Viscoplasticity Theory Research Articles

Viscoelastic or Viscoplastic Glucose Theory (VGT #16): Applying the Theories of Elasticity, Plasticity, Viscoelasticity, and Viscoplasticity to Study the Body Weight and Estimated Daily Glucose (eAG) Over a 13+ Year Period from Y2010 to Y2022 Based on the GH-Method: Math-Physical Medicine (No. 595)

Since 2010, the author has collected his finger-piercing glucoses 4 times (1 FPG and 3 PPG) each day. Over the past 13+ years, he has collected a total of 17,644 glucose data from 4,411 days. He takes the daily average value from the 4 glucose data as his estimated daily glucose (eAG) value. Furthermore, he measures his body weight, in the early morning, before breakfast and has collected 4,411 data over the same period. From previous research work, he learned that his type 2 diabetes (T2D) conditions are tightly related to body weight. There is a 92% correlation between his fasting plasma glucose (FPG) and body weight during the period from 1/1/2012 to 1/31/2022. Therefore, in this article, the author applies the viscoelasticity and viscoplasticity theories to conduct his research to discover some hidden behavior or relationship between the eAG (outcomes or strain) and body weight (inputs or stress). The hidden behaviors and relationships between these two biomarkers, eAG values and body weights, are time-dependent, which are changing from time to time. The following two defined equations are used to establish the stress-strain diagram in a space-domain (SD):

Viscoelastic and Viscoplastic Glucose Theory (VGT #2): Applying the Concept of Viscoelasticity and Viscoplasticity to Conduct a “Time-Dependent Glucose Analogy” Study of An Elastic Glucose Case Covering 4,056 meals <180 mg/ dL Versus Plastic Glucose Case of 23 Meals >180 mg/dL Using Collected Postprandial Plasma Glucose Data from 4,079 Meals over 3.7-Year Period from 5/8/2018 to 1/10/2022 Based on GH-Method:

The author has studied strength of materials and theory of elasticity through his undergraduate courses at the University of Iowa. He also conducted research work to earn a master’s degree in Biomechanics under Professor James Andrews. He remembers using the spring and dashpot models to simulate the behaviors of human joints, bones, muscles, and tendons to investigate the human-weapon interactions. Later, he went to MIT to pursue his PhD study under Professor Norman Jones, who taught him theory of plasticity and dynamic plastic behaviors of structure elements, and took additional graduate courses in the field of fluid dynamics and thermodynamics. Since then, many advancements have been made in biomechanics in a few application areas, especially tissues of the human body which possess viscoelastic characteristics, such as bone, muscle, cartilage, tendon (connect bone to muscle), ligament (connect bone to bone), fascia, and skin. For example, the night splint dorsiflexes forefoot at the back of the foot increases plantar fascia tension to offer stress-relaxation for plantar fascia pain. This model of muscles and tendons connecting the lower leg and foot is a type of viscoelastic problem. However, when dealing with the human internal organs, it is not easy to conduct live experiments to obtain accurate measurements of material properties. Although blood itself is a viscous material, the viscosity factor may fall between water, honey, syrup, or gel. However, the author’s research subject is “glucose”, the sugar amount in blood carried by cells, not the blood itself. It is nearly impossible to measure material geometry or certain engineering properties of glucose, for example, the viscosity of “glucose”. Therefore, the best he could do is to apply the

Special Issue: Viscoelstic and Viscoplastic Glucose Theory application in medicine

The Author’s Case of Diabetes: The author has been a severe type 2 diabetes (T2D) patient since 1996. He weighed 220 lb. (100 kg, BMI 32.5) at that time. By 2010, he still weighed 198 lb. (BMI 29.2) with average daily glucose of 250 mg/dL (HbA1C of 10%). During that year, his triglycerides reached 1161 and albumin-creatinine ratio (ACR) at 116. He also suffered from five cardiac episodes within a decade. In 2010, three independent physicians warned him regarding his need for kidney dialysis treatment and his future high risk of dying from his severe diabetic complications. Other than the cerebrovascular disease (stroke), he has suffered most of the known diabetic complications, including both macro-vascular and micro-vascular complications.

