Farouk ZOUARI

@enit.rnu.tn

Université de Tunis El Manar, BP. 37, Le Belvédère, 1002 Tunis, Tunisia
Laboratoire de Recherche en Automatique (LARA), École Nationale d’Ingénieurs de Tunis (ENIT), Université de Tunis El Manar, BP. 37, Le Belvédère, 1002 Tunis, Tunisia



                          

https://researchid.co/farouikzouari

Farouk Zouari was born in Tunis, Tunisia, on August 27, 1980. He received his Engineer degree in Electrical Engineering, his magister degree in Automatic and Signal Processing, and his PhD degree in Electrical Engineering from the National Engineering School of Tunis, University of Tunis El Manar, Tunisia, in 2004, 2005 and 2014, respectively. He is currently a researcher at Laboratoire de Recherche en Automatique (LARA), École Nationale d′Ingénieurs de Tunis, Université de Tunis El Manar. His current research interests include fractional-order systems, neural control theory, nonlinear control, and intelligent adaptive control.

EDUCATION

PhD in Electrical, Electronics, and Computer Engineering,
National School Engineers of Tunis, University of Tunis El Manar, Tunisia
Subject: On adaptive neural control of complex dynamic systems
Date of the defense: December 16, 2014
Mention: Right Honorable

Master's degree in electrical engineering,
National School Engineers of Tunis, University of Tunis El Manar, Tunisia
Subject: Implementation of conventional and unconventional identification methods
Date of the defense: August 04, 2005
Mention: Very Good

Electrical engineering bachelor's degree,
National School of Engineers of Tunis, University of Tunis El Manar, Tunisia
Subject: Algorithms for designing artificial neural networks
Date of the defense: June 22, 2004

Baccalaureate Diploma in Mathematics, Tunisia
Graduation Year: 1999

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering

33

Scopus Publications

665

Scholar Citations

17

Scholar h-index

18

Scholar i10-index

Scopus Publications

  • A Nonlinear Optimal Control Approach for Bacterial Infections Under Antibiotics Resistance
    Gerasimos Rigatos, Masoud Abbaszadeh, Pierluigi Siano, Mohammed Al-Numay, and Farouk Zouari
    Springer Science and Business Media LLC


  • Understanding mediators and AI's influence on job performance
    Farouk Zouari and Oumeima Toumia
    IGI Global
    Nowadays, firms are keen to combine artificial intelligence with machine learning to improve productivity. More precisely, artificial intelligence and machine learning play a variety of functions in business, from improving communication between staff and customers to automating repetitive tasks. The chapter investigates the impact of artificial intelligence on job performance, using employees' characteristics and types of sectors as mediators' variables. Both explanatory and confirmatory factor analyses, as well as structural equation modeling, are used in the study. The authors found that artificial intelligence has no impact on job performance. Indeed, both employees' characteristics and types of sectors do not mediate the relationship between artificial intelligence and job performance.

  • Nonlinear Control of a Gas Compressor Driven by an Electric Motor
    G. Rigatos, M. Abbaszadeh, B. Sari, P. Siano, G. Cuccurullo, and F. Zouari
    AIP Publishing

  • The effect of artificial intelligence awareness on job performance: Gender as moderator and experience as mediator
    Oumeima Toumia and Farouk Zouari
    IGI Global
    Since artificial intelligence is still a relatively new technology, research on the effects of artificial intelligence on work performance in developing countries is still limited. Thus, the chapter aims to examine and analyze the effect of artificial intelligence on job performance among Tunisian employees, with gender as the moderator variable and experience as the mediating variable. A questionnaire was developed to test the model based on a dataset of 350 employees in different sectors. The generated data were analyzed using IBM SPSS.26 and IBM SPSS AMOS.26. The results of exploratory and confirmatory factor analyses showed the absence of an impact of artificial intelligence on job performance. In addition, the authors found no moderating effect of gender or mediating effect of experience on the relationship between artificial intelligence and job performance. However, the experience of the employee has a positive and significant impact on job performance.

