Publications

Journal articles

  1. Carlos Cano-Domingo, Ruxandra Stoean, Manuel Soler-Ortiz, Nuria Novas, Manuel Fernández-Ros,
    Gonzalo Joya, Jose A. Gázquez Parra, Deep learning event detector from long-term signal variation for seismic activity warning out of Schumann resonance, Knowledge-Based Systems, vol. 328, 114166, https://doi.org/10.1016/j.knosys.2025.114166, IF 7.6, 2025.
  2. Carlos Cano-Domingo, Ruxandra Stoean, Gonzalo Joya, Nuria Novas, Manuel Fernández-Ros, Jose A. Gázquez Parra, A hybrid deep learning approach for enhancing the Lorentzian curve fit algorithm for Schumann resonance, Expert Systems with Applications, vol. 293, 128681, https://doi.org/10.1016/j.eswa.2025.128681, IF 7.5, 2025.
  3. Ruxandra Stoean, Nebojsa Bacanin, Catalin Stoean, Leonard Ionescu, Bridging the past and present: AI-driven 3D restoration of degraded artefacts for museum digital display, Journal of Cultural Heritage, vol. 69, pp. 18-26, https://doi.org/10.1016/j.culher.2024.07.008, IF 3.5, 2024.
  4. Ruxandra Stoean, Nebojsa Bacanin, Catalin Stoean, Leonard Ionescu, Miguel Atencia, Gonzalo Joya, Computational Framework for the Evaluation of the Composition and Degradation State of Metal Heritage Assets by Deep Learning, Journal of Cultural Heritage, vol. 64, pp. 198-206, https://doi.org/10.1016/j.culher.2023.10.007, IF 3.1, 2023.
  5. Carlos Cano Domingo, Ruxandra Stoean, Gonzalo Joya Caparrós, Nuria Novas Castellano, Manuel Fernandez Ros, Jose Antonio Gázquez Parra, A Machine Learning hourly analysis on the relation the Ionosphere and Schumann Resonance Frequency, Measurement, vol. 208, 112426, https://doi.org/10.1016/j.measurement.2022.112426, IF 5.131, 2023.
  6. Nebojsa Bacanin, Catalin Stoean, Miodrag Zivkovic, Miomir Rakic, Roma Strulak-Wójcikiewicz, Ruxandra Stoean, On the Benefits of Using Metaheuristics in the Hyperparameter Tuning of Deep Learning Models for Energy Load Forecasting, Energies, 16, no. 3: 1434, https://doi.org/10.3390/en16031434, IF 3.252, 2023.
  7. Catalin Stoean, Miodrag Zivkovic, Aleksandra Bozovic, Nebojsa Bacanin, Roma Strulak-Wójcikiewicz, Milos Antonijevic, Ruxandra Stoean, Metaheuristic-Based Hyperparameter Tuning for Recurrent Deep Learning: Application to the Prediction of Solar Energy Generation, Axioms 12, no. 3: 266, IF 1.824, 2023.
  8. Anda Ungureanu, Andreea Marcu, Ciprian Laurentiu Patru, Dan Ruican, Rodica Nagy, Ruxandra Stoean, Catalin Stoean, Dominic Gabriel Iliescu, Learning deep architectures for the Interpretation of first-trimester Fetal Echocardiography (LIFE) - a study protocol for developing an automated Intelligent Decision Support System for early fetal echocardiography, BMC Pregnancy, Volume 23, 20, https://doi.org/10.1186/s12884-022-05204-x, IF 3.105, 2023.
  9. Carlos Cano Domingo, Nuria Novas Castellano, Ruxandra Stoean, Manuel Fernandez Ros and Jose A. Gazquez Parra, Schumann resonance modes and ionosphere parameters: An annual variability comparison, IEEE Transactions on Instrumentation & Measurement, vol. 71, pp. 1-10, https://doi.org/10.1109/TIM.2022.3194912, IF 5.332, 2022.
  10. Nebojsa Bacanin, Ruxandra Stoean, Miodrag Zivkovic, Aleksandar Petrovic, Tarik A. Rashid, Timea Bezdan, Performance of Novel Chaotic Firefly Algorithm with Enhanced Exploration for Tackling Global Optimization Problems: Application for Dropout Regularization, Mathematics, 9(21), 2705; https://doi.org/10.3390/math9212705, IF 2.258, 2021.
