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Evidence of mini-jet emission in a large emission zone from a magnetically-dominated gamma-ray burst jet

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arXiv 2023年

作者： Yi, S.-X. Wang, C.-W. Shao, X. Moradi, R. Gao, H. Zhang, B. Xiong, S.-L. Zhang, S.-N. Tan, W.-J. Liu, J.-C. Xue, W.-C. Zhang, Y.-Q. Zheng, C. Wang, Y. Zhang, P. An, Z.-H. Cai, C. Feng, P.-Y. Gong, K. Guo, D.-Y. Huang, Y. Li, B. Li, X.-B. Li, X.-Q. Liu, X.-J. Liu, Y.-Q. Ma, X. Peng, W.-X. Qiao, R. Song, L.-M. Wang, J. Wang, P. Wang, Y. Wen, X.-Y. Xiao, S. Xu, Y.-B. Yang, S. Yi, Q.-B. Zhang, D.-L. Zhang, F. Zhang, H.-M. Zhang, J.-P. Zhang, Z. Zhao, X.-Y. Zhao, Y. Zheng, S.-J. Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100049 China Department of Astronomy Beijing Normal University Beijing100875 China Nevada Center for Astrophysics University of Nevada Las Vegas NV89154 United States Department of Physics and Astronomy University of Nevada Las Vegas NV89154 United States ICRANet Piazza della Repubblica 10 PescaraI-65122 Italy ICRA Dipartamento di Fisica Sapienza Università di Roma Piazzale Aldo Moro 5 RomeI-00185 Italy INAF Osservatorio Astronomico d’Abruzzo Via M. Maggini snc TeramoI-64100 Italy College of Electronic and Information Engineering Tongji University Shanghai201804 China College of Physics Hebei Key Laboratory of Photophysics Research and Application Hebei Normal University Hebei Shijiazhuang050024 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China School of Physics and Optoelectronics Xiangtan University Yuhu District Hunan Xiangtan411105 China School of Computer and Information Dezhou University Dezhou253023 China

The second brightest GRB in history, GRB230307A, provides an ideal laboratory to study the mechanism of GRB prompt emission thanks to its extraordinarily high photon statistics and its single episode activity. Here we demonstrate that the rapidly variable components of its prompt emission compose an overall broad single pulse-like profile. Although these individual rapid components are aligned in time across all energy bands, this overall profile conspires to show a well-defined energy-dependent behavior which is typically seen in single GRB pulses. Such a feature demonstrates that the prompt emission of this burst is from many individual emitting units that are casually linked in a emission site at a large distance from the central engine. Such a scenario is in natural consistency with the internal-collision-induced magnetic reconnection and turbulence framework, which invokes many mini-jets due to local magnetic reconnection that constantly appear and disappear in a global magnetically-dominated jet. © 2023, CC BY.

关键词： Gamma radiography

Spatial-temporal radiogenomics in predicting neoadjuvant chemotherapy efficacy for breast cancer: a comprehensive review

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Journal of translational medicine 2025年第1期23卷 681页

作者： Tingfeng Zhang Liang Zhao Tingting Cui Yi Zhou Peifen Li Chuqiao Luo Junkang Wei Hong Hu Division of Breast Surgery Department of General Surgery The Second Clinical Medical College The First Affiliated Hospital Shenzhen People's Hospital Jinan University Southern University of Science and Technology) Shenzhen 518020 Guangdong China. Shenzhen Health Development Research and Data Management Center Shenzhen 518000 Guangdong China. Key Laboratory of Biomedical Engineering of Guangdong Province South China University of Technology Guangzhou 510006 China. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong SAR 100871 China. weijk@link.cuhk.edu.hk. Division of Breast Surgery Department of General Surgery The Second Clinical Medical College The First Affiliated Hospital Shenzhen People's Hospital Jinan University Southern University of Science and Technology) Shenzhen 518020 Guangdong China. chris_huhong@***.

