Volume-6 , Issue-2 , Apr 2019, ISSN 2348-4519 (Online) Go Back

• Open Access   Article

  On Secondary J-Normal Matrices

  K. Jaikumar, K.Sindhu1

  Research Paper | Isroset-Journal (IJSRMSS)

  Vol.6 , Issue.2 , pp.61-67, Apr-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i2.6167

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  Abstract
  In this paper we presented the ideas of secondary J-Normal matrices ( -normal matrix) and derived few properties of -normal matrices of twenty conditions is given.

  Key-Words / Index Term
  Secondary transpose, Conjugate Secondary transpose, - normal matrix, Symmetric matrix

  References
  [1] M. Ghasemi Kamalvand and P. Karimi Beiranvand, J-Conjugate-Normal Matrices, Sarajevo Journal of mathematics. Vol. 10, Issue 23, pp. 155-160, 2014.
  [2] H. Fassbender and Kh.D. Ikramov, Conjugate Normal Matrices A survey, Linear Algebra Appl., Vol. 429, pp. 1425 -1441, 2008.
  [3] Christian Mehl and Leiba Rodman, Classes of Normal Matrices in indefinite inner product, Linear Algebra Appl., Vol. 336, pp. 71-91, 2001.
  [4] Anna Lee, Secondary Symmetric, skew symmetric and orthogonal matrices, Periodica Mathematica Hungarica, Vol. 7, Issue 1, pp. 63-70, 1976.
  [5] Anna Lee, On s-symmetric, s-skew symmetric and s-orthogonal matrices, Periodica Mathematica Hungarica, Vol. 7, Issue 1, pp. 61-76, 1976.
  [6] S. Krishnamoorthy and K. Jaikumar, On s-orthogonal matrices, Global Journal of Computational Science and Mathematics, Vol. 1, Issue 1, pp. 1-8, 2011.
  [7] S. Krishnamoorthy and R. Vijayakumar, On s-normal matrices, Journal of Analysis and computation, Vol. 2, 2009.
  [8] S. Krishnamoorthy and A. Govindarasu, On Secondary Unitary Matrices, International Journal of Computational Science and Mathematics, Vol. 2, Issue 3, pp. 247-253, 2010).
  

  Citation
  K. Jaikumar, K.Sindhu1, "On Secondary J-Normal Matrices," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.2, pp.61-67, 2019

• Open Access   Article

  Wave Forces Due To Interaction of Water Wave with A Cylindrical Hollow Structure in Water of Finite Depth

  M. Hassan, P. Borah, M. Viswakarma

  Research Paper | Isroset-Journal (IJSRMSS)

  Vol.6 , Issue.2 , pp.68-72, Apr-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i2.6872

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  Abstract
  We consider a single floating hollow cylinder in water of finite depth and investigated associated diffraction problem due to interaction of water wave with hollow cylinderical structure. This device can be considered as wave energy device because a single hollow cylindrical structure represents a particular form of wave energy device (oscillating water column). We used the method of separation of variables to obtain the analytical expressions for the diffracted velocity potentials in clearly identified regions. By using the appropriate matching conditions along the virtual and physical boundaries between the regions, we obtained and then solved a system of linear equations for the unknown coefficients. We obtained wave forces which play a significant part for a floating structure. It is observed that the changes in radius and draft of the cylinder have significant effect on exciting forces. For higher range of frequencies, the exciting forces diminished and tends to zero. The values of exciting forces with various parameters are depicted graphically and compared with available results.

  Key-Words / Index Term
  Diffraction, Wave force, finite depth, virtual boundary, frequency

