Tamil University

This study examines the drilling characteristics of crab carapace-impregnated coir polyester composite material. The chemical structure of the crab carapace offers better mechanical properties; however, optimizing drilling performance in a full factorial design under various drilling conditions, with the parameters of drill bit diameter, feed rate, and spindle speed. A reduction in feed rate combined with a small drill diameter and high spindle speed creates the optimal conditions to minimize the delamination value of 1.05 while maintaining structural strength and producing a strong thrust of 100 N and torque effects of 5.0 N-m on the composite material. The ANN was used to predict the drilling parameters, and the ANN model provides a precise prediction of drilling parameters within the defined boundaries for improved operation efficiency of this composite material, with average absolute percentage error of less than 3%. The ANN-based methodology shows efficient modeling capabilities for drilling operations in complex structured composite materials.

The development of high-performance, sustainable biocomposites requires biodegradable matrices and optimized natural reinforcements. In this study, flax fiber-reinforced polylactic acid (PLA) hybrid biocomposites incorporating waste pistachio nut shells (WPNS), waste tea leaf fiber (WTLF), and waste quail eggshell (WQES) were developed and evaluated, with direct comparison to previously reported jute-based hybrid systems to assess the benefits of fiber substitution. The composites were fabricated via compression molding and characterized for their mechanical, thermal, acoustic, surface, and moisture-related properties. Replacing the jute with flax resulted in a consistent performance enhancement. Among the hybrids, the flax-WPNS composite exhibited the highest tensile and flexural performance, achieving tensile strength improvements of approximately 30-40% over neat PLA due to effective stress transfer and crack deflection. The flax-WTLF composite showed superior acoustic behavior, attaining a maximum sound absorption coefficient of approximately 0.65-0.70 at mid-to-high frequencies, attributed to its porous microstructure. In contrast, the flax-WQES composite demonstrated the highest thermal conductivity (0.54 W/(mK)) and apparent density (2.24 g/cm3), reflecting dense packing and the presence of CaCO3-rich particles. Scanning electron microscopy revealed distinct microstructural mechanisms governing these property-specific responses, including differences in interfacial bonding, void distribution, and filler packing efficiency. An integrated fuzzy CRITIC-COPRAS multicriteria decision-making approach identified the flax-WPNS hybrid as the optimal overall formulation. The results clearly demonstrate that flax fibers outperform jute as a reinforcement in PLA-based hybrid biocomposites, and that targeted combinations of flax and waste-derived fillers enable multifunctional performance optimization for sustainable engineering applications.

Zinc stannate (ZnSnO 3 -ZS) is a promising nonlinear optical (NLO) material due to its wide bandgap, high transparency, and structural stability. This study explores the impact of rare-earth doping (Eu[Formula: see text], Dy[Formula: see text], and Sm[Formula: see text]) on the NLO properties of ZS NPs synthesized via a solution combustion method using Aloe vera gel extract as a reducing agent. Structural analysis confirms successful doping, with slight lattice distortions enhancing optical responses. Bragg reflections of all the samples confirm the formation of inverse spinel cubic perovskite structure. UV-Visible absorption studies reveal bandgap modulation. The optical energy band gap was found to be 3.11, 3.1, 3.08 and 3.04 eV for ZS, ZS:Eu (9 mol%), ZS:Dy (9 mol%) and ZS:Sm (9 mol%) NPs, respectively. NLO studies using the Z-scan technique indicate significant enhancement in two-photon absorption and reverse saturable absorption, with Sm[Formula: see text] (9 mol%)-doped ZS exhibiting the highest NLO response. These findings suggest that rare-earth doped ZS is a strong candidate for photonic and optoelectronic applications, particularly in laser protection and optical switching.

Transformer models have greatly changed how deep learning works, particularly in natural language processing, computer vision, and multi-modal learning problems. While these models have done great empirically, the mathematical foundations and stability of these models remain poorly understood. In this paper, we perform a holistic analysis of the mathematical foundations of the Transformer architecture such as self-attention and positional encodings, and mathematical properties of layer normalization. Specifically, our work explores the stability conditions given different settings of training and inference, using principles from linear algebra, spectral analysis, and dynamical systems. In our analysis, we elaborate on how architectural parameters (e.g., depth, width, and initialization scheme) could influence the behaviour of gradients, convergence, and robustness. We also investigate the conditions by which we can assure stable propagation of information through layers and identified the conditions in which transformers exhibit vanishing or exploding gradients. We hope our work will offer reliable theoretical prospects of robustness for practical implementations of Transformer-based systems, and offer ways to practically choose a model that is stable and demonstrably efficient. The paper offers a connection for theoretical analysis and practical implementation, which are the foundations we believe will facilitate the next generation of theoretically-based grounded deep learning architectures.

First Chapter is about “Basics of IoT And its Architecture” this chapter is contributed by the author Mrs R Praghatha, Assistant Professor in the Department of Computer Science and Engineering at St. Jospeh’s College of Engineering and Technology- Thanjavur. Second Chapter is about “Design Foundations for Iot-Enabled Devices” this chapter is contributed by the author Mrs T Vinotha, Assistant Professor in the Department of Computer Science and Engineering at St. Jospeh’s College of Engineering and Technology- Thanjavur. Third Chapter is about “Web Integration and connectivity essentials” this chapter is contributed by the author Mrs V C Priyadharshini, Assistant Professor in the Department of Computer Science and Engineering at St. Jospeh’s College of Engineering and Technology- Thanjavur. Fourth Chapter is about “Foundations of Internet-Based connectivity systems” this chapter is contributed by the author Mrs P Latha Assistant Professor in the Department of Computer Science and Engineering at St. Jospeh’s College of Engineering and Technology- Thanjavur. Fifth Chapter is about “Modern Methods For Data Acquisition Analytical processing” this chapter is contributed by the author Mrs G SUBBULAKSHMI Assistant Professor in the Department of Computer Science and Engineering at St. Jospeh’s College of Engineering and Technology- Thanjavur. Fifth Chapter is about “Smart Data collection and Cloud based computing” this chapter is contributed by the author Mrs R Kavitha, Assistant Professor in the Department of Computer Science and Engineering at St. Jospeh’s College of Engineering and Technology- Thanjavur.