Sir Syed University of Engineering and Technology

In this investigation, an artificial intelligence-based neural network is used to estimate solutions for the flow and heat transfer characteristics of Ag-CuO/Water hybrid nanofluid past a rotating stretching sheet. Heat source/sink, convective movement, nonlinear thermal radiation, and chemical reactions are all taken into consideration in the energy and concentration equations. The modelled equations are transformed into ordinary differential equations by implementing a suitable similarity transformation. The Levenberg–Marquardt method is used by an artificial neural network (ANN) for solving equations computationally. The outcomes of training, testing, and validation are examined using regression plots, error histograms, performance charts, and transition state analysis to assess the accuracy of the suggested methodology. An enhancement in the Magnetic (M), Rotation ( ε ), and porosity (K) parameters leads to a rise in temperature and concentration profiles; however, a converse effect has been observed with an increment in the Stretching (λ) and ratio (γ) parameters. Growing the Space-dependent heat source/sink ( A^*) and nonlinear thermal radiation parameter (Rd) appreciates the velocity profiles; nonetheless, it decreases for the stretching ratio parameter (λ). To illustrate the precision of the numerical method used, a comparison table is shown, showing an impressive degree of agreement with earlier reported results. The findings show that the hybrid nanofluid offers the possibility of increased thermal performance in application in engineering applications by dramatically improving the heat transmission properties under the applicable circumstances. The quantitative findings of our research work are that velocity, temperature, and concentration fields exhibit strong parametric sensitivity: λ and γ generally suppress flow, heat, and mass transport, while M, K, Rd, and related parameters enhance boundary-layer thickness, temperature, and concentration but may reduce transfer rates due to Lorentz and radiative effects. Skin friction increases with M, Rc, λ, and related parameters, whereas heat and mass transfer rates decline at higher M and Rd owing to thermal boundary-layer thickening and magnetic damping. The AINN trained via the Levenberg–Marquardt algorithm demonstrated near-perfect accuracy (MSE ≈ 0, R = 1), minimal error dispersion, and robust convergence, validating its effectiveness in modeling complex HNF transport phenomena. The results demonstrate that heat transfer is improved for a strong non-linear thermal radiation parameter by 30

In recent years, silver nanoparticles have emerged as potent antimicrobial agents capable of combating extensively drug-resistant pathogenic bacteria that pose serious health risks. The primary aim of our research was to explore the green synthesis of AgNPs using Candida glabrata as an antibacterial agent. A single clinical isolate of Candida glabrata was re-examined via traditional yeast identification methods. Biosynthesis of AgNPs was accomplished by incubating Candida glabrata cell-free supernatant with silver nitrate. AgNP formation was verified by UV-Vis spectroscopy, and the XRD technique assessed the physical properties of the lyophilized AgNPs. EDX and SEM provided insights into the AgNPs' composition, shape, and size. The antibacterial efficacy was evaluated against pathogenic bacteria through the Agar Well Diffusion method. The formation of AgNPs was evidenced by a shift in color to dark brown. The formation of AgNPs at an absorbance wavelength of 430 nm revealed a polycrystalline structure with an average crystal size of 21.91 nm. The silver constituted 29.50% of the composition and indicated a spherical shape with sizes ranging from 74.96 to 100.40 nm. Significant antimicrobial activity was obtained against pathogenic bacteria. Hence, the proposed research highlights a single-step, cost-effective, and environmentally friendly AgNP synthesis approach that exhibits considerable antibacterial properties.

This paper highlights the Prandtl nanofluid flow past a linearly stretchable surface in the existence of nanoparticles and gyrotactic microorganisms. The Prandtl fluid is a non-Newtonian behaviour which is represented by an extra stress tensor. Similarity variables are used to transform the PDEs into a coupled system of non-linear ordinary differential equations. An artificial neural network (ANN) method is also employed to estimate and validate the solution profiles. The results show that the activation energy and chemical reaction greatly affect mass transport, while the heat source/sink strongly affects heat transfer. Moreover, the gyrotactic microorganisms affect the bioconvective response of the nanofluid and improve the stability of the system in given circumstances. The comparison of the numerical and ANN-reliant solutions shows high correspondence, which proves the reliability and efficiency of the suggested ANN framework. The findings may support the design engineering of bio-nanofluid thermal and bioconvective transport systems.

The purpose of the present investigation is to explore the implications of Cross fluid in a Darcy-Forchheimer porous medium due to the tri-hybrid nanofluid past a porous cylinder. Thermal radiation, heat generation, thermal convection, solutal convective and chemical reaction have been encountered in this analysis. Entropy generation has been accounted for under the fluidic friction, heat rate analysis, and porosity analysis. Three different nanoparticles of multiwall carbon nanotube (MWCNT), aluminum oxide (Al2O3), and silver (Ag) are utilized to illustrate the tri-hybrid nanofluid flow with Ethlene Glycol (EG) as the base fluid. The governance model, consisting of linked inadequate differential conditions, is transformed into an ordinary configuration of nonlinear coupled differential conditions by acceptable adjustments. The obtained outcomes in combination with the bvp4c approach are then used to resolve the generated ODEs. For discussion purposes, the impacts of the physical limitations on temperature profile, velocity, and concentration have also been illustrated. Numerical results have been obtained for the diffusion rate, heat transfer rate, drag force, and other factors. While the Forchheimer parameter and the inclination angle reduce the fluid flow’s velocity, the Biot number of heat and mass transfer influences the fluid’s temperature. According to the findings, hybrid nanofluid is the most effective way to improve heat transmission and may also be utilized for cooling. Three different kinds of nanofluids were used in a comparative examination to clarify the study’s conclusions. Changes in viscosity and porousness caused the nanofluids’ velocity to drop by 13.12% and 15.8%, respectively; however, trihybrid nanofluids with improved convection showed a 13.12% rise.

Underwater Acoustic Sensor Networks (UASNs) play a critical role in underwater exploration, yet face challenges like high energy consumption, and uneven load distribution in multi-hop routing. While existing clustering protocols like Anchor Nodes assisted Cluster-based Routing Protocol (ANCRP) have improved energy-efficiency through uniform cluster formation, they still suffer from unequal energy depletion among cluster heads (CHs), particularly in shallower layers. This paper introduces key innovations that advance cluster-based routing in UASNs. We propose an Energy-Adaptive Non-uniform Clustering Protocol (EANCP) with three novel aspects: 1) depth-optimized cluster sizing where deeper CHs manage larger clusters with lower communication demands while shallower CHs handle smaller clusters to prevent early energy depletion; 2) an adaptive beaconing mechanism where transmission rate varies with depth to conserve energy in stable deep regions; and 3) a multi-criteria CH selection strategy considering residual energy, congestion, and distance to sink. The protocol was designed and simulated in Python, leveraging its scientific computing ecosystem (NumPy, SciPy, and Matplotlib) for flexible modeling of underwater acoustic channels and energy consumption. Unlike ANCRP’s uniform approach, EANCP’s depth-aware design addresses the uneven energy consumption in UASNs. Simulations demonstrate significant improvements: a 14% lower energy consumption, 11.5% longer network lifetime, $5-8\% $ higher packet delivery ratio (PDR) and 20% lower delays compared to ANCRP. The protocol particularly excels in large-scale deployment by preventing routing bottlenecks through congestion-aware forwarding. These advancements make EANCP a significant improvement in underwater routing protocols, offering a more robust solution to balancing energy efficiency with reliable communication in harsh underwater environments.