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Journal articles on the topic 'Probabilistic analysi'

Author: Grafiati

Published: 9 March 2023

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1

De la Sen, M. "On Probabilistic Alpha-Fuzzy Fixed Points and Related Convergence Results in Probabilistic Metric and Menger Spaces under Some Pompeiu-Hausdorff-Like Probabilistic Contractive Conditions." Journal of Function Spaces 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/213174.

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In the framework of complete probabilistic metric spaces and, in particular, in probabilistic Menger spaces, this paper investigates some relevant properties of convergence of sequences to probabilisticα-fuzzy fixed points under some types of probabilistic contractive conditions.

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2

Riveros, Bruno, Mariana Rosim, Gabriel Pedro, Rosa Lucchetta, and Marcelo Nita. "Análise de custo-efetividade e a análise de sensibilidade, um roteiro para execução de uma abordagem probabilística: Introdução à análise de sensibilidade probabilística (Parte I)." Jornal de Assistência Farmacêutica e Farmacoeconomia 1, no. 2 (December 2016): 33–38. http://dx.doi.org/10.22563/2525-7323.2016.v1.n2.p.33-38.

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3

Pavese, Carlotta. "Probabilistic Knowledge in Action." Analysis 80, no. 2 (April 1, 2020): 342–56. http://dx.doi.org/10.1093/analys/anz094.

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4

Irzik, G. "Armstrong's account of probabilistic laws." Analysis 51, no. 4 (October 1, 1991): 214–17. http://dx.doi.org/10.1093/analys/51.4.214.

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5

Fitelson, B. "A probabilistic theory of coherence." Analysis 63, no. 3 (July 1, 2003): 194–99. http://dx.doi.org/10.1093/analys/63.3.194.

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6

Tsujimoto, Kazuki, and Toshiaki Omori. "Switching Probabilistic Slow Feature Analysis for Time Series Data." International Journal of Machine Learning and Computing 10, no. 6 (December 2020): 740–45. http://dx.doi.org/10.18178/ijmlc.2020.10.6.999.

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7

Chase, J. "The non-probabilistic two envelope paradox." Analysis 62, no. 2 (April 1, 2002): 157–60. http://dx.doi.org/10.1093/analys/62.2.157.

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8

Flandoli, Franco, and Marco Romito. "Probabilistic analysis of singularities for the 3D Navier-Stokes equations." Mathematica Bohemica 127, no. 2 (2002): 211–18. http://dx.doi.org/10.21136/mb.2002.134166.

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9

LIU, Jinlin, and Changhong PENG. "ICONE23-1839 AN OVERVIEW-PROBABILISTIC SAFETY ANALYSIS FOR RESEARCH REACTORS." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2015.23 (2015): _ICONE23–1—_ICONE23–1. http://dx.doi.org/10.1299/jsmeicone.2015.23._icone23-1_397.

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10

Friederichs, Petra, Martin Göber, Sabrina Bentzien, Anne Lenz, and Rebekka Krampitz. "A probabilistic analysis of wind gusts using extreme value statistics." Meteorologische Zeitschrift 18, no. 6 (December 1, 2009): 615–29. http://dx.doi.org/10.1127/0941-2948/2009/0413.

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11

Akiba, K. "Shogenji's probabilistic measure of coherence is incoherent." Analysis 60, no. 4 (October 1, 2000): 356–59. http://dx.doi.org/10.1093/analys/60.4.356.

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12

Udiyani, Pande Made, and Sri Kuntjoro. "PENGARUH KONDISI ATMOSFERIK TERHADAP PERHITUNGAN PROBABILISTIK DAMPAK RADIOLOGI KECELAKAAN PWR 1000-MWe." JURNAL TEKNOLOGI REAKTOR NUKLIR TRI DASA MEGA 17, no. 3 (October 3, 2015): 149. http://dx.doi.org/10.17146/tdm.2015.17.3.2326.

