ABSTRACT This paper proposes a new Fractional Order Sliding Mode Controller (FOSMC) based on a Robust Disturbance Observer (RDO) and a hybrid k-means Grasshopper Optimization Algorithm (k-GOA) for a grid-connected solar-wind Hybrid Generating System (HGS). The RDO-based k-GOA-tuned FOSMC (k-GOA FOSMC) achieves Maximum Power Point Tracking (MPPT) and grid-side control by combining the robustness and reasonable control costs of Sliding Mode Controller (SMC) with the improved performance of a fractional controller using a fractional Proportional plus Derivative (PD) sliding surface. A fractional controller tuning approach based on hybrid k-means clustering and grasshopper optimization eliminates random initialization and enables faster convergence. The developed k-GOA FOSMC is more effective and robust in compensating uncertainties estimated by the RDO. Extensive MATLAB/Simulink simulations under various climatic conditions validate the approach. Computational complexity, system efficiency, and control costs are compared with PID, FOPID, k-GOA FOPID, and FOSMC, demonstrating the superiority of the proposed controller.

ABSTRACT This paper presents a critical review of parameter estimation techniques for solar photovoltaic (PV) systems, highlighting their merits and limitations. The techniques are classified into three groups based on the number of data samples used and the estimation approach adopted for the single-diode model (SDM). Further sub-classifications are included to emphasize the limitations within each category. Various objective functions and performance parameters used to evaluate the effectiveness of these methods are also discussed. In addition, a comparative analysis of major estimation approaches—Numerical, Analytical, Metaheuristic, and Hybrid—is presented to identify the most suitable method. It is observed that hybrid approaches are relatively simple, faster, and offer improved performance indices compared to other methods. Moreover, they require fewer assumptions and approximations, resulting in characteristics closer to actual PV behavior. This review compiles SDM parameter estimation techniques in one place, providing a clearer understanding of this widely used PV modeling approach.

ABSTRACT The analysis of flow, mass, and heat transferral properties of a tri-hybrid nanofluid dissolved in mineral oil through a stretched spinning disk is the primary focus of this manuscript. The Joule heating effect, injection effect, non-linear thermal radiation effect, and the influences of activation energy, and chemical reactions activation energy are also included in the present flow model. The spherical-shaped graphene oxide, Mo S 2 and Ag are chosen as nanoparticles. The modeled equations are solved numerically by employing the bvp4c method. Results are illustrated in graphs and the numerical computations of the Nusselt number, skin friction, and Sherwood number are reported in tabular form. The outcomes reveal that the hybrid nanofluid has 0.42% and 0.43%, respectively, higher heat and mass transfer rates than the nanofluid, while the tri-hybrid nanofluid has 27.58% and 1.88%, respectively, higher heat and mass transferral rates than the nanofluid. Both radial and angular velocities are influenced positively by the rotation parameter, although the temperature profile and axial velocity show the opposite pattern.

ABSTRACT The present investigation deals with a fractional-order prey–predator–disease model along with the effect of wind, global warming, and fear in the prey–predator interaction process. It is important to note that such environmental disturbance affects the biological activities, including biological growth, behavior, and disease transmission process. In this sense, global warming plays an important role in affecting the birth rate of the prey species. The effect of fear in the prey-predator process has been studied separately. The interaction between susceptible prey and predators is governed by the Hassell–Varley type functional response, whereas the interaction between the infected prey and predators follows Holling type II functional response. Necessary conditions for the existence of equilibrium points have been obtained, and further conditions for local stability analysis have been discussed. Also, it is shown that Hopf bifurcation occurs around the predation rate of the infected prey.

ABSTRACT This article presents a machine-learning-based investigation of entropy generation in the magnetohydrodynamic flow of a hybrid nanofluid over a rotating disk embedded in a Darcy Forchheimer porous medium. The effects of a magnetic field on unsteady flow and heat transfer are examined for an incompressible hybrid nanofluid. The governing highly nonlinear partial differential equations are transformed into nonlinear ordinary differential equations using suitable similarity transformations and solved numerically via the spectral quasi-linearization technique. Local skin friction coefficients in the radial and tangential directions, along with the local heat transfer rate at the disk surface, are evaluated for various physical parameters. Results are presented through tables and dimensionless velocity and temperature profiles. Regression analysis is employed to estimate skin friction and heat transfer coefficients, while an artificial neural network model predicts average entropy generation with excellent accuracy (R=1). Increasing the magnetic parameter and Forchheimer number reduces fluid velocity, whereas higher radiation enhances temperature.

