A scaled boundary polygon formulation for elasto-plastic analyses
- Authors: Ooi, Ean Tat , Song, Chongmin , Tin-Loi, Francis
- Date: 2014
- Type: Text , Journal article
- Relation: Computer Methods in Applied Mechanics and Engineering Vol. 268, no. (January 2014 2014), p. 905-937
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- Description: This study presents a novel scaled boundary polygon formulation to model elasto-plastic material responses in structures. The polygons have flexible mesh generation capabilities and are more accurate than standard finite elements, especially for problems with cracks and notches. Shape functions of arbitrary n-sided polygons are constructed using the scaled boundary finite element method. These shape functions are conforming and linearly complete. When modeling a crack, strain singularities are analytically modeled without enrichment. Standard finite element procedures are used to formulate the stiffness matrix and residual load vector. The nonlinear material constitutive matrix and the internal stresses are approximated locally in each polygon by a polynomial function. The stiffness matrix and the residual load vector are matrix power integrals that can be evaluated analytically even when a strain singularity is present. Standard nonlinear equation solvers e.g. the modified Newton–Raphson algorithm are used to obtain the nonlinear response of the structure. The proposed formulation is validated using several numerical benchmarks.
Min-max optimal control of linear systems with uncertainty and terminal state constraints
- Authors: Wu, Changzhi , Lay Teo, Kok , Wu, Soonyi
- Date: 2013
- Type: Text , Journal article
- Relation: Automatica Vol. 49, no. 6 (2013), p. 1809-1815
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- Description: In this paper, a class of min-max optimal control problems with continuous dynamical systems and quadratic terminal constraints is studied. The main contribution is that the original terminal state constraint in which the disturbance is involved is transformed into an equivalent linear matrix inequality without disturbance under certain conditions. Then, the original min-max optimal control problem is solved via solving a sequence of semi-definite programming problems. An example is presented to illustrate the proposed method. © 2013 Elsevier Ltd. All rights reserved.
- Description: 2003011022
Predicting protein protein interfaces as clusters of optimal docking area points
- Authors: Arafat, Yasir , Kamruzzaman, Joarder , Karmakar, Gour , Fernandez-Recio, Juan
- Date: 2009
- Type: Text , Journal article
- Relation: International Journal of data mining and bioinformatics Vol. 3, no. 1 (2009), p. 55-67
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- Description: Abstract: Desolvation property is used here to predict protein-protein binding sites exploiting the fact that lower-valued 'optimal docking area' ODA (Fernandez-Recio et al., 2005) points form cluster at the interface. The proposed method involves two steps; clustering the ODA points and representing ODA points by average ODA values. On 51 nonredundant proteins, results show the success rate improved considerably. Considering only significant ODA, the previous ODA method has obtained a success rate of 65% with overall success rate of 39%. The proposed method improved the overall success rate to 61%. Further, comparable results were found for X-ray and NMR structures.
Extended HP model for protein structure prediction
- Authors: Hoque, Md Tamjidul , Chetty, Madhu , Sattar, Abdul
- Date: 2009
- Type: Text , Journal article
- Relation: Computational Biology and Bioinformatics Vol. Jan-Feb 2011, no. (2009 ), p. 234-245
- Full Text: false
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- Description: This paper presents the impact of twins and the measures for their removal from the population of genetic algorithm (GA) when applied to effective conformational searching. It is conclusively shown that a twin removal strategy for a GA provides considerably enhanced performance when investigating solutions to complex ab initio protein structure prediction (PSP) problems in low-resolution model. Without twin removal, GA crossover and mutation operations can become ineffectual as generations lose their ability to produce significant differences, which can lead to the solution stalling. The paper relaxes the definition of chromosomal twins in the removal strategy to not only encompass identical, but also highly correlated chromosomes within the GA population, with empirical results consistently exhibiting significant improvements solving PSP problems.
