Journal Vol – 19 No – 11, November 2024

Abstract:

The proliferation of non-expensive commercially available renewable energy systems along with the regular interruption of electrical energy from local power producers has resulted in more DIY (do-it-yourself) enthusiasts. Many of these enthusiasts are from the lower to middle-income classes and thus seek to empower themselves to purchase and install a basic off-grid renewable energy system. It's crucial to emphasize the significance of acquiring a wiring certificate for the electrical setup. National standards, quality management, and human lives are all at risk, so this step cannot be overlooked. However, several components need to be connected in the most efficient and effective way, thereby promoting safety and efficiency. The purpose of this study is to evaluate different electrical connections between two of the main components, the battery (storage device) and the solar charger (or an inverter) to enable an informed decision regarding the optimal type of connection. An experimental setup is used to gather empirical data for seven different electrical connections. The worst type of connection is a solid 1,5 mm cable with battery clamps (or clips) that results in a higher voltage drop of 0,42 V when compared to the ideal type of connection that is a solid 2,5 mm cable with unsoldered crimped lugs. It is recommended that every DIY enthusiast working with electrical connections purchase a non-expensive crimping tool to effectively connect lugs to the correct wire diameter required for their application.

Keywords:

Crimping-tool,DIY enthusiasts,Off-grid,Optimal,Solar charger ,

Refference:

I. Bakır, Hale. “Detection of Faults in Photovoltaic Modules of Spps in Turkey; Infrared Thermographic Diagnosis and Recommendations.” Journal of Electrical Engineering & Technology, vol. 18, no. 3, 2023, pp. 1945-57.
II. Beszédes, Bertalan et al. “The Practice of Troubleshooting and Maintenance in a Small-Scale Off-Grid Industrial Environment.” 2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI), IEEE, 2023, pp. 000213-18.
III. Brainy Quote. “Homepage.” http://www.brainyquote.com/quotes/. Accessed 10 March 2020.
IV. Desai, Alpesh et al. “Temperature Effects on Dc Cable Voltage Drop in Utility Scale Rooftop Solar Pv Plant Based on Empirical Model.” 2020 47th IEEE Photovoltaic Specialists Conference (PVSC), IEEE, 2020, pp. 2397-2402.
V. Du, Pin et al. “Research Progress Towards the Corrosion and Protection of Electrodes in Energy-Storage Batteries.” Energy Storage Materials, 2023.
VI. Fidai, Aamir et al. “Internet of Things (Iot) Instructional Devices in Stem Classrooms: Past, Present and Future Directions.” 2019 IEEE Frontiers in Education Conference (FIE), IEEE, 2019, pp. 1-9.
VII. Generation, Dispersed and Energy Storage. “Ieee Recommended Practice for Installation and Maintenance of Lead-Acid Batteries for Photovoltaic (Pv) Systems.”
VIII. Iderus, Samat et al. “Optimization and Design of a Sustainable Industrial Grid System.” Mathematical Problems in Engineering, vol. 2022, 2022.
IX. John, Obukoeroro and HE Uguru. “Appraisal of Electrical Wiring and Installations Status in Isoko Area of Delta State, Nigeria.” Journal of Physical Science and Environmental Studies, vol. 7, no. 1, 2021, pp. 1-8.
X. Paul, Kamal Chandra et al. “Series Ac Arc Fault Detection Using Decision Tree-Based Machine Learning Algorithm and Raw Current.” 2022 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE, 2022, pp. 1-8.
XI. Ramay, Muhammad Bin Zubaid et al. “Corrosion Effect in Underground Lv Distribution Networks in Domestic and Commercial Buildings.” Engineering Proceedings, vol. 22, no. 1, 2022, p. 16.
XII. Sun, RL et al. “A New Method for Charging and Repairing Lead-Acid Batteries.” IOP Conference Series: Earth and Environmental Science, vol. 461, IOP Publishing, 2020, p. 012031.
XIII. Szabó, Gabriella-Stefánia et al. “A Review of the Mitigating Methods against the Energy Conversion Decrease in Solar Panels.” Energies, vol. 15, no. 18, 2022, p. 6558.
XIV. Vaideeswaran, V et al. “Battery Management Systems for Electric Vehicles Using Lithium Ion Batteries.” 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), vol. 1, 2019, pp. 1-9.
XV. Wang, Yang-Yang et al. “Mechanism, Quantitative Characterization, and Inhibition of Corrosion in Lithium Batteries.” Nano Research Energy, vol. 2, no. 1, 2023, p. e9120046.
XVI. Yao, Xing-Yan and Michael G Pecht. “Tab Design and Failures in Cylindrical Li-Ion Batteries.” IEEE Access, vol. 7, 2019, pp. 24082-95.

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Abstract:

The growth trend and increasing global population are leading to new healthcare challenges that require prompt and effective solutions to meet the clinical demands. Currently, three-dimensional (3D) printing is emerging as a rapidly advancing technology to produce metal implants and other biomedical applications. This method creates intricate designs with biomimetic characteristics in a shorter timeframe, enabling healthcare providers to meet the needs of their patients better. This study thoroughly analyzes the demand and manufacturing methods for biomedical implants, particularly metal bio-implants. It also delves into biomaterials used in additive manufacturing, accompanied by a comprehensive bibliometric study covering scientific production by country, highly cited nations, productive authors, collaboration networks, and source rankings. The paper further investigates top author contributions, affiliations, and trends, featuring various analytical tools, such as co-citation networks, keyword co-occurrence analysis, and reference publication year spectroscopy, culminating in presenting key findings through insightful field plots. The current study uses network analysis and scientometric methodologies to analyze data taken from the Scopus journal database, which includes articles from the period between 2014 and 2023, to accomplish this goal. Through this analysis, the article aims to offer valuable insights into the relevance and real-world implications of previous research on the additive manufacturing of metal bio-implants.

Keywords:

Biomedical implant,Bibliographical analysis,3D-printing,literature review,RStudio,

Refference:

I. Al-Khoury, A., Hussein, S. A., Abdulwhab, M., Aljuboori, Z. M., Haddad, H., Ali, M. A., Abed, I. A., & Flayyih, H. H. (2022). Intellectual Capital History and Trends: A Bibliometric Analysis Using Scopus Database. Sustainability, 14(18), 11615. 10.3390/su141811615

II. Al-Shalawi, F. D., Mohamed Ariff, A. H., Jung, D.-W., Mohd Ariffin, M. K. A., Seng Kim, C. L., Brabazon, D., & Al-Osaimi, M. O. (2023). Biomaterials as Implants in the Orthopedic Field for Regenerative Medicine: Metal versus Synthetic Polymers. Polymers, 15(12), 2601. 10.3390/polym15122601

III. Bandyopadhyay, A., Traxel, K. D., & Bose, S. (2021). Nature-inspired materials and structures using 3D Printing. Materials Science and Engineering: R: Reports, 145, 100609. 10.1016/j.mser.2021.100609

IV. Castanha, R. G., Grácio, M. C. C., & Perianes-Rodríguez, A. (2024). Co-citation analysis between coupler authors of a scientific domain’s citation identity: a case study in scientometrics. Scientometrics, 129(3), 1545–1566. 10.1007/s11192-023-04927-8

V. Chang, Y., & Huang, M. (2012). A study of the evolution of interdisciplinarity in library and information science: Using three bibliometric methods. Journal of the American Society for Information Science and Technology, 63(1), 22–33. 10.1002/asi.21649

VI. Everton, S. K., Hirsch, M., Stravroulakis, P., Leach, R. K., & Clare, A. T. (2016). Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing. Materials & Design, 95, 431–445. 10.1016/j.matdes.2016.01.099

VII. Gutiérrez-Salcedo, M., Martínez, M. Á., Moral-Munoz, J. A., Herrera-Viedma, E., & Cobo, M. J. (2017). Some bibliometric procedures for analyzing and evaluating research fields. Applied Intelligence. 10.1007/s10489-017-1105-y

VIII. Kalantari, A., Kamsin, A., Kamaruddin, H. S., Ale Ebrahim, N., Gani, A., Ebrahimi, A., & Shamshirband, S. (2017). A bibliometric approach to tracking big data research trends. Journal of Big Data, 4(1), 30. 10.1186/s40537-017-0088-1

IX. Lewandowski, J. J., & Seifi, M. (2016). Metal Additive Manufacturing: A Review of Mechanical Properties. Annual Review of Materials Research, 46(1), 151–186. 10.1146/annurev-matsci-070115-032024

X. Li, C., Pisignano, D., Zhao, Y., & Xue, J. (2020). Advances in Medical Applications of Additive Manufacturing. Engineering, 6(11), 1222–1231. 10.1016/j.eng.2020.02.018

XI. Meho, L. I., & Yang, K. (2007). Impact of data sources on citation counts and rankings of LIS faculty: Web of science versus scopus and google scholar. Journal of the American Society for Information Science and Technology, 58(13), 2105–2125. 10.1002/asi.20677

XII. Mehrpouya, M., Dehghanghadikolaei, A., Fotovvati, B., Vosooghnia, A., Emamian, S. S., & Gisario, A. (2019). The Potential of Additive Manufacturing in the Smart Factory Industrial 4.0: A Review. Applied Sciences, 9(18), 3865. 10.3390/app9183865

XIII. Mejia, C., Wu, M., Zhang, Y., & Kajikawa, Y. (2021). Exploring Topics in Bibliometric Research Through Citation Networks and Semantic Analysis. Frontiers in Research Metrics and Analytics, 6. 10.3389/frma.2021.742311

XIV. ]Murr, L.E. (2020). Metallurgy principles applied to powder bed fusion 3D printing/additive manufacturing of personalized and optimized metal and alloy biomedical implants: an overview. Journal of Materials Research and Technology, 9(1), 1087–1103. 10.1016/j.jmrt.2019.12.015

XV. 15] Murr, Lawrence E., Gaytan, S. M., Ramirez, D. A., Martinez, E., Hernandez, J., Amato, K. N., Shindo, P. W., Medina, F. R., & Wicker, R. B. (2012). Metal Fabrication by Additive Manufacturing Using Laser and Electron Beam Melting Technologies. Journal of Materials Science & Technology, 28(1), 1–14. 10.1016/S1005-0302(12)60016-4

XVI. OSAREH, F. (1996). Bibliometrics, Citation Analysis and Co-Citation Analysis: A Review of Literature I. Libri, 46(3). 10.1515/libr.1996.46.3.149

XVII. Pandey, A., Awasthi, A., & Saxena, K. K. (2020). Metallic implants with properties and latest production techniques: a review. Advances in Materials and Processing Technologies, 6(2), 405–440. 10.1080/2374068X.2020.1731236

XVIII. Paul, S., Nath, A., & Roy, S. S. (2021). Additive manufacturing of multi-functional biomaterials for bioimplants: a review. IOP Conference Series: Materials Science and Engineering, 1136(1), 012016. 10.1088/1757-899X/1136/1/012016

XIX. Pranckutė, R. (2021). Web of Science (WoS) and Scopus: The Titans of Bibliographic Information in Today’s Academic World. Publications, 9(1), 12. 10.3390/publications9010012