  References
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  [7] M. Hassan, S. N. Bora, “Exciting Forces for a Pair of Coaxial Hollow Cylinder and Bottom-mounted Cylinder in Water of Finite Depth”, Ocean Engineering, Vol. 50, pp. 38-43, 2012.
  [8] M. Hassan, S. N. Bora, “Exciting forces for a wave energy device consisting of a pair of coaxial cylinders in water of finite depth”, Journal of Marine Science and Application}, Vol. 12, pp. 315-324, 2013.
  [9] M. Rahman, D. D. Bhatta,”Second order wave forces on a pair of cylinders”, Canadian Application of Mathematics Quarterly, Vol. 1, Issue. 3, pp. 343-382, 1993.
  [10] Y. M. Shen, Y. H. Zhang, Y. G. You, “On the radiation and diffraction of linear water waves by a rectangular structure over a sill. Part I. Infinite domain of finite water depth”, Ocean Engineering, Vol. 32, pp. 1073-1097, 2005.
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  [14] R. C. MacCamy, R. A. Fuchs, “Wave Forces on Piles: A Diffraction Theory Technical”, Memo,No. 69 US Army Beach Erosion Board, pp.17, 1954.
  [15] A. Kumar and G. C. Sharma, “Performance and analysis of flow between Annular Space Surrounded by a Rotating Coaxial cylinder with Co-axial Cylindrical Porous Medium”, International Journal of Scientific Research in Computer Sciences and Engineering, Vol. 5, No.3, pp. 1-6, 2017.
  

  Citation
  M. Hassan, P. Borah, M. Viswakarma, "Wave Forces Due To Interaction of Water Wave with A Cylindrical Hollow Structure in Water of Finite Depth," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.2, pp.68-72, 2019

• Open Access   Article

  A Comment on gpα-Closed Sets in Topoogical Spaces

  J.B. Toranagatti

  Research Paper | Isroset-Journal (IJSRMSS)

  Vol.6 , Issue.2 , pp.73-74, Apr-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i2.7374

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  Abstract
  In this paper, we will show that the notations of generalized pre α-closed(gpα-closed) set and pre-closed set are equivalent.

  Key-Words / Index Term
  pre-closed sets, αspg-closed sets, gαb-closed sets,gpα-closed sets

  References
  [1] D. Andrijivic, “On semi pre open sets”, Mat. Vesnic, Vol 38 Issue 1, pp.24-32,1986.
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  [10] Palermo, Vol 19, pp.89-96, 1970.
  [11] H. Maki, J. Umehara, and T. Noiri, “Every topological space is pre-T1/2”, Mem. Fac. Sci. Kochi Univ. Math., Vol 17, pp.33-42, 1996.
  [12] A. S. Mashhour, M. E. Abd El-Monsef, and S. N. EL-Deeb, “ On pre-continuous and weak pre continuous mappings” , Proc. Math and Phys.Soc. Egypt, Vol 53, pp.47-53, 1982.
  [13] O,Njastad, “ On some classes of nearly open sets” , Pacific.J.Math., Vol 15, pp.961-970, 1965.
  [14] P.H. Patil and P.G. Patil, “ Generalized pre
  -closed sets in topology” , J.New Theory, Vol 20, pp.48-56, 2018.
  [15] Toshiiro Ohba and Jun-iti Umehara, “ Every topological space is semipre-α ” , Mem.Fac.Sci.Kochi Univ.(Math.), Vol 21, pp.89-
  92, 2000.
  [16] L. Vinayagamoorthi and N. Nagaveni, “ On generalized
  αb-closed set”, Proceeding ICMD- Allahabad, Pusbha Publication, 1, 2011.
  

  Citation
  J.B. Toranagatti, "A Comment on gpα-Closed Sets in Topoogical Spaces," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.2, pp.73-74, 2019

• Open Access   Article

  Computation of leap hyper-Zagreb indices of certain windmill graphs

  V. R. Kulli, Praveen Jakkannavar, B. Basavanagoud

  Research Paper | Isroset-Journal (IJSRMSS)

  Vol.6 , Issue.2 , pp.75-79, Apr-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i2.7579

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  Abstract
  In this paper, we establish the expressions for the first and second leap hyper-Zagreb indices of certain windmill graphs such as French windmill graph F_n^m, Dutch windmill graph D_n^m, Kulli cycle windmill graph C_(n+1)^m and Kulli path windmill graph P_(n+1)^m.

  Key-Words / Index Term
  French Windmill graph; Dutch Windmill graph; Kulli cycle windmill graph; Kulli path windmill graph; leap hyper-Zagreb index.

