scholarly journals THREE-DIMENSIONAL POSTURE ESTIMATION OF FOOT BONES BY USING PLANTAR PLATE

2017 ◽  
Vol 20 (01) ◽  
pp. 1750011
Author(s):  
Kenta Nomura ◽  
Teru Yonezawa ◽  
Shinichi Kosugi ◽  
Yasuhito Tanaka ◽  
Hiroshi Mizoguchi ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Three Dimensional ◽  
Estimation Accuracy ◽  
Coefficient Of Determination ◽  
Plantar Plate ◽  
Foot Bones ◽  
Estimation Function ◽  
Almost All

Purpose: This paper proposes a method to easily and quantitatively estimate the changes in the foot bone three-dimensional (3D) posture from the 3D posture of a plantar plate without using X-ray or computed tomography (CT). Methods: The estimation functions from the posture of the plantar plate attached to the sole of a foot to the posture of the each bone are calculated using multiple regression analysis (MRA). Because we assumed that the posture of the plantar plate is related to each bone posture. Each bone posture can be estimated by substituting the plantar plate posture into the estimation function. Results: The adjusted coefficient of determination of the linear regression model (estimation function) of more than 90% was obtained by the estimation function, which was higher than 0.70. The estimation accuracy root mean square error (RMSE) of the translation and rotation were approximately within [Formula: see text][Formula: see text]mm and [Formula: see text], respectively. The RMSE/range of motion (RoM) values of the translation and rotation were approximately within [Formula: see text] and [Formula: see text], respectively. Conclusion: The experimental results suggest that the 3D posture of almost all types of foot bones can be easily estimated using plantar plate posture and the linear regression model. This is an inexpensive, easy-to-apply method that can perform real-time measurement.

Whole Facility Measurement for Quantifying Energy Saving in an Office Building, Malaysia

2015 ◽  
Vol 785 ◽  
pp. 676-681 ◽  
Author(s):  
Nor Shahida Razali ◽  
Nofri Yenita Dahlan
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Energy Saving ◽  
Linear Regression Model ◽  
Energy Use ◽  
Multiple Linear Regression Model ◽  
Office Building ◽  
Coefficient Of Determination ◽  
Independent Variable

This paper presents the concept of International Performance Measurement and Verification Protocol (IPMVP) for determining energy saving at whole facility level for an office building in Malaysia. Regression analysis is used to develop baseline model from a set of baseline data which correlates baseline energy with appropriate independents variables, i.e. Cooling Degree Days (CDD) and Number of Working Days (NWD) in this paper. In determining energy savings, the baseline energy is adjusted to the same set condition of reporting period using energy cost avoidance approach. Two types of energy saving analyses have been presented in the case study; 1) Single linear regression for each independent variable, 2) Multiple linear regression for each independent variable. Results show that NWD has coefficient of determination, R2 higher than CDD which indicates that NWD has stronger correlation with the energy use than CDD in the building. Finding also shows that the R2 for multiple linear regression model are higher than single linear regression model. This shows the fact that more than one component are affecting the energy use in the building.

scholarly journals The Distribution of the Coefficient of determination in Linear Regression Model: A Review

2021 ◽  
Vol 23 (09) ◽  
pp. 126-127
Author(s):  
El Houssainy A. Rady ◽  
◽  
Ahmed Amin El-Sheikh ◽  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Regression Models ◽  
Error Term ◽  
Coefficient Of Determination ◽  
Linear Regression Models ◽  
Common Assumption ◽  
Normal Distributions ◽  
Error Terms

In this article, we review the different studies about the coefficient of determination in linear regression models and make a highlight about the inferences and the density function of the coefficient of determination which presented under the most common assumption when the error terms obey the normal distributions, and also analyzed the certain effects of departures from normality of the error term

On Small Sample Properties of R2 in a Linear Regression Model with Multivariate t Errors and Proxy Variables

1993 ◽  
Vol 9 (3) ◽  
pp. 504-515 ◽  
Author(s):  
Kazuhiro Ohtani ◽  
Hikaru Hasegawa
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Important Variable ◽  
Small Sample ◽  
Small Samples ◽  
Coefficient Of Determination ◽  
Proxy Variables ◽  
Small Sample Properties ◽  
Multivariate T

In this paper we consider the small sample properties of the coefficient of determination in a linear regression model with multivariate t errors when proxy variables are used instead of unobservable regressors. The results show that if the unobservable variable is an important variable, the adjusted coefficient of determination can be more unreliable in small samples than the unadjusted coefficient of determination from both viewpoints of the bias and the MSE.

scholarly journals Predicting Virulence of Fusarium oxysporum f. sp. Cubense Based on the Production of Mycotoxin Using a Linear Regression Model

Toxins ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 254 ◽  
Author(s):  
Chuange Shao ◽  
Dandan Xiang ◽  
Hong Wei ◽  
Siwen Liu ◽  
Ganjun Yi ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Fusarium Oxysporum ◽  
Fusarium Wilt ◽  
Linear Regression Model ◽  
Fusaric Acid ◽  
Coefficient Of Determination ◽  
Banana Plant ◽  
Vegetative Compatibility Groups ◽  
Linearity Test

