Hybrid nanofluids, due to their enhanced thermal conductivity, have applications in electronic cooling systems, solar thermal collectors, energy storage, and biomedical devices. This study investigates the unsteady mono-nanofluid and hybrid nanofluid flow towards a stagnation point over a stretching permeable sheet in cylindrical coordinates. The model incorporates magnetohydrodynamic (MHD) effects, thermal radiation, activation energy, Brownian motion, thermophoretic, and the Cattaneo-Christov heat flux model. The governing partial differential equations are transformed into nonlinear ordinary differential equations using similarity transformations and solved numerically using the shooting method combined with the fourth-order Runge-Kutta scheme. The results show that the unsteady parameter leads to a reduction in the heat transfer rate by up to 5.31%. Furthermore, a 26.67% increase in the temperature ratio parameter results in a 4.72% increase in the mass transfer rate. These findings provide valuable insights into thermal and mass transport mechanisms, supporting the optimization of advanced thermal management systems.
Multidrug-resistant tuberculosis (MDR-TB) is a serious health concern and a global challenge due to its prolonged treatment duration and complex response to therapies. This study presents a novel computational modeling framework using fractional calculus and fractal theory to address the complex dynamics of MDR-TB by incorporating first and second-line treatment strategies. The Caputo fractional and fractal-fractional (FF) operators are employed to formulate the model that leverages the strength of these mathematical frameworks. The fundamental characteristics of both models, including existence and uniqueness of solutions, are rigorously investigated. The stability analysis is performed using Ulam-Hyers and Ulam-Hyers-Rassias stability criteria. The normalized sensitivity indices of model embedded parameters are evaluated to identify the influential parameters. Furthermore, computational schemes have been developed for both fractional and fractal-fractional models, utilizing interpolation techniques to perform simulations. Detailed simulation is conducted for various fractional and fractal orders demonstrating the stability of these models. This study aims to empower researchers by integrating advanced computational techniques into the modeling and control of infectious diseases, ultimately contributing to more effective interventions.
The current analysis involves the combined convection of aluminium oxide (Al2O3), silver (Ag) hybrid non-magnetic particles, and base fluid water (H2O) flowing over a solid horizontal circular cylinder with magnetohydrodynamic and Joule heating effects. The system of partial differential equations governing flow and heat transfer in a hybrid nanofluid has been derived. These equations have been solved using the Keller-Box technique, while Response Surface Methodology has been employed to optimize flow problems by identifying sensitive parameters. The numerical results are presented in terms of the Nusselt number ( Re-12Nu ) and skin friction coefficient ( Re12Cf ). It has been observed that Eckert number and Joule heating parameter increase Re12Cf by up to 30% and decrease Re-12Nu by up to 40%. The magnetic parameter reduces Re12Cf by 15% at the maximum xi value. Sensitivity analysis reveals that Re12Cf is most negatively affected by magnetic parameter, while Re-12Nu is most negatively affected by Eckert number and Joule heating parameter.
We propose a shrinkage estimator for gamma regression models that combines principal component dimension reduction with a Stein-type adjustment. In the presence of multicollinearity, the maximum likelihood estimator can exhibit substantial variance inflation, motivating the use of biased alternatives. The proposed estimator applies a Stein-type shrinkage rule to the principal component regression estimator, thereby integrating dimension reduction and risk reduction within a unified framework. We derive its analytical properties, establish conditions under which it dominates the maximum likelihood estimator under scalar mean squared error, and characterize its risk behavior relative to existing biased estimators. Finite-sample performance is investigated through Monte Carlo experiments across varying correlation structures and sample sizes. An empirical application illustrates the practical implications of the method. The results demonstrate that combining principal component regression with Stein-type shrinkage yields systematic risk improvements in gamma regression under multicollinearity.
Entropy generation is crucial in optimizing fluid flow processes, particularly in minimizing energy loss during heat transfer. Despite its importance, limited work has been done on the optimization of entropy and heat transfer in immiscible fluid flow systems, such as those used in enhanced oil recovery. This paper presents a scientific investigation of the time-dependent, generalized flow of two immiscible fluids focusing on the intricate behavior of Al2O3-EG-water nanofluid and Saffman dusty fluid through a horizontal duct. The analysis incorporates a system of coupled partial differential equations and a Radial Basis Function Pseudospectral method to approximate the derivatives velocity and temperature vectors. This study further addresses the knowledge gap in immiscible fluid flow systems by incorporating magnetic field effects, viscous dissipation, and entropy generation. The outcomes of this study have scientific implications, shedding light on heat transfer mechanisms and entropy generation in complex flow configurations. The investigation reveals that the presence of Al2O3 nanoparticles in the lower zone of the duct significantly enhances heat transfer compared to base fluids. The nanofluid exhibits improved convective heat transfer coefficients, leading to more efficient heat transfer processes.