  References
  [1] B. Basavanagoud, Chitra E, On the leap Zagreb indices of generalized xyz-Point-Line transformation graphs T^xyz (G) when z=1, Int. J. Math. Combin., 2 (2018) 44-66.
  [2] B. Basavanagoud, Chitra E, On leap Zagreb indices of some nanostructures, Malaya J. Mat., 6(4) (2018) 816-822.
  [3] B. Basavanagoud, Chitra E, On leap hyper-Zagreb indices of some nanostructures, Int. J. Math. Trends Tech., 64(1) (2018) 30-36.
  [4] B. Basavanagoud, Praveen Jakkannavar, Computing first leap Zagreb index of some nano structures, Int. J. Math.And Appl., 6(2-B) (2018) 141-150.
  [5] B. Basavanagoud, Praveen Jakkannavar, Computing leap Zagreb indices of generalized xyz-Point-Line transformation graphs T^xyz (G) when z=+, J. Comp.Math. Sci., 9(10) (2018) 1360-1383.
  [6] J. A. Gallian, Dynamic Survey DS6: Graph Labeling, Electronic J. Combin., DS6, (2007) 1-58.
  [7] I. Gutman, N. Trinajstic`, Graph theory and molecular orbitals, Total π-electron energy of alternant hydrocarbons, Chem. Phys. Lett., 17 (1972) 535-538.
  [8] A. M. Nazi, N. D. Soner, I. Gutman, On leap Zagreb indices of graphs, Commun. Comb. Optim., 2 (2017) 99-107.
  [9] A. M. Nazi, N. D. Soner, The first leap Zagreb index of some graph operations, Int. J. Appl. Graph Theory, 2(1) (2018) 07-18.
  [10] V. R. Kulli, College Graph Theory, Vishwa International Publications, Gulbarga India. (2012).
  [11] V. R. Kulli, B. Chaluvaraju, H. S. Boregowda, Some degree based connectivity indices of Kulli cycle windmill graph, South Asain J. Maths. 6(6) (2016) 263-268.
  [12] V. R. Kulli, B. Chaluvaraj, H. S. Boregowda, Computation of connectivity indices of Kulli path windmill graph, TWMS J. Appl. Eng. Math., 6(1) (2016) 1-8.
  [13] V. R. Kulli, Leap hyper-Zagreb indices and their polynomials of certain graphs, Int. J. Current Research in Life Sciences, 7(10) (2018) 2783-2791.
  

  Citation
  V. R. Kulli, Praveen Jakkannavar, B. Basavanagoud, "Computation of leap hyper-Zagreb indices of certain windmill graphs," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.2, pp.75-79, 2019

• Open Access   Article

  A Review on the Methods of Detecting Space Time Clusters

  Jaisankar R, Ranjani M, Kausalya N

  Review Paper | Isroset-Journal (IJSRMSS)

  Vol.6 , Issue.2 , pp.80-85, Apr-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i2.8085

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  Abstract
  In disease, cluster is an unusually high incidence of a particular disease or disorder occurring in close proximity in terms of both time and geography. Detecting the clusters of disease cases resulting from emerging disease outbreaks is an important part of spatial epidemiology because it is used to identifying hotspots and also environmental factors related with disease and thus guide for the investigation of etiology of diseases. These disease clusters can be as purely spatial or temporal or both spatial and temporal. The present work is aims on reviewing the methods for detecting the space time clusters.

  Key-Words / Index Term
  Spatial clusters, Cluster detection, Space-time, Window shapes