GlobalMIT: learning globally optimal dynamic Bayesian network with the mutual information test criterion
- Authors: Nguyen, Vinh , Chetty, Madhu , Coppel, Ross , Wangikar, Pramod
- Date: 2011
- Type: Text , Journal article
- Relation: Bioinformatics Vol. 27, no. 19 (2011), p.2765-2766
- Full Text: false
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Detecting K-complexes for sleep stage identification using nonsmooth optimization
- Authors: Moloney, David , Sukhorukova, Nadezda , Vamplew, Peter , Ugon, Julien , Li, Gang , Beliakov, Gleb , Philippe, Carole , Amiel, Hélène , Ugon, Adrien
- Date: 2012
- Type: Text , Journal article
- Relation: ANZIAM Journal Vol. 52, no. 4 (2012), p. 319-332
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- Description: The process of sleep stage identification is a labour-intensive task that involves the specialized interpretation of the polysomnographic signals captured from a patient's overnight sleep session. Automating this task has proven to be challenging for data mining algorithms because of noise, complexity and the extreme size of data. In this paper we apply nonsmooth optimization to extract key features that lead to better accuracy. We develop a specific procedure for identifying K-complexes, a special type of brain wave crucial for distinguishing sleep stages. The procedure contains two steps. We first extract "easily classified" K-complexes, and then apply nonsmooth optimization methods to extract features from the remaining data and refine the results from the first step. Numerical experiments show that this procedure is efficient for detecting K-complexes. It is also found that most classification methods perform significantly better on the extracted features. © 2012 Australian Mathematical Society.
Convergence and accuracy of displacement based finite element formulations over arbitrary polygons: Laplace interpolants, strain smoothing and scaled boundary polygon formulation
- Authors: Natarajan, Sundararajan , Ooi, Ean Tat , Chiong, Irene , Song, Chongmin
- Date: 2014
- Type: Text , Journal article
- Relation: Finite Elements in Analysis and Design Vol. 85, no. (August 2014 2014), p. 101-122
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- Description: Three different displacement based finite element formulations over arbitrary polygons are studied in this paper. The formulations considered are the conventional polygonal finite element method (FEM) with Laplace interpolants, the cell-based smoothed polygonal FEM with simple averaging technique and the scaled boundary polygon formulation. For the purpose of numerical integration, we employ the sub-triangulation for polygonal FEM and classical Gaussian quadrature for the smoothed FEM and the scaled boundary polygon formulation. The accuracy and the convergence properties of these formulations are studied with a few benchmark problems in the context of linear elasticity and the linear elastic fracture mechanics. The extension of scaled boundary polygon to higher order polygons is also discussed.
Where is the rate in the rule?
- Authors: Herbert, Elizabeth
- Date: 2008
- Type: Text , Journal article
- Relation: Australian Senior Mathematics Journal Vol. 22, no. 2 (2008), p. 28-26
- Full Text: false
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- Description: The article reports on additional data collected during interviews for a project investigating the different ways rate may be experience by pre-calculus students. It evaluates the participants' understanding in a specific rate context. Insights into perceptions of rate in several different representations were provided by detailed analysis of the video record of each participant's interview. The article also presents the Victorian Essential Learning Standards (VELS) expectations with respect to the concept of rate.
- Description: C1
Studies on the structural stability of rabbit prion probed by molecular dyanamics simulations of its wild-type and mutants
- Authors: Zhang, Jiapu
- Date: 2010
- Type: Text , Journal article
- Relation: Journal of Theoretical Biology Vol. 264, no. (2010), p. 119-122
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- Description: Prion diseases are invariabiably fatal and highly infectious neurodegenerative diseases that affect humans and animals. Rabbits are the only mammalian species reported to be resistant to infection from prion diseases isolated from other species (Vorber et.al., 2003). Fortunately, the NMR structure of rabbit prion (124-228) (PDB entry 2FJ3), the NMR structure of rabbit prion protein mutation s173N (PDB entry 2JOH) and the NMR structure of rabbit prion protein I214V [PDB entry 2JOM} were released recently. This paper studies these NMR structures by molecular dyanmaics simulations. Simulation results confirm the structural ability of wild-type rabbit prion, and show that the salt bridge between D177 and R163 greatly contributes to the structural stability of rabbity prion. Crown Copyright Published by Elsevier.
Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network
- Authors: Nguyen, Vinh , Chetty, Madhu , Coppel, Ross , Wangikar, Pramod
- Date: 2012
- Type: Text , Journal article
- Relation: BMC Bioinformatics Vol. 13, no. 131 (2012), p. 1-16
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- Description: Abstract Background Dynamic Bayesian network (DBN) is among the mainstream approaches for modeling various biological networks, including the gene regulatory network (GRN). Most current methods for learning DBN employ either local search such as hill-climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing, which are only able to locate sub-optimal solutions. Further, current DBN applications have essentially been limited to small sized networks. Results To overcome the above difficulties, we introduce here a deterministic global optimization based DBN approach for reverse engineering genetic networks from time course gene expression data. For such DBN models that consist only of inter time slice arcs, we show that there exists a polynomial time algorithm for learning the globally optimal network structure. The proposed approach, named GlobalMIT+, employs the recently proposed information theoretic scoring metric named mutual information test (MIT). GlobalMIT+ is able to learn high-order time delayed genetic interactions, which are common to most biological systems. Evaluation of the approach using both synthetic and real data sets, including a 733 cyanobacterial gene expression data set, shows significantly improved performance over other techniques. Conclusions Our studies demonstrate that deterministic global optimization approaches can infer large scale genetic networks.
Reliability analysis of shuffle-exchange network systems
- Authors: Gunawan, Indra
- Date: 2012
- Type: Text , Journal article
- Relation: Reliability Engineering and System Safety Vol. 93, no. (2012), p. 271-276
- Full Text: false
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- Description: Shuffle-exchange networks (SENs) have been widely considered as practical interconnection systems due to their size of its switching elements (SEs) and uncomplicated configuration. SEN is a network among a large class of topologically equivalent multistage interconnection networks (MINs) that includes omega, indirect binary n-cube, baseline, and generalized cube. In this paper, SEN with additional stages that provide more redundant paths are analyzed. A common network topology with a 2×2 basic building block in a SEN and its variants in terms of extra-stages is investigated. As an illustration, three types of SENs are compared: SEN, SEN with an additional stage (SEN+), and SEN with two additional stages (SEN+2). Finally, three measures of reliability: terminal, broadcast, and network reliability for the three SEN systems are analyzed.
A model of the circadian clock in the cyanobacterium Cyanothece sp. ATCC 51142
- Authors: Nguyen, Vinh , Chetty, Madhu , Coppel, Ross , Gaudana, Sandeep , Wangikar, Pramod
- Date: 2013
- Type: Text , Journal article
- Relation: BMC Bioinformatics Vol. 14, no. (Supplement 2) (2013), p. s14-1-s14-9
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- Description: Background The over consumption of fossil fuels has led to growing concerns over climate change and global warming. Increasing research activities have been carried out towards alternative viable biofuel sources. Of several different biofuel platforms, cyanobacteria possess great potential, for their ability to accumulate biomass tens of times faster than traditional oilseed crops. The cyanobacterium Cyanothece sp. ATCC 51142 has recently attracted lots of research interest as a model organism for such research. Cyanothece can perform efficiently both photosynthesis and nitrogen fixation within the same cell, and has been recently shown to produce biohydrogen--a byproduct of nitrogen fixation--at very high rates of several folds higher than previously described hydrogen-producing photosynthetic microbes. Since the key enzyme for nitrogen fixation is very sensitive to oxygen produced by photosynthesis, Cyanothece employs a sophisticated temporal separation scheme, where nitrogen fixation occurs at night and photosynthesis at day. At the core of this temporal separation scheme is a robust clocking mechanism, which so far has not been thoroughly studied. Understanding how this circadian clock interacts with and harmonizes global transcription of key cellular processes is one of the keys to realize the inherent potential of this organism. Results In this paper, we employ several state of the art bioinformatics techniques for studying the core circadian clock in Cyanothece sp. ATCC 51142, and its interactions with other key cellular processes. We employ comparative genomics techniques to map the circadian clock genes and genetic interactions from another cyanobacterial species, namely Synechococcus elongatus PCC 7942, of which the circadian clock has been much more thoroughly investigated. Using time series gene expression data for Cyanothece, we employ gene regulatory network reconstruction techniques to learn this network de novo, and compare the reconstructed network against the interactions currently reported in the literature. Next, we build a computational model of the interactions between the core clock and other cellular processes, and show how this model can predict the behaviour of the system under changing environmental conditions. The constructed models significantly advance our understanding of the Cyanothece circadian clock functional mechanisms.
