Monash University (Australia)
  • Melbourne, Victoria, Australia
Recent publications
The COVID-19 pandemic has caused a great “reset” and has challenged many assumptions about work and life in general. Our focus in this paper is on the future of global work in the context of multinational enterprises (MNEs). We take a phenomenon-based approach to describe the important trends and challenges affecting the where, who, how and why of global work. As we highlight implications for organizations and individuals, we offer a set of research questions to guide future research and inform IHRM practitioners.
Parallel to the fast uptake of renewable energy sources (RESs) connected to the grid, the electric power industry has experienced a number of issues related to system strength and inertia. Battery energy storage systems (BESSs) have been proved effective in mitigating numerous stability problems related to the high penetration of renewable energy sources. This paper investigates the role of BESSs in mitigating the voltage and frequency stability issues in weak grids. We utilize a binary grey wolf optimization approach to define the locations and sizes of BESSs to improve voltage and frequency stability in a weak grid. Simulation results show that compared to existing solutions, significant improvements in terms of voltage and frequency stability can be achieved by implementing our proposed solution.
Geological sequestration of CO2 is an effective way to achieve the goal of reducing global warming and carbon neutralization. The complex CO2–coal interaction will lead to the change of mechanical properties of coal. Therefore, evaluating the mechanical characteristics of deep coal seam after CO2 adsorption is of great significance for analyzing the geological characteristics and structural stability of deep strata and storage site selection. In this study, the triaxial compression test and acoustic emission (AE) test were used to evaluate the effect of CO2 adsorption pressure on the mechanical properties of coal mass. On the basis of the evolution characteristics of AE ring down count (RDC), fractal theory was used to analyze the deformation and fracturing evolution of coal. Besides, an elastic damage constitutive model describing the nonlinear stress-strain relationship of coal under CO2 adsorption is established. Results show that the growth of adsorption pressure develops the risk of damage and fracturing of coal. With the increase in adsorption pressure, the value of singularity index width Δα presents a downward trend, indicating the development of internal cracks and the increase in the complexity of microunit fracturing process. When the adsorption pressure is low, the gas in large cracks and pores leads to the generation of small AE signal that occupies the dominant position. High adsorption pressure increases the complexity and nonlinearity of coal deformation, strengthens the proportion of large AE signal, and leads to the increase in the average value of multifractal spectrum width Δf. With the increase in adsorption pressure, the fracturing degree of coal is higher, a mass of large-size cracks appears, and more AE spectrum presents intensive distribution. In the plastic stage, the load level is close to the strength limit, the microcracks intersect with each other, and the average value of Δα in this stage is the smallest.
As one of the most important indexes of coal quality, accurate and rapid prediction of ash content is urgent and important significance for the coal processing industry. In this work, combined with Shapley Additive exPlanations (SHAP), a machine learning model has been developed to predict the ash content of coal samples based on composition data of XRF analysis. Among the many supervised regression learning algorithms, Poly, RFR, XGBoost, and DNN are used in this predictive model to overcome the ash content prediction research gap. The input parameters were the elements content and ash contents of the coal samples. To evaluate the proposed method, a dataset of XRF data was constructed, containing 217 sets of element content with different ash content labels. Specifically, the dataset is divided into a training set and test set in the proportion 8:2, and RandomizedSearchCV is used to optimize hyperparameters during model training. Experimental results show that the RFR model produced a superior prediction performance over other models (the RMSE, MAE and R² were 1.3278, 0.9339 and 0.9916, respectively). The contribution and role of each element to the ash prediction model are explained and investigated. Moreover, as a result of SHAP interpretation, the nine most important elements (Al, S, Si, Fe, Ca, Ti, K, Sr and Zr) has the greatest contribution to model performance. The case of this paper suggests that interpreted machine learning models and XRF data are good alternatives to ash content prediction.
Background: Immigration detention is associated with detrimental mental health outcomes but little is known about the underlying psychological processes. Moral injury, the experience of transgression of moral beliefs, may play an important role. Objective: Our aim was to explore moral injury appraisals and associated mental health outcomes related to immigration detention on Nauru. Methods: In this retrospective study, we conducted in-depth interviews with 13 individuals who had sought refuge in Australia and, due to arriving by boat, had been transferred to immigration detention on Nauru. At the time of the study, they lived in Australia following medical transfer. We used reflexive thematic analysis to develop themes from the data. Results: Major themes included 1) how participants' home country experience and the expectation to get protection led them to seek safety in Australia; 2) how they experienced deprivation, lack of agency, violence, and dehumanization after arrival, with the Australian government seen as the driving force behind these experiences; and 3) how these experiences led to feeling irreparably damaged. The participant statement 'In my country they torture your body but in Australia they kill your mind.' conveyed these three key themes in our analysis. Conclusion: Our findings suggest that moral injury may be one of the processes by which mandatory immigration detention can cause harm. Although refugees returned to Australia from offshore detention may benefit from interventions that specifically target moral injury, collective steps are needed to diminish deterioration of refugee mental health. Our results highlight the potentially deleterious mental health impact of experiencing multiple subtle and substantial transgressions of one's moral frameworks. Policy makers should incorporate moral injury considerations to prevent eroding refugee mental health.