ABSTRACT The forced Burgers’ equation is investigated through the homotopy analysis method (HAM). Various forms of forcing terms and initial conditions are considered, leading to different types of solutions such as wavefronts. The HAM is directly applied to the nonlinear partial differential equation under consideration and the analytical solutions are compared with numerical ones, showing excellent agreement. The results suggest a significant influence of the forcing term.

ABSTRACT This study investigates the combined effects of rotation, Hall current, chemical reaction, and nonlinear thermal radiation on the flow and heat transfer of a rotating hybrid nanofluid (Cu–Al₂O₃/water) over a stretching surface with an internal heat source. The governing partial differential equations are transformed into nonlinear ordinary differential equations using similarity transformations and solved numerically via the shooting method. The results indicate that an increase in the Hall current parameter enhances transverse velocity and temperature while reducing longitudinal velocity. A higher chemical reaction parameter decreases nanoparticle concentration due to intensified species depletion. Variations in skin friction coefficients, Nusselt number, and Sherwood number are also analyzed. Furthermore, a neural network model is developed to predict skin friction coefficients (Cfx and Cfy). Comparative analysis shows that the Levenberg–Marquardt algorithm achieves higher accuracy, whereas the BFGS method ensures faster convergence, demonstrating the effectiveness of machine learning in modeling complex nanofluid transport phenomena.

ABSTRACT This research introduces a sine cosine algorithm with pattern search (SCAPS), a hybrid optimization algorithm for digital infinite impulse response (IIR) filter system identification. SCAPS combines the global exploration of SCA with the local exploitation of pattern search, addressing SCA’s limitations in complex optimization landscapes. It was benchmarked against SCA, genetic algorithm, particle swarm optimization, and cooperation search algorithm. Performance was evaluated using fifth-order IIR plant models and reduced-order fifth- and sixth-order systems representing diverse dynamics. A mean square error (MSE)-based fitness function was used, with statistical metrics (best, worst, mean, standard deviation) and Wilcoxon’s rank-sum test for evaluation. Sensitivity and computational cost analyses were also conducted. Results show that SCAPS achieves faster convergence, lower MSE, and improved robustness, demonstrating its potential for accurate system identification and engineering applications requiring precise parameter estimation.

ABSTRACT In this work, we propose and analyze a new fractional-order two-stage species model with recruitment, which combines a well-known integer-order two-stage species model with the Caputo fractional derivative, to discover memory effects on population dynamics. Firstly, the positivity and boundedness of solutions are investigated by using some standard comparison results. Next, a simple approach is utilized to study stability properties of the fractional-order model. This approach is based on the Lyapunov stability theory in combination with some nonstandard techniques for fractional dynamical systems. More clearly, we use general quadratic Lyapunov candidate functions and combine them with characteristics of quadratic forms associated with real matrices to establish the stability properties. Consequently, global asymptotic stability, uniform and Mittag-Leffler stability and, therefore, population dynamics of the proposed fractional-order model are analyzed rigorously. In addition, we extend Mickens’ methodology to construct a dynamically consistent nonstandard finite difference (NSFD) scheme for the purpose of numerical simulation. It is proved that the NSFD scheme preserves the positivity and boundedness of the fractional-order model regardless of the values of the step size; moreover, it is also simple and efficient. Lastly, the theoretical results and advantages of the NSFD scheme are supported by illustrative numerical experiments.

ABSTRACT Surface mines supply more than 96% of the raw minerals used across industrial sectors, making its transportation systems a critical component of operational efficiency and environmental performance. This study develops an integrated stochastic discrete–continuous simulation-based optimization framework for transportation decision-making in surface mining operations. The framework couples a mixed-integer linear programming (MILP) optimization model with a discrete–continuous simulation model to evaluate system performance under operational uncertainty. The proposed approach simultaneously considers economic and environmental objectives by minimizing transportation costs while reducing greenhouse gas (GHG) emissions. The framework is applied to an operating surface copper mine to determine the optimal size of the transportation fleet. Results demonstrate that the integrated simulation–optimization approach improves transporter waiting time at loading points by 33%, leading to a 3.5% increase in production. In addition, the framework enables significant environmental benefits, achieving a 72% reduction in carbon dioxide emissions. Sensitivity analysis is conducted to evaluate the influence of key operational parameters on system performance and to examine trade-offs between cost efficiency and emission reduction.