In this article, we study the degree-based topological indices in a random polyomino chain. The key purpose of this manuscript is to obtain the asymptotic distribution, expected value and variance for the degree-based topological indices in a random polyomino chain by using a martingale approach. Consequently, we compute the degree-based topological indices in a polyomino chain, hence some known results from the existing literature about polyomino chains are obtained as corollaries. Also, in order to apply the results, we obtain the expected value of several degree-based topological indices such as Sombor, Forgotten, Zagreb, atom-bond-connectivity, Randić and geometric-arithmetic index of a random polyomino chain.
Citation: Saylé C. Sigarreta, Saylí M. Sigarreta, Hugo Cruz-Suárez. On degree–based topological indices of random polyomino chains[J]. Mathematical Biosciences and Engineering, 2022, 19(9): 8760-8773. doi: 10.3934/mbe.2022406
[1] | Lernik Asserian, Susan E. Luczak, I. G. Rosen . Computation of nonparametric, mixed effects, maximum likelihood, biosensor data based-estimators for the distributions of random parameters in an abstract parabolic model for the transdermal transport of alcohol. Mathematical Biosciences and Engineering, 2023, 20(11): 20345-20377. doi: 10.3934/mbe.2023900 |
[2] | Keenan Hawekotte, Susan E. Luczak, I. G. Rosen . Deconvolving breath alcohol concentration from biosensor measured transdermal alcohol level under uncertainty: a Bayesian approach. Mathematical Biosciences and Engineering, 2021, 18(5): 6739-6770. doi: 10.3934/mbe.2021335 |
[3] | Gigi Thomas, Edward M. Lungu . A two-sex model for the influence of heavy alcohol consumption on the spread of HIV/AIDS. Mathematical Biosciences and Engineering, 2010, 7(4): 871-904. doi: 10.3934/mbe.2010.7.871 |
[4] | Salih Djillali, Soufiane Bentout, Tarik Mohammed Touaoula, Abdessamad Tridane . Global dynamics of alcoholism epidemic model with distributed delays. Mathematical Biosciences and Engineering, 2021, 18(6): 8245-8256. doi: 10.3934/mbe.2021409 |
[5] | Hai-Feng Huo, Shuang-Lin Jing, Xun-Yang Wang, Hong Xiang . Modelling and analysis of an alcoholism model with treatment and effect of Twitter. Mathematical Biosciences and Engineering, 2019, 16(5): 3561-3622. doi: 10.3934/mbe.2019179 |
[6] | Ridouan Bani, Rasheed Hameed, Steve Szymanowski, Priscilla Greenwood, Christopher M. Kribs-Zaleta, Anuj Mubayi . Influence of environmental factors on college alcohol drinking patterns. Mathematical Biosciences and Engineering, 2013, 10(5&6): 1281-1300. doi: 10.3934/mbe.2013.10.1281 |
[7] | Peixian Zhuang, Xinghao Ding, Jinming Duan . Subspace-based non-blind deconvolution. Mathematical Biosciences and Engineering, 2019, 16(4): 2202-2218. doi: 10.3934/mbe.2019108 |
[8] | Biyun Hong, Yang Zhang . Research on the influence of attention and emotion of tea drinkers based on artificial neural network. Mathematical Biosciences and Engineering, 2021, 18(4): 3423-3434. doi: 10.3934/mbe.2021171 |
[9] | Colette Calmelet, John Hotchkiss, Philip Crooke . A mathematical model for antibiotic control of bacteria in peritoneal dialysis associated peritonitis. Mathematical Biosciences and Engineering, 2014, 11(6): 1449-1464. doi: 10.3934/mbe.2014.11.1449 |
[10] | Piotr Klejment . Application of supervised machine learning as a method for identifying DEM contact law parameters. Mathematical Biosciences and Engineering, 2021, 18(6): 7490-7505. doi: 10.3934/mbe.2021370 |
In this article, we study the degree-based topological indices in a random polyomino chain. The key purpose of this manuscript is to obtain the asymptotic distribution, expected value and variance for the degree-based topological indices in a random polyomino chain by using a martingale approach. Consequently, we compute the degree-based topological indices in a polyomino chain, hence some known results from the existing literature about polyomino chains are obtained as corollaries. Also, in order to apply the results, we obtain the expected value of several degree-based topological indices such as Sombor, Forgotten, Zagreb, atom-bond-connectivity, Randić and geometric-arithmetic index of a random polyomino chain.
