Author Index

* Please note this includes full papers only

A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z

A

• Abell, A.D.
  • Artificial Neural Networks & Random Forest Classification of druggable molecules and disease targets via scoring functions (SFs)
    

    Hudson, I.L., Leemaqz, S.Y. and Abell, A.D.
    https://doi.org/10.36334/modsim.2019.A1.hudson

• Abidin, S.Z.
  • Impact of positive and negative price shocks on investment portfolios in selected Asian markets
    

    Abidin, S.Z. and Banchit, A.
    https://doi.org/10.36334/modsim.2019.E3.abidin2

  • Causality and cointegration of stock markets within the Asian growth triangle
    

    Abidin, S.Z. and Banchit, A.
    https://doi.org/10.36334/modsim.2019.E1.abidin

• Acharya, S.C.
  • Applying rainfall ensembles to explore hydrological uncertainty
    

    Kumari, N., Acharya, S.C., Renzullo, L.J. and Yetemen, O.
    https://doi.org/10.36334/modsim.2019.K14.kumari2

• Adams, M.P.
  • Towards Ecologically Relevant Targets: Impact of flow and sediment discharge on seagrass communities in the Great Barrier Reef
    

    Lambert, V., Adams, M.P., Collier, C., Carter, A., Saunders, M., Brodie, J., Bainbridge, Z., Rasheed, M., Turner, R. and O'Brien, K.R.
    https://doi.org/10.36334/modsim.2019.G1.lambert

• Albrecht, A.R.
  • Optimal partitioning of photovoltaic modules on a curved solar collector
    

    Kapsis, M., Albrecht, A.R. and Pudney, P.J.
    https://doi.org/10.36334/modsim.2019.A5.kapsis

• Al-Deen, S.
  • Evaluation of P-Delta instability in hardening oscillators using dynamical systems approach
    

    Pathak, S., Watt, S.D., Khennane, A. and Al-Deen, S.
    https://doi.org/10.36334/modsim.2019.A5.pathak

• Alhuzali, T.
  • Correspondence analysis approach to examine the Nobel Prize
    

    Alhuzali, T., Stojanovski, E. and Beh, E.J.
    https://doi.org/10.36334/modsim.2019.B5.alhuzali

• Altarawni, I.
  • An improved stochastic modelling framework for biological networks
    

    Altarawni, I., Samarasinghe, S. and Kulasiri, D.
    https://doi.org/10.36334/modsim.2019.A1.altarawni

• Alzahrani, A.R.
  • Evaluation of Mathematics teaching strategies in Australian High Schools
    

    Alzahrani, A.R. and Stojanovski, E.
    https://doi.org/10.36334/modsim.2019.J9.alzahrani

• Amirghasemi, M.
  • Meta-moderation for crowdsourced disaster management and urban participatory applications
    

    Ogie, R.I., Amirghasemi, M. and Perez, P.
    https://doi.org/10.36334/modsim.2019.F4.ogie

• Anderson, R.J.
  • Simulation design and analysis to support decision making in the management of capability programs
    

    Jusaitis, D., Baddams, K., Sardesai, N. and Anderson, R.J.
    https://doi.org/10.36334/modsim.2019.B7.jusaitis

• Anderssen, R.S.
  • A functional strategy for nonlinear functionals
    

    Anderssen, R.S., Haak, B. and Hegland, M.
    https://doi.org/10.36334/modsim.2019.A3.anderssen2

  • Deconvolution for oscillatory shear rheometry using the Landweber iteration
    

    Anderssen, R.S., de Hoog, F.R. and Loy, R.J.
    https://doi.org/10.36334/modsim.2019.A3.anderssen

• Anh, D.L.T.
  • Inadequate corporate profitability and marketability efficiency: A case of Vietnam manufacturing enterprises
    

    Anh, D.L.T. and Gan, C.
    https://doi.org/10.36334/modsim.2019.E2.anh

• Arena, C.
  • A multi-scenario Decision Support System for real-time operation of over-year multi-reservoir systems 1. Model structure
    

    Arena, C., Cannarozzo, M., Oliva, F. and Mazzola, M.R.
    https://doi.org/10.36334/modsim.2019.K9.arena