  11. Ruxandra Stoean, Analysis on the potential of an EA–surrogate modelling tandem for deep learning parametrization: an example for cancer classification from medical images, Neural Computing and Applications, 32, pp. 313–322, https://doi.org/10.1007/s00521-018-3709-5, IF 4.68, 2020.
  12. Ruxandra Stoean, Catalin Stoean, Miguel Atencia, Roberto Rodríguez-Labrada, Gonzalo Joya, Ranking Information Extracted from Uncertainty Quantification of the Prediction of a Deep Learning Model on Medical Time Series Data, Mathematics 8, no. 7: 1078, IF 1.747, 2020.
  13. Ruxandra Stoean, Catalin Stoean, Roberto Becerra-García, Rodolfo García-Bermúdez, Miguel Atencia, Francisco García-Lagos, Luis Velázquez-Pérez, Gonzalo Joya, A hybrid unsupervised—Deep learning tandem for electrooculography time series analysis. PLoS ONE 15(7): e0236401. https://doi.org/10.1371/journal.pone.0236401, IF 2.776, 2020.
  14. Miguel Atencia, Ruxandra Stoean, Gonzalo Joya, Uncertainty Quantification through Dropout in Time Series Prediction by Echo State Networks. Mathematics 8, no. 8: 1374, IF 1.747, 2020.
  15. Catalin Stoean, Ruxandra Stoean, Miguel Atencia, Moloud Abdar, Luis Velázquez-Pérez, Abbas Khosravi, Saeid Nahavandi, U. Rajendra Acharya, Gonzalo Joya, Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals, Sensors, Vol. 20, No. 11, 3032, https://doi.org/10.3390/s20113032, IF 3.031, 2020.
  16. Catalin Stoean, Wieslaw Paja, Ruxandra Stoean, Adrian Sandita, Deep architectures for long-term stock price prediction with a heuristic-based strategy for trading simulations. PLOS ONE 14(10): e0223593. https://doi.org/10.1371/journal.pone.0223593, IF 2.776, 2019.
  17. Adriana Samide, Ruxandra Stoean, Catalin Stoean, Bogdan Tutunaru, Roxana Grecu, Nicoleta Cioatera, Investigation of Polymer Coatings Formed by Polyvinyl Alcohol and Silver Nanoparticles on Copper Surface in Acid Medium by Means of Deep Convolutional Neural Networks, Coatings, Vol. 9, No. 2, Art. No. 105, 10.3390/coatings9020105, IF 2.436, 2019.
  18. Adriana Samide, Catalin Stoean, Ruxandra Stoean, Surface study of inhibitor films formed by polyvinyl alcohol and silver nanoparticles on stainless steel in hydrochloric acid solution using Convolutional Neural Networks, Applied Surface Science, 475, pp. 1-5, https://doi.org/10.1016/j.apsusc.2018.12.255, IF 5.27, 2019.
  19. Catalin Stoean, Ruxandra Stoean, Adrian Sandita, Cristian Mesina, Corina Lavinia Gruia, Daniela Ciobanu, How Much and Where to Use Manual Guidance in the Computational Detection of Contours for Histopathological Images?, Soft Computing, 23, pp. 3707–3722, https://doi.org/10.1007/s00500-018-3029-9, IF 2.784, 2018.
  20. Ruxandra Stoean, Catalin Stoean, Adrian Sandita, Daniela Ciobanu, Cristian Mesina, Interpreting Decision Support from Multiple Classifiers for Predicting Length of Stay in Patients with Colorectal Carcinoma, Neural Processing Letters, DOI 10.1007/s11063-017-9585-7, pp. 811-827, IF 2.591, 2017.