No previous research has examined age and sex differences in balance outcomes in individuals with chronic obstructive pulmonary disease (COPD) at risk of falls. A secondary analysis of baseline data from an ongoing trial of fall prevention in COPD was conducted. Age and sex differences were analyzed for the Berg Balance scale (BBS), Balance Evaluation System Test (BEST test) and Activities-specific Balance Confidence Scale (ABC). Overall, 223 individuals with COPD were included. Females had higher balance impairments than males [BBS: mean (SD) = 47 (8) vs. 49 (6) points; BEST test: 73 (16) vs. 80 (16) points], and a lower confidence to perform functional activities [ABC = 66 (21) vs. 77 (19)]. Compared to a younger age (50-65 years) group, age >65 years was moderately associated with poor balance control [BBS (r = - 0.37), BEST test (r = - 0.33)] and weakly with the ABC scale (r = - 0.13). After controlling for the effect of balance risk factors, age, baseline dyspnea index (BDI), and the 6-min walk test (6-MWT) explained 38% of the variability in the BBS; age, sex, BDI, and 6-MWT explained 40% of the variability in the BEST test; And BDI and the 6-MWT explained 44% of the variability in the ABC scale. This study highlights age and sex differences in balance outcomes among individuals with COPD at risk of falls. Recognition of these differences has implications for pulmonary rehabilitation and fall prevention in COPD, particularly among females and older adults.
The Large Hadron Collider beauty (LHCb) experiment at CERN is undergoing an upgrade in preparation for the Run 3 data collection period at the Large Hadron Collider (LHC). As part of this upgrade, the trigger is moving to a full software implementation operating at the LHC bunch crossing rate. We present an evaluation of a CPU-based and a GPU-based implementation of the first stage of the high-level trigger. After a detailed comparison, both options are found to be viable. This document summarizes the performance and implementation details of these options, the outcome of which has led to the choice of the GPU-based implementation as the baseline.
Spatially resolved transcriptomics is an emerging class of high-throughput technologies that enable biologists to systematically investigate the expression of genes along with spatial information. Upon data acquisition, one major hurdle is the subsequent interpretation and visualization of the datasets acquired. To address this challenge, VR-Cardiomics is presented, which is a novel data visualization system with interactive functionalities designed to help biologists interpret spatially resolved transcriptomic datasets. By implementing the system in two separate immersive environments, fish tank virtual reality (FTVR) and head-mounted display virtual reality (HMD-VR), biologists can interact with the data in novel ways not previously possible, such as visually exploring the gene expression patterns of an organ, and comparing genes based on their 3D expression profiles. Further, a biologist-driven use-case is presented, in which immersive environments facilitate biologists to explore and compare the heart expression profiles of different genes.
Background Tonsillectomy, with or without adenoidectomy, is the leading reason for paediatric unplanned hospital readmission, some of which are potentially avoidable. Reducing unplanned hospital revisits would improve patient safety and decrease use of healthcare resources. This study aimed to describe the incidence, timing and risk factors for any surgery-related hospital revisits (both emergency presentation and readmission) following paediatric tonsillectomy and adenotonsillectomy in a large state-wide cohort. Methods We conducted a population-based cohort study using linked administrative datasets capturing all paediatric tonsillectomy and adenotonsillectomy surgeries performed between 2010 and 2015 in the state of Victoria, Australia. The primary outcome was presentation to the emergency department or hospital readmission within 30-day post-surgery. Results Between 2010 and 2015, 46,583 patients underwent 47,054 surgeries. There was a total of 4758 emergency department presentations (10.11% total surgeries) and 2750 readmissions (5.84% total surgeries). Haemorrhage was the most common reason for both revisit types, associated with 33.02% of ED presentations (3.34% total surgeries) and 67.93% of readmissions (3.97% total surgeries). Day 5 post-surgery was the median revisit time for both ED presentations (IQR 3–7) and readmission (IQR 3–8). Predictors of revisit included older age, public and metropolitan hospitals and peri-operative complications during surgery. Conclusions Haemorrhage was the most common reason for both emergency department presentation and hospital readmission. The higher risk of revisits associated with older children, surgeries performed in public and metropolitan hospitals, and in patients experiencing peri-operative complications, suggest the need for improved education of postoperative care for caregivers, and avoidance of inappropriate early discharge. Graphical Abstract
In this paper, we assess the role of investment in research and development (R&D) and economic policy uncertainty (EPU) in Sri Lanka’s economic growth experience. We do this by first determining which endogenous growth theories best explain the evolution of total factor productivity (TFP) in the country. Using historical time series data (1980–2018), we find that semi-endogenous growth theories best explain the evolution of TFP in Sri Lanka. This evidence suggests that R&D is critical to the country’s TFP expansion. We find that, through R&D, EPU has a crucial detrimental impact on TFP growth, although it is short-lived. Our findings are robust and have important implications for R&D investment and for moderating EPU.
