Journal Vol – 19 No – 3, March 2024

PREDICTION OF CONCRETE MIXTURE DESIGN AND COMPRESSIVE STRENGTH THROUGH DATA ANALYSIS AND MACHINE LEARNING

Authors:

Mohammad Hematibahar, Makhmud Kharun

DOI NO:

https://doi.org/10.26782/jmcms.2024.03.00001

Abstract:

Concrete is the most used building material in civil engineering. The mechanical properties of concrete depend on the percentage of materials used in the mix design. There are different types of mixture methods, and the purpose of this study is to investigate the mechanical properties of concrete using the mixture method through data analysis. In this case, more than 45 mixture designs are collected to find the estimated mixture design. The estimated mixture design was found by correlation matrix and the correlation between materials of concrete. Moreover, to find the reliability of the compressive strength of concrete through data mining, two models have been established. In this term, Linear Regression (LR), Ridge Regression (RR), Support Vector Machine Regression (SVR), and Polynomial Regression (PR) have been applied to predict compressive strength. In this study, the stress-strain curve of the compressive strength of concrete was also investigated. To find the accuracy of machine learning models, Correlation Coefficient (R2), Mean Absolute Errors (MAE), and Root Mean Squared Errors (RMSE) are established. However, the machine learning prediction model of RR and PR shows the best results of prediction with R2 0.93, MAE 3.7, and RMSE 5.3 for RR. The PR R2 was more than 0.91, moreover, the stress-strain of compressive strengths has been predicted with high accuracy through Logistic Algorithm Function. The experimental results were acceptable. In the compressive strength experimental results R2 was 0.91 MAE was 1.07, and RMSE was 2.71 from prediction mixture designs. Finally, the prediction and experimental results have indicated that the current study was reliable.

Keywords:

Data Mining,Concrete Compressive Strength,Prediction Method,Reliability,Artificial Intelligence,Machine Learning,

Refference:

I. A. Sultan A, A. Mashrei M, A. Washer G. Utilization of Wilcoxon-Mann-Whitney Statistics in Assessing the Reliability of Nondestructive Evaluation Technologies. Structures, 27, (2020), 780–7.
II. Adnan Ikram RM, A. Ewees A, Singh Parmar K, Yaseen Z, Shahid S, Kisi O. The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction, 131, (2022a), 109739.
III. Adnan Ikram RM, Dai HL, Mirshekari Chargari M, Al-Bahrani M, Mamlooki M. Prediction of the FRP reinforced concrete beam shear capacity by using ELM-CRFOA, ; 205, (2022b ),112230.
IV. Adnan Ikram RM, Goliatt L, Kisi O, Trajkovic S, Shahid S. Covariance Matrix Adaptation Evolution Strategy for Improving Machine Learning Approaches in Streamflow Prediction. Mathematics, 10, 2022c, 2971.
V. Agrawal R. Sustainable design guidelines for additive manufacturing applications. Rapid Prototyping Journal, 28(7), (2022), 1221–40.
VI. Akande KO, Owolabi TO, Twaha S, Olatunji SO. Performance Comparison of SVM and ANN in Predicting Compressive Strength of Concrete. IOSR J Comput Eng,16(5), (2014), 88–94.
VII. Akter S, Ali RME, Karim S, Khatun M, Alam MF. Geomorphological, Geological and Engineering Geological Aspects for Sustainable Urban Planning of Mymensingh City, Bangladesh. Open J Geol, 8:73 (2018) 7–52.
VIII. AlAlaween W, Abueed O, Gharaibeh B, Alalawin A, Mahfouf M, Alsoussi A, et al. The development of a radial based integrated network for the modelling of 3D fused deposition. Rapid Prototyping Journal, (2022) ahead-of-print(ahead-of-print).
IX. ASTM C293 / C293M – 16 Standard Test Method for Flexural Strength of Concrete (Using Simple Beam with Center-Point Loading). Vol. 2016. ASTM International. Epub ahead of print; Available from: https://doi.org/10.1520/C0293_C0293M-16
X. ASTM C1202-19 Standard Test Method for Electrical Indication of Concrete’s Ability to Resist Chloride. ASTM International. Epub ahead of print; 2009. Available from: https://doi.org/10.1520/C1202-19

XI. Chiadighikaobi PC, Hematibahar M, Kharun M, A. Stashevskaya N, Camara K. Predicting mechanical properties of self-healing concrete with Trichoderma Reesei Fungus using machine learning. Cogent Engineering. 11(1), (2024) :2307193.
XII. Chiadighikaobi PC, Kharun M, Hematibahar M. Historical structure design method through data analysis and soft programming. Cogent Engineering, 10, 1 , (2023) 2220499.
XIII. Chopra P, KumarSharma R, Kumar M. Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming. Advances in Materials Science and Engineering, 7648467, (2016), 10.
XIV. Dębska B. Assessment of the Applicability of Selected Data Mining Techniques for the Classification of Mortars Containing Recycled Aggregate. Materials, 15, (2022),8111.
XV. Erdal HI. Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction. Eng Appl Artif Intell, 26, (2013), 1689–97.
XVI. Farooq F F, Czarnecki S, Niewiadomski P, Aslam F, Alabduljabbar H, Ostrowski KA, et al. A comparative study for the prediction of the compressive strength of self-compacting concrete modified with fly ash. Materials, 14, (2021), 4934.
XVII. Fl ̈ory S, Pottmann H. Ruled surfaces for rationalization and design in architecture. LIFE information On Responsive Information and Variations in Architecture, (2010) 103–9.
XVIII. GOST10180. Betony. Metody opredeleniya prochnosti po kontrolnym obraztsam [Concretes. Methods for determination of strength by control samples]. Vol. 36. 2013.
XIX. Hasanzadeh A, Vatin NI, Hematibahar M, Kharun M, Shooshpasha I. Prediction of the Mechanical Properties of Basalt Fiber Reinforced High-Performance Concrete Using Machine Learning Techniques. Materials.15, (2022), 20, 7165.
XX. Hazarika BB, Gupta D, Natarajan N. Wavelet kernel least square twin support vector regression for wind speed prediction, 29 (2022), 86320–36.
XXI. Hematibahar M, Esparham A, Vatin NI, Kharun M, Gebre TH. Effect of Gelatin Powder, Almond Shell, and Recycled Aggregates on Chemical and Mechanical Properties of Conventional Concrete. STRUCTURAL MECHANICS OF ENGINEERING CONSTRUCTIONS AND BUILDINGS. (2023); 19.