Fusarium wilt caused by Fusarium oxysporum f.sp. cubense (Foc) is one of the most destructive diseases for banana. For their risk assessment and hazard characterization, it is vital to quickly determine the virulence of Foc isolates. However, this usually takes weeks or months using banana plant assays, which demands a better approach to speed up the process with reliable results. Foc produces various mycotoxins, such as fusaric acid (FSA), beauvericin (BEA), and enniatins (ENs) to facilitate their infection. In this study, we developed a linear regression model to predict Foc virulence using the production levels of the three mycotoxins. We collected data of 40 Foc isolates from 20 vegetative compatibility groups (VCGs), including their mycotoxin profiles (LC-MS) and their plant disease index (PDI) values on Pisang Awak plantlets in greenhouse. A linear regression model was trained from the collected data using FSA, BEA and ENs as predictor variables and PDI values as the response variable. Linearity test statistics showed this model meets all linearity assumptions. We used all data to predict PDI with high fitness of the model (coefficient of determination (R2 = 0.906) and adjust coefficient (R2adj = 0.898)) indicating a strong predictive power of the model. In summary, we developed a linear regression model useful for the prediction of Foc virulence on banana plants from the quantification of mycotoxins in Foc strains, which will facilitate quick determination of virulence in newly isolated Foc emerging Fusarium wilt of banana epidemics threatening banana plantations worldwide.

scholarly journals Linear and non-linear regression model fitting of testicular three-dimensional growth in Miranda donkeys

2021 ◽  
Vol 20 (1) ◽  
pp. 1518-1531
Author(s):  
Ana Martins-Bessa ◽  
Miguel Quaresma ◽  
Belén Leiva ◽  
Ana Calado ◽  
Ander Arando ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Model Fitting ◽  
Three Dimensional ◽  
Non Linear ◽  
Dimensional Growth

scholarly journals Multiple Linear Regression Model Based on Neural Network and Its Application in the MBR Simulation

2012 ◽  
Vol 2012 ◽  
pp. 1-18
Author(s):  
Chunqing Li ◽  
Zixiang Yang ◽  
Yiquan Deng ◽  
Tao Wang
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
High Efficiency ◽  
Three Dimensional ◽  
Multiple Linear Regression Model ◽  
Simulation System ◽  
Future Research ◽  
Dimensional Simulation

The computer simulation of the membrane bioreactor MBR has become the research focus of the MBR simulation. In order to compensate for the defects, for example, long test period, high cost, invisible equipment seal, and so forth, on the basis of conducting in-depth study of the mathematical model of the MBR, combining with neural network theory, this paper proposed a three-dimensional simulation system for MBR wastewater treatment, with fast speed, high efficiency, and good visualization. The system is researched and developed with the hybrid programming of VC++ programming language and OpenGL, with a multifactor linear regression model of affecting MBR membrane fluxes based on neural network, applying modeling method of integer instead of float and quad tree recursion. The experiments show that the three-dimensional simulation system, using the above models and methods, has the inspiration and reference for the future research and application of the MBR simulation technology.

scholarly journals Estimation of Health-Related Physical Fitness Using Multiple Linear Regression in Korean Adults: National Fitness Award 2015–2019

2021 ◽  
Vol 12 ◽  
Author(s):  
Sung-Woo Kim ◽  
Hun-Young Park ◽  
Hoeryong Jung ◽  
Jinkue Lee ◽  
Kiwon Lim
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Cardiorespiratory Fitness ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Coefficient Of Determination ◽  
Muscular Endurance ◽  
Dependent Variables ◽  
Health Related

Continuous health care and the measurement of health-related physical fitness (HRPF) is necessary for prevention against chronic diseases; however, HRPF measurements including laboratory methods may not be practical for large populations owing to constraints such as time, cost, and the requirement for qualified technicians. This study aimed to develop a multiple linear regression model to estimate the HRPF of Korean adults, using easy-to-measure dependent variables, such as gender, age, body mass index, and percent body fat. The National Fitness Award datasets of South Korea were used in this analysis. The participants were aged 19–64 years, including 319,643 male and 147,600 females. HRPF included hand grip strength (HGS), flexibility (sit and reach), muscular endurance (sit-ups), and cardiorespiratory fitness (estimated VO2max). An estimation multiple linear regression model was developed using the stepwise technique. The outlier data in the multiple regression model was identified and removed when the absolute value of the studentized residual was ≥2. In the regression model, the coefficient of determination for HGS (adjusted R2: 0.870, P < 0.001), muscular endurance (adjusted R2: 0.751, P < 0.001), and cardiorespiratory fitness (adjusted R2: 0.885, P < 0.001) were significantly high. However, the coefficient of determination for flexibility was low (adjusted R2: 0.298, P < 0.001). Our findings suggest that easy-to-measure dependent variables can predict HGS, muscular endurance, and cardiorespiratory fitness in adults. The prediction equation will allow coaches, athletes, healthcare professionals, researchers, and the general public to better estimate the expected HRPF.

Physico-Chemical Analysis of Groundwater and Effect of Climatic Change in Angul-Talcher Area

2018 ◽  
Vol 6 (12) ◽  
pp. 26
Author(s):  
Aliva Bera ◽  
D.P. Satapathy
Keyword(s):  
Water Resources ◽  
Linear Regression ◽  
Regression Model ◽  
Climatic Change ◽  
Linear Regression Model ◽  
Ann Model ◽  
Quality Of Water ◽  
Physico Chemical ◽  
Simulation Based

In this paper, the linear regression model using ANN and the linear regression model using MS Excel were developed to estimate the physico-chemical concentrations in groundwater using pH, EC, TDS, TH, HCO3 as input parameters and Ca, Mg and K as output parameters. A comparison was made which indicated that ANN model had the better ability to estimate the physic-chemical concentrations in groundwater. An analytical survey along with simulation based tests for finding the climatic change and its effect on agriculture and water bodies in Angul-Talcher area is done. The various seasonal parameters such as pH, BOD, COD, TDS,TSS along with heavy elements like Pb, Cd, Zn, Cu, Fe, Mn concentration in water resources has been analyzed. For past 30 years rainfall data has been analyzed and water quality index values has been studied to find normal and abnormal quality of water resources and matlab based simulation has been done for performance analysis. All results has been analyzed and it is found that the condition is stable. 

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