Regression Equation Research Articles

Optimization of piezo pump and air impact cooling based on a synthetic jet

A synthetic jet piezo (PE) pump with an inlet channel structured akin to a diffuser/nozzle, featuring smoothed walls, was developed. The synthetic jet was characterized by analyzing the internal flow field of the PE pump. Furthermore, the flow performance of this novel PE pump was compared with that of a basic PE pump, thereby demonstrating the advantages of the proposed design. Utilizing regression equations and standardized effect plots derived from the response surface method, both the main and interaction effects of parameters on the flow rate were assessed. Subsequently, the flow performance of the optimized PE pump was evaluated through experimental testing. Additionally, air impact cooling experiments were conducted, focusing on the influence of the impact distance on cooling performance. The findings indicated that the PE pump's open cavity height, closed cavity height, outlet diameter, and orifice diameter were 1.3 mm, 0.5 mm, 1.5 mm, and 0.8 mm, respectively. The proposed synthetic jet PE pump achieved a flow rate of 1832.3 ml/min at 50 Vpp and 4.6 kHz. Furthermore, it was observed that the operation of the PE pump reduced the temperature of the heater by 63 % at an impact distance of 20 mm.

Arctic and Barents Sea ice extent variability and trends in 1979–2022

The aim of the paper is to assess interannual and decadal variability of the Barents Sea and Arctic ice extent in various seasons for the period from 1979 to 2022.The material for the study was satellite data on the Barents Sea and Arctic ice extent, climate indices, oceanographic data obtained during surveys in the Barents Sea by PINRO and other available information on hydrometeorological conditions of the sea in 1979–2022.Methods of descriptive statistics as well as comparative, correlation, regression, harmonic and cluster analyses were applied.Results: The year-to-year changes in the Barents Sea and Arctic ice extent in various seasons are described for 1979–2022. The modern climate changes, manifested in substantial warming of air and water masses in the Arctic, resulted in a considerable ice extent reduction in the studied areas in recent decades. Well-marked negative trends took place in all months and seasons. The annual mean ice extent was decreasing at a rate of 532 thousand km2 per decade in the Arctic, and at a rate of 105 thousand km2 per decade in the Barents Sea. Over the past 40 years, the ice extent in the Arctic has decreased by 14%, and in the Barents Sea — by half (by 51%), while the ice-free period duration has increased in the Barents Sea from one to three months. Hydrometeorological parameters were identified that are most closely related to the ice extent in the Arctic (|r| ≥ 0.40) and Barents Sea (|r| ≥ 0.60). Reliable regression models were developed that explain 45.2 and 88.0% of its variability, respectively. The contribution of individual factors to the ice extent variability explained by regression equations was quantitatively assessed, and the leading role of air temperature was noted.Practical significance: The obtained results are useful for a better understanding of the climate processes occurring in the Arctic and Barents Sea in recent decades and can be used to assess the impact of climate changes on the main objects of Russian fishery in these areas.

Gender Differences in the Relationship between Forearm Length and Height across Different Age Groups: A Correlation and Regression Analysis

Abstract Introduction: Forearm length (FrL) and height (Ht) are important anthropometric measurements used in clinical practice and research. This study aimed to investigate the correlation and regression between FrL and Ht in males and females across different age groups. Materials and Methods: Participants aged 18 to under 23 years were recruited, and FrL and Ht were measured using standardised techniques. Pearson’s correlation coefficient and regression analysis were used to analyse the data. Results: The correlation analysis revealed a moderate-to-strong positive correlation between FrL and Ht in males across all age groups, with correlation coefficients ranging from 0.318 to 0.866. In females, the correlation was weaker, ranging from 0.123 to 0.561. Regression equations were derived to estimate FrL from Ht, showing that Ht explains a larger proportion of the variability in FrL in males compared to females. These findings suggest that FrL can be used as a predictor of Ht, particularly in males, but caution is advised when applying this relationship to females. These results have implications for growth assessment and nutritional status evaluation in clinical practice, especially in populations, where direct measurement of Ht may be challenging. Conclusion: The study provides insights into the relationship between FrL and Ht, with implications for Ht estimation and growth assessment in clinical practice. Further research is needed to validate these findings in larger populations.