XXII. Hematibahar M, Ivanovich Vatin N, A. Alaraza H, Khalilavi A, Kharun M. The Prediction of Compressive Strength and Compressive Stress-Strain of Basalt Fiber Reinforced High-Performance Concrete Using Classical Programming and Logistic Map Algorithm. Materials.;15 , (2022a) 19,:6975.
XXIII. Hematibahar M, Vatin NI, Alaraza HAA, Khalilavi A, Kharun M. The Prediction of Compressive Strength and Compressive Stress–Strain of Basalt Fiber Reinforced High-Performance Concrete Using Classical Programming and Logistic Map Algorithm. Materials.;19, (2022b) 15:6975.
XXIV. Hsieh SC. Prediction of Compressive Strength of Concrete and Rock Using an Elementary Instance-Based Learning Algorithm. Advances in Civil Engineering, (2021),10.
XXV. J. Alghamdi S. Classifying High Strength Concrete Mix Design Methods Using Decision Trees. Materials, 15, (2022) 1950.
XXVI. Kaewunruen S, Sresakoolchai J, Huang J, Zhu Y, Ngamkhanong C, M. Remennikov A. Machine Learning Based Design of Railway Prestressed Concrete Sleepers. Appl Sci.12, (2022), 10311.
XXVII. Kashyzadeh KR, Amiri N, Ghorban S, Souri K. Prediction of Concrete Compressive Strength Using a Back-Propagation Neural Network Optimized by a Genetic Algorithm and Response Surface Analysis Considering the Appearance of Aggregates and Curing Conditions. Buildings.;12 , (2022) ,438.
XXVIII. Khan MA, Memon SA, Farooq F, Javed MF, Aslam F, Alyousef R. Compressive strength of fly-ash-based geopolymer concrete by gene expression programming and random forest. Adv Civ Eng, 2021, 6618407.
XXIX. Khorasani M, Loy J, Ghasemi A, Sharabian E, Leary M, Mirafzal H, et al. A review of Industry 4.0 and additive manufacturing synergy. Rapid Prototyping Journal, 28, 8 (2022) , 1462–75.
XXX. Kumar A, Harish CA, Raj Kapoor N, Mazin AM, Kumar K, Majumdar A, et al. Compressive Strength Prediction of Lightweight Concrete: Machine Learning Models. Sustainability, 14, (2022), 2404.
XXXI. Liu Y. High-Performance Concrete Strength Prediction Based on Machine Learning. Hindawi.; (2022), 5802217,7.
XXXII. Mao F, Zhao X, Ma P, Chi S, Richards K, M. Hannah D, et al. Revision of biological indices for aquatic systems: A ridge-regression solution. Ecological Indicators, 106, (2019),105478.
XXXIII. Nafees A, Khan S, Javed MF, Alrowais R, Mohamed AM, Vatin NI. Forecasting the Mechanical Properties of Plastic Concrete Employing Experimental Data Using Machine Learning Algorithms: DT, MLPNN, SVM, and RF. Polymers,14, (2022),1538.
XXXIV. Nguyen KT, Nguyen QD, Le TA, Shin J, Lee K. Analyzing the compressive strength of green fly ash based geopolymer concrete using experiment and machine learning approaches. Constr Build Mater, 247, (2020), 118581.
XXXV. Peng X, Zhuang Z, Yang Q. Predictive Modeling of Compressive Strength for Concrete at Super Early Age. Materials, 15, (2022), 4914.
XXXVI. Riener C, Schabert R. Linear slices of hyperbolic polynomials and positivity of symmetric polynomial functions. arXiv preprint.
XXXVII. Sami Ullah H, Khushnood RA, Farooq F, Ahmad J, Vatin NI, Zakaria Ewais DY. Prediction of Compressive Strength of Sustainable Foam Concrete Using Individual and Ensemble Machine Learning Approaches. Materials, 15, (2022)3166.
XXXVIII. Shahmansouri AA, Yazdani M, Hosseini M, Akbarzadeh Bengar H, Farrokh Ghatte H. The prediction analysis of compressive strength and electrical resistivity of environmentally friendly concrete incorporating natural zeolite using artificial neural network. Construction and Building Materials, 317, (2022), 125876.
XXXIX. Shen Z, Farouk Deifalla A, Kaminski P, Dyczko A. Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning. Materials, 15, (2022) 3523.
XL. Son J, Yang S. A New Approach to Machine Learning Model Development for Prediction of Concrete Fatigue Life under Uniaxial Compression. Appl Sci,12 (2022),9766.
XLI. Topçu İB, Sarıdemir M. Prediction of properties of waste AAC aggregate concrete using artificial neural network. Computational Materials Science, 41, 1, (2007) 117–25.
XLII. Vakharia V, Gujar R. Prediction of compressive strength and portland cement composition using cross-validation and feature ranking techniques. Construction and Building Materials,225 , (2019), 292–301.
XLIII. Yang D, Yan C, Liu S, Jia Z, Wang C. Prediction of Concrete Compressive Strength in Saline Soil Environments. Materials, 15, (2022), 4663.
XLIV. Zheng X, Peng X, Zhao J, Wang X. Trajectory Prediction of Marine Moving Target Using Deep Neural Networks with Trajectory Data. Appl Sci, 12, (2022), 11905.

