Bangalore Institute of Technology

The linear instability of double-diffusive convection in a viscoelastic fluid, described by the Navier-Stokes-Voigt (NSV) model, is investigated. The effects of viscoelasticity and diffusive transport parameters on the onset of convection are analyzed for rigid-free, free-free, and rigid-rigid boundary configurations. The stability eigenvalue problem is solved numerically using the Galerkin method and validated against available analytical and numerical results. The analysis shows that viscoelasticity does not influence stationary convection but significantly affects oscillatory instability. The Voigt parameter alters the balance between viscous dissipation, elastic relaxation, and solutal buoyancy. This leads to a decrease in the critical Rayleigh number for small values of the Voigt parameter and an increase for larger values due to dominant viscoelastic damping. A clear stability hierarchy is observed among the boundary configurations: free-free boundaries are the least stable, while rigid-rigid boundaries are the most stable. Increasing the solutal Rayleigh number promotes oscillatory modes and mode transitions. The Prandtl number and Lewis number modify instability thresholds through their influence on thermal and solutal diffusion. Streamline and isotherm patterns at the critical state reveal complex convective structures. These results provide insight into the interaction between viscoelastic stresses and double-diffusive transport, and are relevant to geophysical and industrial processes involving coupled heat and mass transfer.

This paper investigates the wear behaviour of copper-based metal matrix composites (MMCs) reinforced with carbon nanotubes (CNTs) and micro titanium (Ti) particles. Composites containing 0.5–1.5 Wt

This Paper examines the corrosion behaviour of copper-based metal matrix composites reinforced with hybrid combinations of carbon nanotubes (CNTs) and micro-titanium particles, and evaluates the use of machine-learning models for corrosion prediction. Ten compositions (C0–C9) were fabricated by varying CNT content (0.5–1.5 wt

Federated Learning (FL) is a machine learning paradigm emphasizing data privacy, widely adopted for handling sensitive data. Federated Averaging (FedAvg) is the most commonly implemented FL aggregation technique due to its simplicity and effectiveness. However, FedAvg suffers from information loss during the aggregation stage. This study theoretically and empirically analyzes the Weighted Aggregation via Probability-based Ranking (FedWAPR) technique, an enhancement to FedAvg that retains its simplicity while addressing its limitations. FedWAPR employs a weighted aggregation strategy based on Log-Cauchy and Exponential probability density functions, assigning weights to local models based on their performance. This approach ensures accurate aggregation that reflects the contributions of individual clients. FedWAPR was tested across various model architectures, including Dense Neural Networks, Long Short-Term Memory networks, and Convolutional Neural Networks with results showing performance equal to or surpassing FedAvg. The Log-Cauchy and Exponential distribution functions allow customization of aggregation based on the number of participating clients, with exponential distribution excelling in smaller client setups and Log-Cauchy in larger ones. FedWAPR's ability to integrate with advanced aggregation techniques like FedProx, makes it a robust solution to enhance FL. Additionally, a theoretical analysis confirms the convergence of FedWAPR under standard FL assumptions and thereby ensuring method's robustness and reliability.

The paper is a scientific exploration of the optimization of the mechanical characteristics of biodegradable polyhydroxyalkanoates (PHA) reinforced with Kevlar Fibre (KF) fabricated through Fused Deposition Modeling (FDM). Composite filaments with different weight fractions of 0 wt