Optimal knots for smoothing splines and adaptive smoothing splines in generalized linear models: Design approach

Smoothing splines play a crucial role as a prevalent tool for estimating unknown functions in non parametric regression. In fitting functions with varying roughness, adaptive smoothing splines provide the flexibility to adjust the smoothing parameter adaptively with the changes over the predictor variable within specific intervals, facilitating accommodation for changing roughness. Despite its intuitive appeal and high flexibility, the implementation of this estimator poses challenges, especially in the absence of specified knot numbers, knot locations, and certain simplifying assumptions on the smoothing parameter. D-optimal designs are considered to specify the knot locations in this models. The prior information from data as the prior curvature knowledge can be useful for this optimization problem. For this purpose, the smoothing parameter in this optimization problem can be considered as a function based on the prior curvature. We apply this method to responses from the exponential family of distributions. The mathematical expression of the estimator of the curve in a generalized linear mixed model enables us to derive an information matrix. The derived information matrix and hence the optimum design will depend on the values of the model’s unknown parameters. In such situations, numerical techniques should be considered for evaluating and optimizing this criterion.

Polynomial Regression on Lie Groups and Application to SE(3).

In this paper, we address the problem of estimating the position of a mobile such as a drone from noisy position measurements using the framework of Lie groups. To model the motion of a rigid body, the relevant Lie group happens to be the Special Euclidean group SE(n), with n=2 or 3. Our work was carried out using a previously used parametric framework which derived equations for geodesic regression and polynomial regression on Riemannian manifolds. Based on this approach, our goal was to implement this technique in the Lie group SE(3) context. Given a set of noisy points in SE(3) representing measurements on the trajectory of a mobile, one wants to find the geodesic that best fits those points in a Riemannian least squares sense. Finally, applications to simulated data are proposed to illustrate this work. The limitations of such a method and future perspectives are discussed.

Heat recovery integration in a hybrid geothermal-based system producing power and heating using machine learning approach to maximize outputs

This study offers an in-depth thermodynamic analysis and optimization of an integrated renewable energy system that merges a double-flash geothermal system with a transcritical carbon dioxide Rankine cycle, utilizing machine learning algorithms. The innovative design aims to maximize the concurrent generation of heat and electricity, ultimately benefiting environmental sustainability and energy security. By employing regression machine learning algorithms, the research evaluates and enhances system performance, achieving remarkable R-squared accuracy levels of 98.86 % for heating output and 99.89 % for power output predictions. The thermodynamic modeling, which has been validated against recognized benchmarks, confirms the accuracy of the system's design. Optimization findings indicate that operating pressures between 840 and 870 kPa and pressure ratios of 1.56–1.60 deliver optimal outputs, with power production between 2582 and 2585 kW and heating output ranging from 12260 to 12280 kW. The system reaches its maximum performance at a pressure of 850 kPa and a pressure ratio of 1.57, resulting in a power output of 2583.97 kW and a heating output of 12279.3 kW. These results highlight the potential of combining advanced thermodynamic systems with machine learning methodologies to improve the efficiency and effectiveness of renewable energy sources.

A structural modeling approach to determine the effects of orthorexia nervosa and social appearance anxiety on healthy lifestyle behaviors in young adults

Aims: The aim of this study is to examine the relationship between orthorexia nervosa (ON) and social appearance anxiety (SAA) and to investigate the effects of these variables on healthy lifestyle behaviors (HLB) in young adults aged 18-30 years. Methods: A total of 408 young adult male and female volunteers, with a mean age of 21.7±2.95 years, participated in this study. A structural model was proposed to examine the effects of SAA and ON on HLB. To assess how well the study variables represented the underlying components, we first evaluated a confirmatory measurement model. Subsequently, we analyzed a structural model that allowed for the examination of multiple regression equations involving several dependent variables. Results: SAA had a negative influence on HLB (std β=-0.673; t-value=-8.425), a significant negative relationship between ON, HLB was also promoted (std β=-0.554; t-value=-9.388). It was determined that there is a positive correlation between SAA, ON which is statistically significant (correlation coefficient=0.830; t-value=10.244). Conclusion: It is concluded that the individuals with high social appearance anxiety do not adopt healthy lifestyle behaviors and are more prone to orthorexia nervosa.

Pengaruh Harga dan Promosi Terhadap Loyalitas Konsumen Pengguna Aplikasi Grab-Food di Yogyakarta

This research aims to determine the influence of price variables and promotion variables on consumer loyalty among Grab-Food users in Yogyakarta. The variables used in this study consist of independent variables, namely price (X1) and promotion (X2), as well as the dependent variable, which is consumer loyalty (Y). The data was analyzed using descriptive analysis, instrument quality testing (validity and reliability tests), and hypothesis testing through multiple linear regression (t-test and R² test), with a total of 100 respondents. The results of the analysis using the multiple linear regression equation show that price has a significant effect on consumer loyalty among Grab-Food users in Yogyakarta. This means that price significantly influences consumer loyalty and has a simultaneous significant effect on consumer loyalty. Consumer loyalty of Grab-Food users in Yogyakarta is influenced by price (X1) and promotion (X2), while the remaining factors are influenced by other variables not included in this study.