View Download

SIZE-DEPENDENT VIBRATION ANALYSIS OF CRACKED MICRO BEAMS REINFORCED WITH FUNCTIONALLY GRADED BORON NITRIDE NANOTUBES IN COMPOSITE STRUCTURES

Authors:

L. Anitha, J. Sudha

DOI NO:

https://doi.org/10.26782/jmcms.2024.03.00002

Abstract:

The Boron Nitride Nanotubes (BNNTs) are cylindrical nanostructures made up of nitrogen and boron atoms stacked hexagonally. Comparable to carbon nanotubes, BNNTs have exceptional mechanical, electrical, and thermal capabilities. The increasing prevalence of micro-electromechanical systems in different technological fields underscores the necessity of gaining a comprehension of their mechanical behavior. The behaviour of Functionally Graded Boron Nitride Nanotube-Reinforced Composite (FG-BNNTRC) concerning microbeam cracks during free movement is investigated in this study. BNNT can be added to a matrix of polymers in four distinct manners to give reinforcements. The BNNTRC substance features are expected by the standard of integrating fractured microbeams. This study's primary goal is to investigate the free vibration properties of FG-BNNTRC cracked micro beams. It is crucial to focus on evaluating how different BNNT reinforcing structures, volume %, dimension/thickness ratio, and length scale elements affect vibration frequencies. This paper evaluates the vibration of fractured microbeams having length dependency using the modified couple stress theory. Following examining the effects of various causes, it emerges that the frequencies exhibit noticeable variances. The study shows that when the thickness of the beam becomes closer to the length scale parameter, the size impact gets stronger. The thickness of the beam grows, and the size impact decreases. The results are significant consequences with the design in addition to developing innovative composite materials for micro-scale applications, demonstrating the details of the complex interplay among nanoscale reinforcements and structural integrity.

Keywords:

Beam Theories,Boron Nitride Nanotube,Vibration,Size Effect,Functionally Graded Boron Nitride Nanotube-Reinforced Composite (FG-BNNTRC,

Refference:

I. Arshid, Ehsan, and Saeed Amir. “Size-dependent vibration analysis of fluid-infiltrated porous curved microbeams integrated with reinforced functionally graded graphene platelets face sheets considering thickness stretching effect.” Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications 235, no. 5 (2021): 1077-1099.

II. Bakhtiari-Nejad, F. and Nazemizadeh, M., 2016. Size-dependent dynamic modeling and vibration analysis of MEMS/NEMS-based nanomechanical beam based on the nonlocal elasticity theory. Acta Mechanica, 227(5), pp.1363-1379.

III. Chen, D., Zheng, S., Wang, Y., Yang, L. and Li, Z., 2020. Nonlinear free vibration analysis of a rotating two-dimensional functionally graded porous micro-beam using isogeometric analysis. European Journal of Mechanics-A/Solids, 84, p.104083.

IV. Civalek, Ö., Akbaş, Ş.D., Akgöz, B. and Dastjerdi, S., 2021. Forced vibration analysis of composite beams reinforced by carbon nanotubes. Nanomaterials, 11(3), p.571.

V. Eghbali, M., Hosseini, S.A. and Pourseifi, M., 2022. Free transverse vibrations analysis of size-dependent cracked piezoelectric nano-beam based on the strain gradient theory under mechanic-electro forces. Engineering Analysis with Boundary Elements, 143, pp.606-612.

VI. Guo, L.J., Mao, J.J., Zhang, W. and Wu, M., 2023. Stability Analyses of Cracked Functionally Graded Graphene-Platelets Reinforced Composite Beam Covered with Piezoelectric Layers. International Journal of Structural Stability and Dynamics, p.2350164.
VII. Heo, J., Yang, Z., Xia, W., Oterkus, S. and Oterkus, E., 2020. Free vibration analysis of cracked plates using peridynamics. Ships and Offshore Structures, 15(sup1), pp.S220-S229.

VIII. Huang, T., Li, Y., Chen, M. and Wu, L., 2020. Bi-directional high thermal conductive epoxy composites with radially aligned boron nitride nanosheets lamellae. Composites Science and Technology, 198, p.108322.

IX. Jones, R.S., Gonzalez-Munoz, S., Griffiths, I., Holdway, P., Evers, K., Luanwuthi, S., Maciejewska, B.M., Kolosov, O. and Grobert, N., 2023. Thermal Conductivity of Carbon/Boron Nitride Heteronanotube and Boron Nitride Nanotube Buckypapers: Implications for Thermal Management Composites. ACS Applied Nano Materials.

X. Ko, J., Kim, D., Sim, G., Moon, S.Y., Lee, S.S., Jang, S.G., Ahn, S., Im, S.G. and Joo, Y., 2023. Scalable, Highly Pure, and Diameter‐Sorted Boron Nitride Nanotube by Aqueous Polymer Two‐Phase Extraction. Small Methods, 7(4), p.2201341.

XI. Kumar, M. and Sarangi, S.K., 2022. Bending and vibration study of carbon nanotubes reinforced functionally graded smart composite beams. Engineering Research Express, 4(2), p.025043.

XII. Larkin, K., 2020. Nonlinear Size Dependent Analysis and Crack Network Modeling of Micro/Nano-systems (Doctoral dissertation, New Mexico State University).

XIII. Mercan, K. and Civalek, Ö., 2022. Comparative Stability Analysis of Boron Nitride Nanotube using MD Simulation and Nonlocal Elasticity Theory. International Journal of Engineering and Applied Sciences, 13(4), pp.189-200.

XIV. Numanoğlu, H.M. and Civalek, Ö., 2022. Novel size-dependent finite element formulation for modal analysis of cracked nanorods. Materials Today Communications, 31, p.103545.

XV. Rahi, A., 2018. Crack mathematical modeling to study the vibration analysis of cracked micro beams based on the MCST. Microsystem Technologies, 24(7), pp.3201-3215.

