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Статті в журналах з теми "Predictive Spectral Analysis"
Robert, P., D. Bertrand, M. Crochon, and J. Sabino. "A New Mathematical Procedure for NIR Analysis: The Lattice Technique. Application to the Prediction of Sugar Content of Apples." Applied Spectroscopy 43, no. 6 (August 1989): 1045–49. http://dx.doi.org/10.1366/0003702894203723.
Повний текст джерелаBaishya, Nystha, Mohammad Mamouei, Karthik Budidha, Meha Qassem, Pankaj Vadgama, and Panayiotis A. Kyriacou. "Comparison of Dual Beam Dispersive and FTNIR Spectroscopy for Lactate Detection." Sensors 21, no. 5 (March 8, 2021): 1891. http://dx.doi.org/10.3390/s21051891.
Повний текст джерелаLilo, Taha, Camilo Morais, Kate Ashton, Ana Pardilho, Timothy Dawson, Nihal Gurusinghe, Charles Davis, and Frank Martin. "Predicting meningioma recurrence using spectrochemical analysis of tissues and subsequent predictive computational algorithms." Neuro-Oncology 21, Supplement_4 (October 2019): iv5. http://dx.doi.org/10.1093/neuonc/noz167.020.
Повний текст джерелаCollette, Timothy W., and Adam J. Szladow. "Use of Rough Sets and Spectral Data for Building Predictive Models of Reaction Rate Constants." Applied Spectroscopy 48, no. 11 (November 1994): 1379–86. http://dx.doi.org/10.1366/0003702944028047.
Повний текст джерелаFischer, G., A. Neurauter, L. Wieser, H. U. Strohmenger, and C. N. Nowak. "Prediction of Countershock Success." Methods of Information in Medicine 48, no. 05 (2009): 486–92. http://dx.doi.org/10.3414/me0580.
Повний текст джерелаVrazhnov, Denis, Anastasia Knyazkova, Maria Konnikova, Oleg Shevelev, Ivan Razumov, Evgeny Zavjalov, Yury Kistenev, Alexander Shkurinov, and Olga Cherkasova. "Analysis of Mouse Blood Serum in the Dynamics of U87 Glioblastoma by Terahertz Spectroscopy and Machine Learning." Applied Sciences 12, no. 20 (October 19, 2022): 10533. http://dx.doi.org/10.3390/app122010533.
Повний текст джерелаTang, Siyu, Chong Du, and Tangzhe Nie. "Inversion Estimation of Soil Organic Matter in Songnen Plain Based on Multispectral Analysis." Land 11, no. 5 (April 21, 2022): 608. http://dx.doi.org/10.3390/land11050608.
Повний текст джерелаDean, Roger T., Andrew J. Milne, and Freya Bailes. "Spectral Pitch Similarity is a Predictor of Perceived Change in Sound- as Well as Note-Based Music." Music & Science 2 (January 1, 2019): 205920431984735. http://dx.doi.org/10.1177/2059204319847351.
Повний текст джерелаBurke, Harry B. "Proteomics: Analysis of Spectral Data." Cancer Informatics 1 (January 2005): 117693510500100. http://dx.doi.org/10.1177/117693510500100102.
Повний текст джерелаZhang, Xianglin, Jie Xue, Yi Xiao, Zhou Shi, and Songchao Chen. "Towards Optimal Variable Selection Methods for Soil Property Prediction Using a Regional Soil Vis-NIR Spectral Library." Remote Sensing 15, no. 2 (January 12, 2023): 465. http://dx.doi.org/10.3390/rs15020465.
Повний текст джерелаДисертації з теми "Predictive Spectral Analysis"
Chhatwal, Harprit Singh. "Spectral modelling techniques for speech signals based on linear predictive analysis." Thesis, Imperial College London, 1988. http://hdl.handle.net/10044/1/46996.
Повний текст джерелаLosik, Len. "Adapting Fourier Analysis for Predicting Earth, Mars and Lunar Orbiting Satellite's Telemetry Behavior." International Foundation for Telemetering, 2011. http://hdl.handle.net/10150/595773.
Повний текст джерелаPrognostic technology uses a series of algorithms, combined forms a prognostic-based inference engine (PBIE) for the identification of deterministic behavior embedded in completely normal appearing telemetry from fully functional equipment. The algorithms used to define normal behavior in the PBIE from which deterministic behavior is identified can be adapted to quantify normal spacecraft telemetry behavior while in orbit about a moon or planet or during interplanetary travel. Time-series analog engineering data (telemetry) from orbiting satellites and interplanetary spacecraft are defined by harmonic and non-harmonic influences, which shape it behavior. Spectrum analysis can be used to understand and quantify the fundamental behavior of spacecraft analog telemetry and relate the behavior's frequency and phase to its time-series behavior through Fourier analysis.