  • A multi-scenario Decision Support System for real-time operation of over-year multi-reservoir systems 2. DSS simulation
    

    Arena, C., Cannarozzo, M., Oliva, F. and Mazzola, M.R.
    https://doi.org/10.36334/modsim.2019.K9.arena2

• Aslam, Q.
  • Exploring the effects of linguistic and cultural similarities between interviewer and respondents' behavior in large scale surveys
    

    Cheema, S.A., Hudson, I.L., Aslam, Q., Shafqat, M., Kifayat, T. and Hussain, Z.
    https://doi.org/10.36334/modsim.2019.J9.cheema2

• Aswal, A.
  • Relational algebraic approaches to decision making under uncertainty
    

    Chandrababu, A., Aswal, A. and Prasanna, G.N.S.
    https://doi.org/10.36334/modsim.2019.B1.chandrababu

• Atkinson, J-A.
  • “Stopping before you start”: Reducing and preventing initiation of tobacco use in the ACT
    

    Freebairn, L., Kreuger, K., Watson, C., Yang, O., Prodan, A., Graham, K.I., Draper, G., McDonnell, G., Nixon, E. and Atkinson, J-A.
    https://doi.org/10.36334/modsim.2019.I2.freebairn

• Atzeni, M.G.
  • Modelling feedlots using the MEDLI model framework
    

    Vieritz, A.M., Lowry, M.M., Sullivan, T.J., Atzeni, M.G., Hope-O'Connor, A.J. and Watts, P.J.
    https://doi.org/10.36334/modsim.2019.C1.vieritz

• Austin, J.M.
  • Using pedotransfer functions to improve the precision of spatially predicted available water capacity
    

    Austin, J.M., Stockmann, U. and Verburg, K.
    https://doi.org/10.36334/modsim.2019.C1.austin

• Awad, J.
  • Irrigation water resource management: 'IW-QC2' software tool
    

    Awad, J., Pezzaniti, D., Vanderzalm, J., Esu, O.-O. and van Leeuwen, J.
    https://doi.org/10.36334/modsim.2019.K9.awad

  • Simulation of the suppressive effect of zinc on cyanobacteria in paper mill wastewater
    

    Burger, H., Awad, J., Hewa, G.A. and van Leeuwen, J.
    https://doi.org/10.36334/modsim.2019.K15.burger

B

• Bach, P.M.
  • Future Water: Comparing and contrasting approaches to predicting water quality
    

    Guo, D., Lintern, A., Prodanovic, V., Kuller, M., Bach, P.M., Deletic, A., Shi, B., McCarthy, D., Ryu, D., Webb, J.A., Liu, S. and Western, A.W.
    https://doi.org/10.36334/modsim.2019.K15.guo

• Baddams, K.
  • Simulation design and analysis to support decision making in the management of capability programs
    

    Jusaitis, D., Baddams, K., Sardesai, N. and Anderson, R.J.
    https://doi.org/10.36334/modsim.2019.B7.jusaitis

• Badlan, R.L.
  • Insights into the role of fire geometry and violent pyroconvection
    

    Badlan, R.L., Sharples, J.J., Evans, J.P. and McRae, R.H.D.
    https://doi.org/10.36334/modsim.2019.H7.badlan

• Bae, S.
  • A flexible family of hypertabastic models
    

    Singh, K., Harrison, N., Eby, W., Tabatabai, M., Bursac, Z., Uhm, M., Bartolucci, A. and Bae, S.
    https://doi.org/10.36334/modsim.2019.I1.singh

• Baheerathan, R.
  • Bayesian uncertainty analyses of Great Barrier Reef catchment water quality model parameters without likelihood assumptions
    

    Baheerathan, R. and Bennett, F.R.
    https://doi.org/10.36334/modsim.2019.G1.baheerathan

• Bainbridge, Z.
  • Towards Ecologically Relevant Targets: Impact of flow and sediment discharge on seagrass communities in the Great Barrier Reef
    

    Lambert, V., Adams, M.P., Collier, C., Carter, A., Saunders, M., Brodie, J., Bainbridge, Z., Rasheed, M., Turner, R. and O'Brien, K.R.
    https://doi.org/10.36334/modsim.2019.G1.lambert

• Baker, D.
  • What do we need from catchment water quality models in Australia?
    

