{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T04:50:57Z","timestamp":1747198257143,"version":"3.40.5"},"reference-count":12,"publisher":"Walter de Gruyter GmbH","issue":"3","funder":[{"DOI":"10.13039\/501100006769","name":"Russian Science Foundation","doi-asserted-by":"publisher","award":["24-11-00107"],"award-info":[{"award-number":["24-11-00107"]}],"id":[{"id":"10.13039\/501100006769","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In this study we solve the following problem: Simulate random 2D Poisson point processes with a desired correlation function.\nTo solve this problem we suggest the following algorithm: (1)\nsimulate a positive valued random process with the desired correlation function, (2) use this process as an intensity of the doubly stochastic Poisson random point process.\nWe apply this algorithm to simulate random distribution of nanocrystals on a plane.\nThen we apply the developed methods to calculate excitonic\nfluxes to the family of generated nanocrystals.<\/jats:p>","DOI":"10.1515\/mcma-2024-2014","type":"journal-article","created":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T19:39:39Z","timestamp":1724355579000},"page":"315-330","source":"Crossref","is-referenced-by-count":0,"title":["Simulation of doubly stochastic Poisson point processes and application to nucleation of nanocrystals and evaluation of exciton fluxes"],"prefix":"10.1515","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3698-7540","authenticated-orcid":false,"given":"Karl K.","family":"Sabelfeld","sequence":"first","affiliation":[{"name":"Institute of Computational Mathematics and Mathematical Geophysics , Russian Academy of Sciences , Novosibirsk , Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stepan","family":"Glazkov","sequence":"additional","affiliation":[{"name":"Institute of Computational Mathematics and Mathematical Geophysics , Russian Academy of Sciences , Novosibirsk , Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2024,8,23]]},"reference":[{"key":"2024083008370661897_j_mcma-2024-2014_ref_001","doi-asserted-by":"crossref","unstructured":"S.  Basu and A.  Dassios,\nA Cox process with log-normal intensity,\nInsurance Math. Econom. 31 (2002), no. 2, 297\u2013302.","DOI":"10.1016\/S0167-6687(02)00152-X"},{"key":"2024083008370661897_j_mcma-2024-2014_ref_002","unstructured":"Y.  Chen,\nThinning algorithms for simulating point processes,\nTalk, Florida State University, Tallahassee, 2016."},{"key":"2024083008370661897_j_mcma-2024-2014_ref_003","unstructured":"D. R.  Cox and V.  Isham,\nPoint Processes,\nMonogr. Appl. Probab. Statist.,\nChapman & Hall, London, 1980."},{"key":"2024083008370661897_j_mcma-2024-2014_ref_004","doi-asserted-by":"crossref","unstructured":"L.  Devroye,\nThe series method for random variate generation and its application to the Kolmogorov\u2013Smirnov distribution,\nAmer. J. Math. Manag. Sci. 1 (1981), no. 4, 359\u2013379.","DOI":"10.1080\/01966324.1981.10737080"},{"key":"2024083008370661897_j_mcma-2024-2014_ref_005","doi-asserted-by":"crossref","unstructured":"F.  Feix, T.  Flissikowski, K. K.  Sabelfeld, V. M.  Kaganer, M.  W\u00f6lz, L.  Geelhaar, H. T.  Grahn and O.  Brandt,\nGa-polar (In, Ga)N\/GaN quantum wells versus N-polar (In, Ga) N quantum disks in GaN nanowires: A comparative analysis of carrier recombination, diffusion, and radiative efficiency,\nPhys. Rev. Appl. 8 (2017), Article ID 014032.","DOI":"10.1103\/PhysRevApplied.8.014032"},{"key":"2024083008370661897_j_mcma-2024-2014_ref_006","doi-asserted-by":"crossref","unstructured":"D.  Grebenkov,\nEfficient Monte Carlo methods for simulating diffusion-reaction processes in complex systems,\nFirst-Passage Phenomena and Their Applications,\nWorld Scientific, Hackensack (2014), 571\u2013595.","DOI":"10.1142\/9789814590297_0023"},{"key":"2024083008370661897_j_mcma-2024-2014_ref_007","doi-asserted-by":"crossref","unstructured":"J.  L\u00e4hnemann, V. M.  Kaganer, K. K.  Sabelfeld, A. E.  Kireeva, U.  Jahn, C.  Cheze, R.  Calarco and O.  Brandt,\nCarrier diffusion in GaN: A cathodoluminescence study. III: Nature of nonradiative recombination at threading dislocations,\nPhys. Rev. Appl. 17 (2022), no. 2, Article ID 024019.","DOI":"10.1103\/PhysRevApplied.17.024019"},{"key":"2024083008370661897_j_mcma-2024-2014_ref_008","doi-asserted-by":"crossref","unstructured":"O. N.  Oliveira, Jr., L.  Caseli and K.  Ariga,\nThe past and the future of Langmuir and Langmuir\u2013Blodgett films,\nChem. Rev. 122 (2022), no. 6, 6459\u20136513.","DOI":"10.1021\/acs.chemrev.1c00754"},{"key":"2024083008370661897_j_mcma-2024-2014_ref_009","doi-asserted-by":"crossref","unstructured":"K. K.  Sabelfeld,\nMonte Carlo Methods in Boundary Value Problems,\nSpringer Ser. Comput. Math.,\nSpringer, Berlin, 1991.","DOI":"10.1007\/978-3-642-75977-2"},{"key":"2024083008370661897_j_mcma-2024-2014_ref_010","doi-asserted-by":"crossref","unstructured":"K. K.  Sabelfeld,\nRandom Fields and Stochastic Lagrangian Models,\nWalter de Gruyter, Berlin, 2012.","DOI":"10.1515\/9783110296815"},{"key":"2024083008370661897_j_mcma-2024-2014_ref_011","doi-asserted-by":"crossref","unstructured":"K. K.  Sabelfeld,\nRandom walk on spheres algorithm for solving transient drift-diffusion-reaction problems,\nMonte Carlo Methods Appl. 23 (2017), no. 3, 189\u2013212.","DOI":"10.1515\/mcma-2017-0113"},{"key":"2024083008370661897_j_mcma-2024-2014_ref_012","doi-asserted-by":"crossref","unstructured":"K.  Svit, K.  Zhuravlev, S.  Kireev and K. K.  Sabelfeld,\nA stochastic model, simulation, and application to aggregation of cadmium sulfide nanocrystals upon evaporation of the Langmuir\u2013Blodgett matrix,\nMonte Carlo Methods Appl. 27 (2021), no. 4, 289\u2013299.","DOI":"10.1515\/mcma-2021-2100"}],"container-title":["Monte Carlo Methods and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mcma-2024-2014\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mcma-2024-2014\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T08:38:03Z","timestamp":1725007083000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mcma-2024-2014\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,23]]},"references-count":12,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,6,18]]},"published-print":{"date-parts":[[2024,9,1]]}},"alternative-id":["10.1515\/mcma-2024-2014"],"URL":"https:\/\/doi.org\/10.1515\/mcma-2024-2014","relation":{},"ISSN":["0929-9629","1569-3961"],"issn-type":[{"type":"print","value":"0929-9629"},{"type":"electronic","value":"1569-3961"}],"subject":[],"published":{"date-parts":[[2024,8,23]]}}}