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Institution

Curtin University

EducationPerth, Western Australia, Australia
About: Curtin University is a education organization based out in Perth, Western Australia, Australia. It is known for research contribution in the topics: Population & Zircon. The organization has 14257 authors who have published 48997 publications receiving 1336531 citations. The organization is also known as: WAIT & Western Australian Institute of Technology.


Papers
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Journal ArticleDOI
01 Feb 2013
TL;DR: A forecasting model based on chaotic mapping, firefly algorithm, and support vector regression (SVR) is proposed to predict stock market price and performs best based on two error measures, namely mean squared error (MSE) and mean absolute percent error (MAPE).
Abstract: Due to the inherent non-linearity and non-stationary characteristics of financial stock market price time series, conventional modeling techniques such as the Box-Jenkins autoregressive integrated moving average (ARIMA) are not adequate for stock market price forecasting. In this paper, a forecasting model based on chaotic mapping, firefly algorithm, and support vector regression (SVR) is proposed to predict stock market price. The forecasting model has three stages. In the first stage, a delay coordinate embedding method is used to reconstruct unseen phase space dynamics. In the second stage, a chaotic firefly algorithm is employed to optimize SVR hyperparameters. Finally in the third stage, the optimized SVR is used to forecast stock market price. The significance of the proposed algorithm is 3-fold. First, it integrates both chaos theory and the firefly algorithm to optimize SVR hyperparameters, whereas previous studies employ a genetic algorithm (GA) to optimize these parameters. Second, it uses a delay coordinate embedding method to reconstruct phase space dynamics. Third, it has high prediction accuracy due to its implementation of structural risk minimization (SRM). To show the applicability and superiority of the proposed algorithm, we selected the three most challenging stock market time series data from NASDAQ historical quotes, namely Intel, National Bank shares and Microsoft daily closed (last) stock price, and applied the proposed algorithm to these data. Compared with genetic algorithm-based SVR (SVR-GA), chaotic genetic algorithm-based SVR (SVR-CGA), firefly-based SVR (SVR-FA), artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS), the proposed model performs best based on two error measures, namely mean squared error (MSE) and mean absolute percent error (MAPE).

391 citations

Journal ArticleDOI
TL;DR: In this paper, an in situ isotopic study of 68 Jack Hills zircons was conducted, in which the Hf and Pb isotope ratios were measured concurrently, allowing a better integration of isotope tracer information (176Hf/177Hf).

390 citations

Journal ArticleDOI
TL;DR: Perovskite oxides are demonstrated for the first time as efficient electrocatalysts for the hydrogen evolution reaction (HER) in alkaline solutions with improved HER performance originates from the modified surface electronic structures and properties of Pr0.5BSCF induced by the Pr-doping.
Abstract: Perovskite oxides are demonstrated for the first time as efficient electrocatalysts for the hydrogen evolution reaction (HER) in alkaline solutions. A-site praseodymium-doped Pr0.5 (Ba0.5 Sr0.5 )0.5 Co0.8 Fe0.2 O3- δ (Pr0.5BSCF) exhibits dramatically enhanced HER activity and stability compared to Ba0.5 Sr0.5 Co0.8 Fe0.2 O3- δ (BSCF), superior to many well-developed bulk/nanosized nonprecious electrocatalysts. The improved HER performance originates from the modified surface electronic structures and properties of Pr0.5BSCF induced by the Pr-doping.

390 citations

Journal ArticleDOI
TL;DR: The North China Craton (NCC) was originally formed by the amalgamation of the eastern and western blocks along an orogenic belt at ∼1.9 Ga.
Abstract: The North China Craton (NCC) was originally formed by the amalgamation of the eastern and western blocks along an orogenic belt at ∼1.9 Ga. After cratonization, the NCC was essentially stable until...

389 citations

Journal ArticleDOI
Richard Lowe1
TL;DR: This paper found that much of the information extracted from animated weather map sequences was perceptually salient rather than thematically relevant, and that subjects' use of selective attention to control cognitive load in complex, demanding processing situation and the effects of their lack of domain-specific background knowledge.
Abstract: The construction of a high quality mental model from a complex visual display relies the capacity of learners to extract appropriate information from that display. Beginning students of meteorology complied written records of generalisations extracted from animated weather map sequences in order to prepare themselves for a subsequent prediction task. Analysis of these records revealed that much of the information extracted was perceptually salient rather than thematically relevant. This perceptual dominance effect was found for both visuospatial and temporal aspects of the display. The statements produced were deficient with regard to the causal explanations that would be necessary to build a satisfactory mental model of the depicted situation. These deficiencies involved both the proportion of causal material recorded and the attribution of causality on an everyday rather than a domain-appropriate basis. The limitations of the information extracted were interpreted as evidence of subjects’ use of selective attention to control cognitive load in a complex, demanding processing situation and the effects of their lack of domain-specific background knowledge. Contrary to prevailing orthodoxies, the results raise the possibility that in some circumstances, animations may not be instructionally superior to static depictions because the processing demands involved can have negative effects on learning.

389 citations


Authors

Showing all 14504 results

NameH-indexPapersCitations
David Smith1292184100917
Christopher G. Maher12894073131
Mike Wright12777564030
Shaobin Wang12687252463
Mietek Jaroniec12357179561
John B. Holcomb12073353760
Simon A. Wilde11839045547
Jian Liu117209073156
Meilin Liu11782752603
Guochun Zhao11340640886
Mark W. Chase11151950783
Robert U. Newton10975342527
Simon P. Driver10945546299
Peter R. Schofield10969350892
Gao Qing Lu10854653914
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202398
2022454
20214,200
20203,818
20193,822
20183,543