Simplified cooperative viscoplasticity theory based on overstress model for nanocomposites

AbstractCooperative‐viscoplasticity theory based on overstress (C‐VBO) theory proposed to cover the total time, temperature, and graphene fraction dependent mechanical behavior of the nanocomposite material is simplified in this work. The previous version of the VBO model for nanocomposites includes the agglomeration effect of nanofiller by dividing the polymer nanocomposite into two parts as “agglomerated” and “effective matrix” phases. In this work, to be able to define the behavior of nanocomposites when nanofiller is evenly distributed into the matrix, the material model is simplified by defining the bulk and shear modulus without agglomerated and effective matrix phases and they are calculated separately using the Mori‐Tanaka homogenization scheme. The capabilities of the modified C‐VBO model for nanocomposites are investigated by simulating rate‐dependent uniaxial compression, creep, and relaxation behaviors for different graphene fractions (0.1 and 0.5 wt.%). In addition, to investigate high strain rate behavior, Split Hopkinson pressure bar tests are performed. The adiabatic heating effect that occurs at high strain rates is taken into account and incorporated into the model. All behaviors mentioned above are simulated well by simplified C‐VBO for nanocomposites. The developed constitutive model is ready to implement into finite element codes to be used in the structural analysis.

Advances in Permanent Deformation Modeling of Asphalt Concrete-A Review.

Permanent deformation is one of the dominant asphalt concrete damages. Significant progress has been made to realistically predict the damage. In the last decade, the mechanistic approach has been the focus of research, and the fundamental theories of viscoelasticity, viscoplasticity, continuum mechanics, and micromechanics are applied to develop the material laws (constitutive equations). This paper reviews the advancement of permanent deformation models including analogical, microstructural, and continuum-based methods. Pavement analysis using the nonlinear damage approach (PANDA) is the most comprehensive and theoretically sound approach that is available in the literature. The model coupled different damages and other phenomena (such as cracking, moisture, and phenomena such as healing, aging, etc.). The anisotropic microstructure approach can be incorporated into the PANDA approach for a more realistic prediction. Moreover, the interaction of fatigue and permanent deformation is the gap that is lacking in the literature. The mechanistic approaches have the capacity to couple these damages for unified asphalt concrete damage prediction.

Applying Linear Elastic Glucose Theory (LEGT) and Viscoelastic or Viscoplastic Glucose Theory (VGT) While using Data Collected Over an~ 7-year Period, from 7/1/2015 to 3/30/2022, Organized into 8 Annual Data Points, 81 Monthly Data Points, and 2,463 Daily Data Points, Along with a Biomedical Explanation of 8 Sub-cellular Pathology Pathways of Chronic Diseases to Study the Relationship among Pancreatic Beta

The author was a professional engineer working in the fields of space shuttle, naval battleships, nuclear power plants, computer hardware and software, artificial intelligence, and semiconductor chips. After retiring from his work, he has initiated self-study and research on internal medicine with an emphasis on biomarker relationships exploration and disease prevention. Since 2010, he has utilized these disciplines learned from 7 different universities along with various work experiences to formulate his current medical research work during the past 13 years.

Viscoelastic and viscoplastic Glucose Theory (VGT #26): Investigation of the Relationships between an Overall Metabolism Index and type 2 Diabetes Utilizing HbA1C, Diabetic Nephropathy via Urinary Albumin-to-Creatinine Ratio, along with Diabetic Retinopathy with Hypothyroidism through Thyroid Stimulating Hormone using Statistical Correlation Coefficient and VGT of GH-Method: Math-Physical Medicine (No. 607)

Over the past two years, the author self-studied certain biomarkers and their relationships with certain diabetic complications, such as albumin-to-creatinine ratio (ACR) for diabetic nephropathy, thyroid stimulating hormone (TSH) for hypothyroidism and diabetic retinopathy (DR). The medical papers he reviewed utilized statistical tools, including regression analysis, with input data collected from hundreds of different patients in hospitals (References 1-14). In this article, he uses his own collected data from the past ~9 years, where he applies a combined math-physical medicine tools, such as correlations (a statistics tool), viscoelastic and viscoplastic glucose theory (VGT, a physics tool) from engineering, and perturbation theory from quantum mechanics to investigate certain hidden behaviors of selected biomarkers, such as MI, HbA1C, ACR, TSH, and their associated diseases. Initially, he attempts to find relationships between two biomarkers, ACR and TSH. Although these two biomarkers belong to output variables of the human body, he tries to alter their roles with the following two assumptions: TSH influences ACR and ACR influences TSH.