Fusion regression methods with repeated functional data

Linear regression and classification methods with repeated functional data are considered. For each statistical unit in the sample, a real-valued parameter is observed over time under different conditions related by some neighborhood structure (spatial, group, etc.). Two regression methods based on fusion penalties are proposed to consider the dependence induced by this structure. These methods aim to obtain parsimonious coefficient regression functions, by determining if close conditions are associated with common regression coefficient functions. The first method is a generalization to functional data of the variable fusion methodology based on the 1-nearest neighbor. The second one relies on the group fusion lasso penalty which assumes some grouping structure of conditions and allows for homogeneity among the regression coefficient functions within groups. Numerical simulations and an application of electroencephalography data are presented.

The oxygen uptake efficiency slope does not accurately predict O2peak of children - the Arkansas Active Kids study.

Cardiorespiratory fitness (CRF) is a vital indicator of health. However, accurately measuring peak oxygen consumption ( O2peak) to determine CRF in children can be challenging. The oxygen uptake efficiency slope (OUES) has been proposed as an alternative metric for predicting O2peak in children, but its accuracy and agreement with measured O2peak remain unclear. A post hoc analysis was conducted in 94 children (ages 7-10years) who completed an incremental cycle ergometer test to measure O2peak. Body composition (Dual-energy X-ray absorptiometry) was measured, and fat mass index (FMI, kg/m2) and fat-free mass index (FFMI, kg/m2) were calculated. OUES was determined using all respiratory data (OUES100%) collected during the cycle ergometer test and using data only up to 60% of heart rate reserve (OUES60%HRR). Regression equations to predict O2peak (Pred- O2peak) were derived from simple and multiple linear regression analysis. Bland-Altman analysis assessed the level of agreement between Pred- O2peak and measured O2peak. OUES60%HRR (β = 0.46, p < 0.0001), age (β = 56.0, p = 0.0004), White race (β = 173.3, p < 0.0003), FFMI (β = 0.98.6, p < 0.000), and FMI (β = -0.40.8, p < 0.000) were retained in the final model. The difference between measured O2peak and Pred- O2peak was not different from zero (p = 0.999). There was a positive association between the difference of measured O2peak and Pred- O2peak and the average of the two methods (β = 0.79, p = 0.0028). There was no mean bias between measured O2peak and Pred- O2peak. However, magnitude bias was present even after considering other significant predictors of O2peak (FMI, FFMI, race, and age) in the regression equation. Caution is advised when using OUES to predict O2peak in children.

Analysis of The Probability od Choosing a Public Transportation Mode Using A Binomial Model of Differential Logit

Urbanization and mobility of the population have increased interest in efficient and convenient transportation systems, especially for trips from Cirebon to Jakarta via the Cipali toll road. Public transportation modes that can be used from Cirebon to Jakarta include buses, travel and trains. This study reviews public transportation buses and travel, focusing on the characteristics and probability of mode selection by travelers. The crucial factors studied are cost, travel time, and access time. Primary data was obtained from questionnaires using the stated preference method and secondary data obtained from related sources. The method used is linear regression modeling and binomial logit difference, which is obtained from data on travel cost (X1), travel time (X2), and travel access time (X3), which is tested using SPSS software with the regression equation obtained is Y = 2.386 + 0.251 X1.3 + X2 + 0.301 X1.4 + 0.497 X3.1. with a travel cost of Rp. 110,000 and with a travel time of 4 hours and 30 minutes, travelers prefer the bus mode (64.2%) with the most implicative attribute is the cost of travel, then the percentage value calculated using probability calculations and binomial models logits, the difference is (99.9%) choosing the bus and (0.1%) choosing travel. If the trevel wants to intensify the opportunity to choose the mode used by travelers with a percentage above 50%, then travel must reduce the fare at least Rp. 25,000 from the bus fare.