XX. Sakata, I., Sasaki, H., Akiyama, M., Sawatani, Y., Shibata, N., & Kajikawa, Y. (2013). Bibliometric analysis of service innovation research: Identifying knowledge domain and global network of knowledge. Technological Forecasting and Social Change, 80(6), 1085–1093. 10.1016/j.techfore.2012.03.009

XXI. Schmitt, P., Zorn, S., & Gericke, K. (2021). ADDITIVE MANUFACTURING RESEARCH LANDSCAPE: A LITERATURE REVIEW. Proceedings of the Design Society, 1, 333–344. 10.1017/pds.2021.34

XXII. Singh, V. K., Singh, P., Karmakar, M., Leta, J., & Mayr, P. (2021). The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis. Scientometrics, 126(6), 5113–5142. 10.1007/s11192-021-03948-5

XXIII. Sridhar, T. M., Vinodhini, S. P., Kamachi Mudali, U., Venkatachalapathy, B., & Ravichandran, K. (2016). Load-bearing metallic implants: electrochemical characterisation of corrosion phenomena. Materials Technology, 31(12), 705–718. 10.1080/10667857.2016.1220752

XXIV. Tilton, M., Lewis, G. S., & Manogharan, G. P. (2018). Additive Manufacturing of Orthopedic Implants. In Orthopedic Biomaterials (pp. 21–55). Springer International Publishing. 10.1007/978-3-319-89542-0_2

XXV. van Raan, A. F. J. (2006). Statistical properties of bibliometric indicators: Research group indicator distributions and correlations. Journal of the American Society for Information Science and Technology, 57(3), 408–430. 10.1002/asi.20284

XXVI. Wang, J., Zhang, Y., Aghda, N. H., Pillai, A. R., Thakkar, R., Nokhodchi, A., & Maniruzzaman, M. (2021). Emerging 3D printing technologies for drug delivery devices: Current status and future perspective. Advanced Drug Delivery Reviews, 174, 294–316. 10.1016/j.addr.2021.04.019

XXVII. Weismayer, C., & Pezenka, I. (2017). Identifying emerging research fields: a longitudinal latent semantic keyword analysis. Scientometrics, 113(3), 1757–1785. 10.1007/s11192-017-2555-z

XXVIII. Yadav, L. K., Misra, J. P., Kumar, V., Saxena, K. K., & Buddhi, D. (2022). Additive manufacturing for metal-based bio-implant development: A bibliometric analysis. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 095440892211327. 10.1177/09544089221132737

XXIX. Zhou, Q., Su, X., Wu, J., Zhang, X., Su, R., Ma, L., Sun, Q., & He, R. (2023). Additive Manufacturing of Bioceramic Implants for Restoration Bone Engineering: Technologies, Advances, and Future Perspectives. ACS Biomaterials Science & Engineering, 9(3), 1164–1189. 10.1021/acsbiomaterials.2c01164

XXX. Zhu, W., & Guan, J. (2013). A bibliometric study of service innovation research: based on complex network analysis. Scientometrics, 94(3), 1195–1216. 10.1007/s11192-012-0888-1

XXXI. Zwawi, M. (2022). Recent advances in bio-medical implants; mechanical properties, surface modifications and applications. Engineering Research Express, 4(3), 032003. 10.1088/2631-8695/ac8ae2

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Abstract:

This research primarily presents a model involving R-serial service channels connected to S non-serial service channels. Feedback mechanisms are applied to the serial queues, while balking and reneging behaviors, triggered by urgent calls/messages or customer impatience, are analyzed in both serial and non-serial queues. After developing the queuing model, the system’s differential-difference equations are formulated in a compact form, and their solutions are derived by reducing them to the steady-state form for unlimited waiting capacity. Marginal probabilities and mean queue lengths are calculated to evaluate the system's performance in this scenario.

Keywords:

Differential-difference equations,Exponential,Impatient behaviour,Poisson,Probabilities,Queue discipline,Service channels,Steady-state,Urgent message,Waiting space,

Refference:

I. Ahmed, M.M.S. “Multi-channel bi-level heterogeneous servers bulk arrivals queuing system with Erlangian service time”. Mathematical and Computational Applications. Vol. 12(2) pp.97-1010.3390/mca12020097.
II. Barrer, D.Y.A, “Waiting line problem characterized by impatient customers and indifferent clerk”. Journal of Operations Research Society of America, vol.3, pp. 360-367, 1955.
III. Cox, D.R,“The statistical analysis of congestion”. Journal of the Royal Statistical Society ; vol.118(3), pp. 324-335, 10.2307/2342496
IV. Gupta Meenu, Singh, Man and Gupta, Deepak, “Analysis of queueing system consisting of multiple of parallel channels in series connected to non-serial servers with finite waiting space and balking, reneging”. Journal of Natural Sciences Research, vol.5(3),2015.
V. Singh, Man. ,”Study of some queuing problems”, 1975, Kurukshetra University, Kurukshetra.
VI. Singh, Man. “Steady-state behaviour of serial queuing processes with impatient customers”. Math. Operations forsch. U.Statist. Ser. Statist., vol. 15 (2), pp. 289-298,1984, 10.1080/02331888408801769
VII. Singh, Satyabir and Singh, Man.”The steady-state solution of serial channels with feedback and reneging connected with non-serial queuing processes with reneging and balking”, Indian Journal of Scientific Research and Engineering” vol.3(10),pp. 1-5,2015.
VIII. Singh, Satyabir; Singh, Man, ”Study of some serial and non-serial queuing processes with various types of customers’ behaviour”,2016, Kurukshetra University, Kurukshetra.

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Abstract:

Partial differential equations are essential to every branch of science and engineering. They are regarded as the fundamental components of the majority of mathematical and physical simulations with practical uses. Numerous partial differential equations may be useful in the description of a physical phenomenon that could help in a deeper comprehension of its behaviour. The importance of PDEs has drawn more attention in recent years, which motivates researchers to solve these equations analytically and numerically. In this study, we propose a new hybrid technique for solving partial differential equations arising during liquid drop formations. The proposed hybrid technique is the combustion of double Elzaki transform and the classical Adomian decomposition method. To illustrate the simplicity and accuracy of the proposed scheme, some experimental work has been carried out.

Keywords:

Double Elzaki transform,Adomian decomposition method,Rosenau Hyman equations,Test examples,

Refference:

I. Ahmed, S., ‘Application of Sumudu Decomposition Method for Solving Burger’s Equation,’ Advances in Theoretical and Applied Mathematics, Vol. 9(1), pp. 23-26, (2014).

II. Alderremy, A. A. and Elzaki, T.M., ‘On the New Double Integral Transform for Solving Singular System of Hyperbolic Equations,’ Journal of Nonlinear Sciences and Applications, Vol. 11, pp. 1207-1214, (2018). 10.22436/jnsa.011.10.08
III. Eltayeb, H. and Kilicman, A., ‘A Note on Double Laplace Transform and Telegraphic Equations,’ Abstract and Applied Analysis, 2013, pp. 1-6, (2013). 10.1155/2013/932578

IV. Elzaki, T., ‘The New Integral Transform Elzaki Transform,’ Global Journal of Pure and Applied Mathematics, Vol. 7(1), pp. 57-64, (2011). http://www.ripublication.com/gjpam.htm

V. Elzaki, T. M. and Hilal, E.M., ‘Solution of Telegraph Equation by Modified Double Sumudu Transform ‘Elzaki Transform,’ Mathematical Theory and Modeling, Vol. 2, pp. 95-103, (2012). 10.4236/am.2015.63056

VI. Elzaki, T. and Hilal, M.A.,‘Solution of Linear and Non-Linear Partial Differential Equations Using Mixture of Elzaki Transform and the Projected Differential Transform Method,’ Mathematical Theory and Modeling, Vol. 2(4), pp. 50-59, (2012).

VII. Elzaki, T. and Elzaki, S.M., ‘On the Connections Between Laplace and Elzaki Transforms,’ Advances in Theoretical and Applied Mathematics, Vol. 6(1), pp. 1-10, (2011). http://www.ripublication.com/atam.htm
VIII. Elzaki, T., and Elzaki, S. M., ‘On the Elzaki Transform and Ordinary Differential Equation with Variable Coefficients,’ Advances in Theoretical and Applied Mathematics, Vol. 6(1), pp. 41-46, (2011). http://www.ripublication.com/atam.htm

IX. Elzaki, T., Elzaki, J.M. and Hilal, M.A., ‘Elzaki and Sumudu Transforms for Solving Some Differential Equations,’ Global Journal of Pure and Applied Mathematics, Vol. 8(2), pp. 167-173 (2012). http://www.ripublication.com/gjpam.htm

X. Hassaballa, I. A., and Salih, Y.A., ‘On Double Elzaki Transform and Double Laplace Transform,’ IOSR Journal of Mathematics, Vol. 11(1), pp. 35-41, (2015). 10.9790/5728-11163541

XI. Hassan, M. A., and Elzaki, T.M., ‘Double Elzaki Transform Decomposition Method for Solving Non-Linear Partial Differential Equations,’ Journal of Applied Mathematics and Physics, Vol. 8, pp. 1463-1471 (2020). 10.4236/jamp.2020.88112.

XII. Hassan, M. A., and Elzaki, T.M., ‘Double Elzaki Transform Decomposition Method for Solving Third Order Korteweg-De Vries Equations,’ Journal of Applied Mathematics and Physics, Vol. 9, pp. 21-30 (2021). 10.4236/jamp.2021.91003.

XIII. Idrees, M. I., Ahmed, Z., Awais, M. and Perveen, Z., ‘On the Convergence of Double Elzaki Transform,’ International Journal of Advanced and Applied Sciences, Vol. 5, pp. 19-24,(2018). 10.21833/ijaas.2018.06.003

XIV. Ige, O. E., Heilio, M., Oderinu, R.A. and Elzaki, T.M., ‘Adomian Polynomial and Elzaki Transform Method of Solving Third Order Korteweg-De Vries Equations,’ Pure and Applied Mathematics, Vol. 15, pp. 261-277, (2019). 10.12732/ijam.v32i3.7

XV. Sharma, S and Singh, I., ‘Elzaki transform homotopy analysis techniques for solving fractional (2+1)-D and (3+1)-D nonlinear Schrodinger equations,’ Communications on Applied Nonlinear Analysis, Vol. 31 (6s), pp. 305-317, (2024). 10.52783/cana.v31.1224

XVI. Singh, I. and Kumari, U., ‘Elzaki transform homotopy perturbation method for solving two- dimensional time fractionl Rosenau-Hyman equation,’ MATHEMATIKA, MJIM, Vol. 39 (2), pp. 159-171, (2023).