Viscoelastic or Viscoplastic Glucose Theory (VGT #44): Applying VGT to Study the Relationship of CGM Sensor Fasting Plasma Glucose (FPG) Versus Body Temperature in Early Morning, while using a Viscoelastic Perturbation Model to Predict FPG Values Over a 18-month Period from 10/1/2020 to 3/7/2022 based on the GH-Method: Math-Physical Medicine (No. 625)

Since 2012, the author has been collecting his body weight (BW) and finger-piercing glucose values each day. In addition, he accumulates medical conditions data including a combination of data for blood pressure (BP) and heart rate (HR), and blood lipids along with lifestyle details of diet, exercise, sleep, stress, water intake, and daily routine details. Based on the collected big data, he further organized them into two main groups. The first group is medical conditions (MC) with 4 categories: weight, glucose, BP, and blood lipids. The second group is lifestyle details (LD) with 6 categories: food & diet, exercise, water intake, sleep, stress, and daily routines. He gathers his daily data and then calculates a unique combined score for each MC and LD with their 10 categories. The combined scores of the 2 groups, 10 categories, and 500+ elements constitute an overall “metabolism index (MI) model”. This MI model includes the root causes of 6 lifestyle inputs and 4 symptoms of the disease including the rudimentary chronic diseases: obesity, diabetes, hypertension, and hyperlipidemia. As we know, lifestyle details cause rudimentary chronic diseases which further influence more complicated diseases, such as heart problems (CVD & CHD), chronic kidney disease (CKD), stroke, diabetic retinopathy (DR), neuropathy, hypothyroidism, and others. In addition to the lifestyle-induced chronic disease and complications, environmental factors, such as radiation, air and water pollution, food poison and pollution, toxic chemicals, and hormonal therapy, can contribute to the causes for a variety of cancers. Some genetic conditions and lifetime unhealthy habits, such as smoking, alcohol consumption, illicit drug use would account for approximately 15% to 25% of the root cause for rudimentary chronic diseases along with their complications, and cancers. All of the above-described diseases fall into the “symptoms” category which are the “rootcauses” of poor and unhealthy lifestyles. In this article, the author applies the viscoelasticity and viscoplasticity theories to conduct his research to discover some hidden behavior or possible relationship among 3 key biomarkers, CVD risk (a symptom disease) probability, daily average sensor glucose (eAG), and its related sensor HbA1C (A1C). The hidden behaviors and possible inter-relationships among these three biomarkers are “time-dependent” which change from time to time. This is why he applies viscoelastic & viscoplastic theories (VGT) to conduct his recent research work. The author previously conducted similar analyses for the same sets of biomarkers using a traditional statistical regression method. Generally speaking, statistical methods only deal with numerical characteristics of collected datasets and do not connect with the internal physical characteristics of biomarkers of organs. Incidentally, the accuracy and applicability of results using any statistical method are heavily dependent on internal characteristics of the data sample, data size, and the time-window of the chosen dataset. Therefore, we must be careful in selecting appropriate statistical methods and treat their analysis conclusions cautiously.

Viscoelastic or Viscoplastic Glucose Theory (VGT #35): Applying the VEGT or VPGT to Study the Cardiovascular Disease Risk Probability Percentage Versus Medical Conditions and Lifestyle Details over a 10+ Year Period from Y2012 to Y2022 Based on the GH-Method: Math-Physical Medicine (No. 616)

Since 2012, the author has collected his body weight and finger-piercing glucose values each day. Next, he accumulated medical conditions data including blood pressure (BP), heart rate (HR), blood lipids along with lifestyle details of diet, exercise, sleep, stress, water intake and daily routine details. Based on these collected big data, he organized them into two main categories. The first is medical conditions (MC) with 4 categories: weight, glucose, BP, and lipids. The second is lifestyle details (LD) with 6 categories: food, exercise, water intake, sleep, stress, and daily routines. Furthermore, he summarized and calculated the two separate individual category scores of MC and LD. As we know, diabetes in combination with hypertension, hyperlipidemia, and obesity would cause many chronic disease complications, including but not limited to cardiovascular disease (CVD) or chronic heart disease (CHD). In this article, the author applies the viscoelasticity and viscoplasticity theories to conduct his research to discover some hidden behavior or relationship between the CVD risk probability (as an output or strain) versus either MC score or LD score (as inputs or stresses). The hidden behaviors and relationships between the output biomarker of CVD Risk and the two input biomarkers, MC score and LD score, are time-dependent which change from time to time.