This paper introduces a novel Chaotic-Based BVM (CBVM) algorithm, a meta-heuristic optimization approach inspired by the Hindu Trimurti-Lord Brahma, Vishnu, and Mahesh. Reflecting their divine roles, the algorithm consists of three phases: population generation (Brahma), position updating to preserve diversity (Vishnu), and elimination of inferior solutions (Mahesh). To enhance the balance between exploration and exploitation, CBVM incorporates chaotic maps, improving search efficiency, population diversity, and convergence speed while reducing the risk of premature convergence. The performance of CBVM is evaluated on the CEC2005 and CEC2019 benchmark suites and statistically validated using t-test, Friedman test, and Wilcoxon rank-sum test. Experimental results demonstrate that CBVM consistently outperforms several existing algorithms by effectively avoiding local optima and achieving superior global convergence. Furthermore, its successful application to real-world engineering design problems highlights its robustness, adaptability, and effectiveness in solving both constrained and unconstrained optimization problems.
Peristaltic transport of magnetohydrodynamic, double-diffusive nanofluids plays a vital role in many emerging technologies and biomedical microchannel cooling. In this study, a comprehensive mathematical framework is developed to investigate the combined influence of activation energy and double-diffusive convection on the flow of a nanofluid through an inclined, non-uniform porous channel with compliant walls, motivated by bio-inspired strategies for mitigating hazardous substances. The Sutterby fluid model is employed to capture pronounced shear-thinning and shear-thickening behavior. At the same time, additional physical mechanisms, including internal heat generation, thermal radiation, Brownian motion, thermophoresis, Soret and Dufour effects, and magnetohydrodynamic forces, are incorporated. By applying lubrication theory, the moving boundary problem is transformed into a stationary formulation, and the resulting non-dimensional system is solved numerically using a Lobatto IIIA finite-difference scheme implemented in MATLAB's bvp5c solver. The results reveal that thermal and transport characteristics are strongly governed by Brownian motion and Dufour effects, while flow behavior is significantly modulated by wall compliance, porous resistance, and magnetic interaction. The novelty of the present work lies in the unified treatment of compliant wall dynamics, chemically reactive Sutterby nanofluid rheology, and double-diffusive transport under radiative and magnetic effects within an inclined porous configuration.
Analog and radio frequency (RF) complementary metal oxide semiconductor (CMOS) integrated circuit design poses numerous challenges due to the need to balance completely different characteristics. The shrinking of technology in the CMOS process, resulting in lower supply voltages and short channel effects like velocity saturation and vertical field mobility reduction (VFMR), has further complicated these challenges. This paper introduces a novel pre-simulation program with integrated circuits emphasis (pre-SPICE) design methodology for optimizing analog/RF building blocks based on a semi-empirical model. This method utilizes a dedicated tool and the essential parameter of the inversion coefficient (IC) for circuit design. Two examples of analog/RF design have been studied in detail to clarify the design flow and effectiveness of the proposed methodology. A cascode common-source low-noise amplifier (Cascode CS-LNA) and an LC-voltage-controlled oscillator (LC-VCO) have been implemented in CMOS technology, and their results were compared to state-of-the-art performance.
This study examines the natural convective flow of a Casson hybrid nanofluid in a vertical porous microchannel with alternately heated walls under a transverse magnetic field. One wall satisfies the no-slip condition, while the opposite wall is superhydrophobic. The analysis focuses on the effects of magnetic forces, porosity, nonlinear temperature-dependent density variation, and hydrophobic boundary conditions on fluid flow and heat transfer characteristics. Exact analytical solutions are developed to solve the governing equations. Results indicate that wall heating reduces skin friction because of magnetic influences, while increasing magnetic field strength suppresses fluid velocity in both heating configurations. Furthermore, heating the superhydrophobic wall with low temperature-jump coefficients decreases the Nusselt number significantly. Response surface methodology combined with multilinear regression is also employed to determine the influence of governing parameters on heat transfer rates for water at 10 degrees C. The findings are relevant for microfluidics, thermal management systems, and advanced nanomaterial applications.