  References
  [1] Cromley, E., and McLafferty: GIS and Public Health. 2nd edition. New York, NY: Guilford 2011.
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  [9] Shiode, S: Street-level Spatial Scan Statistic and STAC for analyzing Street Crime Concentrations. Transactions in GIS, 15 (3): 365-383, 2011.
  [10] Shino Shiode and Narushige Shiode: Network based Space-time Search-window technique for hotspot detection of street-level crime incidents. International Journal of Geographic Information Science, 5:866-882, 2013.
  [11] Kunihiko Takahashi, Martin Kulldorff, Toshiro Tango and Katherine Yih: A flexibly shaped space-time scan statistic for disease outbreak detection and monitoring. International Journal of Health Geographics, 7:1-14, 2008.
  [12] Peter A. Rogerson and Ikuho Yamada: Monitoring change in spatial patterns of disease: comparing univariate and multivariate cumulative sum approaches. Statistics in Medicine, 23:2195-2214, 2004.
  [13] Jacquez GM, Kaufmann A, Meliker J, Goovaerts P, AuRuskin G, Nriagu J: Global, local and focused geographic clustering for cease control data with residential histories. Environmental Health, 4:4 2005.
  [14] Geoffrey M Jacquez, Jaymie R Meliker, Gilllian A AvRuskin Pierre Goovaerts, Andy Kaufmann, Mark L Wilson and Jerome Nriagu: Case-Control Geographic Clustering for Residential Histories Accounting for Risk factors and Covariates. International Journal of Health Geographics, 5:32, 2006.
  [15] Andrea J. Cook, Diane R Gold and Yi Li: Spatial Cluster Detection for Repeatedly Measured Outcomes while Accounting for Residential History, Biometric Journal, 51 (5): 801-818, 2009.
  [16] Tomoki Nakaya, Keiji Yano: Visualising Crime in a Space-Time cube: An explanatory Data-analysis Approach Using Space-Time Kernel Density Estimation and Scan Statistics. Transactions in GIS, 14(3): 223-239, 2010.
  [17] Brunsdon C, Corcoran J, and Higgs G: Visualising Space time in crime patterns: A comparison of methods. Computers, Environment and Urban Systems, 31: 52-75, 2007.
  [18] Toshiro Tango, Kunihiko Takahashi and Kazuaki Kohriyama: Space-Time Scan Statistics for Detecting Emerging Outbreaks, Biometrics, International Biometric Society, 67, 106-115, 2011.
  [19] Weishan Dong, Xin Zhang, Li Li, Changhua Sun, Lei Shi, Wei Sun: Detecting Irregularly Shaped Significant Spatial and Spatio-Temporal Clusters. SIAM International Conference on Data Mining, 732-743, 2012. DOI:10.1137.1.9781611972825.63.
  [20] Hadi Fanaee-T and Joae Gama: Eigenspace method for spatiotemporal hotspot detection, Expert Systems, 32(3):454-464, 2014. doi:10.1111/exsy.12088.
  [21] Sami Ullah, Hanita Daud, Sarat C. Dass, Habib Nawaz Khan, Alamgir Khalil: Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach, Geospatial Health, 12(2):567, 2017.
  [22] Martin Kulldorff: Prospective time geographical disease surveillance using a scan statistic, Royal Statistical Society, 164(1):61-72, 2001.
  [23] G. Li, R. Haining, S. Richardson, N. Beset: Space-Time Variability in Burglary Risk: A Bayesian Spatio-Temporal Modelling Approach, Spatial Statistics, Elsevier, 9:180-191, 2014.
  [24] Sara Mclafferty: Disease Cluster detection methods: recent developments and public health implications, Annals of GIS, 21(2): 127-133, 2015.
  

  Citation
  Jaisankar R, Ranjani M, Kausalya N, "A Review on the Methods of Detecting Space Time Clusters," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.2, pp.80-85, 2019

• Open Access   Article

  Nano δG-Continuous Functions and Nano δG-Irresolute Functions in Nano Topological Spaces

  R. Vijayalakshmi, Mookambika.A.P.

  Research Paper | Isroset-Journal (IJSRMSS)

  Vol.6 , Issue.2 , pp.86-90, Apr-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i2.8690

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  Abstract
  In this paper we presented Nano δG-continuous functions and discussed some of their properties. Also we investigate the relationships between the other existing Nano continuous functions .Further, we define and study the concept of Nano δG-irresolute functions in Nano topological spaces and studied some of their characterizations.