    Fu, B., Merritt, W.S., Cuddy, S.M., Jakeman, A.J., Croke, B., Weber, T., Hall, J., Waters, D.K. and Baker, D.
    https://doi.org/10.36334/modsim.2019.K15.fuB

• Balev, S.
  • Modelling a multi-modal logistic network with agents and dynamic graphs
    

    Démare, T., Balev, S., Bertelle, C., Dutot, A., Fournier, D. and Sanlaville, E.
    https://doi.org/10.36334/modsim.2019.B8.demare

• Banchit, A.
  • Impact of positive and negative price shocks on investment portfolios in selected Asian markets
    

    Abidin, S.Z. and Banchit, A.
    https://doi.org/10.36334/modsim.2019.E3.abidin2

  • Causality and cointegration of stock markets within the Asian growth triangle
    

    Abidin, S.Z. and Banchit, A.
    https://doi.org/10.36334/modsim.2019.E1.abidin

• Barnetson, J.
  • Improving evapotranspiration estimation in pasture and native vegetation models using flux tower data, remote sensing and global optimisation
    

    Owens, J., Carter, J., Fraser, G., Cleverly, J., Hutley, L. and Barnetson, J.
    https://doi.org/10.36334/modsim.2019.C6.owens

• Barnhart, B.L.
  • Bilevel optimisation as a solution method for an agri-environmental principal-agent problem
    

    Barnhart, B.L., Bostian, M.B., Hasan, S., Smart, J.C.R. and Fleming, C.
    https://doi.org/10.36334/modsim.2019.J6.barnhart

• Bartolucci, A.
  • A flexible family of hypertabastic models
    

    Singh, K., Harrison, N., Eby, W., Tabatabai, M., Bursac, Z., Uhm, M., Bartolucci, A. and Bae, S.
    https://doi.org/10.36334/modsim.2019.I1.singh

• Baumeister, J.
  • Water and energy-based optimisation of a “MiniCity”: A system dynamics approach
    

    Suprun, E., de Oliveira, G., Bertone, E., Sahin, O., Ottmann, D. and Baumeister, J.
    https://doi.org/10.36334/modsim.2019.J2.suprun

• Beck, H.E.
  • A global, near real-time system measuring river, lake, and reservoir dynamics
    

    Hou, J., Van Dijk, A.I.J.M. and Beck, H.E.
    https://doi.org/10.36334/modsim.2019.H5.houJ

• Beh, E.J.
  • Correspondence analysis approach to examine the Nobel Prize
    

    Alhuzali, T., Stojanovski, E. and Beh, E.J.
    https://doi.org/10.36334/modsim.2019.B5.alhuzali

• Beliakov, G.
  • Aggregation of dependent criteria in multicriteria decision making problems by means of capacities
    

    Beliakov, G., Bowsell, S., Cao, T., Dazeley, R., Mak-Hau, V., Nguyen, M.-T., Wilkin, T. and Yearwood, J.
    https://doi.org/10.36334/modsim.2019.B3.beliakov

• Bennett, F.R.
  • Gradient boosting machine assisted approximate Bayesian computation for uncertainty analysis of rainfall-runoff model parameters
    

    Bennett, F.R.
    https://doi.org/10.36334/modsim.2019.K14.bennett

  • Bayesian uncertainty analyses of Great Barrier Reef catchment water quality model parameters without likelihood assumptions
    

    Baheerathan, R. and Bennett, F.R.
    https://doi.org/10.36334/modsim.2019.G1.baheerathan

• Bennett, J.C.
  • Estimating Lake Mulwala diversions for calibration of a semi-distributed hydrologic model of the Murray River
    

    Ng, T.L., Robertson, D.E. and Bennett, J.C.
    https://doi.org/10.36334/modsim.2019.K13.ngTL

• Benthuysen, J.
  • Predicting subsurface water temperature from sea surface temperature in the Great Barrier Reef
    

    Quinlan, R., Robson, B.J. and Benthuysen, J.
    https://doi.org/10.36334/modsim.2019.G1.quinlan