Viscoelastic or Viscoplastic Glucose Theory (VGT #36): Applying the VEGT or VPGT to Study the Cancer Risk Probability Percentage Versus Medical Conditions and Lifestyle Details over a 10+ Year Period from Y2012 to Y2022 Based on the GH-Method: Math-Physical Medicine (No. 617)

Since 2012, the author has collected his body weight and finger-piercing glucose values each day. Next, he accumulated medical conditions data including blood pressure (BP), heart rate (HR), blood lipids along with lifestyle details of diet, exercise, sleep, stress, water intake and daily routine details. Based on these collected big data, he organized them into two main categories. The first is medical conditions (MC) with 4 categories: weight, glucose, BP, and lipids. The second is lifestyle details (LD) with 6 categories: food, exercise, water intake, sleep, stress, and daily routines. Furthermore, he summarized and calculated the two separate individual category scores of MC and LD. In this article, the author applies the viscoelasticity and viscoplasticity theories to conduct his research to discover some hidden behavior or relationship between the Cancer risk probability (as an output or strain) versus either MC score or LD score (as inputs or stresses). The hidden behaviors and relationships between the output biomarker of Cancer Risk and the two input biomarkers, MC score and LD score, are time-dependent which change from time to time.

Viscoelastic or Viscoplastic Glucose Theory (VGT #24): Applying Perturbation Theory and the Theories of Viscoelasticity and Viscoplasticity to Predict the Cardiovascular Disease (CVD) Risk Probability Percentages Using Collected Data from the Estimated Daily Glucose Values and Overall Metabolism Index Values over a 10+ Year Period from Y2012 to Y2022 Based on the GH-Method: Math-Physical Medicine (No. 604)

This specific article is a combination of the author’s papers No. 593 and No. 594 regarding the cardiovascular disease (CVD) risk probability percentages or CVD risk. For his research work, recently, he received an invitation to be a speaker at a cardiology conference. He then reorganized and combined the above-mentioned two papers into one with an emphasis on the prediction of CVD/CHD Risk utilizing visco-perturbation model. As a result, the emphasis of this combined article is no longer focusing on the time-dependency characters due to viscosity of CVD risk, i.e. estimated average glucose (eAG), and metabolism index (MI). As we know, diabetes (continuous high glucose level) causes many complications, including but not limited to CVD or chronic heart disease (CHD). As a matter of fact, around 75% to 80% of patients with various heart diseases also have diabetes. Furthermore, there is approximately 20% or less resulting mainly from genetic conditions. Therefore, most patients with heart diseases should pay attention to overall metabolism conditions, particularly the glucose situation which is also under the influence of metabolism. In this research, the author applies the concept of viscoelasticity and viscoplasticity theories to discover some hidden behavior or relationship between the CVD risk probability % (outcomes or strain) resulting from eAG values and MI values respectively (inputs or stress). The hidden behaviors and relationships between the output biomarker for the CVD Risk and two input biomarkers, eAG and MI values, are time-dependent, which are changing from time to time.

Viscoelastic glucose theory (VGT #27): Applying the Concept of Viscoelasticity Theory and using the Estimated Daily Glucose (eAG) over a 3.8-Year Period from 5/8/2018 to 2/17/2022 as the Strain to Study the Role and Influences of the Corresponding Stress as well as their Impact on the Predicted eAG Results Based on the GH-Method: Math-Physical Medicine (No. 608)

