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Artículos de revistas sobre el tema "Similarity of structure"

Autor: Grafiati

Publicado: 10 de marzo de 2023

Última modificación: 31 de julio de 2025

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1

Peng Ding, Peng Ding, Dan Liu Peng Ding, Zhiyuan Zhang Dan Liu, Jie Hu Zhiyuan Zhang, and Ning Liu Jie Hu. "A Novel Discrimination Structure for Assessing Text Semantic Similarity." 網際網路技術學刊 23, no. 4 (2022): 709–17. http://dx.doi.org/10.53106/160792642022072304006.

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<p>Discrimination of semantic textual similarity refers to comparing the similarity between two or more entities (including words, short texts and documents) through certain strategies to obtain a specific quantitative similarity value. Traditional research put more experience into the similarity calculation of the original text content, using the matching degree or distance of characters or words as the yardstick to judge whether the text pairs are similar. However, there are still some problems to be solved in the following aspects: the key points of sentence meaning and word semantics

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2

Wang, Yong Fu, Qi Dou Zhou, Zhi Yong Xie, and Xiao Jun Lv. "An Investigation of Acoustic Similarity on an Underwater Structure." Applied Mechanics and Materials 105-107 (September 2011): 84–91. http://dx.doi.org/10.4028/www.scientific.net/amm.105-107.84.

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An acoustic similarity due to two geometrically similar structures which are vibrating in heavy flow, such as in water, is investigated. The acoustic similarity states that for two geometrically similar structures, if a group of dimensionless similarity numbers are constant, the dimensionless acoustic pressure coefficient keep constant at the corresponding acoustic field points for the two flow–loaded vibrating structure systems. Numerical simulations and experiment results are presented to validate the acoustic similarity. This acoustic similarity may be useful when a small structure is emplo

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3

Fukukawa, Tomoya, Kosuke Sekiyama, and Yasuhisa Hasegawa. "Vanishing point detection focusing on similarity structure in vineyard environments." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2015.6 (2015): 278–79. http://dx.doi.org/10.1299/jsmeicam.2015.6.278.

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4

Kang, Zhao, Xiao Lu, Yiwei Lu, Chong Peng, Wenyu Chen, and Zenglin Xu. "Structure learning with similarity preserving." Neural Networks 129 (September 2020): 138–48. http://dx.doi.org/10.1016/j.neunet.2020.05.030.

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5

Rypdal, Kristoffer, Jens Juul Rasmussen, and Knud Thomsen. "Similarity structure of wave-collapse." Physica D: Nonlinear Phenomena 16, no. 3 (1985): 339–57. http://dx.doi.org/10.1016/0167-2789(85)90013-2.

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6

Eva, Benjamin, Reuben Stern, and Stephan Hartmann. "The Similarity of Causal Structure." Philosophy of Science 86, no. 5 (2019): 821–35. http://dx.doi.org/10.1086/705566.

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7

Fletcher, Samuel C. "Similarity Structure and Emergent Properties." Philosophy of Science 87, no. 2 (2020): 281–301. http://dx.doi.org/10.1086/707563.

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8

Chaudhari, N. S., and Xiangrui Wang. "Language Structure Using Fuzzy Similarity." IEEE Transactions on Fuzzy Systems 17, no. 5 (2009): 1011–24. http://dx.doi.org/10.1109/tfuzz.2009.2020155.

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9

Rifai, Muhamad Aldi, and Indra Gita Anugrah. "Semantic Search for Scientific Articles by Language Using Cosine Similarity Algorithm and Weighted Tree Similarity." Journal of Development Research 5, no. 2 (2021): 106–14. http://dx.doi.org/10.28926/jdr.v5i2.150.

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The activity of writing scientific articles by academics at universities is one of the activities that is often carried out, but when writing scientific articles problems arise regarding the difficulty of finding ideas, literature studies, and reference sources that you want to use as references when writing. Sometimes when searching on a search engine, we have trouble finding the right document, because usually, the keywords we are looking for are not in the title section but another part of the structure. Since most search engines only match titles, other structures are usually excluded from

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10

Bartlett, James C., and W. Jay Dowling. "Scale Structure and Similarity of Melodies." Music Perception 5, no. 3 (1988): 285–314. http://dx.doi.org/10.2307/40285401.

