quality and potentially combat food insecurity globally.

Differences in plant biomass of rice cv. Sherpa at harvest when grown with Nirtogen (N) alone (left), N + CeO2 (middle) and N + TiO2 (right). ENM added at 50 mg kg-1.

Figure 1. Differences in plant biomass of rice cv. Sherpa at harvest when grown with Nirtogen (N) alone (left), N + CeO2 (middle) and N + TiO2 (right). ENM added at 50 mg kg-1.

 RELATED ARTICLES

1. Lav R. Khot, Sindhuja Sankaran, Joe Mari Maja, Reza Ehsani, Edmund W. Schuster (2012) Applications of nanomaterials in agricultural production and crop protection: A review, Crop Protection, 35, 64-70.
2. Cornelis G., Kirby J.K., Beak D., Chittleborough D and McLaughlin M.J., A method for determination of retention of silver and cerium oxide manufactured nanoparticles in soils, Environmental Chemistry, 7, 298-308,2010.
3. Fang J., Shan X-Q., Wen B., Lin J-M., Owens G. and Zhou S-R., Transport of copper as affected by titania nanoparticles in soil columns, Environmental Pollution, 159(5), 1248-1256, 2011.
4. Cyren M. Rico, Sanghamitra Majumdar, Maria Duarte-Gardea, Jose R. Peralta-Videa and Jorge L. Gardea-Torresdey (2011) Interaction of Nanoparticles with Edible Plants and Their Possible Implications in the Food Chain, Journal of Agricultural and Food Chemistry, 59, 3485–3498.

Transforming Agricultural Wastes: Biogenic synthesis of nanocomposites for wastewater treatment.

Project Keywords: Environmental Science and Engineering: environmental rehabilitation, green synthesis, materials science, nanomaterials, nanotechnology, plant extracts, recycling, waste reuse, water treatment.

RESEARCH PROJECT
The two most important global issues today are the sustainable provision of clean water and nutritious food. However, driven by an ever increasing world population and industrialisation water pollution is increasing; particularly in Australia where water resources are already severely stretched. Methodologies that allow wastewater to be reused are therefore of significant national benefit allowing pristine waters to be allocated for alternative uses. In addition coincident with the demand for increased food supply, agricultural waste; which is already a significant issue will only increase in the future. This project simultaneously provides a novel solution to both of these problems by developing advanced biogenic engineered nanomaterials (ENMs) from agricultural waste biomass for wastewater treatment.

While engineered nanomaterials (ENMs) are an emerging class of potential adsorbents their manufacture often involves toxic chemicals. Recently green synthesis of ENMs using simple plant extracts was proposed to be safer. In this project metal oxide nanomaterials will be prepared via a green synthetic route and characterised for removal efficiency for one or more common pollutants (i.e. arsenate, azo dyes cadmium, DDT,).nitrate, phosphate). The project further embraces the latest advances in green techniques ENM fabrication by not only using agricultural waste plant extracts to facilitate ENM reduction and capping but also uses the modified residual plant waste biomass simultaneously as a ENM biosupport thus obtaining better water treatment efficiencies.

The figure on the right hand side represents the overall process from raw waste (straw) to biosupport and finally to a biogenic ENM (nFe) decorated nanocomposite.

Figure 1. The figure on the right hand side represents the overall process from raw waste (straw) to biosupport and finally to a biogenic ENM (nFe) decorated nanocomposite.

RELATED ARTICLES
1. F. Luo, Z. Chen, M. Megharaj, R. Naidu, RSC Adv., 4 (2014) 53467-53474.
2. Y. Xie, B. Yan, H. Xu, J. Chen, Q. Liu, Y. Deng, H. Zeng, ACS Appl Mater Interfaces, 6 (2014) 8845-8852.
3. X. Jin, Y. Liu, J. Tan, G. Owens, Z. Chen, J. of Clean. Prod., 176 (2018) 929-936.
4. J. Fang, X.Q. Shan, B. Wen, J.M. Lin, G. Owens, S.R. Zhou, Environ. Pollut., 159 (2011) 1248-1256.
5. G. Cornelis, J.K. Kirby, D. Beak, D. Chittleborough, M.J. McLaughlin, Env. Chem, 7 (2010) 298.

Areas of study and research

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