[1] |
Z. Shao, A. Jahanbani, S. M. Sheikholeslami, Multiplicative topological indices of molecular structure in anticancer drugs, Polycycl. Aromat. Comp., 42 (2020), 475–488. https://doi.org/10.1080/10406638.2020.1743329 doi: 10.1080/10406638.2020.1743329
![]() |
[2] |
C. P. Li, C. Zhonglin, M. Munir, K. Yasmin, J. B. Liu, M-polynomials and topological indices of linear chains of benzene, napthalene and anthracene, Math. Biosci. Eng., 17 (2020), 2384–2398. https://10.3934/mbe.2020127 doi: 10.3934/mbe.2020127
![]() |
[3] |
A. Mehler, A. Lücking, P. Weiß, A network model of interpersonal alignment in dialog, Entropy, 12 (2010), 1440–1483. https://doi.org/10.3390/e12061440 doi: 10.3390/e12061440
![]() |
[4] |
J. J. Pineda-Pineda, C. T. Martínez-Martínez, J. A. Méndez-Bermúdez, J. Muñoz-Rojas, J. M. Sigarreta, Application of bipartite networks to the study of water quality, Sustainability, 12 (2020). https://doi.org/10.3390/su12125143 doi: 10.3390/su12125143
![]() |
[5] |
I. Gutman, Degree-based topological indices, Croat. Chem. Acta, 86 (2013), 351–361. http://dx.doi.org/10.5562/cca2294 doi: 10.5562/cca2294
![]() |
[6] |
B. Furtula, I. Gutman, A forgotten topological index, J. Math. Chem., 53 (2015), 1184–1190. https://doi.org/10.1007/s10910-015-0480-z doi: 10.1007/s10910-015-0480-z
![]() |
[7] |
W. Gao, W. Wang, M. K. Jamil, M. R. Farahani, Electron energy studying of molecular structures via forgotten topological index computation, J. Chem-NY, 2016 (2016), 1–7. https://doi.org/10.1155/2016/1053183 doi: 10.1155/2016/1053183
![]() |
[8] |
D. Vukičević, B. Furtula, Topological index based on the ratios of geometrical and arithmetical means of end-vertex degrees of edges, J. Math. Chem., 46 (2009), 1369–1376. https://doi.org/10.1007/s10910-009-9520-x doi: 10.1007/s10910-009-9520-x
![]() |
[9] | E. Estrada, L. Torres, L. Rodriguez, I. Gutman, An atom-bond connectivity index: modelling the enthalpy of formation of alkanes, Indian J. Chem. 37A (1998), 849–855. http://nopr.niscpr.res.in/handle/123456789/40308 |
[10] | S. W. Golomb, Polyominoes, 2 edition, Princeton University Press, 1994. http://doi.org/10.1515/9780691215051 |
[11] | X. Zhou, H. Zhang, A minimax result for perfect matchings of a polyomino graph, Discret. Appl. Math., 06 (2016), 165–171. https://doi.org/10.1016/j.dam.2016.01.033 |
[12] |
Y. Lin, F. Zhang, A linear algorithm for a perfect matching in polyomino graphs, Theor. Comput. Sci., 675 (2017), 82–88. https://doi.org/10.1016/j.tcs.2017.02.028 doi: 10.1016/j.tcs.2017.02.028
![]() |
[13] | A. Pegu, B. Deka, I. J. Gogoi, A. Bharali, Two generalized topological indices of some graph structures, J. Math. Comput. Sci., 11 (2021), 5549–5564. |
[14] |
N. Iqbal, A. A. Bhatti, A. Ali, A. M. Alanazi, On bond incident connection indices of polyomino and benzenoid chains, Polycycl. Aromat. Comp., (2022), 1–8. https://doi.org/10.1080/10406638.2022.2035414 doi: 10.1080/10406638.2022.2035414
![]() |
[15] |