This study presents numerical results for the freelancing model using a proposed stochastic scheme. The mathematical form of the freelancing model is divided into three categories: the working population, the freelancer population, and the dissemination of information among freelancers. The process of a stochastic computing feed-forward neural network is outlined, utilizing the log-sigmoid function as the activation function and incorporating 10 neurons. The error function is constructed using the differential freelancing model and then optimized through a hybrid search scheme that combines global genetic algorithms with a local active-set method. The efficiency of the solver is tested through the obtained and reference results based on the Runge-Kutta method, whereas the absolute error values around 10-05 to 10-07 improve the worth of the technique. Furthermore, the statistical performances are implemented to authenticate the reliability of the proposed scheme for the numerical solutions of the freelancing model.
The ability of artificial neural networks (ANNs) to accurately simulate nonlinear functions has made them incredibly helpful for modeling complex phenomena. Their flexibility and adaptability are ideal for addressing complex issues in various domains, including epidemiology, engineering, and the applied sciences. To model the behavior of hybrid nanofluids, this work presents a novel computational framework incorporating the Morlet Wavelet Neural Network (MWNN) with the Hybrid Cuckoo Search Algorithm (HCSA). The heat transportation mechanism is explored utilizing the Cattaneo-Christov heat flux model, which incorporates thermal stratification, energy sources, and Thomson-Troian boundary conditions. Using sophisticated wavelet theory and stochastic optimization approaches, the MWNN-HCSA solver is made to capture the intricate nonlinear dynamics present in hybrid nanofluid systems. A comprehensive performance evaluation is ensured by carefully assessing the MWNN-HCSA framework's efficacy using a variety of error metrics, such as mean absolute error (10 degrees-10-07), fitness curves, root mean square error (10-1-10-05), and the ENSE measure. The MWNN-HCSA model's effectiveness in handling challenging modeling issues is confirmed by extensive validation against reference solutions, which shows its precision, convergence, and reliability. Moreover, statistical analyses demonstrate that the solver can accurately utilize an exponential incidence function to represent the nonlinear evolution of hybrid nanofluids.
Verification of the dynamic load transient response of DC-DC converters is an essential process for modern semiconductor devices such as CPUs, GPUs, etc., which require rigorous power supply testing. Conventional electronic loads cannot achieve the rigorous testing conditions of low voltage, high current and high current slew rates. To solve this problem, a novel high-speed variable current sink with a MOSFET-based load is proposed in this study. The proposed device features a linear controllable current from 0 A to 120 A, a scalable and compact footprint, and negligible electrical noise. For stable step response, an optimally compensated control system with 29.95 MHz bandwidth and 51.14 degrees phase margin is used. The study finally demonstrates that this load device can deliver excellent performance with a current slew rate of >900 A/& micro;s and peak load current of 120 A directly sourced from a 1 V power source.
Mathematical modelling of biological fluids is essential for understanding physiologically relevant transport mechanisms in micro-scale biomedical systems. This study examines the peristaltic transport of an ionized non-Newtonian biological fluid, modelled as a fractional second-grade fluid, through a ciliated micro-vessel under electro kinetic effects. The analysis emphasizes the role of the electric double layer formed near the peristaltic wall and its influence on fluid motion. The governing nonlinear equations are simplified using the long-wavelength and low-Reynolds-number approximations together with the Debye-H & uuml;ckel linearization. Thermal effects are incorporated through a modified bio heat equation accounting for viscous dissipation and heat conduction. Thermodynamic irreversibility is evaluated by quantifying entropy generation due to temperature gradients, viscous effects, and electric field interactions. Exact analytical solutions of the resulting boundary value problem are derived and illustrated using Mathematica. The results reveal that increasing the Helmholtz-Smoluchowski velocity $\left({{U_{Hs}}} ight)$UHs and Debye length parameters $ \left(m ight)$m significantly enhances axial velocity due to intensified electroosmotic forces. Parameter polarity strongly influences near-wall and core flow behavior. Additionally, increased cilia length $(\varepsilon)$(epsilon) and electrokinetic width retard core flow while accelerating transport near the walls. These findings provide valuable insights for the modelling-based design and optimization of biomedical microfluidic devices, including artificial cilia systems.
