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Abstract:

In this research, we study the dynamical behaviors of a mass-spring system on a massless moving cart. The Lagrangian of the system was first constructed, which resulted in obtaining the Euler-Lagrange equation (ELE) of the system. As a next step, we used the Laplace transformation technique to attain an exact solution for ELE of the system. Furthermore, numerical and simulation techniques were applied with the help of MATLAB software, where we solved ELE numerically for some specified initial conditions. Simulation results indicate that they are in good agreement with the exact analytical solution. Finally, some simulation results were presented in this research.

Keywords:

Mass-Spring System,Lagrangian,Euler-Lagrange equation,Laplace Transformation,Simulation,MATLAB Software,

Refference:

I. Abergel, F., et al. “Differential Equations.” Sovremennye Problemy Matematiki, vol. 5, VINITI, Moscow, 1989.
II. Abdullah, M., A. R. Butt, and N. Raza. “Heat Transfer Analysis of Walters’-B Fluid with Newtonian Heating through an Oscillating Vertical Plate by Using Fractional Caputo–Fabrizio Derivatives.” Mechanics of Time-Dependent Materials, vol. 23, no. 2, 2019, pp. 133–151.
III. Alkhader, Taqwa, Dilip K. Maiti, Tapas Roy, Olivia Florea, and Jihad Asad. “Time Dependent Harmonic Oscillator via OM-HPM.” Journal of Computational Applied Mechanics, vol. 56, no. 1, 2025, pp. 264–275.
IV. Ali, A., et al. “Magnetohydrodynamic Oscillating and Rotating Flows of Maxwell Electrically Conducting Fluids in a Porous Plane.” Punjab University Journal of Mathematics, vol. 50, no. 4, 2020, pp. 61–71.
V. Bajkowski, J. M., and R. Zalewski. “Transient Response Analysis of a Steel Beam with Vacuum Packed Particles.” Mechanics Research Communications, vol. 60, 2014, pp. 1–6.
VI. Brenner, J. L. Problems in Differential Equations. Dover Publications, 2013.
VII. Brown, E., et al. “Universal Robotic Gripper Based on the Jamming of Granular Material.” Proceedings of the National Academy of Sciences of the United States of America, vol. 107, 2010, pp. 18809–18814.
VIII. Fowles, G. R., and G. L. Cassiday. Analytical Mechanics. Thomson Brooks/Cole, 2005.
IX. Goldstein, H., C. P. Poole, and J. Safko. Classical Mechanics. Vol. 2, Addison-Wesley, 1950.
X. Hand, L. N., and J. D. Finch. Analytical Mechanics. Cambridge University Press, 1998.
XI. Imran, M., et al. “The Solutions of Non-Integer Order Burgers’ Fluid Flowing through a Round Channel with Semi-Analytical Technique.” Symmetry, vol. 11, no. 8, 2019, p. 962.
XII. Landau, L. D., and E. M. Lifshitz. Mechanics: Volume 1. Butterworth-Heinemann, 1976.
XIII. Makowski, M., and L. Knap. “Reduction of Wheel Force Variations with Magnetorheological Devices.” Journal of Vibration and Control, vol. 20, no. 10, 2014, pp. 1552–1564.
XIV. Makowski, M., and R. Zalewski. “Vibration Analysis for Vehicle with Vacuum Packed Particles Suspension.” Journal of Theoretical and Applied Mechanics, vol. 53, no. 1, 2015, pp. 109–117.
XV. Marion, J. B. Classical Dynamics of Particles and Systems. Academic Press, 2013.
XVI. Sadiq, N., et al. “Analytic and Semi-Analytic Solution for Motion of Fractional Second Grade Fluid in a Circular Cylinder.” Journal of Mathematical Analysis, vol. 9, 2018, pp. 28–47.
XVII. Safdar, R., M. Imran, and C. M. Khalique. “Time-Dependent Flow Model of a Generalized Burgers’ Fluid with Fractional Derivatives through a Cylindrical Domain: An Exact and Numerical Approach.” Results in Physics, vol. 9, 2018, pp. 237–245.
XVIII. Spiegel, M. R. Laplace Transforms. McGraw-Hill, 1965.
XIX. Trench, W. Elementary Differential Equations. 1st ed., Brooks Cole, 1999.
XX. Zalewski, R. “Constitutive Model for Special Granular Structures.” International Journal of Non-Linear Mechanics, vol. 45, no. 3, 2010, pp. 279–285.
XXI. Zalewski, R., and M. Pyrz. “Experimental Study and Modeling of Polymer Granular Structures Submitted to Internal Underpressure.” Mechanics of Materials, vol. 57, 2013, pp. 75–85.
XXII. Zalewski, R., et al. “Dynamic Model for a Magnetorheological Damper.” Applied Mathematical Modelling, vol. 38, no. 9-10, 2014, pp. 2366–2376.
XXIII. Zalewski, R., and T. Szmidt. “Application of Special Granular Structures for Semi-Active Damping of Lateral Beam Vibrations.” Engineering Structures, vol. 65, 2014, pp. 13–20.
XXIV. Zhang, J. P., and J. L. Li. “Application Study on the Laplace Transformation and Its Properties.” Journal of Taiyuan University of Science and Technology, vol. 3, 2011, pp. 1–22.
XXV. Zill, D. G. First Course in Differential Equations: The Classic Fifth Edition.

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Abstract:

Variations in the visual characteristics of leaf diameters allow for the differentiation of ill states, making leaves valuable indicators for the diagnosis of sickness. Accurate disease diagnosis depends on identifying the distinctive patterns that illnesses leave on foliage. Specialists or cultivators have frequently performed plant inspections, which may be costly and time-consuming. Automation of disease diagnosis is therefore crucial, particularly in areas with limited access to specialists. This work employs five classification algorithms Inception V3, Decision Tree, Support Vector Machine (SVM), and Random Forest to create a model for detecting diseases on apple leaves. The study's prime focus is Apple Rust, Apple Spot, and Apple Scab. To detect these illnesses, a relative examination of machine learning and deep learning models is carried out using the "Apple Leaves Disease Dataset."Among all the models tested, VGG19 achieved the highest test accuracy, reaching an impressive 95 percent.

Keywords:

Classification,Deep Learning,Apple,Rust Leaf,Disease,Machine Learning,Scab,Spot,

Refference:

I. Abdullah, Dakhaz Mustafa, and Adnan Mohsin Abdulazeez. “Machine learning applications based on SVM classification a review.” Qubahan Academic Journal 1.2 (2021): 81-90. 10.48161/QAJ.V1N2A50
II. Barhate, Deepti, Sunil Pathak, and Ashutosh Kumar Dubey. “Hyperparameter-tuned batch-updated stochastic gradient descent: Plant species identification by using hybrid deep learning.” Ecological Informatics 75 (2023): 102094. 10.1016/j.ecoinf.2023.102094
III. Bednarz, Craig W., W. Don Shurley, and W. Stanley Anthony. “Losses in yield, quality, and profitability of cotton from improper harvest timing.” Agronomy Journal 94.5 (2002): 1004-1011. 10.2134/agronj2002.1004
IV. Bonkra, Anupam, et al. “A systematic study: implication of deep learning in plant disease detection.” 2022 IEEE international conference on current development in engineering and technology (CCET). IEEE, 2022. 10.1109/CCET56606.2022.10080181
V. Bonkra, Anupam, Ajit Noonia, and Amandeep Kaur. “Apple leaf diseases detection system: a review of the different segmentation and deep learning methods.” International Conference on Artificial Intelligence and Data Science. Cham: Springer Nature Switzerland, 2021. 10.1007/978-3-031-21385-4_23
VI. Bonkra, Anupam, et al. “Apple leave disease detection using collaborative ml/dl and artificial intelligence methods: Scientometric analysis.” International journal of environmental research and public health 20.4 (2023): 3222. 10.3390/ijerph20043222
VII. Di Franco, Giovanni, and Michele Santurro. “Machine learning, artificial neural networks and social research.” Quality & quantity 55.3 (2021): 1007-1025. 10.1007/s11135-020-01037-y
VIII. Gené-Mola, Jordi, et al. “Multi-modal deep learning for Fuji apple detection using RGB-D cameras and their radiometric capabilities.” Computers and Electronics in Agriculture 162 (2019): 689-698. doi: 10.1016/j.compag.2019.05.016
IX. Kaur, Prabhjot, et al. “Performance analysis of segmentation models to detect leaf diseases in tomato plant.” Multimedia Tools and Applications 83.6 (2024): 16019-16043. 10.1007/s11042-023-16238-4
X. Kaur, Prabhjot, et al. “A novel transfer deep learning method for detection and classification of plant leaf disease.” Journal of Ambient Intelligence and Humanized Computing 14.9 (2023): 12407-12424. 10.1007/s12652-022-04331-9
XI. Khirade, Sachin D., and Amit B. Patil. “Plant disease detection using image processing.” 2015 International conference on computing communication control and automation. IEEE, 2015. 10.1109/ICCUBEA.2015.153
XII. Li, Huishan, et al. “Real-Time Detection of Apple Leaf Diseases in Natural Scenes Based on YOLOv5.” Agriculture 13.4 (2023): 878. 10.3390/agriculture13040878
XIII. Li, Lili, et al. “Diagnosis and mobile application of apple leaf disease degree based on a small-sample dataset.” Plants 12.4 (2023): 786. 10.3390/plants12040786
XIV. Li, Lili, Shujuan Zhang, and Bin Wang. “Apple leaf disease identification with a small and imbalanced dataset based on lightweight convolutional networks.” Sensors 22.1 (2021): 173. 10.3390/s22010173
XV. Liu, Sha, et al. “An improved lightweight network for real-time detection of apple leaf diseases in natural scenes.” Agronomy 12.10 (2022): 2363.doi: 10.3390/agronomy12102363
XVI. Perveen, Kahkashan, et al. “[Retracted] Multidimensional Attention‐Based CNN Model for Identifying Apple Leaf Disease.” Journal of Food Quality 2023.1 (2023): 9504186. 10.1155/2023/9504186
XVII. Rao, Anusha, and S. B. Kulkarni. “RETRACTED: A Hybrid Approach for Plant Leaf Disease Detection and Classification Using Digital Image Processing Methods.” International Journal of Electrical Engineering & Education 60.1_suppl (2023): 3428-3446. 10.1177/0020720920953126
XVIII. Rohini, V., and R. Jyothsna. “Disease detection in apple tree leaves using CNN algorithms.” Journal of Survey in Fisheries Sciences 10.4S (2023): 1097-1101. 10.17762/sfs.v10i4S.1158
XIX. Sabzi, Sajad, et al. “Segmentation of apples in aerial images under sixteen different lighting conditions using color and texture for optimal irrigation.” Water 10.11 (2018): 1634. 10.3390/w10111634
XX. Sai, A. M., & Patil, N. (2022, October). Sai, Andhavaram Mohan, and Nagamma Patil. “Comparative Analysis of Machine Learning Algorithms for Disease Detection in Apple Leaves.” 2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER). IEEE, 2022. 10.1109/DISCOVER55800.2022.9974840
XXI. Sangeetha, K., et al. “Apple leaf disease detection using deep learning.” 2022 6th International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2022. doi: 10.1109/ICCMC53470.2022.9753985
XXII. Sharma, Ochin, et al. “Predicting Agriculture Leaf Diseases (Potato): An Automated Approach using Hyper-parameter Tuning and Deep Learning.” 2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC). IEEE, 2023. 10.1109/ICSCCC58608.2023.10176819
XXIII. Sivakamasundari, G., and V. Seenivasagam. “Classification of leaf diseases in apple using support vector machine.” International Journal of Advanced Research in Computer Science 9.1 (2018): 261-265. 10.26483/ijarcs.v9i1.5124
XXIV. Sheikh, Sophiya, Manmohan Sharma, and Amar Singh, eds. “Recent Advances in Computing Sciences: Proceedings of RACS 2022.” (2023). 10.1201/9781003405573
XXV. Sood, Shivani, and Harjeet Singh. “A comparative study of grape crop disease classification using various transfer learning techniques.” Multimedia Tools and Applications 83.2 (2024): 4359-4382. 10.1007/s11042-023-14808-0
XXVI. Tian, Liangliang, et al. “VMF-SSD: A Novel v-space based multi-scale feature fusion SSD for apple leaf disease detection.” IEEE/ACM Transactions on Computational Biology and Bioinformatics 20.3 (2022): 2016-2028. 10.1109/TCBB.2022.3229114
XXVII. Vishnoi, Vibhor Kumar, et al. “Detection of apple plant diseases using leaf images through convolutional neural network.” IEEE Access 11 (2022): 6594-6609. 10.1109/ACCESS.2022.3232917
XXVIII. Zhu, Ruilin, et al. “Apple-Net: A model based on improved YOLOv5 to detect the apple leaf diseases.” Plants 12.1 (2022): 169. 10.3390/plants12010169

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Abstract:

This paper introduces a novel quadrature rule of precision 5 designed for the numerical integration of definite integrals involving analytic functions. The proposed method synergistically combines Simpson’s 1/3 rd rule with a quadrature rule derived from Hermite interpolation of degree 3. By harnessing the strengths of both techniques, we establish a new quadrature rule that delivers superior precision, thereby ensuring enhanced accuracy in integration tasks. The theoretical framework underpinning the new rule is developed, and we provide a comprehensive analysis of its convergence properties. Numerical experiments demonstrate the superior performance of the proposed method in comparison to traditional quadrature techniques. The results reveal significant improvements in accuracy and efficiency when applied to various classes of analytic functions. This work aims to advance numerical integration strategies and demonstrates the valuable potential of hybrid methods in enhancing computational performance for the integration of analytic functions.

Keywords:

Simpson’s 1/3 rd rule,Hermite interpolation,Degree of Precision,Analytic functions ,

Refference:

I. B.P. Acharya,: ‘Compound Birkhoff-Young rule for numerical integration of analytic functions’, Int. J. Math. Educ. Sci. Technol.. Vol. 14(1), pp.101-103, 1983. 10.1080/0020739830140116
II. E. Suli and David F. Mayer, : An Introduction to Numerical Analysis, Cambridge University press,2003. http://www.statslab.cam.ac.uk/~lab85/resources/schedules2223.pdf
III. E. Denich, P. Novati,: ‘Numerical quadrature for integrals involving oscillating Functions’. Journal of Approximation Software, Vol. 1, pp:1-14, 2024. https://ojs.unito.it/index.php/JAS/article/view/10097/8890
IV. Jun Zhoua, Pieter J. Barendrecht, Michael Barton, Jirí Kosinka ,: ‘Numerical
quadrature for Gregory quads’. Applied Mathematics and Computation, vol. 453, pp: 1-14, 2023. https://www.cs.rug.nl/~jiri/papers/23ZhBaBaKo.pdf
V. K Malik, M. M. Shaikh, M.S. Chandio, A.W. Shaikh,: ‘Some new and efficient derivative based schemes for numerical cubature’. J. Mech. Cont. & Math. Sci.,Vol.1. ,pp:67-78. 2020. 10.26782/jmcms.2020.10.00005.
VI. Lin Ma,: ‘The Taylor’s theorem and its application, highlights in science’,
engineering and Technology. Vol.72, pp:1368-1372. 10.54097/98knf072
VII. Memon K., M. M. Shaikh, M. S. Chandio, A. W. Shaikh,: ‘A Modified
Derivative-Based Scheme for the Riemann-Stieltjes Integral’, Sindh University Research Journal,Vol. 52(01), pp:37-40, 2020 10.26692/sujo/2020.03.06
VIII. P. A. A. Magalhaes, P.A.A. Magalhaes Junior, C. A Magalhaes, A.L.M.A Magalhaes,: ‘New Formulas of Numerical Quadrature Using Spline Interpolation’. Archieves of Computational Methods in Engineering,
Vol. 28, pages 553–576, 2021. 10.1007/s11831-019-09391-3
IX. Rana, K.: ‘Harmonic Mean and Contra-Harmonic Mean Derivative-Based Closed Newton-Cotes Quadrature’. Integr. J. Res. Arts Humanit. Vol. 2, pp: 55– 61, 2022 . 10.55544/ijrah.2.3.36
X. R. L. Burden, J.D Faires, : Numerical Analysis, Cengage Learning, Ninth Edition, 2011. https://www.pdfdrive.com/numerical-analysis-9th-edition-e157182502.html
XI. S. Mahesar, M.M. Shaikh, M.S. Chandio, A. W. Shaikh ,: ‘Some New Time and Cost Efficient Quadrature Formulas to Compute Integrals Using Derivatives with Error Analysis’. Symmetry, Vol.14(12), pp:2611, 2022. 10.3390/sym14122611

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Abstract:

Palliative care improves the quality of life for patients with serious, life-limiting illnesses. Palliative care is so critical and nurses, as primary care providers, are essential. However, their training is lacking somewhat, and they are challenged when it comes to symptom management and emotional support. The purpose of this study was to determine whether palliative care education and training programs affect nursing outcomes. The study was conducted following the PRISMA guidelines, as a systematic review and meta-analysis. PICOS criteria were used to include studies that involved enrolled registered nurses and healthcare providers in palliative care education. A literature search across PubMed, Scopus, CINAHL, and Cochrane Library was conducted to find 40 studies on 6,500 nurses within the context of their impact on nursing knowledge and skills, emotional well-being, and patient outcomes. Significant improvements in nursing knowledge (SMD = 0.85, p < 0.001), skills and competency enhancement (SMD = 0.72, p < 0.001), and emotional outcomes – a decrease of 30% in burnout and 50% increase in empathy (SMD = 0.65, p < 0.001) – were reported. There was a significant improvement in patient satisfaction (SMD = 0.78, p < 0.001). The results indicate that structured training in palliative care for nurses boosts both nurse competence and patient care quality. Nurses will gain knowledge, skills, and emotional resilience through significant improvement in patient outcomes through the palliative care education program. Due to these findings, it is necessary to include specialized (palliative) care training in nursing curricula so that we can meet the multidimensional needs of the patient at his or her end of life.

Keywords:

Education,nursing,training programs,palliative care,knowledge improvement,emotional well-being,patient outcomes,palliative care,systematic review,

Refference:

I. Abu-Odah, H., Molassiotis, A., & Liu, J. (2020). Challenges on the provision of palliative care for patients with cancer in low-and middle-income countries: a systematic review of reviews. BMC palliative care, 19, 1-16.
II. Aldridge, M. D., Hasselaar, J., Garralda, E., van der Eerden, M., Stevenson, D., McKendrick, K., … & Meier, D. E. (2016). Education, implementation, and policy barriers to greater integration of palliative care: a literature review. Palliative medicine, 30(3), 224-239.
III. Carpenter, J. G., Lam, K., Ritter, A. Z., & Ersek, M. (2020). A systematic review of nursing home palliative care interventions: characteristics and outcomes. Journal of the American Medical Directors Association, 21(5), 583-596.
IV. Durojaiye, A., Ryan, R., & Doody, O. (2023). Student nurse education and preparation for palliative care: A scoping review. PloS one, 18(7), e0286678. 10.1371/journal.pone.0286678
V. Feldenzer, K., Rosenzweig, M., Soodalter, J. A., & Schenker, Y. (2019). Nurses’ perspectives on the personal and professional impact of providing nurse-led primary palliative care in outpatient oncology settings. International journal of palliative nursing, 25(1), 30-37.
VI. Ferrell, B. R., & Paice, J. A. (Eds.). (2019). Oxford textbook of palliative nursing. Oxford University Press.
VII. Ghoshal, A., Talawadekar, P., Palleri, A., Marston, J., & Muckaden, M. (2018). Impact of educational training in improving skills, practice, attitude, and knowledge of healthcare workers in pediatric palliative care: Children’s Palliative Care Project in the Indian State of Maharashtra. Indian journal of palliative care, 24(4), 411.

VIII. Gillman, L., Adams, J., Kovac, R., Kilcullen, A., House, A., & Doyle, C. (2015). Strategies to promote coping and resilience in oncology and palliative care nurses caring for adult patients with malignancy: a comprehensive systematic review. JBI Evidence Synthesis, 13(5), 131-204.
IX. Jounaidi, K., Hamdoune, M., Daoudi, K., Barka, N., & Gantare, A. (2024). Advancing Palliative Care through Advanced Nursing Practice: A Rapid Review. Indian journal of palliative care, 30(2), 155–162. 10.25259/IJPC_308_2023
X. Kelley, A. S., & Morrison, R. S. (2015). Palliative care for the seriously ill. New England Journal of Medicine, 373(8), 747-755.
XI. Li, W. W., Chhabra, J., & Singh, S. (2021). Palliative care education and its effectiveness: a systematic review. Public Health, 194, 96-108.
XII. Parekh de Campos, A., Levoy, K., Pandey, S., Wisniewski, R., DiMauro, P., Ferrell, B. R., & Rosa, W. E. (2022). Integrating Palliative Care into Nursing Care. The American journal of nursing, 122(11), 40–45. 10.1097/01.NAJ.0000897124.77291.7d
XIII. Powell, M. J., Froggatt, K., & Giga, S. (2020). Resilience in inpatient palliative care nursing: a qualitative systematic review. BMJ supportive & palliative care, 10(1), 79-90.
XIV. Zanatta, F., Maffoni, M., & Giardini, A. (2020). Resilience in palliative healthcare professionals: a systematic review. Supportive care in cancer: official journal of the Multinational Association of Supportive Care in Cancer, 28(3), 971–978. 10.1007/s00520-019-05194-1
XV. Zhai, X., Ren, L. N., Liu, Y., Liu, C. J., Su, X. G., & Feng, B. E. (2021). Resilience training for nurses: A meta-analysis. Journal of Hospice & Palliative Nursing, 23(6), 544-550.

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Abstract:

In this paper, behaviour of a milk food plant is analyzed which is separated by binary components, one component is milk manufacturing and the binary component is secondary manufacturing goods like raw milk, milk packets, desi Ghee, etc that are manufactured by another unit. By using some assumptions, the availability of the structure is found out with respect to the failure rate vs repair rate of the subsystems. For this purpose, the state transition diagram Figure 1 is made and the states of possibilities of the system are discussed the calculation of the availability of the structure with the help of Regenerative point to graphical technique (RPGT) is analyzed. Table 7 and Figure 2 reveal the analysis of the availability of the system.