XVI. Sahmani, S. and Safaei, B., 2019. Nonlinear free vibrations of bi-directional functionally graded micro/nano-beams including nonlocal stress and microstructural strain gradient size effects. Thin-Walled Structures, 140, pp.342-356.

XVII. Sedighi, H.M., Malikan, M., Valipour, A. and Żur, K.K., 2020. Nonlocal vibration of carbon/boron-nitride nano-hetero-structure in thermal and magnetic fields by means of nonlinear finite element method. Journal of Computational Design and Engineering, 7(5), pp.591-602.
XVIII. Shafiei, H. and Setoodeh, A.R., 2020. An analytical study on the nonlinear forced vibration of functionally graded carbon nanotube-reinforced composite beams on nonlinear viscoelastic foundation. Arch. Mech, 72(2), pp.81-107.

XIX. Sh Khoram-Nejad, E., Moradi, S. and Shishesaz, M., 2021. Free vibration analysis of the cracked post-buckled axially functionally graded beam under compressive load. Journal of Computational Applied Mechanics, 52(2), pp.256-270.

XX. Song, M., Gong, Y., Yang, J., Zhu, W. and Kitipornchai, S., 2020. Nonlinear free vibration of cracked functionally graded graphene platelet-reinforced nanocomposite beams in thermal environments. Journal of Sound and Vibration, 468, p.115115.

XXI. Vandecruys, E., Van de Velde, M., Reynders, E., Lombaert, G. and Verstrynge, E., 2023. Experimental study on acoustic emission sensing and vibration monitoring of corroding reinforced concrete beams. Engineering Structures, 293, p.116553.

XXII. Xu, C., Rong, D., Zhou, Z., Deng, Z. and Lim, C.W., 2020. Vibration and buckling characteristics of cracked natural fiber reinforced composite plates with corner point-supports. Engineering Structures, 214, p.110614.

XXIII. Yan, J.W. , He, J.B. and Tong, L.H., 2019. Longitudinal and torsional vibration characteristics of boron nitride nanotubes. Journal of Vibration Engineering & Technologies, 7, pp. 205-215.

XXIV. Zeighampour, H., Tadi Beni, Y. and Kiani, Y., 2020. Electric field effects on buckling analysis of boron–nitride nanotubes using surface elasticity theory. International Journal of Structural Stability and Dynamics, 20 (12), p.2050137.

XXV. Zeighampour, H. and Tadi Beni, Y., 2020. Buckling analysis of boron nitride nanotube with and without defect using molecular dynamic simulation. Molecular Simulation, 46(4), pp.279-288.

XXVI. Zhao, J.L., Chen, X., She, G.L., Jing, Y., Bai, R.Q., Yi, J., Pu, H.Y. and Luo, J., 2022. Vibration characteristics of functionally graded carbon nanotube-reinforced composite double-beams in thermal environments. Steel Compos Struct, 43(6), pp.797-808.

XXVII. Zhu, L.F., Ke, L.L., Xiang, Y., Zhu, X.Q. and Wang, Y.S., 2020. Vibrational power flow analysis of cracked functionally graded beams. Thin-Walled Structures, 150, p.106626.

View Download

UWB TAPERED-SLOT PATCH ANTENNA WITH RECONFIGURABLE DUAL BAND-NOTCHES CHARACTERISTICS

Authors:

Adham R. Azeez, Sadiq A, Zaid A. Abdul Hassain, Amer Abbood Al-behadili, Hind S. Ghazi, Yaqeen S. Mezaal, Ahmed A. Hashim, Aqeel Ali Al-Hilali, Kadhum Al-Majdi

DOI NO:

https://doi.org/10.26782/jmcms.2024.03.00003

Abstract:

An ultra-wideband patch antenna (UWB) that makes use of tapered slot technology is designed and analyzed in this article. Coplanar waveguide feeds the projected antenna. The presented antenna displayed superior UWB performances with -10 dB return-loss bandwidth, ranging from 1.9 to 12 GHz. The projected slot antenna has another benefit of minimizing the interference effect of the narrow band communications conducted by two notch bands operating at 3.3–3.8 GHz (WiMAX) and 5.1-6 GHz  (WLAN and HIPERLAN/2), respectively. The Dual-Bands rejection is generated by etching out a complementary split ring resonator (CSRR) from the patch and placing a trapezoidal split ring resonator (TSRR). Adaptable single or dual-band rejection characteristics have been added to the behavior of the UWB antenna, by mounting electronic switching across SRR and CSRR. Furthermore, the presented UWB slot antenna is printed on an FR4-epoxy substrate (εr = 4.4) and it has an overall size of . 55x48x1.5 mm3

Keywords:

Bi-directional Antenna,UWB,Split Ring Resonator,Dual Band-Notch Antenna,Reconfigurable Antenna,

Refference:

I. Adham R. Azeez, Sadiq Kadhim Ahmed, A. M. Zalzala, Zaid A. Abdul Hassain, Taha A. Elwi,” Design of High Gain UWB Vivaldi Antenna with Dual Band-Notches Characteristics,” International Journal on Engineering Applications (IREA), Vol.11, No.2, pp.128-136, 2023.
II. Alnahwi F, Abdulhasan K, Islam N. An ultra-wideband to dual-band switchable antenna design for wireless communication applications. IEEE Antenn Wirel Pr let 2015; 14: 1685-1688.
III. Ansoft HFSS [Online]. Available: http://www.ansoft.com
IV. B. T. P. Madhav, M. Venkateswara Rao, and T. Anilkumar, Conformal band notched circular monopole antenna loaded with split ring resonator, Wireless Person. Communic. 103 (2018), 1965–1976.
V. J. K. Ali, Y. S. Miz’el, “A new miniature Peano fractal-based bandpass filter design with 2nd harmonic suppression,” In 2009 3rd IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, 2009.
VI. J. Y. Siddiqui, C. Saha, and Y. M. Antar, Compact dual-SRRloaded UWB monopole antenna with dual frequency and wideband notch characteristics, IEEE Antenn. Wireless Propagat. Ltr. 14 (2014), 100–103