Prediction of physico-mechanical rock characteristics from electrical resistivity tests

The indirect estimation of intact rock properties is particularly useful for preliminary investigations in engineering projects. In this paper we examine the usability of electrical resistivity, a nondestructive measurement, for the prediction of physical and mechanical rock characteristics. Physico-mechanical tests (uniaxial compression, Brazilian tensile, density, and porosity tests) and electrical resistivity measurements were performed on specimens of 36 rock types. Before the resistivity tests, the specimens were completely saturated with saline solution. Evaluation of the test results showed that there are medium or strong correlations between resistivity and rock properties. There are also strong or stronger correlations between the two parameters for the rock classes. The regression equations developed were statistically tested, and their validity was confirmed. The results were also compared with previous studies. The conclusion is that electrical resistivity measurement can be used for reliably estimating physical and mechanical rock characteristics.

Development and Validation of a Stability Indicating RP-HPLC Method for Simultaneous Estimation of Metformin, Tenegliptin, and Pioglitazone in bulk and its Pharmaceutical Dosage Form .

A straightforward, reliable, and thorough approach was created for contemporaneously estimating Teneligliptin, Metformin, and Pioglitazone in solid dosage form. It was conducted by means of Agilent C18 150x4.6mm, 5µ. The mobile phase comprising buffer, acetonitrile, and Methanol (55:35:10 v/v) passed across the column at a pace of 0.9ml/min. The procedure relied on a 0.01N K2HPO4 buffer having a pH of 3.5, which was achieved by diluting an orthophosphoric acid solution. The temperature was kept constant at 30°C—optimized wavelength for Teneligliptin. Metformin and Pioglitazone were 220.0 nm. Retention times of Teneligliptin, Metformin, and Pioglitazone were 2.970 min, 2.395 min, and 3.642 min. %RSD of a system for Teneligliptin, Metformin, and Pioglitazone were 1.3, 0.6, and 1.0, respectively. %RSD of a method for Teneligliptin, Metformin, and Pioglitazone was 0.6 for each drug. The % recovery was 100.43, 100.45, and 100.22% for Teneligliptin, Metformin, and Pioglitazone, accordingly. The regression equations are used to generate the LoD and LoQ values of Teneligliptin, Metformin, and Pioglitazone, and LoD values were 0.016 ppm, 0.26 ppm, 0.42ppm, while LOQ values of Teneligliptin, Metformin and Pioglitazone were 0.048 ppm, 0.78 ppm,1.28 ppm respectively. The regression equation of Teneligliptin was y = 56655x + 920.87. Metformin was y = 14194x + 3082.1 and of Pioglitazone was y = = 41330x + 792.61.Reduced retention durations make the new approach easy and cost-effective for use in routine industrial quality control testing.

Zenith tropospheric delay model in mainland China based on geographically convolutional neural network weighted regression and tensor spline function

Zenith tropospheric delay model in mainland China based on geographically convolutional neural network weighted regression and tensor spline function

Antimicrobial mechanisms and antifungal activity of compounds generated by banana rhizosphere Pseudomonas aeruginosa Gxun-2 against fusarium oxysporum f. sp. cubense.

Fusarium wilt of banana, also recognized as Panama disease, is caused by the soil-borne fungus Fusarium oxysporum f. sp. cubense tropical race 4 (FOC TR4). In recent years, strategies utilizing biocontrol agents, comprising antifungal microorganisms and their associated bioactive compounds from various environments, have been implemented to control this destructive disease. Our previous study showed that Pseudomonas aeruginosa Gxun-2 had significant antifungal effects against FOC TR4. However, there has been little scientific investigation of the antibacterial or antifungal activity. The aim of this study was to isolate, identify and evaluate the inhibition strength of active compounds in P. aeruginosa Gxun-2, so as to explain the mechanism of the strain inhibition on FOC TR4 from the perspective of compounds. The main antibacterial compounds of strain Gxun-2 were isolated, purified and identified using by fermentation extraction, silica gel column chromatography, thin-layer chromatography (TLC), high-performance liquid chromatography (HPLC), and nuclear magnetic resonance (NMR) techniques. The effect of the compounds on the mycelial growth, morphology and spore germination of strain FOC TR4 was observed by 96-well plate method and AGAR diffusion method. Among the metabolites produced by the strain, four antifungal compounds which were identified phenazine (C12H8N2), phenazine-1-carboxylic acid (PCA) (C13H8N2O2), 2-acetamidophenol (C8H9NO2) and aeruginaldehyde (C10H7NO2S) were identified through HPLC and NMR. Of these compounds, phenazine and PCA exhibited the most pronounced inhibitory effects on the spore germination and mycelial growth of FOC TR4. Phenazine demonstrated potent antifungal activity against FOC TR4 with a minimum inhibitory concentration (MIC) of 6.25 mg/L. The half-maximal effective concentration (EC50) was calculated to be 26.24 mg/L using the toxicity regression equation. PCA exhibited antifungal activity against FOC TR4 with an MIC of 25 mg/L and an EC50 of 89.63 mg/L. Furthermore, phenazine and PCA triggered substantial morphological transformations in the mycelia of FOC TR4, encompassing folding, bending, fracturing, and diminished spore formation. These findings indicate that strain Gxun-2 plays a crucial role in controlling FOC TR4 pathogenesis, predominantly through producing the antifungal compounds phenazine and PCA, and possesses potential as a cost-efficient and sustainable biocontrol agent against Fusarium wilt of banana in forthcoming times.