M. Cancan, M. Imran, S. Akhter, M. K. Siddiqui, M. F. Hanif, Computing forgotten topological index of extremal cactus chains, AMNS, 6 (2021), 439–446. https://doi.org/10.2478/amns.2020.2.00075 doi: 10.2478/amns.2020.2.00075
![]() |
[16] |
M. K. Jamil, S. Ahmed, M. I. Qureshi, A. Fahad, Zagreb connection index of drugs related chemical structures, Biointerface Res. Appl. Chem, 11 (2020), 11920–11930. https://doi.org/10.33263/briac114.1192011930 doi: 10.33263/briac114.1192011930
![]() |
[17] | A. Ali, B. Furtula, I. Gutman, D. Vukicevic, Augmented Zagreb index: extremal results and bounds, MATCH Commun. Math. Comput. Chem., 85 (2021), 211–244. |
[18] | Z. Yarahmadi, Finding extremal total irregularity of polyomino chain by transformation method, J. New Res. Math., 7 (2021), 141–150. |
[19] |
A. Ali, K. C. Das, D. Dimitrov, B. Furtula, Atom–bond connectivity index of graphs: a review over extremal results and bounds, Discrete Math. Lett., 5(2021), 68–93. https://doi.org/10.47443/dml.2020.0069 doi: 10.47443/dml.2020.0069
![]() |
[20] |
R. Cruz, J. Rada, Extremal polyomino chains of VDB topological indices, Appl. Math. Sci, 9 (2015), 5371–5388. http://dx.doi.org/10.12988/ams.2015.54368 doi: 10.12988/ams.2015.54368
![]() |
[21] |
J. Rada, The linear chain as an extremal value of VDB topological indices of polyomino chains, Appl. Math. Sci, 8 (2014), 5133–5143. http://dx.doi.org/10.12988/ams.2014.46507 doi: 10.12988/ams.2014.46507
![]() |
[22] | J. Rada, The zig-zag chain as an extremal value of VDB topological indices of polyomino chains, J. Combin. Math. Combin. Comput., 96 (2016), 103–111. |
[23] |
T. Wu, H. Lü, X. Zhang, Extremal matching energy of random polyomino chains, Entropy, 19 (2017), 684. https://doi.org/10.3390/e19120684 doi: 10.3390/e19120684
![]() |
[24] |
S. Wei, W. C. Shiu, Enumeration of Wiener indices in random polygonal chains, J. Math. Anal. Appl., 469 (2019), 537–548. https://doi.org/10.1016/j.jmaa.2018.09.027 doi: 10.1016/j.jmaa.2018.09.027
![]() |
[25] |
C. Xiao, H. Chen, Dimer coverings on random polyomino chains, Z. Naturforsch. A, 70 (2015), 465–470. https://doi.org/10.1515/zna-2015-0121 doi: 10.1515/zna-2015-0121
![]() |
[26] |
S. Wei, X. Ke, F. Lin, Perfect matchings in random polyomino chain graphs, J. Math. Chem., 54 (2016), 690–697. https://doi.org/10.1007/s10910-015-0580-9 doi: 10.1007/s10910-015-0580-9
![]() |
[27] |
J. Li, W. Wang, The (degree-) Kirchhoff indices in random polygonal chains, Discret. Appl. Math., 304 (2021), 63–75. https://doi.org/10.1016/j.dam.2021.06.020 doi: 10.1016/j.dam.2021.06.020
![]() |
[28] |
T. Došlić, T. Réti, D. Vukičević, On the vertex degree indices of connected graphs, Chem. Phys. Lett., 512 (2011), 283–286. https://doi.org/10.1016/j.cplett.2011.07.040 doi: 10.1016/j.cplett.2011.07.040
![]() |
[29] | P. Hall, C. C. Heyde, Martingale limit theory and its Application, Academic press, New York, 2014. |
[30] |
A. Ali, Z. Raza, A. A. Bhatti, Bond incident degree (BID) indices of polyomino chains: A unified approach, Appl. Math. Comput., 287 (2016), 28–37. https://doi.org/10.1016/j.amc.2016.04.012 doi: 10.1016/j.amc.2016.04.012
