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Journal articles on the topic 'Multivariant'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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1

Gouras, Peter. "Multivariant color vision." Behavioral and Brain Sciences 15, no. 1 (March 1992): 37. http://dx.doi.org/10.1017/s0140525x00067364.

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Borràs Català, Vicent, J. Óscar Fernández Santana, Margarita Latiesa, and Olga Del Río. "Bibliografia sobre anàlisi multivariant." Papers. Revista de Sociologia 37 (July 1, 1991): 135. http://dx.doi.org/10.5565/rev/papers/v37n0.1600.

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3

Kovalev, Aristarkh M. "Multivariant System of Perspectives." Key Engineering Materials 437 (May 2010): 18–21. http://dx.doi.org/10.4028/www.scientific.net/kem.437.18.

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The article describes a patented method of displaying objects that secures natural visual perception of the objects displayed without distortions in their scaling relation and the dimensional depth. The author considers a group of linear perspectives with homographic function to display/map 3D visual scenes. The article also describes matrices for projective transformation.
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4

Alekseev, A. O. "A multivariant travelling-salesman problem." USSR Computational Mathematics and Mathematical Physics 25, no. 2 (January 1985): 200–201. http://dx.doi.org/10.1016/0041-5553(85)90130-2.

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5

Li, Bao Lei, Xin Ling Shi, Chang Xing Gou, Tian Song Li, Ya Jie Liu, Lan Juan Liu, and Qin Hu Zhang. "Multivariant Optimization Algorithm for Multimodal Optimization." Applied Mechanics and Materials 483 (December 2013): 453–57. http://dx.doi.org/10.4028/www.scientific.net/amm.483.453.

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In this paper, a heuristic Multivariant Optimization Algorithm (MOA), which has the ability to locate multiple optima through alternating global-local search iterations by multivariant search groups including global exploration groups and local exploitation groups based on a structure where the optimizing process is saved, is described in detail. The performances of MOA are compared with that of other heuristic algorithms including GA and PSO based on six benchmark functions and the experiment results indicate that MOA outperforms GA and PSO in success rate and convergence efficiency on multimodal functions.
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Andrew, Matthew. "Permeability Prediction using multivariant structural regression." E3S Web of Conferences 146 (2020): 04001. http://dx.doi.org/10.1051/e3sconf/202014604001.

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A novel method for permeability prediction is presented using multivariant structural regression. A machine learning based model is trained using a large number (2,190, extrapolated to 219,000) of synthetic datasets constructed using a variety of object-based techniques. Permeability, calculated on each of these networks using traditional digital rock approaches, was used as a target function for a multivariant description of the pore network structure, created from the statistics of a discrete description of grains, pores and throats, generated through image analysis. A regression model was created using an Extra-Trees method with an error of <4% on the target set. This model was then validated using a composite series of data created both from proprietary datasets of carbonate and sandstone samples and open source data available from the Digital Rocks Portal (www.digitalrocksporta.org) with a Root Mean Square Fractional Error of <25%. Such an approach has wide applicability to problems of heterogeneity and scale in pore scale analysis of porous media, particularly as it has the potential of being applicable on 2D as well as 3D data.
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Franz, Michael. "Making Multivariant Programming Practical and Inexpensive." IEEE Security & Privacy 16, no. 3 (May 2018): 90–94. http://dx.doi.org/10.1109/msp.2018.2701161.

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Hsin-Hui Lin, Kaung-Hwa Chen, and Rong-Tsorng Wang. "A multivariant exponential shared-load model." IEEE Transactions on Reliability 42, no. 1 (March 1993): 165–71. http://dx.doi.org/10.1109/24.210291.

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Zhang, Qin-Hu, Bao-Lei Li, Ya-Jie Liu, Lian Gao, Lan-Juan Liu, and Xin-Ling Shi. "Data clustering using multivariant optimization algorithm." International Journal of Machine Learning and Cybernetics 7, no. 5 (August 26, 2014): 773–82. http://dx.doi.org/10.1007/s13042-014-0294-5.

