Pawan Kumar Singh

The main aim of this book is to introduce and give an overview of a novel, easy, and highly effective heat transfer augmentation technique for single-phase micro/minichannel heat sink. The specific objectives of the volume are to: Introduce a novel planar oblique fin microchannel and cylindrical oblique fin minichannel heat sink design using passive heat transfer enhancement techniques Investigate the thermal transport in both planar and cylindrical oblique fin structures through numerical simulation and systematic experimental studies. Evaluate the feasibility of employing the proposed solution in cooling non-uniform heat fluxes and hotspot suppression Conduct the similarity analysis and parametric study to obtain empirical correlations to evaluate the total heat transfer rate of the oblique fin heat sink Investigate the flow mechanism and optimize the dimensions of cylindrical oblique fin heat sink Investigate the influence of edge effect on flow and temperature uniformity in these oblique fin channels.

The scope of harnessing information science has been experienced in personalized decision support systems. This involves minimizing the volume of information and making deductions, such as in the case of Amazon's recommendation system. Traditional recommendation systems are becoming outdated and inadequate in meeting user requirements and technological trends. New recommendation systems like contextual, group, and social recommendation have been discovered. These systems have been investigated and analyzed using nature-inspired algorithms, evolutionary algorithms, swarm intelligence algorithms, and machine learning techniques to provide more precise personalized recommendations. A community-based filtering algorithm is proposed as well as an innovative hybrid intelligent algorithm to handle non-erroneous recommendations in a context-aware framework and address threats from intruders using optimization techniques and. The work aims to provide efficient solutions to problems faced by users, including sparsity, novelty, precise recommendation, and optimum decision-making solutions. The proposed models have been extensively experimented with and show superior learning mechanisms.