XVII. Singh, I. and Kumari, U., ‘Solving 2D and 3D Telegraph equations with Elzaki transform and Homotopy perturbation method,’ Journal of Mechanics of Continua and Mathematical Sciences, Specal Issue , Vol. 11, pp. 18-29, (2024). 10.26782/jmcms.spl.11/2024.05.00002

XVIII. ZeinEldin, R.A., Singh, I., Singh, G., Elgarhy, M. and El-Wahed Khalifa, H.A., ‘Efficient technique for solving (3+1)-D fourth order parabolic PDEs with time fractional derivatives,’ Axioms, Vol. 12(4), pp. 347, (2023). 10.3390/axioms12040347

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Abstract:

The article is devoted to the actual problem of studying the dynamic response of bent plate structures with reinforcement represented by rigid metal profiles. Such structures can be used in pedestrian bridges or other span structures. A finite element model is built by using the example of a pedestrian bridge slab with reinforcement in the form of steel T-shape profiles. Deformations of concrete, reinforcement, and rigid reinforcement bars are described by a system of solid and shell finite elements, that take into consideration modern models of physical, geometric, and structural nonlinearity. The dynamic impact is modeled at low speed in two variants. The first is a blast load is applied in the middle of the span according to a symmetrical scheme, and the second pursuant to an asymmetric scheme. The structural and inertial damping of vibrations of the damaged system is taken into account. In this case, an implicit integration method is used. The time variation of the dynamic load implies a residual mass of the impacting body that vibrates with the slab structure after the onset of impact. The bond between concrete and stiff rebar is evaluated by the level of cohesion stresses in the vicinity of the profile with maximum strains. The finite element model is verified with a full-scale experiment in which a slab with rigid reinforcement is built and tested. Numerical studies have shown that asymmetrical loading can have a more negative effect on the structure than symmetrical loading, with structure deflections varying by up to 42%. As a result, the effectiveness of experimental theoretical modeling of the dynamics of such structures is shown, which can be used for both typical and individual designs.

Keywords:

Dynamic load,Numerical simulation,Pedestrian bridge,Reinforced concrete.,

Refference:

I. Al Ali, Mohamad & Kvočák, Vincent & Dubecký, Daniel & Alekseytsev, Anatoliy. (2023). Experimental research on composite deck bridges with encased steel beams. Delta University Scientific Journal. 6. 31-38. 10.21608/dusj.2023.291004.
II. Alekhin V, Budarin A, Pletnev M, Avdonina L. MATEC Web of Conferences. 2019. 279 (02):02005. DOI: 10.1051/matecconf/201927902005
III. Alekseytsev A., Gaile L., Drukis P., : “Optimization of Steel Beam Structures for Frame Buildings Subject to Their Safety Requirements.” DOAJ (DOAJ: Directory of Open Access Journals), Nov. 2019, 10.18720/mce.91.1.

IV. Alekseytsev A., Sazonova S. : Numerical Analysis of the Buried Fiber Concrete Slabs Dynamics under Blast Loads. Magazine of Civil Engineering 2023, 117, doi:10.34910/MCE.117.3.

V. Alekseytsev, A., : “Mechanical Safety of Reinforced Concrete Frames Under Complex Emergency Actions.” DOAJ (DOAJ: Directory of Open Access Journals), Apr. 2021, https://doi.org/10.34910/mce.103.6.
VI. Ansys 2021. Mechanical APDL feature archive.
VII. Bregoli Guido, et al. “Static and Dynamic Tests on Steel Joints Equipped With Novel Structural Details for Progressive Collapse Mitigation.” Engineering Structures, vol. 232, Jan. 2021, p. 111829. https://doi.org/10.1016/j.engstruct.2020.111829.
VIII. Budarin A.M., Rempel G.I., Kamzolkin A.A., Alekhin V.N. Concrete damage–plasticity model with double independent hardening. Vestnik MGSU [Monthly Journal on Construction and Architecture]. 2024;19(4):527-543. (In Russ.) https://doi.org/10.22227/1997-0935.2024.4.527-543
IX. Chen Y., May I., : “Reinforced Concrete Members Under Drop-weight Impacts.” Proceedings of the Institution of Civil Engineers – Structures and Buildings, vol. 162, no. 1, Jan. 2009, pp. 45–56. https://doi.org/10.1680/stbu.2009.162.1.45.
X. Dmitriev A., Novozhilov Y., Mikhalyuk D., Lalin, V. Calibration and Validation of the Menetrey-Willam Constitutive Model for Concrete // Construction of Unique Buildings and Structures. 2020. Volume 88. Article No 8804. pp. 84-91.
XI. Fan Wei, et al., : “Reinforced Concrete Bridge Structures Under Barge Impacts: FE Modeling, Dynamic Behaviors, and UHPFRC-based Strengthening.” Ocean Engineering, vol. 216, Sept. 2020, p. 108116. 10.1016/j.oceaneng.2020.108116.

XII. Fu Tao, et al., : “Study on the Time-dependent Reliability of Corroded Reinforced Concrete Bridge Structures Due to Ship Impact.” Advances in Civil Engineering, vol. 2022, Jan. 2022, pp. 1–13. 10.1155/2022/8190297.
XIII. Inelastic analysis of structures, Milan J, Zdeněk P. ISBN 0-471-98716-6, 758 Pages. 10.1007/s00158-002-0217-z
XIV. Isaac, O.S.; Jagadeesh, G. Impulse Loading of Plates Using a Diverging Shock Tube. Exp Mech 2020, 60, doi:10.1007/s11340-019-00573-5.

XV. Kasilingam S., Sharma R., Senthil R., Iqbal M., Gupta N., : “Influence of Reinforcement Bar on the Performance of Reinforced Concrete Slab Under Impact Loading.” Springer Proceedings in Materials, 2023, pp. 331–42. 10.1007/978-981-99-6030-9_29.
XVI. Korsun V.I., Karpenko S.N., Makarenko S.Yu., Nedoresov A.V. Modern strength criteria for concrete under triaxial stress states. Building and Reconstruction. 2021;(5):16-30. 10.33979/2073-7416-2021-97-5-16-30
XVII. Kristoffersen, M., Hauge K., Minoretti A., Børvik T., : “Experimental and Numerical Studies of Tubular Concrete Structures Subjected to Blast Loading.” Engineering Structures, vol. 233, Feb. 2021, p. 111543. 10.1016/j.engstruct.2020.111543.

XVIII. Kumpyak, O., Galyautdinov Z., Kokorin D., : “Strength of Concrete Structures Under Dynamic Loading.” AIP Conference Proceedings, Jan. 2016, https://doi.org/10.1063/1.4937876.

XIX. Li Z., Zhang X., Shi Y., Wu C., Li J., : “Finite Element Modeling of FRP Retrofitted RC Column Against Blast Loading.” Composite Structures, vol. 263, Feb. 2021, p. 113727. https://doi.org/10.1016/j.compstruct.2021.113727.
XX. Li Zhong-Xian, et al. “Finite Element Modeling of FRP Retrofitted RC Column Against Blast Loading.” Composite Structures, vol. 263, Feb. 2021, p. 113727. 10.1016/j.compstruct.2021.113727.

XXI. Liu Z., Lu Z., Li Y., Hu R, Tu B., : “Study on Impact Resistance of Flexible Shed Tunnel for Bridges in Mountainous Areas”. Tiedao Xuebao/Journal of the China Railway Society 2023, 45. doi:10.3969/j.issn.1001-8360.2023.03.015.

XXII. Maazoun A., Matthys S., Belkassem B., Atoui O., Lecompte, D., : “Experimental Study of the Bond Interaction Between CFRP and Concrete Under Blast Loading.” Composite Structures, vol. 277, Aug. 2021, p. 114608. 10.1016/j.compstruct.2021.114608.

XXIII. Mahmoud Khaled Ahmed. : “Lateral Deformation Behavior of Eccentrically Loaded Slender RC Columns With Different Levels of Rotational End Restraint at Elevated Temperatures.” Journal of Structural Fire Engineering, vol. 12, no. 1, Sept. 2020, pp. 35–64. 10.1108/jsfe-04-2020-0014.
XXIV. Menetrey P. Numerical analysis of punching failure in reinforced concrete structures, PhD thesis, Ecole Polytechnique Federale de Lausanne, Lausanne. 1994. p. 179.
XXV. Minhas A., None S., : “Numerical Simulation of Projectile Impact on Reinforced Concrete Structures: A Study of Slab Performance Under Varying Projectile Velocities Using ANSYS.” Innovative Infrastructure Solutions, vol. 9, no. 10, Sept. 2024, 10.1007/s41062-024-01671-7.

XXVI. Mishra N, Netula, O., : “Behaviour of Reinforced Concrete Framed Structure Subjected to Blast Loading”. International Journal of Advanced Research in Engineering and Technology (IJARET) 2021, 12, pp: 173-181. 10.34218/ijaret.12.1.2021.014
XXVII. Model Code for Concrete Structures 2010. International Federation for Structural Concrete, 2013. ISBN: 978-3-433-03061-5, 434 p
XXVIII. Ngo T., Mendis P., : Ngo, Tuan, and Priyan Mendis. “Modelling the Dynamic Response and Failure Modes of Reinforced Concrete Structures Subjected to Blast and Impact Loading.” STRUCTURAL ENGINEERING AND MECHANICS, vol. 32, no. 2, May 2009, pp. 269–82. 10.12989/sem.2009.32.2.269.

XXIX. Niroomandi A., Pampanin S., Dhakal R., Ashtiani M., De La Torre C., : “Rectangular RC walls under bi-directional loading: recent experimental and numerical findings”. The Concrete NZ Conference, 2018.

XXX. Qasrawi Y., Heffernan P., Fam A., : Qasrawi, Yazan, et al. “Dynamic Behaviour of Concrete Filled FRP Tubes Subjected to Impact Loading.” Engineering Structures, vol. 100, June 2015, pp. 212–25. 10.1016/j.engstruct.2015.06.012.

XXXI. Shokrabadi M., Henry B., : “Risk-based Assessment of Aftershock and Mainshock-aftershock Seismic Performance of Reinforced Concrete Frames.” Structural Safety, vol. 73, Mar. 2018, pp. 64–74. 10.1016/j.strusafe.2018.03.003.

XXXII. Sidorov V., Badina E., Detina E. Sidorov V., : “Nonlocal in time model of material damping in composite structural elements dynamic analysis.” International Journal for Computational Civil and Structural Engineering, vol. 17, no. 4, Dec. 2021, pp. 14–21. 10.22337/2587-9618-2021-17-4-14-21.

XXXIII. Siguerdjidjene H., Leonid D., : “Study of Dynamic Impact of Speed Trains on Bridge Structures.” International Journal of Mechanics, vol. 15, Mar. 2021, pp. 30–36. 10.46300/9104.2021.15.4.

XXXIV. Sinković L., et al., : “Risk‐based Seismic Design for Collapse Safety.” Earthquake Engineering & Structural Dynamics, vol. 45, no. 9, Mar. 2016, pp. 1451–71. 10.1002/eqe.2717.

XXXV. Sinković L., Matjaž D., : “Fatality Risk and Its Application to the Seismic Performance Assessment of a Building.” Engineering Structures, vol. 205, Dec. 2019, p. 110108. https://doi.org/10.1016/j.engstruct.2019.110108.

XXXVI. Terrenzi, M., et al. “Collapse Limit State Definition for Seismic Assessment of Code-conforming RC Buildings.” International Journal of Advanced Structural Engineering, vol. 10, no. 3, Sept. 2018, pp. 325–37. 10.1007/s40091-018-0200-6.

XXXVII. Thai, D.K., Pham, T.H., Nguyen, D.L. Thai, Duc‐Kien, et al. “Damage Assessment of Reinforced Concrete Columns Retrofitted by Steel Jacket Under Blast Loading.” The Structural Design of Tall and Special Buildings, vol. 29, no. 1, Nov. 2019, 10.1002/tal.1676.

