Paper Title

MACHINE LEARNING-DRIVEN FOCUSED TECHNIQUES FOR VIRTUAL MACHINE SELF-ABSORPTION

Abstract

Due to its ability to supply reliable, robust and scalable computational power, cloud computing is becoming increasingly popular in industry, government, and academia. High-speed networks connect both virtual and real machines in cloud computing data centres. The system’s dynamic provisioning environment depends on the requirements of end-user computer resources. Hence, the operational costs of a particular data center are relatively high. To meet service level agreements (SLAs), it is essential to assign an appropriate maximum number of resources. Virtualization is a fundamental technology used in cloud computing. It assists cloud providers to manage data centre resources effectively, and, hence, improves resource usage by creating several virtual machine (VM) instances. Furthermore, VMs can be dynamically integrated into a few physical nodes based on current resource requirements using live migration, while meeting SLAs. As a result, un optimised and inefficient VM consolidation can reduce performance when an application is exposed to varying workloads. This paper introduces a new machine-learning-based approach for dynamically integrating VMs based on adaptive predictions of usage thresholds to achieve acceptable service level agreement (SLAs) standards. Dynamic data was generated during runtime to validate the efficiency of the proposed technique compared with other machine learning algorithms.
KEYWORDS : machine learning; virtual machine; migration; allocation; cloud computing

Paper Title

MICROWAVE IRRADIATION TECHNIQUES FOR SCALABLE PRODUCTION OF GRAPHENE-NANODIAMOND COMPOSITE CARBON SPHERES

Abstract

This study explores the development of graphene-nanodiamond composite carbon spheres using microwave-assisted synthesis methods. By leveraging the unique heating capabilities of microwaves, we achieve rapid and uniform temperature control, promoting the formation of hybrid structures with enhanced properties. The process combines graphene and nanodiamond precursors to produce composite spheres with improved mechanical, thermal, and electrical performance. Key factors such as irradiation power, duration, and precursor ratios are optimized to ensure consistent quality and scalability. Characterization techniques, including Raman spectroscopy, X-ray diffraction, and electron microscopy, confirm the successful integration of graphene and nanodiamond within the carbon spheres. These materials hold significant potential for applications in energy storage, catalysis, and advanced coatings, demonstrating that microwave irradiation can be a transformative tool for the cost-effective production of high-performance carbon composites.
KEYWORDS : Microwave Irradiation, Techniques, Scalable Production, Graphene-Nanodiamond Composite Carbon Spheres

Paper Title

APPROACHES FOR THE LEVODOPA DRUGS SYNTHESIS OF LEVODOPA FOR PARKINSON’S DISEASE

Abstract

The synthesis of levodopa, a dopamine precursor, has undergone significant advancements to meet the growing demand for high-quality, cost-effective, and environmentally sustainable production. Various synthetic approaches have been developed, including chemical, biocatalytic, and chemoenzymatic methodologies. Chemical synthesis typically employs asymmetric catalysis or chiral pool strategies to achieve high enantiomeric purity. Biocatalytic processes, leveraging the specificity of enzymes such as tyrosine phenol-lyase (TPL), enable efficient and eco-friendly production routes. Chemoenzymatic methods, combining chemical synthesis with enzymatic resolution, represent a hybrid approach offering improved yield and scalability. This review explores these methodologies, highlighting recent innovations, challenges, and future perspectives in levodopa synthesis, emphasizing the need for greener and more sustainable production processes to ensure continued therapeutic availability.
KEYWORDS : Approaches, levodopa drugs, synthesis, levodopa, parkinson’s disease