scholarly journals Using a Modified Candlestick Charting Technique (OHCA) and the Synthesized PPG Waveform to Develop Two Simple Formulas for PPG Prediction (GH-Method: Math-Physical Medicine)

2020 ◽  
Vol 3 (2) ◽  
Keyword(s):  
Control Mechanism ◽  
Chemical Characteristics ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Related Data ◽  
Modeling Techniques ◽  
Mathematical Equations ◽  
Engineering Modeling ◽  
And Control ◽  
Physical Phenomena

This paper describes development of simple formulas for postprandial plasma glucose (PPG) prediction based on sensor-monitored data using a modified candlestick charting technique. The GH-Method: Math-physical medicine (MPM) starts with the observation of the human body’s physical phenomena not biological or chemical characteristics, collecting elements of the disease related data (preferring big data), utilizing applicable engineering modeling techniques, developing appropriate mathematical equations not just statistical analysis, and finally predicting the direction of the development and control mechanism of the disease.

scholarly journals Using Time-Series and Spatial Analysis (Math-Physical Medicine) to Investigate the Relationships among Weight, Blood Pressure,Glucose, Food and Exercise

2020 ◽  
Keyword(s):  
Physical Medicine ◽  
Related Data ◽  
Modeling Techniques ◽  
Metabolic Conditions ◽  
Mathematical Equations ◽  
Engineering Modeling ◽  
And Control ◽  
Physical Phenomena

The author uses his own type 2 diabetes metabolic conditions control as a case study using the “math-physical medicine” approach of a non-traditional methodology in medical research. Math-physical medicine (MPM) starts with the observation of the human body’s physical phenomena (not biological or chemical characteristics), collecting elements of the disease related data (preferring big data), utilizing applicable engineering modeling techniques, developing appropriate mathematical equations (not just statistical analysis), and finally predicting the direction of the development and control mechanism of the disease.

scholarly journals Methodology of Math-Physical Medicine (GH-Method)

2020 ◽  
Vol 2 (2) ◽  
Keyword(s):  
Physical Medicine ◽  
Related Data ◽  
Modeling Techniques ◽  
Metabolic Conditions ◽  
Mathematical Equations ◽  
Engineering Modeling ◽  
And Control ◽  
Physical Phenomena

This paper discusses the “math-physical medicine (MPM)” approach of a non-traditional methodology in medical research. The author uses his own type 2 diabetes (T2D) metabolic conditions control as a case study for some detailed illustration and explanation of this methodology. Math-physical medicine starts with the observation of the human body’s physical phenomena (not biological or chemical characteristics), collecting elements of the disease related data (preferring big data), utilizing applicable engineering modeling techniques, developing appropriate mathematical equations (not just statistical analysis), and finally predicting the direction of the development and control mechanism of the disease.

scholarly journals Using GH-Method: Math-Physical Medicine, Fourier Transform, and Frequency Segmentation Pattern Analysis to Investigate Relative Energy Associated With Glucose

2020 ◽  
Vol 3 (1) ◽  
Keyword(s):  
Relative Energy ◽  
Physical Medicine ◽  
Related Data ◽  
Segmentation Pattern ◽  
Metabolic Conditions ◽  
Research Findings ◽  
Mathematical Equations ◽  
Engineering Modeling ◽  
And Control ◽  
Physical Phenomena

This paper provides research findings on glucose created relative energy by using sensor collected glucose data from a period of 376 days from 5/5/2018 to 5/15/20. The dataset is provided by the author, who uses his own type 2 diabetes metabolic conditions control, as a case study via the “math-physical medicine” approach of a nontraditional methodology in medical research. Math-physical medicine (MPM) starts with the observation of the human body’s physical phenomena (not biological or chemical characteristics), collecting elements of the disease related data (preferring big data), utilizing applicable engineering modeling techniques, developing appropriate mathematical equations (not just statistical analysis), and finally predicting the direction of the development and control mechanism of the disease.