  21. Catalin Stoean, Ruxandra Stoean, Post-evolution of variable-length class prototypes to unlock decision making within support vector machines, Applied Soft Computing (ISI 2013 impact factor: 2.679), Elsevier, Vol. 25, pp. 159-173, ISSN 1568-4946, IF 2.679, 2014. WoS: Q2-AIS, Q1-IF
  22. Ruxandra Stoean, Catalin Stoean, Modeling Medical Decision Making by Support Vector Machines, Explaining by Rules of Evolutionary Algorithms with Feature Selection, Expert Systems With Applications, Elsevier, Vol. 40, No. 7, pp. 2677?2686, ISSN 0957-4174, IF 2.203, 2013. WoS: Q2-AIS, Q1-IF, 12 citations
  23. Catalin Stoean, Ruxandra Stoean, Monica Lupsor, Horia Stefanescu, Radu Badea, Feature Selection for a Cooperative Coevolutionary Classifier in Liver Fibrosis Diagnosis, Computers in Biology and Medicine, Elsevier, Vol. 41, Issue 4, pp. 238-246, ISSN 0010-4825, IF 1.269, 2011.  WoS: Q3-AIS, Q3-IF, 8 citations
  24. Ruxandra Stoean, Catalin Stoean, Monica Lupsor, Horia Stefanescu, Radu Badea, Evolutionary-Driven Support Vector Machines for Determining the Degree of Liver Fibrosis in Chronic Hepatitis C, Artificial Intelligence in Medicine, Elsevier, Vol. 51, Issue 1, pp. 53-65, IF 1.645, 2011. WoS: Q2-AIS, Q1-IF, 12 citations
  25. Catalin Stoean, Mike Preuss, Ruxandra Stoean, D. Dumitrescu, Multimodal Optimization by means of a Topological Species Conservation Algorithm, IEEE Transactions on Evolutionary Computation, IEEE Intelligence Computational Society, Vol. 14, Issue 6, pp. 842-864, ISSN 1089-778X, IF 4.589, 2010. TSC2 code can be found here. WoS: Q1-AIS, Q1-IF, 37 citations
  26. Ruxandra Stoean, Mike Preuss, Catalin Stoean, Elia El-Darzi, D. Dumitrescu, Support Vector Machine Learning with an Evolutionary Engine, Journal of the Operational Research Society (ISI indexed, 2009 Impact Factor: 1.009), Palgrave Macmillan, Vol. 60, Issue 8 (August 2009), Special Issue: Data Mining and Operational Research: Techniques and Applications, Kweku-Muata Osei-Bryson and Vic J Rayward-Smith (Guest Editors), pp. 1116-1122, ISSN 0160-5682, 2009. WoS: Q3-AIS, Q3-IF, 8 citations

Books

  1. Catalin Stoean, Ruxandra Stoean, Support Vector Machines and Evolutionary Algorithms for Classification - Single or Together?, Intelligent Systems Reference Library, Springer, Vol. 69, ISBN 978-3-319-06940-1, 2014.
  2. Ruxandra Stoean, Catalin Stoean, Evolutie si inteligenta artificiala. Paradigme moderne si aplicatii, Editura Albastra - Grupul MicroInformatica, ISBN: 978-973-650-277-4, 2010.
  3. Ruxandra Stoean, Support Vector Machines. An Evolutionary Resembling Approach, Research Center for Artificial Intelligence, Computer Science Series, Universitaria Publishing House, Craiova, ISBN: 978-606-510-161-6, 2008.

Springer book chapters

  1. Ruxandra Stoean, Mike Preuss, Catalin Stoean, Elia El-Darzi, D. Dumitrescu, An Evolutionary Approximation for the Coefficients of Decision Functions within a Support Vector Machine Learning Strategy, Foundations on Computational Intelligence, Springer, 978-3-642-01082-8, 1, pp. 83-114, 2009.
  2. Catalin Stoean, Ruxandra Stoean, Evolution of Cooperating Classification Rules with an Archiving Strategy to Underpin Collaboration, Intelligent Systems and Technologies - Methods and Applications, Springer, ISBN 978-3-642-01885-5, pp. 47-65, 2009.

Selected conferences

  1. Ruxandra Stoean, Nebojsa Bacanin, Leonard Ionescu, Catalin Stoean, Marinela Boicea, Alina-Maria Garau, Cristina-Camelia Ghitescu, Deep learning for a swift non-invasive recognition and delineation of corrosive iron compounds present on the surface of unrestored archaeological artefacts, 26th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, https://doi.org/10.1016/j.procs.2022.09.186, Procedia Computer Science, vol. 207, pp. 1303-1311, 2022.