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Four experiments explored an asymmetry in the perceived similarity of melodies: If a first-presented melody is "scalar" (conforms to a diatonic major scale), and is followed by a second melody slightly altered to be " nonscalar" (deviating from a diatonic major scale), subjects judge similarity to be lower than if the nonscalar melody comes first. Experiment 1 produced evidence that asymmetric similarity is not due simply to more strongly scalar melodies having greater memorability. Experiment 2 ruled out the hypothesis that asymmetric similarity depends on a taskspecific strategy reflecting d

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11

MINAMI, Shintaro, and George CHIKENJI. "Non-sequential Structure Similarity in Proteins." Seibutsu Butsuri 56, no. 1 (2016): 027–29. http://dx.doi.org/10.2142/biophys.56.027.

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12

Rychlewski, L., and A. Godzik. "Secondary structure prediction using segment similarity." Protein Engineering Design and Selection 10, no. 10 (1997): 1143–53. http://dx.doi.org/10.1093/protein/10.10.1143.

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13

Gentner, Dedre, and Arthur B. Markman. "Structure mapping in analogy and similarity." American Psychologist 52, no. 1 (1997): 45–56. http://dx.doi.org/10.1037/0003-066x.52.1.45.

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14

Dzyabchenko, A. V. "Method of crystal-structure similarity searching." Acta Crystallographica Section B Structural Science 50, no. 4 (1994): 414–25. http://dx.doi.org/10.1107/s0108768193013552.

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15

Xia, Kelin. "Persistent similarity for biomolecular structure comparison." Communications in Information and Systems 18, no. 4 (2018): 269–98. http://dx.doi.org/10.4310/cis.2018.v18.n4.a4.

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16

Kenyon, Richard. "Inflationary tilings with a similarity structure." Commentarii Mathematici Helvetici 69, no. 1 (1994): 169–98. http://dx.doi.org/10.1007/bf02564481.

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17

Collins, Anna, Richard I. Cooper, and David J. Watkin. "Structure matching: measures of similarity and pseudosymmetry." Journal of Applied Crystallography 39, no. 6 (2006): 842–49. http://dx.doi.org/10.1107/s0021889806038489.

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A sizeable proportion of structures withZ′ = 2 are thought to exhibit pseudosymmetry, but establishing the extent of the deviation from true symmetry is problematic. By considering both the conformational similarity between the independent molecules and the way in which they are related in space, assessment of the pseudosymmetry of a structure becomes possible. A method of matching two groups of atoms where both these factors are quantified usingCRYSTALS[Betteridge, Carruthers, Cooper, Prout & Watkin (2003).J. Appl. Cryst.36, 1487] is described.

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18

Ekaney, Lena Y. E., Donatus B. Eni, and Fidele Ntie-Kang. "Chemical similarity methods for analyzing secondary metabolite structures." Physical Sciences Reviews 6, no. 7 (2021): 247–64. http://dx.doi.org/10.1515/psr-2018-0129.

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Abstract The relation that exists between the structure of a compound and its function is an integral part of chemoinformatics. The similarity principle states that “structurally similar molecules tend to have similar properties and similar molecules exert similar biological activities”. The similarity of the molecules can either be studied at the structure level or at the descriptor level (properties level). Generally, the objective of chemical similarity measures is to enhance prediction of the biological activities of molecules. In this article, an overview of various methods used to compar

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19

Gentner, Dedre. "Exhuming similarity." Behavioral and Brain Sciences 24, no. 4 (2001): 669. http://dx.doi.org/10.1017/s0140525x01350082.

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Tenenbaum and Griffiths' paper attempts to subsume theories of similarity – including spatial models, featural models, and structure-mapping models – into a framework based on Bayesian generalization. But in so doing it misses significant phenomena of comparison. It would be more fruitful to examine how comparison processes suggest hypotheses than to try to derive similarity from Bayesian reasoning. [Shepard; Tenenbaum & Griffiths]

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20

Chisholm, James Alexander, and Sam Motherwell. "COMPACK: a program for identifying crystal structure similarity using distances." Journal of Applied Crystallography 38, no. 1 (2005): 228–31. http://dx.doi.org/10.1107/s0021889804027074.

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A method is presented for comparing crystal structures to identify similarity in molecular packing environments. The relative position and orientation of molecules is captured using interatomic distances, which provide a representation of structure that avoids the use of space-group and cell information. The method can be used to determine whether two crystal structures are the same to within specified tolerances and can also provide a measure of similarity for structures that do not match exactly, but have structural features in common. Example applications are presented that include the iden

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21

TERASHIMA, Chieko, Yoshiaki TANIDA, Toshio MANABE, and Hiroyuki SATO. "The Correlation between Similarity of Amino Acid Interaction Potentials and Structure Similarity." Journal of Computer Chemistry, Japan 20, no. 4 (2021): 144–46. http://dx.doi.org/10.2477/jccj.2022-0003.