  Key-Words / Index Term
  Nano δ closed sets, Nano δG closed sets, Nano δG-continuous functions and Nano δG-irresolute functions

  References
  [1] K. Bhuvaneshwari and K. Mythili Gnanapriya, “Nano Generalizesd closed sets”, International Journal of Scientific and Research Publications, 4 (5) (2014), 1-3.
  [2] K. Bhuvaneswari and K. M. Gnanapriya, “On Nano Generalised Pre Closed Sets and Nano Pre Generalised Closed Sets in Nano Topological Spaces”, International Journal of Innovative Research in Science, Engineering and Technology, 3 (10) (2014), 16825-16829.
  [3] S.Chandrasekar,T.Rajesh Kannan, M Suresh,,”δωα-Closed Sets in Topological Spaces”, Global Journal of Mathematical Sciences: Theory and Practical.9 (2), 103-116(2017)
  [4] S.Chandrasekar,T Rajesh Kannan,R.Selvaraj,δωα closed functions and δωα Closed Functions in Topological Spaces”, International Journal of Mathematical Archive ,8(11),2017.
  [5] S.Chandrasekar, M.Suresh and T.Rajesh Kannan, “Nano Sg-Interior And Nano Sg-Closure In Nano Topological Spaces”, International Journal of Mathematical Archive, 8(4) ,94-100,(2017).
  [6] M. L. Thivagar and C. Richard, “On Nano forms of weakly open sets”, International Journal of Mathematics and Statistics Invention, 1(1) 2013, 31-37.
  [7] C. R. Parvathy and , S. Praveena, “On Nano Generalized Pre Regular Closed Sets in Nano Topological Spaces”, IOSR Journal of Mathematics (IOSR-JM), 13 (2) (2017), 56-60.
  [8] I.Rajasekaran and O. Nethaji, “On some new subsets of nano topological spaces”, Journal of New Theory, 16 (2017), 52-58.
  [9] I.Rajasekaran and O. Nethaji, “On nano πgp-closed sets”, Journal of New Theory, 19 ,(2017), 20-26.
  [10] I.Rajasekaran and O. Nethaji, “on nano πgs-closed sets”, Journal of New Theory ,19, ( 2017) ,56-62.
  [11] R.Vijayalakshmi,Mookambika.A.P.“Some Properties Of Nano δG-Closed Sets In Nano Topological Spaces”, Journal of Applied Science and Computations,. Volume V, Issue XII, December/2018 ,1260-1263.
  [12] R. Vijayalakshmi ,Mookambika.A.P., “Nano δg-Interior And Nano δg-Closure In Nano Topological Spaces”, International Journal of Management, Technology And EngineeringVolume 8, Issue XII, December,2018 ,1591- 1596.
  [13] R.Vijayalakshmi,Mookambika,.A.P., “Totally and Slightly Nano Gδ-Continuous Functions, ,International Journal of Research in Advent Technology”, Vol.6, No.12, December 2018, 3330-3333.
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  Citation
  R. Vijayalakshmi, Mookambika.A.P., "Nano δG-Continuous Functions and Nano δG-Irresolute Functions in Nano Topological Spaces," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.2, pp.86-90, 2019

• Open Access   Article

  Analysis of Australian Twin Data by using Shared Frailty Models based on Reversed Hazard

  A. Pandey, Lalpawimawha , P.K. Misra

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.6 , Issue.2 , pp.91-98, Apr-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i2.9198

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  Abstract
  In this paper, we propose shared frailty models based on reversed hazard rate with exponentiated moment exponential distribution as baseline distribution. The Bayesian approach of Markov Chain Monte Carlo (MCMC) technique was employed to estimate the parameters involved in the models. A simulation study also performed to compare the true value and the estimated value of the parameters. Comparison with the non-parametric model also done by using Bayesian comparison techniques . We apply to the Australian twin data set and better model is suggested.