Novel weighting in single hidden layer feedforward neural networks for data classification
- Authors: Seifollahi, Sattar , Yearwood, John , Ofoghi, Bahadorreza
- Date: 2012
- Type: Text , Journal article
- Relation: Computers and Mathematics with Applications Vol. 64, no. 2 (2012), p. 128-136
- Full Text: false
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- Description: We propose a binary classifier based on the single hidden layer feedforward neural network (SLFN) using radial basis functions (RBFs) and sigmoid functions in the hidden layer. We use a modified attribute-class correlation measure to determine the weights of attributes in the networks. Moreover, we propose new weights called as influence weights to utilize in the weights connecting the input layer and the hidden layer nodes (hidden weights) of the network with sigmoid hidden nodes. These weights are calculated as the sum of conditional probabilities of attribute values given class labels. Our learning procedure of the networks is based on the extreme learning machines; in which the parameters of the hidden nodes are first calculated and then the weights connecting the hidden nodes and output nodes (output weights) are found. The results of the networks with the proposed weights on some benchmark data sets show improvements over those of the conventional networks. © 2012 Elsevier Ltd. All rights reserved.
Optimization and matrix constructions for classification of data
- Authors: Kelarev, Andrei , Yearwood, John , Vamplew, Peter , Abawajy, Jemal , Chowdhury, Morshed
- Date: 2011
- Type: Journal article
- Relation: New Zealand Journal of Mathematics Vol. 41, no. 2011 (2011), p. 65-73
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- Description: Max-plus alegbras and more general semirings have many useful applications and have been actively investigated. On the other hand, structural matrix rings are also well known and have been considered by many authors. The main theorem of this article completely describes all optimal ideas in the more general structural matrix semirings. Originally, our investigation of these ideals was motivated by applications in data mining for the design of multiple classification systems combining several individual classifiers.
Chemical characterization of MEA degradation in PCC pilot plants operating in Australia
- Authors: Cruickshank, Alicia , Verheyen, Vincent , Adeloju, Samuel , Meuleman, Erik , Chaffee, Alan , Cottrell, Aaron , Feron, Paul
- Date: 2013
- Type: Text , Journal article
- Relation: Energy Procedia Vol. 37, no. (2013), p. 877-882
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- Description: An important step towards commercial scale post-combustion CO2 capture from coal-fired power stations is understanding solvent degradation. Laboratory scale trials have identified three main solvent degradation pathways for 30% MEA: oxidative degradation, carbamate polymerization and formation of heat stable salts. This paper probes the semi-volatile organic compounds produced from a single batch of 30% MEA which was used to capture CO2 from a black coal-fired power station (Tarong, Queensland, Australia) for approximately 700 hours, followed by 500 hours at the brown coal-fired power station (Loy Yang, Victoria, Australia). Comparisons are made between the compounds identified in this aged solvent system with MEA degradation reactions described in literature. Most of semi-volatile compounds tentatively identified by GC/MS have previously been reported in laboratory scale degradation trials. Our preliminary results show low levels of degradation products were present in samples after its use in the pilot plant at Tarong (black coal) and consequent 13 months storage, but much higher concentrations were later found in the same solvent during its at use in the pilot plant at Loy Yang Power (brown coal). Further work includes identifying the cause of poor GC/MS repeatability and investigating the relative rates of reactions described in literature. The impact of inorganic anions and dissolved metals on MEA degradation will also be explored.
Twin removal in genetic algorithms for protein structure prediction using low-resolution model
- Authors: Hoque, Md Tamjidul , Chetty, Madhu , Lewis, Andrew , Sattar, Abdul
- Date: 2011
- Type: Text , Journal article
- Relation: IEEE/ACM Transactions on Computational Biology and Bioinformatics Vol. 8, no. 1 (2011), p. 234-245
- Full Text: false
- Reviewed:
- Description: This paper presents the impact of twins and the measures for their removal from the population of genetic algorithm (GA) when applied to effective conformational searching. It is conclusively shown that a twin removal strategy for a GA provides considerably enhanced performance when investigating solutions to complex ab initio protein structure prediction (PSP) problems in low-resolution model. Without twin removal, GA crossover and mutation operations can become ineffectual as generations lose their ability to produce significant differences, which can lead to the solution stalling. The paper relaxes the definition of chromosomal twins in the removal strategy to not only encompass identical, but also highly correlated chromosomes within the GA population, with empirical results consistently exhibiting significant improvements solving PSP problems.
