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Fractional calculus plays a pivotal role in modern scientific and engineering disciplines, providing more accurate solutions for complex fluid dynamics phenomena due to its non-locality and inherent memory characteristics. In this study, Caputo's time fractional derivative operator approach is employed for heat and mass transfer modeling in unsteady Maxwell fluid within a cylinder. Governing equations within a cylinder involve a system of coupled, nonlinear fractional partial differential equations (PDEs). A machine learning technique based on the Levenberg–Marquardt scheme with a backpropagation neural network (LMS-BPNN) is employed to evaluate the predicted solution of governing flow equations up to the required level of accuracy. The numerical data sheet is obtained using series solution approach Homotopy perturbation methods. The data sheet is divided into three portions i.e., 80 % is used for training, 10 % for validation, and 10 % for testing. The mean-squared error (MSE), error histograms, correlation coefficient (R), and function fitting are computed to examine the effectiveness and consistency of the proposed machine learning technique i.e., LMS-BPNN. Moreover, additional error metrics, such as R-squared, residual plots, and confidence intervals, are incorporated to provide a more comprehensive evaluation of model accuracy. The comparison of predicted solutions with LMS-BPNN and an approximate series solution are compared and the goodness of fit is found. The momentum boundary layer became higher and higher as there was an enhancement in the value of Caputo, fractional order α = 0.5 to α = 0.9. Higher thermal boundary layer (TBL) profiles were observed with the rising value of the heat source. [ABSTRACT FROM AUTHOR]

For modeling of complex systems, in signal processing, and the explorations of nonlinear dynamics and memory effects, the utilization of noninteger-order dynamics provides adaptable control over oscillation patterns and frequencies. Moreover, various waveforms result in unique sonic qualities. By manipulation of these waveforms, musicians and sound designers have the capacity to craft a wide spectrum of auditory experiences, ranging from basic tones to intricate sound scape. This paper is about study of such vibrating systems using the noninteger-order derivative operator approach. In particular, we will discuss fractional relaxation oscillator and Scott-Blair oscillator employing constant proportional Caputo fractional derivative operator. Laplace transform method and Tzou's numerical inversion algorithm are utilized to solve these vibrating models with respective initial conditions. A thorough graphical analysis is done to discuss the control of noninteger-order parameters and the parameters involved in the definition of fractional derivative operator. Finally, useful conclusions are recorded to highlight the behavior of oscillators and influence of noninteger-order parameters, the parameters involved in the definition of fractional derivative operator and system parameters that are helpful in controlling over oscillation pattern, frequencies and shape of vibrations produced by the vibrating systems. [ABSTRACT FROM AUTHOR]

The main purpose of this article is to reconsider the theory of variational iteration method and embed it with Laplace transform to construct the conjoined technique variational iteration Laplace transform method. And then, by using the proposed method we solve a class of integral equations, especially fractional order Volterra type integro‐partial‐differential equations occurring in different fields of natural and social sciences. For this, we first turn to some prelude concepts and different types of equations especially integral equations of different kinds from the literature. Then, we will present step by step the embedded technique. Next, some fractional order model integral equations will be tackled using the proposed method along with some existing techniques, like the Adomian decomposition method and successive approximations method. The efficacy and authentication of the proposed technique will be analysed with the help of the obtained results and graphical exhibitions of the particular solved examples. Finally, use of the initiated method in the future for different types of equations occurring in different fields of science will be presented. [ABSTRACT FROM AUTHOR]

Retrofitting building designs is crucial given the global aging infrastructure and increased in frequency of natural hazards like earthquakes. While traditional data‐driven models are widely used for predicting building conditions, there has been limited exploration of recent artificial intelligence (AI) techniques in structural design. This study introduces a novel explainable AI framework that utilizes data‐driven models for assessing, designing, and retrofitting of structures. The framework highlights the key global features of the model and further investigates them locally to adjust the input design parameters. It suggests the necessary changes in these inputs to achieve the desired structural performance. To achieve this, the framework employs interpretability techniques such as feature importance, feature interactions, Shapley Additive exPlanations, local interpretable model‐agnostic explanations, partial dependence plot (PDP), and individual conditional expectation to highlight the important features. Additionally, a novel counterfactual) technique is applied for the first time as a design tool in seismic assessment and retrofitting of structures. The effectiveness of this framework is validated on a real benchmark structure through nonlinear time history analysis and natural earthquakes. The results show that the proposed framework is highly effective, especially under design‐level earthquake conditions in achieving the necessary change in stiffness and strength of structures to meet the required seismic design objectives across different earthquake scenarios. This framework holds promise for wider adoption and applications in various other structural and civil engineering domains. [ABSTRACT FROM AUTHOR]

This study utilizes artificial neural networks (ANN) to estimate prediction intervals (PI) for seismic performance assessment of buildings subjected to long-term ground motion. To address the uncertainty quantification in structural health monitoring (SHM), the quality-driven lower upper bound estimation (QD-LUBE) has been opted for global probabilistic assessment of damage at local and global levels, unlike traditional methods. A distribution-free machine learning model has been adopted for enhanced reliability in quantifying uncertainty and ensuring robustness in post-earthquake probabilistic assessments and early warning systems. The distribution-free machine learning model is capable of quantifying uncertainty with high accuracy as compared to previous methods such as the bootstrap method, etc. This research demonstrates the efficacy of the QD-LUBE method in complex seismic risk assessment scenarios, thereby contributing significant enhancement in building resilience and disaster management strategies. This study also validates the findings through fragility curve analysis, offering comprehensive insights into structural damage assessment and mitigation strategies. [ABSTRACT FROM AUTHOR]

