Sharnbasva University

Three-dimensional (3D) printing has emerged as a versatile and sustainable manufacturing route for developing natural fiber-reinforced polymer composites. In this study, the objective is to enhance the mechanical performance and surface quality of 3D-printed polylactic acid (PLA) composites by incorporating powdered pineapple leaf fiber (PLF). Composite filaments were produced using a single-screw extruder and subsequently used to fabricate test specimens through fused deposition modeling. A response surface methodology based on a central composite design was employed to evaluate the influence of fiber content (SFC), infill density (SID), and printing speed (SPS) on ultimate tensile strength (UTS) and surface roughness (Ra). The developed models demonstrated strong statistical reliability, with R2 values of 0.9823 for UTS and 0.9689 for Ra, as supported by ANOVA. Multi-response optimization yielded an overall desirability of 0.973, corresponding to 34.80 wt% SFC, 77.42% SID, and 62.89 mm/s SPS, resulting in a maximum tensile strength of 73.89 MPa and a minimum surface roughness of 3.36 & micro;m. The optimized composite was further examined to establish the processing-structure-property relationship. Hydrogen-bonding interactions between the hydroxyl groups of poly (lactide) fibers and the carbonyl groups of poly (lactide) were detected using FTIR spectroscopy. X-ray diffraction studies showed a moderate increase in the degree of crystallinity of the composite as a result of fiber-induced nucleation. Thermal gravimetric analysis (TGA) indicated an increase in the thermal stability of the composites. Differential scanning calorimetry (DSC) showed that the thermal transitions were more uniform. The results provide evidence that optimized processing results in good bonding between the polymer and the fiber; the composites are, therefore, characterized by structural uniformity, superior thermomechanical properties, and a data-driven approach to the creation of sustainable, high-performance PLA-based biocomposite materials for use in additive manufacturing processes.

The digital transformation of academic ecosystems requires systems capable of addressing diverse cognitive behaviors. Traditional models lack personalized pathways, which reduces student engagement. In this chapter per the authors, a comprehensive adaptive framework is proposed to enhance academic outcomes. By integrating predictive analytics, the system dynamically assesses performance, identifies knowledge gaps, and recommends personalized resources. A hybrid approach combining Random Forest for static prediction and Long Short-Term Memory networks for sequential analysis captures structural and temporal data patterns. Experimental evaluations demonstrate significantly improved prediction accuracy, faster dropout risk identification, and higher engagement. This architecture provides a robust, data-driven foundation for developing student-centric environments while democratizing intelligence for scalable institutional deployment.

This paper investigates the application of a hybrid deep learning model for analyzing human behavior from handwritten data, focusing on prediction accuracy, recall, F1-score, and privacy preservation. The proposed hybrid model integrates Convolutional Neural Networks (CNNs) for feature extraction, Recurrent Neural Networks (RNNs) for sequence learning, and Autoencoders for dimensionality reduction. The model achieved 92.5% accuracy, surpassing the traditional centralized model (90.5%). Although the hybrid model showed slightly lower precision (86.5%), it maintained comparable recall (87.8%) and F1-score (88.4%). The most significant advantage of the hybrid model lies in its privacy preservation, with 95% privacy ensured through federated learning, making it a suitable choice for applications handling sensitive data. While the hybrid model required slightly more training time (15 minutes per round) compared to the centralized model (12 minutes per round), it strikes a balance between privacy protection and computational efficiency. The ablation study showed that reducing the number of local models from 5 to 3 resulted in a drop in accuracy to 86.5%, underscoring the importance of model configuration in federated learning. This study confirms that the hybrid deep learning model, while slightly less efficient in terms of time, offers notable improvements in privacy and predictive performance. The findings suggest that federated learning in conjunction with deep learning models is a promising solution for scenarios requiring high privacy protection and accurate behavioral predictions. Future research will focus on optimizing the hybrid model's training time, improving its scalability, and exploring transfer learning across different domains and datasets.

ABSTRACT This study examines the magnetohydrodynamic (MHD) flow of an electrically conducting fluid through a porous medium in a vertical channel. The influence of an applied magnetic field on fluid motion is analyzed by accounting for both the electrical conductivity of the fluid and the resistance offered by the porous structure. The governing momentum equation is formulated in conjunction with Maxwell's equations for the electromagnetic field and Darcy's law to represent the porous medium effects. The investigation emphasizes the impact of magnetic field intensity, permeability (porosity), and electrical conductivity on the velocity, temperature, and concentration distributions within the channel. The results indicate that increasing magnetic field strength generates a resistive Lorentz force, which suppresses the fluid velocity and reduces the flow rate. Additionally, the porous medium significantly alters the velocity and thermal profiles. The findings are useful for practical applications such as cooling technologies, nuclear reactor design, and industrial transport processes involving porous materials.

The flow and thermal characteristics of a viscous fluid undergoing a first-order chemical reaction, while either absorbing heat or generating heat internally, as it flows vertically through a channel are discussed in this paper. Assumed all fluid properties (viscosity, thermal conductivity, etc.) to depend on temperature and the channel walls are held at constant but different temperatures. In order to obtain solutions to the three-dimensional nonlinear coupled dimensionless equations arising from the problem, the author proposed an analytical method. The non dimensional coupled nonlinear ordinary differential equations are solved analytically by the method of perturbation scheme. Using MATLAB, the distributions of velocity and temperature are presented graphically, and their variations with respect to each parameter are studied numerically. It is observed that the variation of thermal properties and chemical reactions has a remarkable effect on the velocity and temperature distributions in the case of viscous flow through a vertical channel.