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ABSTRAK PENGARUH KONDISI ATMOSFERIK TERHADAP PERHITUNGAN PROBABILISTIK DAMPAK RADIOLOGI KECELAKAAN PWR 1000-MWe. Perhitungan dampak kecelakaan radiologi terhadap lepasan produk fisi akibat kecelakaan potensial yang mungkin terjadi di Pressurized Water Reactor (PWR) diperlukan secara probabilistik. Mengingat kondisi atmosfer sangat berperan terhadap dispersi radionuklida di lingkungan, dalam penelitian ini akan dianalisis pengaruh kondisi atmosferik terhadap perhitungan probabilistik dari konsekuensi kecelakaan reaktor. Tujuan penelitian adalah melakukan analisis terhadap pengaruh kondisi atmosfer berdasarkan model data input meteorologi terhadap dampak radiologi kecelakaan PWR 1000-MWe yang disimulasikan pada tapak yang mempunyai kondisi meteorologi yang berbeda. Simulasi menggunakan program PC-Cosyma dengan moda perhitungan probabilistik, dengan data input meteorologi yang dieksekusi secara cyclic dan stratified, dan disimulasikan di Tapak Semenanjung Muria dan Pesisir Serang. Data meteorologi diambil setiap jam untuk jangka waktu satu tahun. Hasil perhitungan menunjukkan bahwa frekuensi kumulatif untuk model input yang sama untuk Tapak pesisir Serang lebih tinggi dibandingkan dengan Semenanjung Muria. Untuk tapak yang sama, frekuensi kumulatif model input cyclic lebih tinggi dibandingkan model stratified. Model cyclic memberikan keleluasan dalam menentukan tingkat ketelitian perhitungan dan tidak membutuhkan data acuan dibandingkan dengan model stratified. Penggunaan model cyclic dan stratified melibatkan jumlah data yang besar dan pengulangan perhitungan akan meningkatkan ketelitian nilai-nilai statistika perhitungan. Kata kunci: dampak kecelakaan, PWR 1000-MWe, probabilistik, atmosferik, PC-Cosyma ABSTRACT THE INFLUENCE OF ATMOSPHERIC CONDITIONS TO PROBABILISTIC CALCULATION OF IMPACT OF RADIOLOGY ACCIDENT ON PWR-1000MWe. The calculation of the radiological impact of the fission products releases due to potential accidents that may occur in the PWR (Pressurized Water Reactor) is required in a probabilistic. The atmospheric conditions greatly contribute to the dispersion of radionuclides in the environment, so that in this study will be analyzed the influence of atmospheric conditions on probabilistic calculation of the reactor accidents consequences. The objective of this study is to conduct an analysis of the influence of atmospheric conditions based on meteorological input data models on the radiological consequences of PWR-1000MWe accidents. Simulations using PC-Cosyma code with probabilistic calculations mode, the meteorological data input executed cyclic and stratified, the meteorological input data are executed in the cyclic and stratified, and simulated in Muria Peninsula and Serang Coastal. Meteorological data were taken every hour for the duration of the year. The result showed that the cumulative frequency for the same input models for Serang coastal is higher than the Muria Peninsula. For the same site, cumulative frequency on cyclic input models is higher than stratified models. The cyclic models provide flexibility in determining the level of accuracy of calculations and do not require reference data compared to stratified models. The use of cyclic and stratified models involving large amounts of data and calculation repetition will improve the accuracy of statistical calculation values. Keywords: accident impact, PWR 1000 MWe, probabilistic, atmospheric, PC-Cosyma

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13

Tennant, N. "A note on the irrelevance of probabilistic irrelevance." Analysis 66, no. 1 (January 1, 2006): 32–35. http://dx.doi.org/10.1093/analys/66.1.32.

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14

Shogenji, T. "Reply to Akiba on the probabilistic measure of coherence." Analysis 61, no. 2 (April 1, 2001): 147–50. http://dx.doi.org/10.1093/analys/61.2.147.

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15

Anwar, Fadrinsyah. "Analisis Kinerja Pelayanan dan Keamanan di Terminal Penumpang Bandar Udara dengan Menggunakan Pendekatan Jaringan Probabilistik." Warta Penelitian Perhubungan 28, no. 6 (December 30, 2016): 367. http://dx.doi.org/10.25104/warlit.v28i6.305.

