A functional MRI study of the relationship between naming treatment outcomes and resting state functional connectivity in post-stroke aphasia

Background: The majority of studies investigating the neural mechanisms underlying treatment in people with aphasia have examined task-based brain activity. However, the use of resting-state fMRI may provide another method of examining the brain mechanisms responsible for treatment-induced recovery, and allows for investigation into connectivity within complex functional networks Methods: Eight people with aphasia underwent 12 treatment sessions that aimed to improve object naming. Half the sessions employed a phonologically-based task, and half the sessions employed a semantic-based task, with resting-state fMRI conducted pre- and post-treatment. Brain regions in which the amplitude of low frequency fluctuations (ALFF) correlated with treatment outcomes were used as seeds for functional connectivity (FC) analysis. FC maps were compared from pre- to post-treatment, as well as with a group of 12 healthy older controls Results: Pre-treatment ALFF in the right middle temporal gyrus (MTG) correlated with greater outcomes for the phonological treatment, with a shift to the left MTG and supramarginal gyrus, as well as the right inferior frontal gyrus, post-treatment. When compared to controls, participants with aphasia showed both normalization and up-regulation of connectivity within language networks post-treatment, predominantly in the left hemisphere Conclusions: The results provide preliminary evidence that treatments for naming impairments affect the FC of language networks, and may aid in understanding the neural mechanisms underlying the rehabilitation of language post-stroke.

Real-time traffic state estimation in urban corridors from heterogeneous data

In recent years, rapid advances in information technology have led to various data collection systems which are enriching the sources of empirical data for use in transport systems. Currently, traffic data are collected through various sensors including loop detectors, probe vehicles, cell-phones, Bluetooth, video cameras, remote sensing and public transport smart cards. It has been argued that combining the complementary information from multiple sources will generally result in better accuracy, increased robustness and reduced ambiguity. Despite the fact that there have been substantial advances in data assimilation techniques to reconstruct and predict the traffic state from multiple data sources, such methods are generally data-driven and do not fully utilize the power of traffic models. Furthermore, the existing methods are still limited to freeway networks and are not yet applicable in the urban context due to the enhanced complexity of the flow behavior. The main traffic phenomena on urban links are generally caused by the boundary conditions at intersections, un-signalized or signalized, at which the switching of the traffic lights and the turning maneuvers of the road users lead to shock-wave phenomena that propagate upstream of the intersections. This paper develops a new model-based methodology to build up a real-time traffic prediction model for arterial corridors using data from multiple sources, particularly from loop detectors and partial observations from Bluetooth and GPS devices.

State of Education for Energy Efficiency in Australian Engineering Education - Summary of Questionnaire Results

In 2007 the National Framework for Energy Efficiency provided funding for the first survey of energy efficiency education across all Australian universities teaching engineering education. The survey asked the question, ‘What is the state of education for energy efficiency in Australian engineering education?’. There was an excellent response to the survey, with 48 course responses from lecturers across 27 universities from every state and territory in Australia, and 260 student responses from 18 courses across 8 universities from all 6 states. It is concluded from the survey findings that the state of education for energy efficiency in Australian engineering education is currently highly variable and ad hoc across universities and engineering disciplines.

Computer analysis of impact behavior of concrete filled steel tube columns

This paper presents a numerical study of the response of axially loaded concrete filled steel tube (CFST) columns under lateral impact loading using explicit non-linear finite element techniques. The aims of this paper are to evaluate the vulnerability of existing columns to credible impact events as well as to contribute new information towards the safe design of such vulnerable columns. The model incorporates concrete confinement, strain rate effects of steel and concrete, contact between the steel tube and concrete and dynamic relaxation for pre-loading, which is a relatively recent method for applying a pre-loading in the explicit solver. The finite element model was first verified by comparing results with existing experimental results and then employed to conduct a parametric sensitivity analysis. The effects of various structural and load parameters on the impact response of the CFST column were evaluated to identify the key controlling factors. Overall, the major parameters which influence the impact response of the column are the steel tube thickness to diameter ratio, the slenderness ratio and the impact velocity. The findings of this study will enhance the current state of knowledge in this area and can serve as a benchmark reference for future analysis and design of CFST columns under lateral impact.

Lateralization of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging and machine learning

Lateralization of temporal lobe epilepsy (TLE) is critical for successful outcome of surgery to relieve seizures. TLE affects brain regions beyond the temporal lobes and has been associated with aberrant brain networks, based on evidence from functional magnetic resonance imaging. We present here a machine learning-based method for determining the laterality of TLE, using features extracted from resting-state functional connectivity of the brain. A comprehensive feature space was constructed to include network properties within local brain regions, between brain regions, and across the whole network. Feature selection was performed based on random forest and a support vector machine was employed to train a linear model to predict the laterality of TLE on unseen patients. A leave-one-patient-out cross validation was carried out on 12 patients and a prediction accuracy of 83% was achieved. The importance of selected features was analyzed to demonstrate the contribution of resting-state connectivity attributes at voxel, region, and network levels to TLE lateralization.