  2. M. Cumbajin, R. Stoean, J. Aguado, G. Joya, Hybrid Deep Learning Architecture Approach for Photovoltaic Power Plant Output Prediction, Sustainability, Energy and City (CSECity 2021), Lecture Notes in Networks and Systems, vol 379, 26-37, Springer, Cham, https://doi.org/10.1007/978-3-030-94262-5_3, 2022.
  3. Catalin Stoean, Nebojsa Bacanin, Ruxandra Stoean, Leonard Ionescu, Cristian Alecsa, Mircea Hotoleanu, Miguel Atencia, Gonzalo Joya, On Using Perceptual Loss within the U-Net Architecture for the Semantic Inpainting of Textile Artefacts with Traditional Motifs, 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2022.
  4. Catalin Stoean, Nebojsa Bacanin, Wieslaw Paja, Ruxandra Stoean, Dominic Iliescu, Ciprian Patru, Rodica Nagy, Semantic segmentation of fetal heart components in second trimester echocardiography, Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 26th International Conference, Procedia Computer Science, Vol. 207, pp. 3085-3092, 2022.
  5. Ruxandra Stoean, Leonard Ionescu, Catalin Stoean, Marinela Boicea, Miguel Atencia, Gonzalo Joya, A Deep Learning-based Surrogate for the XRF Approximation of Elemental Composition within Archaeological Artefacts before Restoration, 25th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES), September 8-10, vol. 192, pp. 2002-2011, https://doi.org/10.1016/j.procs.2021.08.206, 2021.
  6. Catalin Stoean, Leonard Ionescu, Ruxandra Stoean, Marinela Boicea, Miguel Atencia, Gonzalo Joya, A convolutional neural network as a proxy for the XRF approximation of the chemical composition of archaeological artefacts in the presence of inter-microscope variability, 16th International Work-Conference on Artificial Neural Networks (IWANN), June 16-18, LNCS 12862, pp. 260-271, https://doi.org/10.1007/978-3-030-85099-9_21, 2021.
  7. Ruxandra Stoean, Dominic Iliescu, Catalin Stoean, Vlad Ilie, Ciprian Patru, Mircea Hotoleanu, Rodica Nagy, Dan Ruican, Rares Trocan, Andreea Marcu, Miguel Atencia and Gonzalo Joya , Deep Learning for the Detection of Frames of Interest in Fetal Heart Assessment from First Trimester Ultrasound, 16th International Work-Conference on Artificial Neural Networks, June 16-18, LNCS 12861, pp. 3-14, https://doi.org/10.1007/978-3-030-85030-2_1, 2021.
  8. Ruxandra Stoean, Nebojsa Bacanin, Leonard Ionescu, Marinela Boicea, Alina-Maria Garau, Cristina-Camelia Ghitescu, Semantic Segmentation for Corrosion Detection in Archaeological Artefacts before Restoration, 23rd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), December 7-10, 2021.
  9. Catalin Stoean, Ruxandra Stoean, Mircea Hotoleanu, Dominic Gabriel Iliescu, Ciprian Patru, Rodica Nagy, An assessment of the usefulness of image pre-processing for the classification of first trimester fetal heart ultrasound using convolutional neural networks, 25th International Conference on System Theory, Control and Computing (ICSTCC), October 20-23, IEEE, pp. 242-248, 2021.
  10. Ruxandra Stoean, Leonard Ionescu, Catalin Stoean, Marinela Boicea, Alina-Maria Garau, Cristina-Camelia Ghitescu, Artificial intelligence can “see” the chemical composition of archaeological objects before restoration, 8th International Conference on Matter and Materials in/for Heritage Conservation (MATCONS), October 11-14, pp. 279 - 287, ISSN 2810 – 2797, 2021.