Radiomics is undergoing a paradigm shift from single-omics to multi-omics, from single-temporal to multi-temporal analysis, and from global to subregional analysis. These transformations have shown great potential in addressing key challenges related to imaging changes before and after neoadjuvant chemotherapy (NAC) in breast cancer. Furthermore, radiomics has achieved remarkable progress in tasks such as exploring tumor heterogeneity and uncovering underlying biological mechanisms. Integrating imaging data with gene data offers novel perspectives for understanding imaging changes driven by specific genetic alterations. However, current radiomics studies on neoadjuvant chemotherapy for breast cancer have not yet achieved a close integration of imaging changes with underlying biological mechanisms. They are largely limited to simple associations between models and genomic data, without in-depth interpretation of the biological significance inherent in imaging features, which is essential to directly link these features with the dynamic progression of the disease. This review seeks to explore the spatial-temporal heterogeneity of imaging alterations observed during NAC for breast cancer, while assessing their biological implications using established analytical approaches. It highlights the distinct advantages of spatial-temporal radiomics in predictive model development and examines potential correlations between imaging dynamics and gene expression profiles before and after NAC. Additionally, we critically examines previous radiogenomics studies, providing theoretical insights into their limitations. Finally, the review proposes future directions and innovative approaches for applying spatial-temporal radiogenomics in NAC for breast cancer, serving as a valuable reference and roadmap for researchers and clinical practitioners in this field.

关键词： Breast cancer Magnetic resonance imaging Neoadjuvant chemotherapy Radiogenomics Spatial-temporal heterogeneity

VaTEST III: Validation of 8 Potential Super-Earths from TESS data

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arXiv 2023年

作者： Mistry, Priyashkumar Prasad, Aniket Maity, Mousam Pathak, Kamlesh Gharat, Sarvesh Lekkas, Georgios Bhattarai, Surendra Kumar, Dhruv Lissauer, Jack J. Twicken, Joseph D. Soubkiou, Abderahmane Pozuelos, Francisco J. Jenkins, Jon Horne, Keith Giacalone, Steven Barkaoui, Khalid Timmermans, Mathilde Watkins, Cristilyn N. Sefako, Ramotholo Collins, Karen A. Ciardi, David R. Clark, Catherine A. Safonov, Boris S. Shporer, Avi Schlieder, Joshua E. Benkhaldoun, Zouhair Stockdale, Chris Ziegler, Carl Gilbert, Emily A. Jehin, Emmanuël Murgas, Felipe Crossfield, Ian J.M. Paegert, Martin Lund, Michael B. Narita, Norio Schwarz, Richard P. Goeke, Robert F. Fajardo-Acosta, Sergio B. Howell, Steve B. Tan, Thiam-Guan Barclay, Thomas Kawai, Yugo Department of Physics Sardar Vallabhbhai National Institute of Technology Gujarat Surat395007 India School of Physics Faculty of Science University of New South Wales Kensington SydneyNSW2052 Australia Department of Earth and Space Science Indian Institute of Space Science and Technology Thiruvananthapuram India Department of Physics Presidency University West Bengal Kolkata700073 India Centre for Machine Intelligence and Data Science Indian Institute of Technology Bombay India Department of Computer Science International Hellenic University Kavala65404 Greece Department of Physics Indian Institute of Science Education and Research Kolkata West Bengal Mohanpur741246 India Department of Physics National Institute of Technology Tripura Agartala799046 India NASA Ames Research Center Moffett FieldCA94035 United States SETI Institute Mountain ViewCA94043 United States Oukaimeden Observatory High Energy Physics and Astrophysics Laboratory Cadi Ayyad University Marrakech Morocco Astrobiology Research Unit Université de Liège 19C Allée du 6 Août Liège4000 Belgium Glorieta de la Astronomía s/n Granada18008 Spain SUPA Physics and Astronomy University of St. Andrews FifeKY16 9SS United Kingdom Department of Astronomy University of California Berkeley BerkeleyCA94720 United States Department of Earth Atmospheric and Planetary Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Calle Vía Láctea s/n Tenerife La Laguna38200 Spain Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States South African Astronomical Observatory P.O. Box 9 Observatory Cape Town7935 South Africa NASA Exoplanet Science Institute IPAC California Institute of Technology PasadenaCA91125 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Sternberg Astronomical Institute Lomonosov Moscow State University Russia Depart