Losik, Len. "Adapting Fourier Analysis for Predicting Earth, Mars and Lunar Orbiting Satellite's Telemetry Behavior." International Foundation for Telemetering, 2010. http://hdl.handle.net/10150/604279.
Повний текст джерелаPrognostic technology uses a series of algorithms, combined forms a prognostic-based inference engine (PBIE) for the identification of deterministic behavior embedded in completely normal appearing telemetry from fully functional equipment. The algorithms used to define normal behavior in the PBIE from which deterministic behavior is identified can be adapted to quantify normal spacecraft telemetry behavior while in orbit about a moon or planet or during interplanetary travel. Time-series analog engineering data (telemetry) from orbiting satellites and interplanetary spacecraft are defined by harmonic and non-harmonic influences, which shape it behavior. Spectrum analysis can be used to understand and quantify the fundamental behavior of spacecraft analog telemetry and relate the behavior's frequency and phase to its time-series behavior through Fourier analysis.
Losik, Len. "Using Telemetry Science, An Adaptation of Prognostic Algorithms for Predicting Normal Space Vehicle Telemetry Behavior from Space for Earth and Lunar Satellites and Interplanetary Spacecraft." International Foundation for Telemetering, 2009. http://hdl.handle.net/10150/606150.
Повний текст джерелаPrognostic technology uses a series of algorithms, combined forms a prognostic-based inference engine (PBIE) for the identification of deterministic behavior embedded in completely normal appearing telemetry from fully functional equipment. The algorithms used to define normal behavior in the PBIE from which deterministic behavior is identified can be adapted to quantify normal spacecraft telemetry behavior while in orbit about a moon or planet or during interplanetary travel. Time-series analog engineering data (telemetry) from orbiting satellites and interplanetary spacecraft are defined by harmonic and non-harmonic influences which shape it behavior. Spectrum analysis can be used to understand and quantify the fundamental behavior of spacecraft analog telemetry and relate the behavior's frequency and phase to its time-series behavior through Fourier analysis.
Guldemir, Hanifi. "Prediction of induction motor line current spectra from design data." Thesis, University of Nottingham, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287180.
Повний текст джерелаKwag, Jae-Hwan. "A comparative study of LP methods in MR spectral analysis /." free to MU campus, to others for purchase, 1999. http://wwwlib.umi.com/cr/mo/fullcit?p9962536.
Повний текст джерелаWang, Nancy. "Spectral Portfolio Optimisation with LSTM Stock Price Prediction." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273611.
Повний текст джерелаDen nobelprisvinnande moderna portföjlteorin (MPT) är utan tvekan en av de mest framgångsrika investeringsmodellerna inom finansvärlden och investeringsstrategier. MPT antar att investerarna är mindre benägna till risktagande och approximerar riskexponering med variansen av tillgångarnasränteavkastningar. Nyckeln till en lyckad portföljförvaltning är därmed goda riskestimat och goda förutsägelser av tillgångspris. Riskestimering görs vanligtvis genom traditionella prissättningsmodellerna som tillåter risken att variera i tiden, dock inte i frekvensrummet. Denna begränsning utgör bland annat ett större fel i riskestimering. För att tackla med detta har intresset för tillämpningar av spektraanalys på finansiella tidsserier ökat de senast åren. Bland annat är ett nytt tillvägagångssätt för att behandla detta den nyintroducerade spektralportföljteorin och spektralfak- tormodellen som påvisade ökad portföljenprestanda genom spektralriskskattning [1][11]. Samtidigt har prediktering av aktierpriser länge varit en stor utmaning på grund av dess icke-linjära och icke-stationära egenskaper medan maskininlärning har kunnat använts för att lösa annars omöjliga uppgifter. Färska studier har påvisat signifikant resultat i aktieprisprediktering med hjälp av artificiella LSTM neurala nätverk [6][34]. Detta arbete undersöker kombinerade effekten av dessa två framsteg i ett portföljoptimeringsproblem genom att optimera en spektral portfölj med framtida avkastningar predikterade av ett LSTM neuralt nätverk. Arbetet börjar med matematisk härledningar och teoretisk introduktion och sedan studera portföljprestation som genereras av spektra risk, LSTM aktieprispredikteringen samt en kombination av dessa två. Resultaten visar på att LSTM-predikteringen ensam presterade bättre än kombinationen, vilket i sin tur presterade bättre än enbart spektralriskskattningen.