  11. M. Rebolledo, R. Stoean, A.E. Eiben, T. Bartz-Beielstein, Hybrid Variable Selection and Support Vector Regression for Gas Sensor Optimization. In: Filipic B., Minisci E., Vasile M. (eds) Bioinspired Optimization Methods and Their Applications. BIOMA 2020. Lecture Notes in Computer Science, vol 12438, pp. 281-293. Springer, Cham, 2020.
  12. Catalin Stoean, Ruxandra Stoean, Roberto Antonio Becerra-García, Rodolfo García-Bermúdez, Miguel Atencia, Francisco García-Lagos, Luis Velázquez-Pérez, Gonzalo Joya, Unsupervised Learning as a Complement to Convolutional Neural Network Classification in the Analysis of Saccadic Eye Movement in Spino-Cerebellar Ataxia Type 2, International Work-Conference on Artificial Neural Networks (IWANN 2019), Advances in Computational Intelligence, Gran Canaria, Spain, 12-14 June 2019, pp. 26-37, 2019
  13. Davide Bacciu, Paulo J.G. Lisboa, Jose D. Martin, Ruxandra Stoean, Alfredo Vellido, Bioinformatics and Medicine in the Era of Deep Learning, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018), pp. 345-354, 2018.
  14. Miguel Atencia, Ruxandra Stoean, Non-negative Matrix Factorization for Medical Imaging, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018), pp. 379-384, 2018.
  15. Ruxandra Stoean, Catalin Stoean, Adrian Sandita, Evolutionary Regressor Selection in ARIMA Model for Stock Price Time Series Forecasting, Intelligent Decision Technologies (IDT 2017), Smart Innovation, Systems and Technologies, Springer, Vilamoura, Portugal, 21-23 June 2017, pp. 117-126, 2017.
  16. Stefan Postavaru, Ruxandra Stoean, Catalin Stoean, Gonzalo Joya Caparros, Adaptation of Deep Convolutional Neural Networks for Cancer Grading from Histopathological Images, International Work-Conference on Artificial Neural Networks (IWANN 2017), Advances in Computational Intelligence, Springer, Cadiz, Spain, 14-16 June 2017, pp. 38-49, 2017.
  17. Catalin Stoean, Ruxandra Stoean, Adrian Sandita, Daniela Ciobanu, Cristian Mesina, Corina Lavinia Gruia, SVM-Based Cancer Grading from Histopathological Images using Morphological and Topological Features of Glands and Nuclei, 9th International KES Conference on Intelligent Interactive Multimedia: Systems and Services, Puerto de la Cruz, Tenerife, Spain, 15-17 June 2016, Intelligent Interactive Multimedia Systems and Services 2016, Vol. 55, Smart Innovation, Systems and Technologies, pp. 145-155, 2016. The data set used in the article can be found here.
  18. Ruxandra Stoean, Catalin Stoean, Adrian Sandita, Daniela Ciobanu, Cristian Mesina, Ensemble of Classifiers for Length of Stay Prediction in Colorectal Cancer, International Work-Conference on Artificial Neural Networks (IWANN 2015), Advances in Computational Intelligence, Lecture Notes in Computer Science, Springer, Volume 9094, Palma de Mallorca, Spain, 10-12 June, pp. 444-457, 2015.
  19. Catalin Stoean, Ruxandra Stoean, Adrian Sandita, Cristian Mesina, Daniela Ciobanu, Corina Lavinia Gruia, Investigation on Parameter Effect for Semi-Automatic Contour Detection in Histopathological Image Processing, IEEE Post-Proceedings of the 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2015), IEEE Computer Society, September 21 – 24, pp. 445-452, 2015, Timisoara, Romania.
  20. Catalin Stoean, Ruxandra Stoean, Adrian Sandita, Cristian Mesina, Corina Lavinia Gruia, Daniela Ciobanu, Evolutionary Search for An Accurate Contour Segmentation in Histopathological Images, The ACM Genetic and Evolutionary Computation Conference (GECCO 2015), GECCO Companion, Madrid, Spain, 11-15 July, pp. 1491-1492, 2015.
  21. Catalin Stoean, Ruxandra Stoean, Adrian Sandita, Investigation of Alternative Evolutionary Prototype Generation in Medical Classification, IEEE Post-Proceedings of the 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2014), September 22 – 25, 2014, Timisoara, Romania, pp. 537-543, ISBN 978-1-4799-8447-3. 