Estimating net energy requirements of male Djallonke sheep

Context Energy requirements in sheep can vary significantly due to various factors, including age, breed, sex and environmental conditions. Genetic selection and crossbreeding further contribute to this variability. Although guidelines from the US and UK national research councils are commonly used for formulating sheep diets, they may not adequately account for the unique characteristics of sheep in Africa. Aims The study aimed to estimate the net maintenance energy requirement of male Djallonke sheep. This estimation will provide a basis for comparing the energy requirements of Djallonke sheep with national recommendations and recent estimates for other sheep breeds. Methods Sixteen entire male Djallonke sheep were selected and grouped by weight. They were then randomly allocated to four treatment groups in a 4 × 4 Latin square design. The sheep were fed to achieve intake levels ranging from 1 to 1.75 multiples of their maintenance energy requirement. Gas exchange data were collected using the GreenFeed system over a 3-day period at the conclusion of each treatment period. Heat production was calculated using the established Brouwer’s equation. A linear regression equation was developed to relate heat production to metabolisable energy intake scaled by metabolic bodyweight to calculate the net energy for maintenance. Key results The estimated net energy for maintenance for male Djallonke sheep was determined to be 0.354 MJ/kg BW0.75. This estimation indicated that the energy requirements of male Djallonke sheep exceeded the recommendations provided by the US National Research Council and the UK Agricultural and Food Research Council. However, it closely aligned with an updated estimate of 0.358 MJ/kg BW0.75. Conclusions Based on the study’s findings, male Djallonke sheep have higher energy requirements for maintenance than what is commonly recommended by existing guidelines. Therefore, it is advisable to update ration formulation software for male Djallonke sheep to better reflect their actual energy requirements for maintenance. Implications These results have practical implications for the formulation of diets and feeding management practices for male Djallonke sheep. Adhering to updated energy requirement estimates can help improve the efficiency and productivity of sheep production in the context of Djallonke sheep farming.

Geostatistical evaluation of the impact of climate variability on malaria incidence In the South-West of Nigeria

Malaria remains a significant health concern in Nigeria, particularly in the South-West region. This study assesses the impact of temperature and rainfall on malaria incidence and prevalence in South-West Nigeria using remotely sensed and modelled data sourced from the Malaria Atlas Project and NASA's POWER database covering 2000 to 2020. The study adopts the Geographically Weighted Regression geostatistical model to establish the relationship between malaria and rainfall and temperature in the study area. The result shows a rising oscillating annual mean temperature trend of 0.0088oC/yr-1 from 2000 to 2020. The malaria incidence exceeds 8 million cases annually, peaking in 2020 at almost 10 million cases. The rising trend of malaria incidence highlights the inadequacy of the malaria intervention programmes to meet their goal of reducing malaria incidence by 40% by 2020. The study highlights the spatial variations, with high incidence in urban centres like Lagos and Ibadan metropolises, their satellite towns, as well as other prominent and capital towns including Oshogbo, Ilesa, Akure, Ijebu-Ode and Abeokuta. Contrary to this, the greater malaria prevalence was recorded in less densely populated areas of Oyo state, Imeko-Afon, Odeda, Yewa and Ijebu-Waterside areas in Ogun state as well as Ose and Idanre in Ondo state. The Geographically Weighted Regression equation model shows a strong positive correlation between malaria prevalence and temperature at a significance of 0.76 compared to rainfall which exhibits no association indicating the relevance of temperature as an explanatory indicator of malaria. With the continuous endemicity of malaria in the South-West, malaria management and control efforts should be focused on high-incidence areas in the South-West and Nigeria in general to fulfil the Sustainable Development Goal of Good health and well-being and the eradication of malaria by 2030.