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22

Gao, Nansha, Jiu Hui Wu, and Lie Yu. "Large band gaps in two-dimensional phononic crystals with self-similarity structure." International Journal of Modern Physics B 29, no. 04 (2015): 1550017. http://dx.doi.org/10.1142/s0217979215500174.

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In this paper, we study the band gaps (BGs) of two-dimensional (2D) phononic crystals (PCs) composed of self-similarity shape inclusions embedded in the homogenous matrix. The dispersion relations, transmission spectra, and displacement fields of eigenmodes of the proposed structures are calculated by use of finite element method. Due to the simultaneous mechanisms of the Bragg scattering, the structure can exhibit low-frequency BGs, which can be effectively shifted by changing the geometries and degree of the self-similarity structure. The BGs are significantly dependent upon the geometrical

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23

Lamont, Alexandra, and Nicola Dibben. "Motivic Structure and the Perception of Similarity." Music Perception 18, no. 3 (2001): 245–74. http://dx.doi.org/10.1525/mp.2001.18.3.245.

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This paper presents a theoretical and empirical investigation into the ways in which different listeners perceive similarity relationships in different kinds of music. We first extend the current understanding of similarity relations in music by drawing together theory and evidence from general cognitive psychology, cognitive psychology of music, and music theory. In the empirical study, trained musicians and nonmusicians rated the similarity of pairs of extracts from piano pieces by Beethoven (Sonata op. 10, no. 1, first movement) and Schoenberg (Klavierstüück op. 33a) and provided adjective

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24

Taipale, Joona. "Similarity and asymmetry." Phänomenologische Forschungen 2014, no. 1 (2014): 141–54. http://dx.doi.org/10.28937/1000107780.

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This article suggests that the asymmetrical structure of the self-other relationship can be traced back to the relation between empathy and transcendental intersubjectivity. Drawing on Husserl in particular, I will first recapitulate the argument that empathy is necessarily preceded by, and built upon, structural implications to potential others, and I will then argue that the empathically encountered actual other is bound to arrive as the fulfilment or concretization of this anonymous, emptily appresented “anybody”. Because of this foundedness, empathy is necessarily built on expectations con

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25

Mukherjee, Partha, Youakim Badr, Srushti Karvekar, and Shanmugapriya Viswanathan. "Coronavirus Genome Sequence Similarity and Protein Sequence Classification." Journal of Digital Science 3, no. 2 (2021): 3–18. http://dx.doi.org/10.33847/2686-8296.3.2_1.

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The world currently is going through a serious pandemic due to the coronavirus disease (COVID-19). In this study, we investigate the gene structure similarity of coronavirus genomes isolated from COVID-19 patients, Severe Acute Respiratory Syndrome (SARS) patients and bats genes. We also explore the extent of similarity between their genome structures to find if the new coronavirus is similar to either of the other genome structures. Our experimental results show that there is 82.42% similarity between the CoV-2 genome structure and the bat genome structure. Moreover, we have used a bidirectio

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26

Malod-Dognin, Noël, Rumen Andonov, and Nicola Yanev. "Solving Maximum Clique Problem for Protein Structure Similarity." Serdica Journal of Computing 4, no. 1 (2010): 93–100. http://dx.doi.org/10.55630/sjc.2010.4.93-100.

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Computing the similarity between two protein structures is a crucial task in molecular biology, and has been extensively investigated. Many protein structure comparison methods can be modeled as maximum weighted clique problems in specific k-partite graphs, referred here as alignment graphs. In this paper we present both a new integer programming formulation for solving such clique problems and a dedicated branch and bound algorithm for solving the maximum cardinality clique problem. Both approaches have been integrated in VAST, a software for aligning protein 3D structures largely used in the

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27

PARK, SUNG-HEE, KEUN HO RYU, and DAVID GILBERT. "FAST SIMILARITY SEARCH FOR PROTEIN 3D STRUCTURES USING TOPOLOGICAL PATTERN MATCHING BASED ON SPATIAL RELATIONS." International Journal of Neural Systems 15, no. 04 (2005): 287–96. http://dx.doi.org/10.1142/s0129065705000244.

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Similarity search for protein 3D structures become complex and computationally expensive due to the fact that the size of protein structure databases continues to grow tremendously. Recently, fast structural similarity search systems have been required to put them into practical use in protein structure classification whilst existing comparison systems do not provide comparison results on time. Our approach uses multi-step processing that composes of a preprocessing step to represent geometry of protein structures with spatial objects, a filter step to generate a small candidate set using appr

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28

Clavero, Maria, Pedro Folgueras, Pilar Diaz-Carrasco, Miguel Ortega-Sanchez, and Miguel A. Losada. "A SIMILARITY PARAMETER FOR BREAKWATERS: THE MODIFIED IRIBARREN NUMBER." Coastal Engineering Proceedings, no. 36 (December 30, 2018): 28. http://dx.doi.org/10.9753/icce.v36.structures.28.