  • Artificial intelligence and venture capital decision-making
    Oumeima Toumia and Farouk Zouari
    IGI Global
    Decision making in venture capital involves a lot of work. Venture capitalists must consider a number of issues when selecting an investment. To date, however, little research has been conducted on how artificial intelligence would impact the venture capital decision-making market. Therefore, this chapter extends previous contributions aimed at exploring the relationship between artificial intelligence and venture capital decisions. Evidence shows that artificial intelligence may influence venture capitalists' decisions in a number of ways, such as recognizing firms with high chances of success, assisting venture capitalists in choosing better investments, etc. However, there are also a number of barriers that venture capitalists face in adopting artificial intelligence. The originality of this chapter is the development of items that can be used to measure stages of artificial intelligence. Indeed, it provides some recommendations for how best to integrate artificial intelligence into the decision-making process of venture capitalists.

  • Flatness-Based Control in Successive Loops for Autonomous Quadrotors
    G. Rigatos, M. Abbaszadeh, K. Busawon, L. Dala, J. Pomares, and F. Zouari
    ASME International
    Abstract The control problem for the multivariable and nonlinear dynamics of unmanned rotorcrafts is treated with the use of a flatness-based control approach which is implemented in successive loops. The state-space model of 6DOF autonomous quadrotors is separated into two subsystems, which are connected between them in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input–output linearized flat systems. The state variables of the second subsystem become virtual control inputs for the first subsystem. In turn, exogenous control inputs are applied to the second subsystem. The whole control method is implemented in two successive loops and its global stability properties are also proven through Lyapunov stability analysis. The validity of the control method is further confirmed through simulation experiments showing precise tracking of 3D flight paths by the 6DOF quadrotor.

  • Practical Finite-Time Fuzzy Synchronization of Chaotic Systems with Non-Integer Orders: Two Chattering-Free Approaches
    Abdesselem Boulkroune, Amina Boubellouta, Amel Bouzeriba, and Farouk Zouari
    Springer Science and Business Media LLC

  • Flatness-based control in successive loops for dual-arm robotic manipulators
    Gerasimos Rigatos, Masoud Abbaszadeh, and Zouari Farouk
    SAGE Publications
    Dual-arm robotic manipulators are used in industry and for assisting humans since they enable dexterous handling of objects and more agile and secure execution of pick-and-place, grasping and lifting, or assembling tasks. In this article, a multi-loop flatness-based controller is proposed for the dynamic model of a dual-arm robot. In the considered application, the dual-arm robotic system has to lift and transfer an object under synchronized motion of its two end-effectors while it has also to compensate for the grasping forces between the two end-effectors and the object. The dynamic model of this robotic system is formulated while it is proven that the state-space description of the robot’s dynamics is differentially flat. The control problem for this robotic system is solved with the use of a flatness-based control approach which is implemented in successive loops. To apply the multi-loop flatness-based control method, the state-space model of the dual-arm robotic manipulator is separated into subsystems, which are connected in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input–output linearized flat systems. The state variables of the preceding i-th subsystem become virtual control inputs for the subsequent ( i + 1)-th subsystem. In turn, real control inputs are applied to the last subsystem. The whole control method is implemented in successive loops and its global stability properties are also proven through Lyapunov stability analysis. The multi-loop flatness-based control approach is an exact nonlinear control scheme which (i) does not use approximate linearization of the dynamic model of the controlled system and (ii) does not need changes of state variables and complicated state-space model transformations.

  • Flatness-based control in successive loops for dual-arm robotic manipulators
    Gerasimos Rigatos, Krishna Busawon, Masoud Abbaszadeh, Jorge Pomares, Zhiwei Gao, and Farouk Zouari
    IEEE
    In this article, a multi-loop flatness-based controller is proposed for the dynamic model of a dual-arm robot. The control problem for this robotic system is solved with the use of a flatness-based control approach which is implemented in successive loops. To apply the multi-loop flatness-based control scheme, the state-space model of the dual-arm robotic manipulator is separated into subsystems, which are connected in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input-output linearized flat systems. The state variables of the subsequent (i + 1)-th subsystem become virtual control inputs for the preceding i-th subsystem. In turn, real control inputs are applied to the last subsystem. The whole control method is implemented in successive loops and its global stability properties are also proven through Lyapunov stability analysis.