While protein-truncating variants in RAD51C have been shown to predispose to triple-negative (TN) breast cancer (BC) and ovarian cancer, little is known about the pathogenicity of missense (MS) variants. The frequency of rare RAD51C MS variants was assessed in the BEACCON study of 5734 familial BC cases and 14,382 population controls, and findings were integrated with tumour sequencing data from 21 cases carrying a candidate variant. Collectively, a significant enrichment of rare MS variants was detected in cases (MAF < 0.001, OR 1.57, 95% CI 1.00–2.44, p = 0.05), particularly for variants with a REVEL score >0.5 (OR 3.95, 95% CI 1.40–12.01, p = 0.006). Sequencing of 21 tumours from 20 heterozygous and 1 homozygous carriers of nine candidate MS variants identified four cases with biallelic inactivation through loss of the wild-type allele, while six lost the variant allele and ten that remained heterozygous. Biallelic loss of the wild-type alleles corresponded strongly with ER- and TN breast tumours, high homologous recombination deficiency scores and mutational signature 3. Using this approach, the p.Gly264Ser variant, which was previously suspected to be pathogenic based on small case–control analyses and loss of activity in in vitro functional assays, was shown to be benign with similar prevalence in cases and controls and seven out of eight tumours showing no biallelic inactivation or characteristic mutational signature. Conversely, evaluation of case–control findings and tumour sequencing data identified p.Ile144Thr, p.Arg212His, p.Gln143Arg and p.Gly114Arg as variants warranting further investigation.
Clinical Trial Networks in which trialists work collaboratively enable multi-site, large-scale, high-quality clinical trials to be efficiently run. Although the benefits of Clinical Trial Networks are largely known, establishing a Clinical Trial Network can be complex. There are many factors for clinicians and researchers to consider, and there is currently a paucity of information on how to form a Clinical Trial Network. This article provides a suggested roadmap on how to establish a Clinical Trial Network. The Australian Clinical Trials Alliance (ACTA) is the peak body for Clinical Trial Networks, Coordinating Centres and Registries in Australia, and has produced several resources to support the effective and efficient running of clinical trials. This guide has come about through discussions with members of the ACTA Clinical Trial Network Sector Expansion Reference Group consisting of clinical trialists, clinicians, researchers, and consumers.
It has been a challenge for solving the motor imagery classification problem in the brain informatics area. Accuracy and efficiency are the major obstacles for motor imagery analysis in the past decades since the computational capability and algorithmic availability cannot satisfy complex brain signal analysis. In recent years, the rapid development of machine learning (ML) methods has empowered people to tackle the motor imagery classification problem with more efficient methods. Among various ML methods, the Graph neural networks (GNNs) method has shown its efficiency and accuracy in dealing with inter-related complex networks. The use of GNN provides new possibilities for feature extraction from brain structure connection. In this paper, we proposed a new model called MCGNet⁺, which improves the performance of our previous model MutualGraphNet. In this latest model, the mutual information of the input columns forms the initial adjacency matrix for the cosine similarity calculation between columns to generate a new adjacency matrix in each iteration. The dynamic adjacency matrix combined with the spatial temporal graph convolution network (ST-GCN) has better performance than the unchanged matrix model. The experimental results indicate that MCGNet⁺ is robust enough to learn the interpretable features and outperforms the current state-of-the-art methods.
Gravitational-wave detections are enabling measurements of the rate of coalescences of binaries composed of two compact objects—neutron stars and/or black holes. The coalescence rate of binaries containing neutron stars is further constrained by electromagnetic observations, including Galactic radio binary pulsars and short gamma-ray bursts. Meanwhile, increasingly sophisticated models of compact objects merging through a variety of evolutionary channels produce a range of theoretically predicted rates. Rapid improvements in instrument sensitivity, along with plans for new and improved surveys, make this an opportune time to summarise the existing observational and theoretical knowledge of compact-binary coalescence rates.
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35,105 members
Pathmanathan Rajadurai
  • School of Medicine and Health Sciences, Sunway
Naveen Vankadari
  • Monash Biomedical Discovery Institute
Farhad Fatehi
  • School of Psychological Sciences
Girdhar Singh Deora
  • Monash Institute of Pharmaceutical Sciences
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Address
3168, Melbourne, Victoria, Australia
Head of institution
Professor Margaret Gardner AO
Website
http://monash.edu.au/
Phone
+61 3 9594 2726