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Institution

Queensland University of Technology

EducationBrisbane, 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.


Papers
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Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
03 Aug 2016-Sensors
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

Journal ArticleDOI
Lianne Schmaal1, Derrek P. Hibar2, Philipp G. Sämann3, Geoffrey B. Hall4, Bernhard T. Baune5, Neda Jahanshad2, Joshua W. Cheung2, T.G.M. van Erp6, Daniel Bos7, M. A. Ikram7, Meike W. Vernooij7, Wiro J. Niessen7, Wiro J. Niessen8, Henning Tiemeier7, Henning Tiemeier9, A. Hofman7, Katharina Wittfeld10, Hans-Jörgen Grabe11, Hans-Jörgen Grabe10, Deborah Janowitz11, Robin Bülow11, M Selonke11, Henry Völzke11, Dominik Grotegerd12, Udo Dannlowski13, Udo Dannlowski12, Volker Arolt12, Nils Opel12, Walter Heindel12, Harald Kugel12, D. Hoehn3, Michael Czisch3, Baptiste Couvy-Duchesne14, Baptiste Couvy-Duchesne15, Miguel E. Rentería14, Lachlan T. Strike15, Margaret J. Wright15, Natalie T. Mills15, Natalie T. Mills14, G.I. de Zubicaray16, Katie L. McMahon15, Sarah E. Medland14, Nicholas G. Martin14, Nathan A. Gillespie17, Roberto Goya-Maldonado18, Oliver Gruber19, Bernd Krämer19, Sean N. Hatton20, Jim Lagopoulos20, Ian B. Hickie20, Thomas Frodl21, Thomas Frodl22, Angela Carballedo22, Eva-Maria Frey23, L. S. van Velzen1, B.W.J.H. Penninx1, M-J van Tol24, N.J. van der Wee25, Christopher G. Davey26, Ben J. Harrison26, Benson Mwangi27, Bo Cao27, Jair C. Soares27, Ilya M. Veer28, Henrik Walter28, D. Schoepf29, Bartosz Zurowski30, Carsten Konrad13, Elisabeth Schramm31, Claus Normann31, Knut Schnell19, Matthew D. Sacchet32, Ian H. Gotlib32, Glenda MacQueen33, Beata R. Godlewska34, Thomas Nickson35, Andrew M. McIntosh35, Andrew M. McIntosh36, Martina Papmeyer35, Martina Papmeyer37, Heather C. Whalley35, Jeremy Hall35, Jeremy Hall38, J.E. Sussmann35, Meng Li39, Martin Walter40, Martin Walter39, Lyubomir I. Aftanas, Ivan Brack, Nikolay A. Bokhan41, Nikolay A. Bokhan42, Nikolay A. Bokhan43, Paul M. Thompson2, Dick J. Veltman1 
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

Journal ArticleDOI
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

NameH-indexPapersCitations
Nicholas G. Martin1921770161952
Paul M. Thompson1832271146736
Christopher J. O'Donnell159869126278
Robert G. Parton13645959737
Tim J Cole13682792998
Daniel I. Chasman13448472180
David Smith1292184100917
Dmitri Golberg129102461788
Chao Zhang127311984711
Shi Xue Dou122202874031
Thomas H. Marwick121106358763
Peter J. Anderson12096663635
Bruno S. Frey11990065368
David M. Evans11663274420
Michael Pollak11466357793
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023205
2022641
20214,218
20204,026
20193,623
20183,374