Recently, the author applied theories of elasticity, plasticity, viscoelasticity, and viscoplasticity from engineering and perturbation theory from quantum mechanics, along with the wave theory and energy theory from physics to conduct his biomedical research on certain output biomarkers, such as glucose (symptoms or behaviors) resulting from a selected input biomarker, such as the carbs/sugar amount, exercise level, or others (causes or stressors). In this article, he chose his synthesized estimated daily glucose (eAG) data and waveform over the past 3.8 years as the strain (ε or output, symptom). Since 5/8/2018, he has been utilizing a continuously monitoring glucose (CGM) sensor device to collect 96 glucose data each day at 15-minute time intervals. In order to investigate and learn the behaviors of the strain (ε) or eAG, and its stress (σ) or input/cause as defined to be the eAG change rate multiplied by a selected viscosity factor (η), he purposely select two values of η as 1 and 2. After conducting the stress-strain analysis based on 1 and 2 as his viscosity factors (η), he then uses another set of measured values: average daily carbs/sugar intake amount of 41.13 grams and average daily walking exercise of 16.02 k-steps, as the second set of viscosity factors (η). There are two main purposes of this research article. First, to check the stress-strain diagram’s sensitivity due to the selection of different viscosity factors. Second, to examine the correlation between predicted strain (eAG) waveform using the viscoelastic perturbation model and the measured CGM sensor eAG waveform due to the selection of different viscosity factors.

Viscoelastic Glucose Theory (VGT #28) & Nonlinear Plastic Glucose Theory (NPGT #7): Nonlinear Dynamic Plastic and Viscoplastic Glucose Behaviors Versus Linear Elastic Glucose Behavior of Postprandial Plasma Glucose (PPG) Waveforms over a 3.8-Year Period from 5/8/2018 to 2/18/2022 along with the Plastic PPG Waveforms Impact on the Cardiovascular Disease Risk Probability Based on the GH-Method: Math-Physical Medicine (No. 609)

Recently, the author applied theories of elasticity, plasticity, viscoelasticity, and viscoplasticity from engineering and perturbation theory from quantum mechanics, along with the wave theory and energy theory from physics to conduct his biomedical research on certain output biomarkers, such as glucose (symptoms or behaviors) resulting from a selected input biomarker, such as the carbs/sugar amount, exercise level, or others (causes or stressors). In this article, he chose his synthesized postprandial plasma glucose (PPG) data and waveform over the past 3.8 years as the strain (ε or output, symptom). Since 5/8/2018, he has been utilizing a continuously monitoring glucose sensor (CGM) device to collect 96 glucose data each day and 13 glucose data each meal at 15-minute time intervals. In order to investigate and learn the behaviors of the strain (ε) or PPG, and its stress (σ) or input/cause as defined to be the estimated average glucose (eAG) change rate multiplied by a selected viscosity factor (η), which is the carbs/sugar intake amount. In this study, he selected “elastic” PPG case with average “Finger-piercing PPG <120 mg/dL along with four “plastic” PPG cases, >120 mg/dL, >140 mg/dL, >180 mg/dL, and >200 mg/dL. The procedure of the analysis contains four steps. Step 1, he draws the 5 PPG (1 elastic and 4 plastic) waveforms in a timedomain (TD) with 15-minutes time interval within a 3-hour time span. Step 2, he calculates the stress values to draw the corresponding stress-strain diagram in a spatial-domain (SD). Step 3, he calculates 4 predicted plastic PPG values using the viscoelastic perturbation model. Step 4, he calculates the corresponding cardiovascular disease (CVD) risk probabilities induced by the 5 cases.

Viscoelastic and Viscoplastic Glucose Theory (VGT #25): Applying the Concepts of Viscoelasticity, Viscoplasticity and Perturbation Theories to Predict PPG Dataset and Waveforms for the Pre-Virus and Virus Periods Using the Total PPG Values as a Baseline Along with the Estimated PPG Relative Energy Levels in Both Time-Domain and Frequency-Domain Based on the GH-Method: MathPhysical Medicine (No. 605)

Recently, the author applied theories of viscoelasticity and viscoplasticity from engineering and perturbation theory from quantum mechanics along with the wave theory and energy theory from physics to conduct his biomedical research on the output biomarkers of postprandial plasma glucose (PPG), a symptom of diabetes, resulting from the input biomarker of carbs/sugar amount (a cause of high glucose). In this article, he calculated two additional PPG datasets and curves, which are the predicted PPG for the pre-virus period from 5/1/2018 to 1/18/2020 and the virus period from 1/19/2020 to 2/12/2022. He used the total period from 5/1/2018 to 2/12/2022 PPG values as the dataset baseline. All PPG data from the three periods are collected via a continuous glucose monitoring (CGM) sensor device at 15-minute time-interval over a period of 1,379 days. For this article, there are two primary objectives to check the different PPG behaviors between two time periods with different lifestyles due to the COVID-19 virus quarantine. First, he applies the visco-perturbation model to predict these two different PPG behaviors and verify their associated correlation coefficients along with the prediction accuracy percentages. Second, he estimates and compares the three relative energies associated with PPG in both time-domain (TD) and frequency-domain (FD) for these three periods, including the total period of both pre-virus and virus periods.

