Institution
Queensland University of Technology
Education•Brisbane, Queensland, Australia•
About: Queensland University of Technology is a education organization based out in Brisbane, Queensland, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 14188 authors who have published 55022 publications receiving 1496237 citations. The organization is also known as: QUT.
Topics: Population, Poison control, Raman spectroscopy, Health care, Curriculum
Papers published on a yearly basis
Papers
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TL;DR: The malnutrition screening tool (MST), which consisted of two questions regarding appetite and recent unintentional weight loss, is a simple, quick, valid, and reliable tool which can be used to identify patients at risk of malnutrition.
736 citations
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TL;DR: The size distributions of expiratory droplets expelled during coughing and speaking and the velocities of the expiration air jets of healthy volunteers were measured using the interferometric Mie imaging and particle image velocimetry techniques to avoid air sampling losses.
730 citations
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TL;DR: A novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0.807 to 0.838 for the detection of sweet pepper.
Abstract: This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). We adapt this model, through transfer learning, for the task of fruit detection using imagery obtained from two modalities: colour (RGB) and Near-Infrared (NIR). Early and late fusion methods are explored for combining the multi-modal (RGB and NIR) information. This leads to a novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0 . 807 to 0 . 838 for the detection of sweet pepper. In addition to improved accuracy, this approach is also much quicker to deploy for new fruits, as it requires bounding box annotation rather than pixel-level annotation (annotating bounding boxes is approximately an order of magnitude quicker to perform). The model is retrained to perform the detection of seven fruits, with the entire process taking four hours to annotate and train the new model per fruit.
729 citations
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VU University Medical Center1, University of Southern California2, Max Planck Society3, McMaster University4, University of Adelaide5, University of California, Irvine6, Erasmus University Rotterdam7, Delft University of Technology8, Erasmus University Medical Center9, German Center for Neurodegenerative Diseases10, Greifswald University Hospital11, University of Münster12, University of Marburg13, QIMR Berghofer Medical Research Institute14, University of Queensland15, Queensland University of Technology16, Virginia Commonwealth University17, University of Göttingen18, University Hospital Heidelberg19, University of Sydney20, Otto-von-Guericke University Magdeburg21, Trinity College, Dublin22, University of Regensburg23, University Medical Center Groningen24, Leiden University Medical Center25, University of Melbourne26, University of Texas Health Science Center at Houston27, Charité28, University of Bonn29, University of Lübeck30, University Medical Center Freiburg31, Stanford University32, University of Calgary33, Warneford Hospital34, Royal Edinburgh Hospital35, University of Edinburgh36, University of Bern37, Cardiff University38, Leibniz Institute for Neurobiology39, University of Tübingen40, Siberian State Medical University41, Tomsk State University42, Mental Health Research Institute43
TL;DR: In this article, the authors present the largest ever worldwide study by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Major Depressive Disorder Working Group on cortical structural alterations in MDD.
Abstract: The neuro-anatomical substrates of major depressive disorder (MDD) are still not well understood, despite many neuroimaging studies over the past few decades. Here we present the largest ever worldwide study by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Major Depressive Disorder Working Group on cortical structural alterations in MDD. Structural T1-weighted brain magnetic resonance imaging (MRI) scans from 2148 MDD patients and 7957 healthy controls were analysed with harmonized protocols at 20 sites around the world. To detect consistent effects of MDD and its modulators on cortical thickness and surface area estimates derived from MRI, statistical effects from sites were meta-analysed separately for adults and adolescents. Adults with MDD had thinner cortical gray matter than controls in the orbitofrontal cortex (OFC), anterior and posterior cingulate, insula and temporal lobes (Cohen's d effect sizes: -0.10 to -0.14). These effects were most pronounced in first episode and adult-onset patients (>21 years). Compared to matched controls, adolescents with MDD had lower total surface area (but no differences in cortical thickness) and regional reductions in frontal regions (medial OFC and superior frontal gyrus) and primary and higher-order visual, somatosensory and motor areas (d: -0.26 to -0.57). The strongest effects were found in recurrent adolescent patients. This highly powered global effort to identify consistent brain abnormalities showed widespread cortical alterations in MDD patients as compared to controls and suggests that MDD may impact brain structure in a highly dynamic way, with different patterns of alterations at different stages of life.
728 citations
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TL;DR: In this article, the authors demonstrate that 2D MXenes, like Ti2C, V2C and Ti3C2, are terminated by a mixture of oxygen atoms and hydroxyl.
Abstract: Developing highly conductive, stable, and active nonprecious hydrogen evolution reaction (HER) catalysts is a key step for the proposed hydrogen economy. However, few catalysts, except for noble metals, meet all the requirements. By using state-of-the-art density functional calculations, herein we demonstrate that 2D MXenes, like Ti2C, V2C, and Ti3C2, are terminated by a mixture of oxygen atoms and hydroxyl, while Nb2C and Nb4C3O2 are fully terminated by oxygen atoms under standard conditions [pH 0, p(H2) = 1 bar, U = 0 V vs standard hydrogen electrode], findings in good agreement with experimental observation. Furthermore, all these MXenes are conductive under standard conditions, thus allowing high charge transfer kinetics during the HER. Remarkably, the Gibbs free energy for the adsorption of atomic hydrogen (ΔGH*0) on the terminated O atoms (e.g., Ti2CO2) is close to the ideal value (0 eV). Our results demonstrate terminated oxygens as catalytic active sites for the HER at these materials and highligh...
726 citations
Authors
Showing all 14597 results
Name | H-index | Papers | Citations |
---|---|---|---|
Nicholas G. Martin | 192 | 1770 | 161952 |
Paul M. Thompson | 183 | 2271 | 146736 |
Christopher J. O'Donnell | 159 | 869 | 126278 |
Robert G. Parton | 136 | 459 | 59737 |
Tim J Cole | 136 | 827 | 92998 |
Daniel I. Chasman | 134 | 484 | 72180 |
David Smith | 129 | 2184 | 100917 |
Dmitri Golberg | 129 | 1024 | 61788 |
Chao Zhang | 127 | 3119 | 84711 |
Shi Xue Dou | 122 | 2028 | 74031 |
Thomas H. Marwick | 121 | 1063 | 58763 |
Peter J. Anderson | 120 | 966 | 63635 |
Bruno S. Frey | 119 | 900 | 65368 |
David M. Evans | 116 | 632 | 74420 |
Michael Pollak | 114 | 663 | 57793 |