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Peningkatan pelayanan dan keamanan di bandar udara merupakan hal yang penting untuk dilakukan dalam upaya memenuhi harapan pengguna jasa dalam mendapatkan pelayanan terbaik. Untuk itu diperlukan data dan informasi yang baik tentang kualitas kinerja pelayanan yang ada, guna dapat merumuskan langkah-langkah yang perlu diambil untuk meningkatkannya. Penelitian ini dimaksudkan untuk memberi opsi dalam menganalisis kinerja pelayanan di terminal bandara secara mudah dan cepat, namun berkualitas. Metode yang digunakan adalah pendekatan jaringan probabilistik atau dikenal dengan Bayesian Network. Metode ini mulai banyak digunakan sejak dua dasawarsa terakhir untuk menganalisis berbagai kepentingan. Hal yang digali dengan metode ini adalah hubungan kausalitas antara variabel-variabel penelitian melalui inferensi probabilistik terhadap jaringan probabilisik yang terbentuk. Hasil analisis kinerja pelayanan bandara yang telah dilakukan menjelaskan bagaimana hubungan antara kinerja pelayanan satu fasilitas dengan kinerja pelayanan fasilitas lainnya. Selanjutnya berdasarkan inferensi probabilistik diperoleh bahwa antara kinerja pelayanan Parkir dan Curbside memiliki hubungan yang paling kuat dibandingkan hubungan-hubungan kinerja pelayanan antara area lainnya.

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16

Anwar, Fadrinsyah. "Analisis Kualitas Pelayanan Terminal Kargo Bandara Dengan Jaringan Probabilistik." WARTA ARDHIA 41, no. 2 (August 16, 2017): 59. http://dx.doi.org/10.25104/wa.v41i2.145.59-68.

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Service quality measurement at the airport can give a lot of essentialinput for the airport management, especially in determining the appropriate airport management strategy. This study aims to assess the causal relationship of several factors that are assumed may affect the service process at the cargo terminal. The causal relationship model is built on the perception of the service user satisfaction on the performance of the facilities, the performance of Human Resources (HR), and the performance of services at the airport cargo terminal. Thedevelopment of causal relationship model using probabilistic networks. The analysis results on the case of cargo terminal of Juanda Airport in Surabaya show that the performance improvement of facilities and HR can improve the performance of services in the cargo terminal. Furthermore, the important factors that affect the performance improvement of SERVICES arethe storage facilities, the availability of handling equipment in apron, and the skill and knowledge of the officer in the cargo terminal. Keywords: cargo terminal, service quality, probabilistic network. Pengukuran kualitas pelayanan di bandara dapat memberikan banyak masukan penting bagi manajemen bandara terutama dalam menentukan strategi pengelolaan bandara yang baik. Penelitian ini bertujuan untuk mengkaji hubungan kausal faktor-faktor yang diasumsikan dapat mempengaruhi proses pelayanan di terminal kargo. Model hubungan kausal dibangun berdasarkan persepsi kepuasan pengguna jasa terhadap kinerja fasilitas, kinerja Sumber Daya Manusia (SDM), dan kinerja pelayanan di terminal kargo bandara. Pengembangan model hubungan kausal menggunakan metode jaringan probabilistik. Hasil analisis pada kasus terminal kargo Bandara Juanda - Surabaya menunjukkan bahwa peningkatan kinerja fasilitas dan kinerja SDM dapat meningkatkan kinerja pelayanan di terminal kargo. Adapun faktor-faktor penting yang mempengaruhi peningkatan kinerja PELAYANAN adalah fasilitas penyimpanan barang, kelengkapan atau ketersediaan peralatan penanganan barang di apron, dan keterampilan kerja dan pengetahuan petugas terminal kargo. Kata kunci: terminal kargo, kualitas pelayanan, jaringan probabilistik.

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17

Speranskii, S. O. "Collapsing probabilistic hierarchies. I." Algebra and Logic 52, no. 2 (May 2013): 159–71. http://dx.doi.org/10.1007/s10469-013-9230-0.

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18

Беккер, Сусанна, and Susanna Bekker. "Analysis of electron density calculations using deterministic-probabilistic model of the ionospheric D-region." Solar-Terrestrial Physics 4, no. 3 (September 28, 2018): 67–75. http://dx.doi.org/10.12737/stp-43201809.

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The work is devoted to the development of a fundamentally new way of modeling the ionospheric D-region – deterministic-probabilistic. The results of Ne calculations using this technique are analyzed. Research of this kind is of fundamental importance, related to the rejection of a purely deterministic description of a continuously changing environment such as the ionosphere. In this work, the electron density is calculated using a five-component system of ionization-recombination cycle equations. Probability density functions (PDFs) of input parameters of the model are used to solve the system. The most important sources of the D-region ionization are taken into account to calculate PDFs of the ionization rate. The necessary number of iterations is determined by the convergence of PDFs of the electron density from 50 km to 85 km at midlatitudes under different heliogeophysical conditions. Theoretical Ne PDFs have been shown to be in good agreement with two experimental databases on electron density, especially at large D-region heights. The next important stage of modeling is the thorough verification of Ne PDFs from experimental radiophysical data on VLF–LF propagation.