Viscoelastic or Viscoplastic Glucose Theory (VGT #41): Applying VGT to Study the Relationship of CVD Risk Probability % Versus Sensor Daily Average Glucose (eAG) and its Calculated HbA1C, while using the Viscoelastic Perturbation Model to Predict Two CVD risk % Values over a 15-Month Period from October of Y2020 to February of Y2022 based on the GH-Method: MathPhysical Medicine (No.

Since 2012, the author has been collecting his body weight (m1) and finger-piercing glucose values (m2) each day. In addition, he accumulates medical conditions data including a combination of data for blood pressure (BP) and heart rate (HR), and blood lipids along with lifestyle details of diet, exercise, sleep, stress, water intake and daily routine details. Based on the collected big data, he further organized them into two main groups. The first group is medical conditions (MC) with 4 categories: weight, glucose, BP, and blood lipids. The second group is lifestyle details (LD) with 6 categories: food & diet, exercise, water intake, sleep, stress, and daily routines. He collects his daily data and then calculates a unique combined score for each MC and LD with their 10 categories. The combined scores of the 2 groups, 10 categories, and 500+ elements constitute an overall “metabolism index (MI) model”. This MI model includes the root causes of 6 lifestyle inputs and 4 symptoms of the disease including the rudimentary chronic diseases: obesity, diabetes, hypertension, and hyperlipidemia. As we know, lifestyle details cause rudimentary chronic diseases which further influence more complicated diseases, such as heart problems (CVD & CHD), chronic kidney disease (CKD), stroke, diabetic retinopathy (DR), neuropathy, hypothyroidism, and others. However, in addition to the lifestyle-induced chronic disease and complications, environmental factors, such as radiation, air and water pollution, food poison and pollution, toxic chemicals, and hormonal therapy, can contribute to the causes for a variety of cancer. Some genetic conditions and lifetime unhealthy habits, such as smoking, alcohol consumption, illicit drug use would account for approximately 15% to 25% of the root cause for rudimentary chronic diseases, complications, and cancer. All of the above-described diseases fall into the “symptoms” category which are the “root-causes” due to poor and unhealthy lifestyles.

Viscoelastic and Viscoplastic Glucose Theory (VGT #30): Applying VGT with two Different CGM Sensor Measured 3-Hour Postprandial Plasma Glucose (PPG) Values from 240 Liquid Egg Meals and 191 Solid Egg Meals as the Strain, Along with the PPG Change Rate Multiplied by a Viscosity Factor of Average Carbs/Sugar Amounts for Each Egg Meal Type as the Stress, and then Applying

The author has studied strength of materials and theory of elasticity through his undergraduate courses at the University of Iowa. He also conducted research work on UI campus to earn a master’s degree in Biomechanics under Professor James Andrews. At that time, he used a combined spring and dashpot model to simulate the behaviors of human joints, bones, muscles, and tendons in order to investigate the human-weapon interactions during the Vietnam war era. Later, he went to MIT to pursue his PhD study under Professor Norman Jones, who taught him theory of plasticity and dynamic plastic behaviors of various structure elements. He also took additional graduate courses in the field of fluid dynamics and thermodynamics along the way of his schooling years. Since then, many advancements have been made in the biomechanics branch, especially with human body tissues that possess certain viscoelastic characteristics, such as bones, muscles, cartilages, tendons (connect bone to muscle), ligaments (connect bone to bone), fascia, and skin. For example, the author suffered plantar fasciitis for many years. He understood that the night splint dorsiflexes forefoot, at the back of the foot, increases plantar fascia tension to offer stress-relief for the pain. This model where muscles and tendons connect the lower leg and foot is a form of viscoelastic study for medical problem solving. When dealing with human internal organs, it is not easy to conduct live experiments to obtain accurate measurements for the biomedical material properties. Blood itself is a viscous material (time-dependent) and its viscosity factor may fall between water, honey, syrup, or gel. The author’s research focuses on “glucose” where the blood sugar amount is produced by the liver and carried by red blood cells, not the blood itself. The postprandial plasma glucose (PPG) is strongly influenced by both energy input via carbs/sugar intake amount (~60%) and energy output via post-meal exercise level (~40%). Fundamentally, the PPG level is also dependent on the individual’s health conditions in regard to liver cells and pancreatic beta cells, which produce glucose and release insulin to control the glucose level in blood. Therefore, it is nearly impossible to measure the material geometry or material properties to determine the viscosity of “glucose” like in engineering research work. As a result, the best the author could do is to apply the “concept” of viscoelasticity and/or viscoplasticity” to construct an analogy model of time-dependent glucose behaviors.