XXXVIII. Yilmaz Tolga, et al. : “Experimental Investigation of Axially Loaded Reinforced Concrete Square Column Subjected to Lateral Low‐velocity Impact Loading.” Structural Concrete, vol. 20, no. 4, Apr. 2019, pp. 1358–78. 10.1002/suco.201800276.

XXXIX. Zhang Jingfeng, et al., : “A Comprehensive Approach for Bridge Performance Evaluation Under Rockfall Impact Integrated With Geological Hazard Analysis.” Engineering Failure Analysis, vol. 141, Aug. 2022, p. 106668. 10.1016/j.engfailanal.2022.106668.

XL. Zhao Wuchao, et al., : “Performance of Bridge Structures Under Heavy Goods Vehicle Impact.” Computers and Concrete, an International Journal, vol. 22, no. 6, Dec. 2018, p. 515. 10.12989/cac.2018.22.6.515.

XLI. Zheng X., Song J., Liu L., Sun B., Gao Z., : “Static and Dynamic Load Test on Concrete Hollow-core Slab Bridge.” Advances in Engineering Technology Research, vol. 11, no. 1, July 2024, p. 327. 10.56028/aetr.11.1.327.2024.

XLII. Zhou Xiongfei, Lin Jing., : “Deflection Analysis of Clamped Square Sandwich Panels With Layered-gradient Foam Cores Under Blast Loading.” Thin-Walled Structures, vol. 157, Sept. 2020, p. 107141. 10.1016/j.tws.2020.107141.

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Abstract:

Wireless communication in marine environments is hindered by the unique properties of seawater and the rugged ocean floor. In contrast to land-based communication, underwater conditions are distinct due to the specific characteristics of seawater. This research explores the potential of Network Function Virtualization (NFV) to enhance the monitoring of seaweed farms and underwater properties. As seaweed production is vital for the development of nutritional products, biochemical compounds, and pharmacological research, optimizing its monitoring is crucial. The goal of this study is to leverage NFV to support various aquatic activities. To achieve this, a chain of Virtual Network Functions (VNFs) is proposed to manage service flows, capitalizing on the advancements in NFV. The research employs both simulation and analytical Stochastic Network Calculus (SNC) models to evaluate key performance indicators, including delay bounds, throughput, packet delivery ratio, and energy utilization. Notably, the SNC-based NFV model outperforms simulation results, demonstrating superior performance and potential for improved packet delivery and throughput.

Keywords:

Underwater acoustic wireless communication,Network Function Virtualization,Stochastic Network Calculus,Delay bound,

Refference:

I. Awan, Khalid Mahmood, et al. “Underwater wireless sensor networks: A review of recent issues and challenges.” Wireless Communications and Mobile Computing 2019.1 (2019): 6470359, 10.1155/2019/6470359.
II. Bennouri, Hajar, and Amine Berqia. “U-NewReno transmission control protocol to improve TCP performance in Underwater Wireless Sensors Networks.” Journal of King Saud University-Computer and Information Sciences 34.8 (2022): 5746-5758, 10.1016/j.jksuci.2021.08.006.
III. Bhamare, Deval, et al. “Optimal virtual network function placement in multi-cloud service function chaining architecture.” Computer Communications 102 (2017): 1-16, 10.1016/j.comcom.2017.02.011.
IV Bari, Faizul, et al. “Orchestrating virtualized network functions.” IEEE Transactions on Network and Service Management 13.4 (2016): 725-739, 10.1109/TNSM.2016.2569020.
V. Coutinho, Rodolfo WL, et al. “Underwater wireless sensor networks: A new challenge for topology control–based systems.” ACM Computing Surveys (CSUR) 51.1 (2018): 1-36. 10.1145/3154834.
VI. Data Plane Development Kit, Jan. 2021, [online] Available: https://www.dpdk.org/.
VII. Duan, Qiang. “Modeling and performance analysis for service function chaining in the SDN/NFV architecture.” 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft). IEEE, 2018.. IEEE, 10.1109/NETSOFT.2018.8460068.
VIII. Fattah, Salmah, et al. “A survey on underwater wireless sensor networks: Requirements, taxonomy, recent advances, and open research challenges.” Sensors 20.18 (2020): 5393, 10.3390/s20185393.
IX. Fidler, Markus. “Survey of deterministic and stochastic service curve models in the network calculus.” IEEE Communications surveys & tutorials 12.1 (2010): 59-86, 10.1109/SURV.2010.020110.00019.
X. Gouareb, Racha, Vasilis Friderikos, and Abdol-Hamid Aghvami. “Virtual network functions routing and placement for edge cloud latency minimization.” IEEE Journal on Selected Areas in Communications 36.10 (2018): 2346-2357. 10.1109/JSAC.2018.2869955.
XI. Haque, Khandaker Foysal, K. Habibul Kabir, and Ahmed Abdelgawad. “Advancement of routing protocols and applications of underwater wireless sensor network (UWSN) — A survey.” Journal of Sensor and Actuator Networks 9.2 (2020): 19. 10.3390/jsan9020019.
XII. Hassan, Mohamed Khalafalla, et al. “A Short Review on the Dynamic Slice Management in Software-Defined Network Virtualization.” Engineering, Technology & Applied Science Research 13.6 (2023): 12074-12079, 10.48084/etasr.6394.
XIII. Huang, Xiangdang, Shijie Sun, and Qiuling Yang. “Data uploading strategy for underwater wireless sensor networks.” Sensors 19.23 (2019): 5265. 10.3390/s19235265.
XIV. Huh, Jun-Ho. “Reliable user datagram protocol as a solution to latencies in network games.” Electronics 7.11 (2018): 295, 10.3390/electronics7110295.
XV. Le Boudec, Jean-Yves, and Patrick Thiran, eds. Network calculus: a theory of deterministic queuing systems for the internet. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001, https://doi.org/10.1007/3-540-45318-0.
XVI. Liang Y, Ji-Liu Z. et al. “A study of transmission control protocol for satellite network”. In2009 5th International Conference on Wireless Communications, Networking and Mobile Computing (2009), https://doi.org/10.1109/WICOM.2009.5303023.
XVII. Miao, Wang, et al. “Performance modelling and analysis of software-defined networking under bursty multimedia traffic.” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 12.5s (2016): 1-19, https://doi.org/10.1145/2983637.
XVIII. Miao, Wang, et al. “Stochastic performance analysis of network function virtualization in future internet.” IEEE Journal on Selected Areas in Communications 37.3 (2019): 613-626, 10.1109/JSAC.2019.2894304.
XIX. Mijumbi, Rashid, et al. “Network function virtualization: State-of-the-art and research challenges.” IEEE Communications surveys & tutorials 18.1 (2015): 236-262. 10.1109/COMST.2015.2477041.
XX. Omnet++ User Guide. In Version 5.4.1, Budapest, Hungary, 2016.
XXI. Qu, Long, et al. “A reliability-aware network service chain provisioning with delay guarantees in NFV-enabled enterprise datacenter networks.” IEEE Transactions on Network and Service Management 14.3 (2017): 554-568. 10.1109/TNSM.2017.2723090.
XXII. Saravanan, M., et al. “Medium Access Control layer protocol design based on stochastic network calculus for underwater wireless communication in open‐ocean fish farming.” International Journal of Communication Systems 35.8 (2022): e5118, 10.1002/dac.5118.
XXIII. Saravanan, M., Sukumaran, R. et al. “Underwater Cross Layer Protocol Design for Data Link Layer: Stochastic Network Calculus”. International Journal of Computing, (2023), 367-380, 10.47839/ijc.22.3.3233.
XXIV. Stiliadis, Dimitrios, and Anujan Varma. “Latency-rate servers: a general model for analysis of traffic scheduling algorithms.” IEEE/ACM Transactions on networking 6.5 (1998): 611-624, 10.1109/90.731196.
XXV. Stojanovic M. et al. “Acoustic (underwater) communications”. Wiley Encyclopedia of Telecommunications. (2003). 10.1002/0471219282.eot110.
XXVI. Wang, Zenan, Jiao Zhang, and Tao Huang. “Determining delay bounds for a chain of virtual network functions using network calculus.” IEEE Communications Letters 25.8 (2021): 2550-2553. 10.1109/lcomm.2021.3065147.
XXVII. Yang, Guang, Lie Dai, and Zhiqiang Wei. “Challenges, threats, security issues and new trends of underwater wireless sensor networks.” Sensors 18.11 (2018): 3907. 10.3390/s18113907.
XXVIII. Yi, Bo, et al. “A comprehensive survey of network function virtualization.” Computer Networks 133 (2018): 212-262, 10.1016/j.comnet.2018.01.021.
XXIX. https://inet.omnetpp.org/docs/users-guide/

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Abstract:

It is well known that methods of pattern synthesis reported in the open literature are mostly conventional. The methods include either standard distribution, empirical techniques, or analytical techniques. Every method has its own advantages and disadvantages regarding the overall pattern structure. The pattern structure is characterized by the main lobe and the side lobe behavior in the case of the sum pattern. On the other hand, difference patterns are the patterns characterized by the two different lobes and side lobe structures. Sequentially generating sum and difference patterns is advantageous in IFF radar applications. To simplify the design procedure and improve the pattern characteristics, an attempt is made to use random weights as amplitude excitation. Interestingly, useful results are obtained. The sum and difference are designed using the random approach and are presented in the sinθ domain for the arrays of dipoles and microstrip elements. The results are helpful for the array design depending on the applications and user requirements.

Keywords:

Antenna array,difference pattern,pattern synthesis,sector beam,sum pattern,

Refference:

I. Albert, Chirappanath. “Comparative Analysis of Antenna Array Radiation Patterns Under the Influence of Number of Elements and Spacing Between the Elements with Uniform and Non-uniform Excitations.” 2017, 10.13140/RG.2.2.22180.37760.

II. Al-Zoubi, A. S., Anas Amaireh, and Nihad Dib. “Comparative and Comprehensive Study of Linear Antenna Arrays’ Synthesis.” International Journal of Electrical and Computer Engineering, vol. 12, no. 3, 2022, pp. 2645-2654. 10.11591/ijece. v12i3.pp2645-2654.

III. Banerjee, S., and V. V. Dwivedi. “Linear Antenna Array Synthesis to Reduce the Interference in the Side Lobe Using Continuous Genetic Algorithm.” 2015 Fifth International Conference on Advances in Computing and Communications (ICACC), 2015, pp. 291-296. IEEE, 10.1109/ICACC.2015.23.

IV. Blank, S. J., and M. F. Hutt. “On the Empirical Optimization of Antenna Arrays.” IEEE Antennas and Propagation Magazine, vol. 47, no. 2, Apr. 2005, pp. 58-67. 10.1109/MAP.2005.1487780.

V. Kaur, Jaspreet, and Sonia Goyal. “A Comparative Study on Linear Array Antenna Pattern Synthesis Using Evolutionary Algorithms.” International Journal of Advanced Research in Computer Science, vol. 8, no. 5, 2017.

VI. Krishna, M., G. Raju, and Shrey Mishra. “Design of Linear and Circular Arrays Using Natural Search Algorithms for Generation of Low Side Lobe Patterns.” Advances in Electrical and Computer Engineering, 2018. 10.1007/978-981-10-4280-5_52.

