G.S. Science, Arts And Commerce College

In this work , the compound (4-Formyl N,N-dimethyl anilinium picrate (FDAP) was synthesized and characterized by spectroscopic techniques. The crystal structure was elucidated using single crystal X-ray diffraction method. From the 3D molecular structure it is inferred that the crystal packing is stabilized by several intra and intermolecular, Cg…Cg interactions their nature, strength and percentage contribution are corroborated by the Hirshfeld surface analysis, while the 2D fingerprint plots provided a visual representation of the intermolecular interactions via the surface area corresponding to each type of interaction. The N–H···O and C–H···O types of strong intra and intermolecular hydrogen bond interactions resulted in the formation of S(5) self-motifs and R 2 1 (6) supramolecular ring motif. The structural and electronic properties of the compound FDAP using density functional theory (DFT). The optimized geometry, total energy, HOMO-LUMO gap and vibrational spectra were calculated at the B3LYP/6–311 +G(d,p) level of theory. The calculated results showed that the molecule has a stable geometry with a trans-conformation. The HOMO-LUMO gap of 3.8178 eV indicates that the molecule has moderate stability. In order to aid in the development of better vaccines and treatments, more in-silico research, including molecular docking, was conducted against the structure of the human collapsin response mediator protein-1, a lung cancer suppressor protein. Intriguingly, which shed light on possible inhibitors and new therapeutic candidates due to the high concentration of non-covalent interactions.

Creating lifelike human movement in animation often requires countless hours of manual work, which can limit creativity, flexibility, and production speed. Procedural animation offers a smarter and more efficient solution by using mathematical models and computer algorithms to automatically generate realistic motion. This research explores how principles inspired by human biomechanics can be applied to produce natural, adaptive, and responsive character animation without relying heavily on traditional keyframing. In this study, we integrate harmonic motion, inverse kinematics, and physical constraints to simulate actions such as walking, running, and turning with greater realism. The system dynamically adjusts a character’s movement in real time according to changes in terrain, balance, or speed, resulting in more believable and interactive animations. Additionally, the approach supports procedural blending between animation states, allowing smoother transitions and enhanced control for animators. Our evaluation demonstrates that this method significantly improves motion accuracy and fluidity while reducing both production time and computational cost. The findings suggest that procedural techniques can bridge the gap between physics-based realism and artistic freedom, offering a promising direction for next-generation animation pipelines.

The rapid evolution of robotics is reshaping the landscape of personal transportation, driving a paradigm shift toward safer, smarter, and more efficient mobility solutions. As autonomous systems become increasingly sophisticated, robots are transitioning from industrial and service environments into everyday transportation platforms. Autonomous vehicles, delivery robots, intelligent drones, and robotic personal mobility devices demonstrate how robotics can enhance convenience while reducing human error, a major cause of road accidents. Integration of advanced sensors, machine learning algorithms, and real-time decision-making capabilities enables robotic transportation systems to navigate complex environments with minimal human intervention. Moreover, robotics supports sustainable transportation by optimizing energy use, reducing congestion through coordinated traffic flow, and enabling compact mobility solutions such as self-balancing scooters and robotic wheelchairs. The rise of connected vehicle ecosystems further amplifies these benefits, allowing robots and vehicles to communicate seamlessly with infrastructure and one another. Despite these advancements, challenges remain—including regulatory frameworks, ethical considerations, cyber security risks, and the need for robust public acceptance of autonomous technologies. Looking ahead, the convergence of robotics with artificial intelligence, 5G connectivity, and smart city initiatives will accelerate the deployment of personalized, autonomous transportation options. These innovations promise not only improved mobility but also greater accessibility for individuals with disabilities, reduced environmental impact, and enhanced quality of urban life. Ultimately, robotics is poised to transform personal transportation into a more intelligent, adaptive, and sustainable system, redefining how people move within both urban and rural environments.

Heart disease remains one of the leading causes of mortality worldwide, creating an urgent need for early prediction and preventive strategies to reduce severe cardiac events. This study aims to develop a data-driven predictive model to estimate the likelihood of heart attack occurrence by analyzing major clinical and lifestyle-related risk factors. A publicly available dataset obtained from Kaggle was utilized, consisting of attributes such as age, gender, cholesterol levels, blood pressure, blood sugar levels, heart rate, smoking behavior, obesity, physical inactivity, and dietary habits, all of which contribute significantly to cardiovascular health. The data was processed and analyzed using statistical techniques to evaluate feature relevance, correlation patterns, and risk influence. Multiple machine learning algorithms were then implemented to build a predictive model capable of assessing heart attack risk with improved accuracy. The findings revealed that factors including cholesterol levels, blood pressure, lifestyle behaviors, and physiological health indicators were among the most influential contributors to cardiac risk. The predictive model demonstrated strong potential in risk estimation, supporting the significance of machine learning for medical prognosis. These results indicate that predictive analytics can significantly enhance early diagnosis, assist healthcare professionals in risk stratification, and support timely clinical decision-making. The study concludes that integrating machine learning-based prediction systems into healthcare practice can improve preventive care, enable personalized treatment planning, and contribute to reducing heart disease-related complications and mortality rates.