Abstract
The author is a mathematician and engineer who has conducted medical research work over the past 13 years in the fields of endocrinology, metabolic disorder-induced chronic diseases (especially diabetes), and their resulting various medical complications. Thus far, he has written and published 676 research papers in various journals using different math-physical medicine methodologies (MPM). Beginning with paper No. 578 dated 1/8/2022, he has written a total of 82 medical research and 4 economic research papers using viscoelasticity and viscoplasticity theories (VGT) tools from physics and engineering disciplines. These 86 papers aim to explore some hidden physical behaviors and provide a deeper quantitative understanding of the inter-relationships of a selected output (or symptom) versus singular input or multiple inputs (or root causes, risk factors, influential factors). The hidden biophysical behaviors and possible inter-relationships exist among lifestyle details, medical conditions, chronic diseases, and certain severe medical complications, such as heart attacks, stroke, cancers, dementia, and even longevity concerns. The chosen medical subjects with their associated data, multiple symptoms, and influential factors are “timedependent” which means that all biomedical variables change from time to time because body living cells are dynamically changing. This is what Professor Norman Jones, the author’s adviser at MIT, suggested to him in December 2021 and why he utilizes the VGT tools from physics and engineering to conduct his medical research work since then. Papers No. 671 through No. 674 focused on the COVID infectious disease using three key US economic measurements. From this economic exercise, he realized that the established theory of viscoelasticity and viscoplasticity (from the physics branch of science) should not only be limited to a small scope of engineering applications. Its ability to link certain time-dependent variables and their physical characteristics and associated hysteresis loop areas are equally powerful for applications in many other fields, including economics and medicine. The author would like to describe the essence of the VGT in 6 simple steps in plain English instead of mathematical equations for readers who do not have an extensive academic background in engineering, physics & mathematics - an excerpt from Wikipedia is included in the Method section of the full-text article. In this article, the first step is to collect the output data (strain or ε) on a time scale, e.g. quarterly cardiovascular disease (CVD) risk, gross domestic product (GDP), or postprandial plasma glucose (PPG). The second step is to calculate the output change rate with time (dε/dt), e.g. the change rate of averaged PPG over each year. The third step is to collect the input data (viscosity or η) on a time scale, e.g. annual average numbers of fasting plasma glucose (FPG), carbohydrates/sugar (carbs) intake amounts, and post-meal walking k-steps (k-steps). body weight (BW) and food quantity The fourth step is to calculate the time-dependent input (time-dependent stress or σ) by multiplying dε/dt and η together. The “time-dependent input equation” is stress σ = strain change rate of dε/dt * viscosity η. The fifth step is to plot the input-output (i.e. stressstrain or cause-symptom) curve in a space domain (x-axis versus y-axis) with strain (output or symptom) on the x-axis and stresses (time-dependent inputs, causes, or stresses) on the y-axis. The sixth step is to calculate the total enclosed area within these input-output curves (or hysteresis loop areas), which is also the indicator of associated energies (either created energy or dissipated energy) of this input and output dataset. These energy values can also be considered the degrees of influence on output by inputs.
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