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In the 14th ICCE, Battjes (1974) showed that a single similarity parameter only, embodying both the effects of slope angle and incident wave steepness, was important for many aspects of waves breaking on impermeable slopes, and suggested to call it the "Iribarren number", denoted by "Ir". Ahrens and McCartney (1975) verified the usefulness of Ir to describe run-up and stability on rough permeable slopes. Since then, many researchers applied Ir to characterize and to develop formulae for the design of breakwaters and to verify their stability. On the other hand, depending on their typology, bre

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29

Charette, Bradley D., Richard G. MacDonald, Stefan Wetzel, David B. Berkowitz, and Herbert Waldmann. "Protein Structure Similarity Clustering: Dynamic Treatment of PDB Structures Facilitates Clustering." Angewandte Chemie 118, no. 46 (2006): 7930–34. http://dx.doi.org/10.1002/ange.200602125.

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30

Charette, Bradley D., Richard G. MacDonald, Stefan Wetzel, David B. Berkowitz, and Herbert Waldmann. "Protein Structure Similarity Clustering: Dynamic Treatment of PDB Structures Facilitates Clustering." Angewandte Chemie International Edition 45, no. 46 (2006): 7766–70. http://dx.doi.org/10.1002/anie.200602125.

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31

Li, Xiao, and Qingsheng Li. "Calculation of Sentence Semantic Similarity Based on Syntactic Structure." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/203475.

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Combined with the problem of single direction of the solution of the existing sentence similarity algorithms, an algorithm for sentence semantic similarity based on syntactic structure was proposed. Firstly, analyze the sentence constituent, then through analysis convert sentence similarity into words similarity on the basis of syntactic structure, then convert words similarity into concept similarity through words disambiguation, and, finally, realize the semantic similarity comparison. It also gives the comparison rules in more detail for the modifier words in the sentence which also have ce

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32

Lowet, Adam S., Chaz Firestone, and Brian J. Scholl. "Seeing structure: Shape skeletons modulate perceived similarity." Attention, Perception, & Psychophysics 80, no. 5 (2018): 1278–89. http://dx.doi.org/10.3758/s13414-017-1457-8.

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33

Good, Andrew C., Sung Sau So, and W. Graham Richards. "Structure-activity relationships from molecular similarity matrices." Journal of Medicinal Chemistry 36, no. 4 (1993): 433–38. http://dx.doi.org/10.1021/jm00056a002.

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34

Ganesan, Prasanna, Hector Garcia-Molina, and Jennifer Widom. "Exploiting hierarchical domain structure to compute similarity." ACM Transactions on Information Systems 21, no. 1 (2003): 64–93. http://dx.doi.org/10.1145/635484.635487.

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35

Schill, Hayden M., and Timothy F. Brady. "Global scene similarity structure predicts memory performance." Journal of Vision 20, no. 11 (2020): 614. http://dx.doi.org/10.1167/jov.20.11.614.

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36

Vacher, René, Thierry Woignier, Jacques Pelous, and Eric Courtens. "Structure and self-similarity of silica aerogels." Physical Review B 37, no. 11 (1988): 6500–6503. http://dx.doi.org/10.1103/physrevb.37.6500.

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37

Akbal-Delibas, Bahar, Marc Pomplun, and Nurit Haspel. "Accurate Prediction of Docked Protein Structure Similarity." Journal of Computational Biology 22, no. 9 (2015): 892–904. http://dx.doi.org/10.1089/cmb.2015.0114.

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38

Al-Saleh, Mohammad Fraiwan. "On the Similarity Structure of Order Statistics." Communications in Statistics - Theory and Methods 36, no. 7 (2007): 1433–39. http://dx.doi.org/10.1080/03610920601077204.

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39

Qingfeng Chen and Y.-P. P. Chen. "Function Annotation for Pseudoknot Using Structure Similarity." IEEE/ACM Transactions on Computational Biology and Bioinformatics 8, no. 6 (2011): 1535–44. http://dx.doi.org/10.1109/tcbb.2011.50.

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40

Galgonek, Jakub, David Hoksza, and Tomáš Skopal. "SProt: sphere-based protein structure similarity algorithm." Proteome Science 9, Suppl 1 (2011): S20. http://dx.doi.org/10.1186/1477-5956-9-s1-s20.

