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    <name>Article</name>
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              <text>Regression analysis and features of negative activation energy for MHD nanofluid flow model: A comparative study</text>
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              <text>Negative activation energy; Porous medium; Regression analysis; Successive linearization method</text>
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              <text>This article elucidates the impact of activation energy on magnetohydrodynamic (MHD) stagnation point nanofluid flow over a slippery surface in a porous regime with thermophoretic and Brownian diffusions. Negative activation energy is scarce in practice, but the impact of negative activation energy could not be neglected as it is noticed in chemical processes. The rate of some Arrhenius-compliant reactions is retarded by increasing the temperature and is therefore associated with negative activation energies, such as exothermic binding of urea or water. In some processes, the temperature dependence of the pressure-induced unfolding and the urea-induced unfolding of proteins at ambient pressure give negative activation energies. The present mathematical model is solved with successive linearization method (a spectral technique). A comparison of results is made for negative and positive values of activation energy. Apart from it, the quadratic multiple regression model is discussed briefly and explained with bar diagrams. It is observed that with rise in unsteadiness parameter from 0 to 1 (taking positive activation energy), skin friction and Sherwood number are increased by 9.36% and 19% respectively, and Nusselt number is decreased by 26%. However, for negative activation energy, 9.36% and 112% enhancement is observed in skin friction and Sherwood number, respectively.  2023 The Authors</text>
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              <text>Kumar B.; Prachi; Singhal A.; Nandkeolyar R.; Kumar P.; Chamkha A.J.</text>
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              <text>Propulsion and Power Research, Vol-12, No. 2, pp. 273-283.</text>
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              <text>KeAi Communications Co.</text>
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              <text>2023-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1016/j.jppr.2023.02.005" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1016/j.jppr.2023.02.005&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153075409&amp;amp;doi=10.1016%2Fj.jppr.2023.02.005&amp;amp;partnerID=40&amp;amp;md5=f7b91d4f1dc60135d146cb462b123f44" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153075409&amp;amp;doi=10.1016%2fj.jppr.2023.02.005&amp;amp;partnerID=40&amp;amp;md5=f7b91d4f1dc60135d146cb462b123f44&lt;/a&gt;</text>
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              <text>All Open Access; Gold Open Access</text>
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              <text>ISSN: 2212540X</text>
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              <text>Kumar B., School of Mathematics, Thapar Institute of Engineering and Technology, Punjab, Patiala, 147004, India; Prachi, Department of Applied Sciences and Humanities, RIT, Roorkee, 247667, India; Singhal A., School of Sciences, Christ (Deemed to be University), Delhi NCR, 201003, India; Nandkeolyar R., Department of Mathematics, National Institute of Technology, Jamshedpur, 831014, India; Kumar P., Department of Applied Mechanics, Indian Institute of Technology, Delhi, 110016, India; Chamkha A.J., Faculty of Engineering, Kuwait College of Science and Technology, Doha District, Kuwait</text>
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