Osteoarthritis (OA), a degenerative joint disease, is the most common chronic condition of the joints, which cannot be prevented effectively. Computational modeling of joint degradation allows to estimate the patient-specific progression of OA, which can aid clinicians to estimate the most suitable time window for surgical intervention in osteoarthritic patients. This paper gives an overview of the different approaches used to model different aspects of joint degeneration, thereby focusing mostly on the knee joint. The paper starts by discussing how OA affects the different components of the joint and how these are accounted for in the models. Subsequently, it discusses the different modeling approaches that can be used to answer questions related to OA etiology, progression and treatment. These models are ordered based on their underlying assumptions and technologies: musculoskeletal models, Finite Element models, (gene) regulatory models, multiscale models and data-driven models (artificial intelligence/machine learning). Finally, it is concluded that in the future, efforts should be made to integrate the different modeling techniques into a more robust computational framework that should not only be efficient to predict OA progression but also easily allow a patient's individualized risk assessment as screening tool for use in clinical practice.

The objective of the present work is to use multi body dynamics package (like MSC Adams) for motivating undergraduate students in a Dynamics course by means of simulations—a step which lies between traditional board work and actual experimentation. It also deepens his understanding of mechanics by allowing him to compare ADAMS solution with manual solution and solution obtained through small user developed program segments. The student learns to appreciate why the manual solutions are specialized cases of general solutions and how and why trajectories are determined. In the process it discusses the logical structure of Dynamics as implemented in these packages. Such an approach would also introduce them to such powerful tools at an early stage. It provides detailed solution of three problems using Adams and brief solutions to eight more. It is expected once these examples are mastered, most of the problems in undergraduate dynamics can be tackled using Adams.

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