Keywords:

Availability,Analyse,Transition Diagram,Manufacturing plant.,

Refference:

I. Al Oraimi, Sara Salim, Syed Mohd Rizwan, and Kajal Sachdeva. “SENSITIVITY AND PROFITABILITY ANALYSIS OF TWO-UNITS AMMONIA/UREA PLANT.” Reliability: Theory & Applications 19.1 (77) (2024): 376-386. SENSITIVITY AND PROFITABILITY ANALYSIS OF TWO-UNITS… – Google Scholar
II. Kumari, Sunita, Pooja Khurana, and Shakuntla Singla. “Behavior and profit analysis of a thresher plant under steady state.” International Journal of System Assurance Engineering and Management (2022): 1-6. Behavior and profit analysis of a thresher plant… – Google Scholar
III. Kumari, Sunita, Pooja Khurana, and Shakuntla Singla. “RAP via constraint optimization genetic algorithm.” Life Cycle Reliability and Safety Engineering 10.4 (2021): 341-345. RAP via constraint optimization genetic algorithm – Google Scholar
IV. Rizwan, Syed Mohd, et al. “Reliability and sensitivity analysis of membrane biofilm fuel cell.” International Journal of Engineering Trends and Technology 71.3 (2023): 73-80. 10.14445/22315381/IJETT-V71I3P209.
V. Singla, Shakuntla, and Pooja Dhawan. “Mathematical analysis of regenerative point graphical technique (RPGT).” Mathematical Analysis and its Contemporary Applications 4.4 (2022): 49-56. Mathematical analysis of regenerative point graphical… – Google Scholar
VI. Singla, Shakuntla, Sonia, and Poonam Panwar. “STOCHASTIC OPTIMIZATION AND RELIABILITY ANALYSIS OF MUSHROOM PLANT.” Reliability: Theory & Applications 19.1 (77) (2024): 729-743. STOCHASTIC OPTIMIZATION AND RELIABILITY ANALYSIS… – Google Scholar
VII. Singla, Shakuntla, et al. “Mathematical model for analysing availability of threshing combine machine under reduced capacity.” Yugoslav Journal of Operations Research 32.4 (2022): 425-437. Mathematical model for analysing availability of… – Google Scholar
VIII. Singla, Shakuntla, and Sonia. “EXPLORE THE DYNAMICS OF MANUFACTURING INDUSTRIES: RELIABILITY ANALYSIS THROUGH STOCHASTIC PROCESS MODELING.” Reliability: Theory & Applications 19.2 (78) (2024): 467-471. EXPLORE THE DYNAMICS OF MANUFACTURING INDUSTRIES:… – Google Scholar
IX. Taj, Syed Zegham, et al. “Reliability analysis of a 3-unit subsystem of a cable plant.” Advances and Applications in Statistics 52.6 (2018): 413-429. Reliability analysis of a 3-unit subsystem of a cable plant. – Google Scholar

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Abstract:

Historically, airfoil design optimization has been done through many different methods, or with the use of improving the aerodynamics in some ways as will be discussed below. Of all the PMS methods that are currently available in the literature, PARSEC marks itself out as the best fitting. One of the most effective methods for changing the shape of an airfoil because of high flexibility and accuracy. The current study aims to determine the utility of the PARSEC parameterization method on two airfoil models. Airfoils NACA 4412, and SG6043 within an angle attack of -10 to 15 degrees, by employing a genetic algorithm. The reliability of the PARSEC method is also assessed by reconstructing the geometries of the airfoil and then comparing the shapes with the original airfoils where these characteristics have a significant influence on the airfoil efficiency. For instance lift coefficient (CL), drag coefficient (CD), and the ratio CL/Cd at different angles of attack. The study also includes the improvement of the aerodynamic design of both airfoils through the use of a genetic algorithm which is coded and run in MATLAB, with the PARSEC parameters used as the base for optimization. It is also important for one to conduct some comparisons between the PARSEC-optimized airfoils and the standard airfoil. The performance of the PARSEC method in making the wing shape the same and similar to the original one is accurate. Aerodynamic characteristics. Significantly, the same was realized in the optimized airfoil and the original airfoil which recorded the maximum pushover speed, drag, and lift characteristics to ±0.3 at an angle of attack of 8 ° and Reynolds number of 10 e5. This paper supports the efficiency of the used PARSEC parameterization. It is found to act as an effective means of support to engineers and researchers who would like to use this method to improve airfoil’s characteristics in aviation, and aeronautical, Such as aerospace applications, and wind turbines. The findings show a positive change in the dependent measures, showing how the variables in the present study fare compared to other studies. By comparing the aerodynamic efficiency of the optimized airfoils, paying attention to the results indicates the use of a genetic algorithm should help to increase several aspects of the wing’s performance

Keywords:

Compressive Strength,GGBS,Metakaoline,Regression Analysis,Split Tensile Strength,

Refference:

I. Anitha, D., et al. “Air Foil Shape Optimization Using CFD and Parametrization Methods.” Materials Today: Proceedings, vol. 5, no. 2, 2018, pp. 5364-5373. 10.1016/j.matpr.2017.12.122
II. Antonini, Enrico GA, David A. Romero, and Cristina H. Amon. “Optimal design of wind farms in complex terrains using computational fluid dynamics and adjoint methods.” Applied Energy 261 (2020): 114426. 10.1016/j.apenergy.2019.114426
III. Bashir, Musavir, et al. “Aerodynamic design optimization of a morphing leading edge and trailing edge airfoil–application on the uas-s45.” Applied Sciences 11.4 (2021): 1664. 10.3390/app11041664
IV. Das, Biranchi Narayana, Manoj Ukamanal, and Atal Bihari Harichandan. “Adjoint Based Optimization of NACA 4412 Airfoil.” 2022 International Interdisciplinary Conference on Mathematics, Engineering and Science (MESIICON). IEEE, 2022. 10.1109/MESIICON55227.2022.10093324
V. El Maani, Rabii, et al. “Multiobjective aerodynamic shape optimization of NACA0012 airfoil based mesh morphing.” International Journal for Simulation and Multidisciplinary Design Optimization 11 (2020): 11. 10.1051/smdo/2020006
VI. Kaya, Mehmet Numan, et al. “Aerodynamic optimization of a swept horizontal axis wind turbine blade.” Journal of Energy Resources Technology 143.9 (2021): 091301. 10.1115/1.4049287
VII. Martins, Joaquim RRA. “Perspectives on aerodynamic design optimization.” AIAA Scitech 2020 Forum. 2020. 10.2514/6.2020-0043
VIII. Tadjfar, M., Siroos Kasmaiee, and S. Noori. “Optimization of NACA 0012 airfoil performance in dynamics stall using continuous suction jet.” Fluids Engineering Division Summer Meeting. Vol. 83723. American Society of Mechanical Engineers, 2020. 10.1115/FEDSM2020-20147
IX. Ümütlü, Hatice Cansu Ayaz, and Zeki Kiral. “Airfoil Shape Optimization Using Bézier Curve and Genetic Algorithm.” Aviation, vol. 26, no. 1, 2022, pp. 32-40. 10.3846/aviation.2022.16471
X. Win, Shwe Yee, and Mongkol Thianwiboon. “Parametric optimization of NACA 4412 airfoil in ground effect using full factorial design of experiment.” Engineering Journal 25.12 (2021): pp. 9-19. 10.4186/ej.2021.25.12.9

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Abstract:

A flow shop is a workspace where machines, which could be humans or machines perform a range of tasks. It involves figuring out how to arrange several jobs in the most effective way possible. In the manufacturing sector, production scheduling is essential for several reasons, including lower product costs, higher productivity, customer happiness, and competitiveness. To adequately satisfy consumer needs and meet product demand, proper scheduling offers and promotes the proper usage of criteria such as available commodities, labour, and machines. This study illustrates the general algorithm and methodology comparison using fuzzy numbers, which is beneficial in figuring out the order of tasks. The aim is to provide the best way to minimize the makespan required to distribute shared resources over time to finish competing tasks. Furthermore, the machine processing times are not fixed; rather, they are interpreted as trapezoidal fuzzy numbers (TrFN). First, during the initial step, three parallel equipotential machines are taken and one machine is in the subsequent phase. Three parallel equipotential machines are taken initially and a single machine in the subsequent phase. Then a comparative study between branch and bound and heuristic methods like CDS (Campbell, Dudek, and Smith) and NEH (Nawaz, Enscore, and Ham) is done.