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Gohn, Douglas, Tom Edel, Cathy Pollard, Steve Moore, Michael Firstenberg, William McGuinn, Tony Simmons, Richard Trohman, and James Maloney. "High defibrillation thresholds: Univariant and multivariant analysis." Journal of the American College of Cardiology 17, no. 2 (February 1991): A86. http://dx.doi.org/10.1016/0735-1097(91)91311-2.

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11

Wood, Mary A., Austin Nguyen, Adam J. Struck, Kyle Ellrott, Abhinav Nellore, and Reid F. Thompson. "neoepiscope improves neoepitope prediction with multivariant phasing." Bioinformatics 36, no. 3 (August 19, 2019): 713–20. http://dx.doi.org/10.1093/bioinformatics/btz653.

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Abstract Motivation The vast majority of tools for neoepitope prediction from DNA sequencing of complementary tumor and normal patient samples do not consider germline context or the potential for the co-occurrence of two or more somatic variants on the same mRNA transcript. Without consideration of these phenomena, existing approaches are likely to produce both false-positive and false-negative results, resulting in an inaccurate and incomplete picture of the cancer neoepitope landscape. We developed neoepiscope chiefly to address this issue for single nucleotide variants (SNVs) and insertions/deletions (indels). Results Herein, we illustrate how germline and somatic variant phasing affects neoepitope prediction across multiple datasets. We estimate that up to ∼5% of neoepitopes arising from SNVs and indels may require variant phasing for their accurate assessment. neoepiscope is performant, flexible and supports several major histocompatibility complex binding affinity prediction tools. Availability and implementation neoepiscope is available on GitHub at https://github.com/pdxgx/neoepiscope under the MIT license. Scripts for reproducing results described in the text are available at https://github.com/pdxgx/neoepiscope-paper under the MIT license. Additional data from this study, including summaries of variant phasing incidence and benchmarking wallclock times, are available in Supplementary Files 1, 2 and 3. Supplementary File 1 contains Supplementary Table 1, Supplementary Figures 1 and 2, and descriptions of Supplementary Tables 2–8. Supplementary File 2 contains Supplementary Tables 2–6 and 8. Supplementary File 3 contains Supplementary Table 7. Raw sequencing data used for the analyses in this manuscript are available from the Sequence Read Archive under accessions PRJNA278450, PRJNA312948, PRJNA307199, PRJNA343789, PRJNA357321, PRJNA293912, PRJNA369259, PRJNA305077, PRJNA306070, PRJNA82745 and PRJNA324705; from the European Genome-phenome Archive under accessions EGAD00001004352 and EGAD00001002731; and by direct request to the authors. Supplementary information Supplementary data are available at Bioinformatics online.
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12

Firsov, A. V., and A. Yu Strashnov. "A Modular System for Multivariant Fabric Design." Fibre Chemistry 37, no. 2 (March 2005): 116–18. http://dx.doi.org/10.1007/s10692-005-0067-8.

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13

Li, Baolei, Jianhua Chen, Xinling Shi, Yufeng Zhang, Danjv Lv, Lanjuan Liu, and Qinhu Zhang. "On the convergence of multivariant optimization algorithm." Applied Soft Computing 48 (November 2016): 230–39. http://dx.doi.org/10.1016/j.asoc.2016.07.001.

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14

Liu, Lan Juan, Bao Lei Li, Qin Hu Zhang, Dan Jv Lv, Xin Ling Shi, and Jing Jing Li. "Multivariant Optimization Algorithm for the 0-1 Knapsack Problem." Applied Mechanics and Materials 556-562 (May 2014): 3514–18. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3514.

