Paper Title

DESIGN AND DEPLOYMENT OF SELF-ABSORBING VIRTUAL MACHINES USING HYBRID DEEP LEARNING MODELS

Abstract

Using virtual computers to absorb themselves is a crucial strategy for optimal efficiency and effectiveness in cloud computing. This study presents a novel approach to training hybrid deep learning models with a focus on VM self-absorption. We present a method that uses RNNs in conjunction with CNNs to improve workload prediction and adaptation. We use focused tactics, such as attention mechanisms and transfer learning, to enhance the absorption process. Our experimental results demonstrate that our technique considerably enhances absorption rate, convergence speed, and resource use in comparison to prior methods. Our research has important implications for data Centre, the cloud, and edge computing, and it calls for further research into virtual machines and the self-absorption of virtual machines. As a result of integrating supervised and deep reinforcement learning (DRL), the proposed method generates a controller for virtual machines that can dynamically distribute resources in response to workload, power consumption, and benchmarks. Following training on historical VM data including CPU, memory, disc I/O, and network usage, the hybrid model is adjusted for real-time decision-making via reinforcement learning techniques.
KEYWORDS : self-absorption, virtual machines, deep reinforcement learning, hybrid deep learning models

Paper Title

OPTIMIZATION AND PERFORMANCE EVALUATION OF HIGH-STRENGTH FIBER-REINFORCED CONCRETE WITH CEMENT, SILICA FUME, METAKAOLIN, AND ALCOFINE

Abstract

This study investigates the performance of a high-performance concrete mix containing 70% cement, 12% silica fume, 7.5% metakaolin, 7.5% alcofine, 0.8% steel fibers, and a water-cement ratio of 0.42. The research evaluates the mechanical properties and durability characteristics of this optimized concrete formulation. Experimental results demonstrate superior performance compared to conventional concrete, achieving a 28-day compressive strength of 75 MPa, representing a 35-40% strength enhancement. The steel fiber reinforcement contributes to a 30% improvement in flexural strength and enhanced post-crack ductility. Durability testing reveals excellent resistance to environmental degradation, with a 50% reduction in chloride ion penetration and increased sulfate resistance. The combination of silica fume, metakaolin, and alcofine creates synergistic effects that enhance both strength and durability while maintaining adequate workability. These findings indicate that the proposed mix design offers significant advantages for structural applications requiring high mechanical performance and long-term durability, particularly in aggressive environments. The optimized composition presents a viable solution for infrastructure projects where enhanced service life and reduced maintenance are critical considerations.
KEYWORDS : cementitious materials, silica fume, metakaolin, alcofine, mechanical properties, durability, compressive strength, flexural strength.

Paper Title

EVALUATION OF AGRICULTURAL RESIDUE-BASED FIBERS FOR USE IN AUTOMOTIVE AND CONSTRUCTION INDUSTRIES

Abstract

Environmentally friendly composites materials generated from natural resources have been produced in response to the increased environmental impact involved in the manufacture, disposal, and recycling of petroleum-based fiber reinforced polymer matrix composites. Disposal of agricultural-waste crops has recently become a major concern in developing countries. Because agro-waste is readily available, its use in the creation of sustainable composite materials rising, making it a particularly appealing alternative for the construction sector. From the standpoint of recently published research in this sector, the use of agricultural waste and residual materials in composites is both environmentally beneficial and much more valuable. Therefore, this is a complete analysis exploring how agricultural waste and residues may be used as reinforcements or additions for NFPCs in the future. This review paper will serve as a comprehensive data base for future study into the utilization of agricultural waste and residues fiber-reinforced polymer composites as low-cost primary building materials. In the final section, the potential for agro-waste materials to partially take the place of traditional composites in building material research is examined. Water absorption\'s effect on composite mechanical characteristics is also illustrated. The literature review analyses prior studies\' moisture/water content levels to conventional materials in use and the parameters for building and construction materials
KEYWORDS : Natural fiber; agricultural waste; composites; buildings; moisture absorption

Paper Title

CRITICAL REVIEW OF EFFECT OF VARIOUS PARAMETERS ON NATURAL FIBRE REINFORCED POLYMER MATRIX COMPOSITE

Abstract

The materials available today are bearing the disadvantages like environmental issues, weight inconvenience, cost parameters and availability. Therefore, the researchers have shifted their focus on experimenting the abundantly available materials in nature so that a new material can be developed which will be an alternative for the existing materials. Natural fibers and the thermoplastics as well as thermosetting plastics are the components are clubbed together and used to make a composite material. To have the complete and partial biodegradability of the composite, the matrix material is chosen accordingly. In this paper the discussion of various natural fibers along with the matrix materials are overviewed. The effect of various parameters such as, chemical treatment of fibers, fiber length variation, fiber volume variation, fiber orientation on mechanical and tribological properties of the composites are studied and mentioned. The morphology of the developed composites is observed by studying the SEM analysis of that composite. The objective of the discussion is to understand the dos and don’ts of the composite development process.
KEYWORDS :

Paper Title

DESIGN, SYNTHESIS, AND CHARACTERIZATION OF NOVEL HETEROCYCLIC SCAFFOLDS FOR DUAL BIOLOGICAL AND MATERIAL APPLICATIONS

Abstract

Heterocyclic scaffolds of natural as well as synthetic origin provide almost all categories of drugs exhibiting a wide range of pharmacological activities, such as antibiotics, antidiabetic and anticancer agents, and so on. Under normal homeostasis, aldose reductase 2 (ALR2) regulates vital metabolic functions; however, in pathological conditions like diabetes, ALR2 is unable to function and leads to secondary diabetic complications. ALR2 inhibitors are a novel target for the treatment of retinopathy (cataract) influenced by diabetes. Epalrestat (stat), an ALR2 inhibitor, is the only drug candidate that was approved in the last four decades; the other drugs from the stat class were retracted after clinical trial studies due to untoward iatrogenic effects. The present study summarizes the recent development of this pharmacologically active ALR2 heterocyclic scaffold and illustrates the rationale behind the design, structure–activity relationships, and biological studies performed on these molecules. Heterocyclic nucleus plays a fundamental role in the medicinal chemistry and serves as a key template for the development of various therapeutic agents including broad spectrum antibacterial drugs. In an effort to develop new antibacterial agents, a bicyclic twelve-membered heterocyclic nucleus derived from coumarin was prepared by an uncomplicated method. The rate of ring closure for this nucleus, which was given the name coumacine, in addition to two of its derivatives was monitored spectroscopic ally and this rate followed zero order kinetics.
KEYWORDS : novel heterocyclic scaffolds, drugs exhibiting, biological studies, pharmacological activities

Paper Title

LANE LINE DETECTION USING ARTIFICIAL INTELLIGENCE: TECHNIQUES, CHALLENGES, AND APPLICATIONS

Abstract

Lane line detection is very important for autonomous vehicles and Advanced Driver Assistance Systems (ADAS). Traditional methods generally do not perform well under complex conditions that lead to the adoption of Artificial Intelligence (AI) for enhanced robustness. Furthermore deep learning methods may be used to have more precise performance .The paper examines various AI-based lane line detection techniques, and then discusses associated challenges; paper also explores potential real-world applications. The research also presents, a practical implementation for lane line detection using deep learning.
KEYWORDS : Lane line detection, Autonomous vehicles, Advanced Driver Assistance Systems (ADAS), Artificial Intelligence (AI), Deep Learning.