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    Intelligent computing for electromagnetohydrodynamic bioconvection flow of micropolar nanofluid with thermal radiation and stratification: Levenberg–Marquardt backpropagation algorithm
    (AIP Advances, 2024-03) Khan, Zeeshan; Alfwzan, Wafa F.; Ali, Aatif; Nisreen, Innab; Zuhra, Samina; Islam, Saeed; Asamoah, Joshua Kiddy K.; 0000-0002-7066-246X
    The Levenberg–Marquardt (LM) backpropagation optimization algorithm, an artificial neural network algorithm, is used in this study to perform integrated numerical computing to evaluate the electromagnetohydrodynamic bioconvection flow of micropolar nanofluid with thermal radiation and stratification. The model is then reduced to a collection of boundary value problems, which are solved with the help of a numerical technique and the proposed scheme, i.e., the LM algorithm, which is an iterative approach to determine the minimum of a nonlinear function defined as the sum of squares. As a blend of the steepest descent and the Gauss–Newton method, it has become a typical approach for nonlinear least-squares problems. Furthermore, the stability and consistency of the algorithm are ensured. For validation purposes, the results are also compared with those of previous research and the MATLAB bvp4c solver. Neural networking is also utilized for velocity, temperature, and concentration profile mapping from input to output. These findings demonstrate the accuracy of forecasts and optimizations produced by artificial neural networks. The performance of the bvp4c solver, which is used to reduce the mean square error, is used to generalize a dataset. The artificial neural network-based LM backpropagation optimization algorithm operates using data based on the ratio of testing (13%), validation (17%), and training (70%). This stochastic computing work presents an activation log-sigmoid function based LM backpropagation optimization algorithm, in which tens of neurons and hidden and output layers are used for solving the learning language model. The overlapping of the results and the small computed absolute errors, which range from 10−3 to 10−10 and from 106 to 108 for each model class, indicate the accuracy of the artificial neural network-based LM backpropagation optimization algorithm. Furthermore, each model case’s regression performance is evaluated as if it were an ideal model. In addition, function fitness and histogram are used to validate the dependability of the algorithm. Numerical approaches and artificial neural networks are an excellent combination for fluid dynamics, and this could lead to new advancements in many domains. The findings of this research could contribute to the optimization of fluid systems, resulting in increased efficiency and production across various technical domains.
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    Heat transfer analysis of reactive boundary layer flow over a wedge in a nanofluid using Buongiorno’s model
    (AIP Advances, 2024-10) Jan, Saeed Ullah; Ali, Aatif; Sharaf, Mohamed; Asamoah, Joshua Kiddy K.; 0000-0002-7066-246X
    heat transfer of nanofluids. This study investigates the effects of three different chemical reactions—Arrhenius, bimolecular, and sensitized reactions—using Buongiorno’s model. Through similarity transformations, the system of partial differential equations (PDEs) is converted into ordinary differential equations, which are then solved by combining the shooting method with the Runge–Kutta–Fehlberg numerical technique. The findings show that the skin friction coefficient is greatly increased by raising the pressure gradient and stretching/contracting wedge parameters. On the other hand, as the thermophoresis parameter, Brownian motion parameter, activation energy, and Lewis number increase, the Nusselt number decreases, signifying a decrease in the efficiency of heat transfer. A higher Sherwood number, on the other hand, indicates increased mass transfer and is brought about by increases in the Lewis number, thermophoresis parameter, activation energy, and Falkner–Skan power-law parameter. These findings provide important information for maximizing heat and mass transfer in nanofluid systems. Key values for the skin friction coefficient, local Nusselt number, and the Sherwood number are given in tabular form, and the results are graphically represented.
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    Some New Variants of Hermite–Hadamard and Fej´er-Type Inequalities for Godunova–Levin Preinvex Class of Interval- Valued Functions
    (Wiley, 2024-09) Khan, Zareen A.; Afzal, Waqar; Nazeer, Waqas; Asamoah, Joshua Kiddy K.; 0000-0002-7066-246X
    The theory of inequalities is greatly in2uenced by interval-valued concepts, and this contribution is explored from several perspectives and domains. ,e aim of this note is to develop several mathematical inequalities such as Hermite–Hadamard, Fej´er, and the product version based on center radius (CR)-order relations. Furthermore, we develop several nontrivial examples and remarks to support the main 9ndings.
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    A Mathematical Analysis of the Impact of Immature Mosquitoes on the Transmission Dynamics of Malaria
    (Wiley, 2024-09) Sualey, Nantogmah Abdulai; Akuka, Philip N. A.; Seidu, Baba; Asamoah, Joshua Kiddy K.; 0000-0002-7066-246X
    This study delves into the often-overlooked impact of immature mosquitoes on the dynamics of malaria transmission. By employing a mathematical model, we explore how these aquatic stages of the vector shape the spread of the disease. Our analytical findings are corroborated through numerical simulations conducted using the Runge–Kutta fourth-order method in MATLAB. Our research highlights a critical factor in malaria epidemiology: the basic reproduction number R0 . We demonstrate that when R0 is below unity R0 < 1 , the disease-free equilibrium exhibits local asymptotic stability. Conversely, when R0 surpasses unity R0 > 1 , the disease-free equilibrium becomes unstable, potentially resulting in sustained malaria transmission. Furthermore, our analysis covers equilibrium points, stability assessments, bifurcation phenomena, and sensitivity analyses. These insights shed light on essential aspects of malaria control strategies, offering valuable guidance for effective intervention measures.
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    Modified Holling Tanner diffusive and non-diffusive predator–prey models: The impact of prey refuge and fear effect
    (Elsevier, 2024-09) Tripathi, Deepak; Tripathi, Jai Prakash; Tiwari, Satish Kumar; Jana, Debaldev; Hou, Li-Feng; Shi, Yu; Sun, Gui-Quan; Tiwari, Vandana; Asamoah, Joshua Kiddy K.; 0000-0002-7066-246X
    The secondary consequences of predator species on prey species have substantial implications for population dynamics. A deeper comprehension of the dynamics between prey and predator can be achieved through the examination of indirect consequences. This work examines the dynamic behavior of a modified Holling-Tanner model. The interactions between the species are characterized by a functional response of the Beddington– DeAngelis type. Factors such as prey refuge, fear factor, disturbance intensity, and cross diffusion have been taken into account. The boundedness, feasibility of equilibrium points, their stability and restrictions for Hopf bifurcation of non-spatial model system are derived. The study explores the combined effects of prey refuge presence and fear factors on population dynamics. Furthermore, the investigation focuses on the stability of spatial self-diffusion and cross-diffusion model systems, as well as the specific conditions that lead to Turing instability. Ultimately, it has been shown that in the context of self-diffusion, a moderate level of fear promotes the survival of prey, whereas an excessive level of dread hinders the survival of prey. Concurrently, the mean density of prey exhibited a gradual decline as the refuge parameters increased. The spatial patterns of the population have also been investigated. As the mutual interference between prey populations intensifies, the spatial distribution of the prey population transitions from a clustered pattern to a combination of striped and clustered patterns, ultimately settling into a striped pattern. With the gradual growth of the half saturation constant, the prey population reached a state of uniform distribution. In the scenario of cross diffusion, when the prey is heavily impacted by the pursuit of predators, the fear effect, when appropriately used, did not have a significant impact on the survival of the prey. This work adds to the existing body of knowledge by revealing novel insights into the influence of indirect factors on the behavior of predator and prey populations.