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In this paper, a novel heuristic algorithm named Multivariant Optimization Algorithm (MOA) is presented to solve the 0-1 Knapsack Problem (KP). In MOA, multivariant search groups (locate and global search groups) execute the global exploration and local exploitation iteratively to locate the optimal solution automatically. The presented algorithm has been compared with Genetic Algorithm (GA) and Particle swarm algorithm (PSO) based on five data sets, results show that the optimization of MOA is better than GA and PSO when the dimension of problem is high.
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Sato, Kazuhisa, Keigo Yanajima, and Toyohiko J. Konno. "Z-Contrast STEM Imaging of Long-Range Ordered Structures in Epitaxially Grown CoPt Nanoparticles." Journal of Nanomaterials 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/679638.

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We report on atomic structure imaging of epitaxial L10CoPt nanoparticles using chemically sensitive high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM). Highly ordered nanoparticles formed by annealing at 973 K show single-variant structure with perpendicularc-axis orientation, while multivariant ordered domains are frequently observed for specimens annealed at 873 K. It was found that the (001) facets of the multivariant particles are terminated by Co atoms rather than by Pt, presumably due to the intermediate stage of atomic ordering. Coexistence of single-variant particles and multivariant particles in the same specimen film suggests that the interfacial energy between variant domains be small enough to form such structural domains in a nanoparticle as small as 4 nm in diameter.
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Gou, Chang Xing, Xin Ling Shi, Bao Lei Li, Tian Song Li, Ya Jie Liu, Lan Juan Liu, Qin Hu Zhang, and Jing Jing Li. "Multivariant Optimization Algorithm with Absorption for Multimodal Optimization." Applied Mechanics and Materials 483 (December 2013): 458–64. http://dx.doi.org/10.4028/www.scientific.net/amm.483.458.

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Multivariant Optimization Algorithm (MOA) is a newly proposed algorithm and its multi-group property make it a perfect choice for multimodal optimization. In this paper, an absorption mechanism was introduced into MOA to remove the redundant search information that stored in the structure and we named the proposed algorithm as the Absorption Multivariant Optimization Algorithm (AMOA). With this mechanism, more search information will be captured and the search information will be shared more effectively. The proposed algorithm is tested on nine benchmark functions to compare the performance with standard MOA under the same condition. The experimental results suggest that the improved algorithm can keep high success rate as the number of global optima increase and the output result would be more stable.
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17

Chaoqi, Ren, Wang Qiang, and Zhang Dingwu. "Multivariant Performance Study of Vaneless Integral Particle Separator." Procedia Engineering 24 (2011): 743–48. http://dx.doi.org/10.1016/j.proeng.2011.11.2729.

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18

Pei, York, Gavril Hercz, Celia Greenwood, Gino Segre, Arif Manuel, Carl Saiphoo, Stanley Fenton, and Donald Sherrard. "Risk factors for renal osteodystrophy: A multivariant analysis." Journal of Bone and Mineral Research 10, no. 1 (December 3, 2009): 149–56. http://dx.doi.org/10.1002/jbmr.5650100121.

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19

Shu, Y. C., and J. H. Yen. "Multivariant model of martensitic microstructure in thin films." Acta Materialia 56, no. 15 (September 2008): 3969–81. http://dx.doi.org/10.1016/j.actamat.2008.04.018.

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20

Boyne, D. M., E. K. Tan, and S. A. Dregia. "Misfit dislocations in multivariant epitaxy of silver on copper." Proceedings, annual meeting, Electron Microscopy Society of America 49 (August 1991): 592–93. http://dx.doi.org/10.1017/s0424820100087276.