Keywords:

Makespan,Trapezoidal Fuzzy Number (TrFN),Flow shop scheduling (FSS),Yager’s Ranking Formula,Transportation technique,

Refference:

I. Allahverdi, Ali, et al. “A survey of scheduling problems with setup times or costs.” European journal of operational research 187.3 (2008): 985-1032. 10.1016/j.ejor.2006.06.060
II. Gupta, Arun, and S. Chauhan. “A heuristic algorithm for scheduling in a flow shop environment to minimize makespan.” International Journal of Industrial Engineering Computations 6.2 (2015): 173-184. 10.5267/j.ijiec.2014.12.002
III. Lee, Chung-Yee. “Parallel machines scheduling with no simultaneous machine available time.” Discrete Applied Mathematics 30.1 (1991): 53-61. 10.1016/0166-218X(91)90013-M
IV. Dubois, Didier, and Henri Prade. “Fuzzy real algebra: some results.” Fuzzy sets and systems 2.4 (1979): 327-348. 10.1016/0165-0114(79)90005-8
V. Gupta, Deepak, and Sonia Goel. “Three stage flow shop scheduling model with m-equipotential machines.” International Journal on Future Revolution in Computer Science & Communication Engineering 4.3 (2018): 269-274. https://www.ijrar.org/papers/IJRAR19J1166.pdf
VI. Gupta, Deepak, Sonia Goel, and Neeraj Mangla. “Optimization of production scheduling in two stage flow shop scheduling problem with m equipotential machines at first stage.” International Journal of System Assurance Engineering and Management 13.3 (2022): 1162-1169. https://link.springer.com/article/10.1007/s13198-021-01411-5
VII. Gupta, Deepak, and Sonia Goel. “Branch and bound technique for two stage flow shop scheduling model with equipotential machines at every stage.” International Journal of Operational Research 44.4 (2022): 462-472. 10.1504/IJOR.2022.125132
VIII. Ponnialagan, Dhanasekaran, Jeevaraj Selvaraj, and Lakshmana Gomathi Nayagam Velu. “A complete ranking of trapezoidal fuzzy numbers and its applications to multi-criteria decision making.” Neural Computing and Applications 30 (2018): 3303-3315. 10.1007/s00521-017-2898-7
IX. Palmer, Douglas S. “Sequencing jobs through a multi-stage process in the minimum total time—a quick method of obtaining a near optimum.” Journal of the Operational Research Society 16.1 (1965): 101-107. 10.1057/jors.1965.8
X. Ignall, Edward, and Linus Schrage. “Application of the branch and bound technique to some flow-shop scheduling problems.” Operations research 13.3 (1965): 400-412. 10.1287/opre.13.3.400
XI. McMahon, G. B., and P. G. Burton. “Flow-shop scheduling with the branch-and-bound method.” Operations Research 15.3 (1967): 473-481. 10.1287/opre.15.3.473
XII. Ganesan, K., and P. Veeramani. “Fuzzy linear programs with trapezoidal fuzzy numbers.” Annals of operations research143 (2006): 305-315.https://doi.org/10.1007/s10479-006-7390-1
XIII. Malhotra, K., et al. “Bi-objective flow shop scheduling with equipotential parallel machines.” Malays J Math Sci 16.3 (2022): 451-470. 10.47836/mjms.16.3.04
XIV. Zadeh, Lotfi A. “Fuzzy sets.” Information and Control (1965). http://liphy-annuaire.univ-grenoble- alpes.fr/pages_personnelles/bahram_houchmandzadeh/biblio/Zadeh_FuzzySetTheory _1965.pdf
XV. Zadeh, Lotfi Asker. “Fuzzy sets as a basis for a theory of possibility.” Fuzzy sets and systems 1.1 (1978): 3-28. 10.1016/0165-0114(78)90029-5
XVI. Smith, Richard D., and Richard A. Dudek. “A general algorithm for solution of the n-job, M-machine sequencing problem of the flow shop.” Operations Research 15.1 (1967): 71-82. 10.1287/opre.15.1.71]
XVII. Abbasbandy, Saeid, and T. Hajjari. “A new approach for ranking of trapezoidal fuzzy numbers.” Computers & mathematics with applications 57.3 (2009): 413-419. 10.1016/j.camwa.2008.10.090
XVIII. Chen, Shi-Jay, and Shyi-Ming Chen. “Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers.” Applied intelligence 26 (2007): 1-11. 10.1007/s10489-006-0003-5
XIX. Johnson, Selmer Martin. “Optimal two‐and three‐stage production schedules with setup times included.” Naval research logistics quarterly 1.1 (1954): 61-68. 10.1002/nav.3800010110
XX. Sharma, Sameer, and Deepak Gupta. “Minimizing rental cost under specified rental policy in two stage flow shop, the processing time associated with probabilities including break-down interval and job block criteria.” European Journal of Business and Management 3.2 (2011): 85-103.https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=00ebcb8318cb8481a13af7f6096b8da5eccc6820
XXI. Lomnicki, Z. A. “A “branch-and-bound” algorithm for the exact solution of the three-machine scheduling problem.” Journal of the operational research society 16.1 (1965): 89-100. 10.1057/jors.1965.7

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Abstract:

This commentary addresses flaws discovered in Munir et al.’s recent paper [VI] on the M-Polynomial and associated topological descriptors of dendrimers. Our examination reveals inconsistencies in numerous critical aspects of their work. First, the figures representing the nanostar dendrimer formations must be more accurate. This means that the pictures used to depict the production of these molecules need to be revised. Second, flaws exist within the formulae used for the computations. Finally, based on incorrect formulae and figures, the claimed computational findings must be corrected. We suggest a complete correction approach to ensure the correctness of Munir et al.’s [VI] findings on nanostar dendrimers. It includes supplying corrected figures that accurately portray the nanostar dendrimer structures and removing the inconsistencies in the previous images. In addition, we will use revised notations to ensure that all parameters used in computations are explicitly and consistently described to eliminate ambiguities. Furthermore, we will provide the Accurate Formula for the Symmetric Division Index, which is critical for achieving precise results. Finally, all topological indices will be recalculated using these modifications with the revised figures and formulae to represent the genuine values. These corrections are necessary to give the validity of the results and pave the path for future research in this field.

