Optimizing Custom Orthopedic Implants: A Generative Design Approach in the Medical Manufacturing Industry |Chapter 4 | Scientific Research, New Technologies and Applications Vol. 3

This review paper examines the transformative impact of generative design on the development of custom orthopedic implants in the medical manufacturing sector. By integrating advanced algorithm-driven design processes, generative design technology facilitates the creation of orthopedic implants that are precisely tailored to meet the unique anatomical and biomechanical demands of individual patients. This paper synthesizes the latest research and developments, emphasizing how generative design not only enhances the functionality and fit of these implants but also significantly increases the efficiency of their manufacturing processes. The review delves into the critical role of computational techniques such as finite element analysis and machine learning in enabling the swift prototyping of patient-specific implants. Additionally, it discusses the key practical and regulatory challenges that must be navigated to integrate this technology into clinical practice. The paper also highlights the substantial benefits, including potentially shorter recovery periods and lower rates of surgical revisions. By offering a detailed overview of the current trends and prospective advancements, this paper underscores the revolutionary potential of generative design in reshaping orthopedic surgery and improving patient outcomes.

 

Author (s) Details

Denesh
Department of Mechatronics and Automation, School of Mechanical Engineering, Vellore Institute of Technology, Chennai, India.

 

Abhinav
Department of Mechatronics and Automation, School of Mechanical Engineering, Vellore Institute of Technology, Chennai, India.

 

Harish
Department of Mechatronics and Automation, School of Mechanical Engineering, Vellore Institute of Technology, Chennai, India.

Sivarajan S.
Department of Mechatronics and Automation, School of Mechanical Engineering, Vellore Institute of Technology, Chennai, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/srnta/v3/2163

Computational Modeling of New Drugs for the Treatment of Alzheimer’s Disease (AD) using Functional Correlations and Artificial Intelligence (AI)| Chapter 4 | Challenges and Advances in Pharmaceutical Research Vol. 6

 With the aid of our computer modelling techniques, the study seeks to alter the prescription drugs now used to treat Alzheimer's disease (AD). The half maximum inhibitory concentration (IC50) value is a measurement used to determine the binding affinity of newly developed medicines to protein molecules associated with Alzheimer's. This number represents the dosage at which a drug must be present in order to block a particular biological process. The predicted updated IC50 values are predicted using two methods. First, the functional graph techniques provide correlations that result in projected IC50 values for the altered drug molecules utilising the energies and the experimentally determined IC50 values.

Author(s) Details:

Nouf Masarweh,
Department of Chemistry, University of Arkansas at Little Rock, Center for Molecular Design and Development, Arkansas, USA.

Jerry A. Darsey,
Department of Chemistry, University of Arkansas at Little Rock, Center for Molecular Design and Development, Arkansas, USA.

Please see the link here: https://stm.bookpi.org/CAPR-V6/article/view/8092

Improving Mathematical Skills in Geometry Using Visual Python | Chapter 5 | Recent Advances in Mathematical Research and Computer Science Vol. 1

We show students how to use computational modelling to better understand and apply mathematical concepts, especially when dealing with 3D objects like vectors and geometrical forms, in this chapter. Students can use the Visual Python (VPython) integrated development learning environment to construct numerous 3D objects such as arrows, spheres, and boxes without any prior coding experience. Students can develop their spatial thinking in a three-dimensional system by creating 3D arrows and manipulating them by adding or subtracting two or more vectors. The purpose of this research is to use computational modelling to assist students in improving their spatial thinking in geometry so that they may better grasp and apply mathematical concepts and solve issues. Students can use VPython to change the parameters of different vectors, their position, and assign different colours to them based on their preferences. They will also be able to determine the solution directly utilising mathematical formulas depending on some pre-given information. As a result, utilising VPython has the pedagogical benefit of allowing students to conduct more physical modelling and better describe processes, both of which aid in better understanding topics.

 

Author (S) Details

Ergi Bufasi
American University of the Middle East, Kuwait.

Klea Cuka
University of Tirana, Albania.

Erjona Keci
Istanbul American Schools, Turkey.

View Book https://stm.bookpi.org/RAMRCS-V1/article/view/4335