Bahrampouri, Mahdi. "Ground Motion Prediction Equations for Non-Spectral Parameters using the KiK-net Database." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/87704.
Повний текст джерелаWinn, Olivia, and Sivaram Kiran Thekkemadathil. "Near-Infrared Spectral Measurements and Multivariate Analysis for Predicting Glass Contamination of Boiler Fuel." Thesis, Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-36058.
Повний текст джерелаBadenhorst, Dirk Jakobus Pretorius. "Improving the accuracy of prediction using singular spectrum analysis by incorporating internet activity." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/80056.
Повний текст джерелаENGLISH ABSTRACT: Researchers and investors have been attempting to predict stock market activity for years. The possible financial gain that accurate predictions would offer lit a flame of greed and drive that would inspire all kinds of researchers. However, after many of these researchers have failed, they started to hypothesize that a goal such as this is not only improbable, but impossible. Previous predictions were based on historical data of the stock market activity itself and would often incorporate different types of auxiliary data. This auxiliary data ranged as far as imagination allowed in an attempt to find some correlation and some insight into the future, that could in turn lead to the figurative pot of gold. More often than not, the auxiliary data would not prove helpful. However, with the birth of the internet, endless amounts of new sources of auxiliary data presented itself. In this thesis I propose that the near in finite amount of data available on the internet could provide us with information that would improve stock market predictions. With this goal in mind, the different sources of information available on the internet are considered. Previous studies on similar topics presented possible ways in which we can measure internet activity, which might relate to stock market activity. These studies also gave some insights on the advantages and disadvantages of using some of these sources. These considerations are investigated in this thesis. Since a lot of this work is therefore based on the prediction of a time series, it was necessary to choose a prediction algorithm. Previously used linear methods seemed too simple for prediction of stock market activity and a new non-linear method, called Singular Spectrum Analysis, is therefore considered. A detailed study of this algorithm is done to ensure that it is an appropriate prediction methodology to use. Furthermore, since we will be including auxiliary information, multivariate extensions of this algorithm are considered as well. Some of the inaccuracies and inadequacies of these current multivariate extensions are studied and an alternative multivariate technique is proposed and tested. This alternative approach addresses the inadequacies of existing methods. With the appropriate methodology chosen and the appropriate sources of auxiliary information chosen, a concluding chapter is done on whether predictions that includes auxiliary information (obtained from the internet) improve on baseline predictions that are simply based on historical stock market data.
AFRIKAANSE OPSOMMING: Navorsers en beleggers is vir jare al opsoek na maniere om aandeelpryse meer akkuraat te voorspel. Die moontlike finansiële implikasies wat akkurate vooruitskattings kan inhou het 'n vlam van geldgierigheid en dryf wakker gemaak binne navorsers regoor die wêreld. Nadat baie van hierdie navorsers onsuksesvol was, het hulle begin vermoed dat so 'n doel nie net onwaarskynlik is nie, maar onmoontlik. Vorige vooruitskattings was bloot gebaseer op historiese aandeelprys data en sou soms verskillende tipes bykomende data inkorporeer. Die tipes data wat gebruik was het gestrek so ver soos wat die verbeelding toegelaat het, in 'n poging om korrelasie en inligting oor die toekoms te kry wat na die guurlike pot goud sou lei. Navorsers het gereeld gevind dat hierdie verskillende tipes bykomende inligting nie van veel hulp was nie, maar met die geboorte van die internet het 'n oneindige hoeveelheid nuwe bronne van bykomende inligting bekombaar geraak. In hierdie tesis stel ek dus voor dat die data beskikbaar op die internet dalk vir ons kan inligting gee wat verwant is aan toekomstige aandeelpryse. Met hierdie doel in die oog, is die verskillende bronne van inligting op die internet gebestudeer. Vorige studies op verwante werk het sekere spesifieke maniere voorgestel waarop ons internet aktiwiteit kan meet. Hierdie studies het ook insig gegee oor die voordele en die nadele wat sommige bronne inhou. Hierdie oorwegings word ook in hierdie tesis bespreek. Aangesien 'n groot gedeelte van hierdie tesis dus gebasseer word op die vooruitskatting van 'n tydreeks, is dit nodig om 'n toepaslike vooruitskattings algoritme te kies. Baie navorsers het verkies om eenvoudige lineêre metodes te gebruik. Hierdie metodes het egter te eenvoudig voorgekom en 'n relatiewe nuwe nie-lineêre metode (met die naam "Singular Spectrum Analysis") is oorweeg. 'n Deeglike studie van hierdie algoritme is gedoen om te verseker dat die metode van toepassing is op aandeelprys data. Verder, aangesien ons gebruik wou maak van bykomende inligting, is daar ook 'n studie gedoen op huidige multivariaat uitbreidings van hierdie algoritme en die probleme wat dit inhou. 'n Alternatiewe multivariaat metode is toe voorgestel en getoets wat hierdie probleme aanspreek. Met 'n gekose vooruitskattingsmetode en gekose bronne van bykomende data is 'n gevolgtrekkende hoofstuk geskryf oor of vooruitskattings, wat die bykomende internet data inkorporeer, werklik in staat is om te verbeter op die eenvoudige vooruitskattings, wat slegs gebaseer is op die historiese aandeelprys data.