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Misfit dislocations are known to accommodate disregistry at interfaces which separate crystals of different orientation and/or lattice parameter. Most studies of misfit dislocations have addressed epitaxial bicrystals in ‘parallel’ orientation relationships (OR), e.g, cube-on-cube in fee systems. Recent work, though, has demonstrated that non-parallel epitaxial ORs are energetically favored in some systems, e.g, (111)/(001) in fee systems of large misfit (∼12%). In these systems coexistence of equivalent variants of the same OR gives rise to ‘multivariant epitaxy’ wherein a film grows in a special polycrystalline form on a monocrystalline substrate. In this study we examine the accommodation of misfit in the special structures produced by multivariant epitaxy of (111)Ag on (001)Cu.
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21

Vassilyev, Stanislav Nikolaevich, Mikhail Aleksandrovich Loktev, Aleksey Vyacheslavovich Tolok, Natalya Borisovna Tolok, and Sergey Alexandrovich Ulyanov. "Route Planning in 3d Environment with a Multivariant Model." SPIIRAS Proceedings 2, no. 45 (April 4, 2016): 5. http://dx.doi.org/10.15622/sp.45.1.

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22

Tomaszewska, B., M. Tyszer, M. Bodzek, and M. Rajca. "The concept of multivariant use of geothermal water concentrates." DESALINATION AND WATER TREATMENT 128 (2018): 179–86. http://dx.doi.org/10.5004/dwt.2018.22632.

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23

Dean, James. "A Multivariant Assessment and Treatment Technique for Alcohol Problems." International Journal of the Addictions 20, no. 8 (January 1985): 1281–90. http://dx.doi.org/10.3109/10826088509056364.

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24

Samuels, David C., Jennifer E. Below, Scott Ness, Hui Yu, Shuguang Leng, and Yan Guo. "Alternative Applications of Genotyping Array Data Using Multivariant Methods." Trends in Genetics 36, no. 11 (November 2020): 857–67. http://dx.doi.org/10.1016/j.tig.2020.07.006.

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Arany, Andrea, Otto Meth-Cohn, and Miklós Nyerges. "DOTTADs – readily made novel metal ligands with multivariant functionality." Organic & Biomolecular Chemistry 1, no. 9 (April 3, 2003): 1545–51. http://dx.doi.org/10.1039/b301209n.

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26

Kvederytė, N., E. K. Zavadskas, and A. Kaklauskas. "MULTIPLE CRITERIA MULTIVARIANT DESIGN OF A BUILDING LIFE CYCLE." Statyba 6, no. 2 (January 2000): 128–42. http://dx.doi.org/10.1080/13921525.2000.10531576.

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Love, Tanzy M. T., Sung Yong Park, Elena E. Giorgi, Wendy J. Mack, Alan S. Perelson, and Ha Youn Lee. "SPMM: estimating infection duration of multivariant HIV-1 infections." Bioinformatics 32, no. 9 (December 31, 2015): 1308–15. http://dx.doi.org/10.1093/bioinformatics/btv749.

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Kaklauskas, Arturas, Edmundas Kazimieras Zavadskas, and Saulius Raslanas. "Multivariant design and multiple criteria analysis of building refurbishments." Energy and Buildings 37, no. 4 (April 2005): 361–72. http://dx.doi.org/10.1016/j.enbuild.2004.07.005.

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Jervis, R. E., M. M. C. Ko, Tian Junliang, and Liu Puling. "Multivariant analyses of trace element patterns for environmental tracking." Journal of Radioanalytical and Nuclear Chemistry Articles 169, no. 2 (March 1993): 363–79. http://dx.doi.org/10.1007/bf02042994.

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30

Winarna, Winarna, and Heri Santoso. "Karakteristik Emisi CO2 Tanah Gambut di Bawah Tegakan Kelapa Sawit." Jurnal Penelitian Kelapa Sawit 28, no. 1 (April 1, 2020): 41–50. http://dx.doi.org/10.22302/iopri.jur.jpks.v28i1.103.