  • Nonlinear optimal control for a gas compressor driven by an induction motor
    G. Rigatos, M. Abbaszadeh, B. Sari, P. Siano, G. Cuccurullo, and F. Zouari
    Elsevier BV

  • Neural network controller design for fractional-order systems with input nonlinearities and asymmetric time-varying Pseudo-state constraints
    Farouk ZOUARI, Asier IBEAS, Abdesselem BOULKROUNE, Jinde CAO, and Mohammad Mehdi AREFI
    Elsevier BV

  • Flatness-based adaptive fuzzy control for the uzawa-lucas endogenous growth model
    G. Rigatos, F. Zouari, G. Cuccurullo, P. Siano, and T. Ghosh
    AIP Publishing

  • Nonlinear optimal control of autonomous submarines’ diving
    G. Rigatos, P. Siano, F. Zouari, and Sul Ademi
    Springer Science and Business Media LLC

  • Variable-structure backstepping controller for multivariable nonlinear systems with actuator nonlinearities based on adaptive fuzzy system
    Mohammed Haddad, Farouk Zouari, Abdesselem Boulkroune, and Sarah Hamel
    Springer Science and Business Media LLC


  • Neuro-adaptive tracking control of non-integer order systems with input nonlinearities and time-varying output constraints
    Farouk Zouari, Asier Ibeas, Abdesselem Boulkroune, Jinde Cao, and Mohammad Mehdi Arefi
    Elsevier BV

  • Intelligent fuzzy controller for chaos synchronization of uncertain fractional-order chaotic systems with input nonlinearities
    A. Boubellouta, F. Zouari, and A. Boulkroune
    Informa UK Limited
    ABSTRACT In this research work, a novel fuzzy adaptive control is proposed to achieve a projective synchronization for a class of fractional-order chaotic systems with input nonlinearities (dead-zone together with sector nonlinearities). These master-slave systems under consideration are supposed to be with distinct models, different fractional-orders, unknown models, and dynamic external disturbances. The proposed control law consists of two main terms, namely: a fuzzy adaptive control term for appropriately approximating the uncertainties and a fractional-order variable-structure control term for robustly dealing with these inherent input nonlinearities. A Lyapunov approach is used to derive the updated laws and to prove the stability of the closed-loop control system. At last, a set of computer simulation results is carried out to illustrate and further validate the theoretical findings.

  • Nonlinear H-infinity control for optimization of the functioning of mining products mills
    G. Rigatos, P. Siano, P. Wira, M. Abbaszadeh, and Farouk Zouari
    IEEE
    Control of the milling process of mining products (ore milling) is a non-trivial problem due to being related with a strongly nonlinear and multivariable state-space model. To provide an efficient solution to this problem, in this article a nonlinear optimal (H-infinity) control method is developed. In the considered nonlinear optimal control method, the dynamic model of the mining products' mill undergoes first approximate linearization with the use of Taylor series expansion and with the computation of the associated Jacobian matrices. The linearization point (temporary equilibrium) is recomputed at each time step of the control method and comprises the present value of the system's state vector and the last value of the control inputs' vector that was exerted on it. For the linearized description of the mill's functioning the optimal control problem is solved by applying an H-infinity controller. The feedback gain is computed again at each iteration of the control algorithm through the solution of an algebraic Riccati equation. The stability of the control scheme is confirmed through Lyapunov analysis. First, it is shown that the control method satisfies the H-infinity tracking performance, and this signifies elevated robustness against model uncertainty and external perturbations. Next, under moderate conditions, it is proven that the control loop is globally asymptotically stable.


  • Neural approximation-based adaptive control for pure-feedback fractional-order systems with output constraints and actuator nonlinearities
    Farouk Zouari and Amina Boubellouta
    IGI Global
    In this chapter, an adaptive control approach-based neural approximation is developed for a category of uncertain fractional-order systems with actuator nonlinearities and output constraints. First, to overcome the difficulties arising from the actuator nonlinearities and nonaffine structures, the mean value theorem is introduced. Second, to deal with the uncertain nonlinear dynamics, the unknown control directions and the output constraints, neural networks, smooth Nussbaum-type functions, and asymmetric barrier Lyapunov functions are employed, respectively. Moreover, for satisfactorily designing the control updating laws and to carry out the stability analysis of the overall closed-loop system, the Backstepping technique is used. The main advantage about this research is that (1) the number of parameters to be adapted is much reduced, (2) the tracking errors converge to zero, and (3) the output constraints are not transgressed. At last, simulation results demonstrate the feasibility of the newly presented design techniques.