  Key-Words / Index Term
  Bayesian comparison technique, Exponentiated moment exponential distribution, MCMC, Reversed hazard, Shared frailty model

  References
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  [19] P.G. Sankaran, V.L. Gleeja,”On bivariate reversed hazard rates”, J. Jpn. Stat. Soc., Vol. 10,pp. 181-198, 2006.
  [20] P.G. Sankaran, V.L. Gleeja,“Proportional reversed hazard and frailty models”, Metrika , Vol. 68,pp. 333-342, 2008.
  [21] C.A. Santos, J.A. Achcar,”A Bayesian analysis for multivariate survival data in the presence of covariates”, Journal of Statistical Theory and Applications, Vol. 9,pp. 233-253, 2010.
  [22] D. Sengupta, A.K. Nanda,“The proportional reversed hazards regression model”,Statistical Theory and Applications, Vol. 18,pp. 461-476, 2011.
  [23] A. Wienke,“Frailty Models in Survival Analysis” New York, CRC Press, 2010.
  

  Citation
  A. Pandey, Lalpawimawha , P.K. Misra, "Analysis of Australian Twin Data by using Shared Frailty Models based on Reversed Hazard," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.2, pp.91-98, 2019

• Open Access   Article

  To Develop Accurate Model for Prediction of Soil Attributes with The Help of Statistical Modelling Techniques Such as Multiple Linear Regression (MLR) And Partial Least Square Regression (PLSR)

  P. M. Shah, D. C. Vyas

  Research Paper | Isroset-Journal (IJSRMSS)

  Vol.6 , Issue.2 , pp.99-102, Apr-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i2.99102

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  Abstract
  Present research paper signifies the importance of two statistical modelling techniques, Multiple Linear Regression (MLR) and partial Least Square Regression (PLSR). In the present study above mentioned methods are used to develop accurate model for prediction of soil property. MLR showes better result in comparison to PLSR in terms of Regression coefficient. In future, present models can be extended for soil data set of various other regions.

  Key-Words / Index Term
  soil pH, multiple linear regression, partial least square regression (PLSR)

  References
  [1]. Gessler, P., Moore, I., McKenzie, N., Ryan, P., (1995), “Soil-landscape modelling and spatial prediction of soil attributes”, International Journal of Geographical Information Systems 9 (4), 421 – 432
  [2]. Moore, I., Gessler, P., Nielsen, G., Peterson, G., (1993), “Soil attribute prediction using terrain analysis. Soil Science Society of America Journal 57 (2), 443 – 452
  [3]. Hengl, T., Heuvelink, G. B., & Stein. A, (2004), “A generic framework for spatial prediction of soil variables based on regression-kriging”, Geoderma, 120(1-2), 75-93.
  [4]. Bishop, T., McBratney, A., (2001), “A comparison of prediction methods for the creation of field-extent soil property maps”, Geoderma, 103 (1 – 2), 149 – 160
  [5]. Gholap, J., Ingole, A., Gohil, J., Gargade, S., & Attar, V. (2012), “Soil data analysis using classification techniques and soil attribute prediction”. arXiv preprint arXiv,1206.1557.
  [6]. Tranter G, Minasny B, McBratney AB, Murphy BW, McKenzie NJ, Grundy M, Brough D (2007) Building and testing conceptual and empirical models for predicting soil bulk density.
  [7]. Al-Zaidee, S. R., Fadhil, A. T., & Al-Kubaisi, O. K. Using Finite Element to Modify Winkler Model for Raft Foundation Supported on Dry Granular Soils.
  [8]. Looney, S., Gulledge Jr., T., (1985), “Use of the correlation coefficient with normal probability plots”, The American Statistician, 39, 75 – 79
  [9]. Wisnowski, J. W., Montgomery, D. C., & Simpson, J. R. (2001). “A comparative analysis of multiple outlier detection procedures in the linear regression model”, Computational statistics & data analysis, 36(3), 351-382.
  [10]. Bayer, A., Bachmann, M., Müller, A., & Kaufmann, H. (2012). A comparison of feature-based MLR and PLS regression techniques for the prediction of three soil constituents in a degraded South African ecosystem. Applied and Environmental Soil Science, 2012.A.
  [11]. Stevens, B. van Wesemael, H. Bartholomeus, D. Rosillon, B. Tychon, and E. Ben-Dor, “Laboratory, field and airborne spectroscopy for monitoring organic carbon content in agricultural soils,” Geoderma, vol. 144, no. 1-2, pp. 395–404, 2008.
  [12]. A. Stevens, T. Udelhoven, A. Denis et al., “Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy,” Geoderma, vol. 158, no. 1-2, pp. 32–45, 2010.
  