VII. F. Abayaje, S. A. Hashem, H. S. Obaid, Y. S. Mezaal, & S. K. Khaleel, “A miniaturization of the UWB monopole antenna for wireless baseband transmission,” Periodicals of Engineering and Natural Sciences, vol. 8, no. 1, pp. 256–262, 2020.
VIII. Fontana, R. L., “Recent system applications of short pulse ultra-wideband (UWB) technology,” IEEE Trans. MTT, vol. 52, no. 9, pp. 2087-2104, 2004.
IX. Kumar, O.P.; Ali, T.; Kumar,P.; Kumar, P.; Anguera, J. “An Elliptical-Shaped Dual-Band UWBNotch Antenna for Wireless” Applications. Appl. Sci. 2023, 13, 1310. https://doi.org/10.3390/app13031310.
X. Mohamed H, Elkorany A, Saad S, Saleeb D. New simple flower shaped reconfigurable band-notched UWB antenna using single varactor diode. Prog. Electromagn Resc C 2017; 76: 197-206.
XI. Mohamed H, Elkorany A, Saad S, Saleeb D. New simple flower shaped reconfigurable band-notched UWB antenna using single varactor diode. Prog. Electromagn Resc C 2017; 76: 197-206.
XII. Nikolaou S, Kingsley N, Poncha G, Papapolymerou J, Tentzeris M. UWB elliptical monopoles with a reconfigurable band notch using MEMS switches actuated without bias lines. IEEE Trans Antenn Propg 2009; 57: 2242-2251.
XIII. Nickolas Kingsley, etal., “RF MEMS Sequentially Reconfigurable Sierpinski Antenna on a Flexible Organic Substrate With Novel DC–Biasing Technique”, Journal of Microelectro–Mechanical Systems, vol. 16, no. 5, October 2007.
XIV. Ojaroudi N, Ojaroudi M. A novel design of reconfigurable small monopole antenna with switchable band notch and multi-resonance functions for UWB applications. Microw Opt Techn Let 2013; 55: 652-656.
XV. Ojaroudi N, Ghadimi N, Ojaroudi Y, Ojaroudi S. A novel design of microstrip antenna with reconfigurable band rejection for cognitive radio applications. Microw Opt Tecn Let 2014; 56: 2998-3003.

XVI. Qing-Xin Chu, etal, “A Compact Ultra wideband Antenna With 3.4/5.5 GHz Dual Band-Notched Characteristics”, IEEE Transaction on Antennas and Propagation, vol. 56, no.12, 2008.
XVII. Qing-Xin Chu, etal, “A Compact Ultra wideband Antenna With 3.4/5.5 GHz Dual Band-Notched Characteristics”, IEEE Transaction on Antennas and Propagation, vol. 56, no.12, 2008.
XVIII. Tripathi S, Mohan A, Yadav S. A compact fractal UWB antenna with reconfigurable band notch functions. Microw Opt Techn Let 2016; 58: 509-514.
XIX. S. K. Mishra, and J. Mukherjee, “Compact Printed Dual Band-Notched U-Shape UWB Antenna”, Progress In Electromagnetics Research C, vol. 27, 169–181, 2012.
XX. Symeon Nikolaou, etal,. “UWB Elliptical Monopoles with a Reconfigurable Band Notch Using MEMS Switches Actuated Without Bias Lines”, IEEE Transaction on Antennas and Propagation, vol. 57, no. 8, August 2009.
XXI. Y. S. Mezaal, H. H. Saleh, H. Al-saedi, “New compact microstrip filters based on quasi fractal resonator,” Adv. Electromagn., vol. 7, no. 4, pp. 93–102, 2018.
XXII. Y. S. Mezaal, H. T. Eyyuboglu, “A new narrow band dual-mode microstrip slotted patch bandpass filter design based on fractal geometry,” In 2012 7th International Conference on Computing and Convergence Technology (ICCCT), IEEE, pp. 1180–1184, 2012.
XXIII. Y. S. Mezaal, H. T. Eyyuboglu, & J. K. Ali (2013, September). A new design of dual band microstrip bandpass filter based on Peano fractal geometry: Design and simulation results. In 2013 13th Mediterranean Microwave Symposium (MMS) (pp. 1-4). IEEE.
XXIV. Y. S. Mezaal, S. F. Abdulkareem, “New microstrip antenna based on quasi-fractal geometry for recent wireless systems,” In 2018 26th Signal Processing and Communications Applications Conference (SIU), 2018.
XXV. Y. S. Li, W. X. Li and Q. B. Ye, “Compact Reconfigurable UWB Antenna Integrated With Stepped Impedance Stub Loaded Resonator and Switches”, Progress In Electromagnetics Research C, vol. 27, 239–252, 2012.
XXVI. Zaid A. Abdul Hassain, Mustafa Mahdi Ali, and Adham R. Azeez, “Single and Dual Band-Notch UWB Antenna Using SRR/CSRR Resonators, ” Journal of Communications, Vol. 14, No. 6, PP. 504-510, June 2019.
XXVII. Zaid A. Abdul Hassain, Amer A. Osman, and Adham R. Azeez, “First order parallel coupled BPF with wideband rejection based on SRR and CSRR, “Telkomnika, Vol.17, No.6, PP. 2704-2712, December 2019.
XXVIII. Zaid A. Abdul Hassain, Adham R. Azeez, Mustafa M. Ali, and Taha A. Elwi, “A Modified Compact Bi-Directional UWB Tapered Slot Antenna with Double Band-Notch Characteristics, “Advanced Electromagnetics, Vol. 8, No. 4, PP. 74-79, September 2019.

