K. K. College of Engineering and Management

Language models (LLMs) have shown to be very useful in many fields like healthcare and finance, as natural language comprehension and generation have advanced. The capacity of LLM to participate in textual discussion has been the subject of much research, and the findings have proved encouraging across several domains. The inability of conventional image classification networks to comprehend the causes of crop diseases and etiology further impedes precise diagnosis. Agricultural diagnostic models on a grand scale will be based on generative pre-trained transformers (GPT) assisted with agrarian settings. By examining the efficacy of text corpora linked to agriculture for pretraining transformer-based language (TBL) models, this research delves into agricultural natural language processing (ANLP). To make the most of it, we looked at several important aspects, including prompt building, response parsing, and several ChatGPT versions. Despite the proven effectiveness and huge potential, there has been little exploration of LLM and Generative AI to agriculture artificial intelligence (AI). Therefore, this study aims to explore the possibility of LLM and Generative AI in smart agriculture. In particular, we present conceptual tools and technical background to facilitate understanding the problem space and uncover new research directions in this field. The paper presents an overview of the evolution of generative adversarial network (GAN) architectures followed by a first systematic review of various applications in smart agriculture and precision farming systems, involving a diversity of visual recognition tasks for smart farming and livestock, precision agriculture, agricultural language processing (ALP), agricultural robots (AR), plant phenotyping (PP), and postharvest quality assessment. We outline the possibilities, difficulties, constraints, and shortcomings. The study lays forth a road map of accessible areas in agriculture where LLM integration is likely to happen shortly. The research suggests exciting directions for further study in this area, which could lead to better agricultural NLP applications.

A comprehensive analysis of crosslink density is crucial for understanding the functional characteristics of rubber vulcanisates. This study discusses a quantitative methodology for assessing crosslink density through the application of a Dynamic Mechanical Analyser (DMA), in which the storage modulus is evaluated during a temperature sweep on cured vulcanisate samples. To confirm the DMA findings, the Molecular Weight Between Crosslinks (Mw) and crosslink density derived from DMA were compared with results obtained from the solvent method, utilising the Flory-Rehner approach. The solvent method involved refining the swelling and drying periods for natural rubber (NR) matrices. The investigation covered the diverse vulcanisation systems, including conventional, semi-efficient, and efficient systems, and altering the dosage of the Zinc oxide activator. Additionally, the study delved into the influence of crosslink density on mechanical properties such as hardness and stress-strain characteristics. This was accomplished by manipulating the cure time of rubber vulcanisates, systematically adjusting it both below and above the tC90 determined through rheometric studies, covering a broad spectrum of intervals. Significantly, the research established a correlation between crosslink density determined by DMA and the widely accepted solvent approach. This comprehensive study establishes the utilisation of the Dynamic Mechanical Analyser to study the crosslink density and enriches our understanding of rubber vulcanisates, providing valuable insights into the intricate relationship between crosslink density and mechanical properties across various vulcanisation systems.

This investigation examines computational analysis of magnetohydrodynamic-driven thermal convection occurring within an innovative circular–trapezoidal geometry under base-wall thermal excitation. The research evaluates how externally imposed directional magnetic forces and dispersed nanoparticles influence thermal transport efficiency and convective motion characteristics. Governing conservation equations undergo numerical resolution through finite element methodology across diverse boundary specifications. A systematic parameter evaluation determines how key dimensionless quantities including Hartmann parameter, Rayleigh parameter, and nanoparticle concentration affect thermal-fluid behavior. Results demonstrate that magnetic field application substantially suppresses circulation patterns, consequently reducing thermal transport effectiveness with increasing Ha values. Conversely, elevated Ra promotes natural convection strength, resulting in superior thermal mixing and improved energy dissipation. Quantitative analysis reveals that increased Rayleigh parameters boost thermal transport effectiveness by factors reaching 4.8 relative to baseline conditions. The magnetic effect subdues thermal transport up to 70.43

Three-dimensional (3D) printing is an emerging technique in composite manufacturing that allows the design and production of more intricate structures than those possible with traditional manufacturing processes. Polylactic acid (PLA) is the most commonly used filament in 3D printing, as it is safe, affordable, easy to print, and biodegradable. This article highlights the extraction of natural plant fibers, the chemical treatment of these fibers, and the preparation of filaments incorporating natural fiber fillers. Additionally, various methods for fabricating composites using natural fibers, including the 3D printing technique, are discussed. This review comprehensively emphasizes the incorporation of chemically treated natural plant fibers into PLA-based 3D printing filaments. It explicitly highlights the effect of fiber treatment processes on the printability and mechanical properties of natural fiber-reinforced composites. In addition, the review also identifies a significant knowledge gap in standard processing methods and optimization techniques for achieving even fiber dispersion and uniform filament quality, which are essential for further developing this sustainable manufacturing method. The research has revealed that natural fibers treated with alkali could enhance tensile strength by as much as 25

This study investigates the complex dynamics of filler-filler interactions within rubber compounds, utilising advanced characterisation techniques. Effective dispersion of fillers within the rubber matrix is crucial for achieving optimal performance in vulcanised products. The absence of effective filler-rubber interaction can significantly impact the performance, reliability and lifespan of rubber products across diverse industries and applications. Hence, it is necessary to attain optimal filler dispersion and interaction within the rubber matrix to secure the desired properties and quality of the product. In this study, various industrial tools such as the Dynamic Mechanical Analyser (DMA) and Rubber Process Analyser (RPA) were employed to thoroughly examine the filler-filler interplay. Notably, a strong correlation coefficient exceeding 0.9 was observed, indicating a high degree of consistency between these two techniques. While RPA offers valuable insights into the processing behaviour of rubber compounds, DMA provides more detailed information on the structural and mechanical changes occurring in the rubber-filler matrix during and after vulcanisation. The investigation focuses on two rubber matrices: Natural Rubber (NR) and Styrene Butadiene Rubber (SBR). Three series of carbon black with varying particle sizes (N134, N339, N774), as well as silica, either individually or in combination, were utilised as fillers. Additionally, the effects of annealing before and after vulcanisation, along with the resulting mechanical properties, were analysed in depth. The deeper insights afforded by DMA can contribute to a more comprehensive understanding of the underlying mechanisms responsible for performance limitations and product failures, such as insufficient filler dispersion or flocculation during vulcanisation.