![]() |
[31] |
J. Buragohain, B. Deka, A. Bharali, A generalized ISI index of some chemical structures, J. Mol. Struct., 1208 (2020), 28–37. https://doi.org/10.1016/j.molstruc.2020.127843 doi: 10.1016/j.molstruc.2020.127843
![]() |
[32] |
Y. C. Kwun, A. Farooq, W. Nazeer, Z. Zahid, S. Noreen, S. M. Kang, Computations of the M-polynomials and degree-based topological indices for dendrimers and Polyomino Chains, Int. J. Anal. Chem., 2018 (2018). https://doi.org/10.1155/2018/1709073 doi: 10.1155/2018/1709073
![]() |
[33] |
A. Farooq, M. Habib, A. Mahboob, W. Nazeer, S. M. Kang, Zagreb polynomials and redefined Zagreb indices of dendrimers and Polyomino Chains, Open Chem., 17 (2019), 1374–1381. https://doi.org/10.1515/chem-2019-0144 doi: 10.1515/chem-2019-0144
![]() |
[34] | J. Yang, F. Xia, S. Chen, On sum-connectivity index of polyomino chains, Appl. Math. Sci, 5 (2011), 267–271. |
[35] | J. Yang, F. Xia, S. Chen, On the Randić index of polyomino chains, Appl. Math. Sci, 5 (2011), 255–260. |
[36] | W. Gao, L. Yan, L. Shi, Generalized Zagreb index of polyomino chains and nanotubes, Optoelectron. Adv. Mater. Rapid Commun., 11 (2017), 119–124. |
[37] | S. Hayat, S. Ahmad, H. M. Umair, W. Shaohui, Distance property of chemical graphs, Hacettepe J. Math. Stat., 47 (2018), 1071–1093. |
1. | Yan Wang, Daniel J. Fridberg, Robert F. Leeman, Robert L. Cook, Eric C. Porges, Wrist-worn alcohol biosensors: Strengths, limitations, and future directions, 2019, 81, 07418329, 83, 10.1016/j.alcohol.2018.08.013 | |
2. | John D. Roache, Tara E. Karns-Wright, Martin Goros, Nathalie Hill-Kapturczak, Charles W. Mathias, Donald M. Dougherty, Processing transdermal alcohol concentration (TAC) data to detect low-level drinking, 2019, 81, 07418329, 101, 10.1016/j.alcohol.2018.08.014 | |
3. | Melike Sirlanci, I. Gary Rosen, Tamara L. Wall, Susan E. Luczak, Applying a novel population-based model approach to estimating breath alcohol concentration (BrAC) from transdermal alcohol concentration (TAC) biosensor data, 2019, 81, 07418329, 117, 10.1016/j.alcohol.2018.09.005 | |
4. | Jian Li, Susan E. Luczak, I. G. Rosen, Comparing a distributed parameter model-based system identification technique with more conventional methods for inverse problems, 2019, 27, 0928-0219, 703, 10.1515/jiip-2018-0006 | |
5. | Alastair van Heerden, Mark Tomlinson, Sarah Skeen, Charles Parry, Kendal Bryant, Mary Jane Rotheram-Borus, Innovation at the Intersection of Alcohol and HIV Research, 2017, 21, 1090-7165, 274, 10.1007/s10461-017-1926-z | |
6. | Melike Sirlanci, Susan Luczak, I. G. Rosen, 2017, Approximation and convergence in the estimation of random parameters in linear holomorphic semigroups generated by regularly dissipative operators, 978-1-5090-5992-8, 3171, 10.23919/ACC.2017.7963435 | |