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

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

Patriotism’s impact on cooperation with the state: An experimental study on tax compliance

Although it seems reasonable to assume that activating patriotism might motivate citizens to cooperate with the state in reaching societal goals, the empirical evidence supporting this contention is based mostly on correlational rather than experimental studies. In addition, little is known on whether patriotism can be manipulated without simultaneously triggering nationalism and on the psychological processes which determine the patriotism-cooperation relation. This current article reports results of one survey and three experiments that manipulate patriotism by displaying either a national flag or national landscapes or by priming national achievements. The outcomes indicate that reported and manipulated patriotism indirectly increase tax compliance, although the national flag also increases nationalism. National achievements, on the other hand, seemingly increases trust in national public institutions and the voluntary motivation to cooperate, whereas national landscapes only increase the voluntary motivation to cooperate. Hence, it is possible to increase social capital in the form of trust and cooperation through patriotism without fostering nationalism as well.

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

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

Predicting surgical outcomes for paediatric spinal deformity patients using subject specific finite element models

INTRODUCTION Adolescent idiopathic scoliosis (AIS) is a spinal deformity, which may require surgical correction by attaching rods to the patient’s spine using screws inserted into the vertebrae. Complication rates for deformity correction surgery are unacceptably high. Determining an achievable correction without overloading the adjacent spinal tissues or implants requires an understanding of the mechanical interaction between these components. Our novel patient specific modelling software creates individualized finite element models (FEM) representing the thoracolumbar spine and ribcage of scoliosis patients. We have recently applied the model to investigate the influence of increasing magnitudes of surgically applied corrective force on predicted deformity correction...

Quantifying the state of populations and effects of disturbances at large spatio-temporal scales: The case of coral populations in the great barrier reef

This project was a step forward in applying statistical methods and models to provide new insights for more informed decision-making at large spatial scales. The model has been designed to address complicated effects of ecological processes that govern the state of populations and uncertainties inherent in large spatio-temporal datasets. Specifically, the thesis contributes to better understanding and management of the Great Barrier Reef.

Rehabilitation of individuals with bone-anchored prosthesis: State-of-the-art and challenges

Individuals with limb amputation fitted with conventional socket-suspended prostheses often experience socket-related discomfort leading to a significant decrease in quality of life. Bone-anchored prostheses are increasingly acknowledged as viable alternative method of attachment of artificial limb. In this case, the prosthesis is attached directly to the residual skeleton through a percutaneous fixation. To date, a few osseointegration fixations are commercially available. Several devices are at different stages of development particularly in Europe and the US.[1-15] Clearly, surgical procedures are currently blooming worldwide. Indeed, Australia and Queensland in particular have one of the fastest growing populations. Previous studies involving either screw-type implants or press-fit fixations for bone-anchorage have focused on fragmented biomechanics aspects as well as the clinical benefits and safety of the procedure. However, very few publications have synthetized this information and provided an overview of the current developments in bone-anchored prostheses worldwide, let alone in Australia. The purposes of the presentation will be: 1. To provide an overview of the state-of-art developments in bone-anchored prostheses with as strong emphasis on the design of fixations, treatment, benefits, risks as well as future opportunities and challenges, 2. To present the current international developments of procedures for bone-anchored prostheses in terms of numbers of centers, number of cases and typical case-mix, 3. To highlight the current role Australia is playing as a leader worldwide in terms of growing population, broadest range of case-mix, choices of fixations, development of reimbursement schemes, unique clinical outcome registry for evidence-based practice, cutting-edge research, consumer demand and general public interest.

Numerical study of LSF floors made of hollow flange channels in fire

Light gauge steel frame (LSF) floor systems are generally made of lipped channel section joists and lined with gypsum plasterboards to provide adequate fire resistance rating under fire conditions. Recently a new LSF floor system made of welded hollow flange channel (HFC) section was developed and its fire performance was investigated using full scale fire tests. The new floor systems gave higher fire resistance ratings in comparison to conventional LSF floor systems. To avoid expensive and time consuming full scale fire tests, finite element analyses were also performed to simulate the fire performance of LSF floors made of HFC joists using both steady and transient state methods. This paper presents the details of the developed finite element models of HFC joists to simulate the structural fire performance of the LSF floor systems under standard fire conditions. Finite element analyses were performed using the measured time–temperature profiles of the failed joists from the fire tests, and their failure times, temperatures and modes, and deflection versus time curves were obtained. The developed finite element models successfully predicted the structural performance of LSF floors made of HFC joists under fire conditions. They were able to simulate the complex behaviour of thin cold-formed steel joists subjected to non-uniform temperature distributions, and local buckling and yielding effects. This study also confirmed the superior fire performance of the newly developed LSF floors made of HFC joists.

Finite element analysis investigation of response of pumpkin tissue under uniaxial loading

Finite element analysis (FEA) models of uniaxial loading of pumpkin peel and flesh tissues were developed and validated using experimental results. The tensile model was developed for both linear elastic and plastic material models, the compression model was develop d only with the plastic material model. The outcomes of force versus time curves obtained from FEA models followed similar pattern to the experimental curves however the curve resulted with linear elastic material properties had a higher difference with the experimental curves. The values of predicted forces were determined and compared with the experimental curve. An error indicator was introduced and computed for each case and compared. Additionally Root Mean Square Error (RMSE) values were also calculated for each model and compared. The results of modelling were used to develop material model for peel and flesh tissues in FEA modelling of mechanical peeling of tough skinned vegetables.