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19

Basu, Asit P. "Probabilistic Risk Analysis." Technometrics 44, no. 4 (November 2002): 402. http://dx.doi.org/10.1198/tech.2002.s84.

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20

Heinkele, Christophe, and Claude‐Henri Lamarque. "Modal probabilistic analysis." Journal of the Acoustical Society of America 123, no. 5 (May 2008): 3153. http://dx.doi.org/10.1121/1.2933174.

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21

Sennblad, Bengt, and Jens Lagergren. "Probabilistic Orthology Analysis." Systematic Biology 58, no. 4 (August 1, 2009): 411–24. http://dx.doi.org/10.1093/sysbio/syp046.

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22

Tvedt, Lars. "Proban – probabilistic analysis." Structural Safety 28, no. 1-2 (January 2006): 150–63. http://dx.doi.org/10.1016/j.strusafe.2005.03.003.

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23

Seth, Sohan, and Manuel J. A. Eugster. "Probabilistic archetypal analysis." Machine Learning 102, no. 1 (June 5, 2015): 85–113. http://dx.doi.org/10.1007/s10994-015-5498-8.

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24

Ipsen, Ilse C. F., and Hua Zhou. "Probabilistic Error Analysis for Inner Products." SIAM Journal on Matrix Analysis and Applications 41, no. 4 (January 2020): 1726–41. http://dx.doi.org/10.1137/19m1270434.

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25

Peyre, Rémi. "A Probabilistic Approach to Carne’s Bound." Potential Analysis 29, no. 1 (April 5, 2008): 17–36. http://dx.doi.org/10.1007/s11118-008-9083-7.

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26

Üstünel, Ali Süleyman. "Probabilistic solution of the American options." Journal of Functional Analysis 256, no. 9 (May 2009): 3091–105. http://dx.doi.org/10.1016/j.jfa.2008.10.016.

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27

Martín Del Campo-Márquez, Cecilia, Pamela Fran Nelson-Edelstein, and Miguel Ángel García-Vázquez. "La energía del viento en México: Simulación de un parque eólico y aplicación de análisis probabilístico de seguridad." Ingeniería, investigación y tecnología 10, no. 4 (October 1, 2009): 343–52. http://dx.doi.org/10.22201/fi.25940732e.2009.10n4.031.

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28

Garrick, B. J. "Interval Analysis Versus Probabilistic Analysis." Risk Analysis 30, no. 3 (March 2010): 369–70. http://dx.doi.org/10.1111/j.1539-6924.2010.01360.x.

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29

Hartini, Entin, Roziq Himawan, and Mike Susmikanti. "FRACTURE MECHANICS UNCERTAINTY ANALYSIS IN THE RELIABILITY ASSESSMENT OF THE REACTOR PRESSURE VESSEL: (2D) SUBJECTED TO INTERNAL PRESSURE." JURNAL TEKNOLOGI REAKTOR NUKLIR TRI DASA MEGA 18, no. 2 (June 6, 2016): 55. http://dx.doi.org/10.17146/tdm.2016.18.2.2466.