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Penelitian ini bertujuan mengkaji karakteristik emisi CO2 dari tanah gambut di bawah tegakan kelapa sawit yang dihubungkan dengan faktor lingkungan spesifik lokasi penelitian di daerah Kabupaten Labuhan Batu, Sumatera Utara. Gas CO2 diambil dengan menggunakan close chamber technique, sedangkan konsentrasi gas tersebut diukur menggunakan portable micro Gas Chromatograph CP 4900. Faktor lingkungan sebagai variabel bebas yang diamati meliputi suhu tanah (TS), suhu atmosfer (TA), kelembaban tanah lapisan 0 – 5 cm (SM5), kelembaban tanah lapisan 0 – 30 cm (SM30), dan pH gambut (A). Model hubungan CO2 dengan faktor dianalisis menggunakan analisis korelasi Spearman dan regresi multivariant. Analisis regresi multivariant dilakukan menggunakan regresi stepwise untuk mengetahui faktor lingkungan yang mempunyai hubungan dengan CO2 pada taraf beda nyata 5%. Analisis variance inflation factor (vif) dilakukan untuk melihat multicollinearity dari model regresi multivariant. Berdasarkan analisis korelasi Spearman, faktor lingkungan A (pH gambut) mempunyai koefisiensi korelasi yang tegolong sedang (R = 0.637). Hasil regresi stepwise menunjukkan bahwa emisi CO2 nyata dipengaruhi oleh kelembaban tanah SM30 dan A (n = 216; p < 0.05) dengan nilai vif <3. Regresi multivariant, menghasilkan persamaan terbaik (R2 = 0.420) dimana emisi CO2 = 7.394*A – 0.008*SM30 – 16.659. Secara parsial, penelitian ini juga memperoleh model hubungan emisi CO2 dengan kelembaban tanah gambut, dimana emisi CO2 tertinggi dicapai pada kondisi kelembaban tanah sekitar kapasitas lapang (354 – 376% w w-1) dan menurun dengan meningkatnya kelembaban tanah di atas kadar air kapasitas lapangan. Emisi CO2 tanah gambut mengalami penurunan pada kondisi tanah gambut mengering di bawah zone kadar air kritis, hal ini karena hidrofobisitas tanah gambut meningkat.
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Yao, Lili, Wenxiu Yang, Huiling Liu, Jianbo Jia, Guohua Wu, Dan Liu, Ting Liu, Taixing Tan, and Cheng Wang. "Synthesis and ORR electrocatalytic activity of mixed Mn–Co oxides derived from divalent metal-based MIL-53 analogues." Dalton Transactions 46, no. 44 (2017): 15512–19. http://dx.doi.org/10.1039/c7dt03386a.

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Manik, Ngarap Im. "Perancangan Program Peramalan Kanal Banjir Barat Jakarta Menggunakan Autoregresi Multivariant." ComTech: Computer, Mathematics and Engineering Applications 3, no. 1 (June 1, 2012): 186. http://dx.doi.org/10.21512/comtech.v3i1.2402.

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This paper discusses the design of computer programs that is able to discern the characteristics description of water surface elevation data in Manggarai water gate, which variable is the most influential on the water surface elevation model and find a proper flood forecasting model using multivariate autoregressive model. The result of this study is able to assist the water gate officer in delivering early warning, prevention and anticipation of flood countermeasure. The forecast equation model obtained is Yt = 109,.7828 + 0,9291 CHt-6 – 24,484 T t-2 – 0,06245 PM t-2 + 1,4706 KB t-2 in which temperature and water surface elevation is a variable that owns the strongest correlation. This variable owns negative correlation which means that if the temperature falls, the water levels will rise. The coefficient of determination has a value of R2 = 0.4056 and the Durbin Watson statistics for DW = 0.7429.
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Vigliotti, Andrea. "Finite Element Implementation of a Multivariant Shape Memory Alloy Model." Journal of Intelligent Material Systems and Structures 21, no. 7 (March 5, 2010): 685–99. http://dx.doi.org/10.1177/1045389x10364861.

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Huang, Miinshiou, and L. C. Brinson. "A Multivariant model for single crystal shape memory alloy behavior." Journal of the Mechanics and Physics of Solids 46, no. 8 (August 1998): 1379–409. http://dx.doi.org/10.1016/s0022-5096(97)00080-x.