Keywords:

Degree-based Topological Indices,M-polynomials,Nanostar Dendrimers,

Refference:

I. Chu, Y. M., et al. “On M-Polynomial-Based Topological Descriptors of Chemical Crystal Structures and Their Applications.” The European Physical Journal Plus, vol. 135, no. 11, 2020, article no. 874. 10.1140 /epjp/s13360-020-00893-9 .
II. De, N., and S. M. A. Nayeem. “Computing the F-Index of Nanostar Dendrimers.” Pacific Science Review A: Natural Science and Engineering, vol. 18, no. 1, 2016, pp. 14–21. 10.1016/j.psra.2016.06.001
III. Hussain, Zafar, et al. “Imbalance-Based Irregularity Molecular Descriptors of Nanostar Dendrimers.” Processes, vol. 7, no. 8, 2019, article no. 517. 10.3390/pr7080517
IV. Kamaruzzaman, Nor Fadhilah, et al. “Antimicrobial Polymers: The Potential Replacement of Existing Antibiotics?” International Journal of Molecular Sciences, vol. 20, no. 11, 2019, article no. 2747. 10.3390/ijms20112747.
V. Liu, J. B., et al. “Computation of Bond Incident Degree (BID) Indices of Complex Structures in Drugs.” Eurasian Chemical Communications, vol. 2, no. 6, 2020, pp. 672–679. 10.33945/SAMI/ECC.2020.6.4.
VI. Munir M., Nazeer W., Rafique S., Kang S. M.,: “M-Polynomial and Related Topological Indices of Nanostar Dendrimers.” Symmetry, vol. 8, no. 9, 2016, article no. 97. 10.3390/sym8090097.
VII. Singh, Amandeep, and Sarita Pippal. “Solving Nonlinear Coupled Fractional Partial Differential Equations by ZZ Transform and Adomian Polynomials.” J. Mech. Cont.& Math. Sci, special issue no. 11, May 2024, pp. 1-17. 10.26782/jmcms.spl.11/2024.05.00001

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Abstract:

Recommendation systems are very crucial for enhancing the utility of a given product and or service for a specific user in different fields. This paper focuses on the comparison of various filtering techniques in order to determine their effectiveness in identifying the preferences of the user. The paper also looks into basic methods like user-based collaborative filtering and item-based collaborative filtering, which uses the item's attributes. Also, the paper assesses the subsequent methods such as linear regression, ridge regression, Lasso regression, random forest regression, and XGBoost regression. From the performance evaluation metrics, the researchers reach RMSE and MAE to compare the effectiveness of the proposed methods and reveal their weaknesses. This paper aims to evaluate the performances of the above filtering approaches to gain an understanding of the extent to which these methods improve the recommendations' accuracy and contribute to the literature by providing recommendations on filtering models suitable for various recommendation tasks.

Keywords:

Collaborative filtering,Recommendation system,user-based,item-based,Model Comparison,

Refference:

I. Al-Ghobari, M., Muneer, A., & Fati, S. M. (2021). Location-Aware Personalized Traveler Recommender System (LAPTA) Using Collaborative Filtering KNN. Computers, Materials & Continua/Computers, Materials & Continua (Print), 69(2), 1553–1570. 10.32604/cmc.2021.016348
II. Alhijawi, B., Al-Naymat, G., Obeid, N., & Awajan, A. (2021). Novel predictive model to improve the accuracy of collaborative filtering recommender systems. Information Systems, 96, 101670. 10.1016/j.is.2020.101670
III. Alcacer, A., Epifanio, I., Valero, J., & Ballester, A. (2021). Combining Classification and User-Based Collaborative Filtering for Matching Footwear Size. Mathematics, 9(7), 771. 10.3390/math9070771
IV. Aljunid, M. F., & Huchaiah, M. D. (2021). An efficient hybrid recommendation model based on collaborative filtering recommender systems. CAAI Transactions on Intelligence Technology, 6(4), 480–492. 10.1049/cit2.12048
V. Anwar, T., & Uma, V. (2021). Comparative study of recommender system approaches and movie recommendation using collaborative filtering. International Journal of Systems Assurance Engineering and Management, 12(3), 426–436. 10.1007/s13198-021-01087-x
VI. Chen, C., Zhang, M., Zhang, Y., Ma, W., Liu, Y., & Ma, S. (2020). Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 19–26. 10.1609/aaai.v34i01.5329
VII. Dang, C. N., Moreno-García, M. N., & De La Prieta, F. (2021). An Approach to Integrating Sentiment Analysis into Recommender Systems. Sensors, 21(16), 5666. 10.3390/s21165666
VIII. Fang, J., Li, B., & Gao, M. (2020). Collaborative filtering recommendation algorithm based on deep neural network fusion. International Journal of Sensor Networks, 34(2), 71. 10.1504/ijsnet.2020.110460
IX. Fayyaz, Z., Ebrahimian, M., Nawara, D., Ibrahim, A., & Kashef, R. (2020). Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities. Applied Sciences, 10(21), 7748. 10.3390/app10217748
X. Forouzandeh, S., Berahmand, K., & Rostami, M. (2020). Presentation of a recommender system with ensemble learning and graph embedding: a case on MovieLens. Multimedia Tools and Applications, 80(5), 7805–7832. 10.1007/s11042-020-09949-5
XI. Huang, L., Guan, C. R., Huang, Z. W., Gao, Y., Wang, C. D., & Chen, C. L. P. (2024). Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach. IEEE Transactions on Emerging Topics in Computational Intelligence, 1–15. 10.1109/tetci.2024.3378599
XII. Iwendi, C., Ibeke, E., Eggoni, H., Velagala, S., & Srivastava, G. (2021). Pointer-Based Item-to-Item Collaborative Filtering Recommendation System Using a Machine Learning Model. International Journal of Information Technology & Decision Making, 21(01), 463–484. 10.1142/s0219622021500619
XIII. K, R. C., & Srikantaiah, K. (2021). Similarity Based Collaborative Filtering Model for Movie Recommendation Systems. 10.1109/iciccs51141.2021.9432354
XIV. Kim, T. Y., Ko, H., Kim, S. H., & Kim, H. D. (2021). Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering. Sensors, 21(6), 1997. 10.3390/s21061997
XV. Mohamed, M. H., Khafagy, M. H., & Ibrahim, M. H. (2019). Recommender Systems Challenges and Solutions Survey. https://doi.org/10.1109/itce.2019.8646645
XVI. Mu, Y., & Wu, Y. (2023). Multimodal Movie Recommendation System Using Deep Learning. Mathematics, 11(4), 895. 10.3390/math11040895
XVII. Nassar, N., Jafar, A., & Rahhal, Y. (2020). Multi-criteria collaborative filtering recommender by fusing deep neural network and matrix factorization. Journal of Big Data, 7(1). https://doi.org/10.1186/s40537-020-00309-6
XVIII. Natarajan, S., Vairavasundaram, S., Natarajan, S., & Gandomi, A. H. (2020). Resolving data sparsity and cold start problem in collaborative filtering recommender system using Linked Open Data. Expert Systems With Applications, 149, 113248. 10.1016/j.eswa.2020.113248
XIX. Nguyen, L. V., Hong, M. S., Jung, J. J., & Sohn, B. S. (2020). Cognitive Similarity-Based Collaborative Filtering Recommendation System. Applied Sciences, 10(12), 4183. 10.3390/app10124183
XX. Nguyen, L. V., Vo, Q. T., & Nguyen, T. H. (2023). Adaptive KNN-Based Extended Collaborative Filtering Recommendation Services. Big Data and Cognitive Computing, 7(2), 106. 10.3390/bdcc7020106
XXI. Papadakis, H., Papagrigoriou, A., Panagiotakis, C., Kosmas, E., & Fragopoulou, P. (2022). Collaborative filtering recommender systems taxonomy. Knowledge and Information Systems, 64(1), 35–74. 10.1007/s10115-021-01628-7
XXII. Peng, S., Siet, S., Ilkhomjon, S., Kim, D. Y., & Park, D. S. (2024). Integration of Deep Reinforcement Learning with Collaborative Filtering for Movie Recommendation Systems. Applied Sciences, 14(3), 1155. 10.3390/app14031155
XXIII. R. K., & S, M. J. A. (2021). A Hybrid Deep Collaborative Filtering Approach for Recommender Systems. Research Square (Research Square). 10.21203/rs.3.rs-651522/v1
XXIV. Roy, D., & Dutta, M. (2022). A systematic review and research perspective on recommender systems. Journal of Big Data, 9(1). 10.1186/s40537-022-00592-5
XXV. Sharma, S., Rana, V., & Malhotra, M. (2021). Automatic recommendation system based on hybrid filtering algorithm. Education and Information Technologies. 10.1007/s10639-021-10643-8