  • Adaptive neural control for unknown nonlinear time-delay fractional-order systems with input saturation
    Farouk Zouari and Amina Boubellouta
    IGI Global
    This chapter focuses on the adaptive neural control of a class of uncertain multi-input multi-output (MIMO) nonlinear time-delay non-integer order systems with unmeasured states, unknown control direction, and unknown asymmetric saturation actuator. The design of the controller follows a number of steps. Firstly, based on the semi-group property of fractional order derivative, the system is transformed into a normalized fractional order system by means of a state transformation in order to facilitate the control design. Then, a simple linear state observer is constructed to estimate the unmeasured states of the transformed system. A neural network is incorporated to approximate the unknown nonlinear functions while a Nussbaum function is used to deal with the unknown control direction. In addition, the strictly positive real (SPR) condition, the Razumikhin lemma, the frequency distributed model, and the Lyapunov method are utilized to derive the parameter adaptive laws and to perform the stability proof.

  • Observer-based adaptive neural network control for a class of MIMO uncertain nonlinear time-delay non-integer-order systems with asymmetric actuator saturation
    Farouk Zouari, Abdesselem Boulkroune, Asier Ibeas, and Mohammad Mehdi Arefi
    Springer Science and Business Media LLC

  • A nonlinear optimal control methoc for autonomous submarines' diving
    Gerasimos Rigatos, Pierluigi Siano, Farouk Zouari, and Sul Ademi
    IEEE
    A nonlinear H-infinity (optimal) control method is developed for the problem of simultaneous control of the depth and heading angle of an autonomous submarine. This is a multi-variable nonlinear control problem and its solution allows for precise underwater navigation of the submarine. The submarine's dynamic model undergoes approximate linearization around a temporary equilibrium that is recmputed at each iteration of the control algorithm. The linearization procedure is based on Taylor series expansion and on the computation of the submarine's model Jacobian matrices. For the approximately linearized model, the optimal control problem is solved through the design of an H-infinity feedback controller. The computation of the controller's gain requires the solution of an algebraic Riccati equstion, which is repetitively performed at each step of the control method. The stability of the control scheme is proven through Lyapunov analysis. First, it is demonstrated that for the submarine's control loop, the H-infinity tracking performance criterion holds. Moroever, under moderate conditions it is shown that that the control scheme is globally asymptotically stable.

  • High-gain observer-based adaptive fuzzy control for a class of multivariable nonlinear systems
    L. Merazka, F. Zouari, and A. Boulkroune
    IEEE
    We develop a fuzzy adaptive output-feedback control methodology for unknown nonlinear multivariable systems for which the input gains matrix is characterized by non-zero leading principle minors but not necessary symmetric. An high-gain (HG) observer is introduced to estimate the immeasurable states. A linear in parameters fuzzy system is adequately employed to model the uncertainties. A matrix factorization, so-called SDU, is used when designing the controller to factor the input gains matrix. An appropriate Lyapunov function is exploited to study the stability of the corresponding closed-loop control system as well as to derive the adaptation laws. A 2 DOF helicopter system is used to validate, in a simulation framework, the performances of our developed control approach.

RECENT SCHOLAR PUBLICATIONS

  • Adaptive fuzzy control for practical fixed-time synchronization of fractional-order chaotic systems
    A Boulkroune, F Zouari, A Boubellouta
    Journal of Vibration and Control, 10775463251320258 2025

  • Practical finite-time fuzzy synchronization of chaotic systems with non-integer orders: Two chattering-free approaches
    A Boulkroune, A Boubellouta, A Bouzeriba, F Zouari
    Journal of Systems Science and Systems Engineering, 1-26 2024

  • A nonlinear optimal control approach for bacterial infections under antibiotics resistance
    G Rigatos, M Abbaszadeh, P Siano, M Al-Numay, F Zouari
    Journal of Systems Science and Complexity 37 (6), 2293-2317 2024