• Bertelle, C.
  • Modelling a multi-modal logistic network with agents and dynamic graphs
    

    Démare, T., Balev, S., Bertelle, C., Dutot, A., Fournier, D. and Sanlaville, E.
    https://doi.org/10.36334/modsim.2019.B8.demare

• Bertone, E.
  • Water and energy-based optimisation of a “MiniCity”: A system dynamics approach
    

    Suprun, E., de Oliveira, G., Bertone, E., Sahin, O., Ottmann, D. and Baumeister, J.
    https://doi.org/10.36334/modsim.2019.J2.suprun

• Bhosale, B.
  • Modelling curvelet based signal processing problems via wavelet analysis
    

    Bhosale, B.
    https://doi.org/10.36334/modsim.2019.A1.bhosale

• Binesh, A.
  • Effects of soil data input on catchment streamflow and soil moisture prediction
    

    Binesh, A., Yeo, I-Y. and Hancock, G.R.
    https://doi.org/10.36334/modsim.2019.K24.binesh2

• Blackmore, K.L.
  • Evaluating a virtual human storyteller for improved decision support
    

    Blackmore, K.L., Smith, S.P., Nesbitt, K.V., North, L., Wark, S. and Nowina-Krowicki, M.
    https://doi.org/10.36334/modsim.2019.B2.blackmore

• Blackstock, K.L.
  • Science for Sustainability: Using Societal Metabolism Analysis to check the robustness of European Union policy narratives in the water, energy and food nexus
    

    Matthews, K.B., Waylen, K.A., Blackstock, K.L., Juarez-Bourke, A., Miller, D.G., Wardell-Johnson, D.H., Rivington, M. and Giampietro, M.
    https://doi.org/10.36334/modsim.2019.J5.matthews

• Blair, G.S.
  • Uncertainty quantification in classification problems: A Bayesian approach for predicating the effects of further test sampling
    

    Phillipson, J., Blair, G.S. and Henrys, P.
    https://doi.org/10.36334/modsim.2019.B1.phillipson

• Blumson, D.
  • Baselining the whole-of-force capability and capacity of the Australian Defence Force
    

    Peacock, J., Blumson, D., Mangalasinghe, J., Hepworth, A., Coutts, A. and Lo, E.H.S.
    https://doi.org/10.36334/modsim.2019.B3.peacock

• Bokor, Y.
  • Stratified space learning: Reconstructing embedded graphs
    

    Bokor, Y., Grixti-Cheng, D., Hegland, M., Roberts, S. and Turner, K.
    https://doi.org/10.36334/modsim.2019.A3.bokor

• Bolger, M.
  • Workspace – a Scientific Workflow System with commercial impact
    

    Oakes, N., Hetherton, L., Bolger, M., Thomas, D.G., Rucinski, C., Watkins, D. and Cleary, P.W.
    https://doi.org/10.36334/modsim.2019.D2.oakes

  • Translation of stranded IP to commercialisable software applications using Workspace
    

    Cleary, P.W., Hetherton, L., Thomas, D.G., Bolger, M. and Watkins, D.
    https://doi.org/10.36334/modsim.2019.D2.cleary

  • Workspace for Industry 4.0
    

    Hetherton, L., Bolger, M., Zhang, Z., Oakes, N., Watkins, D. and Cleary, P.W.
    https://doi.org/10.36334/modsim.2019.D2.hetherton

• Bosomworth, B.
  • Modelling nitrogen transport in sugar cane from soil to runoff from banded surface and buried fertiliser using HYDRUS2D and a post-processing algorithm
    

    Cook, F.J., Eyles, M., Cowie, B., Bosomworth, B. and Silburn, M.
    https://doi.org/10.36334/modsim.2019.K15.cook

• Bostian, M.B.
  • Bilevel optimisation as a solution method for an agri-environmental principal-agent problem
    

    Barnhart, B.L., Bostian, M.B., Hasan, S., Smart, J.C.R. and Fleming, C.
    https://doi.org/10.36334/modsim.2019.J6.barnhart

• Boston, T.
  • Some experiments in automated identification of Australian plants using convolutional neural networks
    

    Boston, T. and Van Dijk, A.I.J.M.
    https://doi.org/10.36334/modsim.2019.A1.boston