XXVI. Shokrzadeh, Z., Feizi-Derakhshi, M. R., Balafar, M. A., & Mohasefi, J. B. (2024). Knowledge graph-based recommendation system enhanced by neural collaborative filtering and knowledge graph embedding. Ain Shams Engineering Journal/Ain Shams Engineering Journal, 15(1), 102263. 10.1016/j.asej.2023.102263
XXVII. Singh, P. K., Sinha, M., Das, S., & Choudhury, P. (2020). Enhancing recommendation accuracy of item-based collaborative filtering using Bhattacharyya coefficient and most similar item. Applied Intelligence, 50(12), 4708–4731. 10.1007/s10489-020-01775-4
XXVIII. Thakker, U., Patel, R., & Shah, M. (2021). A comprehensive analysis on movie recommendation system employing collaborative filtering. Multimedia Tools and Applications, 80(19), 28647–28672. 10.1007/s11042-021-10965-2
XXIX. Widiyaningtyas, T., Hidayah, I., & Adji, T. B. (2021). User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system. Journal of Big Data, 8(1). 10.1186/s40537-021-00425-x
XXX. Xue, F., He, X., Wang, X., Xu, J., Liu, K., & Hong, R. (2019). Deep Item-based Collaborative Filtering for Top-N Recommendation. ACM Transactions on Office Information Systems, 37(3), 1–25. 10.1145/3314578
XXXI. Yalcin, E., & Bilge, A. (2024). A novel target item-based similarity function in privacy-preserving collaborative filtering. The Journal of Supercomputing. 10.1007/s11227-024-06221-7
XXXII. Yu. S., Guo, M., Chen, X., Qiu, J., & Sun, J. (2023). Personalized Movie Recommendations Based on a Multi-Feature Attention Mechanism with Neural Networks. Mathematics, 11(6), 1355. 10.3390/math11061355
XXXIII. Zhou, K., Yu, H., Zhao, W. X., & Wen, J. R. (2022). Filter-enhanced MLP is All You Need for Sequential Recommendation. Proceedings of the ACM Web Conference 2022. 10.1145/3485447.3512111

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Abstract:

The Mulholland equation is a third-order ordinary differential equation characterized by its nonlinearity. It particularly represents a central restoring force. Mulholland equations have many real applications in various fields, such as modern control theory, phase plane analysis, stability analysis, bifurcation analysis, etc. This paper employed the modified direct iteration method to solve the Mulholland equation, analytically incorporating all its terms (sometimes in approximated forms) during each iterative step. The analytical solutions are compared with existing results. The analytical solutions also showed remarkable precision when compared with numerical outcomes. It also becomes clear that compared to other approaches already in use, the modified direct iteration method is substantially easier to use, more accurate, efficient, and uncomplicated. Moreover, the fourth approximated frequency exhibited only a 0.022 percentage error. The suggested method can be widely applied to different engineering issues, while it is primarily demonstrated in nonlinear models with strong nonlinear factors.

Keywords:

Mulholland Equation,Direct iteration procedure,Analytical solution,Modified Direct iteration procedure,Mathematica,

Refference:

I. Alquran M., T., Doğan, N.: ‘Variational Iteration method for solving two-parameter singularly perturbed two-point boundary value problem. Applications and Applied Mathematics’. Applications and Applied Mathematics: An International Journal (AAM). Vol. 5(1), pp: 81-95, 2010. https://digitalcommons.pvamu.edu/aam/vol5/iss1/7
II. Beléndez, A., Méndez, D., I., Fernández, E., Marini, S., Pascual, I.: ‘An explicit approximate solution to the Duffing-harmonic oscillator by a cubication method’. Physics Letters A. Vol. 373(32), pp: 2805-9, 2009. 10.1016/j.physleta.2009.05.074
III. Guo, Z, Leung, A., Y, and Yang, H., X.: ‘Iterative homotopy harmonic balancing approach for conservative oscillator with strong odd-nonlinearity’. Applied Mathematical Modelling. Vol. 35(4), pp: 1717-28, 2011. doi:10.1016/j.apm.2010.10.004
IV. Haque, B., M., I, Hossain, M., M., A.: ‘A modified solution of the nonlinear singular oscillator by extended iteration procedure’. Journal of Advances in Mathematics and Computer Science. Vol. 34(3), pp: 1-9, 2019. 10.9734/jamcs/2019/v34i3-430204