Книги з теми "Predictive Spectral Analysis"
P, Shepherd Kevin, and Langley Research Center, eds. Wind turbine acoustics. Washington, D.C: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1990.
Знайти повний текст джерелаЧастини книг з теми "Predictive Spectral Analysis"
Elsner, James B., and Anastasios A. Tsonis. "Prediction." In Singular Spectrum Analysis, 133–41. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-2514-8_9.
Повний текст джерелаKhandekar, M. L. "Wave Prediction: Spectral Models." In Operational Analysis and Prediction of Ocean Wind Waves, 68–103. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4613-8952-1_5.
Повний текст джерелаHermansky, Hynek. "Modulation Spectrum in Speech Processing." In Signal Analysis and Prediction, 395–406. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-1768-8_27.
Повний текст джерелаWeber, Rodolphe, and Christian Faye. "Coarsely Quantized Spectral Estimation of Radio Astronomic Sources in Highly Corruptive Environments." In Signal Analysis and Prediction, 103–12. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-1768-8_7.
Повний текст джерелаMicsonai, András, Éva Bulyáki, and József Kardos. "BeStSel: From Secondary Structure Analysis to Protein Fold Prediction by Circular Dichroism Spectroscopy." In Methods in Molecular Biology, 175–89. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0892-0_11.
Повний текст джерелаBidwai, Sandeep, Shilpa Mayannavar, and Uday V. Wali. "Performance Comparison of Markov Chain and LSTM Models for Spectrum Prediction in GSM Bands." In Machine Learning for Predictive Analysis, 289–98. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7106-0_29.
Повний текст джерелаFurferi, Rocco, Lapo Governi, and Yary Volpe. "Methods for Predicting Spectral Response of Fibers Blends." In New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops, 79–86. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23222-5_10.
Повний текст джерелаEdelkamp, Stefan. "Prediction of Regular Search Tree Growth by Spectral Analysis." In KI 2001: Advances in Artificial Intelligence, 154–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45422-5_12.
Повний текст джерелаHammond, J. K., R. F. Harrison, Y. H. Tsao, and J. S. Lee. "The prediction of time—frequency spectra using covariance-equivalent models." In Developments in Time Series Analysis, 355–73. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-4515-0_25.
Повний текст джерелаDrábik, Peter, Petr Šaloun, Ivan Zelinka, and Marie Vraná. "Better and Faster Spectra Analysis Using Analytical Programming on CUDA." In Nostradamus 2014: Prediction, Modeling and Analysis of Complex Systems, 153–60. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07401-6_15.
Повний текст джерелаТези доповідей конференцій з теми "Predictive Spectral Analysis"
Sheahan, Noirin F., Davis Coakley, and James F. Malone. "Comparison of conventional and Linear Predictive spectral analysis techniques." In 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1992. http://dx.doi.org/10.1109/iembs.1992.5761605.
Повний текст джерелаSheahan, Coakley, and Malone. "Comparison Of Conventional And Linear Predictive Spectral Analysis Techniques." In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1992. http://dx.doi.org/10.1109/iembs.1992.592904.
Повний текст джерелаLucchini, Andrea, Paolo Franchin, and Fabrizio Mollaioli. "A Spectrum-to-Spectrum Method for Calculating Uniform Hazard Floor Response Spectra." In ASME 2017 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/pvp2017-65293.
Повний текст джерелаTchrakian, T. T., and B. Basu. "Real time traffic flow forecasting and predictive ramp-metering using spectral analysis." In IET Irish Signals and Systems Conference (ISSC 2009). IET, 2009. http://dx.doi.org/10.1049/cp.2009.1689.
Повний текст джерелаAnteliz Jaimes, Antonio Alexi. "Maintenance predictive in electric transformers, applying spectral analysis to the flow of magnetic dispersion." In 2012 XVII Symposium of Image, Signal Processing, and Artificial Vision (STSIVA). IEEE, 2012. http://dx.doi.org/10.1109/stsiva.2012.6340599.