scholarly journals Using Time-Series and Forecasting to Manage Type 2 Diabetes Conditions (GH-Method: Math-Physical Medicine)

2020 ◽  
Vol 2 (2) ◽  
Keyword(s):  
Type 2 Diabetes ◽  
Time Series ◽  
Physical Medicine ◽  
Related Data ◽  
Metabolic Conditions ◽  
Mathematical Equations ◽  
Engineering Modeling ◽  
And Control ◽  
Physical Phenomena

This paper describes the author’s application of Time-Series Analysis and forecasting to manage type 2 diabetes (T2D) conditions. The dataset is provided by the author, who uses his own T2D metabolic conditions control, as a case study via the “math-physical medicine” approach of a non-traditional methodology in medical research. Math-physical medicine (MPM) starts with the observation of the human body’s physical phenomena (not biological or chemical characteristics), collecting elements of the disease related data (preferring big data), utilizing applicable engineering modeling techniques, developing appropriate mathematical equations (not just statistical analysis), and finally predicting the direction of the development and control mechanism of the disease.

scholarly journals Using GH-Method: Math-Physical Medicine, Fourier Transform, and Frequency Segmentation Pattern Analysis to Investigate Relative Energy Associated with Glucose

2020 ◽  
Vol 5 (3) ◽  
Keyword(s):  
Relative Energy ◽  
Physical Medicine ◽  
Related Data ◽  
Segmentation Pattern ◽  
Metabolic Conditions ◽  
Research Findings ◽  
Mathematical Equations ◽  
Engineering Modeling ◽  
And Control ◽  
Physical Phenomena

This paper provides research findings on glucose created relative energy by using sensor collected glucose data from a period of 376 days from 5/5/2018 to 5/15/20. The dataset is provided by the author, who uses his own type 2 diabetes metabolic conditions control, as a case study via the “math-physical medicine” approach of a nontraditional methodology in medical research. Math-physical medicine (MPM) starts with the observation of the human body’s physical phenomena (not biological or chemical characteristics), collecting elements of the disease related data (preferring big data), utilizing applicable engineering modeling techniques, developing appropriate mathematical equations (not just statistical analysis), and finally predicting the direction of the development and control mechanism of the disease.

Using Candlestick Charting, Segmentation Pattern Analysis, and GHMethod: Math-Physical Medicine to calculate Effective Glucoses based on Finger-Piercing Glucose Measurement

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
C Gerald Hsu
Keyword(s):  
Pattern Analysis ◽  
Glucose Measurement ◽  
Physical Medicine ◽  
Related Data ◽  
Segmentation Pattern ◽  
Calculation Results ◽  
Metabolic Conditions ◽  
Engineering Modeling ◽  
And Control ◽  
Physical Phenomena

This paper describes glucose measurements and their extensive calculation results over a period of 7.5 years based on Finger-Piercing Data (Finger) using both candlestick charting and glucose segmentation pattern analysis. The dataset is provided by the author, who uses his own type 2 diabetes metabolic conditions control, as a case study via the “math-physical medicine” approach of a non- traditional methodology in medical research. Math-Physical Medicine (MPM) starts with the observation of the human body’s physical phenomena (not biological or chemical characteristics), collecting elements of the disease related data (preferring big data), utilizing applicable engineering modeling techniques, developing appropriate mathematical equations (not just statistical analysis), and finally predicting the direction of the development and control mechanism of the disease.

scholarly journals A1C Variance Study and PPG Prediction Methodology over Six Periods Using GH-Method: Math-Physical Medicine

2020 ◽  
Vol 5 (4) ◽  
Keyword(s):  
Plasma Glucose ◽  
Hemoglobin A1c ◽  
Prediction Models ◽  
Big Data Analytics ◽  
Physical Medicine ◽  
Lifestyle Management ◽  
Postprandial Plasma Glucose ◽  
Index Weight ◽  
Engineering Modeling

