Reducing herbicide discharge to sensitive environments using membrane bioreactors

A hybrid MBR/UV/GAC treatment system was researched to remove Ametryn, which is a commonly used herbicide in Australian farmlands, from wastewater. The research revealed that the hybrid system could be successfully used for 100% removal of Ametryn. Two mathematical models were developed to predict the frequency of chemical cleaning of MBR-membrane and the mechanism of fouling of membrane.

A marginalised Markov Chain Monte Carlo approach for model based analysis of EEG data

The work presented in this paper focuses on fitting of a neural mass model to EEG data. Neurophysiology inspired mathematical models were developed for simulating brain's electrical activity imaged through Electroencephalography (EEG) more than three decades ago. At the present well informative models which even describe the functional integration of cortical regions also exists. However, a very limited amount of work is reported in literature on the subject of model fitting to actual EEG data. Here, we present a Bayesian approach for parameter estimation of the EEG model via a marginalized Markov Chain Monte Carlo (MCMC) approach.

DDoS attack detection at local area networks using information theoretical metrics

DDoS attacks are one of the major threats to Internet services. Sophisticated hackers are mimicking the features of legitimate network events, such as flash crowds, to fly under the radar. This poses great challenges to detect DDoS attacks. In this paper, we propose an attack feature independent DDoS flooding attack detection method at local area networks. We employ flow entropy on local area network routers to supervise the network traffic and raise potential DDoS flooding attack alarms when the flow entropy drops significantly in a short period of time. Furthermore, information distance is employed to differentiate DDoS attacks from flash crowds. In general, the attack traffic of one DDoS flooding attack session is generated by many bots from one botnet, and all of these bots are executing the same attack program. As a result, the similarity among attack traffic should higher than that among flash crowds, which are generated by many random users. Mathematical models have been established for the proposed detection strategies. Analysis based on the models indicates that the proposed methods can raise the alarm for potential DDoS flooding attacks and can differentiate DDoS flooding attacks from flash crowds with conditions. The extensive experiments and simulations confirmed the effectiveness of our proposed detection strategies.

Expert systems modeling for assessing climate change impacts and adaptation in agricultural systems at regional level

Australian agriculture is very susceptible to the adverse impacts of climate change, with major shifts in temperature and rainfall projected. In this context, this paper describes a research methodology for assessing potential climate change impacts on, and formulating adaptation options for, agriculture at regional level. The methodology was developed and applied in the analysis of climate change impacts on key horticultural commodities—pome fruits (apples and pears), stone fruits (peaches and nectarines) and wine grapes—in the Goulburn Broken catchment management region, State of Victoria, Australia. Core components of the methodology are mathematical models that enable to spatially represent the degree of biophysical land suitability for the growth of agricultural commodities in the region of interest given current and future climatic conditions. The methodology provides a sound analytic approach to 1) recognise regions under threat of declines in agricultural production due to unfolding climatic changes; 2) identify alternative agricultural systems better adapted to likely future climatic conditions and 3) investigate incremental and transformational adaptation actions to improve the problem situations that are being created by climate change.

Evaluation of pre-treatments for minimizing membrane fouling in water recovery

 RO membrane major foulants were reviewed. Among available pre-treatment technologies four pre-treatments namely; MF, UF, MBR membranes and GMF are qualitatively ranked as best. Further, experiments and fouling mathematical models showed suitability of UF and MF membrane as pre-treatments, based on their higher permeability and lower fouling potentiality than others.

The effective radius and resistance to slippage

This work reveals that parallel gripper flat-jaw configuration affects grasping effectiveness. An important finding is the fact that object grasp reliability is influenced significantly by gripper's ability to develop high resistance to object rotation in the gripper. The concept of effective torque radius, which increases resistance to object rotation in the gripper, is presented here and can be extrapolated to other grasping devices and grasping strategies to improve their reliability and make them more effective. Grippers with full-jaw contact surface and those with discrete contact areas have been investigated using simple experimental setups. Essential mathematical models needed for analytical investigation, based on simple mechanics for full-jaw contact surfaces and discrete-jaw contact surfaces, are presented. These may be useful for gripper jaw design purposes.

Robust optimal motion cueing algorithm based on the linear quadratic regulator method and a genetic algorithm

The aim of this paper is to design and develop an optimal motion cueing algorithm (MCA) based on the genetic algorithm (GA) that can generate high-fidelity motions within the motion simulator's physical limitations. Both, angular velocity and linear acceleration are adopted as the inputs to the MCA for producing the higher order optimal washout filter. The linear quadratic regulator (LQR) method is used to constrain the human perception error between the real and simulated driving tasks. To develop the optimal MCA, the latest mathematical models of the vestibular system and simulator motion are taken into account. A reference frame with the center of rotation at the driver's head to eliminate false motion cues caused by rotation of the simulator to the translational motion of the driver's head as well as to reduce the workspace displacement is employed. To improve the developed LQR-based optimal MCA, a new strategy based on optimal control theory and the GA is devised. The objective is to reproduce a signal that can follow closely the reference signal and avoid false motion cues by adjusting the parameters from the obtained LQR-based optimal washout filter. This is achieved by taking a series of factors into account, which include the vestibular sensation error between the real and simulated cases, the main dynamic limitations, the human threshold limiter in tilt coordination, the cross correlation coefficient, and the human sensation error fluctuation. It is worth pointing out that other related investigations in the literature normally do not consider the effects of these factors. The proposed optimized MCA based on the GA is implemented using the MATLAB/Simulink software. The results show the effectiveness of the proposed GA-based method in enhancing human sensation, maximizing the reference shape tracking, and reducing the workspace usage.