7. | Kelly Egmond, Cassandra J. C. Wright, Michael Livingston, Emmanuel Kuntsche, Wearable Transdermal Alcohol Monitors: A Systematic Review of Detection Validity, and Relationship Between Transdermal and Breath Alcohol Concentration and Influencing Factors, 2020, 44, 0145-6008, 1918, 10.1111/acer.14432 | |
8. | Christian S. Hendershot, Christina N. Nona, A Review of Developmental Considerations in Human Laboratory Alcohol Research, 2017, 4, 2196-2952, 364, 10.1007/s40429-017-0173-8 | |
9. | Nancy P. Barnett, Mark A. Celio, Jennifer W. Tidey, James G. Murphy, Suzanne M. Colby, Robert M. Swift, A preliminary randomized controlled trial of contingency management for alcohol use reduction using a transdermal alcohol sensor, 2017, 112, 0965-2140, 1025, 10.1111/add.13767 | |
10. | Melike Sirlanci, I G Rosen, Susan E Luczak, Catharine E Fairbairn, Konrad Bresin, Dahyeon Kang, Deconvolving the input to random abstract parabolic systems: a population model-based approach to estimating blood/breath alcohol concentration from transdermal alcohol biosensor data, 2018, 34, 0266-5611, 125006, 10.1088/1361-6420/aae791 | |
11. | Susan E. Luczak, Ashley L. Hawkins, Zheng Dai, Raphael Wichmann, Chunming Wang, I.Gary Rosen, Obtaining continuous BrAC/BAC estimates in the field: A hybrid system integrating transdermal alcohol biosensor, Intellidrink smartphone app, and BrAC Estimator software tools, 2018, 83, 03064603, 48, 10.1016/j.addbeh.2017.11.038 | |
12. | Melike Sirlanci, Susan E. Luczak, Catharine E. Fairbairn, Dahyeon Kang, Ruoxi Pan, Xin Yu, I. Gary Rosen, Estimating the distribution of random parameters in a diffusion equation forward model for a transdermal alcohol biosensor, 2019, 106, 00051098, 101, 10.1016/j.automatica.2019.04.026 | |
13. | Sina Kianersi, Maya Luetke, Jon Agley, Ruth Gassman, Christina Ludema, Molly Rosenberg, Validation of transdermal alcohol concentration data collected using wearable alcohol monitors: A systematic review and meta-analysis, 2020, 216, 03768716, 108304, 10.1016/j.drugalcdep.2020.108304 | |
14. | Catharine E. Fairbairn, I. Gary Rosen, Susan E. Luczak, Walter J. Venerable, Estimating the quantity and time course of alcohol consumption from transdermal alcohol sensor data: A combined laboratory-ambulatory study, 2019, 81, 07418329, 111, 10.1016/j.alcohol.2018.08.015 | |
15. | John D. Clapp, Danielle R. Madden, Sheila Pakdaman, Drinking with Friends: Measuring the Two-week Ecology of Drinking Behaviors, 2022, 46, 1087-3244, 96, 10.5993/AJHB.46.2.1 | |
16. | Clemens Oszkinat, Susan E. Luczak, I. G. Rosen, 2022, Physics-Informed Learning: Distributed Parameter Systems, Hidden Markov Models, and the Viterbi Algorithm, 978-1-6654-5196-3, 266, 10.23919/ACC53348.2022.9867145 | |
17. | Baichen Li, R. Scott Downen, Quan Dong, Nam Tran, Maxine LeSaux, Andrew C. Meltzer, Zhenyu Li, A Discreet Wearable IoT Sensor for Continuous Transdermal Alcohol Monitoring—Challenges and Opportunities, 2021, 21, 1530-437X, 5322, 10.1109/JSEN.2020.3030254 | |