  • Nonlinear optimal control of the 6-DOF parallel Stewart robotic platform
    G Rigatos, G Cuccurullo, P Siano, M Abbaszadeh, M AL-Numay, F Zouari
     2024

  • Flatness-based disturbance observer and control for a robotic mining excavator
    G Rigatos, M Abbaszadeh, F Zouari, P Siano, M Al-Numay, G Cuccurullo
     2024

  • Finite-time adaptive event-triggered output feedback intelligent control for noninteger order nonstrict feedback systems with asymmetric time-varying pseudo-state constraints
    F Zouari, A Ibeas, A Boulkroune, J Cao
    Communications in Nonlinear Science and Numerical Simulation 136, 108036 2024

  • Nonlinear optimal control for free-floating space robotic manipulators
    G Rigatos, J Pomares, M Abbaszadeh, K Busawon, Z Gao, F Zouari
    World Scientific Publishing Company 2024

  • Nonlinear control of a gas compressor driven by an electric motor
    G Rigatos, M Abbaszadeh, B Sari, P Siano, G Cuccurullo, F Zouari
    AIP Conference Proceedings 3094 (1) 2024

  • Flatness-based control in successive loops for autonomous quadrotors
    G Rigatos, M Abbaszadeh, K Busawon, L Dala, J Pomares, F Zouari
    Journal of Dynamic Systems, Measurement, and Control 146 (2) 2024

  • Flatness-based control in successive loops for dual-arm robotic manipulators
    G Rigatos, M Abbaszadeh, Z Farouk
    Journal of Vibration and Control 2024

  • Understanding Mediators and AI's Influence on Job Performance
    F Zouari, O Toumia
    Hyperautomation in Business and Society, 244-265 2024

  • The Effect of Artificial Intelligence Awareness on Job Performance: Gender as Moderator and Experience as Mediator
    O Toumia, F Zouari
    AI Innovation in Services Marketing, 110-133 2024

  • Artificial intelligence and venture capital decision-making
    O Toumia, F Zouari
    Fostering Innovation in Venture Capital and Startup Ecosystems, 16-38 2024

  • Effect of Artificial Intelligence Awareness on Job Performance with Employee Experience as a Mediating Variable
    O Toumia, F Zouari
    Reskilling the Workforce for Technological Advancement, 141-161 2024

  • Nonlinear optimal control for a gas compressor driven by an induction motor
    G Rigatos, M Abbaszadeh, B Sari, P Siano, G Cuccurullo, F Zouari
    Results in Control and Optimization 11, 100226 2023

  • Acknowledgment to the Reviewers of Energies in 2022 Part II
    Energies Editorial Office
    Energies 16 (4), 1715 2023

  • Acknowledgment to the Reviewers of Axioms in 2022
    Axioms Editorial Office
    Axioms 12 (2), 96 2023

  • Acknowledgment to the Reviewers of Energies in 2022 Part II
    A Mesloub, AAV Ochoa, A Beyaz, A Panda, AZ Ahmad, A Adham, ...
     2023

  • Acknowledgment to the Reviewers of Axioms in 2022
    A Derbali, A Miftah, AB Makhlouf, A Salem, A Bayad, A Bobomurat, ...
     2023

  • Neural network controller design for fractional-order systems with input nonlinearities and asymmetric time-varying Pseudo-state constraints
    F Zouari, A Ibeas, A Boulkroune, J Cao, MM Arefi
    Chaos, Solitons & Fractals 144, 110742 2021

MOST CITED SCHOLAR PUBLICATIONS

  • Adaptive neural output-feedback control for nonstrict-feedback time-delay fractional-order systems with output constraints and actuator nonlinearities
    F Zouari, A Ibeas, A Boulkroune, J Cao, MM Arefi
    Neural Networks 105, 256-276 2018
    Citations: 75

  • Intelligent fuzzy controller for chaos synchronization of uncertain fractional-order chaotic systems with input nonlinearities
    A Boubellouta, F Zouari, A Boulkroune
    International journal of general systems 48 (3), 211-234 2019
    Citations: 62

  • Neural network controller design for fractional-order systems with input nonlinearities and asymmetric time-varying Pseudo-state constraints
    F Zouari, A Ibeas, A Boulkroune, J Cao, MM Arefi
    Chaos, Solitons & Fractals 144, 110742 2021
    Citations: 59