Повний текст джерелаDe Giorgi, Maria Grazia, Aldebara Sciolti, Elisa Pescini, and Antonio Ficarella. "Frequency Analysis and Predictive Identification of Flame Stability by Image Processing." In ASME 2014 8th International Conference on Energy Sustainability collocated with the ASME 2014 12th International Conference on Fuel Cell Science, Engineering and Technology. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/es2014-6599.
Повний текст джерелаYoon, Heonjun, Byeng D. Youn, and Chulmin Cho. "Piezoelectric Energy Harvesting Analysis Under Non-Stationary Random Vibrations." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-13547.
Повний текст джерелаZhang, Shengli, Jiong Tang, and Yu Ding. "Modeling and Analysis of Time-Periodic Gearbox Vibration." In ASME Turbo Expo 2014: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/gt2014-27230.
Повний текст джерелаSanchez, Rodolfo Bolan˜os, Agusti´n Sa´nchez Arcilla, Jesu´s Gomez, and Abdel Sairouni. "Spectral Evolution and Wave Age Analysis of an Exceptional Storm Off the Mediterranean Spanish Coast." In ASME 2003 22nd International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2003. http://dx.doi.org/10.1115/omae2003-37063.
Повний текст джерелаFeng, Qin, and Richard Large. "Prediction of Fatigue Life of Shallow Water Offshore Platforms Using Spectral Fatigue Analysis Method." In ASME 2010 29th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2010. http://dx.doi.org/10.1115/omae2010-20796.
Повний текст джерелаЗвіти організацій з теми "Predictive Spectral Analysis"
Gamwo, I. K., A. Miller, and D. Gidaspow. Spectral analysis of CFB data: Predictive models of Circulating Fluidized Bed combustors. Office of Scientific and Technical Information (OSTI), April 1992. http://dx.doi.org/10.2172/5098191.
Повний текст джерелаGamwo, I. K., A. Miller, and D. Gidaspow. Spectral analysis of CFB data: Predictive models of Circulating Fluidized Bed combustors. 11th technical progress report. Office of Scientific and Technical Information (OSTI), April 1992. http://dx.doi.org/10.2172/10156489.
Повний текст джерелаAlchanatis, Victor, Stephen W. Searcy, Moshe Meron, W. Lee, G. Y. Li, and A. Ben Porath. Prediction of Nitrogen Stress Using Reflectance Techniques. United States Department of Agriculture, November 2001. http://dx.doi.org/10.32747/2001.7580664.bard.
Повний текст джерелаСоловйов, В. М., та В. В. Соловйова. Моделювання мультиплексних мереж. Видавець Ткачук О.В., 2016. http://dx.doi.org/10.31812/0564/1253.
Повний текст джерелаRao, M. M. Spectral Analysis, Estimation, and Prediction of Multiple Harmonizable Time Series. Fort Belvoir, VA: Defense Technical Information Center, August 1990. http://dx.doi.org/10.21236/ada266758.
Повний текст джерелаKowalski, Nina, Didier A. Depireux, and Shihab A. Shamma. Analysis of Dynamic Spectra in Ferret Primary Auditory Cortex. 2. Prediction of Unit Responses to Arbitrary Dynamic Spectra. Fort Belvoir, VA: Defense Technical Information Center, January 1995. http://dx.doi.org/10.21236/ada445591.
Повний текст джерелаShamma, Shihab A., and Huib Versnel. Ripple Analysis in Ferret Primary Auditory Cortex. 3. Prediction of Unit Responses to Arbitrary Spectral Profiles. Fort Belvoir, VA: Defense Technical Information Center, January 1995. http://dx.doi.org/10.21236/ada455589.
Повний текст джерелаFeng, Zhichao, Zhimin Yan, and Qianyun Liu. MRI Signs for Prenatal Prediction of Placenta Accreta Spectrum Disorders and Invasiveness in High-risk Pregnant Women: A Systematic Review and Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0003.
Повний текст джерелаZhou, Yiwu. Early prediction models for Extended-spectrum β-lactamase-producing Escherichia coli infection in emergency department: A protocol for systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, березень 2021. http://dx.doi.org/10.37766/inplasy2021.3.0049.
Повний текст джерелаDerbentsev, V., A. Ganchuk, and Володимир Миколайович Соловйов. Cross correlations and multifractal properties of Ukraine stock market. Politecnico di Torino, 2006. http://dx.doi.org/10.31812/0564/1117.
Повний текст джерела