NASA’s all-sky survey mission, the Transiting Exoplanet Survey Satellite (TESS), is specifically engineered to detect exoplanets that transit bright stars. Thus far, TESS has successfully identified approximately 400 transiting exoplanets, in addition to roughly 6000 candidate exoplanets pending confirmation. In this study, we present the results of our ongoing project, the Validation of Transiting Exoplanets using Statistical Tools (VaTEST). Our dedicated effort is focused on the confirmation and characterization of new exoplanets through the application of statistical validation tools. Through a combination of ground-based telescope data, high-resolution imaging, and the utilization of the statistical validation tool known as TRICERATOPS, we have successfully discovered eight potential super-Earths. These planets bear the designations: TOI-238b (1.61+0.09–0.10 R⊗), TOI-771b (1.42+0.11–0.09 R⊗), TOI-871b (1.66+0.11–0.11 R⊗), TOI-1467b (1.83+0.16–0.15 R⊗), TOI-1739b (1.69+0.10–0.08 R⊗), TOI-2068b (1.82+0.16–0.15 R⊗), TOI-4559b (1.42+0.13–0.11 R⊗), and TOI-5799b (1.62+0.19–0.13 R⊗). Among all these planets, six of them fall within the region known as’keystone planets,’ which makes them particularly interesting for study. Based on the location of TOI-771b and TOI-4559b below the radius valley we characterized them as likely super-Earths, though radial velocity mass measurements for these planets will provide more details about their characterization. It is noteworthy that planets within the size range investigated herein are absent from our own solar system, making their study crucial for gaining insights into the evolutionary stages between Earth and Neptune. © 2023, CC BY.

关键词： Satellites

Operational research: Methods and applications

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arXiv 2023年

作者： Petropoulos, Fotios Laporte, Gilbert Aktas, Emel Alumur, Sibel A. Archetti, Claudia Ayhan, Hayriye Battarra, Maria Bennell, Julia A. Bourjolly, Jean-Marie Boylan, John E. Breton, Michèle Canca, David Charlin, Laurent Chen, Bo Cicek, Cihan Tugrul Cox, Louis Anthony Currie, Christine S.M. Demeulemeester, Erik Ding, Li Disney, Stephen M. Ehrgott, Matthias Eppler, Martin J. Erdogan, Güneş Fortz, Bernard Franco, L. Alberto Frische, Jens Greco, Salvatore Gregory, Amanda J. Hämäläinen, Raimo P. Herroelen, Willy Hewitt, Mike Holmström, Jan Hooker, John N. Işık, Tugçe Johnes, Jill Kara, Bahar Y. Karsu, Özlem Kent, Katherine Köhler, Charlotte Kunc, Martin Kuo, Yong-Hong Letchford, Adam N. Leung, Janny Li, Dong Li, Haitao Lienert, Judit Ljubić, Ivana Lodi, Andrea Lozano, Sebastián Lurkin, Virginie Martello, Silvano McHale, Ian G. Midgley, Gerald Morecroft, John D.W. Mutha, Akshay Oguz, Ceyda Petrovic, Sanja Pferschy, Ulrich Psaraftis, Harilaos N. Rose, Sam Saarinen, Lauri Salhi, Said Song, Jing-Sheng Sotiros, Dimitrios Stecke, Kathryn E. Strauss, Arne K. Tarhan, Istenç Thielen, Clemens Toth, Paolo Van Woensel, Tom Vanden Berghe, Greet Vasilakis, Christos Vaze, Vikrant Vigo, Daniele Virtanen, Kai Wang, Xun Weron, Rafal White, Leroy Yearworth, Mike Yıldırım, E. Alper Zaccour, Georges Zhao, Xuying School of Management University of Bath Bath United Kingdom Makridakis Open Forecasting Center University of Nicosia Nicosia Cyprus Department of Decision Sciences HEC Montréal Montreal Canada Molde University College Molde Norway Cranfield School of Management Cranfield University Cranfield United Kingdom Department of Management Sciences University of Waterloo Waterloo Canada ESSEC Business School in Paris Department of Information Systems Decision Sciences and Statistics Cergy France H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta United States Centre for Decision Research Leeds University Business School University of Leeds Leeds United Kingdom Université du Québec à Montréal Montreal Canada Centre for Marketing Analytics and Forecasting Lancaster University Management School Lancaster University Lancaster United Kingdom School of Engineering Department of Industrial Engineering and Management Science I University of Seville Seville Spain Mila-Quebec AI Institute Montreal Canada Warwick Business School University of Warwick Coventry United Kingdom Department of Industrial Engineering Atilim University Ankara Turkey Department of Business Analytics University of Colorado School of Business Denver United States Cox Associates Denver United States Mathematical Sciences University of Southampton Southampton United Kingdom Faculty of Economics and Business Research Center for Operations Management KU Leuven Leuven Belgium Durham University Business School Durham University Durham United Kingdom Center for Simulation Analytics and Modelling University of Exeter Business School Exeter United Kingdom Department of Management Science Lancaster University Management School Lancaster University Lancaster United Kingdom University of St. Gallen St. Gallen Switzerland HEC - Management School of the University of Liège Liège Belgium Département d'Informatique Université Libre de Bruxelles Bruss

