Thangavelu Engineering College is an educational institute located on Old Mahabalipuram Road, Chennai, Tamil Nadu, India. It is affiliated with Anna University, Chennai.
This study's principal intention is to explore the mechanical characteristics with carbon nanotubes (CNTs) having different variations, such as 1, 3, and 5 wt.
Clinical research plays a critical role in advancing medical knowledge, developing innovative therapies, and improving patient outcomes. However, traditional research methodologies often face challenges related to complex trial design, high costs, lengthy development timelines, and difficulties in patient recruitment and data management. This chapter, per the authors, examines the transformative potential of artificial intelligence technologies in addressing these challenges and enhancing the efficiency of modern clinical research. The chapter explores the integration of machine learning, natural language processing, predictive analytics, and intelligent data platforms in clinical trial design, drug discovery, patient monitoring, and data analysis. It also discusses ethical considerations, regulatory frameworks, and real-world applications that demonstrate the value of AI-driven research systems. The chapter further highlights emerging trends and future directions that are shaping the evolution of patient-centric and data-driven clinical research ecosystems.
The work on steady laminar forced convection flow of electro magneto-hydrodynamic nanofluid over a uniformly moving sheet has been considered with the aim of evaluating non-equilibrium governing boundary layer momentum, energy and concentration equations using a classical thermodynamic variational technique. The flow, heat and mass transfer of a continuously accelerated sheet extruded in a low electrically conducting fluid, enhanced by induced ponderomotive force and diluted suspension of nanoparticles is the novel intention of the present study. The integral forms of the Lagrangian functional are constructed by determining the dual flow fields inside and on the boundary layer. Then, the necessary conditions for extremum of integral principle are derived as simple algebraic expressions in terms of boundary layer thicknesses. The thermo-physical quantities of the research interest are determined explicitly as polynomial expressions. This study examines how the skin friction coefficient, local heat and mass transfer are affected by electromagnetic force (QH), Joule heating (Ec), thermophoresis (NT) and Brownian motion (NB). The computed results indicate that electromagnetic force increases the velocity, and reduces the temperature. The concentration is increased by thermophoresis effect and decreased by Brownian diffusion. To validate the efficiency of solution procedure and confirm accuracy, certain specific results are compared with the solutions by other numerical methods available in the literature. The precision is confirmed.
The reduction of the bromate anion to bromine on a rotating disk electrode (RDE) at steady-state conditions is discussed. This model is based on a system of nonlinear equations. The most crucial aspect of this investigation is finding the analytical expression of the concentration of reagents by solving the coupled second-order nonlinear equation using hyperbolic function methods. The effects of the kinetic parameters on concentration and current are explored in the unitizing graph and tables. The analytical results are validated in numerical results using MATLAB software. The effects of the parameters such as diffusion layer thickness, rotation rate, rate constants and the ratio of concentration of bromate anion and volume of concentration of bromine ions on current are discussed.
Abstract The advent growth of wireless networks and their applications in different fields has been more prominent in the previous few years and specifically mobile ad-hoc networks (MANETs) has gained the importance from consumers and researchers due to their reliability and sustainability. MANET is widely employed for communicating and sending and receiving packets in a network without any requirement of specific hardware structure, due to which these are employed in different sectors. Because of its wide applicability, there exist numerous challenges in handling MANETs, especially with respect to network security. Intrusion is one of the key security problem faced in MANETs during the transmission of data packets within the communication system. The occurrence of malicious node in MANETs shall lead to removal of information packets during data transfer and thus intrusion detection system (IDS) are successfully designed to handle the node behaviours and identify the malicious nodes in the network and their behaviours. Henceforth, this research study develops a novel IDS model using the proposed improved grey wolf optimizer (ImGWO) hybridized with that of the deep recurrent neural network (DRNN) model for predicting and observing the behaviour of malicious nodes in MANETs. The developed novel hybrid ImGWO-DRNN model is applied on the KDD Cup 1999 datasets and training and testing of the proposed IDS technique is studied. Evaluation metrics was set to analyse and validate the proposed IDS technique and the results prove the superiority of this ImGWO-DRNN intrusion detection system over the previous techniques from literature for the MANETs.