View Download

OSCILLATORY BEHAVIOR OF SOLUTIONS OF FRACTIONAL MATRIX DIFFERENTIAL EQUATIONS

Authors:

N. Sasikala, V Sadhasivam

DOI NO:

https://doi.org/10.26782/jmcms.2024.03.00004

Abstract:

In this article, new oscillation criteria for the second-order self-adjoint Matrix differential equations by using the Riccatti technique are obtained. A suitable example is given to illustrate the significance and effectiveness of the result.       

Keywords:

Matrix Differential equations,oscillation,selfadjoint,damping,

Refference:

I. Basci,Y. Kamenev type oscillation criteria for second order matrix differential systems with damping, Hacettepe Journal of Mathematics and Statistics, volume 47(5) (2018), 1248-1267.
II. Bellmann, R. Introduction to Matrix Analysis, SIAM, Mc. Graw-Hill Book Company, New York, 1970.
III. Chatzarakis, G.E. Deepa,M. Nagajothi, N. Sadhasivam, V. On the oscillatory behavior of a class of mixed fractional order nonlinear differential equations, Pacific Journal of Applied Mathematics,(2018).
IV. Chatzarakis, G.E.Deepa, M.Nagajothi,N. Sadhasivam,V. Oscillatory properties of a class of mixed fractional differential equations., Applied Mathematics & Information Science, 14, No.1.109-117 (2020).
V. Coles, W.J. Oscillation for self adjoint second order matrix differential equations, Differential and integral equations, vol 4, no.1 (1991) pp 195-204.
VI. Cong, Xu. Yan. and Feng, M. Oscillation theorems for certain second order nonlinear Matrix differential systems, Journal of East China Normal University ( Natural Science 2007) vol.2007, issue (5) pp 34-38
VII. Howard, H.C. Oscillation criteria for matrix differential equations., University of Wisconsin., Milwaukee, pp184-199.
VIII. Kreith, K. Oscillation criteria for nonlinear matrix differential equations, Proc. Amer. Maths.Soc, 26(1970) 270-272.
IX. Kumari, T.S. and Umamaheswaran,S. Oscillation criteria for linear matrix hamiltonian systems, J.Differential equations, vol 165(2000) pp 174-198.
X. Kumar, M.S.Deepa,M .Kavitha,J. Sadhasivam,V. Existence theory of fractional order three-dimensional differential system at resonance, Mathematical Modelling and Control, 2023;3(2):127-38.
XI. Kumar, M. S Ganesan, V. Asymptotic behavior of solutions of third-order neutral differential equations with discrete and distributed delay, Aims Mathematics, 5 (2020), pp.3851-3874.
XII. Liu,H. and Meng, F. Oscillation criteria for second order linear matrix differential systems with damping, Journal of computational and applied mathematics, (2009) 222- 229.
XIII. Tong Li, W. and Zhuang, Rong-kun Interval oscillation criteria for second order forced nonlinear matrix differential equations, Electronic Journal of Differential equations, vol (2005) no.69 pp1-6.
XIV. Meng, F. and man, cuiqin Oscillation results for linear second order matrix differential systems with damping, Applied Mathematics and computations, 187 (2007) 844-855.
XV. Nandakumaran, A.K. and.Panigrahi, S. Oscillation criteria for differential equations of second order, Mathematica slovoca, (59) (2009) no.4 pp 433-454.
XVI. Pan, Yuanyuan and Xu, Run. Some new oscillation criteria for a class of nonlinear fractional differential equations, Fractional Differential calculus, 6(1) (2016), 17-33.
XVII. Parhi, N. & Praharaj, N. Oscillation criteria for second order self adjoint matrix differential equations, Annales Polonici mathematics vol 72(1999) pp 1-14.
XVIII. Parhi,N.and Praharaj,P. Suffcient condition for oscillation of linear second order matrix differential systems., Rocky Mountain, Journal of Mathematics, Volume 32, Number 3, fall 2002.
XIX. Parhi, N.& Praharaj, N. Oscillation of nonlinear matrix differential equations, Math. Slovoca, 57 (2007) no.5 455-474.
XX. Pullman, N.J. Matrix theory and its applications, Marcel Dekker, 1976.
XXI. Sadhasivam,V. and Kavitha, J. Oscillation criteria for fractional partial differential equation with damping term, Applied Mathematics, 7 (2016), 272-291.
XXII. Wang, Qi-Ru Oscillation of self adjoint matrix differential systems, Applied Mathematics Letters 17 (2004) 1299-1305.
XXIII. Wenying shi, Shaoqin Gao and Wensheng Zhao, Oscillation results for nonlinear second order matrix differential systems with damping, International Journal of Pure and Applied Mathematics, vol 84. no.1 (2013) 1-12.
XXIV. Xu, Yan Cong and Zhu, De Ming. New oscillation criteria for second order linear matrix differential equations with damping, Acta mathematics Sinica English series (2008) vol.24, no.6, pp 925-936.
XXV. Yang, Qi Gui. Oscillation theorems of second order linear matrix differential systems with damping, Acta Mathematica Sinica English series, (2005), vol. 21, no.1, pp 17-30.
XXVI. Yang,Qui Gui. and cheng, Sui sun. Kamenev type oscillation criteria for second order matrix differential systems with damping , Annales polonici mathematics, 85(2005).
XXVII. Zheng, Z. Interval oscillation criteria of matrix differential systems with damping (2005).

View Download

INVESTIGATION ON PREDICTING FAMILY PLANNING AND WOMEN’S AND CHILDREN’S HEALTH EFFECTS ON BANGLADESH BY CONDUCTING AGE STRUCTURE POPULATION MODEL

Authors:

Rezaul Karim, M. A. Bkar Pk, Md. Asaduzzaman, Pinakee Dey, M. Ali Akbar

DOI NO:

https://doi.org/10.26782/jmcms.2024.03.00005

Abstract:

Bangladesh has a higher population density than most other nations in the world. This project aims to evaluate the effects of experimental family planning and maternal and child health. Bangladesh saw changes in the use of contraceptives, the continuation of contraception, fertility, and infant and child mortality between 2012 and 2022. The project's current goal is to guarantee improved family health. To satisfy the changing needs and priorities of families and to provide better health for all, this paper has proposed several novel initiatives, such as enhanced health and family planning services, and enhancing maternal and child health. The goal of this project is to improve the health of women and children through family planning using an age-structured population model. It also covers the graphical presentation of the data using programs like Matlab, Mathematica, Excel, and others.