Viscoelastic and Viscoplastic Glucose Theory (VGT #31): Applying VGT with Two Different CGM Sensor Measured 2-Hour Postprandial Plasma Glucose (PPG) Values from 240 Liquid Egg Meals and 191 Solid Egg Meals as the Strain, Along with the PPG Change Rate Multiplied by a Viscosity Factor of Average Carbs/Sugar Amounts for Each Egg Meal Type as the Stress, and then Applying

The author has studied strength of materials and theory of elasticity through his undergraduate courses at the University of Iowa. He also conducted research work and earned a master’s degree in Biomechanics under Professor James Andrews. In 1970-1971, he used a combined spring and dashpot model to simulate the behaviors of human joints, bones, muscles, and tendons in order to investigate the human-weapon interactions during the Vietnam war era. Later, he went to MIT to pursue his PhD study under Professor Norman Jones, who taught him theory of plasticity and dynamic plastic behaviors of various structure elements. To further his education, he took additional graduate courses in various fields of fluid dynamics, thermodynamics, bridge design using energy absorption pad, and soil mechanics under earthquake influences which have dealt with “time-dependency” issues. Since then, many advancements have been made in the biomechanics branch, especially with human body live tissues that possess certain viscoelastic characteristics, such as bones, muscles, cartilages, tendons (connect bone to muscle), ligaments (connect bone to bone), fascia, and skin. For example, the author suffered plantar fasciitis for many years. He understood that the night splint dorsiflexes forefoot, at the back of the foot, increases plantar fascia tension to offer stress-relief for the pain. This model where muscles and tendons connect the lower leg and foot is a form of viscoelastic study for medical problem solving. When dealing with human internal organs, it is not easy to conduct live experiments to obtain accurate measurements for the biomedical material properties. Blood itself is a viscous material (time-dependent) and its viscosity factor may fall between water, honey, syrup, or gel. But, the author’s research focus is on “glucose” where the blood sugar amount is produced by the liver and carried by red blood cells, not the blood itself. The postprandial plasma glucose (PPG) is strongly influenced by both energy input via carbs/sugar intake amount (~60%) and energy output via post-meal exercise level (~40%). Fundamentally, the PPG level is also dependent on the individual’s health conditions in regard to liver cells and pancreatic beta cells, which produce glucose and release insulin to control the glucose level in blood. Therefore, it is nearly impossible to measure the material geometry or material properties to determine the viscosity of “glucose” like in engineering research work. As a result, the best the author could do is to apply the “concept” of viscoelasticity and/or viscoplasticity” to construct an analogy model of time-dependent glucose behaviors.

Viscoelastic or Viscoplastic Glucose Theory (VGT #39): Applying the VEGT or VPGT to Study the Effect of Role Switching for Cause and Symptom, CVD Risk Versus Cancer Risk and Cancer Risk Versus CVD Risk, and using Viscoelastic Perturbation Model to Predict the Risks of having CVD or Cancer using Calculated Risk Data of a 12+ Years Period from Y2012 to