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ABSTRACT FRACTURE MECHANICS UNCERTAINTY ANALYSIS IN THE RELIABILITY ASSESSMENT OF THE REACTOR PRESSURE VESSEL: (2D) SUBJECTED TO INTERNAL PRESSURE. The reactor pressure vessel (RPV) is a pressure boundary in the PWR type reactor which serves to confine radioactive material during chain reaction process. The integrity of the RPV must be guaranteed either in a normal operation or accident conditions. In analyzing the integrity of RPV, especially related to the crack behavior which can introduce break to the reactor pressure vessel, a fracture mechanic approach should be taken for this assessment. The uncertainty of input used in the assessment, such as mechanical properties and physical environment, becomes a reason that the assessment is not sufficient if it is perfomed only by deterministic approach. Therefore, the uncertainty approach should be applied. The aim of this study is to analize the uncertainty of fracture mechanics calculations in evaluating the reliability of PWR`s reactor pressure vessel. Random character of input quantity was generated using probabilistic principles and theories. Fracture mechanics analysis is solved by Finite Element Method (FEM) with MSC MARC software, while uncertainty input analysis is done based on probability density function with Latin Hypercube Sampling (LHS) using python script. The output of MSC MARC is a J-integral value, which is converted into stress intensity factor for evaluating the reliability of RPV’s 2D. From the result of the calculation, it can be concluded that the SIF from probabilistic method, reached the limit value of fracture toughness earlier than SIF from deterministic method. The SIF generated by the probabilistic method is 105.240 MPa m0.5. Meanwhile, the SIF generated by deterministic method is 100.876 MPa m0.5. Keywords: Uncertainty analysis, fracture mechanics, LHS, FEM, reactor pressure vessels ABSTRAK ANALISIS KETIDAKPASTIAN FRACTURE MECHANIC PADA EVALUASI KEANDALAN BEJANA TEKAN REAKTOR: 2D DENGAN BEBAN INTERNAL PRESSURE. Bejana tekan reaktor (RPV) merupakan pressure boundary dalam reaktor tipe PWR yang berfungsi untuk mengungkung material radioaktif yang dihasilkan pada proses reaksi berantai. Maka dari itu integritas bejana tekan reaktor harus senantiasa terjamin baik reaktor dalam keadaan operasi normal, maupun kecelakaan. Dalam melakukan analisis integritas RPV, khususnya yang berkaitan dengan pecahnya bejana tekan reaktor akibat adanya retak dilakukan analisis secara fracture mechanics. Adanya ketidakpastian input seperti sifat mekanik bahan, lingkungan fisik, dan input pada data, maka dalam melakukan analisis keandalan tidak hanya dilakukan secara deterministik saja. Tujuan dari penelitian ini adalah melakukan analisis ketidakpastian input pada perhitungan fracture mechanik pada evaluasi keandalan bejana tekan reaktor PWR. Pendekatan untuk karakter random dari kuantitas input menggunakan teori probabilistik. Analisis fracture mechanics dilakukan berdasarkan metode elemen hingga (FEM) menggunakan perangkat lunak MSC MARC. Analisis ketidakpastian input dilakukan berdasarkan probability density function dengan Latin Hypercube Sampling (LHS) menggunakan python script. Output dari MSC MARC adalah nilai J-integral untuk mendapatkan nilai stress intensity factor pada evaluasi keandalan bejana tekan reactor 2D. Dari hasil perhitungan dapat disimpulkan bahwa SIF probabilistik lebih dulu mencapai nilai batas fracture tougness dibanding SIF deterministik. SIF yang dihasilkan dengan metode probabilistik adalah 105,240 MPa m0,5. Sedangkan SIF metode deterministik adalah 100,876 MPa m0,5. Kata kunci: Analisis ketidakpastian, fracture mechanics, LHS, FEM, bejana tekan reaktor

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30

Campbell-Moore, Catrin, and Jason Konek. "Believing Probabilistic Contents: On the Expressive Power and Coherence of Sets of Sets of Probabilities." Analysis 80, no. 2 (November 26, 2019): 316–31. http://dx.doi.org/10.1093/analys/anz076.

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31

Böttcher, Albrecht, Stefan Kunis, and Daniel Potts. "Probabilistic spherical Marcinkiewicz–Zygmund inequalities." Journal of Approximation Theory 157, no. 2 (April 2009): 113–26. http://dx.doi.org/10.1016/j.jat.2008.07.006.

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32

Hegerfeldt, Gerhard C. "Noncommutative analogs of probabilistic notions and results." Journal of Functional Analysis 64, no. 3 (December 1985): 436–56. http://dx.doi.org/10.1016/0022-1236(85)90068-0.

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33

Yeung, Man-Chung, and Tony F. Chan. "Probabilistic Analysis of Gaussian Elimination Without Pivoting." SIAM Journal on Matrix Analysis and Applications 18, no. 2 (April 1997): 499–517. http://dx.doi.org/10.1137/s0895479895291741.

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34

Arous, Gérard Ben, and Fabienne Castell. "A Probabilistic Approach to Semi-classical Approximations." Journal of Functional Analysis 137, no. 1 (April 1996): 243–80. http://dx.doi.org/10.1006/jfan.1996.0046.