18. | Clemens Oszkinat, Susan E. Luczak, I. Gary Rosen, An abstract parabolic system-based physics-informed long short-term memory network for estimating breath alcohol concentration from transdermal alcohol biosensor data, 2022, 34, 0941-0643, 18933, 10.1007/s00521-022-07505-w | |
19. | Mengsha Yao, Susan E. Luczak, I. Gary Rosen, Tracking and blind deconvolution of blood alcohol concentration from transdermal alcohol biosensor data: A population model-based LQG approach in Hilbert space, 2023, 147, 00051098, 110699, 10.1016/j.automatica.2022.110699 | |
20. | Keenan Hawekotte, Susan E. Luczak, I. G. Rosen, Deconvolving breath alcohol concentration from biosensor measured transdermal alcohol level under uncertainty: a Bayesian approach, 2021, 18, 1551-0018, 6739, 10.3934/mbe.2021335 | |
21. | Clemens Oszkinat, Tianlan Shao, Chunming Wang, I G Rosen, Allison D Rosen, Emily B Saldich, Susan E Luczak, Blood and breath alcohol concentration from transdermal alcohol biosensor data: estimation and uncertainty quantification via forward and inverse filtering for a covariate-dependent, physics-informed, hidden Markov model* , 2022, 38, 0266-5611, 055002, 10.1088/1361-6420/ac5ac7 | |
22. | Mengsha Yao, Susan E. Luczak, Emily B. Saldich, I. Gary Rosen, A population model‐based linear‐quadratic Gaussian compensator for the control of intravenously infused alcohol studies and withdrawal symptom prophylaxis using transdermal sensing, 2022, 0143-2087, 10.1002/oca.2934 | |
23. | Bob M. Lansdorp, Flux-Type versus Concentration-Type Sensors in Transdermal Measurements, 2023, 13, 2079-6374, 845, 10.3390/bios13090845 | |
24. | Kyla-Rose Walden, Emily B. Saldich, Georgia Wong, Haoxing Liu, Chunming Wang, I. Gary Rosen, Susan E. Luczak, 2023, 79, 9780443193866, 271, 10.1016/bs.plm.2023.06.002 | |
25. | Clemens Oszkinat, Susan E. Luczak, I. G. Rosen, Uncertainty Quantification in Estimating Blood Alcohol Concentration From Transdermal Alcohol Level With Physics-Informed Neural Networks, 2023, 34, 2162-237X, 8094, 10.1109/TNNLS.2022.3140726 | |
26. | Lernik Asserian, Susan E. Luczak, I. G. Rosen, Computation of nonparametric, mixed effects, maximum likelihood, biosensor data based-estimators for the distributions of random parameters in an abstract parabolic model for the transdermal transport of alcohol, 2023, 20, 1551-0018, 20345, 10.3934/mbe.2023900 | |
27. | J.M. Maestre, P. Chanfreut, L. Aarons, Constrained numerical deconvolution using orthogonal polynomials, 2024, 24058440, e24762, 10.1016/j.heliyon.2024.e24762 | |
28. | Mengsha Yao, Maria Allayioti, Emily B. Saldich, Georgia Y. Wong, Chunming Wang, Susan E. Luczak, I. G. Rosen, Real-time recursive estimation of, and uncertainty quantification for, breath alcohol concentration via LQ tracking control-based inverse filtering of transdermal alcohol biosensor signals, 2024, 2, 2994-7669, 38, 10.3934/ammc.2024003 | |
29. | Joseph C. Anderson, A new approach to modeling transdermal ethanol kinetics, 2024, 12, 2051-817X, 10.14814/phy2.70070 | |
30. | Mengsha Yao, Gary Rosen, 2025, 10.5772/intechopen.1010428 |