  • Neural adaptive quantized output-feedback control-based synchronization of uncertain time-delay incommensurate fractional-order chaotic systems with input nonlinearities
    F Zouari, A Boulkroune, A Ibeas
    Neurocomputing 237, 200-225 2017
    Citations: 59

  • Neuro-adaptive tracking control of non-integer order systems with input nonlinearities and time-varying output constraints
    F Zouari, A Ibeas, A Boulkroune, J Cao, MM Arefi
    Information Sciences 485, 170-199 2019
    Citations: 51

  • Observer-based adaptive neural network control for a class of MIMO uncertain nonlinear time-delay non-integer-order systems with asymmetric actuator saturation
    F Zouari, A Boulkroune, A Ibeas, MM Arefi
    Neural Computing and Applications 28, 993-1010 2017
    Citations: 45

  • Neural network based adaptive backstepping dynamic surface control of drug dosage regimens in cancer treatment
    F Zouari
    Neurocomputing 366, 248-263 2019
    Citations: 33

  • Finite-time adaptive event-triggered output feedback intelligent control for noninteger order nonstrict feedback systems with asymmetric time-varying pseudo-state constraints
    F Zouari, A Ibeas, A Boulkroune, J Cao
    Communications in Nonlinear Science and Numerical Simulation 136, 108036 2024
    Citations: 26

  • Variable-structure backstepping controller for multivariable nonlinear systems with actuator nonlinearities based on adaptive fuzzy system
    M Haddad, F Zouari, A Boulkroune, S Hamel
    Soft Computing 23 (23), 12277-12293 2019
    Citations: 24

  • Discrete-time observer-based state feedback control of heart rate during treadmill exercise
    A Ibeas, A Esmaeili, J Herrera, F Zouari
    2016 20th International conference on system theory, control and computing 2016
    Citations: 23

  • Neural approximation-based adaptive control for pure-feedback fractional-order systems with output constraints and actuator nonlinearities
    F Zouari, A Boubellouta
    Advanced synchronization control and bifurcation of chaotic fractional-order 2018
    Citations: 21

  • Adaptive neural control for unknown nonlinear time-delay fractional-order systems with input saturation
    F Zouari, A Boubellouta
    Advanced Synchronization Control and Bifurcation of Chaotic Fractional-Order 2018
    Citations: 21

  • Fuzzy state-feedback control of uncertain nonlinear MIMO systems
    L Merazka, F Zouari, A Boulkroune
    2017 6th International Conference on Systems and Control (ICSC), 103-108 2017
    Citations: 21

  • High-gain observer-based adaptive fuzzy control for a class of multivariable nonlinear systems
    L Merazka, F Zouari, A Boulkroune
    2017 6th International Conference on Systems and Control (ICSC), 96-102 2017
    Citations: 21

  • Robust adaptive control for a class of nonlinear systems using the backstepping method
    F Zouari, KB Saad, M Benrejeb
    International Journal of Advanced Robotic Systems 10 (3), 166 2013
    Citations: 21

  • Adaptive internal model control of a DC motor drive system using dynamic neural network
    F Zouari, KB Saad, M Benrejeb
    Scientific Research Publishing 2012
    Citations: 20

  • Adaptive backstepping control for a single-link flexible robot manipulator driven DC motor
    F Zouari, KB Saad, M Benrejeb
    2013 International Conference on Control, Decision and Information 2013
    Citations: 19

  • Adaptive backstepping control for a class of uncertain single input single output nonlinear systems
    F Zouari, KB Saad, M Benrejeb
    10th International Multi-Conferences on Systems, Signals & Devices 2013 2013
    Citations: 11

  • Output‐Feedback Controller Based Projective Lag‐Synchronization of Uncertain Chaotic Systems in the Presence of Input Nonlinearities
    A Boulkroune, S Hamel, F Zouari, A Boukabou, A Ibeas
    Mathematical Problems in Engineering 2017 (1), 8045803 2017
    Citations: 9

  • Robust neural adaptive control for a class of uncertain nonlinear complex dynamical multivariable systems
    F Zouari, KB Saad, M Benrejeb
    International Review on Modelling and Simulations 5 (5), 2075-2103 2012
    Citations: 8