VII. Kumari, U. V. R., G. S. N. Raju, and G. M. V. Prasad. “Generation of Low Sidelobe Beams Using Taylor’s Method and Genetic Algorithm.” 2016 International Conference on ElectroMagnetic Interference & Compatibility (INCEMIC), 2016, pp. 1-5. IEEE. 10.1109/INCEMIC.2016.7921466.

VIII. Raju, G. S. N. Antennas and Wave Propagation. Pearson, 2018.

IX. Raju, G. S. N. Electromagnetic Field Theory and Transmission Lines. Pearson Education India, 2006.

X. Rahman, Saeed Ur, et al. “Analysis of Linear Antenna Array for Minimum Side Lobe Level, Half Power Beamwidth, and Nulls Control Using PSO.” Journal of Microwaves, Optoelectronics and Electromagnetic Applications, vol. 16, no. 2, 2017, pp. 577-591. Accessed 1 July 2022. 10.1590/2179-10742017v16i2913.

XI. Roy, J. S., and P. Nandi. “Optimization of Schelkunoff Array Using Binary and Real Coded Genetic Algorithm.” 2017 IEEE 11th International Conference on Application of Information and Communication Technologies (AICT), 2017, pp. 1-5. IEEE. 10.1109/ICAICT.2017.8687106.

XII. Yang, Xin-She, and Yu-Xin Zhao. “Navigation, Routing and Nature-Inspired Optimization.” Nature Inspired Computation in Navigation and Routing Problems, edited by X.-S. Yang and Y.-X. Zhao, Springer Tracts in Nature-Inspired Computing, 2020, pp. 1-17. 10.1007/978-981-15-1842-3.

XIII. Yang, Xin-She. Nature-Inspired Optimization Algorithms. 2nd ed., Middlesex University London, School of Science and Technology, 2021. ISBN 978-0-12-821986-7. 10.1016/C2019-0-03762-4.

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Abstract:

In eukaryotic organisms, each and every organ takes a major role in ensuring the seamless functioning of the entire system. If we consider about heart then it is treated as a vital part of every human being. Heart-associated ailments are very frequent at present so it is essential to predict such illnesses. This prognosis and prediction of coronary heart-associated illnesses require a lot of accuracy so it must be finished in an environment-friendly manner due to the fact a small mistake can motivate the death of the person. To deal with this hassle there ought to be a gadget which can predict and create consciousness about diseases. It is challenging to decide the ailment manually primarily based on signs and hazard factors. But this ought to be solved with the use of Machine mastering techniques. Artificial brain (AI) in the shape of desktop studying (ML) allows software program purposes to predict results greater precisely whilst functioning unbiased of human input. This study employs various machine learning algorithms, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Random Forest, Decision Tree, and Naïve Bayes, to assess their accuracy in predicting cardiovascular disease and related conditions This paper makes use of the UCI repository dataset for coaching and testing including some basic parameters such as age and sex. After applying all algorithms to our data set, the experimental results concluded that the Logistic Regression model has predicted well with highest accuracy of 92% in comparison with other algorithms.

Keywords:

Cardiovascular Disease,Decision Tree,KNN,ML Algorithms,SVM,Naïve Bayes,Random Forest,Logistic Regression,

Refference:

I. Kaur, B., Kaur, G., Heart Disease Prediction Using Modified Machine Learning Algorithm. International Conference on Innovative Computing and Communications. Lecture Notes in Networks and Systems, vol 473. Springer, Singapore. (2023)
II. V. Rawat, K. Gulati, U. Kaur, J.K Seth, V. Solanki, A.N. Venkatesh, D.P. Singh, N. Singh, M. Loganathan, “A Supervised Learning Identification System for Prognosis of Breast Cancer”, Mathematical Problems in Engineering, vol. 2022, Article ID 7459455, 8 pages, 2022.
III. WHO Data – https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1
IV. Heart Disease Dataset – https://www.kaggle.com/c/heart-disease dated: Sept 2018
V. J. Patel, Tejal Upadhyay, D., and S. Patel, “Predicting heart condition using machine learning and data mining techniques”. (2015)
VI. Soni, J., Ansari, U., Sharma, D., & Soni, S. “Intelligent and effective heart disease prediction system using weighted associative classifiers”. International Journal on Computer Science and Engineering. (2011)
VII. Y. E. Shao, C.-D. Hou, and C.-C. Chiu, “Hybrid intelligent modelling, schemes for heart disease classification,” Applied Soft Computing, vol. 14, pp. (2014).
VIII. V. Chauraisa and S. Pal, “Data Mining Approach to Detect Heart Diseases,” International Journal of Advanced Computer Science and Information Technology (IJACSIT), (2013).
IX. Mrs. G. Subba lakshmi “Decision Support in Heart Disease Prediction System using Naive Bayes”, Indian Journal of Computer Science and Engineering (IJCSE) (2011)
X. Sonam Nikhar, and A. M. Karandikar. “Prediction of Heart Disease Using Machine Learning Algorithms.” International Journal of Advanced Engineering, Management and Science, vol. 2, no. 6, Jun. 2016.
XI. PE Rubini, Dr.C.A. Subasini, A. Vanitha Katharine, V. Kumaresan, S. Gowdham Kumar, T.M. Nithya. “A Cardiovascular Disease Prediction using Machine Learning Algorithms” Annals of R.S.C.B. (2021)
XII. Amin Ul Haq, Jian Ping Li, Muhammad Hammad Memon, Shah Nazir, Ruinan Sun, “A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms”, Mobile Information Systems, (2018).
XIII. Syed Nawaz Pasha, Dadi Ramesh, Sallauddin Mohmmad, A. Harshavardhan and Shabana “cardiovascular disease prediction using deep learning techniques” IOP Conf. Series: Materials Science and Engineering (2020)
XIV. Abhijeet Jagtap, Priya Malewadkar, Omkar Baswat, Harshali Rambade “Heart Disease Prediction Using Machine Learning” International Journal of Research in Engineering, Science and Management (2019)
XV. Rohit Bharti, Aditya Khamparia, Mohammad Shabaz, Gaurav Dhiman, Sagar Pande, Parneet Singh, “Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning”, Computational Intelligence and Neuroscience (2021).
XVI. Bhavesh Dhande, Kartik Bamble, Sahil Chavan, Tabassum Maktum “Diabetes & Heart Disease Prediction” ITM Web of Conferences ICACC-(2022)

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Abstract:

COVID-19 infection is particularly dangerous for individuals with comorbidities such as kidney disease and diabetes due to weakened immunity. While the pandemic has impacted people of all ages and socioeconomic backgrounds, those with underlying medical conditions are more susceptible to severe outcomes. However, the role of vaccination in the co-infection dynamics of COVID-19 among diabetic patients is not well-represented in the literature. This study examines the unique challenges presented by the co-infection of COVID-19 in individuals with diabetes, focusing on disease transmission dynamics. We employ a mathematical modeling approach using a seven-compartment model that incorporates vaccination and comorbidities like diabetes to analyze the dynamics of COVID-19 outbreaks. Analytical investigations were conducted to demonstrate the solutions' existence, boundedness, positivity, and sensitivity. After calculating the basic reproduction number, we performed a stability analysis of the model's equilibrium points. Our findings indicate that when the reproduction number is less than unity, the disease-free equilibrium is both locally and globally stable. Furthermore, as the vaccination rate increases, the incidence of COVID-19 and its co-infections with diabetes decreases. These results suggest that effective disease treatment strategies should consider the potential impact of vaccination on the co-infection of COVID-19 in diabetic patients.

Keywords:

COVID-19,Diabetes,Comorbidity,Co-infection,Vaccination,

Refference:

I. Atkinson, M.A., G.S. Eisenbarth, and A.W. Michels, Type 1 diabetes. The Lancet, 383(9911), 69-82 (2014).
II. Azeez, A., J. Ndege, R. Mutambayi, Y. Qin, A mathematical model for TB/HIV co-infection treatment and transmission mechanism. Asian Journal of Mathematics and Computer Research, 22, 180-192 (2017)
III. Bai, Y., L. Yao, T. Wei, F. Tian, D.-Y. Jin, L. Chen, M. Wang, Presumed asymptomatic carrier transmission of COVID-19. JAMA, 323(14), 1406-1407 (2020).
IV. Bjorgul, K., W.M. Novicoff, K.J. Saleh, Evaluating comorbidities in total
hip and knee arthroplasty: available instruments. Journal of Orthopaedics
and Traumatology, 11, 203-209 (2010).
V. Dang, H.-A.H., M.N. Do, COVID-19 pandemic and the health and well-being of vulnerable people in Vietnam. GLO Discussion Paper (2022).
VI. DiMeglio, L.A., C. Evans-Molina, R.A. Oram, Type 1 diabetes. The Lancet, 391(10138), 2449-2462 (2018).
VII. Egonmwan, A., D. Okuonghae, Mathematical analysis of a tuberculosis model with imperfect vaccine. International Journal of Biomathematics, 12, 1950073 (2019).
VIII. Gomes, C.M., L.A. Favorito, J.V.T. Henriques, A.F. Canalini, K.M. Anzolch, R.d.C. Fernandes, C.H. Bellucci, C.S. Silva, M.L. Wroclawski, A.C.L. Pompeo, Impact of COVID-19 on clinical practice, income, health and lifestyle behavior of Brazilian urologists. International Braz J Urol, 46, 1042-1071 (2020).
IX. Iboi, E.A., C.N. Ngonghala, A.B. Gumel, Will an imperfect vaccine curtail the COVID-19 pandemic in the US? Infectious Disease Modelling, 5, 510-524 (2020).
X. Irena, T.K., S. Gakkhar, A dynamical model for HIV-typhoid co-infection with typhoid vaccine. Journal of Applied Mathematics and Computing, 1-30 (2021).
XI. Mousquer, G.T., A. Peres, M. Fiegenbaum, Pathology of TB/COVID-19 co-infection: the phantom menace. Tuberculosis, 126. 102020 (2021)
XII. Nicola, M., Z. Alsafi, C. Sohrabi, A. Kerwan, A. Al-Jabir, C. Iosifidis, M. Agha, R. Agha, The socio-economic implications of the coronavirus pandemic (COVID-19): A review. International Journal of Surgery, 78, 185-193 (2020)
XIII. Omame, A., U.K. Nwajeri, M. Abbas, C.P. Onyenegecha, A fractional order control model for diabetes and COVID-19 co-dynamics with Mittag-Leffler function. Alexandria Engineering Journal, 61, 7619-7635 (2022).
XIV. Omame, A., N. Sene, I. Nometa, C.I. Nwakanma, E.U. Nwafor, N.O. Iheonu, D. Okuonghae, Analysis of COVID‐19 and comorbidity co‐infection model with optimal control. Optimal Control Applications and Methods, 42(6), 1568-1590 (2021).
XV. World Health Organization, World Health Organization coronavirus disease (COVID-19) dashboard. World Health Organization (2020).
XVI. Polack, F.P., S.J. Thomas, N. Kitchin, J. Absalon, A. Gurtman, S. Lockhart, J.L. Perez, G. Pérez Marc, E.D. Moreira, C. Zerbini, Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine. New England Journal of Medicine, 383, 2603-2615 (2020).
XVII. Prieto Curiel, R., H. González Ramírez, Vaccination strategies against COVID-19 and the diffusion of anti-vaccination views. Scientific Reports, 11, 6626 (2021).
XVIII. Tang, B., X. Wang, Q. Li, N.L. Bragazzi, S. Tang, Y. Xiao, J. Wu, Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions. Journal of Clinical Medicine, 9, 462 (2020).
XIX. Tasman, H., An optimal treatment control of TB-HIV coinfection. International Journal of Mathematics and Mathematical Sciences, 2016, 1-10 (2016).
XX. Van den Driessche, P., J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Mathematical Biosciences, 180, 29-48 (2002).
XXI. Watson, O.J., G. Barnsley, J. Toor, A.B. Hogan, P. Winskill, A.C. Ghani, Global impact of the first year of COVID-19 vaccination: a mathematical modelling study. The Lancet Infectious Diseases, 22, 1293-1302 (2022).
XXII. Zhou, P., X.-L. Yang, X.-G. Wang, B. Hu, L. Zhang, W. Zhang, H.-R. Si, Y. Zhu, B. Li, C.-L. Huang, Addendum: A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature, 588, E6-E6 (2020).
XXIII. Hye, M.A., Biswas, M.H.A., Uddin, M.F., Rahman, M. M., A mathematical model for the transmission of co-infection with COVID-19 and kidney disease. Sci Rep 14, 5680 (2024).
XXIV. Hye, M.A., Biswas, M.H.A., Uddin, M.F,. Correction to: Mathematical Modeling of Covid-19 and Dengue Co-Infection Dynamics in Bangladesh: Optimal Control and Data-Driven Analysis. Comput Math Model 33, 388 (2022). 10.1007/s10598-023-09580-7