Throughout its history, Operational research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes. © 2023, CC BY.

关键词： Decision making

Prediction of sleep quality scores using dynamic functional network connectivity of young adults: A reproducibility analysis

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Alzheimer's & Dementia 2023年第S3期19卷

作者： Mohammad S.E. Sendi Hossein Dini Elaheh Zendehrouh David H Salat Vince D Calhoun School of Electrical and Computer Engineering (ECE) at the Georgia Institute of Technology Atlanta GA USA Tri-Institutional Georgia State University/Georgia Institute of Technology/Emory University Center for Translational Research in Neuroimaging and Data Science Atlanta GA USA Wallace H. Coulter Department of Biomedical Engineering at Emory University and Georgia Tech Atlanta GA USA Aalborg University Copenhagen Denmark Georgia State University Atlanta GA USA Neuroimaging Research for Veterans Center VA Boston Healthcare System Jamaica Plain MA USA Massachusetts General Hospital Charlestown MA USA Harvard Medical School Boston MA USA Athinoula A. Martinos Center for Biomedical Imaging Charlestown MA USA

Background Dynamic functional network connectivity (dFNC) estimated from resting-state functional magnetic resonance imaging (rs-fMRI) is a potentially powerful approach to investigate human behavior and cognition. However, previous studies barely investigate the reproducibility of dFNC features links with cognition. This study aims to examine whether the data collected in four different sessions reproduce the same results using the sleep scores by applying a dFNC method to resting-state fMRI (rs-fMRI) data from the Human Connectome Project (HCP) Young Adult. Method We used the Pittsburgh Sleep Quality Index (PSQI) scores and four sessions of rs-fMRI (15 minutes each) from 833 young adults (age between 22 to 37yrs). We calculated the dFNC for each participant using a fully automated Neuromark independent component analysis and a sliding window technique to estimate dFNCs. A k-means clustering approach partitioned dFNCs into two group-level distinct states and subject-level state vectors. Next, we estimated dFNC features from each individual’s state vector and dFNCs. Finally, we trained a 10-fold support vector regression model to predict the sleep scores based on the dFNC features. We applied all the procedures mentioned above to all four sessions separately. Result We found dFNC features can successfully predict each participant’s night sleep time. The correlation between the actual score and the predicted one was R = 0.0853 (p = 0.0182) for session1, R = 0.0696 (p = 0.0485) for session2, R = 0.0764 (p = 0.0393) for session2, and r = 0.0788 (p = 0.0285) for session4. The dFNC features for none of four sessions could predict other sleep scores. Conclusion We showed that the dFNC feature could predict the amount of sleep during the night in the HCP Young Adult population. Additionally, we showed the prediction result replicates across all four rs-fMRI sessions. Future studies are needed to explore the reproducibility of the result within the age range of the HCP data

