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Artykuły w czasopismach na temat "Surrogate methods"
Ciani, Oriana, Sarah Davis, Paul Tappenden, Ruth Garside, Ken Stein, Anna Cantrell, Everardo D. Saad, Marc Buyse i Rod S. Taylor. "VALIDATION OF SURROGATE ENDPOINTS IN ADVANCED SOLID TUMORS: SYSTEMATIC REVIEW OF STATISTICAL METHODS, RESULTS, AND IMPLICATIONS FOR POLICY MAKERS". International Journal of Technology Assessment in Health Care 30, nr 3 (lipiec 2014): 312–24. http://dx.doi.org/10.1017/s0266462314000300.
Pełny tekst źródłaRios, Ricardo Araújo, Michael Small i Rodrigo Fernandes de Mello. "Testing for Linear and Nonlinear Gaussian Processes in Nonstationary Time Series". International Journal of Bifurcation and Chaos 25, nr 01 (styczeń 2015): 1550013. http://dx.doi.org/10.1142/s0218127415500133.
Pełny tekst źródłaHernandez-Villafuerte, Karla, Alastair Fischer i Nicholas Latimer. "CHALLENGES AND METHODOLOGIES IN USING PROGRESSION FREE SURVIVAL AS A SURROGATE FOR OVERALL SURVIVAL IN ONCOLOGY". International Journal of Technology Assessment in Health Care 34, nr 3 (2018): 300–316. http://dx.doi.org/10.1017/s0266462318000338.
Pełny tekst źródłaLu, Dan, i Daniel Ricciuto. "Efficient surrogate modeling methods for large-scale Earth system models based on machine-learning techniques". Geoscientific Model Development 12, nr 5 (6.05.2019): 1791–807. http://dx.doi.org/10.5194/gmd-12-1791-2019.
Pełny tekst źródłaCiani, Oriana, Bogdan Grigore, Hedwig Blommestein, Saskia de Groot, Meilin Möllenkamp, Stefan Rabbe, Rita Daubner-Bendes i Rod S. Taylor. "Validity of Surrogate Endpoints and Their Impact on Coverage Recommendations: A Retrospective Analysis across International Health Technology Assessment Agencies". Medical Decision Making 41, nr 4 (10.03.2021): 439–52. http://dx.doi.org/10.1177/0272989x21994553.
Pełny tekst źródłaScher, Howard I., Glenn Heller, Arturo Molina, Gerhardt Attard, Daniel C. Danila, Xiaoyu Jia, Weimin Peng i in. "Circulating Tumor Cell Biomarker Panel As an Individual-Level Surrogate for Survival in Metastatic Castration-Resistant Prostate Cancer". Journal of Clinical Oncology 33, nr 12 (20.04.2015): 1348–55. http://dx.doi.org/10.1200/jco.2014.55.3487.
Pełny tekst źródłaHu, Zhen, Saideep Nannapaneni i Sankaran Mahadevan. "Efficient Kriging surrogate modeling approach for system reliability analysis". Artificial Intelligence for Engineering Design, Analysis and Manufacturing 31, nr 2 (maj 2017): 143–60. http://dx.doi.org/10.1017/s089006041700004x.
Pełny tekst źródłaOko, S. O. "Surrogate methods for linear inequalities". Journal of Optimization Theory and Applications 72, nr 2 (luty 1992): 247–68. http://dx.doi.org/10.1007/bf00940518.
Pełny tekst źródłaKim, Hyejin, Janet A. Deatrick i Connie M. Ulrich. "Ethical frameworks for surrogates’ end-of-life planning experiences". Nursing Ethics 24, nr 1 (3.08.2016): 46–69. http://dx.doi.org/10.1177/0969733016638145.
Pełny tekst źródłaRoyce, Trevor Joseph, Ming-Hui Chen, Jing Wu, Marian Loffredo, Andrew A. Renshaw, Philip W. Kantoff i Anthony Victor D'Amico. "A comparison of surrogate endpoints for all cause mortality in men with localized unfavorable-risk prostate cancer." Journal of Clinical Oncology 35, nr 6_suppl (20.02.2017): 21. http://dx.doi.org/10.1200/jco.2017.35.6_suppl.21.
Pełny tekst źródłaRozprawy doktorskie na temat "Surrogate methods"
Conradie, Tanja. "Modelling of nonlinear dynamic systems : using surrogate data methods". Thesis, Stellenbosch : Stellenbosch University, 2000. http://hdl.handle.net/10019.1/51834.
