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All Journal African Multidisciplinary Journal of Sciences and Artificial Intelligence Kwaghe International Journal of Engineering and Information Technology
Suresh Kumar Sahani
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Environmental Science Materials Science & Nanotechnology

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Survival Mechanisms of Clarias batrachus: Glycogen Utilization During Long-Term Starvation Nayan K. Prasad; Kumari Shachi; Sanjeev Kumar; Suresh Kumar Sahani
African Multidisciplinary Journal of Sciences and Artificial Intelligence Vol 1 No 2 (2024): African Multidisciplinary Journal of Sciences and Artificial Intelligence
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/amjsai.v1i2.3978

Abstract

This study aims to analyze the impact of extended food deprivation on glucose storage in different tissues and organs of the freshwater air-breathing catfish Clarias batrachus. The glycogen reserves in the brain, gonads, liver, muscles, and blood of both the male and female Clarias batrachus were estimated after forty days of starvation. The total glycogen was determined by a modification of the colorimetric method of Krishnaswami & Srinivasan in collaboration with Kemp and Heijningen. Even though they had to endure the severe deprivation of food, Clarias batrachus survived during the entire period of experimentation. Nutrient deprivation due to fasting gradually depletes glycogen reserves to a minimal level in all organs. This is partly caused by increased transamination and deamination processes, partly by the inhibition of RNA synthesis, and perhaps becomes increasingly significant during long-term starvation through gluconeogenesis. Importantly, during the first 20 days of starvation, the concentration of glycogen in the brain did not change noticeably in contrast to the liver, muscles, and gonads, which decreased glycogen stores significantly. The fall in blood glucose levels followed a decline in liver and muscle glycogen stores. Glycogen concentration in the liver was higher than in other solid tissues such as muscle, brain, and gonads. Females were observed to have higher glucose stores in all tissues than males when expressed per unit body mass in normal and starvation conditions. After forty days of starvation, the most substantial decrease in glycogen content was observed in the testes, while the brain exhibited the minimum decrease.
Simulation of Realistic Motion in Computer Graphics Using Runge-Kutta Methods Ashok Kumar Mahato; Rahul Das; Suresh Kumar Sahani
African Multidisciplinary Journal of Sciences and Artificial Intelligence Vol 2 No 2 (2025): African Multidisciplinary Journal of Sciences and Artificial Intelligence
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/amjsai.v2i2.5681

Abstract

This article looks into the use of the fourth-order Runge-Kutta (RK4) method in realistic motion simulation within computer graphics. With dynamic animations, there is an emerging need to solve physical systems using ordinary differential equations, for which RK4 is particularly useful due to its accuracy, stability, and balanced computational cost and efficiency. We implement motion phenomena with damped spring-mass systems by changing second-order differential equations into first-order systems that can be integrated using RK4. The results are measured against Euler and Midpoint methods for assessing stability, error control, and visual smoothness. In every instance, RK4 was found to be the most accurate, stable, and free from overshoot and jitter artifacts. The method demonstrates its effectiveness in real-time animation simulation, including but not limited to simulating cloth movement, flexible body motion, and character dynamic movements through progressive simulation and case studies. Even after undergoing extensive simulation durations, RK4 repeated proved to be supremely reliable regarding energy conservation, damping precision, and fidelity. While perhaps more costly in terms of computation than the more straightforward methods, RK4 remains highly tenable with today's processing capabilities. In addition to greatly improving the physics realism in simulations, the method is commendably applicable in Unity and PhysX or Bullet visual engines. The study illustrates smooth and realistic animation with the help of RK4 while further establishing its importance as a fundamental method for motion graphics and simulation in academic research and industry use.
Model-Free Reinforcement Learning for Parabolic Trajectory Optimization in Robotic Arms Aadarsh Karn; Neha Shah; Dilip Kumar Sah; Suresh Kumar Sahani
African Multidisciplinary Journal of Sciences and Artificial Intelligence Vol 3 No 1 (2026): African Multidisciplinary Journal of Sciences and Artificial Intelligence
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/amjsai.v3i1.9338

Abstract

Robotic arms are widely employed in applications that require smooth motion and energy-efficient operation, particularly in tasks such as object throwing and liquid dispensing, where movement often follows a curved path toward a target point. However, conventional trajectory planning methods that rely on predefined mathematical equations may not accurately represent real-world robotic systems due to uncertainties and payload variations. This study aims to optimize the trajectory of a robotic arm moving along a parabolic path using reinforcement learning and to evaluate whether this approach can successfully learn improved trajectory patterns during motion. The research integrates initial classical physics principles for curved motion with a reinforcement learning framework to enhance trajectory following toward a desired point. The findings indicate that reinforcement learning can effectively learn optimized trajectory paths and improve the motion performance of the robotic arm. The study concludes that reinforcement learning offers a promising approach for achieving smoother robotic motion with satisfactory energy efficiency under dynamic conditions. This work contributes to the advancement of intelligent motion planning by demonstrating the potential of reinforcement learning to improve trajectory optimization in robotic systems operating under practical uncertainties.
A Solution of Airy Differential Equation Via Elzaki Transform Nand Kishor Kumar; Suresh Kumar Sahani
Kwaghe International Journal of Engineering and Information Technology Vol 2 No 1 (2025): Kwaghe International Journal of Engineering and Information Technology
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/kijeit.v2i1.4534

Abstract

The Elzaki transform provides an elegant method to solve the Airy differential equation, leveraging its unique properties to handle the mixed derivative and variable coefficient terms. While the traditional methods remain standard, the Elzaki transform offers a powerful alternative, particularly when coupled with numerical techniques.
Co-Authors Aadarsh Karn Ashok Kumar Mahato Dilip Kumar Sah Kumari Shachi Nand Kishor Kumar Nayan K. Prasad Neha Shah Rahul Das Sanjeev Kumar