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An Early Stage researcher's Primer on Systems Medicine Terminology

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Network and Systems Medicine

Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

Adaptive gradient methods with dynamic bound of learning rate

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arXiv 2019年

作者： Luo, Liangchen Xiong, Yuanhao Liu, Yan Sun, Xu MOE Key Lab of Computational Linguistics School of EECS Peking University College of Information Science and Electronic Engineering Zhejiang University Department of Computer Science University of Southern California Center for Data Science Beijing Institute of Big Data Research Peking University

Adaptive optimization methods such as ADAGRAD, RMSPROP and ADAM have been proposed to achieve a rapid training process with an element-wise scaling term on learning rates. Though prevailing, they are observed to generalize poorly compared with SGD or even fail to converge due to unstable and extreme learning rates. Recent work has put forward some algorithms such as AMSGRAD to tackle this issue but they failed to achieve considerable improvement over existing methods. In our paper, we demonstrate that extreme learning rates can lead to poor performance. We provide new variants of ADAM and AMSGRAD, called ADABOUND and AMSBOUND respectively, which employ dynamic bounds on learning rates to achieve a gradual and smooth transition from adaptive methods to SGD and give a theoretical proof of convergence. We further conduct experiments on various popular tasks and models, which is often insufficient in previous work. Experimental results show that new variants can eliminate the generalization gap between adaptive methods and SGD and maintain higher learning speed early in training at the same time. Moreover, they can bring significant improvement over their prototypes, especially on complex deep networks. The implementation of the algorithm can be found at https://***/Luolc/AdaBound. Copyright © 2019, The Authors. All rights reserved.

关键词： Gradient methods

On universal features for high-dimensional learning and inference

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arXiv 2019年

作者： Huang, Shao-Lun Makur, Anuran Wornell, Gregory W. Zheng, Lizhong Data Science and Information Technology Research Center Tsinghua-Berkeley Shenzhen Institute Shenzhen China Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

We consider the problem of identifying universal low-dimensional features from high-dimensional data for inference tasks in settings involving learning. For such problems, we introduce natural notions of universality and we show a local equivalence among them. Our analysis is naturally expressed via information geometry, and represents a conceptually and computationally useful analysis. The development reveals the complementary roles of the singular value decomposition, Hirschfeld-Gebelein-Rényi maximal correlation, the canonical correlation and principle component analyses of Hotelling and Pearson, Tishby's information bottleneck, Wyner's common information, Ky Fan k-norms, and Brieman and Friedman's alternating conditional expectations algorithm. We further illustrate how this framework facilitates understanding and optimizing aspects of learning systems, including multinomial logistic (softmax) regression and the associated neural network architecture, matrix factorization methods for collaborative filtering and other applications, rank-constrained multivariate linear regression, and forms of semi-supervised learning. Copyright © 2019, The Authors. All rights reserved.

关键词： Neural networks

Cover Picture: Laser-scribed Graphene Electrodes as an Electrochemical Immunosensing Platform for Cancer Biomarker ‘eIF3d’ (Electroanalysis 4/2021)

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Electroanalysis 2021年第4期33卷

作者： Simge Balaban Tutku Beduk Ceren Durmus Eda Aydindogan Khaled Nabil Salama Suna Timur Department of Biochemistry Faculty of Science Ege University 35100 Bornova Izmir Turkey Sensors Lab Advanced Membranes and Porous Materials Center Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) 23955-6900 Thuwal Saudi Arabia Central Research Testing and Analysis Laboratory Research and Application Center Ege University 35100 Bornova Izmir Turkey