Pełny tekst źródłaENGLISH ABSTRACT: This study examined nonlinear modelling techniques as applied to dynamic systems, paying specific attention to the Method of Surrogate Data and its possibilities. Within the field of nonlinear modelling, we examined the following areas of study: attractor reconstruction, general model building techniques, cost functions, description length, and a specific modelling methodology. The Method of Surrogate Data was initially applied in a more conventional application, i.e. testing a time series for nonlinear, dynamic structure. Thereafter, it was used in a less conventional application; i.e. testing the residual vectors of a nonlinear model for membership of identically and independently distributed (i.i.d) noise. The importance of the initial surrogate analysis of a time series (determining whether the apparent structure of the time series is due to nonlinear, possibly chaotic behaviour) was illustrated. This study confrrmed that omitting this crucial step could lead to a flawed conclusion. If evidence of nonlinear structure in the time series was identified, a radial basis model was constructed, using sophisticated software based on a specific modelling methodology. The model is an iterative algorithm using minimum description length as the stop criterion. The residual vectors of the models generated by the algorithm, were tested for membership of the dynamic class described as i.i.d noise. The results of this surrogate analysis illustrated that, as the model captures more of the underlying dynamics of the system (description length decreases), the residual vector resembles Li.d noise. It also verified that the minimum description length criterion leads to models that capture the underlying dynamics of the time series, with the residual vector resembling Li.d noise. In the case of the "worst" model (largest description length), the residual vector could be distinguished from Li.d noise, confirming that it is not the "best" model. The residual vector of the "best" model (smallest description length), resembled Li.d noise, confirming that the minimum description length criterion selects a model that captures the underlying dynamics of the time series. These applications were illustrated through analysis and modelling of three time series: a time series generated by the Lorenz equations, a time series generated by electroencephalograhpic signal (EEG), and a series representing the percentage change in the daily closing price of the S&P500 index.
AFRIKAANSE OPSOMMING: In hierdie studie ondersoek ons nie-lineere modelleringstegnieke soos toegepas op dinamiese sisteme. Spesifieke aandag word geskenk aan die Metode van Surrogaat Data en die moontlikhede van hierdie metode. Binne die veld van nie-lineere modellering het ons die volgende terreine ondersoek: attraktor rekonstruksie, algemene modelleringstegnieke, kostefunksies, beskrywingslengte, en 'n spesifieke modelleringsalgoritme. Die Metode and Surrogaat Data is eerstens vir 'n meer algemene toepassing gebruik wat die gekose tydsreeks vir aanduidings van nie-lineere, dimanise struktuur toets. Tweedens, is dit vir 'n minder algemene toepassing gebruik wat die residuvektore van 'n nie-lineere model toets vir lidmaatskap van identiese en onafhanlike verspreide geraas. Die studie illustreer die noodsaaklikheid van die aanvanklike surrogaat analise van 'n tydsreeks, wat bepaal of die struktuur van die tydsreeks toegeskryf kan word aan nie-lineere, dalk chaotiese gedrag. Ons bevesting dat die weglating van hierdie analise tot foutiewelike resultate kan lei. Indien bewyse van nie-lineere gedrag in die tydsreeks gevind is, is 'n model van radiale basisfunksies gebou, deur gebruik te maak van gesofistikeerde programmatuur gebaseer op 'n spesifieke modelleringsmetodologie. Dit is 'n iteratiewe algoritme wat minimum beskrywingslengte as die termineringsmaatstaf gebruik. Die model se residuvektore is getoets vir lidmaatskap van die dinamiese klas wat as identiese en onafhanlike verspreide geraas bekend staan. Die studie verifieer dat die minimum beskrywingslengte as termineringsmaatstaf weI aanleiding tot modelle wat die onderliggende dinamika van die tydsreeks vasvang, met die ooreenstemmende residuvektor wat nie onderskei kan word van indentiese en onafhanklike verspreide geraas nie. In die geval van die "swakste" model (grootse beskrywingslengte), het die surrogaat analise gefaal omrede die residuvektor van indentiese en onafhanklike verspreide geraas onderskei kon word. Die residuvektor van die "beste" model (kleinste beskrywingslengte), kon nie van indentiese en onafhanklike verspreide geraas onderskei word nie en bevestig ons aanname. Hierdie toepassings is aan die hand van drie tydsreekse geillustreer: 'n tydsreeks wat deur die Lorenz vergelykings gegenereer is, 'n tydsreeks wat 'n elektroenkefalogram voorstel en derdens, 'n tydsreeks wat die persentasie verandering van die S&P500 indeks se daaglikse sluitingsprys voorstel.