V. Haque, B., M., I., and Hossain, M., I.: ‘An analytical approach for solving the nonlinear jerk oscillator containing velocity times acceleration-squared by an extended iteration method’. journal of Mechanics of Continua and Mathematical Sciences. Vol. 16(2), pp: 35-47, 2021. 10.26782/jmcms.2021.02.00004
VI. Haque, B., M., I., and Hossain, M., M., A.: ‘An Effective Solution of the Cube-Root Truly Nonlinear Oscillator: Extended Iteration Procedure’. International Journal of Differential Equations. Vol. 2021, pp: 1-11, 2021. 10.1155/2021/7819209
VII. He, J.-H.: ‘Homotopy perturbation technique’. Computer methods in applied mechanics and engineering. Vol. 178(3-4), pp: 257–262, 1999. 10.1016/S0045-7825(99)00018-3
VIII. Hossain, M., M., A., and Haque, B., M., I.: ‘A solitary convergent periodic solution of the Inverse Truly Nonlinear Oscillator by modified Mickens extended iteration procedure’. Journal of Mechanics of Continua and Mathematical Sciences. Vol. 16(8), pp: 1-9, 2021. 10.26782/jmcms.2021.08.00001
IX. Hossain, M., M., A., and Haque, B., M., I.: ‘Fixation of the relation between frequency and amplitude for nonlinear oscillator having fractional terms applying modified Mickens’ extended iteration method’. Journal of Mechanics of Continua and Mathematical Sciences, Vol. 17(1), pp: 88-103, 2022. 10.26782/jmcms.2022.01.00007
X. Hossain, M., M., A., and Haque, B., M., I.: ‘An Analytic Solution for the Helmholtz-Duffing Oscillator by Modified Mickens’ Extended Iteration Procedure’. Springer Proceedings in Mathematics & Statistics. pp: 689-700, 2022. 10.1007/978-981-19-9307-7_53
XI. Hossain, M., M., A., & Haque, B., M., I.: ‘An improved Mickens’ solution for nonlinear vibrations’. Alexandria Engineering Journal. Vol. 95, pp: 352-362, 2024. https://doi.org/10.1016/j.aej.2024.03.077
XII. Hu, H.: ‘Solutions of the Duffing-harmonic oscillator by an iteration procedure’. Journal of Sound and Vibration. Vol. 298(1-2), pp: 446-52, 2006. 10.1016/j.jsv.2006.05.023
XIII. Ismail, G., M., Abu-Zinadah, H.: ‘Analytic approximations to non-linear third-order jerk equations via modified global error minimization method’. Journal of King Saud University-Scienc. Vol. 33(1), pp: 101219, 2021. 10.1016/j.jksus.2020.10.016
XIV. Kevorkian, J., K., and Cole, J., D.: ‘Multiple scale and singular perturbation methods’. Springer Science & Business Media. Vol. 114, 2012. 10.1007/978-1-4612-3968-0
XV. Lakrad, F., Belhaq, M.: ‘Periodic solutions of strongly non-linear oscillators by the multiple scales method’. Journal of Sound and Vibration, Vol. 258(4), pp: 677-700, 2002. 10.1006/jsvi.2002.5145
XVI. Lim, C., W., and Wu, B., S.: ‘A modified Mickens procedure for certain nonlinear oscillators’. Journal of Sound and Vibration, Vol. 257(1), pp: 202-206, 2002. 10.1006/jsvi.2001.423
XVII. Mickens, R., E.: ‘Truly nonlinear oscillations: harmonic balance, parameter expansions, iteration, and averaging methods’. World Scientific, 2010. 10.1016/j.jsv.2010.06.019
XVIII. Mickens, R., E.: ‘Iteration procedure for determining approximate solutions to non-linear oscillator equations’. Journal of Sound Vibration. Vol. 116(1), pp: 185-7, 1987. 10.1016/S0022-460X(87)81330-5
XIX. Mulholland, R., J.: ‘Non-linear oscillations of a third-order differential equation’. International Journal of Non-Linear Mechanics. Vol. 6(3), pp: 279-94, 1971. 10.12691/ajams-3-6-4
XX. Nayfeh, A., H.: ‘Introduction to perturbation techniques’. John Wiley & Sons. 2011.
XXI. Nayfeh, A. H, and Mook, D., T.: ‘Nonlinear oscillations’. John Wiley & Sons, 2008.
XXII. Rahman, M., S., Hosen, M., A., Alam, M., S.: ‘Harmonic Balance Solution of Mulholland Equation’. In Proceedings of the International Multi-Conference of Engineers and Computer Scientists. 2011.
XXIII. Ramos. J., I.: ‘Analytical and approximate solutions to autonomous, nonlinear, third-order ordinary differential equations’. Nonlinear Analysis: Real World Applications. Vol. 11(3), pp: 1613-26, 2010. 10.1016/j.nonrwa.2009.03.023

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Abstract:

A magnetic dipole effects nonlinear thermal radiation from ferromagnetic liquid when stretched provinces are analyzed numerically utilizing various parameters pertinent to the problem. Ferrofluid will undergo a phase shift and become magnetic when it is in a magnetic field. This technique is useful in various fields, including electronics, loudspeakers, and materials research. This research aimed to gain further knowledge about the one-of-a-kind continual flow of ferrofluids via permeable medium, including Brownian and thermophoresis influences. Ordinary differential equations may be generated using the appropriate similarity transformation. After that, the equations are solved by using the approach known as bvp4c. Calculations are carried out to obtain the results of physical parameters with non-dimension quantities. The effects of velocity, temperature, and concentration, as well as the applications of these factors, are shown graphically. The velocity is affected in various ways by two factors, namely, the ferromagnetic parameter and the distance. The concentration is increased due to both the thermophoretic and Brownian variables. The frictional force rises as the ferromagnetic and Brownian motion parameters increase, yet the Sherwood and Nusselt numbers decrease throughout this process.

Keywords:

Thermo-phoresis,Brownian motion,Magnetic dipole,Radiation,

Refference:

I. Asadi, A.H. Nezhad, F. Sarhaddi, T. Keykha, “Laminar ferrofluid heat transfer in presence of non-uniform magnetic field in a channel with sinusoidal wall: a numerical study”, J. Magn. Magn. Mater. 471 (2019) 56–63. 10.1016/j.aej.2022.12.031
II. Dhanke, Jyoti Atul, K. Thanesh Kumar, Pudhari Srilatha, Kurapati Swarnalatha, P. Satish, and S. Abdul Gaffar. “Magnetohydrodynamic radiative simulations of eyring–powell micropolar fluid from an isothermal cone.” International Journal of Applied and Computational Mathematics 8, no. 5 (2022): 232. 10.1007/s40819-022-01436-9
III. G. Dharmaiah, W. Sridhar, K. AL‐Farhany, K. Balamurugan, F. “Ali, Non‐Newtonian nanofluid characteristics over a melting wedge: A numerical study”, Heat Transfer 51(5) (2022) 4620-4640. 10.1002/htj.22515
IV. H.I. Andersson, O.A. Valnes, Flow of a heated ferrofluid over a stretching sheet in the presence of a magnetic dipole, Acta Mech. 128(1) (1998) 39–47. 10.1007/bf01463158
V. K. Rafique, M.I. Anwar, M. Misiran, I. Khan, A.H. Seikh, E.-S.M. Sherif, K.S. Nisar, Brownian motion and thermophoretic diffusion consequences on micropolar type nanofluid flow with Soret and Dufour impacts over an inclined sheet: Keller-box Simulations, Energies 12 (2019) 4191. 10.3390/en12214191
VI. Kotha Gangadhar, Aruna Kumari, etc “Magnetization for Burgers’ Fluid Subject to Convective Heating and Heterogeneous-Homogeneous Reactions” Mathematic Problem in Engineering, Febuary 2022:1- 1. 10.1155/2022/2747676
VII. M. Astanina, M. Sheremet, H. Oztop, N. Abu-Hamdeh, MHD natural convection and entropy generation of ferrofluid in an open trapezoidal cavity partially filled with a porous medium, Int. J. Mech. Sci. 136 (2018) 493–502. 10.1016/j.ijmecsci.2018.01.001
VIII. N. Gibanov, M. Sheremet, H. Oztop, MHD natural convection and entropy generation in an open cavity having different horizontal porous blocks saturated with a ferrofluid, J. Magn. Magn. Mater. 452 (2018) 193–204. 10.1016/j.jmmm.2017.12.075
IX. N.A.A.M. Nasir, T. Sajid, W. Jamshed, G.C. Altamirano, M.R. Eid, F.A.A. ElSeabee, Cubic Chemical Autocatalysis and Oblique Magneto Dipole Effectiveness on Cross Nanofluid Flow via a Symmetric Stretchable Wedge, Symmetry 15(6) (2023) 1145. 10.3390/sym15061145
X. N. Vedavathi, V.S. Sajja, etc.. “Metallic nano particle effect on unsteady convective MHD flow of radiation with rotating frame of reference: A theoretical study” , AIP Conference Proceedings, AIP Publishing LLC, 2021, p. 020033. 10.1063/5.0067493
XI. G. Dharmaiah, S. Dinarvand, K. Balamurugan, “MHD radiative ohmic heating nanofluid flow of a stretching penetrable wedge: A numerical analysis”, Heat Transf. 51(5) (2022) 4522-4543. 10.1002/htj.22511
XII. N. Vedavathi, G. Dharmaiah, K. Venkatadri, S.A. Gaffar, Numerical study of radiative non-Darcy nanofluid flow over a stretching sheet with a convective Nield conditions and energy activation, Nonlinear Eng. 10(1) (2021) 159-176. 10.1515/nleng-2021-0012
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