  22. Catalin Stoean, Mike Preuss, Ruxandra Stoean, EA-based Parameter Tuning of Multimodal Optimization Performance by Means of Different Surrogate Models, The ACM Genetic and Evolutionary Computation Conference (GECCO-2013), Amsterdam, The Netherlands, GECCO-UP workshop, pp. 1063-1070, 2013.
  23. Mike Preuss, Catalin Stoean, Ruxandra Stoean, Niching Foundations: Basin Identification on Fixed-Property Generated Landscapes, The ACM Proceedings of the 13th annual conference on Genetic and evolutionary computation (GECCO-2011), Dublin, Ireland, pp. 837-844, 2011.
  24. Catalin Stoean, Mike Preuss, Ruxandra Stoean, D. Dumitrescu, EA-Powered Basin Number Estimation by Means of Preservation and Exploration, Parallel Problem Solving from Nature ? PPSN X, Lecture Notes in Computer Science, Springer Berlin / Heidelberg, vol. 5199, pp 569-578, 2008, ISBN 978-3-540-87699-1.
  25. Ruxandra Stoean, Catalin Stoean, D. Dumitrescu, Investigating Landscape Topology for Subpopulation Differentiation in Genetic Chromodynamics, IEEE Postproceedings, 10th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing - SYNASC 2008, IEEE Press, pp. 551-554, 2008.
  26. Catalin Stoean, Mike Preuss, Ruxandra Stoean, D. Dumitrescu, Disburdening the Species Conservation Evolutionary Algorithm of Arguing with Radii, The ACM Genetic and Evolutionary Computation Conference - GECCO 2007 (ISI proceedings), London, UK, pp. 1420 - 1427, 2007.
  27. Ruxandra Stoean, Mike Preuss, Catalin Stoean, D. Dumitrescu, Concerning the Potential of Evolutionary Support Vector Machines, The IEEE Congress on Evolutionary Computation - CEC 2007, Singapore, pp. 1436 - 1443, 2007. 
  28. Catalin Stoean, Ruxandra Stoean, Elia El-Darzi, Breast Cancer Diagnosis by Means of Cooperative Coevolution, Proceedings of the Third ACM International Conference on Intelligent Computing and Information Systems (ICICIS 2007), Police Press, Cairo, pp. 493-497, ISBN 977-237-172-3, 2007.
  29. Ruxandra Stoean, Catalin Stoean, Mike Preuss, Elia El-Darzi, D. Dumitrescu, Evolutionary Support Vector Machines for Diabetes Mellitus Diagnosis, IEEE IS 2006, Westminster, London, pp. 182-187, ISBN 1-4244-0196-8.
  30. Catalin Stoean, Mike Preuss, D. Dumitrescu, Ruxandra Stoean, Cooperative Evolution of Rules for Classification, IEEE Post-proceedings SYNASC 2006, IEEE Press, Lisa O'Conner (Ed.), Los Alamitos, CA, USA, pp. 317-322, ISBN 0-7695-2740-X, 2006.
  31. Ruxandra Stoean, Mike Preuss, D. Dumitrescu, Catalin Stoean, Evolutionary Support Vector Regression Machines, IEEE Post-proceedings SYNASC 2006, IEEE Press, Lisa O'Conner (Ed.), Los Alamitos, CA, USA, pp. 330-335, ISBN 0-7695-2740-X, 2006.
  32. Ruxandra Stoean, Catalin Stoean, Mike Preuss, D. Dumitrescu, Evolutionary Support Vector Machines for Spam Filtering, RoEduNet IEEE International Conference, Sibiu, Romania, 2006, pp. 261-266.
  33. Catalin Stoean, Mike Preuss, Ruxandra Gorunescu, D. Dumitrescu, Elitist Generational Genetic Chromodynamics - a New Radii-Based Evolutionary Algorithm for Multimodal Optimization, The 2005 IEEE Congress on Evolutionary Computation - CEC 2005 (ISI proceedings), Edinburgh, UK, September 2-5, 2005, pp. 1839 - 1846, ISBN 0-7803-9363-5.