Asritha, Kotha Sri Lakshmi Kamakshi. "Comparing Random forest and Kriging Methods for Surrogate Modeling". Thesis, Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20230.
Pełny tekst źródłaKamath, Atul Krishna. "Surrogate-assisted optimisation-based verification & validation". Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/15637.
Pełny tekst źródłaHeap, Ryan C. "Real-Time Visualization of Finite Element Models Using Surrogate Modeling Methods". BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/6536.
Pełny tekst źródłaLee, Chang-Hwa 1957. "Analysis of approaches to synchronous faults simulation by surrogate propagation". Thesis, The University of Arizona, 1988. http://hdl.handle.net/10150/276771.
Pełny tekst źródłaShashidhar, Akhil. "Generalized Volterra-Wiener and surrogate data methods for complex time series analysis". Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/41619.
Pełny tekst źródłaIncludes bibliographical references (leaves 133-150).
This thesis describes the current state-of-the-art in nonlinear time series analysis, bringing together approaches from a broad range of disciplines including the non-linear dynamical systems, nonlinear modeling theory, time-series hypothesis testing, information theory, and self-similarity. We stress mathematical and qualitative relationships between key algorithms in the respective disciplines in addition to describing new robust approaches to solving classically intractable problems. Part I presents a comprehensive review of various classical approaches to time series analysis from both deterministic and stochastic points of view. We focus on using these classical methods for quantification of complexity in addition to proposing a unified approach to complexity quantification encapsulating several previous approaches. Part II presents robust modern tools for time series analysis including surrogate data and Volterra-Wiener modeling. We describe new algorithms converging the two approaches that provide both a sensitive test for nonlinear dynamics and a noise-robust metric for chaos intensity.
by Akhil Shashidhar.
M.Eng.
Bilicz, Sandor. "Application of Design-of-Experiment Methods and Surrogate Models in Electromagnetic Nondestructive Evaluation". Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00601753.
Pełny tekst źródłaPeesapati, Lakshmi Narasimham. "Methods To evaluate the effectiveness of certain surrogate measures to assess safety of opposing left-turn interactions". Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/52324.
Pełny tekst źródłaThomas, Sarah Nichole. "Decisions to Seek and Share: A Mixed Methods Approach to Understanding Caregivers Surrogate Information Acquisition Behaviors". The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595545894518707.
Pełny tekst źródłaIsaacs, Amitay Engineering & Information Technology Australian Defence Force Academy UNSW. "Development of optimization methods to solve computationally expensive problems". Awarded by:University of New South Wales - Australian Defence Force Academy. Engineering & Information Technology, 2009. http://handle.unsw.edu.au/1959.4/43758.
Pełny tekst źródłaKsiążki na temat "Surrogate methods"
Alonso, Ariel. Applied Surrogate Endpoint Evaluation Methods with SAS and R. Boca Raton : CRC Press, 2017.: Chapman and Hall/CRC, 2016. http://dx.doi.org/10.1201/9781315372662.
Pełny tekst źródłaForrester, Alexander I. J. Surrogate models in engineering design: A practical guide. Chichester, West Sussex, England: J. Wiley, 2008.
Znajdź pełny tekst źródłaMolenberghs, Geert, Marc Buyse, Tomasz Burzykowski, Ariel Alonso i Theophile Bigirumurame. Applied Surrogate Endpoint Evaluation Methods with SAS and R. Taylor & Francis Group, 2016.
Znajdź pełny tekst źródłaApplied Surrogate Endpoint Evaluation Methods with SAS and R. Taylor & Francis Group, 2016.
Znajdź pełny tekst źródłaApplied Surrogate Endpoint Evaluation Methods with SAS and R. Taylor & Francis Group, 2016.
Znajdź pełny tekst źródłaMolenberghs, Geert, Marc Buyse, Tomasz Burzykowski, Ariel Alonso i Theophile Bigirumurame. Applied Surrogate Endpoint Evaluation Methods with SAS and R. Taylor & Francis Group, 2016.
Znajdź pełny tekst źródłaMolenberghs, Geert, Marc Buyse, Tomasz Burzykowski, Ariel Alonso i Theophile Bigirumurame. Applied Surrogate Endpoint Evaluation Methods with SAS and R. Taylor & Francis Group, 2016.
Znajdź pełny tekst źródłaAlonso, Ariel. Applied Surrogate Endpoint Evaluation Methods with SAS and R. Taylor & Francis Group, 2020.
Znajdź pełny tekst źródłaHuffaker, Ray, Marco Bittelli i Rodolfo Rosa. Entropy and Surrogate Testing. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.003.0005.