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Huang, Miinshiou, Xiujie Gao, and L. Catherine Brinson. "A multivariant micromechanical model for SMAs Part 2. Polycrystal model." International Journal of Plasticity 16, no. 10-11 (January 2000): 1371–90. http://dx.doi.org/10.1016/s0749-6419(00)00014-0.

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Diamond-Friedman, Cassandra. "A Multivariant Model of Alcoholism Specific to Gay-Lesbian Populations." Alcoholism Treatment Quarterly 7, no. 2 (September 14, 1990): 111–17. http://dx.doi.org/10.1300/j020v07n02_07.

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Sysoev, Yu S., V. V. Krivin, and V. G. Beketov. "Use of distribution mixtures with stochastic identification of multivariant objects." Measurement Techniques 50, no. 6 (June 2007): 575–80. http://dx.doi.org/10.1007/s11018-007-0113-4.

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Li, Baolei, Ming Hui, Yongsheng Zhu, Mingyue Cui, Meng Zhang, Yiyuan Cheng, and Tao Hai. "A path planner based on multivariant optimization algorithm with absorption." International Journal of Machine Learning and Cybernetics 8, no. 6 (June 18, 2016): 1743–50. http://dx.doi.org/10.1007/s13042-016-0555-6.

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Adiguzel, Osman. "Self-Accommodating Nature of Martensite Formation in Shape Memory Alloys." Solid State Phenomena 213 (March 2014): 114–18. http://dx.doi.org/10.4028/www.scientific.net/ssp.213.114.

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Shape memory effect is a peculiar property exhibited by certain alloy system. This behavior is facilitated by martensitic transformation, and shape memory properties are intimately related to the microstructures of alloys; in particular, the morphology and orientation relationship between the various martensite variants. Martensitic transformation occurs in thermal manner, on cooling the materials from high temperature parent phase region. Thermal induced martensite called self-accommodated martensite or multivariant martensite occurs as multivariant martensite in self-accommodating manner and consists of lattice twins. Shape memory alloys are deformed in low temperature martensitic phase condition, and deformation proceeds through a martensite variant reorientation. Copper based alloys exhibit this property in metastable β - phase region.
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Wang Mingyu, 王明雨, 石世宏 Shi Shihong, 石拓 Shi Tuo, 傅戈雁 Fu Geyan, 庞祎帆 Pang Yifan, 余司琪 Yu Siqi, and 龚燕琪 Gong Yanqi. "基于光内送粉多元扭曲结构的随形离散分层成形研究." Chinese Journal of Lasers 48, no. 10 (2021): 1002114. http://dx.doi.org/10.3788/cjl202148.1002114.

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Vucetic, Andja, Olivera Petrovic-Obradovic, and Lj Z. Stanisavljevic. "The morphological variation of Myzus persicae (Hemiptera: Aphididae) from peach and tobacco in Serbia and Montenegro." Archives of Biological Sciences 62, no. 3 (2010): 767–74. http://dx.doi.org/10.2298/abs1003767v.

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Multivariate morphometric analysis was used to compare nine characteristics of 47 populations of Myzus persicae (Sulzer) originating from two host-plants, peach and tobacco, from 13 localities in 2004 and 34 localities in 2005, in Serbia and Montenegro. Multivariant discriminant analysis showed there to be a distinct discrimination between the populations from the peach and tobacco host-plants. The most important discrimination characteristics are the ultimate rostral segment length and processes terminalis length, which are greater in the aphids from tobacco than in those from peach. This is the first indication that in this part of Europe there are two subspecies: M. persicae (Sulzer) and M. persicae nicotianae Blackman. .
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Talha, Khalid, Alamgir, Naeem Ahmed, Lin-Hua Xie, Xin Zhang, and Jian-Rong Li. "Construction of a zeolite A-type multivariate metal–organic framework for selective sensing of Fe3+ and Cr2O72−." CrystEngComm 23, no. 28 (2021): 4923–29. http://dx.doi.org/10.1039/d1ce00610j.