Keywords:

Population Model,Sharpe-Lotka model,Gurtin MacCamy model,family planning,women’s and child’s health,

Refference:

I. Abassian et.al,(2020). Five different perspectives on mathematical modeling in mathematics education. Investigations in Mathematics Learning. Vol 12(1), 53–65. https://doi.org/10.1080/19477503.2019.1595360.

II. Abayasekara, A. W. A. D. G. (2019). Forecasting the Sri Lankan Population with the Gompertz and Verhulst Logistic Growth Models. Sri Lanka Journal of Economic Research, 7(1), 1. https://doi.org/10.4038/sljer.v7i1.38.

III. Abeykoon, & A.T.P.L. (2011). The demographic transition: Opportunities and Challenges-The case of Sri Lanka. Institute for Health Policy .1–54.

IV. Ausloos, M. (2013). Gompertz and Verhulst frameworks for growth and decay description. Int. J. Comput. Anticip. Syst. 30 (2014) 15- 36.

V. Barham, T. “Effects of family planning and child health interventions on adolescent cognitive functioning (2008): Evidence from Matlab in Bangladesh.” working paper, University of Colorado, Boulder.

VI. BEDFORD, KATE.(2009) “WORKING WOMEN, CARING MEN, AND THE FAMILY BANK: Ideal Gender Relations after the Washington Consensus.” Developing Partnerships: Gender, Sexuality, and the Reformed World Bank, NED-New edition, University of Minnesota Press, pp. 1–34. JSTOR, http://www.jstor.org/stable/10.5749/j.ctttt2r0.5. Accessed 24

VII. Bangladesh Bureau of Statistics (BBS), ‘‘Statistical year book of Bangladesh 2022,’’ Bangladesh Bureau of Statistics, Dhaka 2022.

VIII. B.B.o.S.(2013a),“StatisticalyearbookofBangladesh,”BangladeshBureauof Statistics, Dhaks, 2012.

IX. Cleland et.al.(2012); Contraception and Health. Lancet. 380(9837):149-156
Doi:10.1016/S0140-6736(12)60609-6

X. D.E.C.D.&.S. J.Bloom, (2003) “Thedemographic dividend: Anewperspectiveonthe economic consequences of population change.” Rand: Santa Monica.
XI. El-doma, Mmohammed (2007). Age-structured Population Model with Cannibalism, Applications and Applied Mathematics: An International Journal (AAM),Vol.-2,Iss.-2. .https://digitalcommons.pvamu.edu/aam/vol2/iss2/3
XII. Fredrick et.al. The Royal Kingdoms of Ghana, Mali, and Songhay Life in Medieval Africa. Macmillan. p. 104. ISBN 978-0-8050-4259-7.

XIII. Fernando, & N. (1991). Demographic situation of Sri Lanka. Asian Population and Development Association 77-86.
XIV. Fred Brauer.(1983). Nonlinear age-dependent population growth under harvesting.Computers& Mathematics with Applications,Volume 9, Issue 3, Pages 345-352.
XV. F.R. Sharpe and A.J. Lotka.L.(1911). A problem in age-distribution.The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science. Vol 21, issue124, Page 435-438, https://doi.org/10.1080/14786440408637050
XVI. Glass, L., &Murray, J. D.(2003). Interdisciplinary Applied Mathematics: Mathematical Biology I. In Physical Review E -Statistical, Nonlinear, and Soft Matter Physics ( (Vol. 17, Issue 4).
XVII. ICDDR, B MCH-FP, “A Rapid Assessment Procedure to Assess Accessibility to and Utilization of Family Planning and Maternal and Child Health. extension Project (Rural) 1996. Services at the Local Level.” Dhaka: 1CDDR.B MCH-FP Extension Project (Rural), Intervention Update (Flyer), Vol. 2, No. 7 (published, May).

XVIII. ICDDR.B MCH-FP. “Maternal and Neonatal Health: Referral and Linkages from Community to Higher Levels.” Dhaka: ICDDR,B MCH-FP Extension Project (Rural), Intervention Update (Flyer), Vol. 2, No. 9 (published, May).

XIX. Islam et.al. (2004)“Fertility transition in Bangladesh: Understanding the role of the proximate determinants of fertility.,” Journal of Biosocial Science, vol. 36, p. 351–369.

XX. JNFPA, Dhaka, Bangladesh (2023). http://niport.gov.bd.

XXI. Karim.et.al. A Study about Forecasting Bangladesh by Using Verhulst Logistic Growth Model and Population Model. Annals of the Romanian Society for Cell Biology (2022) 26(01), 566–578. Retrieved from https://annalsofrscb.ro/index.php/journal/article/view/10845

XXII. Kamal et.al.(1994). “The determinants ofreproductive change in Bangladesh: Success in a challenging environment.”WorldBank, Washington DC.

XXIII. Karim.et.al.(2020). Investigate future population projection of Bangladesh with the help of Malthusian model, Sharpe-lotka model and Gurtin Mac-Camy model. International Journal of Statistics and Applied Mathematics 5(5). https://doi.org/10.22271/maths.2020.v5.i5b.585

XXIV. Lapham, R. J., & Mauldin, W. P. (1984).“Family planning effort and birthrate decline in developing countries,” International Family Planning Perspectives, vol. 10, no. 4, p. 109–118, 1984.