Learning the naive bayes classifier with optimization models
- Authors: Taheri, Sona , Mammadov, Musa
- Date: 2013
- Type: Text , Journal article
- Relation: International Journal of Applied Mathematics and Computer Science Vol. 23, no. 4 (2013), p. 787-795
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- Description: Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in many real world applications, despite the strong assumption that all features are conditionally independent given the class. In the learning process of this classifier with the known structure, class probabilities and conditional probabilities are calculated using training data, and then values of these probabilities are used to classify new observations. In this paper, we introduce three novel optimization models for the naive Bayes classifier where both class probabilities and conditional probabilities are considered as variables. The values of these variables are found by solving the corresponding optimization problems. Numerical experiments are conducted on several real world binary classification data sets, where continuous features are discretized by applying three different methods. The performances of these models are compared with the naive Bayes classifier, tree augmented naive Bayes, the SVM, C4.5 and the nearest neighbor classifier. The obtained results demonstrate that the proposed models can significantly improve the performance of the naive Bayes classifier, yet at the same time maintain its simple structure.
Calibration of an articulated CMM using stochastic approximations
- Authors: Sultan, Ibrahim , Puthiyaveettil, Prajeesh
- Date: 2012
- Type: Text , Journal article
- Relation: International Journal of Advanced Manufacturing Technology Vol. 63, no. 1-4 (2012), p. 201-207
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- Description: A coordinate measuring machine (CMM) is meant to digitise the spatial locations of points and feed the resulting measurements to a CAD system for storing and processing. For reliable utilisation of a CMM, a calibration procedure is often undertaken to eliminate the inaccuracies which result from manufacturing, assembly and installation errors. In this paper, an Immersion digitizer coordinate measuring machine has been calibrated using an accurately manufactured master cuboid fixture. This CMM has been designed as an articulated manipulator to enhance its dexterity and versatility. As such, the calibration problem is tackled with the aid of a kinematic model similar to those employed for the analysis of serial robots. In addition, a stochastic-based optimisation technique is used to identify the parameters of the kinematic model in order for the accurate performance to be achieved. The experimental results demonstrate the effectiveness of this method, whereby the measuring accuracy has been improved considerably. © 2012 Springer-Verlag London Limited.
- Description: 2003010394
A Markov-blanket-based model for gene regulatory network inference
- Authors: Ram, Ramesh , Chetty, Madhu
- Date: 2011
- Type: Text , Journal article
- Relation: Transactions on Computational Biology and Bioinformatics Vol. 8, no. 2 (2011), p.
- Full Text: false
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- Description: An efficient two-step Markov blanket method for modeling and inferring complex regulatory networks from large-scale microarray data sets is presented. The inferred gene regulatory network (GRN) is based on the time series gene expression data capturing the underlying gene interactions. For constructing a highly accurate GRN, the proposed method performs: 1) discovery of a gene's Markov Blanket (MB), 2) formulation of a flexible measure to determine the network's quality, 3) efficient searching with the aid of a guided genetic algorithm, and 4) pruning to obtain a minimal set of correct interactions. Investigations are carried out using both synthetic as well as yeast cell cycle gene expression data sets. The realistic synthetic data sets validate the robustness of the method by varying topology, sample size, time delay, noise, vertex in-degree, and the presence of hidden nodes. It is shown that the proposed approach has excellent inferential capabilities and high accuracy even in the presence of noise. The gene network inferred from yeast cell cycle data is investigated for its biological relevance using well-known interactions, sequence analysis, motif patterns, and GO data. Further, novel interactions are predicted for the unknown genes of the network and their influence on other genes is also discussed.
Integration: Reversing traditional pedagogy
- Authors: Yost, David
- Date: 2008
- Type: Text , Journal article
- Relation: Australian Senior Mathematics Journal Vol. 22, no. 2 (2008), p. 37-40
- Full Text: false
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- Description: The author recommends teaching integration first, and move on to differential calculus only after a sound understanding of integration has been attained. A rationale for teaching integrals before derivatives in a first calculus unit intended primarily for engineering students at the University of Ballarat is presented. It is concluded that students fail to see the connection between getting the right answer and understanding what they are doing. It is noted that the formal manipulations required for high school differential calculus questions are simpler than those for integral calculus.