Since 2012, the author has been collecting his body weight and finger-piercing glucose values each day. In addition, he accumulates medical conditions data including blood pressure (BP), heart rate (HR), and blood lipids along with lifestyle details of diet, exercise, sleep, stress, water intake and daily routine details. Based on these collected big data, he further organized them into two main groups. The first group is medical conditions (MC) with 4 categories: weight, glucose, BP, and lipids. The second group is lifestyle details (LD) with 6 categories: food, exercise, water intake, sleep, stress, and daily routines. He collects his daily data and then calculates a unique combined score for each of these 2 groups of MC and LD and their 10 categories. His calculated risk probability percentage of developing complicated diseases, such as cardiovascular disease (CVD) or cancers, are based on this above-described metabolism model. This metabolism index model (MI) includes the root causes of 6 lifestyle details and 4 disease symptoms including rudimentary chronic diseases: obesity, diabetes, hypertension, and hyperlipidemia. As we know, lifestyle details cause rudimentary chronic diseases which further influence more complicated diseases, such as heart problems (CVD), strokes, and others. However, on top of the lifestyle-induced chronic disease and complications, environmental factors, such as radiation, air and water pollution, food poison and pollution, toxic chemicals, and hormonal therapy, can also contribute to the causes of a variety of cancer. Some genetic conditions and lifetime unhealthy habits, such as smoking, alcohol consumption, illicit drug use would account for approximately 15% to 25% of the root cause for rudimentary chronic diseases, CVD, and cancer.

Viscoelastic or Viscoplastic Glucose Theory (VGT #43): A Research of Statistics Methods Influences on Math-Physical Medicine Subjects by using Statistical Correlations as Initial Examination Tool to Explore the Inter-Relationships of Three Selected Biomarkers, CVD Risk %, Averaged Daily Glucose (eAG), and HbA1C % (A1C), within Three Different Selected Time-Windows, 15-Months, 46-Months, and 8.2 Years (98-Months), also Applying VGT and Viscoelastic

Since 2012, the author has been collecting his body weight and finger-piercing glucose values each day. In addition, he accumulates medical conditions data including a combination of data for blood pressure (BP), heart rate (HR), and blood lipids along with lifestyle details (LD). Based on the collected big data, he further organized them into two main groups. The first is the medical conditions group (MC) with 4 categories: weight, glucose, blood pressures, and blood lipids. The second is the LD group with 6 categories: food & diet, exercise, water intake, sleep, stress, and daily routines. At first, he collected his data on a daily basis since Y2012 and then calculated a unique combined daily score for each of the 10 categories within the MC and LD groups. The combined scores of the 2 groups, 10 categories, and 500+ detailed elements constitute an overall “metabolism index (MI) model”. This MI model includes the root causes from 6 major lifestyle inputs and symptoms from 4 rudimentary chronic diseases: obesity, diabetes, hypertension, and hyperlipidemia. Therefore, it can serve as the foundation and building block for his additional research work that can expand into various diseases associated with different organs, such as cardiovascular diseases (CVD), and various cancers.

Viscoelastic and Viscoplastic Glucose Theory (VGT #32): A Step-By-Step Illustration of How to Apply VGT and Viscoelasticity Perturbation Model with the Average Value of Normalized Body Weight and Body Temperature in Early Morning as the Viscosity Factor to Predict Average Fasting Plasma Glucose Values over 7-Hours of Sleep Time Based on GH-Method: MathPhysical Medicine (No. 613)

The author has studied strength of materials and theory of elasticity through his undergraduate courses at the University of Iowa. He also conducted research work and earned a master’s degree in Biomechanics under Professor James Andrews. In 1970-1971, he used a combined spring and dashpot model to simulate the behaviors of human joints, bones, muscles, and tendons (which he took some related courses at School of Medicine at UI) in order to investigate the soldier-weapon biophysical interactions during the Vietnam war era. Later, he went to MIT to pursue his PhD study under Professor Norman Jones, who taught him theory of plasticity and dynamic plastic behaviors of various structure elements. To further his education, he took additional graduate courses in various fields of fluid dynamics, thermodynamics, bridge design using energy absorption pad, and soil mechanics under earthquake strike forces which deal with “time-dependency” issues. Since then, many advancements have been made in the biomechanics branch, especially with human body live tissues that possess certain viscoelastic characteristics, such as bones, muscles, cartilages, tendons (connect bone to muscle), ligaments (connect bone to bone), fascia, and skin. For example, the author suffered plantar fasciitis for many years. He understood that the night splint dorsiflexes forefoot, at the back of the foot, increases plantar fascia tension to offer stress-relief for the pain. This model where muscles and tendons connect the lower leg and foot is a form of viscoelastic study for medical problem solving.