In this case study, the author analyzed, predicted, and interpreted a type 2 diabetes (T2D) patient’s hemoglobin A1C variances based on six periods data utilizing the GH-Method: math-physical medicine approach by applying mathematics, physics, engineering modeling, and computer science (big data analytics and AI). He believes in “prediction” and has developed five models, including metabolism index, weight, fasting plasma glucose (FPG), postprandial plasma glucose (PPG), and hemoglobin A1C. All prediction models have reached to 95% to 99% accuracy. His focus is on preventive medicine, especially on diabetes control via lifestyle management.

scholarly journals PPG prediction methodology by analyzing a simple lunch using GH-Method:math-physical medicine

2020 ◽  
Keyword(s):  
Plasma Glucose ◽  
Data Analytics ◽  
Hemoglobin A1c ◽  
Prediction Models ◽  
Big Data Analytics ◽  
Physical Medicine ◽  
Lifestyle Management ◽  
Postprandial Plasma Glucose ◽  
Index Weight ◽  
Engineering Modeling

This paper discusses both predicted and measured postprandial plasma glucose (PPG) results from a simple lunch of one small bag of Quaker oatmeal: 18 grams carbs and 0 grams of sugar using the GH-Method: math-physical medicine (MPM). He developed MPM by applying mathematics, physics, engineering modeling, and computer science (big data analytics and AI). He believes in “prediction” and has developed five models, including metabolism index, weight, fasting plasma glucose (FPG), postprandial plasma glucose (PPG), and hemoglobin A1C. All prediction models have reached to 95% to 99% accuracy. His focus is on preventive medicine, especially on diabetes control via lifestyle management.

An evaluation model of 2019-nCoV prevention effectiveness based on immigration data in China (Preprint)

2020 ◽  
Author(s):  
Qiangsheng Huang
Keyword(s):  
Prevention And Control ◽  
Time Window ◽  
Evaluation Model ◽  
Control Measures ◽  
Related Data ◽  
The Face ◽  
Prevention And Control Measures ◽  
And Control ◽  
Best Fit

BACKGROUND As of the end of February 2020, 2019-nCoV is currently well controlled in China. However, the virus is now spreading globally. OBJECTIVE This study aimed to evaluate the effectiveness of outbreak prevention and control measures in a region. METHODS A model is built for find the best fit for two sets of data (the number of daily new diagnosed, and the risk value of incoming immigration population). The parameters (offset and time window) in the model can be used as the evaluation of effectiveness of outbreak prevention and control. RESULTS Through study, it is found that the parameter offset and time window in the model can accurately reflect the prevention effectiveness. Some related data and public news confirm this result. And this method has advantages over the method using R0 in two aspects. CONCLUSIONS If the epidemic situation is well controlled, the virus is not terrible. Now the daily new diagnosed patients in most regions of China is quickly reduced to zero or close to zero. Chinese can do a good job in the face of huge epidemic pressure. Therefore, if other countries can do well in prevention and control, the epidemic in those places can also pass quickly.

scholarly journals Design, Construction, and Control for an Underwater Vehicle Type Sepiida

Robotica ◽  
2020 ◽  
pp. 1-18
Author(s):  
M. Garcia ◽  
P. Castillo ◽  
E. Campos ◽  
R. Lozano
Keyword(s):  
Embedded System ◽  
Closed Loop ◽  
Underwater Vehicle ◽  
Mathematical Representation ◽  
Closed Loop System ◽  
Vehicle Type ◽  
Mathematical Equations ◽  
And Control ◽  
Design Construction

SUMMARY A novel underwater vehicle configuration with an operating principle as the Sepiida animal is presented and developed in this paper. The mathematical equations describing the movements of the vehicle are obtained using the Newton–Euler approach. An analysis of the dynamic model is done for control purposes. A prototype and its embedded system are developed for validating analytically and experimentally the proposed mathematical representation. A real-time characterization of one mass is done to relate the pitch angle with the radio of displacement of the mass. In addition, first validation of the closed-loop system is done using a linear controller.

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