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41

Taylor, William R. "Protein structure modelling from remote sequence similarity." Journal of Biotechnology 35, no. 2-3 (1994): 281–91. http://dx.doi.org/10.1016/0168-1656(94)90042-6.

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42

Ren *, Bin, and Guoxing Ji. "Certain structure of Similarity-preserving linear maps." Linear and Multilinear Algebra 52, no. 1 (2004): 61–68. http://dx.doi.org/10.1080/0308108031000134990.

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43

Hark Gan, Hin, Rebecca A. Perlow, Sharmili Roy, et al. "Analysis of Protein Sequence/Structure Similarity Relationships." Biophysical Journal 83, no. 5 (2002): 2781–91. http://dx.doi.org/10.1016/s0006-3495(02)75287-9.

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44

Snitz, Kobi, Adi Yablonka, Tali Weiss, Idan Frumin, Rehan M. Khan, and Noam Sobel. "Predicting Odor Perceptual Similarity from Odor Structure." PLoS Computational Biology 9, no. 9 (2013): e1003184. http://dx.doi.org/10.1371/journal.pcbi.1003184.

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45

He, Xing-Gang, and Zhi-Ying Wen. "The self-similarity structure on infinite intervals." Journal of Mathematical Analysis and Applications 329, no. 2 (2007): 1094–101. http://dx.doi.org/10.1016/j.jmaa.2006.07.053.

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46

Giegé, Richard, Frank Jühling, Joern Pütz, Peter Stadler, Claude Sauter, and Catherine Florentz. "Structure of transfer RNAs: similarity and variability." Wiley Interdisciplinary Reviews: RNA 3, no. 1 (2011): 37–61. http://dx.doi.org/10.1002/wrna.103.

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47

Schneider, David M., and Damián H. Zanette. "The Structure of Bit-String Similarity Networks." Entropy 27, no. 1 (2025): 57. https://doi.org/10.3390/e27010057.

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We study the structural properties of networks formed by random sets of bit strings—namely the ordered arrays of binary variables representing, for instance, genetic information or cultural profiles. Two bit strings are connected by a network link when they are sufficiently similar to each other, i.e., when their Hamming distance is below a certain threshold. Using both analytical and numerical techniques, we determine the degree distribution and the conditions for the existence of a giant component in this kind of network. In addition, we analyze their clustering, assortativity, and mean geod

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48

Salamaga, Marcin. "Similarity assessment of the commodity structure of foreign trade in the European Union countries." Wiadomości Statystyczne. The Polish Statistician 62, no. 11 (2017): 5–16. http://dx.doi.org/10.5604/01.3001.0014.1059.

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An important element of the foreign trade research is an analysis of its commodity and geographical structure. A method of examining the similarity of foreign trade structure was proposed in this article and applied on the example of certain EU countries. The aim of this article is to assess the degree of similarity of foreign trade structures and to indicate the extent to which it reflects differences in economic development. The study was conducted with the use of an original indicator of commodity structure similarity and a hierarchical cluster analysis for the period 2006—2015. It enabled

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49

An, Xiao Ning. "Research on Structure Design Parameters’ Similarity for Loading Machine." Applied Mechanics and Materials 184-185 (June 2012): 440–44. http://dx.doi.org/10.4028/www.scientific.net/amm.184-185.440.

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Calculation results are showing: There are basically specific relationships between loading machine’s weight and loading machines’ main parameters or other parameters’ combinations. So these specific relationships have certain similarity. And machine’s similarity criteria has invariance. The similarity equations derived from similarity theory basically coincide with quantitative analysis results from practical samples data. So, it is proved that there is a kind of similarity between loading machine’s practical design and development and similarity system’s design of structure size. So, the the

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50

Pan, Ji-Yuan, and Jiang-She Zhang. "Relationship Matrix Nonnegative Decomposition for Clustering." Mathematical Problems in Engineering 2011 (2011): 1–15. http://dx.doi.org/10.1155/2011/864540.

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Nonnegative matrix factorization (NMF) is a popular tool for analyzing the latent structure of nonnegative data. For a positive pairwise similarity matrix, symmetric NMF (SNMF) and weighted NMF (WNMF) can be used to cluster the data. However, both of them are not very efficient for the ill-structured pairwise similarity matrix. In this paper, a novel model, called relationship matrix nonnegative decomposition (RMND), is proposed to discover the latent clustering structure from the pairwise similarity matrix. The RMND model is derived from the nonlinear NMF algorithm. RMND decomposes a pairwise

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