  Citation
  P. M. Shah, D. C. Vyas, "To Develop Accurate Model for Prediction of Soil Attributes with The Help of Statistical Modelling Techniques Such as Multiple Linear Regression (MLR) And Partial Least Square Regression (PLSR)," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.2, pp.99-102, 2019

• Open Access   Article

  A Review on Ripley’s K Function in Spatial Analysis

  Jaisankar R, Kesavan J

  Review Paper | Isroset-Journal (IJSRMSS)

  Vol.6 , Issue.2 , pp.103-107, Apr-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i2.103107

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  Abstract
  Spatial clustering and spatial dependence are the important characteristics of spatial data which are used in methods like disease clustering and spatial dependence in spatial epidemiology. Several procedures like the nearest neighbor method and the quadrat method are developed to contribute these aspects, but the one, called the K- function, developed by Ripley is very significant one on portraying spatial dependence over wide range of scales. This function has salient features both in theory and application. This paper presents a review with reference to the K- function in brief.

  Key-Words / Index Term
  Spatial clustering, Spatial dependence, K- Function

  References
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  [5] Diggle, P. J. Statistical analysis of spatial point pattern. 2nd edition, Academic Press, New York, 2003.
  [6] Dixon, P. M. Ripley’s K function. John Wiley and Sons 3:1796-1803, 2002.
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  [10] Isham, V. Multitype Markov point processes: some applications. Proceedings of the Royal Society of London, Series A 391:39–53, 1984.
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  [12] Lotwick, H. W. and Silverman, B. W. Methods for analyzing spatial processes of several types of points. Journal of the Royal Statistical Society, Series B 44:406–413, 1982.
  [13] Neyman, J. and Scott, E. L. A theory of the spatial distribution of galaxies. Astrophysical Journal 116:144–163,1952.
  [14] Ripley, B. D. Modeling spatial patterns. Journal of the Royal Statistical Society. Series B (Methodological) 39:172-212, 1977.
  [15] Ripley, B. D. The second-order analysis of stationary point processes. Journal of Applied Probability 13(2):255-266, 1976.
  [16] Smith, M. O., Ball, J., Holloway, B. B., Erdelyi, F., Szabo, G., Stone, E., Graham, J and Lawrence, J. J. Measuring Aggregation of Events about a Mass Using Spatial Point Pattern Methods. Spat Stat 13:76-89 2015.
  [17] Strauss, D. J. A model for clustering. Biometrika 62:467–475, 1975. Zhu, G., Ge, Y and Wang, H. A modified ripley’s K function to detecting spatial pattern of urban system. In International Conference on Geoinformatics, 1-5, Kaifeng, China:IEEE, 2013.
  

  Citation
  Jaisankar R, Kesavan J, "A Review on Ripley’s K Function in Spatial Analysis," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.2, pp.103-107, 2019

• Open Access   Article

  Block Related Indices and Coindices of a Graph

  B. Basavanagoud

  Research Paper | Isroset-Journal (IJSRMSS)

  Vol.6 , Issue.2 , pp.108-112, Apr-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i2.108112

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  Abstract
  A nontrivial connected graph with no cutvertices is called a block. A block of a graph is a subgraph of a graph which itself is a block and which is maximal with respect to this property. So far we have seen the graph invariants which are defined on vertices and edges of a graph. In this paper, we introduce new indices and coindices related to blocks of a graph.

  Key-Words / Index Term
  block, block indices, block coindices

  References
  [1] [1] B. Basavanagoud, S. Patil, Line-block graph of a graph,J. Karnatak Univ. Sci., 50 (2015) 14-18.
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  [8] [8] T. Dos ̆lic ̆, B. Furtula, A. Graovac, I. Gutman, S. Moradi, Z. Yarahmadi, On vertex-degree-based molecular structure descriptors, MATCH Commun. Math. Comput.Chem., 66 (2011) 613-626.
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  Citation
  B. Basavanagoud, "Block Related Indices and Coindices of a Graph," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.2, pp.108-112, 2019