The manuscript explores a fractional order heat-mass transfer model for Oldroyd-B fluid on a vertical plate employing the Prabhakar derivative operator. It investigates the MHD flow of Oldroyd-B fluid induced by natural convection and the general motion of the plate. Well known integral transform i.e. Laplace transform and Tzou's numerical inversion algorithm are employed to simulate the model under generalized constraints. Several scenarios involving the generalized motion of the plate and general boundary conditions are investigated. A comprehensive graphical analysis is conducted to investigate the control of system parameters, and valuable findings are concluded that can help to optimize and foster various processes. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present the convergence results to approximate the fixed points of multivalued mean nonexpansive mappings in CAT(0) spaces. Strong and ?-convergence results are established for these mappings using F-iterative scheme. Moreover, a numerical example is given to show the convergence behavior of the iterative scheme for multivalued mean nonexpansive mappings. The results which we derived are generalization of many results existing in literature. [ABSTRACT FROM AUTHOR]

In this manuscript, we aim to establish a connection between the concept of inequalities and the novel Center-Radius order relation. The idea of a Center-Radius (CR)-order interval-valued Godunova-Levin (GL) function is introduced by referring to a total order relation between two intervals. Consequently, convexity and nonconvexity contribute to different kinds of inequalities. In spite of this, convex theory turns to Godunova-Levin functions because they are more efficient at determining inequality terms than other convexity classes. Our application of these new definitions has led to many classical and novel special cases that illustrate the key findings of the paper. Using total order relations between two intervals, this study introduces CR-(h1; h2)-Goduova-Levin functions. It is clear from their properties and widespread usage that the Center-Radius order relation is an ideal tool for studying inequalities. This paper discusses various inequalities based on the Center-Radius order relation. With the CR-order relation, we can first derive Hermite-Hadamard (H.H) inequalities and then develop Jensen-type inequality for interval-valued functions (IVF S) of type (h1; h2)-Godunova- Levin function. Furthermore, the study includes examples to support its conclusions. [ABSTRACT FROM AUTHOR]

The role of integral inequalities can be seen in both applied and theoretical mathematics fields. According to the definition of convexity, it is possible to relate both concepts of convexity and integral inequality. Furthermore, convexity plays a key role in the topic of inclusions as a result of its definitional behavior. The importance and superior applications of convex functions are well known, particularly in the areas of integration, variational inequality, and optimization. In this paper, various types of inequalities are introduced using inclusion relations. The inclusion relation enables us firstly to derive some Hermite-Hadamard inequalities (H.H-inequalities) and then to present Jensen inequality for harmonical h-Godunova-Levin interval-valued functions (GL-IVFS) via Riemann integral operator. Moreover, the findings presented in this study have been verified with the use of useful examples that are not trivial. [ABSTRACT FROM AUTHOR]

In this article, we use the Picard-Thakur hybrid iterative scheme to approximate the fixed points of generalized α-nonexpansive mappings. For generalized α-nonexpansive mappings in hyperbolic spaces, we show several weak and strong convergence results. It is proved numerically and graphically that the Picard-Thakur hybrid iterative scheme converges more faster than other well-known hybrid iterative methods for generalized α-nonexpansive mappings. We also present an application to Fredholm integral equation. [ABSTRACT FROM AUTHOR]

There are different types of order relations that are associated with interval analysis for determining integral inequalities. The purpose of this paper is to connect the inequalities terms to total order relations, often called (CR)-order. In contrast to classical interval-order relations, total order relations are quite different and novel in the literature and are calculated as ω = 〈 ω c , ω r 〉 = 〈 ω ¯ + ω ̲ 2 , ω ¯ − ω ̲ 2 〉 . A major benefit of total order relations is that they produce more efficient results than other order relations. This study introduces the notion of CR- (h 1 , h 2) -convex function using total order relations. Center and Radius order relations are a powerful tool for studying inequalities based on their properties and widespread application. Using this novel notion, we first developed some variants of Hermite–Hadamard inequality and then constructed Jensen inequality. Based on the results, this new concept is extremely useful in connection with a variety of inequalities. There are many new and well-known convex functions unified by this type of convexity. These results will stimulate further research on inequalities for fractional interval-valued functions and fuzzy interval-valued functions, as well as the optimization problems associated with them. For the purpose of verifying our main findings, we provide some nontrivial examples. [ABSTRACT FROM AUTHOR]

Interval analysis distinguishes between inclusion relation and order relation. Under the inclusion relation, convexity and nonconvexity contribute to different kinds of inequalities. The construction and refinement of classical inequalities have received a great deal of attention for many classes of convex as well as nonconvex functions. Convex theory, however, is commonly known to rely on Godunova-Levin functions because their properties enable us to determine inequality terms more precisely than those obtained from convex functions. The purpose of this study was to introduce a (⊆) relation to established Jensen-type and Hermite-Hadamard inequalities using (h1, h2)-Godunova-Levin interval-valued functions. To strengthen the validity of our results, we provide several examples and obtain some new and previously unknown results. [ABSTRACT FROM AUTHOR]