• Bourmistrova, A.
  • Stability test for semi-active suspensions with base disturbance
    

    Storey, I.D. and Bourmistrova, A.
    https://doi.org/10.36334/modsim.2019.A6.storey

• Bowsell, S.
  • Aggregation of dependent criteria in multicriteria decision making problems by means of capacities
    

    Beliakov, G., Bowsell, S., Cao, T., Dazeley, R., Mak-Hau, V., Nguyen, M.-T., Wilkin, T. and Yearwood, J.
    https://doi.org/10.36334/modsim.2019.B3.beliakov

• Bretas, A.
  • Modelling railway traffic management through multi-agent systems and reinforcement learning
    

    Bretas, A., Mendes, A., Chalup, S., Jackson, M., Clement, R. and Sanhueza, C.
    https://doi.org/10.36334/modsim.2019.B6.bretas

• Bretreger, D.
  • Using an artificial neural network to enhance the spatial resolution of satellite soil moisture products based on soil thermal inertia
    

    Senanayake, I.P., Yeo, I-Y., Willgoose, G.R., Hancock, G.R. and Bretreger, D.
    https://doi.org/10.36334/modsim.2019.K10.senanayake

  • The effects of SILO & AWRA wind speeds on irrigation depth simulations
    

    Bretreger, D. and Yeo, I-Y.
    https://doi.org/10.36334/modsim.2019.K17.bretreger2

  • Comparing remote sensing and tabulated crop coefficients to assess irrigation water use
    

    Bretreger, D., Warner, A. and Yeo, I-Y.
    https://doi.org/10.36334/modsim.2019.C1.bretreger

• Brindley, J.
  • 2D chaotic flow in competitive exothermic-endothermic reactions
    

    Huang, Z., Watt, S.D., Sidhu, H.S., McIntosh, A.C., Brindley, J., Jovanoski, Z. and Towers, I.N.
    https://doi.org/10.36334/modsim.2019.A5.huang

• Brodie, J.
  • Towards Ecologically Relevant Targets: Impact of flow and sediment discharge on seagrass communities in the Great Barrier Reef
    

    Lambert, V., Adams, M.P., Collier, C., Carter, A., Saunders, M., Brodie, J., Bainbridge, Z., Rasheed, M., Turner, R. and O'Brien, K.R.
    https://doi.org/10.36334/modsim.2019.G1.lambert

• Brown, T.
  • Victorian fire weather trends and variability
    

    Harris, S., Mills, G. and Brown, T.
    https://doi.org/10.36334/modsim.2019.H7.harris

• Bruggemann, T.
  • Bushfire emergency response uncertainty quantification
    

    Bruggemann, T., Ford, J.J., White, G., Perez, T. and Power, W.
    https://doi.org/10.36334/modsim.2019.A2.bruggemann

  • Bushfire emergency response simulation
    

    Bruggemann, T., Ford, J.J., White, G. and Perez, T.
    https://doi.org/10.36334/modsim.2019.A2.bruggemann2

• Bulovic, N.
  • Quantifying water volumes of ungauged lakes using optical satellite imagery and high-resolution LiDAR DEMs
    

    Bulovic, N., Sari, D.R. and Sabaghy, S.
    https://doi.org/10.36334/modsim.2019.H5.bulovic

• Burger, H.
  • Simulation of the suppressive effect of zinc on cyanobacteria in paper mill wastewater
    

    Burger, H., Awad, J., Hewa, G.A. and van Leeuwen, J.
    https://doi.org/10.36334/modsim.2019.K15.burger

• Bursac, Z.
  • A flexible family of hypertabastic models
    

    Singh, K., Harrison, N., Eby, W., Tabatabai, M., Bursac, Z., Uhm, M., Bartolucci, A. and Bae, S.
    https://doi.org/10.36334/modsim.2019.I1.singh

• Burt, C.
  • Flow balancing in a wastewater network by optimising storage in pumping stations
    

    Burt, C., Candy, B., D'Arpa, R., Koh, E.L., Meng, P., Salamone, D., Sondalini, T. and Taylor, P.
    https://doi.org/10.36334/modsim.2019.B6.burt