Analyzing the impacts on passenger yield of incumbent companies after the entry of a new company into the aviation market: The case of Brazil

The public air transport market is generally oligopolistic, meaning that the market strategies of some companies can influence the decisions of rivals and impact consumer prices. This sector has been marked by a large flow of airline entries and exits. Considering this scenario, the objective of this work is to evaluate how the revenue from airline tickets of incumbent companies in the national aviation market in Brazil was impacted after the entry of a new company. A fixed-effects Difference-in-Differences model (DID) using a feasible generalized least squares (FGLS) estimator with panel data is used to assess this impact. To assess the impact of the entrant on yield dispersion, we apply the regression equation to P90 and P10 of the distribution. We also applied the equation, considering the Gini coefficient as the dependent variable. The results showed that the average income of incumbent companies reduced by an estimated magnitude of 5.9% in the period after the new company entered the market. Our findings also indicate an increase in distribution dispersion in the period following market entry, with a more pronounced reduction of 19% in the P10 and 1.5% in the P90. We conclude that the entry of the new company was readily assimilated by the incumbents, so that the competitive effect can explain the identified reduction in average income. The entry of a new competitor was especially beneficial to consumers in the lower tail of the distribution.

Pertinence of Five Different Regression Equations of Mixed Dentition Analysis in West Godavari District Children: A Comparative Study.

The orthodontic diagnosis and treatment planning are the fundamental goals during the mixed dentition period. Numerous methods have been proposed to date. The study was designed to determine the mesiodistal (MD) widths of the lower permanent canines and premolars from Tanaka and Johnston, regression equation (RE) proposed for north Indian population (RE-NIP), Bernabé and Flores-Mir C, Moyers', and Ling and Wong mixed dentition analysis (MDA) and to determine the correlation coefficients for the Eluru population. The study population was selected using cluster random sampling. A total of 3,572 children were clinically examined from the Outpatient Department of Pediatric and Preventive Dentistry, St Joseph Dental College and Hospital, Eluru district, Andhra Pradesh. Out of these, 140 children aged 11-16 years were randomly selected for the study based on the inclusion and exclusion criteria. The MD width of permanent incisors, canines, premolars, and molars was measured with the help of a digital vernier caliper; summed up and compared with the above-mentioned REs. The data obtained were subjected to statistical analysis using SPSS version 22, employing Student's t-test, Pearson correlation coefficient, and Kruskal-Wallis test. The RE-NIP underestimated the values, whereas the remaining equations overestimated the sum of permanent canines and premolars in both arches in the study population. The predicted values obtained from the abovementioned methods overestimated the actual values, and a new RE for the Eluru population should be suggested to better represent the size of unerupted canines and premolars. Measuring the MD widths of the upper and lower permanent incisors and molars during the mixed dentition period from different REs will help a pedodontist in proper treatment planning to predict the MD width of unerupted canines and premolars. Kanaparthi S, Done V, P Madu GS, et al. Pertinence of Five Different Regression Equations of Mixed Dentition Analysis in West Godavari District Children: A Comparative Study. Int J Clin Pediatr Dent 2024;17(8):871-876.

Calculated estimation of the metabolic clearance rate of insulin measured by glucose clamp examination in out-patient clinical practice.

A subpopulation of Japanese patients with type 2 diabetes mellitus (T2DM) who have elevated insulin clearance (IC) exists. We tested our hypothesis that it is possible to estimate IC using common and simple test results collected in routine clinical practice. We recruited patients with newly diagnosed, treatment-naïve T2DM and measured the metabolic clearance rate of insulin (MCRI) determined by a hyperinsulinemic-euglycemic clamp examination. Multivariable regression analysis was performed with body mass index (BMI), serum uric acid (UA), and fasting plasma insulin (F-IRI) which were independently associated with IC increase in our previous reports as explanatory variables to calculate a prediction equation for MCRI. We enrolled 101 patients in this study. Because MCRI is not normally distributed, we calculated the logarithmically transformed estimated Log10MCRI as a prediction formula for IC. Multivariable regression analysis showed that Log10BMI (β = - 0.3257, P < 0.001), UA (β = - 0.1834, P = 0.0081), and Log10F-IRI (β = - 0.4367, P < 0.001) were significant independent factors for Log10MCRI. The regression equation was as follows: estimated Log10MCRI = - 0.5421 × Log10BMI - 0.0167 × UA - 0.1792 × Log10F-IRI + 3.8251 (r = 0.7677, R 2 = 0.5894, P < 0.001). IC can easily be predicted using BMI, UA, and F-IRI which are common and simple test results collected in routine clinical practice.