XXV. Liu et.al. Child Health Epidemiology Reference Group of WHO and UNICEF. Global, regional, and national causes of child mortality: an updated systematic analysis for 2010 with time trends since 2000. Lancet. 2012 Jun 9; 379(9832):2151-61. doi: 10.1016/S0140-6736(12)60560-1. Epub 2012 May 11. Erratum in: Lancet. 2012 Oct 13; 380(9850):1308. PMID: 22579125

XXVI. Mitra et.al. “Bangladesh Demographic and Health Survey: 1993-1994.” NIPORT and Mitra and Associates, Dhaka and Macro International Inc., Calverton, MD, U.S.A.

XXVII. Murray, J.D. (2002). Mathematical Biology: An Introduction. Springer, New York,1-75. https://doi.org/10.1007/b98868
XXVIII. Ministry of Family Planning (2022). https://mohfw.portal.govt.bd

XXIX. Nelder, J. A. (1961). The Fitting of a Generalization of the Logistic Curve. Biometrics, 17(1), 89. https://doi.org/10.2307/2527498.

XXX. NIPORT (2011). Bangladesh Maternal Mortality and Health Care Survey 2010. Summary of Key Findings and Implications, Dhaka, Bangladesh: NIPORT. LINK: https://bit.ly/36fvObN] NIPORT, DHAKA, (2013), (2022). https://www.niport.govt.bd

XXXI. “National Child Abuse and Neglect Data System Glossary” (PDF). Administration for Children & Families. 2000. Archived from the original (PDF) on 20 October 2020. Retrieved 30 October 2019.

XXXII. United Nations Department of Economic and Social Affairs, Population Division (2022). World Family Planning 2022: Meeting the changing needs for family planning: Contraceptive use by age and method. UNDESA/POP/2022/TR/NO.4(https://www.un.org/development.)

XXXIII. United Nations Department of Economic and Social Affairs, Population Division (2020). World Family Planning 2020 Highlights: Accelerating action to ensure universal access to family planning (ST/ESA/SER.A/450).

XXXIV. UNDP/GOB (2009). The nexus between urban poverty and local environmental degradation of Bangladesh. The International Journal of Environmental, Cultural, Economic and Sustainability 5: 229-240. DOI:10.18848/1832-2077/CGP/v05i02/54583

XXXV. PRAYİTNO et.al.(2022), Identification of Graph Thinking in Solving Mathematical Problems Naturally. Participatory Educational Research ,9(2), 118–135. https://doi.org/10.17275/per.22.32.9.2.
XXXVI. Sastry, N. (2000).The importance of international demographic research for the United States. Population Research and Policy Review 19, 199–232. DOI: https://doi.org/10.1023/A:1026552503626
XXXVII. Shair et.al. (2017). Evaluating extensions to coherent mortality forecasting models. Risks, 5(1), 1–20. https://doi.org/10.3390/risks5010016.

XXXVIII. Terano, H.J.R.(2018). Analysis of mathematical models of population dynamics applied to Philippine population growth. Far East Journal of Mathematical Sciences (FJMS). Vol 103, No: 3, Pages 561-571. http://dx.doi.org/10.17654/MS103030561

XXXIX. Turner et.al. A Generalization of the Logistic Law of Growth. Biometrics, 25(3), 577. https://doi.org/10.2307/2528910.Application, 08(03), 53–61.

XL. Ullah, M., Mostafa, G. , Jahan, N. and Khan, M. (2019).Analyzing and Projection of Future Bangladesh Population Using Logistic Growth Model. International Journal of Modern Nonlinear Theory and Application, 8, 53-61. doi: 10.4236/ijmnta.2019.83004.

XLI. Verhulst, P.F.(1838).Notice sur la loi que la population suit dans son accroissement. Correspondence Mathematiqueet Physique (Ghent), 10, 113-121.

XLII. “What services do family planning clinics provide?” NHS. Archived from the original on 11 November 2014. Retrieved 8 March 2008.

XLIII. World meter(www.Worldometers.info) Elaboration of data by United Nations, Department of Economic and Social Affairs, Population Division.

View Download

TWO PHASE SLIP FLOW OF BLOOD IN HEPATIC ARTERY WITH SPECIAL REFERENCE TO HEPATITIS B

Authors:

Ruma Bagchi, Anup Kumar Karak

DOI NO:

https://doi.org/10.26782/jmcms.2024.03.00006

Abstract:

In this paper, we have presented a model of two-phased arterial hepatic blood flow in hepaticarteries remote from the heart and proximate to the Liver keeping in view the nature of hepatic blood circulation in the human body. Blood is supposed to be non-Newtonian of the power-law type. Solutions of the constitutive equations are obtained in analytical as well as in numerical forms. The role of hematocrit is explicit in the determination of blood pressure drop in the case of Hepatic disease Hepatitis B.

Keywords:

Hepatic Blood Flow,Non-Newtonian power law model,Haematocrit,Blood pressure drop,Hepatitis B,

Refference:

I. Häussinger, Dieter, Liver Regeneration. Berlin: De Gruyter. 2011, 1.

II. Hwang S. Microcirculation of the liver. Venous embolization of the liver. DOI 10.1007/978-1-84882- 122-4_2, 2011.

III. Sinnatamby CS. Last’s anatomy: regional and applied. 11th ed. Edinburgh: Elsevier. 2006, 273. 8. Sheldon GF, Rutledge R. Hepatic trauma. AdvSurg; 22: 179-93, 1989.

IV. Upadhyay, V., Prakash, Om and Pandey, P. N. A mathematical model for two phase hepatic blood flow in artery with special reference to hepatitis-B, The Pharma Journal, 82-9,1.1, 2012.

V. Vollmar B, Menger MD. The hepatic microcirculation, mechanistic contributions and therapeutic target in liver injury and repair.Physiol Rev, 2009; 89:1269-1339.

VI. Vollmar B, Menger MD. The hepatic microcirculation: mechanistic contributions and therapeutic targets in liver injury and repair. Physiol. Rev. 1269-1339, 2009.

View Download