Pełny tekst źródła(Editor), Michael E. Burczynski, i John C. Rockett (Editor), red. Surrogate Tissue Analysis: Genomic, Proteomic, and Metabolomic Approaches. CRC, 2005.
Znajdź pełny tekst źródłaCzęści książek na temat "Surrogate methods"
Koziel, Slawomir, David Echeverría Ciaurri i Leifur Leifsson. "Surrogate-Based Methods". W Computational Optimization, Methods and Algorithms, 33–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20859-1_3.
Pełny tekst źródłaFleming, Thomas R., Victor DeGruttola i David L. Demets. "Surrogate Endpoints". W Methods and Applications of Statistics in Clinical Trials, 878–86. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118596005.ch74.
Pełny tekst źródłaQu, Yongming. "Surrogate Biomarkers". W Statistical Methods in Biomarker and Early Clinical Development, 39–52. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31503-0_3.
Pełny tekst źródłaRehbach, Frederik. "Methods/Contributions". W Enhancing Surrogate-Based Optimization Through Parallelization, 29–94. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30609-9_3.
Pełny tekst źródłaJiang, Ping, Qi Zhou i Xinyu Shao. "Verification Methods for Surrogate Models". W Surrogate Model-Based Engineering Design and Optimization, 89–113. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0731-1_5.
Pełny tekst źródłaMolenberghs, Geert, Ziv Shkedy, Burzykowski Tomasz, Marc Buyse, Ariel Alonso Abad i Wim Van der Elst. "Evaluation of Surrogate Endpoints". W Handbook of Statistical Methods for Randomized Controlled Trials, 567–600. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781315119694-26.
Pełny tekst źródłaGao, Yuehua, Lih-Sheng Turng, Peng Zhao i Huamin Zhou. "Optimization Methods Based on Surrogate Models". W Computer Modeling for Injection Molding, 293–312. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118444887.ch11.
Pełny tekst źródłaYang, Kai, i Katta G. Murty. "Surrogate Constraint Methods for Linear Inequalities". W Combinatorial Optimization, 19–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77489-8_2.
Pełny tekst źródłaAumann, Quirin, Peter Benner, Jens Saak i Julia Vettermann. "Model Order Reduction Strategies for the Computation of Compact Machine Tool Models". W Lecture Notes in Production Engineering, 132–45. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34486-2_10.
Pełny tekst źródłaKansara, Saket, Sumeet Parashar i Abdus Samad. "Chapter 3 Surrogate-Assisted Evolutionary Computing Methods". W Evolutionary Computation, 55–80. 3333 Mistwell Crescent, Oakville, ON L6L 0A2, Canada: Apple Academic Press, 2016. http://dx.doi.org/10.1201/9781315366388-4.
Pełny tekst źródłaStreszczenia konferencji na temat "Surrogate methods"
Freire Neto, José Ilmar Cruz, i André Britto. "Surrogate Methods Applied to Hyperparameter Optimization Problem". W Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/eniac.2022.227594.
Pełny tekst źródłaPhelivan Soak, H., J. Wackers, R. Pellegrini, A. Serani, M. Diez, R. Perali, M. Sacher i in. "Hydrofoil Optimization via Automated Multi-Fidelity Surrogate Models". W 10th Conference on Computational Methods in Marine Engineering. CIMNE, 2023. http://dx.doi.org/10.23967/marine.2023.136.
Pełny tekst źródłaRanftl, Sascha, i Wolfgang von der Linden. "Bayesian Surrogate Analysis and Uncertainty Propagation". W International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. Basel Switzerland: MDPI, 2021. http://dx.doi.org/10.3390/psf2021003006.
Pełny tekst źródłaWackers, J., H. Pehlivan Solak, R. Pellegrini, A. Serani i M. Díez. "Error estimation for surrogate models with noisy small-sized training sets". W VIII International Conference on Particle-Based Methods. CIMNE, 2023. http://dx.doi.org/10.23967/c.particles.2023.007.
Pełny tekst źródłaAlizadeh, Reza, Janet K. Allen i Farrokh Mistree. "Surrogate Models and Time Series for Flow Prediction on the Red River Dam Network". W ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-88163.
Pełny tekst źródłaSimion, Andrei, Michael Collins i Cliff Stein. "Towards a Convex HMM Surrogate for Word Alignment". W Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/d16-1051.
Pełny tekst źródłaAlbert, Christopher G., Ulrich Callies i Udo von Toussaint. "Surrogate-Enhanced Parameter Inference for Function-Valued Models". W International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. Basel Switzerland: MDPI, 2021. http://dx.doi.org/10.3390/psf2021003011.