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Abdelmageed, Shaima, and Mohammed Elmusrati. "Phonocardiogram Based Diagnosis Using Machine Learning : Parametric Estimation with Multivariant Classification." Bioscience & Engineering : An International Journal 5, no. 1/2/3/4 (October 30, 2018): 01–06. http://dx.doi.org/10.5121/bioej.2018.5401.

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Kalinowski, Krzysztof, Cezary Grabowik, Wojciech M. Kempa, and Iwona Paprocka. "The Graph Representation of Multivariant and Complex Processes for Production Scheduling." Advanced Materials Research 837 (November 2013): 422–27. http://dx.doi.org/10.4028/www.scientific.net/amr.837.422.

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The paper presents a solution useful for production scheduling in the context of the constraints arising from the complexity of the structures of realized processes and possibilities of use variants of their implementation. The proposed method uses both activity on arc and activity on node types of graph for describing multivariant processes. Representation of complex (composed) structures the AND-OR graphs are used that combines both conjunction and disjunction relations between nodes.
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Suljkanovic, Midhat, Milovan Jotanovic, Elvis Ahmetovic, and Nidret Ibric. "Multivariant simulator for vacuum cooling processes of three component electrolyte systems." Chemical Industry 64, no. 1 (2010): 21–33. http://dx.doi.org/10.2298/hemind1001021s.

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In this paper, a computer aided analysis and synthesis of the crystallization processes from multicomponent electrolyte systems were studied. In addition, the vacuum crystallization processes with adiabatic cooling of the system are presented. The cooling process of a multicomponent electrolyte system can be considered as a process with the concentration of the system and/or the crystallization of the solid phase from the system. Requirements for multivariant options of the process simulator are the result of practical needs in the design of new processes or the improvement of exploitation processes. According to this, there are needs for a simulation of a simple flashing of the system as well as for the vacuum cooling crystallization processes with the cyclic structure. The possibilities of the created process simulator are illustrated on three component electrolyte systems. Application of the process simulator for any other electrolyte systems requires only an update of the thermodynamic model, and physico-chemical properties related to electrolyte system.
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Arany, Andrea, Otto Meth-Cohn, and Miklós Nyerges. "DOTTADs—readily made novel metal ligands with multivariant functionality and chirality." Tetrahedron Letters 41, no. 7 (February 2000): 1115–18. http://dx.doi.org/10.1016/s0040-4039(99)02243-1.

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47

Reddy, Samala Murali Mohan, George Augustine, Niraikulam Ayyadurai, and Ganesh Shanmugam. "Biocytin-Based pH-Stimuli Responsive Supramolecular Multivariant Hydrogelator for Potential Applications." ACS Applied Bio Materials 1, no. 5 (October 10, 2018): 1382–88. http://dx.doi.org/10.1021/acsabm.8b00340.

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48

Guo, Qingli, Jing Ye, Yu Hu, Guohe Zhang, Xiaowei Li, and Huawei Li. "MultiPAD: A Multivariant Partition-Based Method for Audio Adversarial Examples Detection." IEEE Access 8 (2020): 63368–80. http://dx.doi.org/10.1109/access.2020.2985231.

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49

Gupta, Mahesh, Tai H. Kwon, and Yogesh Jaluria. "Multivariant finite elements for three-dimensional simulation of viscous incompressible flows." International Journal for Numerical Methods in Fluids 14, no. 5 (March 15, 1992): 557–85. http://dx.doi.org/10.1002/fld.1650140505.

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50

Yongming, Lou, and Börje Johansson. "A multivariant interpolation routine for a random distribution of data points." Computer Physics Communications 70, no. 2 (June 1992): 389–400. http://dx.doi.org/10.1016/0010-4655(92)90201-9.

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