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Abstract:

Power transformers play an important role in the efficient delivery of power to consumers. Their failure leads to significantly higher losses and maintenance costs. Therefore, it is essential to have an optimal maintenance strategy in place for the transformers. However, to design an effective maintenance strategy, real failure data of the transformers need to be collected and studied to identify the failure patterns. To facilitate the analysis presented in this paper, five years of real failure data of a transformer system is collected from a power distribution company. The best-fit distribution for the failure times data of the system is found using AIC, BIC, and LKV values. Useful reliability parameters of the system are evaluated using the Maximum Likelihood Estimation and Rank Regression Method. Life data analysis is performed to estimate the reliable life, mean time to failure, and remaining lifetime of the entire system and its subsystems.

Keywords:

Best-fit distribution,Maximum likelihood estimation,Rank regression,reliability,Transformer,

Refference:

I. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. 10.1109/TAC.1974.1100705
II. Chen, T., Tang, W., Lu, Y., and Tu, X. (2014). Rank regression: an alternative regression approach for data with outliers. Shanghai Archives of Psychiatry, 26(5), 310-315. 10.11919/j.issn.1002-0829.214148
III. Cheng, J., Cho, S., Tan, Y.P., and Hu, G. (September 11-14, 2023). Deep learning-enabled statistical model estimation for power transformers with censoring and truncation problems. Asia Pacific Conference of the PHM society, Tokyo, Japan. 10.36001/phmap.2023.v4i1.3762
IV. El-Bassiouny, A., El-Shimy, M., and Hamouda, R. (2019). Probabilistic analysis of the reliability performance for power transformers in Egypt. Journal of Renewable Energy and Sustainable Development, 5(2), 46-56.
V. Jagtap, H.P., Bewoor, A.K., Kumar, R., Ahmadi, M.H., El Haj Assad, M., and Sharifpur, M. (2021). RAM analysis and availability optimization of thermal power plant water circulation system using PSO. Energy Reports, 7, 1133–1153. 10.1016/j.egyr.2020.12.025
VI. Kumar, A., Garg, R., and Barak, M.S. (2022). Performance analysis of computer systems with Weibull distribution subject to software upgrade and load recovery. Life Cycle Reliability and Safety Engineering, 12, 51–63. 10.1007/s41872-022-00211-5
VII. Maihulla, A.S., Yusuf, I., and Bala S.I. (2023). Weibull comparison based on reliability, availability, maintainability, and dependability (RAMD) analysis. Reliability: Theory & Applications, 1(72), 120-132.
VIII. Mirzai, M., Gholami, A., and Aminifar, F. (2006). Failures analysis and reliability calculation for power transformers, Journal of Electrical Systems, 2(1), 1–12.
IX. Myung, I.J. (2003). Tutorial on maximum likelihood estimation. Journal of Mathematical Psychology, 47(1), 90–100. 10.1016/S0022-2496(02)00028-7
X. Nabila Al Balushi. (2021). A review of the reliability analysis of the complex industrial systems, Advances in Dynamical Systems and Applications, 16(1), 257-297.
XI. Nabila Al Balushi, Rizwan, S.M., Taj, S.Z., and Waleed Al Khairi. (2023). Reliability analysis of power transformers of a power distribution company. International Journal of System Assurance Engineering and Management. 10.1007/s13198-023-02042-8
XII. Oliveira Neto, A.B., Costa, E.G., Moraes, V.S., and Ferreira, T.V. (August 27-September 01, 2017). Methodology for reliability analysis of power transformers based on failure data. The 20th International Symposium on High Voltage Engineering, Buenos Aires, Argentina.
XIII. Padmavathi, N., Rizwan, S.M., Pal, A., and Taneja, G. (2012). Reliability analysis of an evaporator of a desalination plant with online repair and emergency shutdowns. Aryabhatta Journal of Mathematics & Informatics, 4(1), 1-12.
XIV. Schwarz, G.E. (1978). Estimating the dimension of a model. Annals of Statistics, 6 (2), 461–464. 10.1214/aos/1176344136
XV. Seyedi, H., Fotuhi, M., and Sanaye-Pasand, M. (2006). An extended Markov model to determine the reliability of protective system, 2006 IEEE Power India Conference. 10.1109/POWERI.2006.1632549
XVI. Singla, S., Mangla, D., Panwar, P., and Taj, S.Z. (2024). Reliability optimization of a degraded system under preventive maintenance using genetic algorithm. Journal of Mechanics of Continua and Mathematical Sciences, 19(1), 1-14.
XVII. Taj, S.Z., and Rizwan, S.M. (2021). Estimation of reliability indices of a complex industrial system using best–fit distribution for repair/restoration times. International Journal of Advanced Research in Engineering and Technology, 12(2), 132-146.
XVIII. Taj, S.Z., Rizwan, S.M., Alkali, B.M., Harrison, D.K., and Taneja, G. (2020). Three reliability models of a building cable manufacturing plant: a comparative analysis. International Journal of Systems Assurance Engineering and Management. 10.1007/s13198-020-01012-8
XIX. Tang, S., Hale, C., and Thaker, H. (2014). Reliability modelling of power transformers with maintenance outage. Systems Science & Control Engineering, 2(1), 316–324. 10.1080/21642583.2014.901930
XX. Vahidi, F., and Tenbohlen, S. (November 2014). Statistical failure analysis of European substation transformers. Conference: 6. ETG-Fachtagung Diagnostik elektrischer Betriebsmittel.
XXI. Wei, X., Wang, Z., and Guo, J. (2022). Reliability assessment of transformer insulating oil using accelerated life testing. Scientific Reports, 12. 10.1038/s41598-022-26247-2
XXII. Yaqoob Al Rahbi, Rizwan, S.M., Alkali, B.M., Cowell, A. and Taneja, G. (2019). Reliability analysis of a rodding anode plant in aluminium industry with multiple units’ failure and single repairman. International Journal of System Assurance Engineering and Management, 10, 97-109. 10.1007/s13198-019-00771-3

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Abstract:

This paper presents a novel approach for solving 2D mathematical models arising in applied sciences, specifically focusing on 2-dimensional time-fractional order Klein-Gordon (TFKGE) and sine-Gordon equations (TFSGE) using the Sumudu transform-homotopy perturbation method (STHPM). The amalgamation of the Sumudu transform with the homotopy perturbation method provides an effective analytical technique for tackling these time-fractional order partial differential equations. The solutions obtained illustrate the precision and efficiency of the method, offering valuable insights for modelling complex physical systems. In this study, we also solve the same numerical problems using the variational iteration method and perform a comparative analysis of the results. This study advances the application of fractional calculus methods to challenging problems in theoretical and applied physics.

Keywords:

Homotopy Perturbation Method,Klein-Gordon Equation,Sine-Gordon Equation,Sumudu Transform,Test Examples,Variational Iteration Method,

Refference:

I. Atangana Abdon and Adem Kılıçman. “The Use of Sumudu Transform for Solving Certain Nonlinear Fractional Heat-Like Equations.” Abstract and Applied Analysis 2013 (2013): 1-12. 10.1155/2013%2F737481.
II. Belayeh W. G., Mussa Y. O., Gizaw A.K., “Approximate analytic solutions of two-dimensional nonlinear Klein-Gordon equation by using the reduced differential transform method.” Mathematical Problems in Engineering, 2020(1), 2020. 10.1155/2020/5753974.
III. Belgacem Fethi Bin Muhammed, Karaballi, Ahmed Abdullatif “Sumudu transform fundamental properties investigations and applications.” Journal of Applied Mathematics and Stochastic Analysis, 2006(6), (2006) pp. 1-23. 10.1155/JAMSA/2006/91083.
IV. Chang, Chih-Wen, Kuo Chia-Chen “A lie-group approach for solving backward two-dimensional nonlinear Klein-Gordon equation.” Procedia Engineering, 79, (2014), pp. 590-598. 10.1016/j.proeng.2014.06.384.
V. Deresse, Alemayehu Tamirie. “Application of iterative three-dimensional Laplace transform method for 2-dimensional non linear Klein Gordon equation.” Trends in sciences, (2023), 20(3). 10.48048/tis.2023.4410.
VI. Deresse Alemayehu Tamirie, Mussa Yesuf Obsie and Gizaw Ademe Kebede. “Analytical solution of two-dimensional sine-Gordon equation”, Advances in Mathematical Physics, 2021 (2021), issue 1, 2021. 10.1155/2021/6610021.
VII. El-Sayed M.A., Elsaid A., I.L. El-Kalla, D. Hammad, “A homotopy perturbation technique for solving partial differential equations of fractional order in finite domains.” Applied Mathematics and Computation, 218(17), (2012), pp. 8329–8340. 10.1016/j.amc.2012.01.057.
VIII. Gill V., Dubey R.S., “New analytical method for Klein-Gordon equations arising in quantum field theory.” European Journal of Advances in Engineering and Technology, 5( 8), (2018), pp. 649-655.
IX. Gupta P.K., Singh M., “Homotopy perturbation method for fractional Fornberg-Whitham equation.” Computer and Mathematics with Applications, 61(2), , 2011, pp. 250-254. 10.1016/j.camwa.2010.10.045.
X. He, Ji-Huan, “Homotopy perturbation technique”, Computer Methods in Applied Mechanics and Engineering, 178(3-4), (1999), pp. 257–262. 10.1016/S0045-7825(99)00018-3.
XI. He Ji-Huan, “Some applications of nonlinear fractional differential equations and their approximations.” Bulletin of Science, Technology & Society, 15(2), , 1999, pp. 86–90.
XII. He Ji-Huan,“A coupling method of a homotopy technique and a perturbation technique for non-linear problems.” International Journal of Non-Linear Mechanics, 35(1), (2000), pp. 37–43. 10.1016/S0020-7462(98)00085-7.
XIII. He Ji-Huan, “Application of homotopy perturbation method to nonlinear wave equations.” Chaos, Solitons and Fractals, 26(3), , 2005, pp. 695–700. 10.1016/j.chaos.2005.03.006.
XIV. Hosseininia M., Heydari M.H., Ghaini F.M.M., Avazzadeh Z., “A wavelet method to solve nonlinear variable order time fractional 2D Klein-Gordon equation.” Computers & Mathematics with Applications, 78(15), (2019), pp. 3713-3730. 10.1016/j.camwa.2019.06.008.
XV. Ibrahim W., Tamiru M., “Solutions of three dimensional non-linear Klein-Gordon equations by using quadruple Laplace transform.” International Journal of Differential Equations, 2022(1), 2022. 10.1155/2022/2544576.
XVI. Kang X., Feng W., Cheng K., Guo, C., “An efficient finite difference scheme for the 2D sine-Gordon equation.” Arxiv, 10(6), (2017), pp. 2998-3012.
XVII. Karbalaie Abdolamir, Montazeri Mohammad Mehdi, and Muhammed Hamed Hamid “Exact Solution of Time-Fractional Partial Differential Equations Using Sumudu Transform.” WSEAS Transactions on Mathematics archive 13 (2014): 142-151.
XVIII. Khader M., “Application of homotopy perturbation method for solving nonlinear fractional heat-like equations using sumudu transform.” Scientia Iranica, 24(2), , (2017), pp. 648-655.
XIX. Li Demei, Lai Huilin, Shi Baochang “Mesoscopic simulation of the (2+1)-dimensional wave equation with non-linear damping and source terms using the lattice Boltzmann BGK model.” MDPI, 21(4), 2019.
XX. Li, X, “Mesh less numerical analysis of a class of nonlinear generalized Klein-Gordon equation with a well shaped moving least square approximation”, Applied Mathematical Modelling, 48, , (2017), pp. 153-182. 10.1016/j.apm.2017.03.063.
XXI. Liu W., Sun J., Wu B. , “Space–time spectral method for the two-dimensional generalized sine-Gordon equation.” Journal of Mathematical Analysis and Applications, 427(2), (2015), pp. 787-804, 10.1016/j.jmaa.2015.02.057.
XXII. Maitama S., Zhao W., “Homotopy perturbation Shehu transform method for solving fractional models arising in applied sciences.” Journal of Applied Mathematics and Computational Mechanics, 20(1), (2021), pp. 71-82. 10.17512/jamcm.2021.1.07.
XXIII. Singh Brajesh Kumar, Kumar Parmod, “Fractional variational iteration method for solving fractional partial differential equations with proportional Delay.” International Journal of Differential Equations, 2017(1), (2017), 10.1155/2017/5206380.
XXIV. Singh Inderdeep, Kumari Umesh “Elzaki Transform Homotopy Perturbation Method for Solving Two-dimensional Time-fractional Rosenau-Hyman Equation.” Matematika, Malaysian Journal of Industrial and Applied Mathematics, 39(2), (2023), pp. 159–171. https://matematika.utm.my/index.php/matematika/article/view/1463.
XXV. Singh P., Sharma D., “On the problem of convergence of series solution of non-linear fractional partial differential equation.” In: AIP Conference Proceeding, 1860: 020027, 2017. 10.1063/1.4990326.
XXVI. Watugala G.K, “Sumudu transform- a new integral transform to solve differential equations and control engineering problems.” International Journal of Mathematical Education in Science and Technology, 24(1), (1993), pp. 35-43. 10.1080/0020739930240105.
XXVII. Yıldırım Ahmet, “Analytical approach to fractional partial differential equations in fluid mechanics by means of the homotopy perturbation method.” International Journal of Numerical Methods for Heat & Fluid Flow, 20(2), (2010), pp. 186-200. 10.1108/09615531011016957.
XXVIII. Yousif Eltayeb A., Hamed Sara H. “Solution of nonlinear fractional differential equations using the homotopy perturbation sumudu transform method”, Applied Mathematical Sciences, 8(44), 2014, pp. 2195-2210.

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Abstract:

The present work in bio-inspired robotics explores the design and implementation of a novel-legged robotic system featuring a modified Peaucellier-Lipkin mechanism with three control points for a single degree of freedom. The emphasis is placed on the robot’s adaptability to various walking gaits in different environments. The paper delves into the robot’s design, construction, and control system, which includes the application of PID control for enhanced stability and efficiency in mimicking biological locomotion. The primary aim is to demonstrate a robot capable of adjusting its form and function for diverse operational challenges, enhancing robotic mobility. The design also addresses repeatability issues, ensuring consistent performance across various tasks and conditions, contributing to the robot’s reliability and practical applicability in real-world scenarios.

Keywords:

Biological locomotion,Peaucellier-Lipkin mechanism,PID controller repeatability,Robotic mobility,

Refference:

I. Alexeev, L., Dobra, A., & Lovasz, E.: “Walking Robot with Modified Jansen Linkage.” In Machine and Industrial Design in Mechanical Engineering, Mechanisms and Machine Science 109, Springer Nature Switzerland, 2022, Ch. 58, p. 577. 10.1007/978-3-030-88465-9_58

II. Bhavsar, Keval, Dharmik Gohel, Pranav Darji, Jitendra Modi, and Umang Parmar.: ‘Kinematic Analysis of Theo Jansen Mechanism-Based Eight-Leg Robot’. In Advances in Fluid Mechanics and Solid Mechanics: Proceedings of the 63rd Congress of ISTAM 2018, pp. 75-82. Singapore: Springer Singapore, 2020. 10.1007/978-981-32-9971-9_30

III. Chen, X., Wang, L. Q., Ye, X. F., Wang, G., & Wang, H. L.: “Prototype Development and Gait Planning of Biologically Inspired Multi-Legged Crablike Robot.” Mechatronics, 2013, 23(4), pp. 429-444. 10.1016/j.mechatronics.2013.03.006

IV. Chwila, S., Zawiski, R., and Babiarz, A.: ‘Developing and Implementation of the Walking Robot Control System’. In Man-Machine Interactions 3, Springer International Publishing, pp. 97-105, 2014. 10.1007/978-3-319-02309-0_10

V. Desai, Shivamanappa G., Anandkumar R. Annigeri, and A. TimmanaGouda.: ‘Analysis of a New Single Degree-of-Freedom Eight Link Leg Mechanism for Walking Machine’. Mechanism and Machine Theory, Vol. 140, pp. 747-764, 2019. 10.1016/j.mechmachtheory.2019.06.002

VI. Gao, H., Kareem, A., Jawarneh, M., Ofori, I., Raffik, R., and Kishore, K.H.: ‘[Retracted] Metaheuristics Based Modeling and Simulation Analysis of New Integrated Mechanized Operation Solution and Position Servo System’. Mathematical Problems in Engineering, 2022(1), p. 1466775. 10.1155/2022/1466775

VII. Ghassaei, Amanda, Professors Phil Choi, and Dwight Whitaker.: “The Design and Optimization of a Crank-Based Leg Mechanism.” Pomona, USA (2011).

VIII. Giesbrecht, Daniel. Design and Optimization of a One-Degree-of-Freedom Eight-Bar Leg Mechanism for a Walking Machine. MS thesis, 2010. http://hdl.handle.net/1993/3922

IX. Haidar, A. M., C. Benachaiba & M. Zahir.: “Software Interfacing of Servo Motor with Microcontroller.” Journal of Electrical Systems, vol. 9, (1) pp. 84-99, 2013. https://ro.uow.edu.au/eispapers/468/

X. Janson, T. The Great Pretender. Uitgeverij, 2007.

XI. Jaichandar, K., Mohan Rajesh, E., Martínez-García, E., and Le Tan-Phuc.: ‘Trajectory Generation and Stability Analysis for Reconfigurable Klann Mechanism Based Walking Robot’. Robotics, Vol. 5, No. 3, pp. 1-12, 2016. https://doi.org/10.3390/robotics5030013

XII. Jaichandar, K., Rajesh Elara M., Martínez-García E., & Tan-Phuc L.: “Synthesizing Reconfigurable Foot Traces Using a Klann Mechanism.” Robotica, 35(1), 2015. Cambridge University Press. https://doi.org/10.1017/S0263574715000089

XIII. Kajita, Shuuji, and Bernard Espiau.: ‘Legged Robot’. Springer Handbook of Robotics. Berlin/Heidelberg, Germany: Springer, pp. 361-389, 2008.

XIV. Khaled, Nassim.: “Acceleration Based Approach for Position Control.” IOP Conference Series: Materials Science and Engineering, Vol. 717, No. 1, IOP Publishing, 2020. 10.1088/1757-899X/717/1/012020

XV. Kim, D.H.: ‘Design and Tuning Approach of 3-DOF Emotion Intelligent PID (3-DOF-PID) Controller’. In 2012 Sixth UKSim/AMSS European Symposium on Computer Modeling and Simulation, IEEE, pp. 74-77, November 2012. https://doi.org/10.1109/EMS.2012.93

XVI. Klann, J.C.: Patent No. 6.260.862, USA, 2001. https://patents.google.com/patent/US6260862B1/en

XVII. Krishnamurthy, Balachandar, Sabari Senbagarajan, and Lokesh Mahendran.: ‘Design and Fabrication of Spider Bot’. AIP Conference Proceedings, Vol. 2946, No. 1, AIP Publishing, pp. 1-5, 2023. https://doi.org/10.1063/5.0178024

XVIII. McCarthy, J. M., and Kevin Chen.: Design of Mechanical Walking Robots. MDA, Press, 2021. https://www.google.co.in/books/edition/Design_of_Mechanical_Walking_Robots/-gfozgEACAAJ?hl=te

XIX. Papoutsidakis, M., Chatzopoulos, A., Symeonaki, E., and Tseles, D.: ‘Methodology of PID Control – A Case Study for Servomotors’. International Journal of Computer Applications, Vol. 179, No. 30, pp. 30-33, 2018. 10.5120/ijca2018916689

XX. Sheba, J.K., Martínez-García, E., Elara, M.R., and Tan-Phuc, L.: ‘Design and Evaluation of Reconfigurable Klann Mechanism Based Four-Legged Walking Robot’. In 2015 10th International Conference on Information, Communications and Signal Processing (ICICS), IEEE, pp. 1-5, December 2015. 10.1109/ICICS.2015.7459939

XXI. Sun, Jiefeng, and Jianguo Zhao.: ‘An Adaptive Walking Robot with Reconfigurable Mechanisms Using Shape Morphing Joints’. IEEE Robotics and Automation Letters, Vol. 4, No. 2, pp. 724-731, 2019. 10.3390/robotics5030013

XXII. Sutyasadi, P., and Parnichkun, M.: ‘Gait Tracking Control of Quadruped Robot Using Differential Evolution Based Structure Specified Mixed Sensitivity H∞ Robust Control’. Journal of Control Science and Engineering, 2016(1), p. 8760215, 2016. 10.1155/2016/8760215

XXIII. Vanitha, U., Premalatha, M., Nithinkumar, S., and Vijayaganapathy, S.: ‘Mechanical Spider Using Klann Mechanism’. Scholarly Journal of Engineering and Technology, Vol. 3, No. 9, pp. 737-740, December 2015. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://saspublishers.com/media/articles/SJET39737-740.pdf

XXIV. Visioli, Antonio. Practical PID Control. Springer Science & Business Media, 2006. https://www.google.co.in/books/edition/Practical_PID_Control/ymyAY01bEe0C?hl=te&gbpv=0

XXV. Zielinska, Teresa.: “Development of Walking Machines; Historical Perspective.” International Symposium on History of Machines and Mechanisms: Proceedings HMM2004. Springer Netherlands, 2004. 10.1016/S0957-4158(01)00017-4

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Abstract:

Life tables are used in many fields in demographic and health research They represent an important indicator of death in society. There are two types of life tables; complete life tables are based on the age at death based on single-age categories and are obtained using a comprehensive survey method. The second type is the abbreviated life tables which are based on the age at death of five-year age groups and are obtained by the sample survey method. In this research, the survival function was estimated for the data obtained from the Central Statistical Organization, social and Economic Survey of the Family in Iraq (IHSES II) using parametric methods (the principle of Maximizing Entropy method (POME), and maximum likelihood method (MLE)), as well as the use of A non-parametric approach, the kernel smoothing method (KS), the compared between the estimation methods using (RMSE) and (MAPE). One of the most important conclusions was the emergence of a preference for the (POME) method for the five-age groups, but in the case of the single-age groups, the (KS) method is the best.