Pełny tekst źródłaDe Villiers, Dirk. "RECENT ADVANCES IN SURROGATE MODELLING OF REFLECTOR ANTENNA SYSTEMS". W VII European Congress on Computational Methods in Applied Sciences and Engineering. Athens: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece, 2016. http://dx.doi.org/10.7712/100016.2111.6166.
Pełny tekst źródłaJacobs, Jan Pieter, i Dirk De Villiers. "SURROGATE MODELING OF ANTENNA RADIATION CHARACTERISTICS BY GAUSSIAN PROCESSES". W VII European Congress on Computational Methods in Applied Sciences and Engineering. Athens: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece, 2016. http://dx.doi.org/10.7712/100016.2113.7849.
Pełny tekst źródłaKotti, M., R. Gonzalez-Echevarria, E. Roca, R. Castro-Lopez, F. V. Fernandez, M. Fakhfakh, J. Sieiro i J. M. Lopez-Villegas. "Surrogate models of Pareto-optimal planar inductors". W 2012 International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD). IEEE, 2012. http://dx.doi.org/10.1109/smacd.2012.6339412.
Pełny tekst źródłaRaporty organizacyjne na temat "Surrogate methods"
Stromer, Bobbi, Rebecca Crouch, Katrinka Wayne, Ashley Kimble, Jared Smith i Anthony Bednar. Methods for simultaneous determination of 29 legacy and insensitive munition (IM) constituents in aqueous, soil-sediment, and tissue matrices by high-performance liquid chromatography (HPLC). Engineer Research and Development Center (U.S.), wrzesień 2021. http://dx.doi.org/10.21079/1168142105.
Pełny tekst źródłaHart, Carl R., D. Keith Wilson, Chris L. Pettit i Edward T. Nykaza. Machine-Learning of Long-Range Sound Propagation Through Simulated Atmospheric Turbulence. U.S. Army Engineer Research and Development Center, lipiec 2021. http://dx.doi.org/10.21079/11681/41182.
Pełny tekst źródłaWalizer, Laura, Robert Haehnel, Luke Allen i Yonghu Wenren. Application of multi-fidelity methods to rotorcraft performance assessment. Engineer Research and Development Center (U.S.), maj 2024. http://dx.doi.org/10.21079/11681/48474.
Pełny tekst źródłaLiu, Tong, i Hadi Meidani. Artificial Intelligence for Optimal Truck Platooning: Impact on Autonomous Freight Delivery. Illinois Center for Transportation, sierpień 2023. http://dx.doi.org/10.36501/0197-9191/23-017.
Pełny tekst źródłaMudge, Christopher, Glenn Suir i Benjamin Sperry. Unmanned aircraft systems and tracer dyes : potential for monitoring herbicide spray distribution. Engineer Research and Development Center (U.S.), październik 2023. http://dx.doi.org/10.21079/11681/47705.
Pełny tekst źródłaTreadwell, Jonathan R., James T. Reston, Benjamin Rouse, Joann Fontanarosa, Neha Patel i Nikhil K. Mull. Automated-Entry Patient-Generated Health Data for Chronic Conditions: The Evidence on Health Outcomes. Agency for Healthcare Research and Quality (AHRQ), marzec 2021. http://dx.doi.org/10.23970/ahrqepctb38.
Pełny tekst źródłaField, Richard V. ,. Jr, i .). A decision-theoretic method for surrogate model selection. Office of Scientific and Technical Information (OSTI), czerwiec 2005. http://dx.doi.org/10.2172/882352.
Pełny tekst źródłaBurke, J., L. Bernstein, J. Escher, L. Ahle, J. Church, F. Dietrich, K. Moody i in. Deducing the 237U(n,f) cross-section using the Surrogate Ratio Method. Office of Scientific and Technical Information (OSTI), sierpień 2005. http://dx.doi.org/10.2172/883605.
Pełny tekst źródłaCrouch, Rebecca, Jared Smith, Bobbi Stromer, Christian Hubley, Samuel Beal, Guilherme Lotufo, Afrachanna Butler i in. Methods for simultaneous determination of legacy and insensitive munition (IM) constituents in aqueous, soil/sediment, and tissue matrices. Engineer Research and Development Center (U.S.), sierpień 2021. http://dx.doi.org/10.21079/11681/41720.
Pełny tekst źródłaEscher, J. Benchmark and Assessment of the Surrogate Reaction Method for Determining Unknown (n,n') and (n,2n) Reaction Cross Sections. Office of Scientific and Technical Information (OSTI), sierpień 2022. http://dx.doi.org/10.2172/1884627.
Pełny tekst źródła