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35

Voinalovych, O. V., O. A. Hnatiuk, L. L. Titova, and O. G. Polischuk. "Probabilistic method of analysis of degree of operating hazards of tractors on basis of detection details." Naukovij žurnal «Tehnìka ta energetika» 10, no. 1 (February 7, 2019): 121–26. http://dx.doi.org/10.31548/machenergy2019.01.121.

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36

D'Antona, O. "A ring underlying probabilistic identities." Journal of Mathematical Analysis and Applications 108, no. 1 (May 1985): 211–15. http://dx.doi.org/10.1016/0022-247x(85)90017-4.

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37

Miheţ, Dorel. "Multivalued generalizations of probabilistic contractions." Journal of Mathematical Analysis and Applications 304, no. 2 (April 2005): 464–72. http://dx.doi.org/10.1016/j.jmaa.2004.09.034.

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38

Alsina, Claudi, Berthold Schweizer, and Abe Sklar. "Continuity Properties of Probabilistic Norms." Journal of Mathematical Analysis and Applications 208, no. 2 (April 1997): 446–52. http://dx.doi.org/10.1006/jmaa.1997.5333.

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39

Lafuerza Guillén, Bernardo, José Antonio Rodrı́guez Lallena, and Carlo Sempi. "Probabilistic Norms for Linear Operators." Journal of Mathematical Analysis and Applications 220, no. 2 (April 1998): 462–76. http://dx.doi.org/10.1006/jmaa.1997.5810.

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40

Wu, Ruiyuan, Wing-Kin Ma, Yuening Li, Anthony Man-Cho So, and Nicholas D. Sidiropoulos. "Probabilistic Simplex Component Analysis." IEEE Transactions on Signal Processing 70 (2022): 582–99. http://dx.doi.org/10.1109/tsp.2021.3133690.

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41

Tipping, Michael E., and Christopher M. Bishop. "Probabilistic Principal Component Analysis." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 61, no. 3 (August 1999): 611–22. http://dx.doi.org/10.1111/1467-9868.00196.

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42

Keren, D., and M. Werman. "Probabilistic analysis of regularization." IEEE Transactions on Pattern Analysis and Machine Intelligence 15, no. 10 (1993): 982–95. http://dx.doi.org/10.1109/34.254057.

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43

Sanzo, D. L., and G. E. Apostolakis. "Limiter probabilistic lifetime analysis." Fusion Engineering and Design 6 (January 1988): 229–67. http://dx.doi.org/10.1016/s0920-3796(88)80111-x.

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44

Al-Ajmi, Adel M., and Mansoor H. Al-Harthy. "Probabilistic wellbore collapse analysis." Journal of Petroleum Science and Engineering 74, no. 3-4 (November 2010): 171–77. http://dx.doi.org/10.1016/j.petrol.2010.09.001.

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45

Baucells, Manel, and Emanuele Borgonovo. "Invariant Probabilistic Sensitivity Analysis." Management Science 59, no. 11 (November 2013): 2536–49. http://dx.doi.org/10.1287/mnsc.2013.1719.

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46

Peng-Sheng Chen, Yuan-Shin Hwang, R. D. C. Ju, and J. K. Lee. "Interprocedural probabilistic pointer analysis." IEEE Transactions on Parallel and Distributed Systems 15, no. 10 (October 2004): 893–907. http://dx.doi.org/10.1109/tpds.2004.56.

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47

Griffiths, D. V., Jinsong Huang, and Gordon A. Fenton. "Probabilistic infinite slope analysis." Computers and Geotechnics 38, no. 4 (June 2011): 577–84. http://dx.doi.org/10.1016/j.compgeo.2011.03.006.

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48

Dhillon, B. S., and A. R. M. Fashandi. "Robotic systems probabilistic analysis." Microelectronics Reliability 37, no. 2 (February 1997): 211–24. http://dx.doi.org/10.1016/s0026-2714(96)00074-1.

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49

Şen, Zekâi. "Probabilistic innovative trend analysis." International Journal of Global Warming 20, no. 2 (2020): 93. http://dx.doi.org/10.1504/ijgw.2020.10027061.

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Şen, Zekâi. "Probabilistic innovative trend analysis." International Journal of Global Warming 20, no. 2 (2020): 93. http://dx.doi.org/10.1504/ijgw.2020.105387.

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