Keywords:

life tables,the principle of maximum entropy method,kernel smoothing method.,

Refference:

I. Calot, Gérard, and Jean-Pierre Sardon. Calculation of Eurostat’s Demographic Indicators. 2004.

II. Central Statistical Organization, Ministry of Planning. Iraqi Household Social and Economic Survey (IHSES II, 2012) Tables Report. Central Statistical Organization Press, 2014. www.cost.gov.iq.

III. Chen, Dong-Guk, and Ying-Chung Lio. “A Note on the Maximum Likelihood Estimation for the Generalized Gamma Distribution Parameters under Progressive Type-Ⅱ Censoring.” International Journal of Intelligent Technologies and Applied Statistics, vol. 2(2), 2009, pp. 145-152.

IV. Cropper, William H. Great Physicists: The Life and Times of Leading Physicists from Galileo to Hawking. Oxford UP, 2004. “The Road to Entropy: Rudolf Clausius.”

V. Jaynes, E. T. Probability Theory in Science and Engineering. No. 4, Socony Mobil Oil Company Field Research Laboratory, 1959.

VI. Jowitt, P. W. “The Extreme-Value Type-1 Distribution and the Principle of Maximum Entropy.” Journal of Hydrology, vol. 42, no. 1-2, 1979, pp. 23-38. 10.1016/0022-1694(79)90004-0

VII. Lagos Álvarez, B., Ferreira, G., and Valenzuela Hube, M. “A Proposed Reparameterization of Gamma Distribution for the Analysis of Data of Rainfall-Runoff Driven Pollution.” Proyecciones (Antofagasta), vol. 30, no. 3, 2011, pp. 415-439. https://www.scielo.cl/pdf/proy/v30n3/art09.pdf

VIII. Qamruz, Z., and Karl, P. “Survival Analysis Medical Research.” InterStat, 2011, http://interstat.statjournals.net/YEAR/2011/abstracts/1105005.php.

IX. Qiao, H., and C. P. Tsokos. “Nonparametric Approach to Reliability Analysis.” Proceedings of SOUTHEASTCON’94, April 1994, pp. 231-235. IEEE.

X. Rao, B. L. S. P. Nonparametric Functional Estimation. Academic Press, 1983.

XI. Reshi, J. A., Ahmed, A., and Mir, K. A. “Some Important Statistical Properties, Information Measures, and Estimations of Size Biased Generalized Gamma Distribution.” Journal of Reliability and Statistical Studies, 2014, pp. 161-179.

XII. Roshani, S., Yahya, S. I., Mezaal, Y. S., Chaudhary, M. A., Al-Hilali, A. A., Mojirleilani, A., & Roshani, S. (2023). Design of a compact quad-channel microstrip diplexer for L and S band applications. Micromachines, 14(3), 553.
XIII. Roshani, S., Yahya, S. I., Alameri, B. M., Mezaal, Y. S., Liu, L. W., & Roshani, S. (2022). Filtering power divider design using resonant LC branches for 5G low-band applications. Sustainability, 14(19), 12291.

XIV. Singh, V. P., and Fiorentino, M. “A Historical Perspective of Entropy Applications in Water Resources.” Entropy and Energy Dissipation in Water Resources, 1992, pp. 21-61. https://link.springer.com/chapter/10.1007/978-94-011-2430-0_2
XV. Singh, V. P., and Guo, H. “Parameter Estimation for 3-Parameter Generalized Pareto Distribution by the Principle of Maximum Entropy (POME).” Hydrological Sciences Journal, vol. 40, no. 2, 1995, pp. 165-181. 10.1080/02626669509491402

XVI. Sulaiman, Abbas Najm, and Ebtihal Hussein Farhan. “Estimation of the Survival Function for Complete Real Data of Lung Cancer Patients.” Ibn Lahitham Journal of Pure and Applied Sciences, vol. 27, no. 3, 2014, pp. 531-541. https://jih.uobaghdad.edu.iq/index.php/j/article/view/318

XVII. S. A. AbdulAmeer et al., “Cyber Security Readiness in Iraq: Role of the Human Rights Activists,” International Journal of Cyber Criminology, vol. 16, no. 2, pp. 1–14-1–14, 2022.

XVIII. Tarrad , K. M. et al., “Cybercrime Challenges in Iraqi Academia: Creating Digital Awareness for Preventing Cybercrimes,” International Journal of Cyber Criminology, vol. 16, no. 2, pp. 15–31-15–31, 2022.

XIX. Tan, P., and C. Drossos. “Invariance Properties of Maximum Likelihood Estimators.” Mathematics Magazine, vol. 48, no. 1, 1975, pp. 37-41.

XX. United Nations. MORT PAK for Windows, version 4.3, New York, 2013.

XXI. Y. S. Mezaal, & Ali, J. K. (2016). Investigation of dual-mode microstrip bandpass filter based on SIR technique. PLoS one, 11(10), e0164916.
XXII. Yahya, Salah I., et al. “A New Design method for class-E power amplifiers using artificial intelligence modeling for wireless power transfer applications.” Electronics 11.21 (2022): 3608.

XXIII. Y. S. Mezaal, K. Al-Majdi, A. Al-Hilalli, A. A. Al-Azzawi, and A. A. Almukhtar, “New miniature microstrip antenna for UWB wireless communications,” Proceedings of the Estonian Academy of Sciences, vol. 71, no. 2, pp. 194-202, 2022.
XXIV. Y. S. Mezaal , H. A. Hussein, and B. M. Alameri, “Miniaturized microstrip diplexer based on fr4 substrate for wireless communications,” Elektronika Ir Elektrotechnika, vol. 27, no. 5, pp. 34-40, 2021.

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Abstract:

A_G = (N, A, σ, μ) be a anti fuzzy graph. A partition of N(A_G) Π = {D₁, D_{2, ….,} D_k} is a regular anti fuzzy partial domatic partition of A_G if (i) for each D_i, < D_i > is an anti fuzzy regular and (ii) D_i is an anti fuzzy dominating set of G_A. The maximum fuzzy cardinality of a regular anti fuzzy partial domatic partition of A_G is called the regular anti fuzzy partial domatic number [RAPDN]of A_G and it is denoted by  Also these numbers are determined for various anti fuzzy graph. In this work, random r- regular anti fuzzy graph, regular partial domatic number in anti fuzzy graphs, regular partial anti domatic number in anti fuzzy graphs are introduced. Some bounds for anti fuzzy domatic numbers are discussed.

Keywords:

Anti fuzzy graph,Dominating set,Domatic number,Vertex degree,

Refference:

I. Akram, Muhammad. “Anti fuzzy structures on graphs.” Middle East Journal of Scientific Research 11.12 (2012): 1641-1648. 10.5829/idosi.mejsr.2012.11.12.131012

II. Allan, Robert B., and Renu Laskar. “On domination and independent domination numbers of a graph.” Discrete mathematics 23.2 (1978): 73-76. 10.1016/0012-365X(78)90105-X
III. Chang, Gerald J. “The domatic number problem.” Discrete Mathematics 125.1-3 (1994): 115-122. 10.1016/0012-365X(94)90151-1
IV. Cockayne, Ernest J., and Stephen T. Hedetniemi. “Towards a theory of domination in graphs.” Networks 7.3 (1977): 247-261. 10.1002/net.3230070305
V. Dharmalingam, K.M., and Valli, K., “Regular Domatic Partition in Fuzzy Graph.” World Journal of Engineering Research and Technology, 5.5 (2019), 100-107.b http://wjert.org/admin/assets/article_issue/34082019/1567157413.pdf
VI. Gani, A. Nagoor, and K. Prasanna Devi. “2-domination in fuzzy graphs.” International Journal of Fuzzy Mathematical Archive 9.1 (2015): 119-124. http://www.researchmathsci.org/IJFMAart/IJFMA-V9n1-14.pdf
VII. Haynes, Teresa W., Stephen Hedetniemi, and Peter Slater. Fundamentals of domination in graphs. CRC press, 2013. 10.1201/9781482246582
VIII. Muthuraj, R., and A. Sasireka. “Domination on anti fuzzy graph.” International Journal of Mathematical Archive 9.5 (2018): 82-92. https://sadakath.ac.in/naac/criterion_iii/research/maths_supportingdocuments.pdf
IX. Muthuraj, R., and A. Sasireka. “Total domination on anti fuzzy graph.” New Trends in Mathematical Sciences 6.4 (2018): 28-39. 10.20852/ntmsci.2018.312
X. Muthuraj, R.,Vijayalakshmi, P., and Sasireka, A., “Domatic Number On Anti Fuzzy Graph. ” AIPCP. [ Accepted]
XI. Muthuraj, R., and A. Sasireka. “On anti fuzzy graph.” Advances in Fuzzy Mathematics 12.5 (2017): 1123-1135. https://www.ripublication.com/afm17/afmv12n5_06.pdf
XII. Somasundaram, A., and S. Somasundaram. “Domination in fuzzy graphs–I.” Pattern recognition letters 19.9 (1998): 787-791. 10.1016/S0167-
8655(98)00064-6
XIII. Zadeh, Lotfi Asker, George J. Klir, and Bo Yuan. Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers. Vol. 6. World scientific, 1996.
https://books.google.co.in/books/about/Fuzzy_Sets_Fuzzy_Logic_and_Fuzzy_Systems.html?id=wu0dMiIHwJkC
XIV. Zelinka, Bohdan. “Antidomatic number of a graph.” Archivum Mathematicum 33.2 (1997): 191-195. https://dml.cz/bitstream/handle/10338.dmlcz/107610/ArchMathRetro_033-1997-2_2.pdf

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